MENDELOVA UNIVERZITA V BRNĚ. Lesnická a dřevařská fakulta. Ústav lesnické botaniky, dendrologie a geobiocenologie

MENDELOVA UNIVERZITA V BRNĚ Lesnická a dřevařská fakulta Ústav lesnické botaniky, dendrologie a geobiocenologie

Populace smrku ztepilého při horní hranici lesa v Hrubém Jeseníku

DISERTAČNÍ PRÁCE

2014

Martin Šenfeldr

Prohlašuji, že jsem disertační práci na téma: Populace smrku ztepilého při horní hranici lesa v Hrubém Jeseníku zpracoval sám v rámci uvedeného autorského podílu na publikacích a uvedl jsem všechny použité prameny. Souhlasím, aby moje disertační práce byla zveřejněna v souladu s § 47b Zákona č. 111/1998 Sb. o vysokých školách a uložena v knihovně Mendelovy univerzity v Brně, zpřístupněna ke studijním účelům ve shodě s Vyhláškou rektora Mendelovy univerzity v Brně o archivaci elektronické podoby závěrečných prací. Autor kvalifikační práce se dále zavazuje, že před sepsáním licenční smlouvy o využití autorských práv díla s jinou osobou (subjektem) si vyžádá písemné stanovisko univerzity o tom, že předmětná licenční smlouva není v rozporu s oprávněnými zájmy univerzity a zavazuje se uhradit případný příspěvek na úhradu nákladů spojených se vznikem díla dle řádné kalkulace. V Brně, dne: Podpis autora:

Poděkování

Děkuji mému školiteli doc. Dr. Ing. Petru Maděrovi za profesionální vedení práce a lidský přístup. Za příjemnou spolupráci a podněty, jak řešit některé dílčí okruhy práce děkuji Ing. Danielu Volaříkovi Ph.D, Ing. Josefu Urbanovi Ph.D. a Mgr. Václavu Tremlovi, Ph.D. Za korektury některých anglických textů děkuji Mgr. Věře Kolářové a Jonathanu Rosenthalovi. Ze všeho nejvíc bych chtěl poděkovat své rodině a Anetě za neustálou podporu. Práce vznikla za podpory projektů Geobiocenózy horní hranice lesa a vliv porostů kleče na horskou krajinu Hrubého Jeseníku (projekt grantové služby LČR s.p.), projektu IGA 15/2009 Populace vegetativně se šířícího smrku (Picea abies L. Karst.) na horní hranici lesa v Hrubém Jeseníku, projektu IGA 12/2010 Využití genetických informací v lesnické botanice, fyziologii dřevin, dendrologii a geobiocenologii, projektu IGA 38/2010 Zhodnocení věkové struktury smrkových populací a ověření expozičního efektu na horní hranici lesa v Hrubém Jeseníku, projektu IGA 25/2011 Vodní provoz smrkového polykormonu nad horní hranicí lesa v Hrubém Jeseník - případová studie redistribuce vody mezi rodičovským a dceřiným jedincem klonálního původu. Dále pak děkuji projektům CZ.1.07/2.3.00/20.0265 Indikátory vitality dřevin, CZ.1.07/2.3.00/20.0004 Vytvoření a rozvoj multidisciplinárního týmu na platformě krajinné ekologie a výzkumnému záměru LDF (MSM 6215648902).

Obsah 1. Úvod......................................................................................................................................................... 4 2. Téma a cíle práce ..................................................................................................................................... 5 3. Struktura práce ....................................................................................................................................... 6 4. Teoretické aspekty studované problematiky .......................................................................................... 8 4.1 Terminologie horní hranice lesa .............................................................................................................. 8 4.2 Faktory ovlivňující vznik hranice lesa ................................................................................................... 10 4.3 Adaptace dřevin na podmínky horní hranici lesa .................................................................................... 13 4.4 Reprodukce dřevin při hranici lesa ........................................................................................................ 16 4.4.1 Generativní reprodukce .................................................................................................................. 16 4.4.2 Vegetativní reprodukce .................................................................................................................. 17 4.5 Horní hranice lesa v Hrubém Jeseníku ................................................................................................... 19 4.5.1 Základní údaje o horní hranici lesa a polohách nad hranicí lesa ....................................................... 19 4.5.2 Přirozený rozsah bezlesí a antropické vlivy na horní hranici lesa ..................................................... 20 4.6 Posuny horní hranice lesa v závislosti na recentní změně klimatu ........................................................... 22 4.7 Literatura .............................................................................................................................................. 24 5. Population Structure and Reproductive Strategy of Norway Spruce (Picea abies L. Karst) Above the Former Pastoral Timberline in the Hrubý Jeseník Mountains, Czech Republic ............................... 33 5.1 Introduction .......................................................................................................................................... 33 5.2 Material and methods ............................................................................................................................ 34 5.2.1 Study area ...................................................................................................................................... 34 5.2.2 Research polygon and control plots ................................................................................................ 35 5.2.3 Field data collection ....................................................................................................................... 36 5.2.4 Laboratory work ............................................................................................................................ 37 5.2.5 Data analysis.................................................................................................................................. 37 5.3 Results .................................................................................................................................................. 38 5.3.1 Population structure in the research polygon ................................................................................... 38 5.3.2 Seed-based and vegetative regeneration in the RP ........................................................................... 42 5.3.3 Age structure of population in control plots .................................................................................... 45 5.4 Discussion ............................................................................................................................................ 45 5.5 Conclusions .......................................................................................................................................... 49 5.6 References ............................................................................................................................................ 50 6.Effects of prostrate dwarf pine on Norway spruce clonal groups in the treeline ecotone of the Hrubý Jeseník Mountains, Czech Republic ......................................................................................................... 53 6.1 Introduction .......................................................................................................................................... 53

6.2 Material and methods ............................................................................................................................ 55 6.2.1 Study sites ..................................................................................................................................... 55 6.2.2 Field data collection ....................................................................................................................... 56 6.2.3 Data analysis.................................................................................................................................. 59 6.3 Results .................................................................................................................................................. 59 6.3.1 Principal component analysis ......................................................................................................... 59 6.3.2 Differences in age-related variables and tree height ........................................................................ 60 6.3.3 Number of trees and number of layering branches .......................................................................... 62 6.3.4 Effect of distance to pine on layering probability ............................................................................ 63 6.4 Discussion ............................................................................................................................................ 64 6.5 Conclusions .......................................................................................................................................... 66 6.6 References ............................................................................................................................................ 67 7. 20th century treeline ecotone advance in Central European medium altitude mountains – a consequence of land abandonment or climate change? ............................................................................ 73 7.1 Introduction .......................................................................................................................................... 74 7.2 Methods................................................................................................................................................ 75 7.2.1Geographical settings ...................................................................................................................... 75 7.2.2 Treeline demography ..................................................................................................................... 76 7.2.3 Changes in tree cover ..................................................................................................................... 77 7.2.4 Growth trends ................................................................................................................................ 78 7.2.5 The effects of temperature, growth and agricultural abandonment ................................................... 78 7.3 Results .................................................................................................................................................. 79 7.3.1 Treeline demography ..................................................................................................................... 79 7.3.2 Changes in tree cover ..................................................................................................................... 82 7.3.3 Growth trends ................................................................................................................................ 83 7.3.4 The effects of temperatures, tree growth and agricultural land abandonment on tree establishment .. 85 7.4 Discussion ............................................................................................................................................ 88 7.4.1 Factors of treeline ecotone advance ................................................................................................ 88 7.4.2 Changes in tree cover and growth trends......................................................................................... 89 7.5 Conclusions .......................................................................................................................................... 90 7.6 References ............................................................................................................................................ 91 8. Bidirectional flows in the layering branches between parent and daughter tree in a Norway spruce polycormon ................................................................................................................................................ 95 8.1 Introduction .......................................................................................................................................... 95 8.2 Materials and methods .......................................................................................................................... 96 8.2.1 Study site and study trees ............................................................................................................... 96 8.2.2 Meteorology, soil and leaf water potential measurements ................................................................ 96 8.2.3 Sap flow measurement and calculation ........................................................................................... 97 8.3 Results .................................................................................................................................................. 98

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8.3.1 Diurnal dynamic of sap flow and shoot water potential ................................................................... 98 8.3.2 Monthly sap flow characteristics .................................................................................................. 100 8.4 Discusion ............................................................................................................................................ 100 8.5 Conclusions ........................................................................................................................................ 101 8.6 References .......................................................................................................................................... 101 9. Redistribution of water via layering branch between conected parent and daughter trees in Norway spruce clonal groups................................................................................................................................ 103 9.1 Introduction ........................................................................................................................................ 103 9.2 Materials and methods ........................................................................................................................ 105 9.2.1 Study site ..................................................................................................................................... 105 9.2.2 Study trees ................................................................................................................................... 105 9.2.3 Meteorology, soil and leaf water potential measurements .............................................................. 107 9.2.4 Sap flow measurement and calculation ......................................................................................... 107 9.2.5 Daughter tree root exposure experiment ....................................................................................... 108 9.3 Results ................................................................................................................................................ 108 9.3.1 Diurnal dynamic of sap flow and shoot water potential in the period before excavation ................. 108 9.3.2 Diurnal sap flow dynamics after exposing daughter tree roots ....................................................... 112 9.3.3 Seasonal sap flow characteristics before daughter tree root exposure ............................................. 112 9.3.4 Seasonal sap flow characteristic after daughter tree roots excavating ............................................. 113 9.4 Discussion .......................................................................................................................................... 114 9.5 Conclusion .......................................................................................................................................... 116 9.6 References .......................................................................................................................................... 116 10. Celkový závěr .................................................................................................................................... 121 11. Summary ........................................................................................................................................... 126 12. Fotografické přílohy .......................................................................................................................... 131

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Šenfeldr, M. Populace smrku při horní hranici lesa v Hrubém Jeseníku

1. Úvod Na počátku roku 2014 uplyne již 9 let od té doby, co se autor na základě svého vlastního popudu začal věnovat výzkumu nejvyšších poloh Hrubého Jeseníku. Nejprve se jednalo o inventarizační výzkum porostů borovice kleče při zpracování bakalářské práce, poté následovala populační analýza smrku ztepilého (Picea abies L. Karst) při zpracování práce diplomové. Daná problematika autora natolik zaujala, že jeho nadšení vyústilo v myšlenku pokračovat ve výzkumu horní hranice lesa v rámci doktorandského studia. Myšlenka se stala skutečností a pod vedením doc. Dr. Ing. Petra Maděry bylo vypsáno téma týkající se problematiky smrku ztepilého při horní hranici lesa v Hrubém Jeseníku, které začal autor oficiálně zpracovávat na podzim roku 2009. Téma smrku při horní hranici lesa bylo pro autora atraktivní v tom smyslu, že se mohl zabývat stavem a vývojem dřevinných populací budujících důležitý ekotonální prvek v krajině, a to jak na úrovni jednotlivých jedinců, tak i celých populací v prostředí krásných hor, kde se narodil. A navíc, výzkum horských lesů a horní hranice lesa má na autorově alma mater dlouholetou tradici (např. Zlatník, Kavinová 1966; Horák 1971; Rypl 1980; Horák 2004; Buček et al. 2004; Gebauer et al. 2008). Problematika horní hranice lesa a alpinských poloh je v české a slovenské literatuře dlouhodobě zakotvena (např. Jeník 1959; Jeník 1961; Bednář 1966; Plesník 1971; Hošek 1972; Hošek 1973; Jeník 1973; Treml, Banaš 2000; Vacek et al. 2003; Buček et al. 2004; Vacek, Podrázský 2006; Treml 2007). V současné době reprezentuje horní hranice lesa ve vědeckém světě významné téma, zejména díky populárnosti globální klimatické změny, jež může být některými charakteristikami horní hranice lesa citlivě indikována (Körner 1999; Holtmeier 2009). Dále je zde velký potenciál pro studium rostlinných adaptací, strategií přežití a interakcí mezi rostlinami na hranici jejich ekologické valence (např. Arno, Hammerly 1984; Maděra 2005; Körner 2012). Důležitým motivem rovněž bylo, že výzkumy probíhající v nejvyšších polohách Hrubého Jeseníku jsou velmi frekventované a autor mohl své poznatky sdílet a konfrontovat s celou řadou výzkumníků různých specializací. A mohl tím udržovat a rozvíjet vědeckou spolupráci, kterou navázal již v předchozích letech při zpracování bakalářské a diplomové práce. Pozitivní motivací pro autora rovněž bylo, že téma, které si vybral ke studiu, má značný potenciál pro využití v praktických oborech, jako je ochrana přírody, lesní hospodářství a má úzkou vazbu na vytváření strategií managementu ve zvláště chráněných územích nejvyšších poloh chráněné krajinné oblasti Hrubý Jeseník. V první fázi byl výzkum soustředěn zejména na posouzení populační věkové struktury smrkových populací a možnosti jejich reprodukcí nad současnou horní hranicí lesa. Již v této počáteční fázi zpracování dizertační práce byl důraz kladen na studium vegetativní strategie šíření smrků, jakožto významné adaptace na přežití a udržení na stanovištích s extrémní mírou stresu (Kuoch, Amiet 1970). V rámci této reprodukční strategie vzniká nový dceřiný jedinec zakořeněním spodní poléhavé větve rodičovského stromu (Tranquillini 1979). Právě fyziologický vztah mezi vzájemně spojenými rodičovskými a klonálními dceřinými stromy z hlediska jejich vodního režimu se stal předmětem výzkumu v další fázi zpracování této práce. Následně autor rozšířil svůj výzkum o studium interakcí mezi výsadbami porostů borovice kleče (Pinus mugo, Turra) a populacemi smrků, při tom se autor zaměřil zejména na posouzení vlivu klečových porostů na schopnost vegetativního zmlazování smrků a výškového růstu. V závěrečné 4


fázi se autor zaměřil na zjištění, do jaké míry jsou historické a současné změny v poloze hranice lesa, ovlivněny ústupem od tradičního historického využívání vysokohorské krajiny formou pastvy, travaření a těžby dříví v první polovině 20. století, a do jaké míry jsou ovlivněny oteplením o 1°C za posledních 100 let.

2. Téma a cíle práce Předkládaná dizertační práce je zaměřena na stanovení věkové struktury a dynamiky smrkových populací v rámci ekotonu horní hranice lesa v Hrubém Jeseníku. Zvláštní důraz je kladen na studium smrkových skupin (polykormonů) nad horní hranicí lesa vznikajících vegetativně prostřednictvím hřížení. Tyto smrkové skupiny jsou studovány jednak z hlediska popsání vybraných biometrických parametrů v rámci skupiny (vzdálenost a směr zakořenění hřížících větví, počty jedinců vzniklých z jedné větve, počty jedinců ve skupině), dále pak intenzitou hřížení a objasnění fyziologického vztahu mezi rodičovským a dceřiným z hlediska jejich vodního režimu. Součástí dizertační práce je rovněž studium vlivu porostů borovice kleče na vegetativně se šířící smrkové skupiny nad horní zapojenou hranicí lesa. Věková struktura dřevin, které formují horní hranici lesa, významným způsobem determinuje polohu a dynamickou tendenci (vzestupnou, sestupnou) hranice lesa (Holtmeier 2009). Poloha ekotonu horní hranice lesa je největší měrou způsobena gradientem klimatických podmínek způsobených změnou nadmořské výšky (Körner 1999). Poloha hranice lesa je však ovlivněna celou řadou dalších faktorů, které popisuji v kapitole 4.2. V současné době jsou podrobné informace o dynamice dřevinných populací na hranici lesa v rámci Evropy dostupné ze skandinávských pohoří (Dalen, Hofgaard 2005; Kullman 2007; Hofgaard 2009), alpského systému (Motta et al. 2006; Gehrig-Fasel 2007) a karpatského oblouku (Kern, Popa 2008; Martazinová et al. 2011), nicméně ucelené informace o populační struktuře a dynamice dřevin v rámci Hrubého Jeseníku, jakožto významného pohoří Vysokých Sudet, nejsou k dispozici. Právě konkurence s keři může zásadním způsobem limitovat expanzi stromů vzhůru v rámci ekotonu hranice lesa v důsledku oteplení (Holtmeier, Broll 2007). Problematikou kompetice mezi populacemi smrku a klečovými keři se v evropských pohořích zabýval pouze (Dullinger 2005) v Alpách, nicméně jeho studie se týkala interakcí mezi smrkem a klečí v rámci spodní části ekotonu hranice lesa, kde převažují generativní smrkové populace. Problematika vlivu kleče na smrkové populace nad horní zapojenou hranicí lesa, kde převažují klonální populace ve vědecké literatuře chybí. Schopnost a možnosti reprodukce dřevin zásadním způsobem předurčují dynamiku ekotonu hranice lesa (Holtmeier 2009). Většina zdrojů se shoduje, že na hranici lesa se generativní schopnost šíření u dřevin snižuje (např. Tranquillini 1979, Körner 1999). Z tohoto důvodu se jeví schopnost vegetativního šíření v podmínkách hranice lesa jako velmi důležitá (Kuoch, Amiet 1970). Typicky se vyskytuje nad zapojenou hranicí lesa, kde již stromy nejsou vyvětvené, a díky nízkému zápoji mají korunu nasazenou u země (Kuoch, Amiet 1970). Při této vegetativní reprodukční strategii vzniká nový dceřiný jedinec zakořeněním spodní poléhavé větve rodičovského stromu. Spojovací (zahřížená větev) následně zprostředkovává spojení mezi těmito stromy. Dle Holtmeiera (2009) může trvat desetiletí, než přestane být dceřiný jedinec závislý na asimilátech rodičovského 5


stromu. Velmi málo je známo o vodním režimu těchto klonálních smrkových skupin a informace o míře a délce závislosti dceřiného jedince na vodních zásobách rodičovského stromu (respektive délky období, než si dceřiný jedinec vytvoří dostatečný kořenový systém), ve vědecké literatuře kompletně chybí. Ve třech výše uvedených odstavcích je stručně nastíněna řešená problematika a zároveň jsou uvedeny tematické okruhy, ve kterých chybí vědecké informace. Cílem dizertační práce bylo navázat na některé výše citované témata a v některých oblastech je rozvinout. Specifickými cíli dizertační práce bylo: - Zhodnotit věkovou strukturu smrkových populací v rámci horní hranice lesa v Hrubém Jeseníku a srovnat ji mezi jednotlivými částmi ekotonu hranice lesa. - Zhodnotit generativní a vegetativní reprodukci smrků v rámci tohoto ekotonu s důrazem na výzkum vegetativně se šířících skupin. V rámci těchto skupin bylo cílem analyzovat: vzdálenost zakořenění hřížících větví a jejich výšku nasazení na rodičovském stromě, klonální vztahy mezi jednotlivými stromy a intenzitu hřížení. - Analyzovat vliv borovice kleče na schopnost vegetativního rozmnožování smrků nad horní hranicí lesa. - Zjistit časovou posloupnost uchycování smrků v rámci posunů hranice lesa a zjistit jaký vliv mělo s ohledem na tento proces ukončení přímého vlivu člověka a navýšení teploty. - Provést měření transpiračního proudu v rámci vegetativně se šířících smrkových skupin. Toto měření zaměřit na zjištění směrů a kvantity vodního toku ve spojovací zahřížené větvi, a kvantitu tohoto toku dát do poměru s celkovou spotřebou vody rodičovským a dceřiným stromem. Identifikovat hlavní řídící faktory pro transpirační proud ve spojovací zahřížené větvi. Výše uvedených cílu jsem se pokoušel dosáhnout prostřednictvím celé řady metod zahrnujících terénní mapování, dendrochronologii, měření transpiračního proudu prostřednictvím metod pracujících na principu tepelné bilance kmene (Čermák et al. 2004; Kučera, Urban 2012), měření vodních potenciálů v koruně prostřednictvím Scholanderovy tlakové bomby (Cochard 1992), zkoušek klíčivosti dle ČSN 48 12 11 a metod zpracování dat pomocí geografických informačních systémů. Metody jsou podrobně popsány v jednotlivých manuscriptech nebo článcích.

3. Struktura práce Práce je předložena ve formě pěti publikací v anglickém jazyce (Tab. 1) a je doplněná o česky psané kapitoly: Úvod, Téma a cíle práce, Struktura práce, Teoretické aspekty studované problematiky, Celkový závěr, Fotografické přílohy a anglicky psané Summary. Při rozhodnutí pro zvolení této formy a struktury dizertační práce jsem se nechal inspirovat rovněž dvojjazyčně „článkově“ pojatou dizertační prácí Mgr. Václava Tremla 6


Ph.D z Univerzity Karlovy (Treml 2007). Způsob zpracování dizertačních prácí ve formě souboru publikací se stává stále více populární i na Lesnické a dřevařské fakultě (např. Kolář 2012; Valtera 2013). Studie, které byly zahrnuty do této dizertační práce, vznikaly postupně. Proto úroveň jednotlivých článků reflektuje vývoj úrovně autorova poznání a pohledu na danou problematiku. První stať ,,Population Structure and Reproductive Strategy of Norway Spruce (Picea abies L. Karst) Above the Former Pastoral Timberline in the Hrubý Jeseník Mountains, Czech Republic“ pojednává o biometrické a věkové struktuře smrkových populací včetně zhodnocení možností jejich reprodukce a tyto charakteristiky srovnává mezi dvěma na sebe navazujícími vertikálními částmi ekotonu hranice lesa. Publikace ,,Effects of prostrate dwarf pine on Norway spruce clonal groups in the treeline ecotone of the Hrubý Jeseník Mountains, Czech Republic“ pojednává o vlivu různě zapojených porostů borovice kleče na minulou i současnou schopnost vegetativního rozmnožování smrků. Manuskript článku ,,20th century treeline ecotone advance in medium altitude mountains of Central Europe – the consequence of land abandonment or climate change?“ pojednavá o historických posunech smrků v rámci ekotonu hranice lesa, které dává do souvislosti s upuštěním od tradičního hospodaření v těchto polohách v 50. letech 20. století a s teplotními charakteristikami. Tento článek zahrnuje kromě Hrubého Jeseníku i pohoří Krkonoš, nicméně výsledky z Krkonoš jsou nad rámec této dizertace, proto v závěrečném souhrnu této práce nejsou brány v potaz. Článek ,,Bidirectional flows in the layering branches between parent and daughter tree in a Norway spruce polycormon“ pojednává o dynamice a kvantitě transpiračního proudu v rodičovském a dceřiném stromě, a rovněž v zahřížené spojovací větvi, která tyto jedince spojuje. V článku je zhodnocen význam kvantity transpiračního toku v zahřížené spojovací větvi s ohledem na celkové množství vody spotřebované rodičovským a dceřinným jedincem. Tento článek byl prezentován při 9th International Workshop on Sap Flow (Ghent–Belgium) ve sborníku vydaném v rámci tohoto workshopu. Na základě jeho velmi dobrého hodnocení vědeckým shromážděním pří 9th International Workshop on Sap Flow (Ghent – Belgium), bylo téma tohoto článku vybráno pro potenciální publikování v rozšířené formě ve speciálním čísle časopisu Trees - Structure and Function (viz. níže). Manuscript článku ,,Redistribution of water via layering branch between connected parent and daughter trees in Norway spruce clonal groups“ se s předchozí půblikací v některých pasážích překrývá, nicméně ji zásadním způsobem rozšiřuje o měření na více stromových dvojicích a v rámci delší časové řady. Článek navíc zahrnuje i experiment umělého navození vodního stresu u dceřiného jedince, při sledování změn transpiračních toků v zahřížené spojovací větvi. U všech publikací, ve kterých je autor této dizertační práce uveden jako hlavní autor na prvním místě (viz. Tab. 1), se dominantním způsobem podílel na vytvoření designu výzkumu, sběru a analýzy dat a psal podstatnou hlavní část manuscriptu. V rámci druhé publikace (Tab. 1) byla analýza dat významnou mírou realizována D. Volaříkem. V případě jednoho článku, kde je autor dizertace uveden jako druhý autor (viz. Tab. 1), se podílel na sběru a zpracování dat a formou připomínek doplňoval manuscript, který byl podstatnou částí napsán jeho prvním autorem.

7


Tab. 1: Přehled publikací zařazených do dizertační práce. Název publikace

Typ

Periodikum/ Sborník

Stav

Autorský podíl

Population Structure and Reproductive Strategy of Norway Spruce (Picea abies L. Karst) Above the Former Pastoral Timberline in the Hrubý Jeseník Mountains, Czech Republic

článek ve vědeckém časopise s impakt faktorem

Mountain Research and Development, 2011, 31: 131-143

vytištěno

Šenfeldr, M. (80%), Maděra, P.

Effects of prostrate dwarf pine on Norway spruce clonal groups in the treeline ecotone of the Hrubý Jeseník Mountains, Czech Republic


Arctic, Antarctic and Alpine Research

přijato do tisku

Šenfeldr, M. (70%), Treml, V., Maděra, P., Volařík, D.

20th century treeline ecotone advance in medium altitude mountains of Central Europe – the consequence of land abandonment or climate change?


v recenzním řízení

Treml, V., Šenfeldr, M. (20%), Chuman, T., Ponocná, T., Demková, K.

vytištěno

Šenfeldr, M. (80%), Urban, J., Maděra, P., Kučera, J.

v recenzním řízení

Šenfeldr, M. (80%), Urban, J., Maděra, P., Kučera, J.

Journal of Biogeography

Acta článek ve sborníku Horticulturae Bidirectional flows in the layering Thomson Reuters 991: IX branches between parent and evidovaný v International daughter tree in a Norway spruce databázi Web of Workshop on polycormon Science Sap Flow, 2013, 277-283 Redistribution of water via layering branch between conected parent and daughter trees in Norway spruce clonal groups


Trees: structure and function

4. Teoretické aspekty studované problematiky 4.1 Terminologie horní hranice lesa Pro fenomén přechodu lesa do bezlesí v důsledku rostoucí nadmořské výšky se používá termín horní nebo alpinská hranice lesa (Jeník, Lokvenc 1962; Plesník 1971; Maděra 2005; Treml 2007). Otázka, zda-li používat termín horní nebo alpinská, je podrobně diskutována v klíčových prácích (Jeník, Lokvenc 1962; Plesník 1971) nověji pak v práci (Treml 2007). Autoři (Jeník, Lokvenc 1962; Treml 2007) používají termín alpinská hranice lesa z důvodu jeho větší zavedenosti a tradice. Plesník (1971) se přiklání k pojmu horní hranice lesa z důvodu jeho obecné platnosti v různých oblastech s různě definovanými vegetačními stupni. Zatímco v karpatské oblasti se hranice lesa obvykle nachází na přechodu z montánního (zpravidla smrkové lesy) do subalpinského stupně (porosty borovice kleče, jalovce, olše zelené či pěnišníku), tak v alpském regionu zase na přechodu 8


ze subalpinského stupně (porosty borovice limby, modřínu opadavého) do alpinského stupně /alpinské trávníky, většinou včetně kleče či pěnišníkových porostů/ (Treml 2007). Někteří autoři rovněž používají termín horská hranice lesa - mountain timberline (Holtmeier 2009) či montánní hranice lesa - montane treeline (Slatyer, Noble 1992) nebo z důvodu celkového zjednodušení pouze termín hranice lesa - „timberline“. V této dizertační práci se striktně nevážu k jednotnému užívání termínu horní nebo alpinská hranice lesa. Protože oba termíny jsou pro studovanou oblast vhodné a různými autory užívané (např. Jeník 1961; Bednář 1966). V česky psaných kapitolách a v názvu dizertační práce upřednostňuji termín horní hranice lesa, protože je obecně známější, nicméně v některých anglických článcích je rovněž používán termín „alpine“ („timberline nebo treeline“), což je ekvivalent k termínu alpinská. Nicméně mnohdy pro jednoduchost používám jen termíny: „treeline“, „treeline ecotone“ nebo „timberline“, „timberline ecotone“. Klíčovou otázkou je, jakým způsobem lze konkrétně definovat vlastní rozhraní lesa a bezlesí. Dle Tremla (2007) představuje horní hranice lesa fraktální objekt, kdy její vymezení záleží na měřítku, ve kterém ji studujeme. Tento objekt může být studován na úrovni linie spojující nejvyšší okraje lesa nebo na úrovni širšího přechodového pásu – ekotonu. Dolní hranice ekotonu přechodu lesa do bezlesí je obvykle vymezena jako linie, na níž se začíná rozvolňovat souvisle zapojený horský les. Ve světové vědecké literatuře se pro tuto linii používá termín „(upper) timberline“ (např. Hadley, Smithm 1983; Arno, Hammerly 1984; Scuderi 1987), „(upper) forest limit“ (např. Gorchakovsky, Shiyatov 1978), „alpine timberline“ (např. Tranquilini 1979; Treml Banaš 2000), „forest line“ (Jobbágy, Jackson 2000) a řada dalších, jejichž přehled je uveden v komplexně pojatých monografiích (Jeník, Lokvenc 1962; Plesník 1971; Holtmeier 2009). V české a slovenské literatuře je tato linie nazývána jako horní hranice lesa (např. Plesník 1971, Maděra 2005; Vacek, Podrázský 2006), nebo alpinská hranice lesa (např. Jeník 1961; Treml 2007; Vacek, Jeník 2010). Horní hranice ekotonu přechodu lesa do bezlesí je vymezena jako horní hranice výskytu stromových druhů, bez ohledu na jejich růstovou formu - ,,tree species line“ (Körner 1999) nebo horní hranice stromů vzpřímeného vzrůstu o různě definované výšce „treeline“, „tree-limit“ (Elenberg 1963; Holtmeier 2009). Pásmo mezi horní hranicí zapojeného lesa a horní hranicí stromů vzpřímeného vzrůstu nebo stromových druhů je označováno jako pásmo boje – v německé terminologii označováno jako „kampfzone“ (Schröter 1926), „kampfwald“ (Strasburger et al. 1991), v anglické terminologii je pak označováno jako „timberline ecotone“ (Tranquillini 1979), „forest tundra ecotone“ (Marr, Marr 1973), „treeline ecotone“ (např. Germino et al. 2002). Pro toto pásmo existuje celá řada dalších termínů, jejichž přehled je např. uvedeny v práci (Holtmeier 2009). Z výše uvedeného přehledu vyplývá značná terminologická nejednotnost používání jednotlivých pojmů, dle Tremla (2007) je proto vždy nutné uvést konkrétní parametry, jimiž je vlastní hranice definována. Těmi jsou zejména korunový zápoj stromů a výška. Kritérium minimální výšky se většinou pohybuje od 2 – 8 metrů a kritérium minimálního korunového zápoje pak od 20 – 50 % (viz. přehledy v monografiích Jeník, Lokvenc 1962; Plesník 1971; Holtmeier 2009).

9


Pro pohoří Hrubého Jeseníku a Krkonoš je obecně přijímána a nejčastěji používána definice dle Jeníka, Lokvence (1962), kdy horní (alpinská) hranice lesa je vegetační linií, která spojuje všechny empiricky zjistitelné nejvyšší okraje lesa, kdy pokryvnost (korunový zápoj) je nejméně 50% plochy, jedná se alespoň o 5 metrů vysoké stromy. Autoři rovněž používají kritérium minimální plochy o velikosti jednoho aru (10x10 m), dalším kritériem je vzdálenost izolované enklávy zařazené do horní hranice lesa od souvislého lesního komplexu, Jeník, Lokvenc (1962) navrhují pro tyto účely vzdálenost 100 m. Nověji byla tato definice použita při vymezení horní hranice lesa v Hrubém Jeseníku, Kralickém Sněžníku a v Krkonoších v práci (Treml, Banaš 2000). Takto vymezená linie odpovídá anglickému termínu ,,timberline“ nebo ,,upper forest limit“ (Treml 2007).

Obr. 1: Ekoton horní hranice lesa ve Švýcarských Alpách.

4.2 Faktory ovlivňující vznik hranice lesa Faktory, které ovlivňují polohu a strukturu ekotonu horní hranice lesa se zabývá v řadě partikulárně zaměřených prací značné množství vědců již od počátku minulého století, výjimečně i dříve. Vhodné shrnující monografie publikovali zejména Tranquillini (1979), Körner (1999), Holtmeier (2003, 2009), Arno, Hammerly (1984), Körner (2012), z českých a slovenských autorů např. Jeník, Lokvenc (1962), Plesník (1971) a Treml (2007). V podstatě se jedná o makrofaktory („general factors“ podle Arno, Hammerly 1984) – hlavním činitelem je makroklima daného biomu či stupeň oceanity a kontinentality (Arno, Hammerly 1984), mezi tyto faktory patří i některé topografické vlastnosti návětrná a závětrná strana pohoří, mohutnost horského masivu (Arno, Hammerly 1984;

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Holtmeier 2009). Makrofaktory vytvářejí rámce podmínek, jež umožňují vystoupat hranici lesa v konkrétním regionu do určité nadmořské výšky. Mikro (mezo) faktory („specific factors“ podle Arno, Hammerly 1984) jsou pak konkrétní podmínky na daném stanovišti, které přímo ovlivňují růst jednotlivých dřevin a zapříčiňují vlastní průběh hranice lesa na daném místě a v daném čase. Klíčovými stanovištními faktory s rozhodujícím vlivem pro formování hranice lesa jsou ty, které působí jako stresové, tzn. na hranici ekologické valence druhu dřeviny, která vytváří hranici lesa. Faktory působící na hranici ekologické valence navíc často umocňují své negativní účinky na dřevinnou vegetaci tím, že působí souběžně (Fowden et al. 1993). Proto většinou na hranici lesa najdeme druhy dřevin se strestolerantní populační strategií (Grimme 1979), která je typická např. pro rody Pinus a Juniperus. Nejpodstatnějším a jediným globálně platným faktorem zapříčiňujícím vznik hranice lesa jsou teplotní podmínky (Körner 1999). Ty jsou dlouhodobě využívány jako indikátory polohy hranice lesa. Podrobný výčet hlavních fyziologických hypotéz, které způsobují snížení růstu stromů na hranici lesa podavá (Körner 1999). V současné době již byly vyvráceny dvě delší dobu prosazované hypotézy, a to hypotéza stresu v důsledku mrazového vysychání (Tranquillini 1979) a hypotéza nedostatečné fotosyntetické aktivity (Pisek, Winkler 1958; Slatyer, Ferrar 1977). Mrazové vysychání nemůže být obecně určující příčinou ústupu stromového růstu (Troll 1961; Körner 1999), protože podmínky pro něj nejsou splněny ve všech pohořích. Hypotéza nedostatečné fotosyntetické aktivity byla rovněž zamítnuta, a to na základě zjištění, že v pletivech stromů na hranici lesa se nachází velké množství asimilátů (Hoch et al. 2002), tudíž nelze uvažovat o nedostatečné aktivitě fotosyntézy. Navíc se ukázalo, že celkové ztráty uhlíku dýcháním během zimního období může strom nahradit během jediného dne vegetačního období (Wieser 1997). Nejpravděpodobněji se dnes jeví dle Körnera (1999) vznik horní hranice lesa díky nedostatečnému zabudování asimilátů vzniklých fotosyntézou do buněčných struktur a růst buněk a pletiv vůbec díky nízkým teplotám ve vegetačním období. Körner, Paulsen (2004) publikovali globální indikátor polohy hranice lesa v podobě průměrných půdních teplot (10 cm pod povrchem) ve vegetačním období v rozmezí 6.7±0.8 °C. Také lignifikace vytvořených pletiv je díky krátkému vegetačnímu období problematická. Krátká vegetační doba způsobuje též nižší produkci a klíčivost semen stromů blízko limitu svého výskytu (Tranquillini 1979). Kromě teplot ve vegetačním období mají na výslednou polohu hranice lesa vliv i limitní teploty, zejména ty pod bodem mrazu. Častým jevem je mrazové poškození asimilačních orgánů nebo vodivých pletiv /kavitace xylému/ (Tyree, Sperry 1989), odumření jemného kořenového vlášení v důsledku nízkých půdních teplot (Kullman 2007). V souvislosti s teplotním režimem je častým předmětem výzkumu i zimní vysýchání (Marchand 1996), které je nebezpečné pro neopadavé dřeviny, které si uchovávají asimilační aparát přes zimu. Jsou-li listy exponovány (tzn. nejsou-li chráněny sněhem), mohou začít při slunečných dnech (tmavé listy absorbují záření) transpirovat, ale protože voda v půdě je zmrzlá, nemohou kořeny saturovat ztrátu vody. Také střídání teplot (např. při inverzi) může způsobit opakované tání a tuhnutí vody v pletivech a vést k jejich poškození, přičemž nejcitlivější jsou báze kmenů mladých stromů (Martinková et al. 2001; Larcher 2003). Vítr převažujícího směru, unášející krystaly ledu, způsobuje abrazi asimilačních orgánů na návětrné straně (Jeník, Lokvenc 1962). Důsledky abraze na návětrných stranách kmenů a větví jsou zcela zřetelné a vedou k tvorbě vlajkových forem, ale měřením abraze 11


se dosud zabývalo jenom málo autorů. Marchand (1996) uvádí příklady poškození dřevin abrazí i způsob měření pomocí exponovaného kůlu s několika vrstvami barevných nátěrů. Největší abrazi zaznamenal do 30–40 cm nad sněhovou pokrývkou. Dahms (1992) však uvádí, že i v létě vítr unáší částice půdy o velikosti 7–17 mikrometrů (nalezeny ve stomatech jehlic), které způsobují abrazi epikutikulárních vosků jehlic na návětrné straně stromu. Mechanické poškození stromů větrem, případně těžkým sněhem je pozorováno také velmi často, zejména v podmínkách s oceánickým klimatem, kde jsou zimní sněhové srážky velmi vysoké (Marchand 1996). Jedná se většinou o korunové zlomy, nikoliv vývraty celých stromů (díky nízké výšce stromu není mechanické namáhání na kořenový systém tak značné), často jsou proto přítomní jedinci s více kmeny. Podobně může působit i silná námraza. Sníh může působit disturbance ve formě lavin, což je např. v Sudetských pohořích typicky podmíněno fenoménem anemo-orografických systémů (Jeník 1961; Jeník 2008). Také výška sněhové pokrývky, resp. její délka trvání je klíčovým faktorem pro růst dřevin (Arno, Hammerly 1984; Marchand 1996, Holtmeier 2009). V místech kde se sníh kumuluje a vytrvává proto až do léta, stromy růst nemohou (Arno, Hammerly 1984). Nárovec et al. (2006) zaznamenali v Orlických horách v roce s vysokými sněhovými srážkami odumření spodních větví stromů, které byly dlouhou dobu pod sněhem v anoxickém režimu, a to proto, že během zimy přišla obleva, která vytvořila ve sněhu ledovou vrstvu neprostupnou pro vzduch. Holtmeier (2009) shrnuje negativní a pozitivní působení sněhu na stromovou vegetaci. Pozitivně sníh působí jako ochrana před mrazem, zimním vysýcháním, abrazí ledovými částicemi, škodám zvěří a je zásobárnou vláhy, zejména na vysýchavých substrátech. Naopak negativní efekt sněhu je ve zkracování vegetační sezóny, zpožďuje oteplování půdy, což ovlivňuje klíčení semen, růst kořenů, dekompozici a koloběh živin, způsobuje mechanická poškození (od zlomů až po laviny) a zapříčiňuje houbové infekce semenáčků (Holtmeier 2009). Reliéf terénu, kromě již zmiňovaných lavin, podmiňuje i další vlastnosti ekotopu hranice lesa, které se projevují modifikací teplotních poměrů, edafických poměrů, délky trvání sněhové pokrývky, směru větru a umožňuje vyšší výskyt určitých disturbancí (Holtemeier 2009). Specifickým příkladem je vrcholový fenomén (Scharfetter 1918). Podstatným reliéfovým faktorem je orientace, sklon a tvar svahu. Na prudkých svazích dochází častěji k rychlým svahovým procesům (mury, sesuvy, sněhové laviny), které zásadně předurčují fyziognomii hranice lesa (Jeník, Lokvenc 1962). V oblastech s dostatkem srážek se zpravidla setkáváme s vyšší polohou hranice lesa na osluněných svazích, naopak v suchých oblastech zase hranice lesa probíhá níže na méně osluněných expozicích (Schickhoff 2005). Důležité jsou rovněž půdní poměry. Holtmeier (2009) udává zejména teplotu, vlhkost a hloubku půdy jako hlavní vlastnosti, které rozhodují o růstu stromů na hranici lesa. Tyto vlastnosti jsou ovlivňovány konvexní či konkávní topografií terénu, texturou a pórovitostí půdy, obsahem organických látek, vodivostí tepla, rostlinným krytem, expozicí, sklonem apod. Intenzita a složení slunečního záření je též pro prostředí ekotonu hranice lesa charakteristická. Přímé poškození může způsobit ultrafialové záření. Ultrafialové záření ovlivňuje též produkci fytohormonů, což vede ke zkrácenému růstu nodů a polštářovitému růstu rostlin (Larcher 1988). Arno, Hammerly (1984) rovněž zmiňují požáry jako jeden z faktorů ovlivňující hranici lesa v severoamerických pohořích. Podstatným faktorem ovlivňujícím pozici 12


hranice lesa je kompetice stromových druhů s dominantními travami, nebo s keřovitými dřevinami (Dullinger et al. 2005). Pro postup lesa vzhůru je nutné i posunutí pásma fungující mykorhizy, díky velkému významu symbiotického vztahu stromů s mikromycety (Tranquilliny 1979; Körner 1999). Proto často hranice lesa při kladných teplotních anomáliích reaguje vzestupem jen mírně nebo vůbec, neboť právě konkurenční tlak neumožňuje expanzi semenáčků nad hranicí lesa (Slatyer, Noble 1992). Nezanedbatelné jsou i některé druhy živočichů, které mohou rozvoji hranice lesa bránit okusem semenáčků či naopak pomáhat šíření disperzí semen – zejména u borovic s bezkřídlými semeny (Arno, Hammerly 1984). Mnozí autoři uvádějí jako jeden z velmi významných faktorů ovlivňujících hranici lesa i lidské aktivity, pastvou a travařením počínaje, přes těžbu dříví, imisní poškození až po výstavbu sportovních areálů (Holtmeier 2009; Novák et al. 2010). V případě některých pohoří, kde byla hranice lesa výrazně ovlivněna pastvou je označována přívlastkem „pastevní“ hranice lesa (anglický termín ,,pastoral timberline“ – Kozak et al. 1995). Holtmeier (2009) zdůrazňuje, že jakékoliv antropické zásahy vedoucí ke snížení hranice lesa nejsou žádoucí z hlediska stability krajiny a navrhuje způsoby péče vedoucí ke stabilizaci hranice lesa (Holtmeier 2009). Ve středoevropských hercynských pohořích jsou první významné antropické zásahy v oblasti hranice lesa nejčastěji datovány již v období raného středověku od 9. – 11. století (Friedman 2000; Novák et al. 2010). V posledním století se ve většině středoevropských oblastí nad hranicí lesa přestalo hospodařit (Holtmeier 2009). Od druhé poloviny 20. století se v těchto oblastech projevovali imisní spady vedoucí ke snížení hranice lesa (Vacek et al. 2003).

4.3 Adaptace dřevin na podmínky horní hranici lesa Morfologické a fyziologické adaptace sehrávají klíčovou roli při přizpůsobení dřevin vůči extrémním klimatickým podmínkám ekotonu hranice lesa (Tranquillini 1979; Körner 2012). Pro přehlednou řešerši jsou nejvhodnější monograficky zpracované přehledy, jaké publikovali například Tranquillini (1979), populárnější formou Arno, Hammerly (1984) či na přirozenou obnovu dřevin zaměřená práce Kuoch, Amiet (1970), z novějších prací je to Holtmeier (2009) a Körner (2012). Komplexní podrobná rešerše literatury zabývající se adaptacemi dřevin na hranici lesa by počtem stran dosáhla obsahu velmi silné knihy. Pro účely této kapitoly, bylo zvoleno pouze stručné pojednání o vybraných adaptacích, které mají určitou vazbu na řešené téma dizertační práce. Obecně jsou dle Marchanda (1996) v mimo tropických podmínkách na hranici lesa zvýhodněnné stálezelené dřeviny. Adaptací na krátkou vegetační sezónu představuje skutečnost, že díky přezimujícím ročníkům jehlic, jsou už v průběhu zimy a na jaře okamžitě připraveny k aktivitě, na rozdíl od listnáčů, které nejdříve musí narašit (Marchand 1996). Známou skutečností je, že respirační ztráty uhlíku u stromů rostoucích na hranici lesa jsou vyšší při jakékoliv teplotě ve srovnání se stromy z nižších nadmořských výšek (Písek, Winkler 1958; Mooney et al. 1964). Tyto vyšší respirační ztráty jsou na hranici lesa kompenzovány nižšími teplotami, při kterých dosahuje fotosyntetický výkon optima, oproti dřevinám z nižších poloh (Tranquillini, Havránek 1985). U jehličnatých dřevin dosahuje fotosyntetický výkon optima při 15 °C (Tranquillini, Havránek 1985). Asimilační aparát na chlad adaptovaných stromů rostoucích na hranici lesa běžně dosahuje 13


30% fotosyntetické kapacity již při teplotě 0 °C a 50–70% při teplotě 5 °C, celkově je teplotní optimum fotosyntetické kapacity velmi široké v rozmezí 5–21 °C (Benecke et al. 1981; Tranquillini, Havránek 1985). Limitní teplotní bod pro fotosyntetickou aktivitu na hranici lesa u jehličnanů činí -4 °C, což je teplota, při které se začíná tvořit led v listech (Tranquillini 1957; Tranquillini, Holzer 1958; Pisek et al. 1967). Celkově je uhlíková bilance dřevin na hranici lesa obdobná nebo jen nevýznamně méně příznivá, ve srovnání se stromy z nížeji položených poloh (Körner 2012). Dřeviny při horní hranici lesa musely vyvinout schopnost výrazné mrazuvzdornosti, neboť asimilační aparát, vyčnívající nad sněhovou pokrývku, je vystaven mrazu, který je často znásoben silnými větry. Körner (2012) uvádí u vybraných dřevin letální teploty nižší než -65 °C, podobně i Holtmeier (2009) publikoval tabulku potenciální odolnosti dřevin proti mrazu s rozlišením na pupeny, listy a kmen s využitím publikací (Sakai, Wieser 1973; Larcher 1985; Tranquillini, Plank 1989; Gansert et al. 1999). Také zde se pohybuje tolerance v rozpětí od -30 °C do -80 °C. Pro smrk ztepilý uvádí hodnotu -40 °C jak pro jehlice, tak i pro kmen. Fyziologické mechanizmy vedoucí k vysoké mrazuvzdornosti dřevin jsou založeny na schopnosti vyhnout se zmrznutí (např. rychlý růst do určité výšky, kde nejsou mrazové podmínky), klíčovou roli rovněž hrají osmoregulační mechanismy vedoucí ke snížení bodu mrazu buněk (Sakay, Larcher 1987), dále pak prevence vzniku intracelulárního ledu, z důvodu jeho formování v extracelulárním prostoru rostlinných pletiv (Körner 2012). V rámci výškového gradientu směrem k hranici lesa a následné hranici stromů dochází rovněž k významným strukturálním změnám ve vlastnostech listoví. Obecně platí, že velikost listů se zmenšuje s nadmořskou výškou, tento trend je obzvláště patrný v gradientu mezi horní hranicí lesa a horní hranicí stromů (Dale 1992). Zmenšení velikosti listů je připisováno chladnějším a celkově méně příznivým klimatickým charakteristikám, zejména zkrácení celkového období vhodného pro formování listů (Körner 2012). Zmenšení velikosti listů je v případě jehličnanů daleko méně znatelné v porovnání s listnatými dřevinami. Se zvyšující se nadmořskou výškou se listy u listnatých dřevin stávají podstatně tlustší, mají silnější vrstvu epidermis a více palisádových vrstev, s většími palisádovými buňkami. Tyto strukturální změny vedou k celkovému snížení projekční plochy listoví a specifické listové plochy na 1 gram sušiny. Stomatální frekvence se směrem k horní hranici lesa zvyšuje, což je vysvětleno vyšší intenzitou radiace a větším poměrem slunných jehlic v důsledku nižšího zápoje (Körner 2012). Rovněž vlastnosti vodivých elementů jehličnanů se mění v gradientu nadmořské výšky směrem k hranici stromů. Dle výsledků, které publikovali Petit et al. (2011) dochází u smrků na hranici lesa k tvorbě výrazně užších tracheid a z tohoto důvodu je pro vodivý systém xylému charakteristický velký hydraulický odpor. Právě z důvodu velkých hydraulických odporů ve vodivém systému dosahují dle Petit et al. (2011) stromy v ekotonu hranice lesa nižších výšek, protože při vyšším vzrůstu by z důvodu velkých hydraulických odporů byla voda nedostatečně transportována k vrcholu stromu. Výše uvedenou teorii, ale nelze považovat za globálně platnou, protože Mayr et al. (2006) nezaznamenal statisticky významné rozdíly ve velikosti vodivých elementů u pěti různých dřevinných druhů (Picea abies, Pinus mugo, Pinus cembra, Larix decidua, Sorbus aucuparia) rostoucích při hranici lesa v Alpách. Stejných výsledků rovněž dosáhl (Körner 2012) u druhů Larix decidua a Picea abies.

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Mechanické vlastnosti dřeva ve větvích se rovněž mění s nadmořskou výškou. Jako velmi podstatné se jeví zvýšení jejich pružnosti, jakožto adaptace proti vzniku zlomů. Z anatomického hlediska dochází ke zvýšení pružnosti prostřednictvím zvýšené lignifikace vodivých elementů a prodloužení vláken vodivých elementů (Speck et al. 1996). Jalkanen, Konopka (1998) kvantifikovali stupnici odolnosti dřevin proti vzniku zlomů větví v důsledku sněhu v severním Finsku, a jako nejméně odolnou dřevinu zaznamenali Pinus sylvestris, jako středně odolný druh Picea abies a vysoký stupeň odolnosti přiřadili rodu Betula. Gallenmüller et al. (1999) vysvětluje dominanci některých druhů formujících hranici lesa (Alnus, Betula, Salix) v některých oblastech (např. Holoarktický region) z důvodu velké pružnosti a ohebnosti jejich kmenů a větví, dobře odolávajících zlomům. Výhodnou adaptací proti mechanickým škodám způsobených abrazí, či přímým poškozením koruny nebo jejich částí (prýtů a jehlic) je schopnost regenerace ze spících pupenů a přizpůsobení tvaru těla vnějším podmínkám. Schopnost zaujmout optimální design (Mosbrugger 1990) se v těchto podmínkách jeví jako klíčové. Jedná se o snížení výškového růstu, podpora keřovitého až poléhavého růstu. Proti škodám těžkým sněhem, se stromy brání tvorbou úzké kuželovité koruny, složené z krátkých větví, nasazených na kmeni pod úhlem větším než 90°. Typickým příkladem jsou úzké koruny severomarických dřevin Abies lasiocarpa či Picea engelmanii (Marchand 1996). Veškeré poškození biomasy dokáží stromy účelně nahradit za současného přizpůsobení tvaru těla, aby se minimalizovaly ztráty asimilačních orgánů a energie vynaložená na jejich ochranu, a to za kladné uhlíkové bilance (Mosbrugger 1990). V případě podzemní biomasy kořenů byly rovněž zaznamenány strukturální změny v gradientu směrem k horní hranici lesa. Hertel, Schöling (2011) zaznamenali významný nárůst biomasy jemných kořínků zároveň zvýšení poměru kořenové biomasy vůči biomase kmene u Picea abies v pohoří Harz. Navýšení biomasy kořenů s nadmořskou výškou považují autoři za adaptační mechanizmus smrku proti nepříznivým půdním podmínkám, zejména proti redukovanému množství dostupných živin a velké sezónní variabilitě jejich dostupnosti (Hertel, Schöling 2011). Výše uvedená teorie nicméně nemusí být globálně platná, protože autoři (Alvarez-Uria, Körner 2011) neprokázali významné rozdíly v parametrech kořenů u šesti hlavních dřevin horní hranice lesa v Evropě ve srovnání s nižšími polohami. Na hranici lesa jsou rovněž vyvinuty adaptace proti vzniku vodního stresu a s ním souvisejícím vznikem embolizmu ve vodivých systémech dřevin. V embolizovaném xylému je transport vody blokován bublinami, které snižují hydraulickou vodivost (Sperry at al. 1988). V evropských pohořích obecně platí, že stres suchem u jehličnanů je méně častý ve vegetačním období (častý výskyt srážek), ve srovnání se zimním obdobím, kdy jsou stromy často postihovány zimním vysycháním (Mayr et al. 2006). Proti těmto druhům stresu mají některé dřeviny vyvinutou schopnost tzv. stomatální regulace, resp. schopnost uzavírat průduchy z důvodu regulování ztrát vody transpirací. Tato schopnost je např. velmi dobře vyvinuta u Pinus cembra a Picea abies (Anfodillo et al. 1998). Také snížení průměru tracheid je považováno za účinnou adaptaci proti vzniku embolizmu ve vodivém systému dřevin (Petit et al. 2011). Klíčovou adaptací, která rozhoduje o existenci a poloze hranice lesa je schopnost dřevin se v těchto podmínkách generativně či vegetativně šířit. Jelikož má toto téma úzkou vazbu k tématu dizertační práce je mu věnována následující samostatná kapitola. 15


4.4 Reprodukce dřevin při hranici lesa 4.4.1 Generativní reprodukce Všechny literární zdroje se shodují, že na hranici lesa se generativní schopnost šíření u dřevin snižuje, prodlužuje se perioda semenných let, a v plodech a šiškách je méně semen, jejichž klíčivost je nižší (např. Tranquillini 1979; Holtmeier 2009). Úspěšná generativní obnova není otázkou vhodných podmínek během jedné vegetační sezóny. Holtmeier (2009) názorně vysvětluje, že musí jít o souhru vhodných podmínek minimálně čtyři po sobě jdoucích let, což může být v podmínkách hranice lesa velmi vzácné. První rok se musí založit květní pupeny, druhý rok strom kvete, je opylen, oplozen a zrají šišky, třetí rok vypadávají semena, která se musí dostat na vhodné mikrostanoviště a teprve čtvrtý rok dojde ke klíčení a poté následuje vysoká mortalita semenáčků v prvních letech po vyklíčení. Například smrk (Picea abies) produkuje dobrou úrodu semen v intervalu - jedenkrát za tři až pět let, v podmínkách horní hranice lesa se tento interval prodlužuje a dobrou úrodu lze očekávat jedenkrát za devět až jedenáct let (Tschermak 1950). V případě borovice limby (Pinus cembra) je frekvence semenných let, obdobná jako u smrku ztepilého, Holtmeier (1974) uvádí její interval v rozmezí sedm až deset let. Frekvence semenných let u severoamerických zástupců dřevin formující hranici lesa Abies lasiocarpa a Picea engelmannii je o něco častější, ve srovnání s evropskými zástupci Picea abies a Pinus cembra. Oosting, Reed (1952) uvádějí její frekvenci v rozmezí tři až šest let. Postupem k hranici lesa sice stromy občasně formují šišky, ale množství a vitalita semen je výrazně snížená. Z tohoto důvodu je klíčivost semen například u Picea engelmannii v pohoří Colorado Front Range téměř nulová (Dahms 1984). Téměř nulová klíčivost byla rovněž zaznamenána u Pinus sylvestris na hranici lesa ve Finském Laponsku (Holtmeier 2005). Dle Hentonnen (1986) je pravděpodobnost produkce dobře vyzrálých vitálních semen u Pinus sylvestris v severních boreálních lesích v časovém intervalu dvakrát za 100 let. Wardle (1970) zaznamenal snížení klíčivosti semen Notofagus solandri z 20% na 5% v rámci výškového gradientu 1000–1350 m n. m. (pohoří Craigieburn – Nový Zéland). Nicméně, někteří autoři (Marr 1977; Maděra 2005) se domnívají, že zásoba klíčivých semen v ekotonu hranice lesa je dostatečná, pro efektivní přirozenou regeneraci, a absence semenáčků je z tohoto důvodu zapříčiněna nedostatkem vhodných ekotopů pro klíčení semen. Klíčivost semen je ovlivněna celou řadou faktorů (teplota, půdní vhkost, půdní pH, vlhkost vzduchu, osvětlení atd.) a rovněž závisí na specifických vlastnostech semene daného druhu (Holtmeier 2009). Bezkřídlá semena dřevin hranice lesa jako např. Pinus cembra, Pinus albicaulis, Pinus pumila, Pinus flexilis jsou charakteristicky těžká a obsahují tuhé osemení. Semena tohoto typu mohou klíčit až po dlouhé stratifikaci při teplotách okolo bodu mrazu a světelné podmínky nehrají v souvislosti s jejich klíčením velkou roli (Granström 1987). Těmito vlastnostmi jsou přizpůsobena k zoochornímu šíření. Na druhou stranu, semena druhů dřevin hranice lesa, která se šíří anemochoricky, jsou schopna klíčit okamžitě po vypadnutí z šišky (Granström 1987), ale ztrácí vitalitu již po 10–16 měsicích. Z tohoto důvodu, druhy dřevin, které produkují semena bez schopnosti udržet si víceletou vitalitu, se mohou úspěšně rozmnožovat pouze, když po úrodě semen následuje dostatečně teplé vegetační období (Kaerney 1982). Na druhou stranu, bezkřídlá semena druhů adaptovaných na zoochorní šíření mohou přeléhat i několik let (Kajimoto et al. 1998). Hustý porost keřů a travinobylinné vegetace často může bránit semenům, aby přišla do kontaktu s vhodným ekotopem pro klíčení (Weih, Karlsson 16


1999). Již Auer (1947) poukazuje na to, že pouze 2 cm tlustá vrstva humusu a mechů výrazně brání klíčení semen modřínu (Larix decidua), protože semenáčky obvykle vyschnou, než jejich kořeny dosáhnou minerální půdy. Na druhou stranu porost keřů a travinobylinné vegetace chrání semenáčky proti nepříznivým klimatickým vlivům (Holtmeier 2009). Velmi vhodný ekotop pro klíčení semen a růst semenáčků představuje otevřená minerální půda (Holtmeier 1995). Velmi zajímavou studii provedli Cui, Smith (1991), kteří sledovali mortalitu semenáčků v pohoří Medicine Bow (jižní Wyoming, USA) v rozmezí nadmořských výšek 2672–2950 m n. m. Tito autoři zjistili velmi vysokou mortalitu v prvních dvou letech po vyklíčení. Tato mortalita činila 60% u zastíněných a 90% u osluněných semenáčků. V dalších letech se téměř žádná mortalita semenáčků nevyskytla (Cui, Smith 1991). Rovněž Mellmann-Brown (2005) potvrzuje, že z hlediska mortality semenáčků jsou nekritičtější první 2–4 roky po vyklíčení.

4.4.2 Vegetativní reprodukce Z důvodu omezené schopnosti generativní reprodukce se jeví vegetativního šíření v podmínkách horní hranice lesa jako velmi důležitá (např. Kuoch, Amiet 1970; Tranquillini 1979). Vegetativní reprodukce se nejčastěji může vyskytovat ve formě výmladků na kmeni, kořenových výmladků, hřížení a obecně je více častá u listnatých stromů (Smith et al. 1997). Hřížení představuje nejčastější formu vegetativní reprodukce u jehličnatých dřevin na hranici lesa (Holtmeier 2009). Typicky se vyskytuje v pásmu boje nad zapojenou hranicí lesa, kde již nejsou stromy vyvětvené, díky hustému zápoji, a mají zde korunu nasazenou nízko u země. Podmínkou hřížení je, aby spodní větve byly v kontaktu se zemí (Kuoch, Amiet 1970).

Obr. 2: Klonální smrkové skupinky vznikající hřížením. 17


Tento způsob šíření je nejčastěji popisován z evropských pohoří u smrku ztepilého (Picea abies), (Kuoch, Amiet 1970; Maděra 2005, Vacek et al. 2012). Plesník (1971) zaznamenal tuto schopnost i u modřínů (Larix decidua). Na Kamčatce se hřížením šíří velmi často Larix gmelinii, který dokonce regeneruje i pomocí kořenových výmladků (Okitsu 1998). V Severní Americe se podobně hřížením šíří například druhy Abies lasiocarpa, A. balsamea, Picea engelmanii, P. glauca, P. mariana, Larix lyalii, Tsuga mertensiana, Chamaecytisus nootkatensis, Pseudotsuga menziesii (Arno, Hammerly 1984; Holtmeier 2009). Vegetativně, zakořeňováním poléhavých větví, se na horní hranici lesa šíří i klečovitě rostoucí borovice (Pinus mugo, P. albicaulis, P. pumila), jalovec (Juniperus nana), ale i listnaté dřeviny /Nothofagus spp., Alnus viridis, Betula turtuosa/ (Holtmeier 2009), Fagus sylvatica (Fanta 1981; Vacek, Jeník 2010; Vacek Hejcman 2012). V případě rodu Pinus, je s výjimkou výše uvedených druhů borovic, vegetativní šíření na hranici lesa spíše výjimečné (Holtmeier 2009). Například borovice limba (Pinus cembra) se šíří pomocí ořešníka pouze semeny (Campell 1950). Výsledky rozsáhlého výzkumu zaměřeného na vegetativní šíření smrku v Alpách publikovali Kuoch, Amiet (1970). Skupiny vegetativně se šířících stromů (klony) mají podle jejich výsledků výhodnější ekologické poměry oproti samostatně rostoucím stromům, protože si samy vytvářejí vnitřní mikroklima, snižující klimatické extrémy, poskytují si vzájemnou ochranu. Prostřednictvím vegetativního rozmnožování vznikají klonální skupinky (v anglické terminologii − ,,clonal group“, Holtmeier 2009; Körner 2012), nebo polykormony (Slavíková 1986; Maděra 2005). Zatímco generativně vzniklý strom uhyne, když se dožije svého fyziologického věku, vegetativně šířící se klon může dosahovat značného stáří. Laberge et al. (2000) objevili klonální skupinu smrku (Picea mariana) složenou z 80 kmenů a starou 1800 let. Rovněž Kullman (1996) popsal v centrálním Švédsku klonální skupinku (Picea abies), u níž byl věk jejich subfosilní zbytků odhadnut na 9000 let. Strom, který založil klonální skupinku bývá nazýván jako mateřský nebo rodičovský strom - ,,parent tree, mother tree“ (Cooper 1911; Vacek et al. 2012). Vegetativně vzniklý jedinec bývá nazýván jako dceřiný strom − ,,daughter tree“ (Cooper 1911), rameta − ,,ramets“ (Jeník 1994), hříženec − ,,layer“ (Holtmeier 2009). Větev, ze které vznikl dceřiný jedinec a která zprostředkovává klonální spojení mezi těmito jedinci bývá nazývána jako hřížící větev − ,,layering branch“ (Cooper 1911). Klonální skupina se chová z hlediska zásobování vodou a živinami jako jednotný celek a to až do té doby, než se dceřiný jedinec stane nezávislý na vodních zásobách a živinách rodičovského stromu (Holtmeier 2009). Nicméně, délka období závislosti dceřiného stromu na rodičovském není doposud objasněna, Holtmeier (2009) pouze konstatuje, že může trvat desetiletí, než se dceřiný jedinec stane nezávislým. Tvar klonálních skupinek je odvislý od prostředí, ve kterém se šíří a obecně platí, že převažuje šíření ve směru od hlavního převažujícího stresového faktoru. Z tohoto důvodu se na prudkých svazích klonální skupiny rozrůstají ve směru ze svahu a na stanovištích exponovaných vytrvalému větru se pak výhradně šíří v jeho převládajícím směru (Holtmeier 2009). Na stanovištích, která nejsou výrazně svažitá a která nejsou pod vlivem silnějšího větru, formují klonální skupiny pravidelný kulovitý tvar (Kuoch, Amiet 1970). Po odumření iniciálního rodičovského stromu uvnitř klonální skupiny, je tato skupina nazývána jako atolls (Griggs 1938). Výskyt klonálních skupin stromů není vázán výhradně do poloh nad horní hranici lesa, ale občasně jsou popisovány i z nižších lokalit (Jeník 1976). Obecně však platí, že jejich frekvence se významně zvyšuje směrem k hranici stromů (Kuoch, Amiet 1970). V drsných 18


klimatických podmínkách je hřížení − resp. formování adventivních kořenů obvykle aktivováno poškozením apikálních větví prostřednictvím klimatických (mráz, zimní vysychání) a mechanických faktorů /abraze, okus, atd./ (Kuoch Amiet 1970). Z tohoto důvodu se mohou spodní poléhavé větve ve zvýšené míře rozrůstat, protože nejsou fytohormonálně kontrolovány vrcholovým prýtem a postupně se stanou upevněné ve svrchním půdním horizontu (Kuoch Amiet 1970; Fanta 1973; Brown 1974). Adventivní kořeny jsou obvykle formovány na spodní straně poléhavé větve, která je ukotvená ve svrchní humusové vrstvě. Huminové kyseliny mají stimulační efekt na tvorbu adventivních kořenů (Fanta 1973).

4.5 Horní hranice lesa v Hrubém Jeseníku 4.5.1 Základní údaje o horní hranici lesa a polohách nad hranicí lesa Z hlediska druhové skladby stromového patra je současná horní hranice lesa tvořena smrkem ztepilým (Picea abies) a místy s příměsí jeřábu ptačího (Sorbus aucuparia). V kontextu přirozeného areálu smrku se v Hrubém Jeseníku jedná o zcela unikátní společenstvo, neboť v jiných středoevropských pohořích jsou porosty smrku na horní hranici lesa doprovázeny dalšími druhy dřevin, které pak vystupují i výše /borovice limba − Pinus cembra, borovice kleč - Pinus mugo, modřín opadavý - Larix decidua, rododendron − Rhododendron sp., olše zelená − Alnus viridis/ (Maděra 2005). Smrk proto mohl zaujmout ekologickou niku borovice kleče, která zde pravděpodobně chorologicky chybí, respektive v průběhu holocénního vývoje vegetace ustoupila (Jeník 1973; Rypl 1980; Rybníček Rybníčková 2004). Proto se adaptoval na drsné podmínky vrcholového fenoménu, takže jedinci roztroušeně vystupují v podobě skupin jedinců vegetativního původu i nad horní souvisleji zapojenou hranici lesa, kde vytváří specifickou jesenickou smrkovou variantu klečového vegetačního stupně (Buček et al. 2004). Nejstarší přibližné vymezení horní hranice lesa je provedeno v práci (Miklitz 1857). Poté byla horní (alpinská) hranice lesa vymezena v práci (Jeník, Lokvenc 1962). Nejnověji je pak horní hranice lesa v Hrubém Jeseníku vymezena v praci (Treml, Banaš 2000). Celková plocha poloh nad horní hranici lesa v Hrubém Jeseníku je 1103 ha. Polohy nad horní hranicí lesa se nacházejí v šesti oddělených enklávách /Šerák, Keprník, Vysoká hole – Pecný, Červená hora, Malý Děd, Praděd/ (Treml, Banaš 2000). Maximální výše horní hranice lesa (1430 m n. m.) je dosaženo na západním svahu Pradědu. Její průměrná výška činí 1302 m n. m. Největší část horní hranice lesa probíhá ve výškovém intervalu 1300 – 1350 m n. m. (Treml, Banaš 2000). Ekoton horní hranice lesa v Hrubém Jeseníku je typický užším pásem boje v porovnání s Krkonošemi, přibližně 70% její délky má šířku pásu boje do 100 m (Treml, Banaš 2000). V Hrubém Jeseníku nalézáme výrazně odlišenou horní hranici lesa a horní hranici stromů (Treml, Banaš 2000), přičemž horní hranice smrků o výšce 2 m je vymezena pouze ve vrcholových polohách Keprníku, Vysoké hole a Pradědu, průměrná výška smrků na současné horní hranici lesa je 10 m (Treml 2007).

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4.5.2 Přirozený rozsah bezlesí a antropické vlivy na horní hranici lesa V Hrubém Jeseníku byla původnost horní hranice lesa a otázka rozsahu přirozeného bezlesí dlouhodobě diskutovaným problémem (např. Jeník 1961; Bureš 1976; Horák 1977), zejména pak v souvislosti s otázkou přirozené absence borovice kleče (Jeník 1961). Pokud bychom přijali myšlenku stálých, v průběhu celého holocénu přítomných rozsáhlých bezlesých ploch nad horní hranicí lesa (Jeník 1961), je velice obtížné vysvětlit přirozenou absenci borovice kleče (Treml, Banaš 2005). Takřka s jistotou lze na základě současných poznatků odmítnout názor, že kleč by mohla být v Hrubém Jeseníku původní, jak zmiňoval (Horák 1977). Výskyt kleče nebyl prokázán ani paleogeobotanickým výzkumem čtyř rašelinných profilů mezi Velkým Dědem a Májem, zahrnujícím období 5000 let (Rybníček, Rybníčková 2004), ani v profilech Mezikotlí a Keprník (Novák et al. 2010). Uhlíky borovice nebyly nalezeny ani při podrobné antrakologické analýze ve vrcholových polohách Hrubého Jeseníku (Novák et al. 2010). Příčiny absence kleče ve Východních Sudetech nejsou dosud uspokojivě vysvětleny (Banaš et al. 2001), takže je třeba spokojit se s tím, že v biogeografii Hrubého Jeseníku hrála velkou roli náhoda, díky níž se sem diaspory kleče v průběhu postglaciální florogeneze nedostaly (Jeník 2005). Za doklady přirozeného alpinského bezlesí jsou považována floristicky bohatá společenstva závětrných turbulentních prostor svazu Calamagrostion arundinaceae, společenstva vyfoukávaných trávníků svazu Juncion trifidi a společenstva sněhových výležisek svazu Salicion herbaceae. Kromě rostlinných společenstev mohou posloužit jako indikátory přirozeného bezlesí i společenstva živočichů (Kuras 2001). Zde je ovšem nutné připustit, že tato společenstva mohla přežívat na stanovištně omezených lokalitách, ze kterých se mohla po antropickém zvětšení ploch (Hošek 1972; Horák 1977; Rybníček, Rybníčková 2004; Novák et al. 2010) rozšířit na území jejich dnešního výskytu. Za další důkaz přirozeného bezlesí bývá považována přítomnost zachovalých, kořenovým systémem stromů nerozrušených periglaciálních tvarů, nutno ovšem podotknout, že v Krkonoších se dochované strukturní půdy nacházejí v místech dlouhodobě porostlých klečí, a že jesenické tvary (s výjimkou Keprníku) celkově nejsou tak dobře vyvinuté, jako tvary v nejvyšších polohách Krkonoš (Treml, Banaš 2005). Většina názorů na nepůvodnost bezlesí ve vrcholové oblasti Hrubého Jeseníku vychází z pylových analýz (Salaschek 1935; Firbas 1949). Salaschek (1935) uvádí v subatlantiku přítomnost jedlobučin na hřebenech Jeseníků. Podle Firbase (1949) pak v subatlantiku musely hřebeny Jeseníků pokrývat uzavřené lesy a bukový stupeň do 14. století sahal nejméně do 1300 m n. m., s tím, že dnešní přítomnost alpinské hranice lesa v její výšce je výsledkem lidské činnosti. Podle Firbase (1949) maximální zdvih alpinské hranice lesa v holocénu (atlantik, subboreál) byl o cca 300 m výše než dnes. Názory Firbase (1949) a Salascheka (1935) jsou značně ovlivněny tím, že zmínění autoři přikládali malou roli záplavě pylu na rašeliništích ve větrně exponovaných vrcholových polohách, což mohlo velmi zkreslit jejich výsledky (Banaš et al. 2001). Zcela odlišný názor na přirozený výskyt hranice lesa a rozsah alpinských poloh v Hrubém Jeseníku publikoval Horák (1977), který vycházel z porovnávání s alpinskými polohami v Tatrách prostřednictvím analogických a paralelních geobiocenologických ploch dle Zlatníkovi geobiocenologické metody (Zlatník 1976; Štykar, Ambros 2004). Na základě geobiocenologického výzkumu došel Horák (1977) k závěru, že jesenické hřebeny byly ještě před příchodem člověka do těchto lokalit, pravděpodobně i v nejvyšších lokalitách, pokryty rozvolněnými porosty lesních dřevin. Dle Horáka (1977) se jednalo o 20


smrčinu s přimíšeným jeřábem. Vlivem vrcholového fenoménu byla omezeného růstu a s omezenou schopností reprodukce. Z těchto důvodů nemohla být plně zapojena. Na příznivých místech byl přimíšen buk a klen, rovněž v zakrslé formě. Druhy horské tundry, zatlačované nástupem lesních edifikátorů, našly útočiště na skalách převyšujících jejich výšku a v mozaice rozvolněných ploch. Byl to dostatečný prostor pro vývin endemických forem. Na ekotopech s Juncus trifidus (návětrné strany skalních bloků a na thufurech) a se Salicetum herbaceae a Festuca supina, se dochovaly druhy horské tundry (Horák 1977). Nejnovější výzkumy týkající se problematiky dynamiky horní hranice lesa v Hrubém Jeseníku, které byly založeny na palynologických a antrakologických analýzách (Treml et al. 2006; Treml et al. 2008, Novák et al. 2010) vytvořily historické schéma vývoje polohy hranice lesa v Hrubém Jeseníku v průběhu Holocénu. Na základě tohoto schématu, rozsah teplotně limitovaného alpinského bezlesí v Hrubém Jeseníku fluktuoval v průběhu holocénu, v omezených vrcholových partiiích. A v průběhu teplotně nadprůměrných period holocénu (např. v období Atlantiku, kdy byla teplota cca o 2 stupně vyšší, Kalis a kol. 2003) tyto teplotně limitované plochy téměř kompletně vymizely (Treml et al. 2006). Nicméně, dle Tremla et al. (2006), přítomnost rostlinných druhů striktně vázaných na bezlesé plochy indikuje, že plošně omezené enklávy bezlesí zde musely být dlouhodobě přítomny. Přítomnost těchto enkláv byla podmíněna půdními vlastnostmi, vodním režimem a sklonem svahu, nikoliv pak teplotními podmínkami. Současný stav horní hranice lesa v Hrubém Jeseníku a vegetace nad touto hranicí je ýslednicí jak přírodních procesů, tak i dlouhodobých vlivů člověka. Dle Nováka a kol. (2010) je současný rozsah bezlesých ploch nad horní hranicí lesa převážně výsledkem historických vlivů člověka. Na základě palynologických a antrakologických analýz je první pravděpodobný impakt člověka do nejvyšších poloh Hrubého Jeseníku datován do konce doby Železné (100 let před n. l.). Dle Nováka et al. (2010), není žádný pochyb o výskytu lokálních požárů v hřebenových polohách Hrubého Jeseníku založených člověkem vlivem pálení stromů v průběhu druhé poloviny raného Středověku (800 – 1000 našeho letopočtu). Extenzivní odlesnění nejvyšších poloh začalo po roce 1300 našeho letopočtu (Novák et al. 2010). Na základě datování a analýzy antrakologických vzorků rekonstruují Novák et al. (2010) v období prvních antropických vlivů, v nejvyšších polohách Hrubého Jeseníku, rozvolněné porosty s dominancí Picea abies, a rovněž je dokladována přítomnost heliofilních druhů dřevin (Betula sp., Sorbus sp., Juniperus sp., Salix sp.). Dlouhodobé vlivy pastvy ve vrcholových polohách Hrubého Jeseníku dokládají historické prameny. Již první lesní řády z let 1541−1574 obsahují zmínky o pastvě a potřebě její regulace (Sokol 1965). Od počátku 17. století, lze nalézt zmínky o travaření a pastvě na všech panstvích, která spravovala vrcholové polohy Hrubého Jeseníku, což dokládá souhrnná rešerše historických pramenů, soustředěných v historických průzkumech lesů v Hrubém Jeseníku (Zmrhalová 2007). Od poloviny 19. století se začíná s pokusy o zdvižení tehdejší horní hranice lesa, z důvodu zabránění antropogenně podmíněných svahových sesuvů (Sokol 1965). K významnějšímu zalesňování dochází po roce 1853, kdy byla v Rakousku vyhlášena soutěž o nejúspěšnější zalesňovací výsledky v horských polohách. V letech 1883–1907 probíhají snad vůbec největší zalesňovací práce na horských holích. Hlavní vysazovanou dřevinou se stává zejména kleč a limba. Zalesnění se nejprve považovalo za úspěšné, po roce 1920 však dochází k náhlým

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lavinovým úhynům limby a porosty tak poměrně rychle mizí (Nožička 1957; Hošek 1973).

Obr. 3: Horní hranice lesa v severní části Velké kotliny.

4.6 Posuny horní hranice lesa v závislosti na recentní změně klimatu V souvislosti s recentní změnou klimatu, vymezuje Grace et al. (2002) tři hlavní aspekty recentní změny klimatu, vůči kterým lze očekávat fyziologickou odezvu rostlin. Jedná se o navýšení teploty, zvýšení koncentrace oxidu uhličitého a zvýšená depozice dusíku (Grace et al. 2002). V případě zvýšení koncentrace oxidu uhličitého, je nepravděpodobné očekávat významnou odezvu v růstu stromu na hranici lesa (Grace et al. 2002). Z toho důvodu, že u těchto stromů není růst limitován nedostatkem produktů fotosyntézy, ale spíše problémem jejich zabudování do rostlinných pletiv a růstu při nízkých teplotách (Körner 1999). V souvislosti s depozicemi dusíku Körner (2012) jednoznačně konstatuje, že minerální výživa stromů a obsah živin v půdě přirozeně ovlivňuje vitalitu stromů, ale zároveň vykazuje velmi malý vliv na polohu hranice lesa. Vzhledem k výše uvedeným důvodům a s ohledem na klíčový vliv teploty na polohu hranice lesa (Körner 1999), lze očekávat nejvýznamnější odezvu polohy hranice lesa právě vůči navýšení teploty (Körner 2012). V průběhu posledního století došlo ke globálnímu navýšení teploty v průměru o 0.6 °C, nicméně, často existují lokální odchylky od této hodnoty (Walther et al. 2002). Velmi výrazně se změny teplot projevují ve vysokých nadmořských výškách a v polohách vysokých zeměpisných šířek (Solomon et al. 2007). Z těchte důvodu jsou v rámci celosvětového měřítka zaznamenávány posuny horní hranice lesa vzhůru a rovněž její expanze více na sever v případě severních hranic výskytu lesa (Harsch et al. 2009). 22


Vzestup teploty redukuje vliv hlavních faktorů, které limitují výskyt stromů ve vysokých nadmořských výškách a to − fyziologického omezení růstu a neschopnosti generativní reprodukce (Körner 1999; Germino et al. 2002; Harsch and Bader 2011; Körner 2012). Nicméně, autoři (Holtmeier, Broll 2005) poukazují na to, že hlavní faktory, které determinují dynamiku hranice lesa, jsou velmi závislé na měřítku, ve kterém dynamiku hranice lesa posuzujeme. V rámci kontinentálního měřítka, teplotní charakteristiky představují nejvýznamnější faktor pro polohu hranice lesa. Zatímco, v případě lokálního měřítka, může být více významný vliv disturbancí, geomorfologických charakteristik a mezoklimatu (Holtmeier, Broll 2005). Citlivost horní hranice lesa ke změnám klimatu se liší v závislosti na rozdílnosti typů horní hranice lesa (Holtmeier, Broll 2005). Hranice lesa, které jsou formovány převážně orografickými vlivy, nejsou příliš citlivé v reakci na oteplení klimatu. Naopak, velmi citlivé jsou dlouhodobě antropicky ovlivňované hranice lesa ve fázi po ukončení lidských aktivit (např. pastvy). Citlivost horní hranice lesa vůči změně environmentálním faktorům souvisejících s globální klimatickou změnou (mocnost a dynamika kumulování sněhu, půdní vlhkost, teplota, evaporace) se mění mezi regiony s různými klimatickými charakteristikami (Holtmeier, Broll 2005). Jako nejvýznamnější indikátor odezvy hranice lesa vůči navýšení teploty, je považována expanze semenáčků lesních dřevin do původně bezlesých poloh nad hranicí lesa a více na sever v případě severní polární hranice lesa. Za další typické indikátory navýšení teploty jsou považovány změny ve fyziognomii stromů – náhlé zvýšení tloušťkového a výškového přírůstu nebo změna původně poléhavého keřovitého vzrůstu v růst stromovitý (Holtmeier, Broll 2005). Globální experiment zahrnující 166 lokalit hranice lesa z celého světa sledující její dynamiku od roku 1900 zaznamenal vzestup hranice lesa v případě 52 % lokalit, pouze v případě 1 % lokalit bylo zaznamenáno snížení polohy hranice lesa a v ostatních případech nedošlo ke změně polohy hranice lesa (Harsch et al. 2009). Autoři poukazují na to, že v případě některých lokalit není teplota hlavním faktorem determinující polohu hranice lesa, ale že její přímý vliv může být potlačen díky interakci s dalšími faktory jako intenzita srážek (Daniels, Veblen 2003; Wang et al. 2006), disturbance (Cullen et al. 2001), interakce mezi rostlinami (Germino et al. 2002). Autoři rovněž sledovali rozdílnou sensitivitu různých typů horní hranice lesa vůči globálnímu oteplení. Z tohoto důvodu kategorizovali horní hranici lesa do třech typů dle struktury tohoto ekotonu: 1) Difúzní typ hranice lesa − ,,difuse treeline form“, pro kterou je charakteristické postupné snižování pokryvnosti stromů s rostoucí nadmořskou výškou, 2) hranice lesa s prudkým přechodem do bezlesí − ,,abrupt treeline form“, jež je charakteristická konstantní pokryvností bez postupného snížení zápoje s nadmořskou výškou, 3) hranice lesa formována dřevinami zakrslého růstu − ,,krummholz treeline form“, která je charakteristická zakrnělým a polykormonálním růstem dřevin (Harsch et al. 2009). Difúzní typ hranice lesa vykazoval nejvyšší poměr lokalit s vzestupnou hranicí lesa, což je vysvětleno silnou determinací teplotami ve vegetačním období, z tohoto důvodu vykazuje tento typ nejvyšší sensitivitu k celkovému zvýšení teploty (Harsch et al. 2009). V případě typů hranice lesa s prudkým přechodem a s dřevinami zakrslého vzrůstu byl poměr lokalit s vzestupnou tendencí významně nižší, což je vysvětleno nižším přímým vlivem teploty na jejich formování a zároveň vyšším vlivem dalších limitujících faktorů /např. vliv větru, zimní vysychání, půdní podmínky/ (Harsch et al. 2009).

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Vacek, S., Hejcmanová, P., Hejcman, M., 2012: Vegetative reproduction of Picea abies by artificial layering at the ecotone of the alpine timberline in the Giant (Krkonoše) Mountains, Czech Republic. Forest Ecology and Management, 263: 199-207. Vacek, S., Jeník, J., 2010: Přirozené hřížení buku lesního (Fagus sylvatica L.) v ekotonu alpinské hranice lesa v Krkonoších. Opera Corcontica, 47. Vacek, S., Podrázský, V., 2006: Lesy a ekosystémy nad horní hranicí lesa v národních parcích Krkonoš. Lesnická práce. Vacek, S., Vančura, K., Zingari, P.C., Jeník, J., Simon, J., Smejkal, J., 2003. Horské lesy České republiky. Ministerstvo zemědělství České republiky. Valtera, M., 2013: Vliv disturbančního režimu přirozeného temperátního lesa na variabilitu půd na různých prostorových úrovních. Dizertační práce. Lesnická a dřevařská fakulta, Mendelova universita v Brně. 130 s. Walther, G.R., Post, E., Convey, P., Menzel, A., Parmesan, C., Beebee, T.J., Bairlein, F., 2002: Ecological responses to recent climate change.Nature, 416(6879): 389-395. Wang, T., Zhang, Q., Ma, K., 2006: Treeline dynamics in relation to climatic variability in the central Tianshan Mountains, northwestern China. Global Ecology and Biogeography, 15: 406–415. Wardle, J., 1970: The ecology of nothofagus solandri: 4. Growth, and general discussion to parts 1 to 4. New Zealand journal of botany, 8(4): 609-646. Weih, M., Karlsson, P.S., 1999: The nitrogen economy of mountain birch seedlings: implications for winter survival. Journal of Ecology, 87(2): 211-219. Wieser, G., 1997: Carbon dioxide gas exchange of cembran pine (Pinus cembra) at the alpine timberline during winter. Tree Physiology, 17(7): 473-477. Zlatník, A., 1976: Přehled skupin typů geobiocénů původně lesních a křovinných v ČSSR. Zprávy geografického ústavu ČSAV, r. VIII, č. 3-4: 55-64. Zlatnik, A., Kavinova, A., 1966: Květiny a hory. Státní pedagogické nakladatelství Praha. Zmrhalová, M., 2007: Historický vývoj porostů borovice kleče a vysokohorského zalesňování v Hrubém Jeseníku a Králickém Sněžníku. In: HOŠEK, J. et al.: VaV SM/6/70/05 Vliv výsadeb borovice kleče na biotopovou a druhovou diverzitu arktoalpinské tundry ve Východních Sudetech (CHKO Jeseníky, NPR Králický Sněžník). Návrh managementu těchto porostů. Zpráva o řešení projektu za rok 2007. s. 24-66. ČSN 48 1211 2006: Lesní semenářství – Sběr, kvalita a zkoušky kvality semenného materiálu lesních dřevin, ČNI Praha, 60 s.

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Šenfeldr, M., Maděra, P. Population structure and Reproductive Strategy of Norway Spruce (Picea abies L. Karst) Above the Former Pastoral Timberline in the Hrubý Jeseník Mountains, Czech Republic. Mountain research and development, 31(2): 131 – 143.

5. Population Structure and Reproductive Strategy of Norway Spruce (Picea abies L. Karst) Above the Former Pastoral Timberline in the Hrubý Jeseník Mountains, Czech Republic Martin Šenfeldr, Petr Maděra Department of Forest Botany, Dendrology and Geobiocoenology, Mendel University in Brno, Zemědelská 3, 613 00 Brno, Czech Republic Abstract The aims of this study were to describe the biometrics and the spatial and age structure of a population of Norway spruce, to compare vertically connected sections of the timberline ecotone, and to assess the possibility of regeneration of a Norway spruce population under the conditions of the ecotone developing above the former pastoral timberline in the Hrubý Jeseník Mountains, Czech Republic. In the first stage, we established the age of the populations by means of an age–girth nonlinear regression model in a research polygon (RP) with an area of 3.5 ha. The degree of coverage was determined by using geographic information system methods. The total number of cones in the RP was counted, and 50 cones were taken for laboratory investigation of their germination capacity and the number of seeds per cone. The distribution of trees in clonal generations was examined for 10 clonal groups, and the characteristics of layering were explored by measuring the preserved layered branches. To verify the trends found in the first stage of research, we conducted an analysis of the age of all specimens on 8 control plots (50 m × 30 m) in other parts of the mountain range. The results show that the timberline ecotone is rising gradually and that the forest-free area may become significantly reduced. Up to 3 generations of trees of clonal origin were found in the RP. A greater intensity of vegetative regeneration was observed in the upper section of the RP. Moreover, the seed-based regeneration proved to be successful in the RP, and living, germinating seeds were found even in the top parts of the RP. Keywords: Norway spruce, alpine timberline, population structure, seed-based regeneration, clonal groups, timberline dynamics, Czech Republic

5.1 Introduction The alpine timberline ecotone (ATLE) is one of the most significant biogeographic borders in a mountain landscape (Troll 1973; Becker et al. 2007; Holtmeier 2009). Because of the response of vegetation to climate change, the ATLE is considered to be a sensitive indicator of this phenomenon (Baker et al. 1995; Kimball, Weihrauch 2000). The only globally determining factor of timberline is temperature conditions (Körner 1999; Körner, Paulsen 2004). However, the position of the timberline and the structure of the woody plant population are affected by several other factors (Plesník 1971; Arno, Hammerly 1984; Treml 2007; Holtmeier 2009), among which the following are important: intense wind pressure (Holtmeier, Brol 2010), local irregularities in the distribution of snow cover (Mellman-Brown 2005), avalanche activity and relief factors (Treml, Banaš 2000). Frequently, these factors interact, e.g., the properties of relief, wind intensity and 33


direction affect the relocation of snow (Holtmeier 2005). A key factor that has influenced the physiognomy of the timberline ecotone in the mountain ranges of Central Europe is the anthropogenic activities (Plesník 1971; Treml, Banaš 2000; Treml 2007; Sarmiento, Frolich 2002; Sitko, Troll 2008; Holtmeier 2009). In the European Alps, the Carpathian Mts. and many other high mountains of Eurasia, and in most tropical high mountains, almost no untouched mountain forests remain. The type and intensity of human impact on timberlines are also different. However, a depression of 150 to 300 m below the uppermost postglacial level of the climatic timberline can be accepted as an average value (Holtmeier 1974). The timberline ecotone in the Hrubý Jeseník Mts. has been considerably affected by grazing and the related burning of trees and by haymaking and logging (Hošek 1973; Jeník, Hampel 1992). Therefore, the current timberline in the Hrubý Jeseník Mts. can be referred to as the former pastoral timberline (Kozak et al. 1995), and it is characterized by a narrow width of the ecotone /the “kampf” zone/ (Treml, Banaš 2000). With regard to tree species composition the current ATLE is naturally formed exclusively by Norway spruce (Picea abies), without the natural presence of dwarf pine – this species was introduced in the area during afforestation in the late 19th and early 20th centuries (Treml, Banaš 2000). In the context of the natural Central European range of spruce, the situation in the Hrubý Jeseník Mts. is unique (Maděra 2004; Král 2009) because in other mountain ranges, the stands of spruce on the alpine timberline (ATL) are accompanied by other woody plant species that reach even higher (Pinus cembra, Pinus mugo, Larix decidua, Rhododendron sp., Alnus viridis). Therefore, spruce may occupy the space for the dwarf pine, which receded during the development of vegetation in the Holocene (Rybníček, Rybníčková 2004). Whether this is a natural timberline (Jeník 1961; Treml, Banaš 2000; Jeník, Štursa 2003) or a timberline artificially conditioned by human activity (Hošek 1973; Novák et al. 2010), it is highly interesting to monitor the behavior of spruce at the limit of its ecological valence. The reduced ability of the Norway spruce to propagate generatively is replaced by vegetative regeneration - when a clonal tree (layer) is formed by the rooting (layering) of a plagiotropic branch (after rooting, the branch is referred to as a layering branch) of the initial parent tree (Kuoch, Amiet 1970; Tranquillini 1979; Holtmeier 2009). The aims of the present study were the following: (1) to describe the biometrics and age structure of the populations and (2) to compare two vertically connected sections of the timberline ecotone and (3) to assess the possibility of regeneration of the Norway spruce population in the conditions of the ecotone developing above the former pastoral timberline in the Hrubý Jeseník Mts.

5.2 Material and methods 5.2.1 Study area The highest elevation of the Hrubý Jeseník Mts. (the approximate center of the study area is 50°04'N and 17°14'E, Fig. 1) and the Giant Mountains are in fact the only islands of natural alpine forest-free areas between the Scandes to the north and the Alps and West Carpathians to the south (Jeník, Štursa 2003). In the Hrubý Jeseník Mts., there are six elevations at which the ATL has been defined (Treml, Banaš 2000); the upper line of 2 m tall spruce trees has only been defined in the areas of Keprník, Vysoká Hole and Praděd 34


(Treml 2007). The average altitude of the ATL (minimal canopy 50%; minimal height of trees 5 m and minimal area 1 are) in the Hrubý Jeseník Mts. is 1301 m a.s.l. (Treml, Banaš 2000). The highest parts of Keprník (1423 m a.s.l.), Vysoká hole (1463 m a.s.l.) and Praděd (1492 m a.s.l.) are the only locations in the Hrubý Jeseník Mts. where there are unique patterned ground (Treml, Křížek 2006) and wind-blown alpine grassland (Chytrý et al. 2001) made up of an alliance of Juncion triffidi (as. Carici-rigidae - Juncetum trifidi and Cetrario-Festucetum supinae Jeník 1961) with arcto-alpine species (Carex bigelowii, Juncus trifidus, Diphasiastrum alpinum, Hieracium alpinum, and abundant lichens and mosses, including Cetraria sp. and Cladonia sp.). 5.2.2 Research polygon and control plots The first, more detailed, stages of field data collection were conducted in the research polygon (RP) located in the area of the summit of Keprník. The RP was bordered by a hiking trail on the east and a vegetation line of an artificially planted closed stand of the dwarf pine on the west. The RP was divided into two sections in the altitudinal direction (Fig. 1). The lower section (lower RP) was limited by the upper line of 5 m tall spruce trees (with an average altitude of 1382 m a.s.l.) and the upper line of 2 m tall spruce trees (with an average altitude of 1407 m a.s.l); these lines were identified by Treml (2007) in the 2005 growing season. The upper section (upper RP) was defined by the upper line of 2 m tall spruce trees /1407 m a.s.l./ (Treml 2007) and the summit of Keprník (1423 m a.s.l.). The areas and inclinations of the lower and upper sections are 20,380 m2, 18%, and 15,258 m2, 11%, respectively. The research polygon is exposed to the northwest. To verify whether the trends found in 2009 can be found in other parts of the mountain range, four control plots (CP) in the area of Praděd and four CP in the area of Vysoká hole were established in the second stage of field data collection (Fig. 1). The CP were located in complementary expositions, i.e., two plots were located with one approximately above the other in each exposition: one of them in the area of the upper line of 5 m tall spruce trees (Treml 2007) and one in the area of the upper line of 2 m tall spruce trees (Treml 2007). The size of the CP was 50 m (contour line) x 30 m (line perpendicular to the contour).

35


Fig. 1: Position of the Hrubý Jeseník Mts. in Europe (A), the elevations in the Hrubý Jeseník Mts. where the alpine timberline was defined according to (Treml, Banaš 2000) and the upper lines of 5m tall spruce trees and 2m tall spruce trees defined according to (Treml 2007), (B), aerial photos of the research polygon and control plots (C-E).

5.2.3 Field data collection The fieldwork in the RP was carried out in 2009. All living and dead specimens of the Norway spruce 10 cm tall or taller were measured (shorter specimens were not recorded due to the thick grass-herb vegetation). For the living specimens, the girth at the stem base (using a tape with 1 cm accuracy) and the height (using a height-measuring staff with 5 cm accuracy) were measured. For dead specimens (including stems on the ground), only the girth at the stem base was measured. All seedlings up to a height of 80 cm that had not layered were recorded. A representative number (lower section, 94; upper section, 21) of 36


randomly chosen specimens was sampled from the stem base by means of a Pressler borer. The age of specimens with a small stem diameter, where a core could not be acquired, was established by destructive methods. The clonal relationships were explored in detail in 10 randomly selected clonal groups (6 in the lower RP and 4 in the upper RP). We determined the numbers of living and dead trees of individual generations within a clonal group by uncovering the preserved layering branches. For layering branches, the height of their position in the parent tree, the distance and the direction of rooting from the parent tree were measured. Individual generations were counted starting from the oldest specimen, even if it was dead already. All fertile specimens and the total number of their cones were recorded in 2008 and 2009. At the end of the vegetation period in 2008, 50 randomly chosen cones (25 pieces in each section of the RP) were taken with the purpose of assessing their basic characteristics (number of seeds in a cone, germination capacity). In 2010, an age analysis was performed at eight CP (Fig. 1). In each plot, all living specimens of spruce 10 cm tall or taller were bored at the stem base. In addition, the number of seedlings was counted in all CP. 5.2.4 Laboratory work The surface of the samples was sanded, and the number of tree rings was counted by using a binocular microscope (LEICA S6D). The cones that had been collected in the field were broken open, and the total number of seeds in each cone was counted. We used randomly selected seeds (4 x 100 seeds for each RP section) to test their germination capacity (the number of germinated seeds during the entire period of the germination test, i.e., 21 days, expressed in % of the seeds) in compliance with valid norms /ČSN 481211 (2006)/.

5.2.5 Data analysis Using the established age and stem girth, a non-linear regression model was created: the Chapman-Richards growth function (Šmelko et al. 1992; Zhang 1996). This was used for the calculation of the population age structure model in the RP. The Chapman-Richards growth function is expressed by the following formula:



b * x (1) y  A * 1  e



1/ c

where y is the girth at age x, b and c are parameters that have been determined by the “least square” method, and A is the asymptote that has been established by fixing it as the value corresponding to the tree on the site with highest girth (Brewer et al. 1985). The goodness of the fit (R2) was calculated by the following equation (Sweda, Kouketsu 1984):

37

Šenfeldr, M., Maděra, P. Population structure and Reproductive Strategy of Norway Spruce (Picea abies L. Karst) Above the Former Pastoral Timberline in the Hrubý Jeseník Mountains, Czech Republic. Mountain research and development, 31(2): 131 – 143. n

(2)

R2  1

 (Y i 1 n

i

 (Y y 1

i

 yi ) 2  Y )2

where: R2 – is the coefficient of determination; Yi is the observed value at age i, yi is the calculated value at age i; Y is the average of the actual observation, n is the total age of the sample tree and f is the number of parameters involved in the equation concerned. To calculate the growth function and to conduct the data analyses, we used the tools of the STATISTICA 9 and MICROSOFT EXCEL 2003 programs. Statistical testing was carried out at a level of significance of α = 0.05. The degree of coverage was determined on the basis of the proportion of the area (the category of tree). The area of the category “tree” was determined by creating a vector layer defining individual trees and clonal groups over a layer of orthogonal images (pixel 20 x 20 cm, taken in: 2008) in the ArcGIS 9 program. The size of the CP was designed so that the age could be established in a direct way for all specimens: the age structure could thus be ascertained without the use of the growth function.

5.3 Results 5.3.1 Population structure in the research polygon In total, 1,389 living specimens and 220 dead specimens of spruce were recorded in the RP. In the lower section, there were 953 living specimens (467.6 specimens per hectare) and 209 dead specimens (102.6 specimens per hectare); in the upper section there were 436 living specimens (285.8 specimens per hectare) and 13 dead specimens (8.5 specimens per hectare). The degree of coverage of spruce in the lower RP was 10.8% (spruce area 2198.4 m2), and in the upper RP, it was 1.6% (spruce area 238.7 m2). In the lower RP, the most specimens were within the 201–400 cm height class; it is important that even though the lower RP was above the upper line of 500 cm-tall spruce trees (Treml 2007), 27 specimens that exceeded that height were found here. In the upper RP, the most specimens were within the 81–200 cm height class. In total, 57 specimens exceeded the height of 200 cm, in spite of the fact that the upper RP is above the upper line of 200 cm-tall spruce trees (Treml 2007) /Tab. 1/.

38


Tab. 1: The number of living trees ≥ 10 cm by height classes. height classes

n - trees 109 230 443 144 27

10–80 cm 81–200 cm 201–400 cm 400–500 cm 501 cm +

lower RP proportion % 11.4% 24.2% 46.5% 15.1% 2.8%

n - trees 158 221 57

upper RP proportion % 36.2% 50.7% 13.1%

In both sections of the RP, the highest proportion of specimens were within the lowest girth class; the proportion decreases with increasing girth. The mortality in the lower section was the highest (31.2%) in the 61+ cm girth class, and the mortality in the upper section was the highest (3.8%) in the 31+ cm girth class. The total mortality in the upper RP was considerably lower (2.9%) than in the lower section (18.0%) /Tab. 2/.

Tab. 2: The ratio of dead trees to the living trees by girth classes. girth class 1–30 cm 31–60 cm 61+ cm sum

total 535 454 173 1162

% 46.0% 39.1% 14.9% 100.0%

girth class 1–15 cm 16–30 cm 31+ cm sum

total 295 128 26 449

% 65.7% 28.5% 5.8% 100.0%

lower RP n - living 496 338 119 953 upper RP n - living 287 124 25 436

% 92.7% 74.4% 68.8% 82.0%

n - dead 39 116 54 209

% 7.3% 25.6% 31.2% 18.0%

% 97.3% 96.9% 96.2% 97.1%

n - dead 8 4 1 13

% 2.7% 3.1% 3.8% 2.9%

The regression between the stem girth and the age of the trees as established by the core was statistically significant. Thus, the age of trees was modeled using the regression formula for the lower and upper sections of the RP (Fig. 2). The growth function has the following form for the RP lower section:



0.003455* x (3) y  217 * 1  e



1 / 0.960064

The growth function has the following form for the RP upper section: (4)



y  62 * 1  e 0.01249* x



1/ 0.996429

39


Fig. 2: Non-linear regression (Ch-R growth function) between stem girth and tree age: (A) in the RP lower section, all parameters are statistically significant, p < 10-7; parameter b interval estimate (0.002585–0.004326), parameter c interval estimate (0.822537–1.097591); R2 = 0.8044, n = 94, asymptote fixed at 217; (B) in the RP upper section, all parameters are statistically significant, p < 10-4, parameter b interval estimate (0.004908–0.020071), parameter c interval estimate (0.451770–1.541088); R2 = 0.7463, n = 21, asymptote fixed at 62.

The highest proportion of trees (23.9%) in the lower section was within the 21–40 year age class. Toward higher ages the proportion decreased to the value of 0.3%, which represents the oldest age class (181–200 years). In the upper section, the highest proportion of specimens was in the youngest age class (59.2%), and there was a gradual decrease in proportion towards older age classes (Fig. 3).

40


Fig. 3: The distribution of specimens in the age classes on the RP and on the CP.

Another insight into the age structure of the population is provided by the proportion of trees in the individual generations in clonal groups (Tab. 3). In the lower section, 11 initial parent trees had produced 49 1st generation layers: these had produced 23 2nd generation layers, and the 2 nd generation layers had given birth to 5 3rd generation layers. In the upper section, one generation fewer was recorded. In total, 5 initial parent trees had produced 64 1st generation layers, and these had produced 10 2nd generation layers. Considering the above mentioned data with respect to the entire RP, one primary parent tree has 7.1 direct descendants, i.e., 1st generation layers. The 1 st generation layers only produced 0.3 2nd generation layers on average and the 2nd generation layers produced 0.23rd generation layers (Tab. 3). The speed of spruce propagation decelerates starting in the 2nd generation in both sections of the RP. A statistically significant difference (Mann-Whitney U test, p < 10-5) in the number of 1 st generation layers produced by one parent tree was recorded between the lower (average value ± SE = 4.5 ± 1.06) and the upper (average value ± SE = 12.8 ± 4.18) sections of the RP.

41


Tab. 3: The distribution of trees in individual generations in clonal groups, their number and mortality (in relation to the total number of trees in clonal groups within a particular section of the RP).

Generation Living/dead Number of trees Proportion % In total

Parent tree f m 9 2 9.4 % 2.1% 11

lower RP 1st generation 2nd generation f m f m 47 2 22 1 49.0% 2.1% 22.9% 1.0% 49 23

Generation Living/dead Number of trees Proportion % In total

Parent tree f m 5 0 6.3% 0.0% 5

upper RP 1st generation 2nd generation f m f m 63 1 10 0 79.7% 1.3% 12.7% 0.0% 64 10

3rd generation f m 5 0 5.2% 0.0% 5

Not identified f m 7 1 7.3% 1.0% 8

3rd generation f m

Not identified f m

5.3.2 Seed-based and vegetative regeneration in the RP The potential of seed-based regeneration of spruce in these conditions is not high; however, it is important that it is still apparent. In the lower RP 1,325 cones in total (650 cones per hectare) were found. Cones were found in 9.9% of specimens. In the upper RP, 29 cones were found (19 cones per hectare). Cones were found in 1.2% of all specimens. The average number of cones per fertile specimen in the lower section (average value ± SE = 14.1 ± 1.88) was higher than in the upper section (average value ± SE = 5.8 ± 1.32). There was a statistically significant difference (Student T-test, p < 10-4) in the number of seeds per cone between the lower (average value ± SE = 70.2 ± 6.50) and the upper (average value ± SE = 42.2 ± 5.42) sections of the RP. The average value of the germination capacity in the lower section (average value ± SE = 27.0 ± 3.67) was slightly higher than in the upper RP (average value ± SE = 20.5 ± 2.90), but the difference for the basic set was not statistically significant (Student T-test, p = 0.21). The estimated potential of the seed-based regeneration (estimation based on the number of cones, the average number of seeds per cone and the average germination capacity) is 12,320 germinating seeds per hectare in the lower RP, and 164 germinating seeds per hectare in the upper RP (Tab. 4). Tab. 4: The number of cones in the RP (per hectare), the percentage of fertile specimens, the average number of cones per fertile specimen, the average number of seeds per cone, the average germination capacity and the estimate of germinating seeds (vegetation period, 2008). number of cones

fertile specimens

cones per a fertile specimen

seed per a cone

seed germination capacity %

germinating seeds estimate

(n.ha-1)

%

average ± SE

average ± SE

average ± SE

(n.ha-1)

upper RP

19

1.2

5.8 ± 1.32

42.2 ± 5.42

20.5 ± 2.90

164

lower RP

650

9.9

14.1 ± 1.88

70.2 ± 6.50

27.0 ± 3.67

12,320

Research plot

42


In total, 109 seedlings were recorded in the RP: 50 in the lower section (25 specimens per hectare) and 59 in the upper section (39 specimens per hectare). The 10–20 cm height class included 5.1% and 2.0% of seedlings in the lower and upper RP, respectively; the 21–40 cm height class included 40.7% and 32.0% of seedlings in the lower and upper RP, respectively. The 41–60 cm height class was represented by 35.6% and 40% of seedlings in the lower and upper RP, respectively, and the 61–80 cm height class by 18.6% and 26.0% of seedlings in the lower and upper RP, respectively The population solves the essential problem of survival and propagation in sites with a high intensity of stress and biomass disturbance by the vegetative regeneration. There was a statistically significant difference (Mann-Whitney U test, p < 10-5) between the layering (rooting) distance in the lower RP (average value ± SE = 93.0 ± 4.64) and the upper RP (average value ± SE = 43.9 ± 3.12). Also, the difference in the height of layering branch position in the parent tree between the lower (average value ± SE = 25.9 ± 2.79) and the upper (average value ± SE = 7.8 ± 0.73) RP was statistically significant (MannWhitney U test, p < 10-4). Similar distribution of values and statistically significant differences (Mann-Whitney U test, p < 10-4) were observed when assessing these characteristics (the distance of layering, the height of layering branch position in the parent tree) in parent trees of similar age (Figure 4 A-C). It was found that multiple trees can originate from one primary layering branch, as the branch can root at more than one spot. We found up to five clonal trees originating from one branch. The average number of trees from one primary branch was slightly higher in the upper RP (average value ± SE = 1.9 ± 0.18) than in the lower RP (average value ± SE = 1.6 ± 0.12), but a statistically significant difference was not found (Mann-Whitney U test, p = 0.22). The same trend in the distribution of values was observed when this feature was assessed in parent trees of similar age (Fig. 4).

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Fig. 4: The distribution of values: (A) the height of layering branch positions in the parent tree, (B) the distance of layering from the parent tree, (C) the number of trees originating from one primary layering branch. T- all parent trees; age classes of parent trees: 1 = 1–30 years, 2 = 31–60 years, 3 = 61–90 years.

In the lower section, layering occurred most often in the NW (20%), SE (20%) and SW (18%) directions; in the upper section, layering was most frequent in the E (27%), NE (19%) and N (19%) directions (Fig. 5).

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Fig. 5: The distribution of the directions of layering (A – lower RP, B – upper RP).

5.3.3 Age structure of population in control plots The distribution of specimens in age classes in the CP in the lower part of the ecotone (Praděd NW5m, Praděd SE5m, Vysoká Hole NW5m, Vysoká Hole SE5m) is similar to that in Keprník. However, in the plots in Praděd, there is a higher proportion of specimens only of the third age class (not the second class), which is the main difference from the age structure at RP Keprník. Spruce populations in the lower part of the ecotone in all CP are different from those of the RP because specimens in the two oldest age classes are absent (Fig. 3). The distribution of specimens in age classes in the CP in the upper part of the ecotone (Praděd NW5m, Praděd SE5m, Vysoká Hole NW5m, Vysoká Hole SE5m) is similar to Keprník as well. The highest number of specimens was always found in the youngest age class, but their relative proportion was not highly significant (42–53%) /Fig. 3/.

5.4 Discussion According to Harsch and Bader (2011), four types of natural timberline are distinguished; these are determined by different factors. The studied area most resembles the “diffuse form“ of timberline – its main determining factor is temperature conditions. However, Harsch and Bader (2011) did not study mid-mountains of Central Europe, where the timberline is often conditioned anthropogenically, as proven by Treml et al. (2006), Novák et al (2010). The distribution of numbers of living specimens in model age classes in the upper RP (Fig. 3) and, at the same time, the very low mortality (2.9%) show that the spruce population here is in the stage of growth (Odum 1983), which is also proved by the lower degree of coverage (1.6%) and the hectare number of all living specimens in comparison with the lower RP. In the lower RP, the growth of the population is decelerating. The proportion of specimens in the youngest age class is not the highest here, and the stand is gradually closing (degree of coverage, 10.8%). The difference in the age structure of the lower and upper RP can be explained by a gradual increase of the ATLE.

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The same trend of the timberline shift, determined on the basis of the differences in age structures, was established in all locations of the CP (Fig. 3). The highly dynamic and intensive height increment is proved by the fact that, within four vegetation periods, the original upper line of 5 m tall spruce, as defined by Treml (2007), was exceeded by 27 specimens, and the original upper limit of 2 m tall spruce, as defined by Treml (2007), was exceeded by 57 specimens. The upper line of 2 m tall spruce was not recorded because specimens taller than 2 m were found in the highest part of the RP. We also observed the ability of the spruce population to take up new spaces by generative specimens (25 specimens per hectare in the lower RP, 39 specimens per hectare in the upper RP), even in the highest part of the RP. The occurrence of seedlings was also recorded in all CP, reaching values similar to the RP (20 specimens/ha – the average value of CP from the area of the upper line of 2 m tall spruce trees; 16.7 specimens/ha - the average value of CP from the area of the upper line of 5 m tall spruce trees). These were seedlings that had already outgrown the grass-herb vegetation and were above 4 years of age, which means they had survived the critical life stage after which mortality is reduced to less than 1% (Mellmann-Brown 2002). The success rate of seed-based regeneration can be affected by the trampling of the vegetation cover by deer, a process that initiates the consequent wind erosion of vegetation (Holtmeier 2010) and thus supports the creation of a suitable seedbed. In a closed grass-herb vegetation community (Calamagrostis villosa, Nardus stricta, Vaccinium sp. Luzula sp.) outside disturbed areas, the seed germination and the survival of seedlings are difficult (Šerá et al. 2000; Maděra 2004). In 2009, only 27 cones per hectare in the lower RP and 0 cones per hectare in the upper RP were recorded, whereas the numbers in 2008 were significantly higher (lower RP, 650 per hectare; upper RP, 19 per hectare). In timberline conditions, spruce produces good seed crops once in nine to eleven years (Tschermak 1950). The considerably lower potential of seed-based regeneration in the upper RP than in the lower RP can be explained by the worse climatic conditions (Tranquiliny 1979; Holtmeier 2009) and the lower average age of the spruce subpopulation and thus by the lower number of fertile specimens. Tjoelker et al. (2007) report that more abundant flowering, coupled with a substantial production of mature seeds, in open-grown trees in Bosnia occurs at 25 years, but not at the alpine timberline ecotone, where the specimens start to be fertile later (Tranquilliny 1979). Whether and to what extent the current increasing tendency of the ATLE is related to the increase in temperature in the vegetation periods of the past two decades (Houghton et al. 2001) cannot be assessed because of the long-term anthropogenic influence on the ATLE in the Hrubý Jeseník Mts. in the past (Hošek 1973; Novák et al. 2010). Good seedbased regeneration of spruce is characteristic of pastoral timberline that has been artificially lowered in the past (Plesník 1971; Kozak et al. 1995). According to Holtmeier (2009), viable seeds are located below the physiological limit of tree growth. In the present study, viable seeds were found to be present in the highest areas of the RP, high above the current upper treeline (upper line of the trees). We can conclude that this is not a natural position of the upper treeline but an artificially lowered one; and now, as a result of several decades of spontaneous succession in the reserve, it is being reformed. Although it has been scientifically substantiated (Treml et al. 2006; Novák et al. 2010), 46


that the timberline in the studied area was lowered artificially, we cannot exclude the concurrent influence of temperature amelioration on the appearance of cones and the timberline ascent (Baker et al. 1995). On many previous alpine pastures in Europe (e.g., the European Alps, the Carpathian Mountains), natural tree invasion can also be observed, and, in the long-term, a gradual natural forest advance to the potential climate limit might be expected if grazing and other kinds of use are excluded (Holtmeier, Broll 2005). The advance of timberline to higher altitudes, both as a consequence of the end of grazing and global change, would cause the fragmentation and reduction of Alpine forest-free land (Holtmeier 2009). The risk of fragmentation or complete loss of arcto-alpine grounds is, in the case of the Hrubý Jeseník and other Hercynian mid-mountains of Central Europe like Vosges, Harz, and Kralický Sněžník Mts. strengthened by the fact that these small, isolated areas are located near the current anthropogenically influenced timberline (Tackenberg et al. 1997; Treml at al. 2006; Novák et al. 2010). An important role in the current ecotonal dynamics in the Hrubý Jeseník Mts. is played by the natural absence of dwarf pine and other competitively effective edificators (Rybníček, Rybníčková 2004), which do not prevent the expansion of spruce (Fig. 6).

Fig. 6: Spruce expanding above the former pastoral timberline. Photo by Martin Šenfeldr.

In the plots we analyzed, there were spruces often present in close vicinity to patterned ground; therefore, we can expect their degradation as a consequence of the disruption by the root system (Treml, Křížek 2006). The negative effect can also be expected if spruce grows in arcto-alpine vegetation, as was often found, as this alpine vegetation is then displaced and the properties of the ecotope are changed. Upward shift of the timberline would also reduce landscape diversity. This might affect aesthetic values and would thus have negative consequences for the economy (Holtmeier 2009). 47


Because of the limited seed-based regeneration in the conditions found at timberline, vegetative regeneration seems to be very important (Kuoch, Amiet 1970). While a specimen of seed-based origin will die, clonal groups may continue to exist indefinitely, as long as the climate does not prevent vegetative growth (Holtmeier 1993). In our RP, 3 generations of clonal origin trees were found (Fig. 7) in the lower section but only two generations in the upper section; this fact can be explained by the overall lower age of the population in the upper RP (Fig. 3).

Fig. 7: The clonal group in which three generations of clonal trees were recorded. Photo by Martin Šenfeldr.

The question remains how the original parent specimens – the founders of clonal groups – came to these places. They can be seen as remnants of the afforestation of abandoned pastures (Maděra 2004) or as generatively rejuvenated specimens that further propagated in a vegetative way. The values of the distance of layering branch rooting and the height of its position in the parent tree are considerably higher in the lower RP which can be explained by the fact that in the upper RP the vegetative propagation occurs with younger trees and with a higher intensity (a higher amount of layers originating from one branch, a higher number of layers per one primary parent tree). Vegetative regeneration increases with altitude (Holtmeier 1999), which is probably caused by a stronger influence of stress factors of the 48


“summit phenomenon“ /wind exposure/ (Scharfetter 1938). Breaks and injuries are processes that activate the development of bottom plagiotropic branches and the formation of adventitious roots (Kuoch, Amiet 1970; Brown 1974). When the apical shoot is damaged (frost, drought, abrasion, mechanical damage), plagiotropic branches can elongate vigorously because they are not controlled hormonally by the leader (Holtmeier 2009). The fine root biomass and the ratio of fine root/stem biomass strongly increased with the timberline ecotone altitude; it is considered to be a mechanism to cope with unfavourable soil conditions (Hertel, Schöling 2011). On the basis of the distribution of the rooting directions, which is perceived as a long-term indicator of wind conditions (Griggs 1938), we can state that wind conditions are of a different character in the two sections. In the lower, less exposed section, there are dominant clonal groups that have a circular physiognomy in plan view, and layering occurs in various directions, which, according to Griggs (1938), indicates no wind influence at a site on horizontal terrain. In the upper RP, which is more noticeably affected by the “summit phenomenon“ (Scharfetter 1938) and more exposed to the direct impact of winds, layering is considerably limited by southerly winds that attain the highest speeds and frequency, especially in winter (Sobíšek 2000). The direction of the flag spruce crowns in the summit areas of Keprník shows that the main devastating attack on treetops has came from the south and southwest.

5.5 Conclusions In the Hrubý Jeseník Mts., which represent mid-mountains of Central Europe, the shift of timberline ecotone into higher altitudes was ascertained based on the differences in age structures of spruce populations in different parts of the timberline ecotone. The vegetative form of regeneration of spruce was determined in the studied area. We found up to three generations of clonal trees. The intensity of the vegetative regeneration within the timberline ecotone increases with the altitude. We also determined seed-based regeneration, appearing even in the topmost parts of the timberline ecotone. Viable seeds were found even above the current upper tree-line. The ascertained ascent of the timberline ecotone is a result of a decades-long effect of natural processes and the absence of the anthropogenic influence provided by the protection regime of the national nature reserves. This rise in timberline has thus become a significant phenomenon deserving the increased attention of scientists. However, the highest areas of Praděd, Vysoká Hole and Keprník, within the Hrubý Jeseník Mts. are the only locations where the patterned ground and wind blown alpine grassland with arcto-alpine species are to be found. Their spread probably expanded in the past due to anthropogenous extension of forest-free areas. These arcto-alpine phenomena are among the most significant subjects of nature protection today. Currently, some trees are growing near these phenomena, affecting them negatively. If the natural succession provided by the status of NNR continues, we can expect the fragmentation of these biotopes, further diminishing the area with the consequent decrease in diversity. Soon, the nature protection authorities will have to decide whether to protect natural processes or significant biotopes often conditioned by historical management and the following successional processes. 49


Acknowledgments This project received support from the Internal Grant Agency of the Facultyof Forestry and Wood Technology at Mendel University in Brno (projects 15/2009 and 12/2010) and the Ministry of Education of the Czech Republic (project MSM 6215648902). Insightful comments from the anonymous reviewers are gratefully acknowledged.

5.6 References Arno, S.F., Hammerly, R.P., 1984: Timberline. Mountain and Arctic Forest Frontiers. The Mountaineers, Seattle, Washington, USA. Baker, W., Honaker, J., Weisberg, P., 1995: Using aerial photography and GIS to map the forest- tundra ecotone in Rocky Mountain National Park, Colorado for global change research. Photogrammetric Engineering and Remote Sensing, 61: 313–320. Becker, A., Körner, Ch., Brun, J.J., Guisan, A., Tappeiner, U., 2007: Ecological and Land Use Studies Along Elevational Gradients. Mountain Research and Development, 27(1): 58-65. Brewer, J.A., Burns, P.Y., Cao, Q.V., 1985: Short-term projection accuracy of the asymptotic height-age curves for loblolly pine. Forest Science, 31: 414-418. Brown, C.L., 1974: Growth and form. In: Zimmermann MH, Brown CL, Tyree MT, editors. Tree structure and function. Berlin, Heidelberg, New York. pp 125 – 167. [CNS] Czech national standard. 2006: Forest Seeds - collecting and testing the quality of seeds [in Czech]. Czech standart institut Prague. Griggs, R.F., 1938: Timberline in the Rocky Mountains. Ecology, 19: 518-564. Harsch, M.A., Bader, M.Y., 2011: Treeline form – a potential key to undestanding treeline dynamics. Global Ecology and Biogeography, 20: 1-15. Hertel, D., Schöling, D., 2011: Below ground response of Norway spruce to climate conditionsat Mt. Brocken (Germany)-A re-assessment of Central Europe's northernmost treeline. Flora, 206: 127-135. Holtmeier, F.K., 1974: Geoecological observations and studies on the subarctic and alpine treeline in a comparative perspective (northern Fenno-Scandinavia / Central Alps [in German]. Geoecological Research. Holtmeier, F.K., 1993: The influence of generative and vegetative reproduction on the distribution patterns of the trees and the ecological differentiation of the timberline. Observations and investigations in the high mountains of the North America and the Alps [in German]. Geoökodynamik, 4: 153-182. Holtmeier, F.K., 1999: Layering at the timberline ecotone in the Front Range, Colorado [in German]. Mitteilungen der Deutschen Dendrologischen Gesellschaft, 84: 39-61. Holtmeier, F.K., 2005: Relocation of snow and its effects in the treeline ecotone – with special regard to the Rocky Mountains, the Alps and northern Europe. Die Erde, 136(4): 343-373. Holtmeier, F.K., 2009: Mountain timberlines. Ecology, Patchiness, and Dynamics. Advances in global change research 36. Springer science. Holtmeier, F.K., Broll, G., 2005: Sensitivity and response of northern hemisphere altitudinal and polar treelines to environmental change at landscape and local scales. Global Ecology and Biogeography, 14: 395-410. Holtmeier, F.K., Broll, G., 2010: Wind as an ecological agent at treelines in North America, the Alps, and the European Subarctic. Physical Geography, 31(3): 203-233.

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Hošek, E., 1973: History of mountain farming in the highest elevations of the Hrubý Jeseník Mts. And its influence on timberline course [in Czech]. Campanula, 4: 69-81. Houghton, J.T., Ding, Y., Griggs, D.J., Noguer, M., van der Linden, P.J., Dai, X., Maskell, K., Johnson, C.A., 2001: Climate change 2001: The scientific basic. Cambridge university press. Chytrý, M., Kučera, T., Kočí, M. (eds), 2001: Habitat Catalogue of the Czech Republic [in Czech]. [ANCLP ČR] The Agency for Nature Conservation and Landscape Protection of the Czech Republic. Prague, Czech Republic. Jeník, J., 1961: Alpine vegetation of the High Sudetes: Theory of Anemo-Orographic systems [in Czech]. NČSAV, Praha, Czech Republic. Jeník, J., Hampel, R., 1992: Die waldfreien Kammlagen des Altvatergebirges (Geschichte und Ökologie). MSSGV. Stuttgart, Germany. Jeník, J., Štursa, J., 2003: Vegetation of the Giant Mountains, Central Europe. In: Nagy, L., Grabherr, G., Körner, Ch., Thompson, D.B.A. (eds). Alpine Biodiversity in Europe. Ecological studies, Springer – Verlag, Berlin Heidelberg New York. pp 47-51. Kimball, K.D., Weihrauch, D., 2000: Alpine vegetation communities and the alpine treeline ecotone boundary in New England as biomonitors for climate change. USDA Forest Service Proceedings, 3: 93–101. Körner Ch., 1999: Alpine plant life: functional plant ecology of high mountain ecosystems. Springer. New York. Körner, Ch., Paulsen, J., 2004: A world-wide study of high altitude treeline temperatures. Journal of Biogeography, 31: 713–732. Kozak, J., Troll, M., Widacki, W., 1995: The athropogenic upper tree line in the Silesian Beskid Mts. In: Environmental aspects of the timberline in Finland and in the Polish Carpathians. Geographic works, 8: 200-207. Král, K., 2009: Classification of Current Vegetation Cover and Alpine Treeline Ecotone in the Praděd Reserve (Czech Republic), Using Remote Sensing. Mountain Research and Development, 29(2): 177-183. Kuoch R, Amiet R. 1970. Die Verjüngung im Bereich der oberen Waldgrenze der Alpen mit Berücksichtigung von Vegetation und Ablegerbildung. Eidgenössischen Anstalt für das Forstliche Versuchswesen, 46: 159-328. Maděra, P., 2004: Growth and population strategy of Norway spruce (Picea abies (L.) Karsten) at alpine timberline in the Praděd nature reserve, Větrná louka site [in Czech]. Geobiocenological papers, 10: 51-70. Mellmann-Brown, S., 2002: The regeneration of whitebark pine in the timberline ecotone on Beartooth Plateau, Montana and Wyoming. [PhD dissertation]. The University of Münster, Germany. Mellman-Brown, S. 2005. Regeneration of Whitebark Pine in the Timberline Ecotone of the Beartooth Plateau, U.S.A.: Spatial Distribution and responsible agents. In: Broll G, Keplin B, editors. Studies in treeline ecology. Springer, Berlin – Heidelberg. pp 97–116. Novák, J., Petr, L., Treml, V., 2010: Late-Holocene human-induced changes to the extent of alpine areas in the East Sudetes, Central Europe. The Holocene, 20(6): 895-905. Odum EP. 1983. Basic Ecology. Saunders Publishers. Philadelphia. Plesník, P.,1971: Upper timberline in the High and Belanské Tatry Mts. SAV [in Slovak]. Bratislava. 475 pp. Rybníček, K., Rybníčková, E., 2004: Pollen analyses of sediments from the summit of the Praděd range in the Hrubý Jeseník Mts. (Eastern Sudetes). Preslia, 76: 331–347.

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Sarmiento, F.O., Frolich, L.M., 2002: Andean Cloud Forest Tree Lines - Naturalness, Agriculture and the Human Dimension. Mountain Research and Development, 22(3): 278-287. Scharfetter, R., 1938: Plant communities of the Eastern Alps [in German]. Wien. Sitko, A., Troll, M., 2008: Timberline Changes in Relation to Summer Farming in the Western Chornohora (Ukrainian Carpathians). Mountain Research and Development, 28(3/4): 263-271. Sobíšek, B., 2000: Wind speed and direction in the Czech Republic in 1961–1990 [in Czech]. National Climate Programme of the Czech Republic. Publishers CHMI. Prague, Czech Republic. Sweda, T., Kouketsu, S., 1984: Applicability of growth equations to the growth of tree in stem radius (II) application to jack pine. Journal of the Japanese Forest Society, 10: 402–411. Šerá, B., Falta, V., Cudlín, P., Chmelíková, E., 2000: Contribution to knowledge of natural growth and development of mountain Norway spršce seedlings. Ekológia Bratislava, 19: 420-434. Šmelko, Š.,Wenk, G., Antanaitis, V., 1992: Growth structure and production of the forest [in Slovak]. Bratislava, Slovakia. Príroda. Tackenberg, O., Poschold, P., Karste, G., 1997: Changes in the sub-alpine vegetation and landscape of the Brockens (Harz) [in German]. Verhandlungen der Gesellschaft für Ökologie, (27): 45–51. Tjoekler, M.G., Boratyński, A., Bugala, W., 2007: Biology and Ecology of Norway Spruce. 2nd edition (1st edtion 1998). Dordrecht. Springer Netherlands. Traquillini, W., 1979: Physiological ecology of the alpine timberline. Tree existence at high altitudes with special reference to the europaean Alps. Ecological studies 31. Berlin: Springer. Treml, V., 2007: Dynamic of the alpine timberline in the High Sudetes [PhD dissertation]. Prague, Czech Republic: Charles University. Treml, V., Banaš, M., 2000: Alpine timberline in the High Sudetes. Acta Univesitatis Carolinae, Geographica, 15(2): 83–99. Treml V, Jankovská V, Petr L. 2006. Holocene timberline fluctuations in the mid-mountains of Central Europe. Fennia, 184(2): 107-119. Treml, V., Křížek, M., 2006: The effect of dwarf pine (Pinus mugo) on patterned ground in the Czech part of the High Sudetes [in Czech]. Opera Corcontica, 43: 45-56. Troll, C., 1973: The upper timberlines in different climatic zones. Artic and Alpine Research, 5(3): 3-18. Tschermak, L., 1950: Waldbau auf pflanzengeographisch-ökologischer Grundlage. Wien: Springer. Zhang, L., 1997: Cross-validation of Non-linear Growth Functions for Modelling Tree Height–Diameter Relationships. Annals of Botany, 79: 251-257.

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Šenfeldr, M., Treml, V., Maděra, P., Volařík, D. Effects of prostrate dwarf pine on Norway spruce clonal groups in the treeline ecotone of the Hrubý Jeseník Mountains, Czech Republic. Arctic, Antarctic and Alpine Research (accepted).

6.Effects of prostrate dwarf pine on Norway spruce clonal groups in the treeline ecotone of the Hrubý Jeseník Mountains, Czech Republic Martin Šenfeldr1, Václav Treml2, Petr Maděra1, Daniel Volařík1 1) Department of Forest Botany, Dendrology and Geobiocoenology, Mendel University in Brno, Zemědelská 3, 613 00 Brno, Czech Republic 2) Department of Physical Geography and Geoecology, Charles University in Prague, Albertov 6, 128 43 Prague, Czech Republic Abstract Global temperature increase would seem likely to result in general upwards shifts of altitudinal margins of tree stands. However, range expansion of trees could be significantly affected by both negative and positive interactions with alpine shrubs in existing treeline ecotones. We examined the effects of dwarf pine (Pinus mugo) shrubs on the vegetative propagation and height growth of Norway spruce (Picea abies) trees in the treeline ecotone of the Hrubý Jeseník Mountains, Czech Republic. Here, the non-native dwarf pine was planted above timberline during the 19th and 20th century. In the treeline ecotone, vegetative propagation is important both for generation of clonal groups from seed-originated individuals and for persistence of such stands. We found that increasing density of dwarf pine stands strongly reduced vegetative propagation of spruce, as shown by the spruce clonal groups surrounded by dense pine having fewer layering branches and ramets than such groups outside pine stands. This has likely resulted from competitive pressure of pine causing decreased spruce layering mainly through mechanical damage and shading. In contrast, dense pine stands increased spruce height growth, presumably by providing shelter against wind and/or browsing. Our results indicate that interactions of prostrate dwarf pine and Norway spruce clonal groups include both competitive and facilitative components, which probably change in importance along climatic stress gradients. Key words: Dwarf pine, Norway spruce, clonal groups, layering, tree-shrub interaction, treeline, Hrubý Jeseník Mts., vegetative reproduction, competition, facilitation

6.1 Introduction The alpine treeline ecotone represents a prominent temperature-driven altitudinal boundary of forests in mountain regions (Holtmeier 2009). Along the gradient of increasing stress due to temperature, trees lose their competitive advantage over low, prostrate shrubs (Körner 2012). Whereas tall trees are thermally coupled with the ambient atmosphere, prostrate shrubs profit from near-ground heating of the surface air layer (Körner 1998; Geiger et al. 2003). Therefore, direct temperature limitation of tree growth is not the only major driver of treeline ecotone dynamics, as the interaction between upright tree species and prostrate shrubs can also have a particularly strong influence (Dullinger et al. 2005; Dufour-Tremblay et al. 2012). The stress-gradient hypothesis (Callaway et al. 2002; He et al. 2013) predicts that prostrate shrubs should have 53


prevalently competitive effects on trees in the lower part of treeline ecotone, with facilitation more common towards the tree species limit (e.g. Maestre et al. 2009). Indeed, a range of positive and negative effects of prostrate shrubs on treeline trees has been documented. Positive effects can include protection against herbivorous insects and mammals (Dullinger et al. 2005; Grau et al. 2010), frost (Michiels 1993) and strong winds (Vacek et al., 2012). In particular, these positive effects facilitate tree seedling survival and performance (Camarero, Gutiérrez, 1999; Gómez-Aparicio et al. 2005; Grau et al. 2010). On the other hand, prostrate shrubs compete with trees for light (Jeník, Lokvenc 1962), nutrients, and water (Schönenberger 1975; Weih, Karlsson 2002; Grau et al. 2012), and can reduce tree germination success (Dullinger et al. 2005) and cool surface microclimates during the growing season (Svoboda 2001; Köck et al. 2003). Trees can have negative effects on prostrate shrubs, with their overstory overgrowing them, thereby likely limiting the responses of shrub species to increasing temperatures (Švajda et al. 2011; Boundreau, Villeneuve-Simard 2012). In the treeline ecotone, trees often occur in clonal groups (Bliss 1971; Tranquillini 1979; Laberge et al. 2000). Indeed, vegetative reproduction is an important strategy enabling trees to form and maintain stands in environments in which seedling growth and survival are limited by cold (Holtmeier 2009). Thus, alpine and northern treeline ecotones often contain clonally reproduced tree groups surrounded by prostrate shrubs (Harsch, Bader 2011; Grau et al. 2012). For example, treeline ecotones in western North American mountains contain Abies lasiocarpa, Picea engelmannii and shrubby Chamaecyparis nootkatensis (Brooke et al. 1970; Arno, Hammerly 1984), those in the Carpathians include Picea abies and Juniperus communis ssp. alpina (Mihai et al. 2007), and those in central Kamchatka have Larix gmelinii and Pinus pumila (Okitsu 1998). Norway spruce (Picea abies) and dwarf pine (Pinus mugo) play the roles, respectively, of clonal group-forming tree and prostrate shrub species in treeline ecotones of Central Europe. There, Norway spruce is the most abundant treeline-forming species (Scotti et al. 2008; Treml, Banaš, 2008; Hertel, Schöling 2011). In the treeline ecotone, the reduced sexual reproduction of Norway spruce is replaced by layering, in which new ramets are generated by the rooting of plagiotropic branches of the parent tree (Kuoch, Amiet, 1970; Tranquillini 1979; Kozlowski 2002; Šenfeldr, Maděra 2011; Vacek et al. 2012). Prostrate dwarf pine occurs in alpine areas of Europe from the Pyrenees to the Balkan peninsula, and is a widespread species in the altitudinal belt above the upper limit of closed forest in the eastern Alps, Sudetes, and Carpathians (Nagy et al. 2003; Úradníček et al. 2010). The distribution and altitudinal limits of Norway spruce and dwarf pine have been strongly influenced by past agricultural activities such as grazing, grass mowing, burning and logging (Dirnböck, Grabherr 2000; Sitko, Troll 2008). Thus, treeline ecotones were shifted downwards and dwarf pine stand distribution became more scattered between the 11th and 18th centuries (Kaltenrieder et al. 2005; Treml et al. 2008). Later, from the second half of the 19th century through the mid-20th century, dwarf pine was frequently planted in deforested or steeply sloping mountain areas to protect soil against mass movement and surface erosion (Bukovčan 1960; Hošek 1964; Souček, Špulák 2011; Roštínský et al. 2013). This occurred in the Hrubý Jeseník Mountains (eastern part of the Sudetes Mts.), where the pine was planted as a non-indigenous species on summit forest-free areas. 54


There, some Norway spruce clonal groups are surrounded by dwarf pine stands of known planting date, whereas others are not. This provides a special opportunity to examine the effects of prostrate dwarf pine on dynamics of Norway spruce clonal groups by comparing the spruce groups inside and outside dwarf pine stands. To date, the literature on interactions between Norway spruce and dwarf pine only reflects examination of the effects of the pine on spruce sexual reproduction and height growth (Dullinger et al. 2005). Studies of similar tree-shrub systems from alpine treeline ecotones indicate both competition among species of different sizes and different competition strategies and facilitation through protective effects of shrubs at early life stages of tree seedlings on extreme sites (Anthelme et al. 2003, Takahashi 2003, Grau et al. 2012). However, no published study has investigated the effect of prostrate shrubs on vegetative reproduction of treeline trees. Since vegetative reproduction is important for the persistence and spread of spruce at its upper distributional limit, in the present study we examine: (1) the influence of dwarf pine stand density on indicators of spruce clonal group vegetative reproduction ability; and (2) the effect of distance from dwarf pine shrubs on actual spruce vegetative reproduction.

6.2 Material and methods 6.2.1 Study sites Our study sites (Praděd, Keprník, Větrná Louka and Vysoká Hole) are situated on the highest peaks of the Hrubý Jeseník Mountains, and comprise all the locations on these mountains in which spruce groups and planted dwarf pine co-occur (see Fig. 1). The Hrubý Jeseník Mountains reach their maximum elevation at Mount Praděd, at 1491 m a.s.l. (Fig. 1). The climate is relatively cold and humid, with the summit areas characterized by annual precipitation of around 1400 mm and average temperature around 1.1 °C (Tolasz et al. 2007). The mountaintops of the Hrubý Jeseník Mts. are among the windiest locations in Europe (Migala 2005). The highest elevations are thought to have been naturally forest-free (Jeník 1961), but the extent of alpine meadows was significantly enlarged by human activities (Novák et al. 2010), e.g. grass mowing, cattle grazing, and woodland burning and logging. The average altitude of the upper limit of closed forest is 1300 m a.s.l. Above this, scattered Norway spruce groups occur. Spruce groups typically consist of one seed-originated parent tree accompanied by variable numbers of its ramets. Indeed, within these groups, trees of clonal origin clearly dominate, accounting for 90–95 % of the trees (Šenfeldr, Maděra 2011). Therefore, we term the spruce groups “clonal groups.” Nevertheless, sparse sexual reproduction is present at treeline, with seedlings of height 10–80 cm at a density of 25–39 specimens per hectare found at the study sites (Šenfeldr, Maděra 2011). Dwarf pine was planted on these mountains during the 19th and 20th centuries, mostly between 1874 and 1928, at spacings of 1.25×1.25 m to 2×2 m square (Hošek 1964). This species now covers 179.2 ha (6.8 %) of the area above the upper limit of closed forest (Treml et al. 2010). Since designation of the Šerák–Keprník and Praděd nature reserves in 1955, both Norway spruce populations and dwarf pine stands have developed spontaneously, without any direct human intervention. Of particular conservation concern, 55


dwarf pine stands rapidly expanded into surrounding alpine grasslands, leading to a loss of rare alpine plants and insects (Kuras et al. 2001; Treml et al. 2010; Zeidler et al. 2012). Fig. 1: Topographic map showing location of study sites and elevation of the upper limit of closed forest in the Hrubý Jeseník Mts.; inset shows position of the Hrubý Jeseník Mts. in Europe.

6.2.2 Field data collection To examine the effects of dwarf pine density on spruce clonal growth characteristics, we distinguished three types of pine stands (see Fig. 2) based on pine canopy cover: no pine 56


presence (type “no-pine”), sparse pine (pine cover 20–70 %; type “sparse”), and dense pine (pine cover 71–100 %; type “dense”). Precise information about dwarf pine cover was obtained from orthorectified images with 0.25 m resolution, using supervised classification followed by manual error correction. Although we searched for all types of stands at each site, two sites lacked dense pine stands (Praděd and Větrná Louka) and one lacked sparse pine stands (Vysoká Hole) /Tab. 1/. Cover of dwarf pine stands was usually related to their age, with some deviations caused by site factors and rates of seedling establishment (Treml et al. 2010). Fig. 2: Photographs showing: (A) extensive clonal spruce groups in a no-pine plot; (B) detail of a clonal group with layering branches growing in a no-pine plot; (C) spruce groups growing in a dense pine stand; and (D), close mechanical contact of pine with spruce resulting in absent layering branches in a dense pine stand.

In each study site, at similar altitudes, we randomly placed 4 to 9 circular 30-m diameter sample plots in each pine stand type (no-pine, sparse, dense). In each of these plots, data were recorded from all clonal spruce groups and solitary spruce trees. For study design simplicity, each solitary tree was treated as a clonal group. For each clonal group, the number of layering branches, the total number of trees, and the height of the highest tree in the group (maximum tree height) were recorded. Additionally, to detect possible effects of dwarf pine on juvenile spruce stem-breaks, for each tree growing in the dense and no57


pine plots, we recorded the presence/absence of multiple stems, up to the height of 130 cm (roughly corresponding to the average height of dwarf pine stands). Next, we randomly chose two spruce clonal groups on each plot for age structure analysis. Two groups per plot were sufficiently representative because across sites, the average number of clonal groups per plot was 3.8 ± 0.8 SD; (n=60). Within each of these groups, all trees were cored using a Pressler borer. Cores were taken from the stem base (5 to 20 cm above ground). Tree rings were measured on a positioned table. If off-centre cores were collected, we used the age correction method employing the mean annual width of the five rings nearest the pith (Batllori, Gutiérrez, 2008). Coring height correction was based on mean height growth rate of seedlings and juvenile ramets (height 10–200 cm, Šenfeldr, Treml – unpublished data). We considered the following three variables derived from age structure analysis: mean age, age of the oldest tree, and number of juvenile ramets (i.e. 20 years old or younger). The numbers of layering branches and juvenile ramets can serve as indicators of spruce vegetative reproduction intensity, as groups with relatively high vegetative reproduction are characterized by numerous juvenile ramets and layering branches and by low mean age. Additionally, the overall number of trees within a group provides an indicator of long-term vegetative reproduction ability (Kuoch, Amiet 1970). The maximum tree height within each clonal group was recorded to evaluate effects of pine presence on spruce height growth. Tab. 1: Basic characteristics of sample plots in the four study sites above upper forest limit in the Hrubý Jeseník Mts. Study site Keprník

Praděd

Větrná Louka

Vysoká Hole

Type of pine Pine stand stands age

Number of plots

Overall number of trees

Mean altitude of Inclination Aspect plots (m a.s.l.) ± SD (%) 1378.3±11.0

38

SE

119

1400.7±9.5

33

E

50

1388.0±16.5

35

E

6

329

1435.0±21.6

38

NE

130

6

153

1441.3±17.6

40

NE

-

-

-

-

no-pine

-

4

181

1378.8±3.8

22

E

sparse

50

4

114

1390.8±4.0

15

E

dense

-

-

-

-

no-pine

-

7

277

1400.8±8.2

sparse

-

-

-

-

dense

90

9

156

1402.9±3.9

no-pine

-

8

129

sparse

130

7

dense

90

9

no-pine

-

sparse dense

NE

E 21

E E

11

E

Further, we assessed the effect of the distance between dwarf pine margins and spruce group crown margins (hereinafter referred as “distance to pine”) on the presence/absence of spruce layering branches. Spruce groups were placed in four distance classes: 0–1 m; 1–3 m; 3–5 m; 5–7 m, with the corresponding mid-range values of 0.5 m; 2 m; 4 m, and 6 m used in the analysis. The distances to the nearest dwarf pine margin were evaluated in the four cardinal directions, and only those spruce groups having distance values within these classes in all four directions were regarded as suitable for analysis. At each site (excluding 58


Keprník), 10 such clonal groups in each distance class were chosen, and the presence or absence of layering branches in each of the clonal groups was recorded.

6.2.3 Data analysis The data from our sample plots had a hierarchical structure (sample plot/clonal group/tree). To avoid pseudoreplication and simplify analyses (Murtaugh 2007), we generally used within-plot average values for analyses at the plot level. The exception was the probability analysis of the relationship between distance to pine and layering branch occurrence, due to its different sampling design. In analyzing our data, we first performed principal component analysis (PCA) to evaluate the relationships among studied spruce clonal group characteristics and also the effect of pine stand type (no-pine/sparse/dense) on the whole set of spruce clonal group characteristics. Data were scaled to unit variance before using PCA. To assess the effect of pine stand type, we fitted this factor onto the the first two main components of the ordination. Its significance was tested using a permutation test. To test differences within studied clonal spruce group characteristics (mean age, age of the oldest tree, maximum height, number of layering branches, and number of juvenile ramets), we used linear mixed effect (LME) models with restricted maximum likelihood (REML), treating pine stand type as a fixed effect and site as a random effect. To evaluate the significance of site effects, we also fitted a simpler model with only pine stand type (i.e., with no random effect) using generalized least squares (GLS) REML estimation. We used likelihood ratio tests and Akaike´s Information Criterion (AIC) to compare this GLS model with the more complex LME model (see Zuur et al. 2009). To account for heteroscedasticity of our dependent variables, we did square-root transformation of all dependent variables except age of the oldest tree. Because maximum height was correlated with mean age, a separate analysis of a subset of trees of similar age (60–80 years) was conducted. This allowed us to evaluate the effect of pine stand type on maximum height independent of mean age. The effect of distance to pine on the probability of spruce layering was analysed using generalized linear models (binomial family and probit link function) with distance to pine as the explanatory variable and probability of layering as the dependent variable. WALD Z was used to evaluate the significance of distance to pine. All statistical analyses were carried out using R statistical environment version 2.14 (R Development Core Team 2011). The 'vegan' package (Oksanen et al. 2012) was used for multivariate analysis, the 'nlme' package (Pinheiro et al. 2013) for LME and GLS, and the 'lattice' package (Sarkar 2008) for boxplot construction.

6.3 Results 6.3.1 Principal component analysis Data on 380 clonal spruce tree groups comprising 1508 trees were collected from a total of 60 plots at the four sites (Tab. 1). The PCA ordination diagram for the six clonal spruce group characteristics showed a clear trend from the dense pine stand type to the no-pine 59


stand type along the first principal component (Fig. 3). The effect of pine stand type on the studied characteristics was significant (p < 0.001). The numbers of juvenile ramets, layering branches and trees in a group were positively correlated with the first principal component and tended to be higher in no-pine stands. In contrast, maximum height and mean age were negatively correlated with the first principal component and tended to be higher in dense pine stands. The ages of the oldest trees tended to be greater in dense stands than in sparse stands, with this variable distinct in that it was correlated with the second principal component, standing apart from the other variables. The ordination plot showed also strong positive correlations between maximum height and mean age and between number of trees in a group and number of layering branches. Fig. 3: PCA ordination plot showing projections of sample plot and clonal spruce groups characteristics. Sample plots in different pine stand types (dense, sparse, no-pine) are distinguished using a spider plot. Dispersion ellipses for pine stand types are plotted using standard deviations of point scores. The first PCA axis explains 52% of variance and the second explains 20% of variance.

6.3.2 Differences in age-related variables and tree height At all sites and in all pine stand types, the spruce populations were younger than the surrounding pine stands (compare pine stand age in Tab. 1 and age of oldest spruce tree in Fig. 4). The age differences between the spruce (oldest tree in group-indicating establishment date) and surrounding pine stands ranged from 10 (dense pine Keprník sites) to 60 years (sparse pine Praděd site). Thus, most of the spruces have been growing, since their early ontogenetic stages, inside gradually closing pine stands. 60


The effect of pine stand type was significant for all variables except the age of the oldest spruce (Tab. 3). In contrast, site did not have effect a significant on any studied variable (Tab. 2). Mean spruce age was greater in dense pine stands than in no-pine stands and sparse pine stands (Fig. 4). The number of juvenile ramets showed the opposite trend. It was lower in dense pine stands (with almost no juveniles) and higher in no-pine stands (on average, two juvenile ramets per spruce clonal group) (Fig. 4). The age of the oldest tree tended to be higher in dense pine stands, but this trend was not significant (Fig. 4). Overall, the oldest tree was 155 years old and it was found in a no-pine stand at Keprník (Fig. 4). Stem breaks were more frequent in no pine plots (55%) than dense pine plots (20%; p < 0.001, t-test). The maximum height of spruce trees in dense pine stands was significantly greater than those of no-pine and sparse pine plots (Tab. 3, Fig. 5). The height findings were similar to the trend for mean age, which is not surprising, as maximum tree height was correlated with mean age (r = 0.75). However, this trend was also found in the subset of spruce trees of similar age (60–80 years, n = 28). In this subset, trees in dense pine stands were significantly taller [mean height (cm): 441 ± 72 SD] than trees in no-pine stands [mean height (cm): 367 ± 56 SD; F = 7.4, p < 0.01].

Tab. 2: Evaluation of site effects on clonal spruce group chracteristics; likelihood ratio test and AIC were used to compare simpler model without site effect (fitted using GLS) and model with site effect (LME). Results show site effects were not significant in any case. Characteristic

AIC GLS

AIC LME

Likelyhood ratio

p-value

Mean age

458.6

460.6

6.45e-8

0.500

Trees in group

108.8

110.8

1.97e-8

0.500

Layering branches

134.6

136.6

1.70e-8

0.500

Juvenile trees

116.2

118.2

2.39e-8

0.500

Age of oldest tree

205.7

207.7

3.56e-8

0.500

Height max

659.2

660.5

0.612

0.217

Tab. 3: Effects of pine stand type (referred to as “pine” in table) on clonal spruce group chracteristics. Because site effect was not significant, F and p-values from only GLS models are shown. Pine stand type had significant effects on all studied characteristic expect the age of the oldest tree. Variable Mean age Trees in group Layering branches Juvenile trees Age of oldest tree Height max

Predictor pine pine pine pine pine pine

D.f. 2; 58 2; 58 2; 58 2; 58 2; 58 2; 58

F 35.481 26.781 21.012 19.366 1.447 15.918

61

p-value <0.001 <0.001 <0.001 <0.001 0.244 <0.001


Fig. 4: Boxplots of mean age, number of juvenile ramets (20 years old or younger), and age of the oldest tree for spruce groups in each pine stand type (dense, sparse, no-pine). The horizontal line in each box represents the median; the hinges represent the 25 th and 75th percentiles; the whiskers represent 1.5 times the interquartile range; open circles represent values outside this interval.

6.3.3 Number of trees and number of layering branches The numbers of trees and layering branches were both significantly affected by pine stand type (Tab. 3), showing similar trends to the number of juvenile ramets: numbers low in dense pine, higher in sparse pine and even higher in no-pine stands (Fig. 5). At the site level, the average number of trees per group ranged from three at Keprník (dense pine stand) to 11 at Praděd (no-pine stands). Layering branches occurred in 41–80% of the clonal spruce groups in no-pine stands, in 20–39 % in sparse pine stands, and in 6–14 % in dense pine stands. The highest number of layering branches for a single clonal spruce group was 20 in a no-pine stand at Praděd (Fig. 5).

62


Fig. 5: Boxplots of number of trees in a clonal group, number of layering branches, and maximum height for spruce groups in each pine stand type (dense, sparse, no-pine). The horizontal line in each box represents the median; the hinges represent the 25 th and 75th percentiles; the whiskers represent 1.5 times the interquartile range; open circles represent values outside this interval.

6.3.4 Effect of distance to pine on layering probability The probability of layering, calculated as the number of spruce clonal groups having layering branches divided by total number of spruce groups in the distance class, was significantly affected by the distance between the spruce group crown and dwarf pine crown margin (p < 0.001; Wald Z value = - 5.603). None of the spruce groups in the distance class 0–1 m had any layering branches, resulting in zero layering probability in this distance class. The probability of layering increased with increasing distance to pine: from 17 % in the 1–3 m distance class, to 43 % in the 3–5 m class and finally 67 % in the 5–7 m class (Fig. 6).

63


Fig. 6: Spruce branches layering probability in relation to distance between spruce tree crown and pine shrub crown margins. Layering probability is gradually increasing from zero to 67 % with increasing distance to pine. Grey area is within 95% confidence intervals. Small circles (slightly jittered to avoid overplotting) represent the observed values.

6.4 Discussion Our results clearly show that vegetative propagation of spruce was strongly affected by the density of surrounding dwarf pine stands. In particular, this was demonstrated by the spruce populations in pine stands being older, with fewer juveniles, than the spruce groups surrounded by alpine meadows. Furthermore, in dense pine stands the reduced occurrence of layering branches indicated the lower potential for vegetative propagation (see Kuoch, Amiet 1970; Schönenberger 1981; Tranquillini 1979). We suggest that the scarcity of layering branches probably resulted from a combination of the competitive pressure of closely occurring pine (causing light deficiency, Soukupová et al. 2001; Wild, Wildová 2002; Dullinger et al. 2005) and increased branch mortality from prolonged snow cover and wetter microclimate in pine stands (Culek 2012). Indeed, both the snow cover prolongation and increased moisture can lead to fungal infection (Vacek et al. 2012). The mortality of actually or potentially layering branches could also be increased by mechanical damage from prostrate branches of dwarf pine (see Fig. 2D). In fact, we have observed high dieback of spruce branches in dense pine stands in the study area. The relative importance of the various explanatory phenomena is likely to differ among the distance classes, as they would operate over different distances. Thus, mechanical damage would likely have been especially important in the class of shortest distances to pine margins (0–1 m) and thereby at least partly explain the zero layering probability found for that class. However, at this distance, shading (Wild, Wildová 2002; Dullinger et al. 2005) and snow cover prolongation (Culek 2012) also likely contributed to the extremely low layering probability. The reduced layering probability of spruces even at 64


relatively large distances from pine (3–5 m), about 25 % less than than for those in meadows, can probably be ascribed largely to snow prolongation and to a lesser degree to shading. Apparently, none of these mechanisms would operate at distances greater than 6 m, as no effect on spruce layering was observed. Layering success is also known to be affected by ground vegetation (Arno, Hamerly 1984; Vacek et al. 2012), waterlogging (Vacek et al. 2012), and soil moisture scarcity, but none of these is likely to have substantially influenced our observed outcomes. In particular, increasing dwarf pine cover is characterized by increasing dominance of ground vegetation cover by Avenella flexuosa (Zeidler et al. 2012). However, ground vegetation dominated by this species is associated with very low mortality of spruce layering branches (Vacek et al. 2012). Therefore, such a change in vegetation cover could not underlie the inhibitory effect we found of dwarf pine on spruce layering (if anything, it would have reduced the strength of this effect). Similarly, mortality of juvenile ramets is higher in waterlogged areas (Vacek et al. 2012), but our sites were not waterlogged (as shown by the absence of hygrophilous vegetation). Too little soil moisture can also limit vegetative reproduction of some woody species in alpine areas (e.g., Salix and Rhododendron at sites in the Alps having substrate that does not retain water), with adventitious root development strongly dependent on available soil moisture (Körner 2003). However, in our study system, there is unlimited water availability in the root zone soil substrate during the entire growing season (Šenfeldr et al. 2013). In contrast to hampering the vegetative reproduction of spruce, dense pine stands positively affected spruce height growth. As shown by the lower ages of spruce groups compared to their surrounding pine stands, many of these spruces have been growing in gradually increasing pine cover. The improved height growth in the dense pine stands might be related to decreased browsing pressure from herbivores (Rao et al. 2003; Russel, Fowler 2004; Dullinger et al. 2005) and/or lower wind abrasion of aboveground biomass in the juvenile ontogenetic phases. The latter explanation in particular is supported by our finding of lower numbers of broken stems in dense pine plots in comparison to no-pine plots. Protection against frost (Michiels 1993) might also have played a role. We suggest that these benefits accelerated height growth at least until the shrub layer was overtopped, with the added growth retained such that trees that lacked this protection as juveniles would not catch up. We do not believe that the higher spruce growth was the result of competition for light and and growing space with pine, because we find no differences in spruce tree slendernesss coeficients between dense pine and no-pine plots (not shown). In most cases, gaps in pine stands were probably large enough for the juvenile phase of the oldest trees in a group not to experience light deficiencies. According to Wild, Wildová (2002), the negative effect of pine on low-stature plants is manifested only within a ca. 0.4–0.6 m wide buffer zone along the pine margin. In our study, it appears that the benefit from sheltering by dwarf pine outweighed possible suppression due to competition for light and nutrients. These findings are in contrast to results from the eastern Alps, where poorer height growth was found in pine stands than in meadows. This dissimilarity is probably related to stronger effects (abrasion, breaks) of wind on alpine/subalpine ecosystems in the Sudetes than in the Alps (see Treml et al. 2012). Besides strong winds, high population densities of browsers (red deer, Cervus elaphus) in the treeline areas of the Sudetes might be key height growth-limiting factors for Norway spruce in its early ontogenetic stages. Our finding of a 65


positive effect of dwarf pines on spruce height growth along with a negative effect on vegetative propagation is consistent with the overall view that interactions between treeline species can have both positive and negative components (Callaway, Walker 1997; Song et al. 2010). At the upper forest limit in the study area, the spruce trees occur as ’competitive stress tolerators’ (C-S strategists, sensu Brzeziecki, Kienast, 1994) in dense sexual populations, reaching heights of about 10 m (Treml 2007). There, the pine-spruce interaction is mainly competitive, and, as documented from many areas, spruce is gradually overgrowing pine stands (Jeník, Lokvenc 1962; Dullinger et al. 2005; Švajda et al. 2011; Šenfeldr et al. 2012). In the upper part of the treeline ecotone, this interaction switches to at least partly facilitative, with pine, representing strong ’stress tolerators’ (S – strategist, sensu Brzeziecki, Kienast 1994), benefitting the height growth of spruce at its range limit (Körner 2012). However, this facilitative role is ambiguous, since spruce layering is supressed. This pattern is consistent with special cases of the stress-gradient hypothesis (Maestre et al. 2009) posed by competition between C-S and S strategists (Wang et al. 2008). In our study system, different spruce morphotypes are represented by high stature sexual populations and low stature clonal populations (Schöb et al. 2013). Moreover, changes between competition and facilitation can also occur during the ontogenies of interacting species (Mirity, 2006). Taking these considerations into account, the pine-spruce interactions along the alpine treeline ecotone can be understood within the framework of the stress-gradient hypothesis (Callaway et al. 2002). Most treelines in Europe have recently been subjected to forest and shrub invasion following the cessation of mountain agriculture, along with climate amelioration (Anthelme et al. 2003; Gehrig-Fasel et al. 2007). Generally, for upward shifts of island-form, wind-affected treelines, as found in the study area, the combination of both sexual and vegetative reproduction is needed (Holtmeier 2009; Šenfeldr, Maděra 2011). Our findings suggest that potentially quick upward expansion of forest within the zone adjacent to the current upper forest limit is likely to be slowed by closed dwarf pine stands, since they hamper both seedling recruitment of spruce (Dullinger et al. 2005) and spruce layering. In the upper part of the treeline ecotone at climatically extreme sites, the interactions are complex as shown by our documentation of both facilitative and competitive effects of dwarf pine on spruce establishment and growth. Future scenarios of spruce-dwarf pine interactions will strongly depend on the texture of dwarf pine stands and the availability of space for spruce germination (Wild, Winkler 2008). Moreover, although interactions of spruce and dwarf pine within the treeline ecotone may follow their current patterns, they will probably also depend on differing individual responses of both species to climate change (Walther et al. 2002).

6.5 Conclusions Our results show that tree-shrub interactions at wind-affected treelines significantly determine dynamics of clonal tree groups. We found that the distance between spruce trees and surrounding dwarf pine proved to be a key limiting factor of the spruce’s vegetative reproduction. As a result of strong competitive pressure of dwarf pine, the numbers of layering branches and juvenile spruce ramets decreased with increasing pine stand density. 66


On the other hand, spruce height growth was facilitated in dense pine stands. This study indicates that both competition and facilitation between shrubs and trees will influence dynamics of the alpine treeline ecotone. The expansion of spruce coverage will probably be slowed significantly at sites with dense dwarf pine stands adjacent to the current upper forest limit. At climatically more extreme sites, facilitative interactions should also be considered. Similar patterns of interactions between shrubs and trees are likely to occur at treelines involving other tree and shrub species. Thus, the results of this study can contribute to the understanding of processes driving treeline dynamics, not only in the particular study system, but more generally, especially in the context of treeline responses to climatic change. Acknowledgments This paper was supported by the Internal Grant Agency of the Faculty of Forestry and Wood Technology at Mendel University in Brno (projects 38/2010) and by the project for creation and development of a multidisciplinary landscape team (no. CZ.1.07/2.3.00/20.0004), with financial contribution from the EU and the state budget of the Czech Republic. V. Treml was supported by project GA ČR P504/11/P557. The authors wish to thank J. Rosenthal for improving the English. Additionally, we are grateful to T. Kyncl for initial suggestions and help in the field.

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Treml, V., Šenfeldr, M., Chuman, T., Ponocná, T., Demková, K. 20th century treeline ecotone advance in medium altitude mountains of Central Europe – the consequence of land abandonment or climate change? Submitted to Journal of Biogeography

7. 20th century treeline ecotone advance in Central European medium altitude mountains – a consequence of land abandonment or climate change? Václav Treml1, Martin Šenfeldr2, Tomáš Chuman1 , Tereza Ponocná1, Katarína Demková1 1) Department of Physical Geography and Geoecology, Faculty of Science, Charles University in Prague; Albertov 6, CZ-128 43 Prague 2) Department of Forest Botany, Dendrology and Geobiocoenology, Faculty of Forestry and Wood Technology, Mendel University in Brno; Zemědělská 3, CZ-613 00 Brno Abstract Aim In Europe, treeline ecotones have been exposed to both temperature increase and agricultural land abandonment during the last 100 years. We aimed to disentangle the effects of these two phenomena on treeline ecotone shifts. Location The study was conducted in the Sudetes Mts., in Central Europe (50°N, 15-17°E). Data were gathered from 38 plots in the treeline ecotone formed by Norway spruce at elevations ranging from 1250 to 1500 m. The study area experienced a 1°C temperature increase over the last 100 years and agricultural land abandonment in the first half of the 20 th century. Methods At the 38 plots situated at different positions within the alpine treeline ecotone, the age structure of all seed-originated Norway spruces (Picea abies) was determined using tree rings. Changes in tree cover over the last 60-70 years were assessed from aerial images. The history of agricultural land use for each plot was compiled and we also constructed average radial growth curves for the study area. Finally, the changes in recruitment were modeled using climatic variables, growth indices and land-use history. Results We found that establishment of trees at treeline stands had occurred with a 30–40 year lag after the main establishment peak at the upper forest limit (UFL) All treeline plots and most UFL plots revealed gradual increases in tree cover. Increases in recruitment were dependent mainly upon agricultural land abandonment at the UFL, whereas climatic variables and tree vigor (indicated by ring widths) affected recruitment at treeline. Main conclusions Treeline ecotone densification was mainly attributable to agricultural land abandonment, whereas positive temperature anomalies were responsible for the upward migration of trees. Based on comparison with other mountain areas, this seems to be typical of treeline ecotone reactions to co-occurring agriculture land-abandonment and rising temperature. Keywords: cover; growth; Norway spruce; recruitment; Sudetes; treeline

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7.1 Introduction The upward shift of treeline ecotones is considered the most remarkable consequence of climate change in mountain regions (Walther et al. 2002). The treeline ecotone is a transitional zone between montane forest and the alpine belt. Conventionally, it is delineated as the zone occurring between the upper limit of closed forest (timberline) and the uppermost tree species individuals /tree species line/ (Körner 2007). On a global scale, the position of the treeline ecotone is driven by heat deficiency (Körner 2012), which is why, unsurprisingly, both upward and northward expansions of tree stands have been reported worldwide in response to recent warming (see review by Harsch et al. 2009). Temperature increase reduces the impact of three main factors that limit tree occurrence at high elevations: physiological growth limitation, dieback and seedling mortality (Germino et al. 2002; Harsch, Bader 2011; Körner 2012). However, the prevailing factors that determine whether treeline ecotones migrate or remain stable are often scale-dependent (Holtmeier, Broll 2005). Whereas, at the continental scale, temperature is the most important factor, at the landscape and local scales, effects of disturbances, geomorphology and mesoclimate can affect treeline dynamics. On the continental scale, purely temperature-driven upward shifts of treeline have been reported from, for example, Siberia (Esper, Schweingruber 2004), the Polar Ural (Shiyatov et al. 2005), northwestern Canada (Danby, Hik 2007), and the Rocky Mountains (Elliot 2012). In these areas, the treeline ecotone has moved upward by several tens of meters over the last six to ten decades. Some mountain regions have displayed stable treeline ecotones, but these are restricted to areas with at least one of the following characteristics: (i) negligible temperature increases (Dalen, Hofgaard 2005; Payette 2007); (ii) inhibitory effects of temperature increase (from increased drought, increased snowpack and winter desiccation; Barber et al. 2000; Kullman 2001; Gamache, Payette 2004); (iii) treelines limited by disturbances or geomorphological features (Kullman 2005; Van Bogaert et al. 2011; Leonelli et al. 2011); or (iv) abrupt treelines formed by broadleaved species (Cuevas 2002; Harsch et al. 2009). In densely populated regions such as much of Europe, treeline movement is, however, also controlled by the history and recent intensity of human impact. This includes mainly cattle or reindeer grazing, grass mowing, logging and burning of trees (Camarero, Gutiérrez 2007; Sitko, Troll 2008; Potthoff 2009; Rundqvist et al. 2011). Agricultural land abandonment (hereinafter also referred to as land abandonment) usually leads to enhanced seedling survival and decreased dieback from grazing (Potthoff 2009). Land abandonment has recently been reported from many mountain regions in Europe (Holtmeier, Broll 2005) and less frequently from other parts of the world (Miehe, Miehe 2000). It is considered to be an important cause of treeline advance in the Pyrenees (Améztegui et al. 2010), Scandes (Rössler et al. 2008), Alps (Gehrig-Fasel et al. 2007; Tasser et al. 2007) and Carpathians (Sitko, Troll 2008). In many cases, the period of land abandonment has overlapped that of temperature rise, making it difficult to determine the individual effects of each of these phenomena. Case studies considering the influences of both factors on treeline advance are, however, rare (Motta et al. 2006). In the present paper, we investigate the influences of agricultural land abandonment and temperature change on the treeline dynamics in the Sudetes Mountains (Czech Republic and Poland), which provide an example of a mountain range with a fragmented 74


alpine zone that occupies a narrow span of elevations. Advancing treeline ecotone might cause further fragmentation and diminution of the small alpine islands associated with likely loss of biodiversity (Theurillat, Guisan 2001). In the Sudetes, mountain agriculture gradually ceased during the first half of the 20th century, and both winter and summer temperatures increased by 1°C over the last century. Therefore, the main objectives of this study are to determine the timing of the treeline ecotone advance and to disentangle the effects of temperature increase from those of land abandonment. To achieve these objectives, we gathered data about treeline demography, tree growth and temporal changes in tree cover.

7.2 Methods 7.2.1Geographical settings The Sudetes Mts., a hercynian mountain range in Central Europe, reach their greatest heights in the Giant Mts. (highest peak, Mt. Sněžka, 1602 m) in the west and the Hrubý Jeseník Mts. (highest peak, Mt. Praděd, 1491 m) in the east (Fig. 1A). The alpine area in the Giant Mts. (GM) is about 5500 ha and in the Hrubý Jeseník Mts. (HJ) about 1600 ha. Treeline ecotones in the Sudetes are usually situated at altitudes ranging from 1200 to 1400 m. Climate is characterized by high precipitation (approximately 1400-1600 mm per year), irregularly distributed snow pack, strong western winds, and mean annual temperature of about 1 to 2°C in the uppermost locations (Tolasz et al. 2007). The forests of the upper montane belt and treeline ecotone are composed of Norway spruce (Picea abies). Graminoids and mountain dwarf pine (Pinus mugo) stands dominate alpine and subalpine communities. The extent of the alpine forest-free area was enlarged by deforestation and grazing from the early medieval period (9th–11th century AD, Speranza et al. 2000; Novák et al. 2010) until the beginning of the 19th century, when mountain agriculture (grazing and grass mowing) peaked (Lokvenc 1995; Jeník, Hampel 1992). Direct human impacts then gradually diminished until the beginning of the 20th century, when the decrease became much more rapid. Since ca. 1950, almost no direct human intervention in the treeline ecotone has occurred. High-elevation forests in the Sudetes also experienced acid air pollution, which resulted in marked growth depression of trees, in the 1970s and 80s (Sander et al. 1995). Our research activities were conducted in 2010-2011 in 38 plots located at the current upper limit of closed forest (UFL) and in the middle (treeline - TL) and in the upper (outpost treeline – OTL, HJ only) parts of the treeline ecotone. Most plots are arranged in pairs or triplets along altitudinal transects that span the upper forest limit, treeline zone and highest-positioned outpost tree islands (Fig. 1). The average distance between adjacent UFL and TL plots is 150 m in HJ and 304 m in GM. The plots are situated outside the area of artificial afforestation. For each plot, the history of human intervention was compiled based on a literature survey (Lokvenc 1978; Lokvenc 1995; Jeník, Hampel 1992), and examination of old forestry maps, as well as cadastral maps covering the 1850s, and aerial images from 1936 (GM) or 1946 (HJ). 75


Fig. 1: Study area (A) and sample plots configuration in Giant Mountains (B) and Hrubý Jeseník Mountains (C).

7.2.2 Treeline demography The demographic aspect of the present study focuses on seed-originated individuals, although ramets are also common at treelines. However, establishment of seed-originated trees may be affected by different variables than their ramets, and large-scale treeline shifts are mainly dependent on seed-based regeneration (Holtmeier 2009), thus providing the basis for focus on seed-originated trees.

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The ages of all seed-originated individuals were analyzed on rectangular 50 x 30 m plots with the longer side oriented perpendicular to the mountain slope. Trees with diameter at breast height (DBH) ≥ 5 cm were cored using an increment borer as close to the trunk base as possible. In cases of off-center cores, tree ages were corrected for missing rings using the method described in Battlori, Guttieréz (2008). Additionally, correction for coring height was applied. For this purpose, functions describing the dependence of tree age on tree height were derived from randomly and destructively sampled seedlings and saplings of various heights ranging from 0.2 to 1 m (N = 50 for each region and UFL, TL, OTL). The ages of saplings (height ≥ 20 cm and DBH < 5 cm) were determined by counting the internodes or by destructive sampling and counting tree rings at the root collar. The numbers of seedlings (height < 20 cm) were counted on two randomly distributed subplots, each 25 m2. Finally, establishment of each tree was ascribed to a calendar year. Given that pith offset distances were usually small (less than 0.5 cm) and coring height was as low to the ground as possible (always less than 0.5 m), the estimated error in our determinations of tree age derived from coring or counting of internodes was less than 10 years (Battlori, Guttieréz 2008). For the sake of simplicity, all seedlings were dated to 2005, i.e., five or six years before data collection, and representing the midpoint of the prior decade. At each plot, the dead trees were counted. The difference between the numbers of established trees in upper forest limit plots and treeline plots was tested on a decadal scale using t-test, after checking for data normality. Also, the differences between establishment dates of the oldest trees in upper forest limit plots and treeline plots was tested using t-test for each area (GM, HJ). These tests were based on the five oldest trees from each plot. 7.2.3 Changes in tree cover In order to supplement the age structure analysis, mainly to enable detection of possible forest thinning or past absence of trees, the changes in spruce cover were assessed in plots that surrounded each age-structure plot. These plots ranged from 0.8 to 8.1 ha in size, with greater plot size reflecting greater homogeneity of environmental characteristics. We classified their tree cover from the series of aerial images covering the period from 1936 to 2005 in GM and from 1946 to 2005 in HJ (Tab. 1). Changes were analyzed in individual time windows comprising 15 to 20 year intervals. Images were first orthorectified with a 10 m resolution digital terrain model. Ground control points were collected from current orthogonal images (Czech Geodetic Survey). We then applied image segmentation followed by supervised classification using textural and spatial features (Coburn, Roberts 2004). Due to shadows from crowns and scratches on images, extensive manual correction of classification errors was performed. The final binary matrix of “tree” and “no-tree” pixels thus represented the best possible result of the classification based on available aerial images. For both GM and HJ, the differences between UFL and TL in changes in tree cover were tested for the periods 1936–64, 1964–85, and 1985–2005 using the Mann-Whitney U test.

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Tab. 1: Attributes of aerial images. Year of acquisition

Scale

Focal length (mm)

1936 1964 1984 1946 1962 1985

1 : 15 000 1 : 12 500 1 : 26 600 1 : 17 000 1 : 15 000 1 : 19 660

Panchromatic Panchromatic Panchromatic Panchromatic Panchromatic Panchromatic

210.73 210.79 151.97 200.25 209.74 152.12

0.31 0.26 0.37 0.35 0.32 0.41

GM GM GM HJ HJ HJ

2005

1 : 20 000

Colour (RGB)

152.14

0.28

GM, HJ

Type

Pixel resolution (m)

Region

7.2.4 Growth trends In our study, tree ring width (TRW) was considered an indicator of tree vigor and examined as an explanatory variable possibly influencing tree recruitment. Therefore, TRW chronologies for GM and HJ were developed to identify periods of above- and below-average radial increments. Since the differences in TRW trends between UFL and TL trees were negligible (Treml et al. 2012), samples were exclusively collected on UFL plots. From each UFL plot or its close vicinity, 20 to 30 trees were sampled (2 cores per tree) at breast height. After mounting and sanding the cores, tree-rings were measured on a positioning table. The resulting two TRW series from each tree were cross-dated and averaged. To identify growth trends, we simply averaged the TRW series from all UFL plots separately in GM and HJ. However, we had to cope with uneven temporal distribution of samples and varying proportions of different cambial ages among calendar years. Therefore, we selected different-aged samples distributed regularly over time and all series were truncated at 50 years. In this way, we assembled a data set with a regular distribution and sufficient replication of samples for the period 1860–2000 (Fig. 4). Each plot was roughly equally represented in the data set. In comparison to commonly used detrended TRW chronologies, this approach does not suffer from removal of lowfrequency signals or from end effects (Büntgen et al. 2012).

7.2.5 The effects of temperature, growth and agricultural abandonment We examined the relationships between temporal changes in tree establishment and temperature, tree growth and land use. Data were treated either on the plot level or as aggregated data sets with annual resolution for UFL, TL, OTL in both study areas. At the plot level, we compared the annual numbers of trees established during the 20 years before and 20 years after cessation of human impact. These differences were tested using the Mann-Whitney U test. For the other variables (climatic, growth), the differences in numbers of trees established over the same interval of years were tested using an ANOVA with an F-test to assess whether agricultural abandonment could, by itself account for them, or, alternatively, these variables had explanatory power. 78


The aggregated data set was analyzed using Pearson correlations and linear regression within the framework of hierarchical partitioning (Chevan, Sutherland 1991). This method reduces collinearity by identifying the independent contribution of each explanatory variable to the response variable and isolating it from the joint effects of correlated variables (Walsh, Mac Nally 2004). In the regression analysis, recruitment was treated as a response variable, and climatic characteristics, cessation of mountain agriculture, and tree growth as explanatory variables. Temperature variables reflected conditions both prior to tree establishment (affecting seed production) and after tree establishment (affecting early stages of seedling growth). Therefore, 5-year backward and forward means of winter (December-February), spring (March-May), summer (June-August), autumn (September-November) and annual temperatures were computed. Prior to regression analysis, the number of climatic variables was reduced to four principal components. 11-year mean TRW was used as a proxy for tree vigor. Cessation of mountain agriculture was quantified as the annual sum of the number of plots that had been abandoned within the preceding 20 years. Because differences among the total numbers of trees on each plot would result in different weights of the plots in the aggregated data set, the annual numbers of trees established on each plot were expressed as ratios between the annual recruitment and the overall number of trees on the plot. These ratios were summed to form the final, aggregated data set, with each plot thereby weighted equally. To enable analyses of recruitment time series of sufficient length for statistical analysis, we assumed a normal distribution of age-determination errors and used annually resolved recruitment time series. However, mainly in old stands, static age structure is biased by the increasing effect of mortality as one looks further into the past. Therefore, we fit recruitment series to a trend line (exponential function, Gamache, Payette 2005). If the fit was statistically significant, we analyzed the residuals; if not, raw data were further employed. In the case of treeline sites in which juveniles were prevalent, models based on both raw data and residuals were tested. To avoid the possible influence of acid pollution in the 1970s through the 1980s (Sander et al. 1995), recruitment series were truncated such that they ended in 1970. All statistical analyses were performed using R statistical software.

7.3 Results 7.3.1 Treeline demography The analysis of tree ages shows that trees were older at the upper forest limit (mean year of establishment 1945 for GM and 1924 for HJ) than in tree stands in the upper part of the treeline ecotone (mean year of establishment 1955 for GM and 1959 for HJ). There were no statistically significant differences in ages of trees located in TL and OTL stands. The standard deviations of mean years of establishment were higher at the upper forest limit (GM – 49 for UFL and 33 for TL; HJ – 32 for UFL, 28 for TL, 29 OTL), suggesting more diversified stands age structure at the upper limit of closed forest than in treeline stands. The oldest trees on individual plots were established significantly earlier in UFL stands than in treeline or outpost treeline stands (t-test p < 0.001), both in GM and in HJ. Establishment dates of oldest trees on plots ranged from 1745 to 1900 at UFL and from

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1802 to 1952 in treeline stands in GM. In HJ, the oldest trees established between 1780 and 1879 at UFL and between 1849 and 1936 in treeline and outpost treeline stands. In GM, in UFL plots, the first pronounced increase in recruitment occurred during the1840s, the second recruitment pulse is in the 1880s, and the third peak, which was very pronounced, in the 1930s-1940s (Fig. 2). A recent (1990s-2000s) peak in UFL recruitment largely reflects seedlings in early stages. The highest proportions of seedlings were observed on north-facing UFL plots (Fig. 5). The most pronounced depression in UFL recruitment occurred during the 1960s-1980s At treeline, the first recruitment pulse happened around 1900, followed by a short depression in 1910s. Then, an abrupt increase in numbers of established trees occurred in the 1920s–1930s, and the first recruitment maximum was recorded in the 1950s–1960s. After a depression in the 1970s–1980s, the second pronounced recruitment maximum was achieved in the 1990s. The highest differences in tree establishment between the UFL and TL sites occurred in the 1840s, 1870s, 1880s and 1890s, in all of which recruitment was significantly higher at forest sites than at TL sites (p < 0.05). In the 1950s–1970s, establishment was significantly higher in TL than UFL sites (p < 0.05). In HJ, the first peak in establishment of trees on UFL sites was in the 1860s. After a short depression in the 1870s, the numbers of established trees gradually increased up to the year 1910. Then, after a depression in the 1920s, the numbers of trees being established increased again, culminating in 1930s. Stands in TL and OTL had their first peak in recruitment around 1890, then, after a slight depression, showed a gradual increase in establishment until culminating in the 1950s–1960s. In the 1970s–1980s, the numbers of trees established dropped abruptly. This decrease was followed by a recruitment peak between 1990 and 2010. Trees established significantly more frequently in UFL sites than in TL and OTL sites from the 1860s to 1930s (p < 0.05). In contrast, recruitment significantly prevailed on TL, OTL sites over UFL sites from the 1950s to 1990s (p < 0.05). Correlations between establishment lag (expressed as delay in either beginning of continual recruitment or in peak of recruitment) and distance between adjacent plots were not significant (Spearman r > 0.05). Some mortality was recorded at each plot. In GM, the highest numbers of dead stems were observed on north-facing UFL sites, whereas in HJ, OTL sites displayed the highest mortality mostly involving ramets situated on windward sides of tree groups (Tab. 2).

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Fig. 2: Recruitment frequency, in terms of sums of established trees standardized per plot (each plot having the same weight in the aggregated dataset). A) Giant Mountains; B) Hrubý Jeseník Mountains. Recruitment time series is smoothed with a 5-year moving average.

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Tab. 2: Basic characteristics of study plots.

Area

GM

HJ

Plot code

Plot type

Altitude (m)

Slope (°)

Aspect

Median Age

Number of dated trees (incl. seedlings and saplings)

FS1 FS2 FS3 FS4 FS5 FN6 FN7 FN8 FN9 FN10 TS11 TS12 TS13 TS14 TN15 TN16 TN17 TN18 JFN1 JFN2 JFN3 JFN4 JFS5 JFS6 JFS7 JTN8 JTN9 JTN10 JTN11 JTS12 JTS13 JTS14 JON15 JON16 JON17 JOS18 JOS19 JOS20

UFL UFL UFL UFL UFL UFL UFL UFL UFL UFL TL TL TL TL TL TL TL TL UFL UFL UFL UFL UFL UFL UFL TL TL TL TL TL TL TL OTL OTL OTL OTL OTL OTL

1252 1321 1312 1321 1288 1284 1281 1290 1278 1255 1483 1350 1387 1391 1348 1490 1398 1392 1386 1393 1369 1357 1390 1392 1349 1404 1390 1399 1421 1420 1400 1417 1426 1414 1467 1464 1434 1470

16 15 28 18 30 21 17 31 21 36 15 13 23 18 26 15 15 20 10 16 15 15 12 10 19 9 10 12 15 11 15 14 13 11 12 11 10 16

SW S SW SW S N NW N N N SW SW SW S N N N NW NW NW N N S SE SW NW N N NW S SW SE N N NW S SW SE

1934 1901 1957 1917 1941 1941 1912 1935 1961 1972 1977 1947 1957 1939 1927 1989 1953 1941 1936 1910 1927 1911 1900 1926 1927 1950 1962 1970 1958 1954 1955 1952 1951 1956 1978 1968 1948 1971

41 38 77 74 103 74 36 84 55 150 110 74 40 49 59 100 40 29 73 55 70 19 41 139 60 73 91 60 106 58 39 81 68 23 111 59 17 127

Number of dead stems

Size of the surrounding area of treecover classification (ha)

6 9 5 7 13 15 19 15 17 16 18 19 9 15 23 12 15 11 11 18 37 18 9 43 26 5 39 16 20 22 17 14 25 10 25 45 4 42

5.1 8.0 8.1 5.8 3.1 4.1 3.2 8.1 6.4 3.3 6.1 5.9 3.4 7.0 5.9 6.6 4.1 2.9 1.5 0.9 3.4 1.7 2.3 1.4 1.3 1.1 2.3 3.1 1.4 1.5 2.0 1.3 1.6 3.1 1.0 1.7 1.6 1.2

7.3.2 Changes in tree cover All treeline plots revealed increases in tree cover between the onset of the study period (1936 for GM and 1946 for HJ) and 2005 (Fig. 3A). At the beginning of this period, the average tree cover in GM (1936) was 1.8 % for TL (OTL not assessed) and in HJ (1946) 82


was 3.8 % for TL and 1.2 % for OTL. In 2005, tree cover ranged from 2.8% (HJ OTL), to 9.3% (HJ TL), with GM at 4.0 %. Maximum tree cover values were achieved mostly in 2005. Upper forest limit plots revealed mostly increases in tree cover between 1936(46) and 2005, however there were two exceptions, comprising north-facing sites FN6 and FN9 which had decreasing spruce cover. Average tree cover at UFL was 12 % in GM and 17 % in HJ at the onset of the study period, whereas in 2005 spruce stands covered 18% (GM) and 28% (HJ) on average. Highest tree cover values at UFL were mostly achieved in 2005, however several plots had their densest tree covers in the 1960s or 1970s (Fig. 3A). Standardized annual changes in tree cover (Fig. 3B) were bigger at UFL sites than TL sites. Obviously, tree cover changes are greater at sites with higher tree cover. Changes were represented mostly by increases in tree cover. Recently (1984/5–2005), several upper forest limit sites showed tree stand thinning. Changes in tree cover were significantly higher at upper forest limit sites than treeline sites only in the 1936–64 period in GM (Mann-Whitney U test, p < 0.05). After standardization for different original tree cover (i.e., to yield annual change per m2 of forested plot), treeline sites revealed significantly higher increases in tree cover than upper forest limit sites in the 1984–2005 period in GM (Mann-Whitney U test, p < 0.05). In HJ, upper forest limit sites showed significantly higher densification than treeline sites in 1962-1985 and the opposite tendency in 1985–2005 (p < 0.05). However, after standardization for different original tree cover, only the second is significant. Fig. 3: Changes in tree cover expressed (A) as percentages of coverage; and (B) as annual change per m2 of plot (corrected for differences in plot size and differences in lengths of time windows).

7.3.3 Growth trends Similar patterns of tree growth characterized both HJ and GM sites (Fig. 4). Distinct periods with above-average growth rates comprised the years ca. 1935–1970 and after 1990. In contrast, below-average radial increments were typical 1860–1870, 1880–1900, 83


1905–1930, and 1970–1990. Whereas in HJ, the radial increments after 1990 were the highest over the last 150 years, in GM these increments were comparable to those from the 1950s and 1960s. In both areas, the deepest drops in TRWs were recorded during the 1970s–1980s.

Fig. 4: Sample size (A) and average tree ring width (B) in both study areas.

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7.3.4 The effects of temperatures, tree growth and agricultural land abandonment on tree establishment Recruitment was significantly correlated with land abandonment at UFL (Fig. 5). At TL, recruitment was in all cases dependent on TRW and on annual and autumn temperatures. Recruitment of TL trees was also correlated with certain indices of spring and summer temperatures (Fig. 5). Establishment of trees at UFL was negatively correlated in some cases with certain indices of autumn and summer temperatures (Fig. 5). All of the relationships were, however, affected by multicollinearity, which is not the case of linear regression within a framework of hierarchical partitioning (Tab. 3). Hierarchical partitioning showed that at the upper forest limit, tree recruitment was closely related with agricultural land abandonment (71 % of explained variability in GM and 23 % in HJ). The recruitment series revealed a slight, but significant, negative association with TRW (GM) and a positive association with climatic component PC1 in HJ (correlated positively with preceding 5yr winter temperatures and negatively with following 5yr summer and autumn temperatures, Tab. 4). The regression models were significant and explained 28 % of variability in recruitment at UFL in GM and 21 % in HJ (Tab. 3). On TL sites, tree establishment responded positively to enhanced radial growth (GM 54 % of explained variability, HJ 34 %) and to following 5-year annual and/or winter temperatures (PC2 – 14 % of explained variability in GM and 7 % in HJ). In HJ, TL recruitment was further contingent on PC1 and on PC3, which in turn was correlated with preceding 5-year annual and spring temperatures. Establishment of trees at OTL sites in HJ was significantly associated with climatic components PC1, PC2 and PC3. The models using raw and residual recruitment series tested for TL and OTL sites produced consistent results with respect to the statistical significance of individual variables. The regression models with raw recruitment as a response variable were, however, substantially stronger. At the plot level, of the entire set of 38 plots, 19 revealed the beginning or peak of tree establishment in or immediately after period of agricultural land abandonment (Fig. 6). Statistically significant differences in the numbers of trees established 20 years before and 20 years after agricultural land abandonment were recorded at four sites (FS1, FS4, TS12, TN18) in GM (Tab. 5). At FS1, the difference in the number of established trees was accompanied by a significant increase in TRW associated with a significant increase in summer temperatures (PC1). At treeline plots (TS12, TN18), there were significant increases in PC1 and PC2 values (TS12) or decreases in PC1 and increase in PC3 values (TN18). Eight sites in HJ (3 – UFL, 3 – TL, 2 – OTL) showed significant increases in number of established trees after land abandonment. At UFL plots, these increases were mostly accompanied by significant increases in PC1, PC2 and TRW. TL and OTL plots revealed significant decreases in PC1 and, in one case (JTN9), also significant increase in PC3 and in TRW. Three UFL sites in HJ, where agricultural land abandonment was dated to 1940, revealed significantly lower recruitment after abandonment compared with before it (Tab. 5).

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Fig. 5: Pearson correlations of tree establishment with agricultural land abandonment, radial growth (TRW), and temperature variables.Abbreviations: prior5-year = average annual temperature of 5 years preceding seedling establishment; prior5-DJF = average winter temperature of preceding 5 years; prior5-MAM = average spring temperature of preceding 5 years; prior5-JJA = average summer temperature of preceding 5 years; prior5SON = average autumn temperature of preceding 5 years; labels with „after5“ designation – indicate comparable(annual or seasonal) temperature averages from the first 5 years after seedling establishment.

Tab. 3: Overall variability in recruitment explained by full model and explained by individual variables as determined by hierarchical partitioning, with significance at p < 0.05 (n.s. – not significant). Variability explained by individual variables (%) Location within ecotone

Variability explained by full model (adjusted R2)

Land abandonment

TRW

PC1

PC2

PC3

PC4

GM – timberline (res)

0.28

71

13 (neg)

n.s.

n.s.

n.s.

n.s.

HJ – timberline (raw)

0.21

23

n.s.

57

n.s.

n.s.

n.s.

0.31

n.s.

54

n.s.

14

n.s.

n.s.

0.49

n.s.

34

25

7

27

n.s.

0.21

n.s.

20

22

16

35

n.s.

GM – treeline (raw) HJ – treeline (raw) HJ – outpost treeline (raw)

86


Tab. 4: Scores of temperature variables along individual axes of PCA analysis. For abbreviations see Figure 5. Temperature variable Prior5-year Prior5-DJF Prior5-MAM Prior5-JJA Prior5-SON After5-year After5- DJF After5- MAM After5- JJA After5- SON

PC1 -0.0729 0.7509 -0.1053 -0.6137 -0.5607 -0.3940 0.4655 -0.1220 -0.8180 -0.6801

PC2 0.1396 -0.1124 0.0464 -0.0252 0.3158 0.8509 0.8434 0.5065 0.1045 0.2229

PC3 0.9514 0.5893 0.6896 0.2876 0.5056 0.0551 -0.0937 -0.1170 0.1501 0.2438

PC4 0.1010 -0.0785 -0.3221 0.2647 0.4134 -0.2664 0.2108 -0.7535 -0.0037 -0.1565

Fig. 6: Numbers of established trees (decadal increments) on individual plots. Occurrence of agricultural activities is indicated by a gray stripe.

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Tab. 5: Statistically significant differences between tree establishment 20 years before and 20 years after land abandonment. Signs indicate positive (+) or negative (-) effect of variables. Plot FS1 FS4 TS12 TN18 JFN2 JFN2 JFN1 JFS5 JFS6 JFS6 JTS12 JTN9 JTN11 JOS20 JON17

Period before/after land abandonment 1910-1929/1930-1949 1860-1879/1880-1899 1880-1899/1900-1919 1900-1919/1920-1939 1861-1880/1881-1900 1920-1939/1940-1959 1880-1899/1900-1919 1920-1939/1940-1959 1880-1899/1900-1919 1920-1939/1940-1959 1920-1939/1940-1959) 1901-1920/1921-1940) 1920-1939/1940-1959) 1920-1939/1940-1959) 1861-1880/1881-1900)

P (after Bonferroni correction) 0.045(+) 0.017 (+) 0.028 (+) 0.017 (+) 0.015 (+) 0.006 (-) 0.016 (+) 0.017 (-) 0.000 (+) 0.018 (-) 0.011 (+) 0.008 (+) 0.015 (+) 0.041 (+) 0.024 (+)

Other variables with significant differences (t-test, p < 0.05) TRW+, PC1– PC1+, PC2+ PC1–, PC3+ PC1– PC1+, PC2+, TRW+ PC1– PC1+, PC2+, TRW+ PC1– PC1– PC1–, PC3+, TRW+ PC1– PC1– -

7.4 Discussion 7.4.1 Factors of treeline ecotone advance Both the age structures and the tree cover data provide evidence of the migration of trees upward into formerly treeless areas in the Sudetes. Our analysis shows that agricultural land abandonment had the most decisive effect on densification of the current upper forest limit. At the upper part of treeline ecotone, tree recruitment was more contingent on climatic variables and tree vigor. In light of these findings from medium-height mountains and also findings from other European alpine areas (Tasser et al. 2007; Rössler et al. 2008; Vittoz et al. 2008; Améztegui et al. 2010; Van Bogaert et al. 2011) changes in land-use seem to be the major drivers of treeline ecotone advance in European mountain ranges. However, this advance has been strongly modulated by climatic variables. Although many studies emphasize strong effects of summer temperatures on early life stages (Szeicz, MacDonald 1995; Camarero, Gutiérrez 2004; Danby, Hik 2007; Vittoz et al. 2008), other research (Wang et al. 2006; Harsch et al. 2009), as well as our present results indicate that other climatic variables, including autumn and annual mean temperatures, may also significantly influence tree recruitment. In high-elevation forests, June-July temperatures mostly govern radial growth (Treml et al. 2012), which was positively related to recruitment pulses in the upper part of the treeline ecotone. However, tree establishment is a more complex process than growth. Seedlings require a sufficiently long growing season without danger of spring frost (Gamache, Payette 2005) as well as a warm autumn to enable accumulating resources (Oberhuber 2004). Mean annual temperatures include all seasons, which might explain their relationship with TL recruitment. 88


In the Sudetes, expansion of trees into the present upper part of the treeline ecotone occurred 30–40 years later than tree establishment at the present upper forest limit. Part of this lag can be attributed to the gradual advance of trees into higher elevations. However, we did not find any significant correlation between the distance of TL plots from UFL and the duration of establishment lags, suggesting that there must be other factors responsible than only migration distance itself. The timing of recruitment pulses at TL and OTL were similar despite their difference in altitude. At TL and OTL sites, the peak of establishment was in the 1950s–1960s in both GM and HJ; however, at UFL, recruitment peaks differed between GM and HJ. In the 1950s–1960s, annual temperatures in the Sudetes were the highest of the first 90 years of the 20th century, suggesting possible temperature forcing of this treeline advance. Intense expansions of trees into alpine/arctic ecosystems have also been documented from the 1950s -1960s in many other areas (Central Siberia – Esper, Schweingruber 2004, Alps - Vittoz et al. 2008, Polar Ural – Devi et al. 2008; Northern Labrador – Payette 2007) with most of these studies agreeing that the treeline advance was associated with rising temperature. In contrast to the prevailing increase in tree establishment associated with cessation of mountain agriculture, three UFL plots in HJ showed the opposite reaction to agricultural abandonment in the 1940s, with their recruitment decreasing. Because tree stands there were already quite dense (tree canopy ca. 20 %) and we do not know the exact boundaries of pastures, it is likely that these sites were not actually subjected to grazing or that their grazing intensity was low. Some study plots revealed relatively weak reactions, in terms of tree establishment, to recent (1990s, 2000s) warming, which might be attributable to absence of suitable microsites (Germino et al. 2002; Dullinger et al. 2004). Recent ground vegetation of the study plots consisted of dense mats with thick litter layer unsuitable for seedling establishment in comparison with disturbed vegetation cover (Hanssen 2003). Spruce regeneration likely took advantage of the residual mat disturbance and thin litter layer that would have persisted briefly after the cessation of grazing or mowing. 7.4.2 Changes in tree cover and growth trends The different basic data sets that were used – age structure data and aerial images – supply different types of information, which in combination are particularly helpful in identifying treeline ecotone dynamics. Thus, whereas age structure data enable identification of the establishment date of each tree, aerial images allow detection of possible past stand thinning, which age structure analysis cannot yield. Additionally, such images furnish evidence that, 70 years ago, current treeline areas were almost treeless. Today their average tree cover is between 4 and 9 %, and some of these plots are covered by denser spruce stands than were UFL plots in 1936. Moreover, in comparison to UFL, tree cover increase has been more pronounced at treeline sites from the 1980s onwards. Possible further upward advance of upper forest limit in the future may therefore be inferred, especially considering that trees produce viable germinating seeds even in the uppermost parts of the alpine treeline ecotone in HJ (Šenfeldr, Maděra 2011). However, one of the main drivers of rapid change – the cessation of human impacts – will be absent, so the further upward advance will probably not be as quick as in the 1950s and 1960s. 89


Relatively slow future upward migration is also supported by current observation of relatively low densities of seedlings at TL and OTL plots, probably associated with the absence of suitable microsites. As an exception, north-facing UFL sites revealed negligible increases or even decreases in tree cover over the study period as a whole. This is probably attributable to the least intensive direct human intervention (and therefore weak effect of land-abandonment) and to strongest effects of acid air pollution there (Hruška et al. 2006). In the 1970s–1980s, the highest mortality was observed in these sites. On the other hand, the current presence of decaying wood and open canopy makes these sites suitable for regeneration (Jonášová et al. 2010), so that quick densification of north-facing tree stands may be expected. Indeed, they now have the highest numbers of seedlings. Besides upward migration of trees, the recent warming has also been manifested in high growth rates. However, we found recent ring widths to be only slightly wider than those from the 1950s and 1960s, in contrast to several other studies (Paulsen et al. 2000; Mamet, Kershaw 2013; Treml et al. 2012) that reported recent ring widths being the highest in a century-long context. Besides regional differences, also the TRW chronologybuilding procedure, which reliably preserves low-frequency variations, might explain the trends detected in tree ring widths. Although current tree stands at UFL are still open, gradual densification, with increasing effect of competition may additionally lead to slight underestimation of TRWs in comparison to periods with lower stand density.

7.5 Conclusions In the Sudetes, medium altitude Central European mountains with alpine areas of limited extent, upward migration of trees into treeless areas and densification of the treeline ecotone were documented. Similarly to other mountain ranges in the region, the treeline ecotone was subjected in the 20th century to both agricultural land-abandonment and rising temperature. Whereas tree establishment in stands at the current upper forest limit was associated mainly with the cessation of mountain agriculture, trees in the upper part of treeline ecotone were established mainly during periods of enhanced radial growth and periods with above-average annual temperatures. The differences in driving forces of tree establishment in the different parts of the treeline ecotone indicate that land abandonment was mainly responsible for treeline ecotone densification, whereas positive temperature anomalies resulted in the upward migration of trees. Based on comparison with other mountain areas, this seems to be general way in which the treeline ecotone reacts to simultaneous land use reduction and rising temperature. High temperatures and distinctly above-average radial growth still characterize the treeline ecotone in the Sudetes, suggesting that the treeline advance will probably continue, with isolated alpine patches becoming more endangered. However, since the effect of agricultural land abandonment is absent, the treeline ecotone advance is likely to not be as rapid as in the 20th century.

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Acknowledgments This study was funded by grant project GACR P504/11/P557. We are grateful to L. Piro and B. Lestinska for field and laboratory assistance. We appreciate the authorities of the KRNAP and CHKO Jeseníky for technical support and for permission to conduct research in protected areas. The authors wish to thank J. Rosenthal for improving the English.

7.6 References Améztegui, A., Brotons, L., Coll, L., 2010: Land-use changes as major drivers of mountain pine (Pinus uncinata Ram.) expansion in the Pyrenees. Global Ecology and Biogeography, 19: 632-641. Barber, V., Juday, G.P., Finney B., 2000: Reduced growth of Alaskan white spruce in the twentieth century from temperature-induced drought stress. Nature, 405, 668-673. Battlori, E., Guttieréz, E., 2008: Regional tree line dynamics in response to global change in the Pyrenees. Journal of Ecology, 96: 1275-1288. Büntgen, U., Tegel, V., Heussner, K.U., Hofmann, J., Kontic, R., Kyncl, T., Cook, E.R., 2012: Effects of sample size in dendroclimatology. Climate Research, 53: 263-269. Camarero, J., Gutiérrez, E., 2004: Pace and pattern of recent treeline dynamics: response of ecotones to climatic variability in the Spanish Pyrenees. Climatic Change, 63: 181-200. Camarero, J., Gutiérrez, E., 2007: Response of Pinus uncinata recruitment to climate warming and changes in grazing pressure in an isolated population of the Iberian systém (NE Spain). Arctic, Antarctic, and Alpine Research, 39: 210-217. Coburn, C.A., Roberts, A., 2004: A multiscale texture analysis procedure for improved forest stand classification. International Journal of Remote Sensing, 25: 4287-4308. Cuevas, J.G., 2002: Episodic regeneration at the Nothofagus pumilio alpine timberline in Tierra del Feugo, Chile. Journal of Ecology, 90: 52-60. Dalen, L. & Hofgaard, A. (2005) Differential regional treeline dynamics in the Scandes Mountains. Arctic, Antarctic, and Alpine Research, 37: 284-296. Danby, R.K., Hik, D., 2007: Variability, contingency and rapid change in recent subarctic alpine tree line dynamics. Journal of Ecology, 95: 352-363. Devi, N., Hagedorn, F., Moiseev, P., Bugmann, H., Shiyatov, S., Mazepa, V., Rigling, A., 2008: Expanding forest and changing growth forms in Siberian larch at the Polar Urals treeline during the 20th century. Global Change Biology, 14: 1581-1591. Didier, L., 2001: Invasion patterns of European larch and Swiss stone pine in subalpine pastures in the French Alps. Forest Ecology and Management, 145: 67-77. Dullinger, S., Dirnböck, T., Grabherr, G., 2004: Modelling climate change-driven treeline shifts: relative effects of temperature increase, dispersal and invasibility. Journal of Ecology, 92: 241-252. Elliot, G., 2012: Extrinsic regime shifts drive abrupt changes in regeneration dynamics at upper treeline in the Rocky Mountains, USA. Ecology, 93: 1614-1625. Esper, J., Schweingruber, F.H., 2004: Large-scale treeline changes recorded in Siberia. Geophysical Research Letters, 31(6).

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Gamache, I., Payette, S., 2004: Height growth response of tree line black spruce to recent climate warming across the forest-tundra ecotone of eatern Canada. Journal of Ecology, 92: 835-845. Gamache, I. Payette, S., 2005: Latitudinal response of subarctic tree lines to recent climate change in eastern Canada. Journal of Biogeography, 32: 849-862. Gehrig-Fasel, J., Guisan, A. & Zimmermann, N.E. (2007) Tree line shifts in the Swiss Alps: Climate change or land abandonment? Journal of Vegetation Science, 18: 571-582. Germino, M.J., Smith, W.K., Resor, C., 2002: Conifer seedling distribution and survival in an alpine-treeline ecotone. Plant Ecology, 162: 157-168. Hanssen, K.H., 2003: Natural regeneration of Picea abies on small clear-cuts in SE Norway. Forest Ecology and Management, 180: 199-213. Harsch, M.A., Bader, M., 2011: Treeline form – a potential key to understanding treeline dynamics. Global Ecology and Biogeography, 20: 582-596. Harsch, M.A., Hulme, P.E., McGlone, M.S., Duncan, R., 2009: Are treelines advancing? A global meta-analysis of treeline response to climate warming. Ecology Letters, 12: 1040-1049. Holtmeier, F.K., 2009: Mountain timberlines. Ecology, patchiness and dynamics, Sprinter, New York. Holtmeier, F.K., Broll, G., 2005: Sensitivity and response of northern hemisphere altitudinal and polar treelines to environmental change at landscape and local scales. Global Ecology and Biogeography, 14: 395-410. Hruška, J., Majer, V., Fottová, D., 2006: The influence of acid rain on surface waters in the Giant Mountains. Opera Corcontica, 43: 95-110. Chevan, A., Sutherland, M., 1991: Hierarchical partitioning. American Statistician, 45: 90-96. Jeník, J., Hampel, R., 1992: Die waldfreien Kammlagen des Altvatergebirges (Geschichte und Ökologie), Mährisch-Schlesischer Sudetengebirgsverein, Stuttgart. Jonášová, M., Vávrová, E., Cudlín, P., 2010: Western Carpathian mountain spruce forest after a windthrow: Natural regeneration in cleared and uncleared areas. Forest Ecology and Management, 259: 1127-1134. Körner, Ch., 2007: Climatic treelines: conventions, global patterns, causes. Erdkunde, 61: 316-324. Körner, Ch., 2012: Alpine treelines: Functional ecology of the global high elevation tree limits, Springer, Basel. Kullman, L., 2001: 20th century climate warming trend and tree-limit rise in the southern Scandes of Sweden. Ambio 30: 72-80. Kullman, L., 2005: Wind-conditioned 20th century decline of birch treeline vegetation in the Swedish Scandes. Arctic, 58: 286-294. Leonelli, G., Pelfini, M., Morra di Cella, U., Garavaglia, V., 2011: Climate warming and the recent treeline shift in the European Alps: the role of geomorphological factors in high-altitude sites. Ambio, 40: 264-273. Lokvenc, T., 1978: Toulky krkonošskou minulostí, Kruh, Hradec Králové. Lokvenc, T., 1995: Analysis of anthropogenic changes of woody plant stands above the alpine timber line in the Krkonoše Mts. Opera Corcontica, 32: 99-114. 92


Mamet, S.D., Kershaw, G.P., 2013: Age-dependency, climate, and environmental controls of recent tree growth trends at subarctic and alpine treelines. Dendrochronologia, 31:75-87. Miehe, G., Miehe, S., 2000: Comparative high mountain research on the treeline ecotone under human impact. Erdkunde, 54: 34-50. Motta, R., Morales, M., Nola, P., 2006: Human land-use, forest dynamics and tree growth at the treeline in the Western Italian Alps. Annals of Forest Science, 63: 739-747. Novák, J., Petr, L., Treml, V., 2010: Late-Holocene human-induced changes to the extent of alpine areas in the East Sudetes, Central Europe. Holocene, 20: 895-905. Oberhuber, W., 2004: Influence of climate on radial growth of Pinus cembra within the alpine timberline ecotone. Tree Physiology: 24: 291-301. Paulsen, J., Weber, U.M., Körner, Ch., 2000: Tree growth near treeline: Abrupt or Gradual Reduction with Altitude? Arctic, Antartctic, and Alpine Research, 32: 14-20. Payette, S., 2007: Contrasted dynamics of northern Labrador tree lines caused by climate change and migrational lag. Ecology, 88: 770-780. Potthoff, K., 2009: Grazing history affects the tree-line ecotone: a case study from Hardanger, Western Norway. Fennia, 187: 81-98. Rössler, O., Bräuning, A., Löffler, J., 2008: Dynamics and driving forces of treeline fluctuation and regeneration in central Norway during the past decades. Erdkunde, 62: 117-128. Rundqvist, S., Hedenas, H., Sandström, A., Emanuelsson, U., Eriksson, H., Jonasson, Ch., Callaghan, T.V., 2011: Tree and shrub expansion over the past 34 years at the treeline near Abisko, Sweden. Ambio, 40: 683-692. Sander, C., Eckstein, D., Kyncl, J., Dobrý, J., 1995: The growth of spruce (Picea abies (L) Karst) in the Krkonoše-(Giant) Mountains as indicated by ring width and wood density. Annals of Forest Science, 52: 401-410. Shiyatov, S.G., Terentev, M.M., Fomin, V.V., 2005: Spatiotemporal dynamics of foresttundra communities in the Polar Urals. Russian Journal of Ecology, 36: 83-90. Sitko, I., Troll, M., 2008: Timberline changes in relation to summer farming in the western Chornohora (Ukrainian Carpathians). Mountain Research and Development, 28: 263-271. Speranza, A., Hanke, J., van Geel, B., Fanta, J., 2000: Late-Holocene human impact and peat development in the Černá hora bog, Krkonoše Mountains, Czech Republic. Holocéne, 10: 575-585. Szeicz, J.M., MacDonald, G.M., 1995 Recent white spruce at the subarctic treeline of northwestern Canada. Journal of Ecology, 63: 873-885. Šenfeldr, M., Maděra, P., 2011: Population structure and reproductive strategy of Norway Spruce (Picea abies L. Karst) above the former pastoral timberline in the Hrubý Jeseník Mountains, Czech Republic. Mountain Research and Development, 31: 131-143. Tasser, E., Walde, J., Tappeiner, U., Teutsch, A., Noggler, W., 2007: Land-use changes and natural reforestation in the eastern central Alps. Agriculture Ecosystems and Environment, 118: 115-129. Theurillat, J.P., Guisan, A., 2001: Potential impact of climate change on vegetation in the European Alps: A review. Climatic Change, 50: 77-109. 93


Tolasz, R., Míková, T., Valeriánová, A., Voženílek, V., 2007: Climate Atlas of Czechia, 1st. edn. Czech Hydrometeorological Institute, Prague. Treml, V., Ponocná, T., Büntgen, U., 2012: Growth trends and temperature responses of treeline Norway spruce in the Czech-Polish Sudetes Mountains. Climate Research, 55: 91-103. Van Bogaert, R., Haneca, K., Hoogesteger, J., Jonasson, Ch., de Dapper, M., Callaghan, T.V., 2011: A century of tree line changes in sub-Arctic Sweden shows local and regional variability and only a minor influence of 20th century climate warming. Journal of Biogeography, 38: 907-921. Vittoz, P., Rulence, B., Freléchoux, F. (2008) Effects of climate and land-use change on the establishment and growth of cembrain pine (Pinus Cembra L.) over the altitudinal treeline ecotone in the Central Swiss Alps. Arctic Antarctic Alpine Research, 40: 225-232. Walsh, C., Mac Nally, R., 2004: hier.part: hierarchical partitioning, version 0.5–3. R Foundation for Statistical Computing, Vienna, Austria. Walter, G.R., Post, E., Convey, P., Menzel, A., Parmesan, C., Beebee, T.J.C., Fromentin, J.M., Hoegh-Guldberg, O., Bairlein, F., 2002: Ecological responses to recent climate change. Nature, 416: 389-395. Wang, T., Zhang, Q.B., Ma, K., 2006: Treeline dynamics in relation to climatic variability in the central Tianshan Mountains, northwestern China. Global Ecology and Biogeography, 15: 406-415.

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Šenfeldr, M., Urban, J., Maděra, P., Kučera, J. Bidirectional flows in the layering branches between parent and daughter tree in a Norway spruce polycormone. Acta Horticulturae, 991: 277-283

8. Bidirectional flows in the layering branches between parent and daughter tree in a Norway spruce polycormon Abstract Layering, in which clonal offsprings are formed by the rooting of lower branches of the parent tree, is an important way of vegetative tree reproduction at treelines. These rooting branches (termed “layering” branches) mediate connection between parent and clonal daughter tree. In this contribution, we aimed to investigate quantity and directions of sap flow in layering branch of Norway spruce clonal tree - polycormone, consisting of 53 years old parent tree and 23 years old daughter tree. Trunk heat balance method (sensors manufactured by EMS Brno) was used for the sap flow measurements. Based on the sap flow measurements we discovered bidirectional flow in layering branch. We found, that in daughter tree was 7 % of the transpired water supplied from the parent’s tree root system and, in some cases, up to 25 % of the instantaneous daughter tree sap flow was supplied from the parent tree. On the other hand, water provided from the offspring’s root system to the parent tree comprised only negligible amount of less than 1 % of the parent’s whole tree sap flow. Our findings suggest that even after 23 years of development is the new tree still semidependent on the parent tree. Combination of water supply to the daughter from the parent tree and from its own root system helps to establish new individual upon harsh conditions of the treeline. Keywords: sap flow, trunk heat balance method, bidirectional flow measurements

8.1 Introduction Plants growing at the treeline ecotone are exposed to various stress factors like a short growing season, high wind speeds, intense radiation, high snow loads and others (Holtmeier 2009). Therefore, tree species have evolved diverse morphological and physiological adaptations and strategies making possible growth and survival in this harsh, inhospitable environment (Mosbrugger 1990; Körner 2003). Flowering and seed production can be a rather risky mode of tree propagation at treeline conditions. Therefore, some tree species have developed various ways of vegetative regeneration, which importance generally tend to increase with increasing elevations (Billings 1974). Important way of vegetative reproduction is layering, when clonal offspring are formed by the rooting of lower branches of a parent tree (Maděra 2004; Vacek et al. 2012). These rooting branches, also called “layering” branches, mediate connection between parent and clonal (daughter) tree. In this way, clonal goups - polycormones are formed (Šenfeldr, Maděra 2011). Little information is available about water regime of Norway spruce polycormones, and, as to our best knowledge, nothing is known about flow of sap between individual stems within these polycormones, when individual trees with already developed root system are still connected by the layering branches. Key issues are, how long and to what extent is clonal daughter tree (DT) dependent on water supply from the parent tree (PT). We can hypothetise, that DT will be supported by water supplies of PT via layering branch until when sufficient root system will have been developed. Later, the water flow from the root systems of both trees should follow difference in water potentials, while the absolute amount of water share should be given by the resistances in hydraulic pathways (Tyree 95


1988; Tyree, Ewers 1991). As a result, bidirectional xylem sap flow - or hydraulic redistribution, (Richards, Caldwell 1987; Xu, Bland 1993; Naděždina et al. 2010) – may occur. Therefore, during the period of daughter tree semidependence (i.e. when functional root system has been developed but layering branch is still conductive) three alternatives of water flow in a layering branch are theoretically possible. First, water is transported only from the parent tree to the daughter. This implies constantly lower water potential in the daughter tree than in parent. Second, water is transported unidirectionally towards the parent tree, thus water is withdrawn from the daughter tree root system by constantly lower water potential in a parent tree. Third, bidirectional water flow, following the difference in water potentials in a parent and daughter tree is possible. Vegetativelly spreading trees above tree-line, complex in their multiple stem and root system interactions, provides a special opportunity to investigate mutuality in water flow among the interconnected individuals. Based on the above-mentioned facts, the aims of this contribution are (1) to measure the direction and quantity of the sap flow in the branches interconnecting parent and daughter trees, (2) to compare amount of water flow in the layering branches to the total tree water use and (3) to identify driving factors for the sap flow in the layering branch.

8.2 Materials and methods 8.2.1 Study site and study trees The study was carried out at tree-line ecotone formed mainly by Norway spruce trees, mostly in form of polycormons in the Hrubý Jeseník Mountains, Czech Republic (17°13'57,821"E and 50°4'17,024"N; 1395 m a. s. l). Hrubý Jeseník Mountains are an old Hercynian mountain range, with the highest peak, Praděd, at 1492 m a.s.l. Climate is relatively cold and humid with annual precipitation around 1400 mm and average temperature around 1.1 °C in the summit regions (Tolasz et al. 2007). Spruce polycormones here usually consist from one parent trees of generative origin and from different number of its vegetative originated offsprings (Šenfeldr, Maděra 2011). Three polycormones of Norway spruce were selected for detailed ecophysiological analyses. First of them, chosen for the long term study (137 days), consisted from PT and one daughter offsprings. Diameter of PT at the base (20 cm above ground) was 18.5 cm, and its height was 5.1 m. The diameter of DT at the base was 2.9 cm, and its height was 1.2 m. The DT originated in layered branch of a parent tree (growing from the east side of PT in a height 40 cm) which rooted 125 cm from the PT. The ages of PT and DT were 53 and 23 years, respectively. The other two Norway spruce tree pairs of similar size and age (with layering branches, and consequently the DT, oriented to the west and east ) were chosen for the short-term (i.e. one week in july and one week august) measurements. 8.2.2 Meteorology, soil and leaf water potential measurements Meteorological variables were measured in an open plot above the grass surface, approximately 40 m from the polycormones. The global radiation, air temperature and air humidity at a height of 2 m were measured by a Minikin RTH (EMS, Brno, Czech Republic) at 1–minute intervals. Fifteen-minute means were stored in the datalogger 96


memory. These data were used to calculate the reference evapotranspiration - EVAP, (Allen et al. 1998). Soil water potential was monitored by six gypsum block sensors (GB2, Delmhorst Inc., USA), connected to the datalogger (Microlog SP, EMS, Brno, Czech Republic). Soil sensors were buried at depths of 15 cm within the roots of PT and DT. Data were collected every hour. Soil water potential sensors indicated unlimited water availability in the root zone of the trees (i.e. soil water potential never dropped below - 0.03 MPa). Leaf water potential was measured with Scholander pressure chamber on excised shoots and twigs of PT and DT. Measurements were conducted (from 8 a.m. to 10 a.m., from 12 a.m to 2 p.m., from 4 p.m. to 6 p.m.) on 21 August, 2011. Eight shoots were sampled from the PT and the same number from the DT in each time of measurement. The twigs were collected both from the sun and shade parts of the crowns, in order to minimize the variability caused by illumination. The differences between PT and DT were tested using paired t–test for each time period of measuring. 8.2.3 Sap flow measurement and calculation We used method based on the trunk heat balance principle (THB) for the sap flow measurements. One stem sensor (EMS 51, EMS, Brno, Czech Republic, (Čermák et al. 2004; Kučera, Urban, 2012) and three branch ‘baby’ sensors (EMS 62, EMS, Brno, Czech Republic, (Lindroth et al. 1995; Urban et al. 2012) were installed on first tree pair (Fig. 1). The stem sensor was installed on PT in a height of 50 cm, one of the baby sensors were installed at the stem of DT in height of 15 cm. Two baby sensors were installed facing the opposite directions on layering branch, which allowed to measure sap flows in both towards the PT as well as towards DT. EMS 62 sap flow sensors were completed with additional thermocouples installed in the xylem around the heater of a ‘baby’ sensor, which were used for the specification of sap flow directions (Fig. 1). Temperature difference between heated and reference part of the measuring points was kept at the constant value of 4 K in both EMS 51 and EMS 62 sap flow sensors. After installation, the sensors were covered by a radiation shield, the upper borders of which were sealed with silicone to protect against stem flow. The voltage outputs of the sensors were registered by the datalogger (EdgeBox V16, EMS, Brno, Czech Republic) at 1-minute intervals, and fifteen-minute averages were stored in the memory. Sap flow was measured for 137 days from 20 May 2011 to 4 October 2011. Same design of sensors installation was used in the second and third tree polycormone. For the purposes of this contribution, we only present results from the first tree pair and from one month period (June). The dependency rate of DT on water supplies of PT was determined from the proportion between total sap flow in layering branch in the direction of DT and total DT transpiration.

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Fig. 1: Illustration of parent and daughter trees connected by layering branch, and sensors placement design. A – stem sensor (EMS 51) on a parent´s tree, branch ‘baby’ sensors (EMS 62) on layering branch measuring in the direction to parent tree (B), in the direction of daughter tree (C) and on daughter´s tree (D). G – gypsum block sensors (GB2, Delmhorst Inc., USA) for measuring of soil water potentials.

8.3 Results 8.3.1 Diurnal dynamic of sap flow and shoot water potential Typical diurnal course of the sap flow in June in both parent and daughter tree reflected the development of a EVAP with a typical time lag (Fig. 2). The morning lag of sap flow in a DT was by about 60 minute shorter than in the PT, thus mutually reflecting eastward facing of a young tree and its smaller water storage capacitances. For the same reasons, evening decrease in sap flow appeared sooner at a DT. One hour time lag was also found between diurnal maxima of the flows (Fig. 2). Sum of daily transpiration in PT (15 kg) was much higher than in the daughter tree (0.5 kg), thus proportionally reflecting different surfaces of the transpiring needles. Bidirectional sap flow was typical for the layering branch (Fig. 3). From morning until the midday, sap flow toward DT (thus DT behaved as a branch), while in the afternoon sap flow towards the parent tree (root system of the DT was utilized by both parent and daughter tree). In a sum, five time more water was provided in a direction to the DT. Maximal flow rates on the layering branch was ca 15 % of the maximal sap flow in a DT and maximally about 25 % of the instantaneous DT sap flow. On the other hand, maximal amount of water supplied to the PT was less than 1 % of the maximal transpiration.

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Fig. 2: Typical diurnal dynamics of reference evapotranspiration (EVAP), sap flow of parent and daughter trees (always sap flow kg. h-1 per whole tree). Data were obtained on 5 June 2011.

Fig. 3: Diurnal dynamic of flows in layering branch. Positive values indicate flow from the parent towards the daughter tree, while negative values indicate reverse flow to the parent tree.

In the morning hours, the recorded values of shot water potential were lower in the crown of DT [DT (Mpa) = -1.9 ± 0.3 SD, PT (Mpa) -1.6 ± 0.3 SD], whereas shortly after midday we recorded lower water potential in the crown of PT [DT (Mpa)= -1.6 ± 0.2 SD, PT (Mpa) = -2.1 ± 0.2 SD] and which relationship hold until late afternoon [DT(Mpa) = -1.2 ± 0.2 SD, PT (Mpa) = -1.6 ± 0.3 SD]. 99


8.3.2 Monthly sap flow characteristics The June sum of transpiration of PT and DT were 209.5 and 5.8 kg, respectively. The total sap flow in layering branch supplied in the direction of DT was 0.4 kg, while reverse flow in the direction of PT was lower 0.07 kg. The maximum sap flow values of PT and DT were 1.85 and 0.08 kg h-1, respectively. The maximum sap flow in layering branch in the direction of daughter tree was very low: 0.01 kg h-1, and even lower in the direction of PT 0.004 kg h-1.

8.4 Discusion Lag of a sap flow behind the transpiration and EVAP is well described and in the literature identified as a result of lag in propagation of hydraulic signal between the foliage and the stem xylem. This lag is thought to be highest between foliage and branches (Köstner et al. 1998) or is inscribed to water storage in branches (Anfodillo et al. 1998). Time lag reported in this study fit well within the common range of 0 – 4 hours (Martin et al. 1997; Ewers, Oren 2000; Čermák et al. 2007). As the water potentials in DT and PT likely equalized during the night, shift in sap flow curves between the DT and PT in a long term studied polycormone must have been mutual effect of different illumination (DT was on the east side of the polycormone) and possibly smaller amount of stored water available for transpiration in the DT plant tissues. Hypothesis on the effect of illumination on the shape and magnitude of lag between sap flow in the PT and DT was confirmed through short measurements on the other two sets of trees with offsprings facing different azimuths (i.e. sap flow in the westward facing DT begun even later than in PT). The other monitored polycormones showed the same general patterns of the water flow and shifts in a shoot water potentials as the first polycormon. Absolute quantity of water supplied from PT root system to DT (dependence rate less than 6.6 %) seems rather unimportant, however in a short time view, especially in the morning, the layering branch supplies up to 25 % of water. On the other hand, the flow is negligible in time of highest evapotranspiration demands when shoot water potentials in both PT and DT equalized. The magnitude of the flows on the layering branch (0.003 – 0.009 kg h-1) is similar to the magnitude of the flows measured by Coners, Leuschner (2005) and to the magnitude of flows measured on grasses by the heat balance sensors (Senock, Ham 1995). Xylem water flow is entirely passive process, that follows the differences in a water potential (Tyree, Ewers 1991; Tyree, Zimmerman 2002). Therefore we expected water flow in a layering branch to reflect deviations in water potential of a DT and PT. This explanation was verified by direct measurements of shoot water potential in the crowns of PT and DT. The differences between PT and DT within individual measuring times were always statistically significant (paired t-test, p ˂ 0.05). Minimum water potentials in our experiment were by about 0.5 MPa lower than those measured on Norway spruce at alpine treeline by (Anfodillo et al. 1998).

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8.5 Conclusions Based on the sap flow measurements we found, that even in 23 year old DT was, in total, about 6.6 % of the transpired water supplied from the parent tree. In some cases, water low in the layering branch reached up to 25 % of the instantaneous transpiration of the daughter tree. On the other hand, water supplied by the offspring’s root system to the parent tree comprised less than 1 % of the parent’s sap flow.

Acknowledgements This contribution was supported by the Internal Grant Agency of the Faculty of Forestry and Wood Technology at Mendel University in Brno (projects 25/2011), COST LD13017 and by project Indicators of trees vitality Reg. No. CZ.1.07/2.3.00/20.0265 is co-financed by the European Social Fund and the state budget of the Czech Republic.

8.6 References Allen R.G., Pereira L.S., Raes D., Smith, M., 1998: Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. FAO, Rome 300. Anfodillo, T., Rento, S., Carraro, V., Furlanetto, L., Urbinatti, C., Carrer M., 1998: Tree water relations and climatic variations at the alpine timberline: seasonal changes of sap flux and xylem water potential in Larix decidua Miller, Picea abies (L.) Karst. and Pinus cembra L. Annals of Forests Science, 55:159–172. Billings, W.D., 1974: Adaptations and origins of alpine plants. Arctic and alpine research, 129-142. Coners, H., Leuschner, C., 2005: In situ measurement of fine root water absorption in three temperate tree species — Temporal variability and control by soil and atmospheric factors. Basic and Applied Ecology, 6(4): 395-405. Čermák, J., Deml, M., Penka, M., 1973: A new method of sap flow rate determination in trees. Biologia Plantarum, 15: 171–178. Čermák, J., Kučera, J., Naděždina, N., 2004: Sap flow measurements with some thermodynamic methods, flow integration within trees and scaling up from sample trees to entire forest stands. Trees-Structure and Function, 18: 529–546. Čermák, J., Kučera, J., Bauerle, W.L., Phillips, N., Hinckley, T.M., 2007: Tree water storage and its diurnal dynamics related to sap flow and changes in stem volume in old-growth Douglas-fir trees. Tree Physiology, 27: 181–198. Ewers, B.E., Oren, R., 2000: Analyses of assumptions and errors in the calculation of stomatal conductance from sap flux measurements. Tree Physiology, 20(9): 579–589. Holtmeier, F. K., 2009: Mountain timberlines. Ecology, Patchiness, and Dynamics. Advances in global change research 36. Springer science. Körner, Ch. 2003. Alpine plant life: functional plant ecology of high mountain ecosystems. Springer Verlag, 2003. Köstner, B., Granier, A. and Čermák, J., 1998: Sapflow measurements in forest stands: methods and uncertainties. Annals of Forest Sciences, 55: 13–27. Kučera, J., Urban, J., 2012: History of the development of the trunk heat balance method in last forty years. Acta Horticulturae, 951: 87–94. 101


Lindroth, A., Čermák, J., Kučera, J., Cienciala, E., Eckersten, H., 1995: Sap flow by the heat balance method applied to small size Salix trees in a short-rotation forest. Biomass and Bioenergy, 8: 7–15. Maděra, P., 2004: Growth and population strategy of Norway spruce (Picea abies (L.) Karsten) at alpine timberline in the Praděd nature reserve, Větrná louka site [in Czech]. Geobiocenological papers, 10: 51-70. Martin, T., Brown, K., Čermak, J., Ceulemans, R., Kučera, J., Meinzer, F., Rombold, J,, Sprugel, D., Hinckley T., 1997: Crown conductance and tree and stand transpiration in a second-growth Abies amabilis forest. Canadian Journal of Forest Research, 27: 797–808. Mosbrugger, V., 1990: The tree habit in land plants-a functional comparison of trunk constructions with a brief introduction into the biomechanics of trees. Lecture notes in earth sciences, (28). Nadězhdina, N., David, T. S., David, J. S., Ferreira, M. I., Dohnal, M., Tesař, M., Morales, D., 2010: Trees never rest: the multiple facets of hydraulic redistribution. Ecohydrology, 3(4): 431-444. Penman, H.L. 1948. Natural evaporation from open water, bare soil and grass. Proceedings of the Royal Society of London. Series A, Mathematical and Physical Sciences, 193: 120–145. Richards, J.H., Caldwell, M.M. 1987. Hydraulic lift: substantial nocturnal water transport between soil layers by Artemisia tridentata roots. Oecologia, 73: 486-489. Senock RS, Ham JM. 1995. Measurements of water use by prairie grasses with heat balance sap flow gauges. Journal of Range Management, 48: 150-158. Šenfeldr, M., Maděra, P. 2011. Population Structure and Reproductive Strategy of Norway Spruce (Picea abies L. Karst) Above the Former Pastoral Timberline in the Hrubý Jeseník Mountains, Czech Republic. Mountain Research and Development, 31: 131-143. Tolasz, R., Míková, T., Valeriánová, A., Voženílek, V., 2007: Climate Atlas of Czechia, 1st. edn. Czech Hydrometeorological Institute, Prague. Tranquillini, W., 1979: Physiological Ecology of the Alpine Timberline. Tree Existence at High Altitudes with Special Reference to the European Alps. Ecological studies 31. Berlin, Germany: Springer. Tyree, M.T., 1988. A dynamic model for water flow in a single tree: evidence that models must account for hydraulic architecture. Tree Physiology, 4: 195–217. Tyree, M., Ewers, F. 1991. The hydraulic architecture of trees and other woody plants. New Phytologist, 119(3): 345–360. Tyree, M., Zimmerman, M.H., 2002: Xylem structure and the ascent of sap. Springer, Berlin, Heidelberg, New York. Urban, J., Krofta, K., Kučera, J., 2012: Calibration of stem heat balance sensors upon a study of water balance of the hop plantation (L Sebastiani, Ed.). Acta Horticulturae, 951: 79–86. Vacek, S., Hejcmanová, P., Hejcman, M., 2012. Vegetative reproduction of Picea abies by artificial layering at the ecotone of the alpine timberline in the Giant (Krkonoše) Mountains, Czech Republic. Forest Ecology and Management, 263: 199-207. Xu, X., Bland, W.L., 1993. Reverse water flow in sorghum roots. Agronomy Journal, 85: 384-388.

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Šenfeldr, M., Urban, J., Maděra, P., Kučera, J. Redistribution of water via layering branch between conected parent and daughter trees in Norway spruce clonal groups. Submitted to Trees – Structure and Function.

9. Redistribution of water via layering branch between conected parent and daughter trees in Norway spruce clonal groups Martin Šenfeldr1, Josef Urban1, Petr Maděra1, Jiří Kučera2 1) Department of Forest Botany, Dendrology and Geobiocoenology, Mendel University in Brno, Zemědělská 3, 613 00 Brno, Czech Republic 2)EMS Brno – Environmental Measuring Systems, Turistická 5, 621 00 Brno, Czech Republic Abstract Layering is an important mode of vegetative reproduction at treeline, in which clonal daughter trees are formed by the rooting of lower (“layering”) branches of the parent tree. These branches mediate the connection between parent (PT) and daughter tree (DT). Here, we measured quantity and direction of sap flow in layering branches as well as PT and DT, and measured shoot water potentials in the crowns of a connected PT and DT. We found bidirectional sap flow pattern in layering branches, with the bidirectionality of the flow resulting from water potential dynamics of the parent and daughter trees varying diurnally. We found that it was 4.3 % of the total water transpired by the DT was supplied by the PT root system, with up to 25 % of the instantaneous daughter tree sap flow coming from the parent tree. In contrast, water provided by the daughter’s root system to the parent tree comprised only a negligible amount, less than 1 % of the parent’s entire sap flow. Additionally, after experimental excavation of part of the DT roots, layering branch flow towards the DT increased, while flows in the opposite direction almost vanished. This study showed that clonal connections can enabling trees to transfer water, enabling exploitation of patchy water resources and amelioration of potential drought stress. Keywords: sap flow, trunk heat balance method, bidirectional flow measurements, clonal reproduction, treeline, roots excavation

9.1 Introduction The alpine treeline ecotone is among the most prominent temperature-driven biogeographic boundaries between mountain plant communities (Körner, Paulsen 2004). Plants growing in this ecotone are exposed to various stress factors such as short growing season, low temperature, high wind speed, intense radiation, high snow loads (e.g., Tranquillini 1979; Holtmeier 2009; Körner 2012). Therefore, tree species have evolved diverse morphological and physiological adaptations and strategies making possible growth and survival in this harsh, inhospitable environment (Mosbrugger 1990; Körner 2003). Flowering and seed production can be a rather risky mode of tree propagation at treeline conditions, Thus, for trees, the importance of vegetative regeneration ability generally tends to increase with increasing elevations (Billings 1974). One major mode of vegetative reproduction consists of layering, in which clonal offspring are generated by the rooting of lower branches of a parent tree (Maděra 2004; Vacek et al. 2012). These rooting (“layering”) branches (maintain connections between parent and clonal (daughter) trees in clonal groups (Treml, Banaš 2008; Šenfeldr, Maděra 2011). Clonal group-forming 103


tree species are found within alpine and northern treelines worldwide, at the ecotone between forest and alpine ecosystems (e.g. Kullman 1996; Treml, Banaš 2008; Holtmeier 2009). For example, clonal group-forming conifers from the three major northern hemisphere continents include Picea abies from the treeline ecotones of Central Europe (Scotti et al. 2008; Hertel, Schöling 2011), Abies lasiocarpa and Picea engelmannii from those of western North American (Brooke et al. 1970), and Larix gmelinii from those of central Kamchatka (Okitsu 1998). Indeed, in Central Europe, Norway spruce is the most abundant treeline-forming species (Scotti et al. 2008; Treml, Banaš 2008; Hertel, Schöling 2011). Generally, clonal plants are characterized by physiological integration that enables the parent and its connected daughters to transfer carbohydrates and mineral nutrients to each other (Alpert, Mooney 1986; Stuefer, Hutchings 1994; Alpert 1996; Wijesinghe, Hutchings 1997), exploit patchy resources (de Kroon, Knops 1990; Evans, Cain 1995; Brewer, Bertness 1996), and tolerate environmental stresses (Salzman 1985; Salzman, Parker 1985; Slade, Hutchings 1987; Pennings, Callaway 2000). However, little information is available about the water regimes of clonal tree groups, and, to the best of our knowledge, nothing is known about sap flow between individual ramets with already developed root systems but still connected by layering branches. Key issues include how long and to what extent a clonal daughter tree (DT) is dependent on water supply from the parent tree (PT). We can hypothesize that a DT will be supported by water provided by the PT via a layering branch until its root system is sufficiently developed. Later, water flow from the root systems of both trees should follow differences in water potentials, while the absolute amount of water use should be determined by the resistances in the hydraulic pathways (Tyree 1988; Tyree, Ewers 1991). As a result, bidirectional xylem sap flow, i.e., hydraulic redistribution, (Richards, Caldwell 1987; Xu, Bland 1993; Naděždina et al. 2010) may occur. Therefore, during the period of daughter tree semi-dependence (i.e., when a functional root system has been developed but the layering branch is still conductive), three alternatives of water flow in a layering branch are theoretically possible. First, water could be transported only from the PT to the DT, which would require water potential to be continuously lower in the DT than in the PT. Second, water could be transported unidirectionally towards the parent tree, with water being withdrawn from the daughter tree root system by continuously lower water potential in the parent tree. Third, bidirectional water flow could occur, with the direction changing based on the difference in water potentials between a parent and daughter tree. Clonal tree groups found above treeline, having complex interactions among their multiple stem and root system, provide a special opportunity to investigate mutuality in water flow among the interconnected individuals. Moreover, the study of the functionality of these clonal connections contributes to the understanding of survival of such trees in the treeline ecotone. The present study aimed to: (1) assess the directions and quantities of sap flow in the layering branches connecting parent and daughter Norway spruce trees; (2) compare the amounts of water flow in these layering branches to the total water uses of the parent and daughter trees; and (3) identify the driving factors determining the direction and amount of sap flow in the layering branches.

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9.2 Materials and methods 9.2.1 Study site The study was conducted in the treeline ecotone of the Hrubý Jeseník Mountains, Czech Republic. This is an Hercynian mountain range, with its highest peak, Praděd, at 1492 m a.s.l. The climate is relatively cold and humid, with average temperature around 1.1 °C and annual precipitation around 1400 mm in the summit regions (Tolasz et al. 2007). The upper limit of closed forest is formed predominantly by Norway spruce trees. Its average altitude is 1300 m a. s. l., but scattered Norway spruce clonal groups of ca. 2 m height are found up to the highest elevations. The highest elevations of these mountains are thought to have been naturally forest-free (Jeník 1961), but the extent of alpine meadows was significantly enlarged by human activities in the past (Novák et al. 2010). Sparsely distributed spruce trees originating from seeds have formed extensive clonal groups by layering. Norway spruce clonal groups here usually consist of one seed-derived parent tree and various numbers of vegetatively derived daughters (Šenfeldr, Maděra 2011). 9.2.2 Study trees Three clonal Norway spruce groups were selected for detailed ecophysiological analyses. Each of these groups consisted of only one parent tree and one daughter tree. They occurred above the upper limit of the closed forest, and in approximately the same part of the mountain range (Tab. 1). The first such group (Group 1) was subjected to long-term measurement, comprising a period of 137 days in 2011. The diameter of the parent tree (PT) at the base (20 cm above ground) was 18.5 cm, and its height was 5.1 m. The diameter of the daughter tree (DT) at the base was 2.9 cm, and its height was 1.2 m. The DT originated from a layered branch of the parent tree, which grew from its east side at of 40 cm height and rooted 125 cm from the PT. The other two Norway spruce clonal groups (Group 2, Group 3), of similar size and age and with layering branches (and consequently DTs), oriented to the east and west, respectively, were employed for short-term measurements (Group 2 - 13-20 of July; Group 3 22– 29 of August) in 2011 (Tab. 1).

Tab. 1: Basic characteristics of studied clonal groups. The characteristics include ages of parent and daughter trees, orientation of daughter tree to parent tree, altitude, geographic coordinates and slope aspect. Clonal group nr.

Age of parent (years)

Age of daughter (years)

Daughter tree orientation

Altitude (m a.s.l.)

Coordinate

Slope aspect

East

1395

17°13'57.82"E, 50°4'17.02"N

Northeast

1

53

23

2

42

19

East

1427

17° 13' 55.28"E, 50° 4' 7.20"N

Northeast

3

49

21

West

1385

17° 13' 50.81"E, 50° 4' 15.39"N

Northeast

After the sap flow measurements on the long-term studied group were completed in October 2011, we harvested it for needle area estimation and age structure analysis. The projected leaf area was estimated from the needle dry mass and known leaf mass per area 105


(LMA). Subsamples for LMA measurement were taken from 33 % of the branches, evenly distributed throughout the tree height and radius. 20 needles were collected from each year’s shot elongation from each of the selected branches. The fresh needles were scanned, and the average one-sided leaf surface area (ELSA) was estimated using ImageTool 3.00 software (University of Texas Health Science Center, San Antonio, TX, USA). The leaf mass per area (LMA) was calculated as the ratio of ELSA to dry mass (DM) of these needles (dried to constant weight at 105°C, weighed with an accuracy 0.001g). The projected leaf area was calculated as the ratio of total DM of the needles of the whole tree to the LMA. Based on this procedure, projected leaf areas of 21.7m2 and 0.49m2 were determined for PT and DT, respectively. The numbers of tree rings in the PT and DT were counted using a binocular microscope and measuring table. The age of the PT (estimated from the number of the rings at the base) was 53 years (see Fig. 1 for schematic representation of PT and DT and the ages of relevant parts). The age of the layered branch at the base was 30 years. The oldest part of the DT, just beyond the layering branch’s rooting place, was 23 years. The oldest root of the DT was 9 years old. The ages of the three remaining coarse roots of the DT were 6, 7,and 7 years In order to determine the ages of the other two clonal groups, their PTs and DTs were sampled by Pressler borer and their rings counted in the laboratory. Fig. 1: Illustration of parent and daughter trees connected by a layering branch in Group 1 (showing their age characteristics). Sensor placement design: (A) stem sensor (EMS 51) on parent tree; (B) branch ‘baby’ sensors (EMS 62) on layering branch measuring flow towards parent tree; (C) ‘baby’ sensors measuring flow towards daughter tree; (D) ’baby’ sensors on daughter tree; (E) ‘baby’ sensors on control branch; (G) gypsum block sensors (GB2, Delmhorst Inc., USA) for measuring soil water potentials.

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9.2.3 Meteorology, soil and leaf water potential measurements Meteorological variables were measured in an open area above the grass surface, approximately 40 m from the clonal groups. The global radiation, air temperature and air humidity at a height of 2 m were measured by a Minikin RTH (EMS, Brno, Czech Republic) at 1–minute intervals. Fifteen-minute means were stored in the data logger memory. These data were used to calculate the grass reference evapotranspiration - EVAP (Allen et al. 1998). Soil water potential was monitored by six gypsum block sensors (GB2, Delmhorst Inc., USA), connected to the data logger (Microlog SP, EMS, Brno, Czech Republic). Soil sensors were buried at depths of 15 cm within the roots of PTs and DTs. Data were collected every hour. Soil water potential sensors indicated unlimited water availability in the root zone of the trees (i.e. soil water potential never dropped below -0.03 MPa). Leaf water potential in Group 1 was measured with a Scholander pressure chamber on excised shoots and twigs of PTs and DTs. These measurements were conducted (from 8 a.m. to 10 a.m., from 12 am to 2 p.m., from 4 p.m. to 6 p.m.) on 21 August, 2011. Eight shoots were sampled from the PT and the same number from the DT in each time of measurement. The twigs were collected both from the sun and shade parts of the crowns in order to minimize the variability caused by illumination. The differences between PT and DT were tested using a paired t–test for each time period of measuring. 9.2.4 Sap flow measurement and calculation We used a method based on the trunk heat balance principle (THB) for the sap flow measurements. For each clonal group, one stem sensor /EMS 51, EMS, Brno, Czech Republic/ (Čermák et al. 1973; Čermák et al. 2004; Tatarinov et al. 2005; Kučera, Urban 2012) and four branch ‘baby’ sensors /EMS 62, EMS, Brno, Czech Republic/ (Lindroth et al. 1995; Urban et al. 2012) were installed (Fig. 1). The stem sensor was installed on the PT at a height of 50 cm, and one of the baby sensors was installed at the stem of DT at the height of 15 cm, under the first whorl. Two baby sensors were installed facing in opposite directions on the layering branch, allowing measurement of sap flows both towards the PT and towards the DT. One baby sensor was installed on a ‘control‘ rootless branch to compare sap flow quantities and directions with the layering branch. The diameter, age, and orientation of the control branch on the trunk of parent tree were similar to the layering branch. EMS 62 sap flow sensors were supplemented with additional thermocouples installed in the xylem around the heater of a ‘baby’ sensor, which were used for identifying sap flow direction. The temperature difference between the heated and reference parts of the measuring points was kept at the constant value of 4 K in both EMS51 and EMS62 sap flow sensors. After installation, the sensors were covered with a radiation shield, the upper borders of which were sealed with silicone to protect against stem flow. The voltage outputs of the sensors were registered by the data logger (EdgeBox V16, EMS, Brno, Czech Republic) at 1-minute intervals, and fifteen-minute averages were stored in the memory. For Group 1, the sap flow was measured for 137 days from 20 May 2011 to 3 October 2011. For Groups 2 and 3, measurements were done for one week each, in July and August, 2011, respectively. The dependence of DTs and PTs on water supplied by 107


layering branches was assessed as a ratio between the total sap flow in layering branch in a particular direction and the total tree transpiration. 9.2.5 Daughter tree root exposure experiment We induced artificial water stress by excavating the soil from around part of the roots of the daughter tree in the Group 1. Manual excavations were done on 27 August 2011. The root system of the daughter tree was left exposed until the end of the research season (3 October). The dry mass of the roots was quantified after the end of the season (dried to constant weight at 105°C) separately for the exposed and the unaltered parts of the DT root system. The total dry mass of the daughter tree root system was 127.6 g. The proportion of roots exposed was 30.6 g, 24 % of the total biomass. This experiment separated the overall sap flow measurement period into two sub-periods – ‘before excavation’ (20 May - 26 August; 99 days) and ‘after excavation’ (27 August - 3 October; 38 days).

9.3 Results 9.3.1 Diurnal dynamic of sap flow and shoot water potential in the period before excavation In the period ‘before excavation’, the typical diurnal course of sap flow in Group 1 in both parent and daughter trees reflected the development of a EVAP with a regular time lag behind the reference EVAP (Fig. 2A and 2B, 20 May 2011). The morning lag of sap flow in the DT was by about 40 minute shorter than in the PT, reflecting both that the daughter tree faced eastward and its smaller water storage capacity. For the same reasons, afternoon decreases in sap flow appeared sooner in the DT. Diurnal maxima of the PT and DT flows also showed approximately one-hour long time lags (Figs. 2A, 2C). The maximal sap flow in the period ‘before excavation‘ in PT was much higher than in the daughter tree, proportionally reflecting the different surface areas of the transpiring needles (Fig. 2A). The tree sap flow per unit of the needle area was higher in the DT than in a PT in the morning and vice versa in the afternoon, resulting from the illumination of the east-facing DT (Fig. 3). Almost every day, bidirectional sap flow was typical of the layering branch within the ‘before excavation’ period (Fig. 2A). From morning until midday, sap flowed toward the DT (the DT thus behaving as a branch), whereas in the afternoon sap flowed towards the PT (the root system of the DT utilized by both parent and daughter trees). Typically, sap flow in the layering branch lagged approximately one hour behind the DT (Fig. 2A). Over the entire the ‘before excavation’ period, a total of twice the amount of water flowed towards the DT than away from it (Tab. 3). The maximal flow rate in the layering branch was ca. 13 % of the maximal daily sap flow in the DT (compare the maximum values in Tab. 2), and maximally ca. 25 % of the instantaneous DT sap flow. In contrast, the maximal amount of water supplied to the PT was less than 1 % of the maximal transpiration. High flow rates towards the PT occurred in the layering branch and simultaneously in the PT in periods of high evaporative demand (e.g. 18.7., 17.8. – 23.8., see Fig. 4). 108


The other two clonal groups also showed bidirectional flows in their layering branches. Group 2 revealed the same sap flow pattern as Group 1. However, in the case of Group 3, the daily periodicity of the bidirectional sap flow in the layering branch was opposite that of Group 1 (Fig. 5). Thus, in Group 3, sap flowed towards PT from the evening to the following morning, when the direction would be reversed (Fig. 5). Night sap flow was often detected in all the measured clonal groups (Figs. 2 and 5), with this phenomenon reflecting the nightly dynamics of vapour pressure deficits. In Group 1, in the morning the recorded values of shoot water potential were lower in the crown of the DT than that of the PT [DT (Mpa) = -1.9 ± 0.1 SE, PT (MPa) = -1.6 ± 0.1 SE], whereas shortly after midday lower water potentials were recorded in the crown of the PT [DT (MPa) = - 1.6 ± 0.1 SE, PT (MPa) = -2.1 ± 0.1 SE], with this relationship holding until late afternoon [DT (MPa) = -1.2 ± 0.1 SE, PT (MPa = -1.6 ± 0.1 SE] The differences between PT and DT within individual measuring times were always statistically significant (p ˂ 0.05) /Fig. 6/. Fig. 2: (A) Diurnal dynamics of sap flow of parent, daughter tree and layering branch (positive values in layering branch indicate flow from parent towards the daughter tree, whereas negative values indicate flow to parent tree) in Group 1“before excavation”; (B) reference evapotranspiration (EVAP) and vapour pressure deficit (VPD) before excavation; and (C) diurnal sap flow dynamics after excavation; (D) reference EVAP and VPD after excavation. Data were obtained on 20 May 2011 in the “before excavation period” and on 1 October 2011 in the “after excavation” period.

109


Fig. 3: (A) Transpiration of parent and daughter trees per 1m2 of projected leaf area of the long-term clonal group. (B) Reference evapotranspiration. Data were obtained on 22 August 2011.

Fig. 4: Seasonal course of daily sums of (A) sap flow in layering branch (separated into the periods before and after daughter tree root system excavation); (B) sap flow of parent and daughter trees; and (C) reference evapotranspiration and vapour pressure deficit.

110


Fig. 5: (A) Diurnal dynamics of sap flow of parent and daughter trees and layering branch in Group 3. Positive values in the layering branch indicate flow from the parent towards the daughter tree, whereas negative values indicate flow to the parent tree; (B) reference evapotranspiration and vapour pressure deficit. Data were obtained on 26 August 2011.

Fig. 6: Diurnal dynamics of shoot water potential in the crowns of parent and daughter trees. Data were obtained on 21 August 2011. Error bars shows standard error of the mean (SE). The differences between PT and DT within individual measuring times were always statistically significant (p ˂ 0.05).

111


9.3.2 Diurnal sap flow dynamics after exposing daughter tree roots After exposing one quarter of the roots of the DT in this experiment, obvious changes in the diurnal course of sap flow in Group 1 were detected. The bidirectional flow in the layering branch changed to almost unidirectional, towards DT (Fig. 2C). The morning time lag between sap flow in the DT and layering branch disappeared as well (compare Fig. 2A and 2C). The daughter tree was saturated by the parent tree’s water supply via layering branch already at the beginning of its morning stem sap flow (Fig. 2C). In addition, maximal flow rates in the layering branch increased to ca. 23 % (compare the maximum values in Tab. 2) of the maximal sap flow in the DT, and maximally about 80 % of the instantaneous DT sap flow. Tab. 2: The maximum and total sap flow values in trees, layering branch (l.b.), control branch in the periods before and after excavation of part of the daughter tree roots in Group 1. maximum (kg/h-1)

total flow (kg)

2.49 0.15 0.04 0.02 0.01 0.63

840.57 24.06 4.99 1.26 0.65 516.94

1.76 0.09 0.02 0.02 0.00 0.47

223.80 7.10 1.69 0.81 0.05 344.29

before excavation parent tree daughter tree control branch l.b. flow to daughter l.b. flow to parent reference evapotranspiration [mm/h] after excavation parent tree daughter tree control branch l.b. flow to daughter l.b. flow to parent reference evapotranspiration [mm/h]

9.3.3 Seasonal sap flow characteristics before daughter tree root exposure The transpiration totals in ‘before excavation’ period were 840.57 kg and 24.06 kg for PT and DT, respectively (Tab. 2). The total sap flow in the control (rootless) branch was higher (4.99 kg) than the sum of total flows in the layering branch in both directions (Tab. 2). The total sap flow in the layering branch supplied in the direction of the DT was 1.26 kg, whereas the flow in the direction of PT was lower, at 0.65 kg (Tab. 2). In other words, 66 % of the water flow was towards the DT (Tab. 3). The maximum sap flow rates of the PT and DT were 2.49 and 0.15 kg h-1, respectively, proportionally reflecting the different total surface areas of the transpiring needles (Tab. 2). The maximum sap flow in the layering branch in the direction of the daughter tree was very low: 0.02 kg h-1, and even lower in the direction of the PT, 0.01 kg h-1 (Tab. 2). There was almost zero flow (less than 0.0001 kg h-1) most of the time (60.1%). Water flowed to the DT for 21.4% of the time and towards 112


the PT 18.5% of the time (Tab. 3). Of the water transpired by the DT, the PT root system supplied 5.3 % through the layering branch. In contrast, water supplied by the DT root system to the PT was less than 0.1 % of the total water transpired by the PT. The abovementioned ratios are presented in (Tab. 4) as dependence rates. Tab. 3: The proportion of sap flow in particular directions in layering branch. The proportion is expressed quantitatively in terms of percentage and time ratio and is divided into the periods before and after excavation. Measuring period

total flow (kg)

total flow (%)

time flow ratios (%)

flow direction before exposing

daughter 1.3

parent 0.7

daughter 66.0%

parent 33.0%

daughter 21.4%

parent 18.5%

zero 60.1%

after exposing

0.8

0.1

94.2%

5.8%

32.8%

7.9%

59.3%

Tab. 4: The dependency rate of daughter tree and parent tree on water supplied through the layering branch (l.b.). These rates were calculated as the proportions of total tree transpiration represented by total sap flow in the layering branch in a particular direction. Measuring period

flow in l.b. to daughter

total flow in daughter tree (kg)

dependency rate (%)

before exposing

1.3

24.1

5.3%

after exposing

0.8

7.1

flow in l.b. to parent

total flow in parent tree (kg)

11.5% dependency rate (%)

before exposing

0.7

840.6

0.1%

after exposing

0.05

223.8

0.0%

9.3.4 Seasonal sap flow characteristic after daughter tree roots excavating In the ‘after excavation’ period, the total sap flows of PT and DT were 223.80 kg and 7.10 kg, respectively. The total sap flow in the control branch was 1.69 kg. The total layering branch sap towards the DT was 0.81 kg (94.2 %), whereas in the towards PT it was only 0.05 kg (5.8 %) /Table 2, Table 3/. The maximum sap flow rate in the layering branch towards the DT was 0.019 kg h-1, and obviously lower in the direction of the PT, 0.002 kg h-1 (Tab. 2). The lower absolute values of sap flow rates in this period compared to ‘before excavation‘ were caused by the shorter measurement period as well as lower evaporative demands (Tab. 2). In terms of time flow proportions, zero flow (less than 0.0001 kg h-1) was measured most of the time (59.3%); however, the proportions of time that flow was towards DT and PT changed from 32.8 % and 7.9 %, respectively, from 21.4 and 18.5 % in the period ‘before excavation‘ (Tab. 3). The dependence rate of DT on PT water storage was 11.5 %, which was higher than the 5.3% before root excavation (Tab. 4). In summary, ‘after excavation‘, the sap flow towards the DT in the layering branch strongly increased and the flows in the opposite direction almost completely disappeared (Tab. 3, Fig. 4). 113


9.4 Discussion The sap flow time lag behind transpiration and EVAP is well described in the scientific literature and is identified as a result of the lag in propagation of the hydraulic signal between the foliage and the stem xylem (Borchert 1994; Goldstein et al. 1998; Čermák et al. 2007). This lag is thought to be highest between foliage and branches (Köstner et al. 1998; Čermák et al. 2007) and is ascribed to water storage in branches (Anfodillo et al. 1998). The time lag reported in this study fits well within the common range of 0 – 4 hours (Martin et al. 1997; Ewers, Oren 2000; Whitley et al. 2009). As the water potentials in DT and PT likely equalized during the night, the shift in sap flow curves between the DT and PT in Goup 1 likely was an effect of different illumination /DT was on the east side of the clonal group/ (Burgess, Dawson 2008) and possibly a smaller amount of stored water available for transpiration in the DT, corresponding to its smaller size (Čermák et al. 1976; Waring, Running 1978). The inference of an effect of different illumination on the shape and magnitude of the lag between sap flow in the PT and DT was supported by the measurements on the other two clonal groups, which each had its daughter facing a different azimuth. Thus, in Group 3, sap flow in the westward-facing DT began later than in the PT, whereas Group 2 showed the same general patterns of water flow as Group 1. Xylem water flow is an entirely passive process that follows the differences in water potential (Tyree, Ewers 1991; Tyree, Zimmerman 2002). Therefore, we expected the typical bidirectional sap flow of the Group 1 layering branch (morning to mid-day, flowed towards DT; afternoon flow towards PT) to reflect deviations in DT and PT water potentials during the day. This explanation was supported by direct measurements of shoot water potential in the crowns of PT and DT in Group 1. Minimum shoot water potentials in our experiment were about - 2.5 MPa. Our values were about 1.2 MPa lower than those found in Norway spruce at treeline in the central Alps (Anfodillo et al. 1998), and about 1.3 MPa higher than found (Mayer et al. 2002) in this species at treeline in the Dolomites Alps. The Group 2 layering branch revealed the same bidirectional sap flow pattern as that of Group 1, as the DT was also eastward-oriented. However, the Group 3 layering branch showed the opposite bidirectional sap flow pattern, caused by the different orientation of its DT, as it was on the west side of the clonal group and therefore did not get illuminated until around noon (see Fig. 5). Naturally, the layering branches showed higher tree ring density compared to stem ring density (Šenfeldr, Maděra – unpublished data). Moreover, the layering branches were characterised by higher tree ring density compared to non-layering branches (Šenfeldr, Maděra – unpublished data). The increased tree ring density of spruce layering branches leads to overall increase of the hydraulic constraints (Tyree, Zimmermann 2002). This fact contributes to the overall low flow rates found in layering branches, which were lower than those in the non-layering control branches (see Tab. 2). It can take several decades before a daughter tree becomes independent of water and assimilates from the parent tree (Holtmeier 2009). This independence is defined functionally, with daughters self-supported by their roots and are fully photoautotrophic (Körner 2003). Thus, some physical connection can remain, in the form of dead layering branches, between independent ramets. Maděra (2004) documented a Norway spruce layering branch that was functionally active for 26 years. This period started with the rooting of the initial plagiotropic branch and ended with the death of the layering branch. 114


Layering branch vitality is linked with the vitality of the PT and is also favoured by having a leeward position in relation to the PT (Vacek et al. 2012). Layering of spruce branches increases in intensity with elevation as conditions become more stressful (Šenfeldr, Maděra 2011). In a previous study in the close vicinity of our study site, we found that in the period of approximately 70 years, a given primary layering branch would give rise to up to three generations of living clonal spruce daughters, comprising approximately 13 clonal trees (Šenfeldr, Maděra 2011). Clonal groups can persist for a long time; in the immediate vicinity of 5-m-tall living Norway spruce groups, Kullman (1996) found many generations of genetically identical subfossil wood remains, with the oldest 9,000 years old. As long as functional connections are maintained, they can take on special importance during periods of disturbance (e.g., herbivory) or patchy resource supply. However, in our case, after 23 years of PT and DT coexistence, the DT was still dependent on the PT. The absolute quantity of water supplied from the PT root system to the DT in the period ‘before excavation‘, (dependence rate 5.3 %) seems rather unimportant, but in the short-term, especially in the morning, the layering branch would supply up to 25 % of the water. On the other hand, the flow was negligible in times of highest evapotranspiration demands, when shoot water potentials in PT and DT would be equalized. The fairly insignificant role of layering branch sap flows for the DT changed completely after the excavation of part of its roots. Then, the DT became more dependent, with the root excavation caused increase in overall layering branch flow magnitude as well as layering branch flows only in the direction to DT. The bidirectional flows in a layering branch represent the physiological phenomenon of hydraulic redistribution (Brooks et al. 2002). Generally, this phenomenon leads mostly to passive water movement from wetter (high-water potential) to drier (low-water potential) sections of root systems via a connected root system network (Richards, Caldwell 1987; Dawson 1993; Xu, Bland 1993). However, in our case, the water was not redistributed by roots, but by rooted branches. This represents a completely new type of hydraulic redistribution than those previously described (Naděždina et al. 2010). A similar pattern can be expected for phloem flow, because clonal plants behave as a single unit allowing the allocation of photosynthates form the larger to the smaller daughters (Hutchings, Wijesinghe 1997; Gardner, Mangel 1999) and not only between daughters but between parent and daughters. Importantly, the daughter ramets can play the role of helpers, not parasites, by moving nutrients not only from, but also to parent tree (Wilson 2013). The results of our study reveal that the clonal connection via a layering branch enables trees to transfer water to exploit patchy water resources and ameliorate potential drought stress. The strategy of clonal growth, thus possibly improves the fitness of clonal plants (see Pitelka, Ashmun 1985; Kroon, Groenendael 1997) growing at species range limits at treeline. Indeed, the resource exploitation and sharing among ramets of clonal plants often enhance their survival and vitality (Hartnett, Bazzaz 1985; Evans 1992; Hester et al. 1994; Brewer, Bertness 1996). These are some of the reasons that clonal trees are so robust in the face perturbations and have a stabilizing effect on ecosystems. Clonal growth can also improve the competitive ability of clonal plants (Herben, Suzuki 2001), which would seem to be very important at treeline, as competition with dwarf shrubs and dense mats of grass and herbs can strongly influence new tree establishment and overall treeline dynamics (Germino et al. 2002; Dullinger et al. 2005; Holtmeier, Broll 2007). 115


9.5 Conclusion We found bidirectional sap flow in layering branches (connecting parent and daughter trees) in all measured clonal Norway groups. The bidirectionality of the flows in layering branches was driven by differences in water potentials of the parent and daughter trees during the day. Importantly, the daughter tree could thus play the role of helpers, and not be parasitic, by moving water to the parent tree. Based on sap flow measurements we found that even in a 23-year-old daughter tree about 5.3 % of the total transpired water was supplied by the parent tree. In some cases, water flow in the layering branch reached up to 25 % of the instantaneous transpiration of the daughter tree. In contrast, water supplied by the daughter’s root system to the parent tree would comprise less than 1 % of the parent’s sap flow. After excavating part of the daughter tree roots, the layering branch sap flow towards the daughter tree increased (with 11.5 % of the daughter’s transpired water supplied by the parent) and the flows to the parent almost completely disappeared. The results of this study have ecological implications, as the water transfer via layering branches can enables trees to exploit patchy water resources and ameliorate potential drought stress. In the harsh conditions of the treeline ecotone, the additional water provided to the daughter by the parent tree, in addition to that from its own root system, can helps the daughters establish as new individuals.

Acknowledgements This contribution was supported by the Internal Grant Agency of the Faculty of Forestry and Wood Technology at Mendel University in Brno (projects 25/2011), COST LD13017 and by the project “Indicators of trees’ vitality”, Reg. No. CZ.1.07/2.3.00/20.0265 cofinanced by the European Social Fund and the state budget of the Czech Republic. The authors wish to thank J. Rosenthal for improving the English.

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10. Celkový závěr Z výsledků hodnocení věkové struktury v rámci spodní a horní části ekotonu horní hranice lesa (tyto části jsou podrobně definovány v kap. 5.2.2), vyplynula příznivá věková struktura smrkových populací nad horní zapojenou hranicí lesa v Hrubém Jeseníku. V rámci vypočtené modelové věkové struktury na lokalitě Keprník, byl nejvyšší podíl stromů (23.9 %) ve spodní části výzkumného polygonu (dále jen VP) zastoupen ve věkové třídě 21–40 let. Směrem ke starším věkovým třídám docházelo k postupnému snižování procentuálního zastoupení jedinců. V horní části VP byl jednoznačně nejvyšší podíl stromů (59.2 %) zastoupen v nejmladší věkové třídě (1–20 let), a struktura populace zde odpovídá populaci ve stádiu růstu. Rozdíl ve věkové struktuře mezi horní a spodní částí ekotonu může být vysvětlen posunem stromů v rámci ekotonu hranice lesa vzhůru, zejména vezmeme-li v potaz, že stromy expandovaly v té době do bezlesých poloh. Stejný trend, jako na lokalitě Keprník, byl zaznamenán na všech kontrolních plochách na lokalitách: Praděd-severozápad, Praděd-jihovýchod, Vysoká hole-severozápad, Vysoká hole-jihovýchod) v ostatních částech pohoří Hrubého Jeseníku. Jiný pohled na věkovou strukturu populace nám poskytuje zastoupení stromů v jednotlivých klonálních generacích v rámci klonálních skupin. Ve spodní části VP na lokalitě Keprník, vyprodukovalo celkem 11 rodičovských stromů 49 dceřiných stromů 1. generace, tyto dále vyprodukovaly 23 dceřiných stromů 2. generace, a dceřiné stromy 2 generace daly dokonce vznik 5 stromům 3. generace. V horní části VP bylo zaznamenáno o jednu generaci dceřiných stromů méně. Celkem 5 rodičovských stromů zde vyprodukovalo 64 dceřiných stromů 1. generace, dceřiné stromy 1. generace vygenerovaly 10 stromů 2. generace. Byl zaznamenán významný rozdíl v počtu dceřiných stromů 1. generace připadajících na jeden RS mezi horní (průměr ± SE = 12.8 ± 4.18) a spodní části VP (průměr ± SE = 4.5 ± 1.06). Rovněž byla zjištěna delší vzdálenost zakořenění hřížících větví od rodičovského stromu mezi spodní (průměr v cm ± SE = 93.0 ± 4.64) a horní částí (průměr v cm ± SE = 43.9 ± 3.12) VP. Stejný trend byl zaznamenán ve výšce nasazení zahřížené větve na rodičovském stromě mezi spodní (průměr v cm ± SE = 25.9 ± 2.79) a horní (průměr v cm ± SE = 7.8 ± 0.73) částí VP. Výše uvedené skutečnosti poukazují na trend zvýšení klonální reprodukce s rostoucí nadmořskou výškou. Z uvedeného vyplývá, že smrky v horní části VP hříží dříve a intenzivněji, ve srovnání se spodní částí VP. Což vypovídá o zvýšení intenzity klonální reprodukce s gradientem stresových faktorů, tudíž lze hřížení smrků v ekotonu horní hranice lesa chápat jakožto odezvu stromů na působení stresových podmínek. Růstové podmínky v nejvyšších polohách jsou do jisté míry vystihnuty výškovými charakteristikami smrků v ekotonu hranice lesa. Horní hranice smrků o maximální výšce 2 metry, která byla v roce 2006 vymezena pouze ve vrcholových polohách Keprníku (1411 m n. m.), Vysoké hole (1451 m n. m.) a Pradědu /1469 m n. m./ (Treml 2007), již při opětovném měření v roce 2009 na lokalitě Keprník zaznamenána nebyla. I některé z nejvýše položených klonálních skupinek, rostoucích v těsné blízkosti vrcholu Keprníku, tuto výšku v roce měření přesáhly. Stejný trend byl zaznamenán při horní hranici smrků o maximální výšce 5 metrů, kde za čtyři roky tuto hranici přerostlo 57 jedinců smrku. Výše uvedené skutečnosti poukazují na intenzivní výškový růst smrků v současnosti v zájmové lokalitě. 121


Potenciál generativní reprodukce, který byl vyjádřen množstvím klíčivých semen, v ekotonu hranice lesa v Hrubém Jeseníku není příliš vysoký, nicméně je dostatečný pro rozmnožování smrků generativní cestou a jejich posun do vyšších nadmořských výšek. Ve spodní části VP bylo v roce 2009 zaznamenáno 650 šišek na hektar, zatímco v horní části VP pouze 19 šišek na hektar. Počet šišek na jednoho plodného jedince byl vyšší ve spodní části VP (průměr ± SE = 14.1 ± 1.88) ve srovnání s horní částí VP (průměr ± SE = 5.8 ± 1.32). Stejný trend byl pozorován i u počtu semen zaznamenaných v jedné šišce při srovnání spodní (průměr ± SE = 70.2 ± 6.50) a horní (průměr ± SE = 42.2 ± 5.42) části VP. Klíčivost semen byla mírně vyšší ve spodní části (průměr ± SE =27.0 ± 3.67) než v horní části (průměr ± SE = 20.5 ± 2.90) VP. Na základě výše uvedených údajů činil odhadnutý potenciál generativní reprodukce 12 320 klíčivých semen na hektar ve spodní části VP a 164 semen na hektar v horní části VP. Hustota odrostlých semenáčků (10–80 cm) činila 25 kusů na ha ve spodní části VP, v horní části pak 39 jedinců na hektar. V některých částech pohoří dochází k interakci mezi alochtonními porosty borovice kleče a smrkovými populacemi. Pokud jde o vliv kleče na klonální smrkové skupinky, tak zde byl zaznamenán negativní vliv borovice kleče na ukazatele intenzity vegetativního rozmnožování smrků. Jako aktuální indikátor intenzity vegetativního rozmnožování byl použit počet aktuálně hřížících větví připadajících na jednu skupinu, zatímco pro účely dlouhodobého indikátoru byl použit počet stromů v klonální skupině. V zapojených porostech borovice kleče (pokryvnost 71–100%) bylo zaznamenáno statisticky významně méně hřížících větví (medián = 0.0) ve srovnání s volnou plochou (medián = 2.7). Rovněž nejmladších jedinců do 20 let bylo zaznamenáno méně v zapojených porostech kleče (medián = 0.0), než na volné ploše (medián = 1.8). Podobný trend byl zjištěn v případě počtu stromů v klonální skupině. Početnější skupinky se vyskytovaly na volné ploše (medián = 8.0), a naopak v zapojené kleči byly vždy méně početné (medián = 3.0). Střední věk smrků byl vždy vyšší v porostech kleče (medián = 64 let) v porovnání s volnou plochou (medián = 34 let). V případě rozvolněných porostů borovice kleče (pokryvnost 1–70 %), tvořily hodnoty sledovaných proměnných přechod mezi volnou plochou a zapojenou klečí. Z výše uvedeného vyplývá, že dochází ke snížení schopnosti vegetativní reprodukce smrků, s rostoucím zápojem porostů borovice kleče. Nižší počty hřížících větví zapříčiňují slabší regeneraci klonálních skupinek v porostech kleče, což vede k celkově nižším počtům jedinců ve skupině a jejich vyššímu věku ve srovnání s volnou plochou. Nižší výskyt hřížících větví v porostech kleče je zapříčiněn jejich vyšší mortalitou, která pravděpodobně souvisí s jejich zastíněním klečí, prodloužením období trvání sněhové pokrývky a mechanickým tlakem kleče. Jako hraniční vzdálenost kleče od okraje klonálních smrkových skupin (průměr ze čtyř světových stran) se ukázala být hodnota 6 metrů. Při delších vzdálenostech kleče od okraje klonálních smrkových skupin přestává být negativní vliv kleče zřetelný, a vegetativní rozmnožování odpovídá svou intenzitou klonálním skupinám na volné ploše. Na rozdíl od negativního ovlivnění vegetativního rozmnožování smrků, působí zapojené klečové porosty pozitivně na výškový růst smrků. Při srovnání výšek podobně starých smrků, byli v zapojené kleči zaznamenáni významně vyšší jedinci (průměrná max. výška v cm ± SD = 441 ± 72) ve srovnání s volnou plochou (průměrná max. výška v cm ± SD = 367 ± 56). Lepší výškový růst smrků v kleči pravděpodobně souvisí s nižším poškozením okusem zvěří a nižším poškozením abrazí nadzemní biomasy u mladých smrků (Dullinger et al. 2005), do té doby, než předrostou okolní kleč. Předchozí 122


vysvětlení bylo podloženo zjištěním nižšího počtu zlomených kmenů v zapojené kleči (20 %) ve srovnání s volnou plochou (55 %). Zjištění jednak negativního vlivu kleče na vegetativní rozmnožování smrků a zároveň pozitivního vlivu na výškový růst, je v souladu s obecně platným názorem, že interakce mezi druhy zahrnuje jak negativní, tak i pozitivní působení (Callaway, Walker 1997). V souvislosti se současným posunem stromů v ekotonu hranice lesa vzhůru, a s ohledem na snížení vegetativní reprodukce smrků s rostoucím zápojem kleče (Dullinger et al. 2005), stejně tak jako snížení generativní reprodukce smrků, lze očekávat, že expanze smrků bude pomalejší na lokalitách se zapojenými porosty kleče. Protože významné posuny hranice lesa závisí výhradně na generativně zmlazených jedincích. Byl poskytnut podrobnější přehled o posunech stromů v rámci ekotonu hranice lesa prostřednictvím zjištění věkové struktury stromů generativního původu (v předchozích případech byl zjišťován věk, jak stromů generativního, tak i vegetativního původu). Výše uvedená metoda byla doplněna analýzou změn pokryvnosti v čase v jednotlivých částech ekotonu. Pro účely sledování výše uvedených ukazatelů, byly vybrány pouze plochy, na kterých dle historických záznamů, neprobíhalo umělé zalesňování. Plochy výjimečně vykazující podezření na zalesňování (homogení věková struktura, homogení změna pokryvnosti) byly vyřazeny z datového souboru. Nicméně, nelze vyloučit lokální vliv zalesňování malého rozsahu, které nebylo evidováno. Tudíž, se ve všech případech v rámci uchycování smrků, nemuselo jednat výhradně o přirozený proces. V Hrubém Jeseníku byly nejstarší stromy v oblasti současné horní hranice lesa uchyceny v letech 1780–1879, a v oblasti současné horní hranice stromů v letech 1849– 1936. První vrchol v uchycování stromů při současné hranici lesa byl v 60. letech 18. století, poté po krátké depresi v 70. letech 18. století, se počet uchycených stromů postupně zvyšoval až do roku 1910. Poté, po depresi ve 20. letech 19. století, se počet uchycených stromů opět postupně zvyšoval a kulminace bylo dosaženo v 30. letech 19. století. První vrchol uchycování smrků v oblasti současné hranice stromů proběhl okolo roku 1890, poté po krátké depresi probíhalo postupné zvyšování počtu uchycených stromů s kulminací v letech 1950–1960. V letech 1970–1980 počet uchycených stromů náhle výrazně poklesl. Na tento pokles navázal další vrchol v uchycování smrků v letech 1990– 2010. Mezi léty 1860–1930 docházelo k četnějšímu uchycování stromů v oblasti současné hranice lesa v porovnání horní hranicí stromů. Naopak, v letech 1950–1990 docházelo k četnějšímu uchycování smrků v oblasti horní hranice stromů. Na všech plochách v oblasti horní hranice stromů docházelo k navyšování zápoje stromů od začátku studované periody v roce 1946 do konce této periody v roce 2005. Na začátku této periody (1946) byla v oblasti horní hranice stromů průměrná pokryvnost smrků 3.8 %, zatímco do konce této periody (2005) vzrostla tato hodnota na 9.3 %. V oblasti horní hranice lesa dosáhla průměrná pokryvnost smrků na začátku studované periody (1946) hodnoty 17 %, zatímco v roce 2005 pokrývaly smrky v průměru 28 % plochy. Jak analýza věkové struktury generativně vzniklých stromů, tak i analýza změn pokryvnosti v čase potvrdily expanzi stromů vzhůru, do v té době bezlesých poloh. Expanze stromů do oblasti současné horní hranice stromů probíhalo přibližně o 30–40 let později než jejich uchycování v oblasti horní hranice lesa. Horní hranice lesa v Hrubém Jeseníku byla v průběhu 20. století vystavena jak ukončení hospodaření, tak i nárůstu teploty. Statistická analýza těchto proměnných poukázala na to, že uchycování stromů v oblasti současné horní hranice lesa souviselo 123


zejména s ukončením hospodaření, zatímco populace smrků v oblasti současné horní hranice stromů, byly uchyceny v průběhu let s nadprůměrnými teplotami a nadprůměrným tloušťkovým přírůstem. Ve studovaném území byl po roce 1990 zaznamenán nejvyšší tloušťkový přírůst za posledních 150 let. Vysoké teploty a nadprůměrný tloušťkový růst smrků, které pozorujeme v současné době, indikují, že posun stromu do vyšších poloh bude pokračovat. Nejvyšší polohy Hrubého Jeseníku představují jediné lokality výskytu strukturních půd a vyfoukávaných alpinských trávníků s arkto-alpinskými druhy v rámci celého pohoří. Tyto fenomény představují jedny z nejvýznamnějších předmětů ochrany přírody v CHKO Hrubý Jeseník. Zaznamenaný posun stromů vzhůru může představovat riziko pro tyto fenomény, protože, již za současných podmínek rostou některé smrky v jejich těsné blízkosti a negativně je ovlivňují. Při dalším posunu stromů vzhůru lze očekávat fragmentaci biotopů nad hranicí lesa, což může vést k významnému snížení diverzity. Na základě přístrojového měření transpiračního proudu v rodičovských a dceřiných stromech včetně zahřížených větvích, které tyto stromy propojují, byly zjištěny některé kompletně nové informace o fyziologii klonálních smrkových skupin. Celkově byl transpirační proud měřen u tří klonálních skupinek, z nichž jedna byla měřena dlouhodobě (137 dní) a u zbývajících ambulantně po dobu jednoho týdne. U dlouhodobě měřené skupiny byl téměř každý den zaznamenán dvousměrný transpirační tok v zahřížené spojovací větvi. V ranních a dopoledních hodinách byl transpirační tok v hřížící větvi realizován ve směru k dceřinému stromu, zatímco kolem poledních hodin docházelo k reverzaci toku, který byl dále realizován v opačném směru k rodičovskému stromu. Vzhledem k tomu, že transpirační tok je pasivní proces, který je řízen dynamikou vodních potenciálů v kontinuu půda-rostlina-atmosféra (Tyree, Zimmerman 2002), byla dvousměrná denní dynamika toku vysvětlena změnami hodnot vodních potenciálu v koruně rodičovského a dceřiného stromu v průběhu dne. Vodní zásoby rodičovského a dceřiného stromu byly v průběhu noci dosycovány. Po východu slunce, začaly oba stromy transpirovat. Dceřiný strom měl výrazně menší a kratší kmen a byl vystaven přímému slunečnímu záření (orientace k V), tudíž velmi brzy po začátku transpirace v koruně, došel transpirační požadavek do spodní části kmene. V té době byl kmen rodičovského stromu dosycený vodou, tento strom transpiroval v koruně, a než došel gradient vodního potenciálu do spodní části kmene (zpravidla v poledních hodinách), tak do této doby mohl dceřiný jedinec využívat jeho vodních zásob prostřednictvím hřížící větve. Jakmile došlo v poledních hodinách ke snížení vodního potenciálu u báze kmene rodičovského stromu pod hodnotu v dceřiném stromu, docházelo k reverzaci a tok se obracel k rodičovskému stromu. Výše uvedené vysvětlení je podpořeno ambulantním měřením změn vodních potenciálů v koruně rodičovského (RS) a dceřiného stromu (DS) v průběhu dne prostřednictvím Scholanderovy bomby. V ranních hodinách byly naměřeny nižší hodnoty vodních potenciálů v koruně dceřiného stromu [DS (Mpa) = -1.9 ± 0.1 SE; RS (MPa) = -1.6 ± 0.1 SE], krátce po poledni byl naopak zaznamenán nižší vodní potenciál v koruně rodičovského stromu [DS (Mpa) = - 1.6 ± 0.1 SE; RS (MPa) = -2.1 ± 0.1 SE] a tento poměr vydržel až do pozdně odpoledních hodin [DS (Mpa) = -1.2 ± 0.1 SE; rodičovský strom (MPa) = -1.6 ± 0.1 SE]. Suma transpirace za sledované období (99 dní) činila pro rodičovský strom 840.57 kg, zatímco, pro dceřiný strom 24.06 kg. Suma toků v hřížící větvi ve směru k dceřinému stromu činila 1.26 kg, zatímco ve směru k rodičovskému stromu pouze 0.65 kg. Jinak řečeno, 66% z celkového 124


množství vody, v zahřížené větvi proteklo ve směru k dceřinému stromu. Z celkového množství vody, kterou vytranspiroval dceřiný strom za sledované období, bylo 5.3 % dotováno rodičovským stromem, prostřednictvím hřížící větve (respektive se jednalo se o míru závislosti dceřiného stromu na rodičovském). Na druhou stranu, dceřiný strom dotoval rodičovský strom prostřednictvím hřížící větve, pouze v množství menším, než 0.1 %. Po experimentálním odhrabání zeminy (po 99 dnech měření) z části kořenového systému dceřiného stromu, byly ve zbývajícím měřeném období (38 dní) zaznamenány výrazné změny v tocích v zahřížené větvi. Míra závislosti dceřiného stromu na rodičovském, vzrostla po odkrytí kořenového systému dceřiného stromu, z předchozí hodnoty 5.3 % na 11.5 %. Po odkrytí kořenového systému, výrazně vzrostl i transpirační tok v hřížící větvi směrem k dceřinému stromu a v opačném směru téměř zmizel. V případě dalších dvou klonálních skupin, byly rovněž zjištěny dvousměrné toky v zahřížených spojovacích větvích. Výše uvedené skutečnosti naznačují, že dvousměrné toky v zahřížených větvích představují běžný mechanismus klonálně propojených stromů na hranici lesa, jak efektivně redistribuovat vodu z nerovnoměrně rozložených zdrojů a tímto eliminovat potenciální vodní stres.

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11. Summary The analysis of Norway spruce populations showed a favourable age structure of spruce trees above the upper forest limit in the Hrubý Jeseník Mts.The highest proportion of trees (23.9%) in the lower section of the research polygon (RP) on the Keprník site was within the 21–40-year model age class. The proportion of trees in older model age classes gradually decreased. The highest proportion of trees in the upper RP section was in the youngest age class (59.2%), and there was a gradual decrease in proportion toward older age classes. This age structure indicates a population at the stage of growth. The differences in the age structure between the lower and the upper RP sections can be explained by a gradual advance of trees within timberline ecotone, especially if we consider that trees have expanded to areas that were treeless at the time. The same trend as on the Keprník site was recorded in all control plots on other sites of the Hrubý Jeseník Mts.: Praděd-northwest, Praděd-southeast, Vysoká hole-northwest, Vysoká holesoutheast). Another insight into the age structure of the population is provided by the proportion of trees in the individual generations in clonal groups. In the lower RP section, 11 initial parent trees had produced 49 first-generation clonal trees: these had produced 23 secondgeneration trees, and the second-generation trees had given birth to 5 third-generation trees. In the upper section, one generation fewer was recorded. In total, 5 initial parent trees had produced 64 first-generation clonal trees, and these had produced 10 secondgeneration trees. A statistically significant difference was found in the number of first-generation clonal trees produced by one parent tree between the upper RP (mean ± SE = 12.8 ± 4.18) and the lower RP (mean ± SE = 4.5 ± 1.06) sections. In addition, a significant difference was found in the layering (rooting) distance from a parent tree between the lower RP (mean of 93.0 ± 4.64) and the upper RP (mean ± SE = 43.9 ± 3.12) sections. Moreover, the difference in the height of layering branch positions in the parent tree between the lower (mean ± SE = 25.9 ± 2.79) and the upper (mean ± SE = 7.8 ± 0.73) RP sections was statistically significant. It follows that spruce trees in the upper RP section are reproduced clonally earlier and more intensively compared to the lower RP, which indicates an increasing intensity of clonal reproduction with a gradient of stress factors, so the layering in the timberline ecotone can be understood as a response of trees to stress conditions. Growing conditions in the timberline ecotone are to some extent expressed by spruce height growth. The upper line of 2-m-tall spruce trees, which had only been defined on Keprník, Vysoká hole and Praděd sites by Treml (2007), was not detected in remeasurements on Keprník site in 2009. Even some of the uppermost clonal groups growing in a close proximity to the Keprník summit exceeded this height of 2 meters. The same spruce height growth trend was observed at the upper limit of 5-m-tall spruce trees defined by Treml (2007), where this line was exceeded by 57 trees during four growing seasons. The above mentioned facts indicate the current intensive height growth of spruce trees in the treeline ecotone of the study site. The generative reproduction potential in the Hrubý Jeseník Mts., which was expressed as the quantity of germinated seeds in the treeline ecotone, is not very high; however, it is sufficient for the spruce tree rejuvenation and migration to higher altitudes. In the lower RP section, 650 cones per hectare were found, whereas in the upper RP 126


section only 19 cones per hectare were found. The number of cones per one fertile tree was higher in the lower RP section (mean ± SE = 14.1 ± 1.88) compared to the upper RP section (mean ± SE = 5.8 ± 1.32). The same trend was observed in the seed numbers recorded in a single-cone when comparing the lower RP (mean ± SE = 70.2 ± 6.50) and the upper RP (mean ± SE = 42.2 ± 5.42) sections. Seed germination was slightly higher in the lower RP (mean ± SE = 27.0 ± 3.67) than in the upper RP (mean ± SE = 20.5 ± 2.90). The estimated potential of generative reproduction was calculated in the amount of 12 320 germinating seeds per hectare in the lower RP and 164 seeds per hectare in the upper RP section. The density of seedlings of height of 10–80 cm was 25 per hectare in the lower RP section and 39 per hectare in the upper RP section. In some parts of the Hrubý Jeseník Mts., an interaction between allochtonous dwarf pine (Pinus mugo) stands and spruce populations within the treeline ecotone can be observed. In terms of dwarf pine influence on clonal spruce groups, we detwcted a negative pine influence on indicators of spruce vegetative reproduction intensity was detected. I used the number of layering branches per one clonal group as a current indicator of vegetative reproduction intensity, whereas the number of trees per one clonal group was used as a long-term indicator of vegetative reproduction intensity. In dense pine plots (pine cover 71–100%) a significantly lower number of layering branches (median = 0.0) was detected compared to no-pine plots (median = 2.7). Also, the number of juvenile trees (1–20 years) was found to be lower (median=0.0) in dense pine plots compared to no-pine plots (median = 1.8). A similar trend was found in the case of the number of trees per a clonal group indicator. The clonal groups with more trees (median=8.0) were found to be in no-pine plots, while fewer trees in clonal groups (median = 3.0) were always in dense-pine plots. The mean spruce age was always higher in dense-pine plots (median=64 years) compared to no-pine plots (median = 34 years). In sparse-pine stands (pine cover 1– 70%), intermediate values between no-pine and dense-pine plots were reached for all of the above mentioned variables. The results clearly showed that vegetative propagation of spruce decreased with increasing density of surrounding dwarf pine stands. The scarcity of layering branches probably resulted from a combination of the competitive pressure of closely occurring pines causing light deficiency and increased branch mortality from prolonged snow cover and wetter microclimate in pine stands. Indeed, the mortality of currently or potentially layering branches could also be increased by mechanical damage from prostrate branches of dwarf pine. The lower amount of layering branches caused weaker rejuvenation of clonal groups in pine plots which led to generally older spruce clonal groups with fewer numbers of trees as well as juveniles. When the distance between pine shrubs and spruce group is more than 6 m, as in sparse pine stands or meadows, the negative effect of pine vanishes, with layering intensity corresponding to no-pine plots. In contrast to hampering the vegetative reproduction of spruce, dense pine stands positively affected spruce height growth. When comparing subsets of the similar-aged trees, the spruce height were significantly greater taller (mean height cm ± SD = 441 ± 72) in dense pine stands than in no-pine stands (mean height cm ± SD = 367 ± 56). The improved height growth in the dense pine stands might be related to a smaller browsing pressure from herbivores and/or lower wind abrasion of aboveground biomass in the juvenile ontogenetic phases. The latter explanation in particular is supported by our finding of lower numbers of broken stems in dense pine plots (20%) in comparison to nopine plots (55%). 127


The finding of a positive effect of dwarf pines on spruce height growth along with a negative effect on vegetative propagation is consistent with the overall view that interactions between treeline species can have both positive and negative components (Callaway, Walker 1997). The pine cover negatively affects both spruce seedling recruitment (Dullinger et al. 2005) and vegetative reproduction, providing the basis for overall negative impacts on spruce expansion. Thus, expansion of spruce will probably be significantly faster in areas lacking dense dwarf pine stands. Since significant large scale timberline shifts mainly depend on seed-based regeneration, the historical reconstruction of spruce tree migration within the timberline ecotone was conducted through the findings of the age structure of seed-originated trees (in previous cases both the age of seed-originated as well as clonally-originated trees was analyzed). The above mentioned method was supplemented by an analysis of spruce coverage changes over a time period (1946–2005) in different parts of the timberline ecotone. For the purpose of the above mentioned analysis, only areas without artificial afforestation were selected based on historical records. Areas exhibiting suspected afforestation (homogeneous age structure, homogeneous change of coverage) were excluded from the data set. However, an influence of local small-scale afforestation which has not been recorded cannot be excluded. Thus, establishment of all spruce trees may not be a result of natural processes entirely. The establishment dates of oldest trees on plots ranged from 1780 and 1879 at upper forest limit sites (UFL) and between 1849 and 1936 in tree-line (TL) sites. The first peak in the establishment of trees on UFL sites was in the 1860s. After a short decline in the 1870s, the numbers of established trees gradually increased up to the year 1910. Then, after a decline in the 1920s, the numbers of trees established increased again, culminating in the 1930s. Sites in TL sites had their first peak in recruitment around 1890, then, after a slight decline, showed a gradual increase in establishment until culminating in the 1950s–1960s. In the 1970s–1980s, the numbers of trees established dropped abruptly. This decrease was followed by a recruitment peak between 1990 and 2010. Trees established significantly more frequently in UFL sites than in TL sites from the 1860s to 1930s. In contrast, recruitment significantly prevailed on TL sites over UFL sites from the 1950s to 1990s. All TL plots revealed increases in tree cover from the beginning to the end of this study period (1946–2005). At the beginning of this period, the average spruce cover was 3.8% in TL sites, while at the end of this period (2005), this value increased to 9.3%. The average tree cover at UFL was 17% at the onset of the study period, whereas in 2005 spruce stands covered 28% on average. Both age structure analysis and tree cover change analysis in time revealed a tree upward expansion to areas that were treeless at the time. In the Hrubý Jeseník Mts., expansion of trees into the present TL sites occurred 30–40 years later than tree establishment at the present UFL sites. The study area experienced a 1°C temperature increase over the last 100 years and agricultural land abandonment in the first half of the 20 th century. The analysis of these variables revealed that recruitment was mainly dependent upon agricultural land abandonment at the UFL, whereas climatic variables and tree vigor (indicated by ring widths) affected recruitment at the tree-line. The study area revealed the highest radial increment for the last 150 years after 1990. At present, the high temperatures as well as the above-average radial growth indicate that the upward migration of trees will continue.

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The highest positions of the Hrubý Jeseník Mts. are the only habitats of patterned ground and windblown alpine grassland with arcto-alpine species from the entire mountain range. These arcto-alpine phenomena are among the most significant subjects of nature protection today. Currently, some spruce trees grow near these phenomena, affecting them negatively. If the upward advance of spruce trees continues, the fragmentation of these alpine biotopes, further diminishing the area with the consequent decrease in diversity can be expected. Based on instrumental measurements of sap flow in parent trees, daughter trees and layering branches connecting them, completely new information about physiology of clonal groups was discovered. The sap flow was measured in three clonal groups, one of which was measured in the long-term period (137 days) and the remaining two in shortterm periods of one week. In the clonal group measured almost every day in the long-term, bidirectional sap flow was typical of the layering branch. From morning until midday, sap flowed towards the daughter tree, whereas in the afternoon sap flowed towards the parent tree. Xylem water flow is an entirely passive process that follows the differences in water potential in the continuum of soil-plant-atmosphere (Tyree, Zimmerman 2002). Therefore, I expected the typical bidirectional sap flow of layering branch to reflect deviations in daughter and parent tree water potentials during the day. The deviation of daughter and parent tree water potentials I expected to result from their different illumination during the day as well as the different amount of stored water relating with the different body size. This explanation was supported by direct measurements of shoot water potential in the crowns of parent and daughter trees during the day using Scholander pressure chamber. In the morning the recorded values of shoot water potential were lower in the crown of the daughter tree (DT) than that of the parent tree (PT) [DT (Mpa) = -1.9 ± 0.1 SE, PT (MPa) = -1.6 ± 0.1 SE], whereas shortly after midday lower water potentials were recorded in the crown of the parent tree [DT (MPa) = - 1.6 ± 0.1 SE, PT (MPa) = -2.1 ± 0.1 SE], with this relationship holding until late afternoon [DT (MPa) = - 1.2 ± 0.1 SD, PT (MPa = -1.6 ± 0.1 SD].The transpiration totals in the period (99 days) were 840.57 kg and 24.06 kg for parent and daughter trees, respectively. The total sap flow in the layering branch supplied in the direction of the daughter tree was 1.26 kg, whereas the flow in the direction of parent tree was lower, 0.65 kg. In other words, 66 % of the water flow was towards the daughter tree. The parent tree root system supplied 5.3% of the water transpired by the daughter tree through the layering branch (in other words this was the dependence rate of the daughter tree on the parent tree). In contrast, water supplied by the daughter tree root system to the parent tree was less than 0.1% of the total water transpired by the parent tree. After the experimental excavation of a part of roots of the daughter tree (after 99 days of measurements), significant changes in the layering branch sap flow were detected in the remaining period of measurements (38 days). The dependence rate of the daughter tree on the parent tree increased from 5.3% to 11.5% after daughter roots excavation. Moreover, the sap flow towards the daughter tree in the layering branch strongly increased and the flows in the opposite direction almost completely disappeared. In the other two measured clonal groups bidirectional flows in layering branches were also detected. The above mentioned results indicate that bidirectional flows in layering branches represent a

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common mechanism of treeline trees to exploit patchy water resources and ameliorate potential drought stress. Literatura Dullinger, S., Dirnböck, T., Köck, R., Hochbichler, E., Englisch, T., Sauberer, N., Grabherr, G., 2005: Interactions among treeline conifers: differential effects of pine on spruce and larch. Journal of Ecology 93: 948-957. Treml, V., 2007: Dynamika alpinské hranice lesa ve Vysokých Sudetech. Doktorská práce, Katedra fyzické geografie a geoekologie, Přírodovědecká fakulta Univerzity Karlovy, 191 s. Tyree, M., Zimmerman, M.H., 2002: Xylem structure and the ascent of sap. Springer, Berlin, Heidelberg, New York.

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12. Fotografické přílohy

Obr. 1: Ekoton horní hranice lesa na severním svahu Petrových kamenů.

Obr. 2: Klonální smrkové skupinky v těsné blízkosti vrcholu Keprníku. 131


Obr. 3: Semenáček smrku v rámci skalního biotopu na lokalitě Keprník. Tento jedinec se nachází v kritické fázi vývoje, s vysokou mortalitou.

Obr. 4: (vlevo) Dobře odrůstající generativně zmlazený jedinec smrku pod vrcholem Pradědu. Obr. 5: (vpravo) Klonální smrková skupinka, u které byly determinovány 2 generace žijících jedinců vegetativního původu na lokalitě Vysoká hole.

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Obr. 6: Smrkový kmen klonálního původu s dobře viditelným pozůstatkem po spojovací větvi při horní hranici lesa na lokalitě Malý Děd.

Obr. 7: Rodičovský strom s velkým množstvím klonálních jedinců 1. generace při horní hranici lesa na jižním svahu Pradědu.

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Obr. 8: Horní hranice lesa tvořená břízou karpatskou a smrkem ztepilým ve Velké kotlině. Hranice lesa je zde formována, periodickou činností lavin, které představují přirozený ekologický faktor, v těchto podmínkách.

Obr. 9: Jedna z nejvýše položených bučin v České Republice se vyskytuje na horní hranici lesa v Malé kotlině.

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Obr. 10: Měření smrků v zapojeném porostu kleče na lokalitě Keprník.

Obr. 11: Odběr vzorků prostřednictvím Presslerova přírůstového nebozezu pro účely následného laboratorního zjištění věku.

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Obr. 12: Přístrojový systém na měření transpiračního proudu u klonálně propojených rodičovských a dceřiných stromů (výrobce: EMS Brno).

Obr. 13: Čidlo pro měření transpiračního proudu u rodičovského stromu metodou tepelné bilance kmene. Zřetelné jsou elektrody s termočlánky umístěnými uvnitř. Obr. 14: (vpravo) Čidlo průtoku “baby” pro měření transpiračního proudu u dceřiného jedince metodou tepelné bilance kmene (výrobce EMS Brno). 136


Obr. 15: Dvě čidla průtoku “baby” pro měření transpiračního toku v obou směrech v zahřížené spojovací větvi. Čidla byla ukryta v radiačních krytech (výrobce: EMS Brno).

Obr. 16: Přístrojová skříň s datalogerem a řídícími jednotkami (výrobce: EMS Brno). 137


Obr. 17: Kabelové spojení datalogeru s počítačem při procesu stahování dat.

Obr. 18: Měření vodních potenciálů pomocí Scholanderovy tlakové bomby. 138


Obr. 19: Vegetativní rozmnožování smrků podmíněné přímořským klimatem v jihozápadní části Norska.

Obr. 20: Interakce mezi borovicí klečí a vznikající klonální skupinkou modřínu opadavého ve Švýcarských Alpách.

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MENDELOVA UNIVERZITA V BRNĚ. Lesnická a dřevařská fakulta. Ústav lesnické botaniky, dendrologie a geobiocenologie

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