Effect of soil drought on vitality and growth on juvenile

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Nov 29, 2010 - Forests and Environmental Sciences, Albert-Ludwigs University of Freiburg, ...... corner points namely E, F, G and H was chosen from the central zone of .... Spade, hand spade, knife, water spray bottle, Munsell ® Soil Color ...

Effect of soil drought on vitality and growth on juvenile and understorey beech (Fagus sylvatica L.) trees Case study from a rocky gneiss outcrop near Freiburg, Black Forest, Germany

A thesis submitted in the partial fulfillment of the requirements for the degree Master of Science (M.Sc.) in Forest Ecology and Management from the Faculty of Forests and Environmental Sciences, Albert-Ludwigs University of Freiburg, Freiburg im Breisgau, Germany

Submitted By

Tamalika Chakraborty

Supervisor: Prof. Dr. Dr. h. c. Albert Reif, Institute of Silviculture, Albert-Ludwigs University of Freiburg Co- supervisor: Prof. Dr. Siegfried Fink, Institute of Forest Botany and Tree Physiology, Albert-Ludwigs University of Freiburg November, 2010 Freiburg im Breisgau, Germany

I hereby declare that I have completed the present thesis without external aid, using only the sources and materials indicated and that I have not submitted the document in question as a master thesis elsewhere.

---------------------------------------------------------Tamalika Chakraborty Date: 29.11.2010

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SUMMARY European beech (Fagus sylvatica L.) is the most important broad-leaved tree species in Central and Western Europe. Beech juveniles have been found to be particularly sensitive to soil drought. The growth and the survival of beech juveniles are influenced by water availability. This thesis aimed to find the effect of soil drought on tree vitality and growth for beech juvenile and understorey. Semi-natural oak stand surrounded with beech stands in Schlossberg near Freiburg in southwestern Germany was selected for the study. Crown dieback or branch mortality, expressed as the percentage of dead above ground biomass (stems, branches and leaves), was used as a measure of tree vitality. The pattern of branch mortality distribution was recorded in different vertical parts of the crown. Biomass was calculated from the regression models, prepared from the harvested samples. Both height and lateral increment were measured for calculating growth. Tree-ring analysis was performed to calculate lateral increment, expressed by basal area increment (BAI), with harvested tree discs. Soil drought was quantified by calculating available soil water storage capacity (ASWSC). A significant negative strong correlation between soil drought and tree vitality was found. Branch mortality threshold was found 40%. In the stand having 67.38 mm mean ASWSC. Highest branch mortality (42%) was noticed in the lower crown. Significant difference was found between height and length of the trees proving stunted growth. ‘2003 summer drought’ had significantly high adverse effect on basal area increment. At the drought limit of beech, drought causes partial up to complete crown dieback of beech individuals. Particularly extreme dry year is resisting the growth and survival of beech. Soil drought together with extreme summer drought impedes the establishment of beech trees in semi-natural oak forests.

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ACKNOWLEDGEMENTS

I am very grateful for all the moments I experienced because of this thesis, I would like to thank all those people who have shared their knowledge and provide me time during this thesis period. I would like to express my deepest gratitude to my supervisor Prof. Dr. Dr. h. c. Albert Reif. He was the starting point of this project and always present with his patience, support and guidance. I would like to thank my co-supervisor Prof. Dr. Siegfried Fink for reading this thesis and giving helpful comments. Special thanks go to Carl Burhop for helping me in the field, Mr. Dieter Thoma, Revierleiter of Forstrevier Roßkopf for his helpful suggestions and all others who helped me during this project. I want to thank Dr. Karl Hocke and Renate Nitschke, for their advice. My warmest thanks go to my husband Somidh, for his never ending support and patience for handling difficulties in my life. Without him this could have not been possible. I would like to thank my parents and sister for the support they provided me through my entire life, especially when I decided to continue my education abroad. I am also thankful to all of my friends from the M.Sc. course and from the Institute of Silviculture especially Dimitris, Cristabel, Patrick, Jan and Pifeng for providing remarkable and entertaining moments over the last two years. This research would not have been possible without the financial support of the Heinrich Böll Stiftung e.V.

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TABLE OF CONTENTS Declaration..................................................................................................................................... I Summary ..................................................................................................................................... II Acknowledgements ................................................................................................................... III Table of Contents ...................................................................................................................... IV List of Figures ........................................................................................................................... VII List of Tables ............................................................................................................................. IX Overview of Thesis ...................................................................................................................... X Acronyms and Abbreviations .................................................................................................. XI 1. INTRODUCTION......................................................................................................................1 1.1 Background and state of knowledge ...................................................................................1 1.1.1 Central European beech forest.........................................................................................1 1.1.2 Drought and its consequences ........................................................................................1 1.1.3 Response of beech to different stress .................................................................................... 2 1.1.4 Drought and vitality of beech ..........................................................................................3 1.1.5 Drought and growth of beech .........................................................................................3 1.2 Study Objective ...................................................................................................................4 1.3 Research questions ..............................................................................................................5 1.3.1 Research question one .....................................................................................................5 1.3.2 Research question two .....................................................................................................5 2. METHODOLOGY ....................................................................................................................6 Flowchart of the methodology ..................................................................................................6 2.1 Study area ............................................................................................................................7 2.1.1 Climate and geology ........................................................................................................7 2.1.2 Stand description and management history .....................................................................8 2.2 Sampling design and plots establishment for field data collection .................................9 2.2.1 Plot design for the soil, tree growth and morphological data ........................................11 2.2.2 Plot design for the biomass and tree-ring analysis data ...............................................11 2.3 Data collection, data generation and database preparation ..........................................12 2.3.1 Spatial and vegetation data ............................................................................................13 2.3.2 Morphological and growth parameters ..........................................................................13 2.3.3 Soil data .........................................................................................................................14

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2.3.3.1 Primary soil data collection ...................................................................................14 2.3.3.2 Secondary soil data generation ..............................................................................15 2.3.4 Inventory database ........................................................................................................16 2.3.5 Biomass study................................................................................................................16 2.3.5.1 Sample collection and preparation for biomass calculation .................................17 2.3.5.2 Regression models for calculating biomass ..........................................................17 2.3.5.3 Average biomass calculation of the annual shoots ...............................................18 2.3.5.4 Secondary database generation .............................................................................19 2.3.6 Tree-ring analysis ..........................................................................................................20 2.3.6.1 Sample collection and preparation for tree-ring analysis .....................................21 2.3.6.2 Secondary database generation .............................................................................21 Calculation of age, BAI and mBAI for the harvested juvenile and understorey beeches ...................................................................................................................21 Calculation of age and mBAI for the living juvenile and understorey beeches of the inventory database............................................................................................22 2.4 Data analysis ......................................................................................................................24 2.4.1 Calculation of branch mortality to measure tree vitality for juvenile and understorey beeches in the inventory database .........................................................................................24 2.4.2 Relation between tree vitality and soil drought .............................................................24 2.4.3 Significance testing of tree vitality differences between dry and less dry plots ...........24 2.4.4 Branch mortality pattern and relation with tree height, diameter and age ...................25 2.4.5 Relation between soil drought and tree height growth ..................................................25 2.4.6 Relation between mBAI and soil drought .....................................................................25 2.4.7 Significance testing of mBAI between dry and less dry plots.......................................25 2.4.8 Effect of ‘2003 summer drought’ on BAI of juvenile and understorey beeches ..........26 3. RESULTS ................................................................................................................................27 3.1 Relation between soil drought and tree vitality....................................................................27 3.2 Difference of tree vitality between dry and less dry plots ...................................................30 3.3 Pattern of branch mortality in different vertical parts of tree crown ...................................33 3.4 Relating rate of branch mortality with tree height, stem diameter and age .........................34 Albert-Ludwigs University of Freiburg

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3.5 Effect of soil drought on tree height growth .......................................................................39 3.6 Relation between soil drought and mean basal area increment (mBAI) ..............................41 3.7 Difference of mBAI of the trees between dry and less dry plots ........................................42 3.8 Effect of ‘2003 summer drought’ on basal area increment .................................................46 4. DISCUSSION ..........................................................................................................................49 4.1 Soil drought effects on the beech vitality ............................................................................49 4.2 Threshold of branch mortality and ASWSC to survive drought stress ...............................50 4.3 Variation in growth with soil drought .................................................................................50 4.4 Decline of growth due to ‘2003 summer drought’ ..............................................................51 4.5 Soil drought effects on the establishment and survivability of beech .................................51 4.6 Future threat for the beech ...................................................................................................52 5. CONCLUSION .......................................................................................................................53 References ................................................................................................................................... 54 Annexure 1 ................................................................................................................................... 61 Annexure 2 ....................................................................................................................................62 Annexure 3 ....................................................................................................................................63 Annexure 4....................................................................................................................................64 Annexure 5.1.................................................................................................................................65 Annexure 5.2.................................................................................................................................66 Annexure 5.3.................................................................................................................................67 Annexure 5.4.................................................................................................................................68 Annexure 6.1.................................................................................................................................69 Annexure 6.2.................................................................................................................................70 Annexure 7....................................................................................................................................70 Annexure 8....................................................................................................................................72

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List of Figures Figure 2.1 Flowchart of the methodology ...................................................................................6 Figure 2.2 Map locating the study area .......................................................................................8 Figure 2.3 Schematic design of the systematic sampling plots in the study area .....................10 Figure 2.4 Schematic design of circular sampling plot for the soil, tree growth and morphological data collection ...................................................................................................11 Figure 2.5 Schematic design of circular sampling plot for the biomass and tree-ring analysis data collection ...........................................................................................................................12 Figure 2.6 Reference figure for denoting three crown compartments for the assessment of proportions of dead branches ....................................................................................................14 Figure 2.7 Regression models for calculating biomass of stem and perennial shoots ..............18 Figure 2.8 Regression model for reconstructing dead tree biomass ..........................................20 Figure 2.9 Regression models for calculating tree age .............................................................22 Figure 2.10 Regression models for mBAI calculation ..............................................................23 Figure 3.1.A Tree vitality or rate of branch mortality (%) and ASWSC (mm) in the inventory trees ...........................................................................................................................................27 Figure 3.1.B Relation between tree vitality or rate of branch mortality (%) and ASWSC (mm) in the trees .................................................................................................................................28 Figure 3.1.C Tree vitality or rate of branch mortality (%) and ASWSC (mm) in the plots ......28 Figure 3.1.D Relation between tree vitality or rate of branch mortality (%) and ASWSC (mm) at the scale of plots ....................................................................................................................29 Figure 3.1.E Inventory trees with their rate of branch mortality (%) and ASWSC (mm) ........29 Figure 3.1.F Branch mortality threshold (red arrow) of the inventory trees .............................30 Figure 3.2.A Overview of the plant condition of the sampling plots ........................................31 Figure 3.2.B Percentage of live and dead biomass parts in the trees on dry and less dry plots .31 Figure 3.2.C Median and range of branch mortality rates (%) of trees on dry and less dry plots at the scale of trees ...................................................................................................................32 Figure 3.2.D Median and range of branch mortality rates (%) of trees on dry and less dry plots at the scale of plots ....................................................................................................................32 Figure 3.3.A Pattern of branch mortality in different vertical crown compartments of the stand ....................................................................................................................................................33 Figure 3.3.B Pattern of branch mortality in different vertical crown compartments of the dry and less dry plots ........................................................................................................................34

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Figure 3.4.A Relation between rate of branch mortality (%) in the stand with height, collar diameter and age of the living inventory trees ...........................................................................35 Figure 3.4.B Relation between rate of branch mortality (%) in the upper crown with height, collar diameter and age of the living inventory trees ................................................................36 Figure 3.4.C Relation between rate of branch mortality (%) in the middle crown with height, collar diameter and age of the living inventory trees ................................................................37 Figure 3.4.D Relation between rate of branch mortality (%) in the lower crown with height, collar diameter and age of the living inventory trees ................................................................38 Figure 3.5.A Relation between tree height (cm) and ASWSC (mm) of the inventory trees ......40 Figure 3.5.B Median and range of height and length values (cm) of trees on the stand ............40 Figure 3.6.A ASWSC (mm) and mBAI (mm2/yr) of the inventory trees .................................41 Figure 3.6.B Relation between mBAI (mm2/yr) and ASWSC (mm) of the inventory trees .....42 Figure 3.7.A mBAI (mm2/yr) of the harvested trees on dry and less dry plots .........................43 Figure 3.7.B mBAI (mm2/yr) of the inventory trees on dry and less dry plots .........................43 Figure 3.7.C Mean mBAI (mm2/yr) on of the harvested trees dry and less dry plots ...............44 Figure 3.7.D Mean mBAI (mm2/yr) of the inventory trees on dry and less dry plots ..............44 Figure 3.7.E Median and range mBAI (mm2/yr) of the harvested trees on dry and less dry plots ....................................................................................................................................................45 Figure 3.7.F Median and range mBAI (mm2/yr) of the inventory trees on dry and less dry plots ....................................................................................................................................................45 Figure 3.8.A Trend of growth based on BAI (mm2/yr) of the harvested trees on dry and less dry plots ......................................................................................................................................47 Figure 3.8.B Mean of 2003 and 2004 BAI (mm2/yr) of the harvested trees on dry and less dry plots ............................................................................................................................................47 Figure 3.8.C Median and range of 2003 and 2004 BAI (mm2/yr) of the harvested trees on dry and less dry plots ........................................................................................................................48

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List of Tables Table 2.1 Geographic and site description of the study area ......................................................7 Table 2.2 Classification table of ASWSC after Arbeitskreis Standortkartierung (2003) with total number of sampled plots found in each class ....................................................................16 Table 2.3 Different branch harvesting groups according to diameter class: (2-41 mm) ............17 Table 2.4 Biomass regression model testing ..............................................................................18 Table 2.5 Calculation of annual shoots’ average biomass (in gm) from three different crown compartments for ‘dry’ and ‘less dry’ plots ..............................................................................18 Table 2.6 Regression model-testing for dead tree simulation ...................................................20 Table 2.7 Different tree harvesting groups according to collar diameter class: (3-41 mm).......21 Table 2.8 Age (year) regression model testing .........................................................................23 Table 2.9 mBAI (mm2/yr) regression model testing .................................................................24 Table 3.1 Descriptive statistics of ASWSC (mm) of the stand .................................................30 Table 3.2 Correlations of rate of branch mortality (%) with tree height, stem diameter and age ....................................................................................................................................................39 Table 3.3 Correlations of rate of branch mortality (%) in different crown compartments with tree height, stem diameter and age ............................................................................................39 Table 3.4 Descriptive statistics of mBAI (mm2/yr) of the stand ...............................................41 Table 3.5 Descriptive statistics of mBAI (mm2/yr) of the harvested trees on dry and less dry plots ...........................................................................................................................................46 Table 3.6 Descriptive statistics of mBAI (mm2/yr) of the inventory trees on dry and less dry plots ...........................................................................................................................................46 Table 3.7 Descriptive statistics of 2003 and 2004 BAI (mm2/yr) of the harvested trees on dry and less dry plots .......................................................................................................................46

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OVERVIEW OF THE THESIS Aim of this research is to find the effect of soil drought on juvenile and understorey beech morphology based on the vitality and growth in a semi-natural oak forest stand surrounded with beech stand in Schlossberg, near Freiburg city in southwestern Germany. The thesis is presented in five chapters. First chapter is the introduction which is sub-divided in three main sections. First is the background with context of the project. The problem statement along with the present knowledge state has been described here with literature review. Second is the project objective. Here the current knowledge gap has been recognized describing how this project could fill up that gap. The last part concludes with the research questions. Second chapter is the methodology which is sub-divided in four main sections. First is the description of the study area. Second is the sampling design where the sampling technique has been described. Third is the data collection; how data had been collected, and measured in the field is described here. Method of calculation and preparation of secondary or derived dataset in the laboratory are also described. And the last part is specifying the statistical methods for analyzing the data. Third chapter is the result. Results have been described according to the research questions. In total, eight research questions are answered using graphs, charts and tables. Fourth chapter is the discussion that relates the study with existing literatures by establishing arguments. Fifth chapter concludes the study by highlighting the key findings and proposing the direction for the future research.

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ACRONYMS AND ABBREVIATIONS • • • • • • • • • • • • • • • • • • • • • • • • •

AGB- above ground biomass Alt. - altitude Annex.- annexure ASWSC- available soil water storage capacity BAI- basal area increment C - centigrade cm- centimeter Dt. - date E – east FAO – Food and Agriculture Organization, Rome Fig.- figure gm - gram m- meter m a.s.l.- meter above sea level mBAI- mean basal area increment mm- millimeter mm2/yr - square millimeter per year N – north no. - number Lat. - latitude Lon. - longitude SOM - soil organic matter Vol. – volume Yr - year % - percentage

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Introduction

1. INTRODUCTION 1.1 Background and state of knowledge 1.1.1

Central European beech forest

European beech (Fagus sylvatica L.) is the most important broad-leaved tree species in Central and Western Europe; also in vegetation classification of Europe this tree share a major role in this century (Peters, 1997). This shade tolerant species often forms pure forest with little or no undergrowth in Europe (Ellenberg, 1996). On the relatively poor sandy soils beech dominates the forest stand (Ellenberg, 1996). However, human manipulation has been a large factor in determining the European beech forest (Peters, 1997). Since the medieval epoch, many forests with European beech were converted into agricultural land, and later into coniferous forests, particularly in its north-eastern range, northeastern Germany and northern Poland, where mainly Scots pine plantations replaced many European beech and mixed deciduous forests (Ellenberg, 1988). But a ‘renaissance’ of European beech can be observed in recent years when Scots pine forests were converted back to pure and mixed deciduous forests (Fritz, 2006), and as European beech spread more extensively throughout its eastern range spontaneously (Peters, 1997). Beech forests cover a large geographic area with wide range of climate zones and soil types (Peters, 1997). Regions with moderately warm summers and high quantities of precipitation are marked as the best beech growing areas (Peters, 1997; Kolling and Zimmermann, 2007). The optimum distribution of this tree is found in areas with mean annual air temperature between 7 and 10°C (Seletkovic et al., 2009) but this tree is able to tolerate mean annual temperature up to 14°C if sufficient water is available (Peters, 1997; Kolling and Zimmermann, 2007). 1.1.2

Drought and its consequences

Thornthwaite (1948) defines drought as a condition in which the amount of water needed for transpiration and direct evaporation exceeds the amount available soil moisture. Drought depends on hydraulic soil properties as well as on climatic features; is a complex edaphicclimatic factor (Gartner et al., 2008). According to the “relative site constancy” theorem (Walter and Walter, 1953), soil properties and climatic features can interact and increase their effect, or compensate each other in way that the result of drought intensity will be similar in sites reflecting different climatic features and soil properties. Drought is defined as the ‘absence of rainfall for a period of time long enough to result in depletion of soil water and injury to plants’ (Kramer, 1983). Drought occurs when demand exceeds the supply of water and is a constraint that limits tree growth not only in arid but often also in humid climates (Gower et al., 1992; Becker et al., 1994). Drought results in the reduction of soil water availability and inhibits a number of tree functions and restricts tree establishment (vanHees, 1997; Lof et al., 2005; Breda et al., 2006) . The ability of trees to function under drought stress depends on various physiological and morphological traits (Bréda et al., 2006).

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Introduction

The most important among them are the capacity of the roots to guarantee water uptake in drying soils and the ability to continue leaf growth during drought periods (Kozlowski and Pallardy, 2002). Reduced precipitation during the growing season shows higher frequency of summer drought in the Central Europe (IPCC, 2007). In last 40 years, the temperature increased almost 2°C in the southern part of Germany causing more summer droughts (Mayer et al., 2005). 2003-2004 weather data from Freiburg Meteorological Station show an acute drought in summer 2003 with most severe occurrence in June and August and extended to June 2004 (Rebetez et al., 2006). In the southern part of Central Europe, the summer of 2003 was exceptionally severe with aboveaverage insolation, elevated daily mean temperatures, and below average precipitation with reduced net primary production of the beech forest (Ciais et al., 2005). 1.1.3

Response of beech to different stress

In permanent beech plots in southern Germany, strong positive deviations from expected growth since 1940s were reported (Pretzsch, 1999). In different regions of central Europe, especially at the higher altitude sites, strong crown damages and abrupt dieback accompanied by growth depressions due to different environmental stresses in the 1970s and 1980s were reported (Schutt and Summerer, 1983; Eckstein et al., 1984; Dittmar et al., 1997). Drought sensitivity is assumed to be a key factor limiting the range of beech in southern and southeastern Europe (Aranda et al., 1996; Backes and Leuschner, 2000). Climate properties like high air temperature and low precipitation influence the crown condition like defoliation and dieback of beech severely (Potocic et al., 2008; Seletkovic et al., 2009). Some authors hypothesize that the expansion of beech in Central Europe in the Holocene was triggered by a climatic change towards wetter and cooler conditions (Tinner and Lotter, 2006) and the future climatic factors may have an important role to the distribution of beech (Sabate et al., 2002; Jump et al., 2006; Gessler et al., 2007; Allen et al., 2010). Many ecophysiological studies involving beech in laboratories and experimental settings simulated changing climate and showed that with increasing frequency of drought, growth and competitiveness of this species were reduced (Gessler et al., 2004; Gessler et al., 2007). Beech juveniles have been found to be sensitive to soil drought (Bolte and Roloff, 1993; Lof and Welander, 2000; Kolling and Zimmermann, 2007) particularly when they are facing competition with the mature trees and the forest floor vegetation for soil water and nutrients (Lof, 2000; Collet and Chenost, 2006; Collet and Le Moguedec, 2007; Lendzion and Leuschner, 2008). The growth and the survival of beech juveniles is influenced by water availability (Fotelli et al., 2001; Lendzion and Leuschner, 2008), inter-species competition (Nakashizuka, 1987; Peters et al., 1992; Yamamoto et al., 1995; Collet and Chenost, 2006) and intra-species competition between different age groups (Collet and Le Moguedec, 2007) influence. Severe crown dieback with 30 to 70% foliage and side-branch loss due to ‘2003 summer drought’ on understorey beech near its drought limit in the southern Germany has been recorded (Kohler et al., 2006).

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Introduction

However, beech is one of the most shade tolerant species; juveniles of this species can survive about 1 % of daylight (Peters, 1997). This species would dominate central European temperate forest because of its high physiological tolerance and competitiveness (Ellenberg et al., 1992; Ellenberg, 1996; Bohn, 2004). In Bavaria, beech achieves its best height growth in the warmest region on deep soil (Felbermeier, 1994). Beech was found highly water-stress tolerant having less effect on growth increment (Dittmar et al., 2003). Beech can dominate sites with low water storage capacity (Gartner et al., 2008). These different conclusions reveal that the drought limit and competitiveness of beech in the central Europe still is a controversial issue (Ammer et al., 2005). 1.1.4

Drought and vitality of beech

The vitality of a plant is a theoretical concept and one of the most important indicators of forest condition (Innes, 1993). Random house Webster’s College Dictionary (Webster, 1992) defines vitality as ‘the capacity for survival, power to live or grow’. Shigo (1990) expressed ‘vitality’ as ‘the ability to grow under the conditions present’. Brang (1998) defines tree vitality as ‘the capacity of an organism to assimilate carbon, to resist stress, to adapt to changing environmental conditions and to reproduce’. Vitality becomes weaker as stress persists (Larcher, 2001). Vitality is impossible to measure directly, so, various indicators like crown foliage and morphology, tree stem growth etc. are used to measure it (Dobbertin, 2005). Drought tolerance of plants can be attributed to their ecophysiological and morphological adjustments with the environment (Kozlowski, 1982; Givnish, 1988) and different age classes may differ in drought tolerance (Grubb, 1977). Morphological plasticity of plants has been studied primarily in leaves and roots and an important feature of drought tolerance (Osonubi and Davies, 1981; Eschrich et al., 1989; vanHees, 1997) Above ground dry biomass is a good indicator for measuring beech tree vitality (Vyskot, 1991). Drought affects biomass distribution within plants and thus plants morphology (vanHees, 1997). Beech shows different morphological responses like reduced shoots biomass and height, and decreasing leaf area resulting in whole-plant productivity loss (Aranda et al., 1996; Fotelli et al., 2001; Czajkowski et al., 2005; Meier and Leuschner, 2008) due to soil drought. 1.1.5

Drought and growth of beech

The growth of beech is highly dependent on soil water availability (Peters, 1997). Growth and competitive ability of beech is strongly affected by a long duration and high frequency of summer droughts (Gessler et al., 2007). Drought as the consequence of soil water deficit reduces beech growth increment (Czajkowski et al., 2005). Tree-ring analysis is an important tool to reflect environmental phenomena on tree growth and vitality (Breda et al., 2006). Variation in tree growth due to environmental impacts like drought

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Introduction

results in variation in the annual growth ring width that is laid down during each growing season. Age determination of standing trees can indicate climatic variables that are responsible for variation in the frequency of sapling establishment (Gervais and MacDonald, 2000; Daniels and Veblen, 2004). This method can integrate positive and negative environmental influences affecting the growth of a tree during the current and previous years (Eckstein, 2004). Tree-ring analysis can be used to estimate survival probabilities for the entire lifespan of trees (Bigler and Bugmann, 2004). This is a suitable technique to study growth trend in beech (Dittmar et al., 2003; Jump et al., 2007). However, ring width in mature trees declines with age. Thus, if a declining growth trend is suspected, it may be impossible to investigate it on the basis of changes in ring width alone. The conversion of ring width into basal area increment (Garcia-Suarez et al., 2009) overcomes this problem. Unlike ring width, age-related trends in unstandardized BAI are generally positive, culminating in a linear phase of high mature BAI that can be maintained for many decades (Phipps and Whiton, 1988; Leblanc, 1990b). Therefore, a negative trend in BAI is a strong indication of a true decline in tree growth (Leblanc, 1990b; Pedersen, 1998). 1.2 Study objective Numbers of ecophysiological studies show how drought and other site conditions affect the growth and survivability of beech. Most of the studies have done in laboratory conditions (Ponge and Ferdy, 1997; vanHees, 1997; Fotelli et al., 2001; Coll et al., 2004; Lof et al., 2005; Lendzion and Leuschner, 2008), or in the experimental plots underlying treatments (Coll et al., 2003; Collet and Le Moguedec, 2007; Robson et al., 2009). Very few studies were performed in naturally regenerated forest floor at local and stand level (Topoliantz and Ponge, 2000; Kohler et al., 2006; Gartner et al., 2008). However, more research is needed to relate soil drought with crown dieback as tree vitality indicator and growth as tree establishment factor for beech juveniles and understorey. Given the global warming trend in Europe and the associated potential changes in water supply and the consequences for beech establishment and survivability, this study aimed to find the effect of soil drought on tree vitality and growth for beech juvenile and understorey. Seminatural oak stand surrounded with beech stands in Schlossberg near Freiburg in southwestern Germany was selected for the study. In this case study, crown dieback or branch mortality, expressed as the percentage of dead above ground biomass (stems, branches and leaves), was used as a measure of tree vitality. The pattern of branch mortality distribution was recorded in different vertical parts of the crown. Both height and lateral increment were considered for growth measurement. Lateral growth was expressed by basal area increment (Garcia-Suarez et al., 2009) for each tree.

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Introduction

1.3 Research questions This study focused on two main research questions divided in respective sub-questions: 1.3.1

Research question one

What is the relationship between soil drought and vitality in juvenile beech? •

Sub-question one How is crown dieback or branch mortality changing with increasing soil drought?



Sub-question two What is the difference of branch mortality between the dry and less dry plots?



Sub-question three How changes branch mortality in different vertical parts of the juvenile beech crowns?



Sub-question four How is branch mortality changing with tree height, collar diameter and age?

1.3.2

Research question two

What is the effect of soil drought on growth of juvenile beech? •

Sub-question five What is the effect of soil drought on height growth of juvenile beech?



Sub-question six How is mean basal area increment changing with increasing soil drought?



Sub-question seven What is the difference of the basal area increment between dry and less dry plots?



Sub-question eight What is the effect of ‘2003 summer drought’ on basal area increment in juvenile beech?

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Methodology

2. METHODOLOGY Flowchart of the methodology

Selection of a study area: Sessile oak stand consisting regenerating beech with scattered juveniles was selected.

Establishment of plots using systematic sampling design for data collection: 24 plots were established in approximately 0.3 hectare forest stand.

Data collection, data generation and database preparation: This part of study was done in three following consecutive steps:

First step: • Tree growth and morphological data collection from the plots. • Primary soil data collection and secondary soil data generation in the laboratory • Inventory database preparation.

Second step: • Tree harvesting from the plots f or biomass study. • Calculation of above ground biomass • Secondary database preparation.

Third step: • Tree harvesting from the plots f or tree-ring analysis. • Calculation of tree age and basal area increment. • Secondary database preparation.

Data analysis: Statistical analyses were d one to find out the effect of soil drought on tree vitality and growth.

Fig. 2.1 Flowchart of the methodology

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Methodology

2.1 Study area The study was conducted in Schlossberg (latitude: 47º 59'38" N, longitude: 7º 51'40" E), located near the Freiburg city of the Black Forest mountain range of southwestern Germany (Fig. 2.2). Criteria for selecting the study area were the following: • •

Sessile oak stand surrounded by beech forest with natural regeneration of beech. Rocky outcrop having gneiss as bedrock, low water storage capacity and shallow soil.

2.1.1 Climate and geology A temperate broad-leaved stand of sessile oak (Quercus petraea (Matt.) Liebl.) surrounded by European beech (Fagus sylvatica L.) stand at the southwest facing rocky slope was chosen as the study area for data collection for this case study. Data were collected during the summer (MayJuly) of 2010. The study area has temperate climate. Climatic and geology of the study area are described after Gauer and Aldinger (2005) (Table 2.1). Name of the growth zone ( in German ‘Wuchsgebiet’)

Black Forest

Name of the growth district (in German ‘Wuchsbezirk’) Central Black Forest between Kinzig and Dreisam Name of the forest district

Rosskopf

Elevation of study area (m a.s.l.)

~ 385 – 435 m a.s.l.

Elevation zone

Submontane

Slope

24 - 43 degrees

Mean annual air temperature1

9-10 ° C

Mean annual precipitation1

800-1000 mm

Mean temperature during vegetation period1

15-16 ° C

Mean precipitation during vegetation period1

450-500 mm

Days per year having temperature > 10 ° C1

>170 days

Underlying bedrock

Gneiss

1

= after Gauer and Aldinger (2005) Table 2.1 Geographic and site description of the study area

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Study area

Freiburg city in BadenWürttemberg

Baden-Württemberg state in Germany

Fig. 2.2 Map locating the study area (Source: http://www.goyellow.de) 2.1.2 Stand description and management history Major area of the stand is occupied by big sessile oaks surrounded by beech trees. Few dead Scots pines (Pinus sylvestris L.) are found there (see Photo 1 of Annex. 8), indicating previous silvicultural attempts of tree species conversion. Few sweet chestnuts (Castanea sativa Mill.), European hornbeams (Carpinus betulus L.) and hazelnuts (Corylus avellana L.) are also found in the stand. The stand has natural regeneration of beech and European hornbeam with seedlings of oak, Sweet chestnut and Scots pines. Less understory vegetation was found during the field work, but in some areas forest floor is covered with the typical acidic forest floor flora, including: grass (Deschampsia flexuosa L.); wood-rusts (Luzula luzuloides (Lam.) Dandy et Wilmott, Luzula sylvatica (Huds.) Gaudin); herbs (Melampyrum pretense L., Veronica officinalis L., Teucrium scorodonia L., Hieracium sabaudum L., Hieracium sylvaticum L.); common honeysuckle (Lonicera periclymenum L.); fern (Polypodium vulgare L.) and acidophilous moss species. Oaks are standing with crooked stem and branches, some of them showing crown dieback and dry branches (see Photo 1 of Annex. 8). Few young beech trees (above 3 meter height) show either high crown dieback with stunted growth and some are standing dead (see Photo 1 of Annex. 8). Young and juvenile beeches (below 3 meter height) have partial crown dieback (see

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Photo 1 of Annex. 8). The stand at south-west facing slope is getting high exposure to sunlight throughout the whole vegetation period. ‘Hagerhumus’ type humus (Arbeitskreis Standortkartierung, 2003) shows a patchy thin (0.5 to 1.5 cm) or no humus layer, bare mineral soil surface or exposed bedrock with a moss cover. The stand grows on nutrient poor shallow sandy soils with low available soil water storage capacity. The ground water table is far below the rooting horizon as the stand is on a high slope. The area has high recreation value and remains commercially unmanaged since last decades. All of these above mentioned stand situations and arguments hypothesized that the stand is under a gradient of drought conditions. 2.2 Sampling design and plots establishment for field data collection Plots were established in the stand with following assumptions: • • •

Negligible variation in environmental conditions (exposition, inclination and irradiance). Negligible variation in below and above ground competition between young beech trees and other vegetation for nutrients and light. Gradient of soil depth and height distribution of young beech.

The approximate size of the oak stand being the study area is 0.3 hectare (70 m X 32 m X 55 m X 44 m; without slope correction) and with four corner points namely A, B, C and D (Fig. 2.3). The coordinates of the corner points were recorded (Table 1 in Annex. 1). The stand is situated in a rocky outcrop on a slope. The canopy is formed by sessile oak and contains few naturally regenerating young beech saplings and understorey trees. The height distribution of the young beech is heterogeneous. Site parameters like soil depth and soil water storage capacity differ within the stand. A systematic sampling design was chosen for this study. A 50 x 30 m sized rectangle with four corner points namely E, F, G and H was chosen from the central zone of the study area to establish the plots. The coordinates of four corner points were recorded (Table 2 in Annex.1). A systematic grid of 4 horizontal transects and 6 vertical transects with the distance of 10 m apart with each other was implemented. The first horizontal line running between E and F points was shifted 2 m apart from the original AB line of the study area to avoid the influence of the hiking trail below the study area. Thereafter 3 other horizontal transects were established keeping a vertical distance of 10 m apart from each other following uphill the stand. In total 24 sampling points with vertical and horizontal distances of 10 m between each were determined (Fig. 2.3).

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D

55 m C

44 m

A

32 m

B

70 m 10 m

H

G 10 m

30 m

E

50 m

F

Circular sampling plot with centrally located sampling point

Fig. 2.3 Schematic design of the systematic sampling plots in the study area

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2.2.1 Plot design for the soil, tree growth and morphological data For each sampling plot four quadrates were established through a cross formed by two lines. One line is the compass direction and the second line running perpendicular to the compass direction line through the sampling point. Each line was 4 meter long, running 2 meter distance from the sampling point at each of four directions. Finally a circular plot of 2 meter radius with four quadrates numbered as 1, 2, 3 and 4 was established. Distance between two circular plots was 6 m in this case (Fig. 2.4). Soil samples were collected from the established sampling points of each circular plot. All individuals of young beech (saplings and juveniles up to 250 cm height) within the quadrates of each circular plot were measured.

1

2

2m

10 m

6m

2m

2m

3

4 2m

6m Sampling point

10 m

Fig. 2.4 Schematic design of circular sampling plot for the soil, tree growth and morphological data collection. Each plot has one sampling point and four quadrates, numbered as 1, 2, 3 and 4. Radial distance of each plot is 2 m.

2.2.2 Plot design for the biomass and tree-ring analysis data The design of the plots for the biomass and tree-ring analysis data collection was similar like for the recording of the soil and tree morphological data. But the radial distance of the circle had been changed here by increasing the radius of each plot. Each line was 10 meter long running 5 meter distance from the sampling point at each of four directions. Finally a circular plot of 5 meter radius with four quadrates numbered as 1, 2, 3 and 4 was established. So each circular plot was touching the edge of the next one both horizontally and vertically (Fig. 2.5). Samples were collected within the quadrates of each circular plot for biomass study and tree-ring analysis.

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1

2

5m

10 m

5m

5m

3

4 5m

10 m

Sampling point

Fig. 2.5 Schematic design of circular sampling plot for the biomass and tree-ring analysis data collection. Each plot has one sampling point and four quadrates, numbered as 1, 2, 3 and 4. Radial distance of each plot is 5 m.

2.3 Data collection, data generation and database preparation • • • • • • • • • • • •

Primary data was collected from the field; secondary data was generated in the laboratory. A database was prepared with primary and secondary data in consecutive steps. Spatial and vegetation data of the sampling plots were collected. Morphological and growth parameters were recorded of each juvenile and understorey beech (below 250 cm height). Soil data were collected from the soil profile in each sampling point from the plots. Available soil water storage capacity (ASWSC) was calculated based on soil depth, soil texture, and skeleton content in the laboratory for all plots. All plots were classified to two plot groups: ‘dry’ and ‘less dry’ on the basis of ASWSC. An inventory database was prepared having morphological and growth parameters of each juvenile and understorey beech, ASWSC and spatial data. Samples including stems, branches and leaves from 19 trees were collected to measure ex situ above ground biomass (AGB). AGB was calculated by weighting collected and oven-dried samples in the laboratory. 44 trees were harvested for the tree-ring analysis. The age of the beech saplings in the understorey was assessed by counting tree rings, the increment growth by measuring the ring widths in the laboratory using the harvested trees. Based on the tree-ring data, biomass – tree age relationship was quantified for two groups of plots.

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2.3.1 Spatial and vegetation data Instruments GPS: 12 CX Garmin, field compass, clinometers, yard stick and measuring tape, datasheet for spatial and vegetation data of plots (Data sheet no. 1 in Annex. 2), datasheet for spatial data of trees (Data sheet no. 2 in Annex. 3) Geographical coordinates (latitude, longitude and altitude); aspect and inclination of 24 sampling plots were recorded at each of 24 sampling points. Also geographical coordinates and position i.e. the quadrate number of the respective circular plot and the distance from the respective Cartesian X - Y axis were measured for all juvenile and understorey beech trees (≤ 2.5 m height) standing within the sampling plots. Type of woody vegetation other than beech was recorded for all sampling plots. 2.3.2 Morphological and growth parameters Data for morphological and growth parameters were collected of all young (juvenile and understorey) beech trees between 30 cm to 250 cm height in the sampling plots. In total 47 individuals (42 live and 5 dead) were found in this category in plots and measured during field data collection. Instruments Slide-calipers, clinometers, measuring tape, yard stick, and data sheet for individual tree measurement (Data sheet no. 2 in Annex. 3). Parameters collected for all living juvenile and understorey beeches •

Height was measured using an yard stick, vertically placed along plant axis from the plant base (Fig. 1 in Annex. 4 );



Length was measured using an yard stick from the ground to the longest shoot tip;



Root collar diameter was measured at 5 cm above ground level;



Crown diameter calculated from the length of four radial measurements along the four different directions north, east, south and west crossing each other through the plant axis;



Diameter of all branches (>1.5 mm) and number of all branches (< 1.5 mm diameter) were measured, counted and recorded;



Location of dead branches was recorded to find out the most affected crown dieback compartment. The crown was equally divided of upper, middle and lower part (Fig. 2.6).

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Parameters collected for all dead juvenile and understorey beeches •

Height (same as for living individuals);



Length (same as for living individuals);



Root collar diameter (same as for living individuals);

Tip of the crown Upper crown Middle crown Lower crown

First green branch

Fig. 2.6 Reference figure for denoting three crown compartments for the assessment of proportions of dead branches 2.3.3 Soil data Soil drought was quantified by calculating ASWSC in mm at each sampling point after Arbeitskreis Standortkartierung (2003). This was performed in two following steps: 2.3.3.1 Primary soil data collection Instruments and literatures Spade, hand spade, knife, water spray bottle, Munsell ® Soil Color Charts (Munsell, 1994), yard stick, plastic bags for soil sample collection, datasheet for soil data collection (Data sheet no. 3 in Annex. 5.1).

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Soil data were collected from the sampling plots to measure ASWSC. 24 soil profiles were systematically selected, prepared and analyzed for each sampling point. Profiles were dug until they touched the bedrock (see Pic.2 of Annex. 8). The different horizons were designated. Soil depth and percentage of soil skeleton of each mineral horizon were measured. Both dry and moist soil colors were recorded for each mineral horizon using Munsell ® Soil Color Charts (Munsell, 1994). Finally soil samples were collected from each mineral horizon of each soil profile for texture analysis in the laboratory. 2.3.3.2 Secondary soil data generation Instruments and literatures Collected soil samples from the field, 2 mm sieve, mortar and pestle, water, Arbeitskreis Standortkartierung (2003), soil texture triangles with FAO and German texture classes modified after Schack-Kirchner (Schack-Kirchner, 2001; FAO, 2006) (Fig. 2 in Annex. 5.2), “Crowfoot” structure for accessing soil texture class (Fig. 3 in Annex. 5.3), Munsell ® Soil Color Charts (Munsell, 1994), classification table of soil humus contents (Schlichting et al., 1995), datasheet for calculating ASWSC (Data sheet no. 3 in Annex. 5.1). Calculation of ASWSC ASWSC was calculated in mm for each plot after Arbeitskreis Standortkartierung (2003) in the laboratory. At first, collected samples were sieved using 2 mm sieve to separate out gravels and stones. Then soils were crushed using mortar and pestle to mix soil aggregates. Water was added until the sample does not darken. Then sand, clay and silt fractions of the soil were determined by field soil type analysis. Soil textures of each mineral horizons for all 24 soil profiles were determined using the “Crowfoot” structure for accessing soil texture class in the soil texture triangles with FAO and German texture classes modified after Schack-Kirchner (SchackKirchner, 2001). Both German and FAO texture classes were recorded. Using the Munsell value and soil texture class in the classification table of soil humus contents (Schlichting et al., 1995), soil organic matter (vandenBerg et al.) content was calculated. Finally ASWSC was calculated for 24 sampling plots using soil texture and SOM content, soil skeleton content and horizon depth for each horizon and profile. Classification of plots based on the calculated ASWSC values Soil drought was expressed by following the ASWSC classification (Table 2.2) after Arbeitskreis Standortkartierung (2003). 12 sampling plots were found having≤ 60 m m ASWSC and the rest 12 having between 60 ≤ 137 mm. Plots having ASWSC ≤ 60 mm was assigned as ‘dry plot’ and ASWSC between 60 ≤ 137 mm as ‘less dry plot’ respectively (Table 3 in Annex. 5.4).

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ASWSC (in mm) < 30 30-60 60-90 90-120 120-180

Status very very low very low low medium high

No of plots found 5 7 5 4 3

Table 2.2 Classification table of ASWSC after Arbeitskreis Standortkartierung (2003) with total number of sampled plots found in each class 2.3.4 Inventory database Inventory database was prepared with spatial, morphological and growth parameters for 47 juvenile beech trees from the sampling plots. Spatial, vegetation and secondary soil data (calculated ASWSC) of all plots were also included in the database. 2.3.5 Biomass study Destructive sampling was done in the study area for biomass data collection. AGB was assessed from ex situ weighing after oven - drying of samples in the laboratory. Two regression models were formulated which calculated biomass and diameter for both ‘dry’ and ‘less dry’ plots. The dead part of the tree was simulated from the living part biomass. Secondary database was prepared which calculated AGB of 47 beech juveniles of the inventory database. This secondary database was further used for the data analysis. Instruments Pruning shear, hand saw, slide calipers, datasheet for sample collection (Data sheet no. 4 in Annex. 6.1), datasheet for biomass calculation (Data sheet no. 5 in Annex. 6.2), weighing machine, drying oven. Criteria for sample collection • Classification of 5 harvesting groups based on the measured diameter of the branches and stems of the inventory trees (Table 2.3). Both stems and branches of the trees were collected within these groups. • Sample collection from the nearest tree of the sampling point from where the soil samples were collected. • Selection of one or more trees to get branches of all 5 groups from a single plot. • Collection of living tree parts only. • Collection of 3 annual shoots from the three crown compartments of the nearest tree of the sampling point.

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Harvesting groups 1 2 3 4 5

Diameter Class (in mm) 2-5 5.1-8 8.1-11 11.1-18 18.1-41

Table 2.3 Different branch harvesting groups according to diameter class: (2-41 mm) 2.3.5.1 Sample collection and preparation for biomass calculation Samples were collected in summer 2010. Samples include all above ground living parts of the tree: stem along with bark, perennial branches with bark, annual shoots with buds and leaves. Because the samples were collected in summer, leaves were included in it. Trees could be used for collecting samples were only found in 16 plots from the 24 sampling plots. So, these 16 plots were selected for sample collection. Total 80 (5 x 16) samples of perennial branches and stems and 48 (3 x 16) annual shoots were collected from the 19 trees of the 16 sampling plots as the harvested samples. All of these 19 sampled trees were below 250 cm height and live. Diameter of each individual perennial branch was recorded. Diameter for all annual shoots was more or less constant (1.5 mm). Samples were oven-dried at the temperature of 105°C for 8 days and then weighted separately for AGB calculation. 2.3.5.2 Regression models for calculating biomass Two separate regression equations with diameter and biomass (Fig. 2.7) of the stems and perennial branches were developed using power function equation (Y = a×Db; where Y was biomass, a = coefficient constant, b = regression coefficient and D = diameter) for both ‘dry’ and ‘less dry’ plots based on the collected samples. Using these equations, modelled biomass dataset was prepared with the collected samples. This dataset was used as modelled data. Then another dataset named as observed data was prepared with original measured biomass values of the collected samples. These two datasets were tested statistically to verify the models before using for the inventory trees. A positive significant correlation was found for the modelled and observed data with Spearman’s rho. Paired sample t-test between observed and modeled means show that there was no significance difference (Table 2.4). So, both equations of ‘dry’ and ‘less dry’ plots were accepted as models for calculating dry biomass of the inventory trees.

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Biomass (gm)

Biomass (gm)

Methodology

Dry plots

Less dry plots

Diameter (mm)

Diameter (mm)

Fig. 2.7 Regression models for calculating biomass of stem and perennial shoots

Statistical tests One-sample KolmogorovSmirnov test Non-parametric correlation by Spearman’s rho Significance testing by paired sample t test

Results (Dry plots)

Results (Less dry plots)

P < 0.05

P < 0.05

rs = 0.98, N = 40, p < 0.01*

rs = 0.96, N = 40, p < 0.01*

t (39) = 0.724; p > 0.05

t (39) = 1.240; p > 0.05

Table 2.4 Biomass regression model-testing

2.3.5.3 Average biomass calculation of the annual shoots Average biomass was calculated of the annual shoots for both ‘dry’ and ‘less dry’ plots from the collected and oven dried samples (Table 2.5).

Plot types Dry plots Less dry plots

Average biomass from different crown compartments (in gm) Upper Middle Lower crown crown crown 0.24 0.19 0.17 0.37 0.14 0.13

Average biomass for two plots (in gm) 0.20 0.21

Table 2.5 Calculation of annual shoots’ average biomass (in gm) from three different crown compartments for ‘dry’ and ‘less dry’ plots

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2.3.5.4 Secondary database generation Secondary database was prepared with the calculated AGB of 47 beech juveniles of the inventory database. Calculation was done in two separate steps for 42 living and 5 dead juveniles. Calculation of AGB for living beech of the inventory database In the inventory database, the diameter of stem and all perennial shoots (both live and dead) and the total numbers of annual shoots (both live and dead) were recorded for all 42 living beech trees. Biomass of stem and each perennial shoot were calculated by using the developed regression models. The average biomass of the annual shoots was multiplied by the total number of annual shoots. The biomass of dead parts was calculated from the model which was derived from the equations derived from the living parts of trees. Then AGB of the whole tree was calculated for each of the 42 living beech juveniles in the inventory database. The equation for the AGB calculation of living young beech was: Y = (A+B), (where Y = AGB of the tree, A = biomass of stem and perennial shoots using regression model and B = total annual shoot biomass). Calculation of AGB for dead beech of the inventory database In total 5 dead trees were measured for the inventory database. Some dead parts already were broken and missing from these trees. AGB of the dead tree was the summation of the existing and the missing dry parts of the tree. So the dead tree biomass was reconstructed using the model based on the living tree biomass measurements. A regression equation (Fig. 2.8) was developed with the collar diameter and the calculated AGB of the living 42 trees from the inventory database using power function equation (Y = a×(CD)b; where Y = AGB of the tree, a = coefficient constant, b = regression coefficient and CD = root collar diameter). Using the equation, AGB was modelled for the 42 living trees. This dataset was used as modelled data. Then another dataset named as calculated data was prepared with calculated AGB values of the 42 living trees. These two datasets were tested statistically to verify the models before using for the dead trees. A positive significant correlation was found for the modelled and calculated data with Spearman’s rho. Paired sample t-test between modeled and calculated means shows that there was no significance difference (Table 2.6). So the equation was accepted and then used as the model for simulating dead tree biomass of the inventory data.

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AGB (gm)

Y = 0.337 x (CD)2.254 R2 = 0.971, p < 0.05

Collar diameter (mm) Fig. 2.8 Regression model derived from the relation between collar diameter and AGB of living trees then used for reconstructing dead tree biomass Statistical tests One-sample Kolmogorov-Smirnov test Non-parametric correlation by Spearman’s rho Significance testing by paired sample t test

Results P < 0.05 rs = 0.98, N = 42, p < 0.01* t (41) = -0.218; p > 0.05

Table 2.6 Regression model-testing for dead tree simulation

2.3.6 Tree-ring analysis Tree ring analysis was done by harvested trees. The WinDENDRO 2009a software (Regent Instruments Canada Inc., 2009) was used for the analysis. Age, basal area increment (GarciaSuarez et al. 2009) and mean basal area increment (mBAI) were calculated for each harvested tree in the laboratory. Two regression models between age and collar diameter and two regression models between mBAI and collar diameter were formulated for both ‘dry’ and ‘less dry’ plots. Age and mBAI of 47 beech juveniles of the inventory database was calculated using those models. A secondary database was prepared with two different types of datasets for the data analysis. One dataset was prepared with the calculated age, the measured BAI for each year and the calculated mBAI of 44 harvested beech juveniles. The second dataset was prepared to calculate age and mBAI of 47

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beech juveniles of the inventory database. Both datasets were used in different data analyses in the study. Instruments Pruning shear, hand saw, slide calipers, datasheet (Data sheet no. 6 in Annex. 7), drying oven, LA1600+ scanner (Regent Instruments Canada Inc.), WinDENDRO 2009a software (Regent Instruments Canada Inc., 2009). Criteria for sample collection • Classification of 6 harvesting groups based on the root collar diameter of the trees of the inventory database (Table 2.7) for tree harvesting to perform tree-ring analysis. • Selection of all live trees for harvesting. • Cutting at the root collar diameter (5 cm above ground level) to harvest the tree.

Harvesting groups 1 2 3 4 5 6

Collar diameter class (in mm) 3-7 7.1-11 11.1-15 15.1-19 19.1-27 27.1-41

Table 2.7 Different tree harvesting groups according to collar diameter class: (3-41 mm)

2.3.6.1 Sample collection and preparation for tree-ring analysis Trees for sampling were found only in 13 plots from the 24 sampling plots. So, these 13 plots were selected for sample collection. In total 44 trees (22 trees from the 7 dry plots and 22 trees from the 6 less dry plots) were harvested. Root collar diameters of the harvested samples were recorded. Samples were oven dried at 40°C for 4 days and then sanded to prepare them for treering analysis using standard dendroecological methods. Finally 0.5-3 cm thick discs were prepared depending from the diameter. Prepared discs were then scanned at 4800 or 6400 d.p.i (dot per inch) using a LA1600+ scanner and saved (see Pic.3 of Annex 8). Ring width was measured in four radii direction from the central pith to the periphery using the WinDENDRO 2009a software and data files were saved. 2.3.6.2 Secondary database generation Secondary database was prepared with two different datasets as mentioned below: Calculation of age, BAI and mBAI for the harvested juvenile and understorey beeches

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Age was calculated counting the tree rings for all harvested trees using WinDENDRO 2009a software. Ring width for each year was averaged from the four radii measurements to produce a final ring width series for each individual. Ring width was converted into tree BAI according to the following formula:

𝑩𝑩𝑩𝑩𝑩𝑩 = 𝝅𝝅�𝑹𝑹𝟐𝟐𝒏𝒏 − 𝑹𝑹𝟐𝟐𝒏𝒏−𝟏𝟏 �

(where R = radius of the tree and n = the year of tree ring formation,

).

BAI for each year were calculated for each harvested tree. By averaging BAI of all year, mBAI was calculated for each of 44 harvested trees. Calculation of age and mBAI for the living inventory juvenile and understorey beeches Calculation of age Two separate regression equations with collar diameter and age (Fig. 2.9) were developed using power function equation (A = a×CDb; where A is age, a = coefficient constant, b = regression coefficient and CD = collar diameter) for both ‘dry’ and ‘less dry’ plots based on the calculated age from the harvested samples. Using the equations, modelled age dataset was prepared with the collected samples. This dataset was used as modelled data. Then another dataset named as observed data was prepared with original calculated age of the collected samples. These two datasets were tested statistically to verify the models before using it for the inventory trees. A positive significant correlation was found in modeled and observed data with Pearson’s r. Paired sample t-test between observed and modeled means show that there was no significance difference (Table 2.8). So, both equations of ‘dry’ and ‘less dry’ plots were accepted as models for calculating age of the inventory trees.

Dry plots

Collar diameter (mm)

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A = 2.038 x (CD)0.780 R2 = 0.775, p < 0.05

Tree age (year)

Tree age (year)

A = 2.382 x (CD)0.718 R2 = 0.760, p < 0.05

Less dry plots

Collar diameter (mm)

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Fig. 2.9 Regression models for calculating tree age Statistical tests One-sample KolmogorovSmirnov test Parametric correlation by Pearson’s r Significance testing by paired sample t test

Results (Dry plots)

Results (Less dry plots)

P > 0.05

P > 0.05

r = 0.85, N = 22, p < 0.01*

r = 0.8, N = 22, p < 0.01*

t (21) = 0.534; p > 0.05

t (21) = 0.368; p > 0.05

Table 2.8 Age (year) regression model-testing Calculation of mBAI Two separate regression models with mBAI and root collar diameter (Fig. 2.10) were developed using power function equation (Y = a×(CD)b; where Y = mBAI, a = coefficient constant, b = power coefficient and CD = tree root collar diameter) for both ‘dry’ and ‘less dry’ plots based on the calculated mBAI from the harvested samples. Using the prepared equations, modelled mBAI dataset was prepared with the collected samples. This dataset was used as modelled data. Then another dataset named as observed data was prepared with original calculated mBAI of the samples. These two datasets were tested statistically to verify the models before using for the inventory trees. A positive significant correlation was found in modeled and observed data with Pearson’s r. Paired sample t-test between observed and modeled means shows that there was no significance difference (Table 2.9). So, both equations of ‘dry’ and ‘less dry’ plots were accepted as models for calculating mBAI of the inventory trees.

Y = 0.077 x (CD)1.803 R2 = 0.873, p < 0.05 mBAI (mm²/yr)

mBAI (mm²/yr)

Y = 0.156 x (CD)1.473 R2 = 0.877, p < 0.05 Dry plots

Collar diameter (mm)

Less dry plots

Collar diameter (mm)

Fig. 2.10 Regression models for mBAI calculation

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Statistical tests One-sample KolmogorovSmirnov test Parametric correlation by Pearson’s r Significance testing by paired sample t test

Results (Dry plots)

Results (Less dry plots)

P > 0.05

P > 0.05

r = 0.83, N = 22, p < 0.01*

r = 0.80, N = 22, p < 0.01*

t (21) = 0.499; p > 0.05

t (21) = 0.591; p > 0.05

Table 2.9 mBAI (mm2/ yr) regression model-testing

2.4 Data analysis 2.4.1 Calculation of branch mortality to measure tree vitality for juvenile and understorey beeches in the inventory database Crown dieback or branch mortality of the tree was considered as the magnitude of tree vitality. This was a derived value and calculated by the percentage of dead AGB of the tree. So, percentage of dead AGB was used as the indicator for the tree vitality. Vitality was calculated for each of the 47 trees (both live and dead) of the inventory database.

𝑷𝑷𝑷𝑷𝑷𝑷𝑷𝑷𝑷𝑷𝑷𝑷𝑷𝑷𝑷𝑷𝑷𝑷𝑷𝑷 𝒐𝒐𝒐𝒐 𝒅𝒅𝒅𝒅𝒅𝒅𝒅𝒅 𝑨𝑨𝑨𝑨𝑨𝑨 =

𝑫𝑫𝑫𝑫𝑫𝑫𝑫𝑫 𝒃𝒃𝒃𝒃𝒃𝒃𝒃𝒃𝒃𝒃𝒃𝒃𝒃𝒃 𝒑𝒑𝒑𝒑𝒑𝒑𝒑𝒑 × 𝟏𝟏𝟏𝟏𝟏𝟏 𝑻𝑻𝑻𝑻𝑻𝑻𝑻𝑻𝑻𝑻 𝒃𝒃𝒃𝒃𝒃𝒃𝒃𝒃𝒃𝒃𝒃𝒃𝒃𝒃

Higher percentage means high rate of branch mortality that shows low vitality of the tree. In case of dead trees, the mortality is 100% expressing “zero” vitality of the tree. 2.4.2 Relation between tree vitality and soil drought Relation between tree vitality and soil drought was assessed both at the scale of trees and plots. Vitality of 47 trees of the inventory database and 19 plots were correlated with their respective ASWSC. Distributions of data were tested with one-sample Kolmogorov-Smirnov test. Because data were not following normal distribution, a non-parametric analysis with Spearman correlation test was performed. 2.4.3 Significance testing of tree vitality differences between dry and less dry plots

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Methodology

To find out the difference of the tree vitality between dry and less dry plots, statistical significance testing was performed. Levene’s test was performed to check the equality of variances. Because variances differ significantly, a non-parametric Mann-Whitney U test was performed. 2.4.4 Branch mortality pattern and relation with tree height, diameter and age The pattern of branch mortality was assessed in different crown compartments for each living tree of the inventory database. Relationship between rate of branch mortality and tree height, stem diameter and age was explained by regression and correlation approach. Rate of branch mortality in different crown compartments was correlated with tree height, diameter and age. Distributions of data were tested with one-sample Kolmogorov-Smirnov test. Because data were not following normal distribution, a non-parametric Spearman correlation test was performed. 2.4.5 Relation between soil drought and tree height growth To find out the relation between tree height and soil drought, the height of 42 living trees of the inventory database were correlated with their respective ASWSC. Distributions of data were tested with one-sample Kolmogorov-Smirnov test. Because data were found normally distributed, a parametric Pearson correlation test was performed. Height and length of 42 living trees were also compared. To compare the two means, significance test with paired sample t test was performed because the two samples had same population size. Average height and length ratio of the trees between dry and less dry plots was compared with independent sample t test since the two samples had different population size. 2.4.6 Relation between mBAI and soil drought To find out the relation between mBAI and soil drought, mBAI of 47 trees of the inventory database were correlated with their respective ASWSC. Distributions of data were tested with one-sample Kolmogorov-Smirnov test. Because data were not following normal distribution, a non-parametric Spearman correlation test was performed. 2.4.7 Significance testing of mBAI between dry and less dry plots Comparison of mBAI of the trees between ‘dry’ and ‘less dry’ plots was done with two types of datasets. One with mBAI calculated for 44 harvested and another for 47 inventory trees. To find out the difference of mBAI between dry and less dry plots, statistical significance testing was performed. Levene’s test was performed to check the equality of variances. Because equal variances were found in both samples for both datasets, a parametric test was performed. In case of harvested trees, paired sample t test was performed because the two samples had same population size. In case of inventory trees, independent sample t test was performed because the two samples had different population size.

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Methodology

2.4.8 Effect of ‘2003 summer drought’ on BAI of juvenile and understorey beeches To compare the mean growth trend of the trees of ‘dry’ and ’less dry’ plots, BAI for each year was averaged over all individuals of the harvested trees at each plot. Effect of ‘2003 summer drought’ on the trees was studied by comparing the growth trend of the year 2003 and 2004 on the harvested trees. To find out the difference of 2004 BAI between the trees of dry and less dry plots, statistical significance testing was performed by paired sample t test after doing Levene’s variance test.

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Results

3. RESULTS 3.1 Relation between soil drought and tree vitality The relation between soil drought (ASWSC, see page 15) and tree vitality (rate of branch mortality or percentage of dead AGB, see page 19) was assessed both at the scale of trees and plots. Trend of branch mortality rates in each tree and for whole stand are shown in Fig. 3.1.A and 3.1.C. Trees from the low soil drought areas had high vitality and low branch mortality rates, and trees from the high soil drought areas had low vitality and high branch mortality rates (Fig. 3.1.A). A negative trend of branch mortality rates with increasing ASWSC with significant negative strong correlation both in trees’ and plots’ scale was found (3.1.B and 3.1.D). Four dead trees with 100 percent branch mortality were found in the dry plots with high soil drought (ASWSC ≤ 60 mm) and one in less dry plot with comparatively low soil drought (ASWSC > 60 mm) (Fig. 3.1.E). Branch mortality threshold was found 40 % (Fig. 3.1.F) for the whole stand having mean ASWSC 67.38 mm (Table 3.1).

Rate of branch mortality (%) and ASWSC (mm)

Interpretation of the results is that soil drought has an adverse effect on the vitality of juvenile and understorey beeches in the whole stand. With decreasing ASWSC, vitality in trees is also decreasing. ASWSC (mm)

Rate of branch mortality (%)

160 140 120 100 80 60 40 20 0 1

3

5

7

9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 Inventoried understorey and juvenile beech

Fig. 3.1.A Inventoried understorey and juvenile beeches with their vitality or rate of branch mortality (%) and ASWSC (mm)

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Results

Rate of branch mortality (%)

Y = 535.546 x (ASWSC)-0.911 R² = 0.27, p < 0.001

ASWSC (mm)

Fig. 3.1.B Relation between tree vitality or rate of branch mortality (%) and ASWSC (mm) in the trees (Spearman’s rho, rs = -0.54, N = 42, p < 0.001*)

Rate of branch mortality (%) and ASWSC (mm)

ASWSC (mm)

Rate of branch mortality (%)

160 140 120 100 80 60 40 20 0 1

2

3

4

5

6

7

8

9

10 11 12 13 14 15 16 17

Plots

Fig. 3.1.C Tree vitality or rate of branch mortality (%) and ASWSC (mm) in the plots

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Results

Rate of branch mortality (%)

Y = 328.354 x (ASWSC)-0.761 R² = 0.3, p = 0.016

ASWSC (mm)

Rate of branch mortality (%) and ASWSC (mm)

Fig. 3.1.D Relation between tree vitality or rate of mortality (%) and ASWSC (mm) at the scale of plots (Spearman’s rho, rs = -0.57, N = 17, p = 0.016*)

ASWSC (mm)

Rate of branch mortality (%)

160 140 120 100 80 60 40 20 0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 Inventoried understorey and juvenile beech

Fig. 3.1.E Inventoried understorey and juvenile beeches with their rate of branch mortality (%) and ASWSC (mm). Five dead beeches (four on dry plots and one on less dry plot) with 100% branch mortality were found

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Results

Branch mortality

Rate of branch mortality (%)

100 90 80 70 60 50 40 30 20 10 0 0

10

20

30

40

50

Inventoried understorey and juvenile beech

Fig. 3.1.F Branch mortality threshold of the inventoried understorey and juvenile beeches N Minimum Maximum Mean Std. Deviation 19 137 67.38 34.932 ASWSC (mm) 24

Table 3.1 Descriptive statistics of ASWSC (mm) of the stand 3.2 Difference of tree vitality between dry and less dry plots Fewer plants were found in dry plots than less dry plots. Vitality of trees was higher on less dry plot than on dry plots. Out of five dead plants, four were found in dry plots (Fig. 3.2.A). The percentage of live and dead AGB differs significantly between the trees of dry and less dry plots (Fig. 3.2.B). The ratio of dead to live AGB was higher in the trees of dry plots showing lower tree vitality than on less dry plots. Mann-Whitney U test was performed to compare the means of branch mortality rates in the trees on dry and less dry plots both at the scale of trees and plots. Rate of branch mortality was significantly higher showing lower tree vitality on dry plots than on less dry plots in both trees and plots scale. Median and range of branch mortality rates in the trees and in the plots of both dry and less dry plots are shown in the box-plots (Fig. 3.2.C and 3.2.D). The range of branch mortality rates and the standard deviation are wider and the median value is higher in the trees on dry plots than less dry plots. In dry plots, 100% mortality was found in some trees where in less dry plots 39% with one outlier was found. Likewise, the range of mortality rates is wider and the median value is higher in the dry plots than less dry. Branch mortality rates or crown dieback was higher in dry plots compared to less dry plots. Trees from the dry plots were much more affected by soil drought, showing lower vitality, than the trees from less dry plots. Albert-Ludwigs University of Freiburg

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Results

Dry

Less dry

Number of trees

1 31

30

4 16

12

Total

Live

Dead

Plant types Fig. 3.2.A Overview of the plant condition of the sampling plots

Live part biomass

Dead part biomass

120

Biomass % in trees

100 80

15 41

60 85

40 59 20 0 Dry

Less dry

Fig. 3.2.B Percentage of live and dead biomass parts in the trees on dry and less dry plots Albert-Ludwigs University of Freiburg

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Branch mortality rates (%) in trees

Results

Branch mortality rates (%) in plots

Fig. 3.2.C Median and range of branch mortality rates (%) of trees on dry and less dry plots at the scale of trees (Mann-Whitney U = 106.000, p = 0.002*)

Fig. 3.2.D Median and range of branch mortality rates (%) of trees on dry and less dry plots at the scale of plots (Mann-Whitney U = 17.000, p = 0.022*)

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Results

3.3 Pattern of branch mortality in different vertical parts of tree crown The pattern of branch mortality in different vertical crown compartments expresses the stand condition and the related vitality of the trees. The distribution was heterogeneous. General trend of branch mortality in whole stand was decreasing with increasing vertical crown height. 28, 30 and 42 % of branch mortality were found in upper, middle and lower crown respectively in the whole stand (Fig. 3.3.A) which means more dead branches in the lower crown compartment. The pattern of branch mortality was different for dry and less dry plots (Fig. 3.3.B). Branch mortality in lower crown was higher on dry plots (49 %) than on less dry plots (39 %). Result shows that the lower crown part of the trees was the most damaged part in the stand on both dry and less dry plots. 42 % branch mortality seems a high value for the whole stand showing low vitality of the trees. Especially trees on the dry plots show lower vitality having more branch mortality rate in lower crown than trees on the less dry plots.

45

Rate of branch mortality (%)

40

42

35 30 25

30

28

20 15 10 5 0 Upper

Middle

Lower

Crown compartments Fig. 3.3.A Pattern of branch mortality in different vertical crown compartments of the stand

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Results

Rate of branch mortality (%)

Dry

Less dry

60 50 40 30 49 20 10

26

29

25

39

32

0 Upper

Middle

Lower

Crown compartments Fig. 3.3.B Pattern of branch mortality in different vertical crown compartments of the dry and less dry plots

3.4 Relating rate of branch mortality with tree height, stem diameter and age Very weak negative correlation between the rate of branch mortality and plant height, positive weak correlation between the rate of branch mortality with diameter and age were found for the whole stand (Fig. 3.4.A; Table 3.2). Branch mortality in upper crown was significantly and strongly negative correlated with plant height, and strongly negative correlated with both stem diameter and age. Very weak positive correlation of branch mortality in middle crown with plant height and very weak negative correlation with stem diameter and age was found. Negative weak correlation of mortality with plant height, stem diameter and age was found in lower crown (Fig. 3.4.B, 3.4.C, 3.4.D; and Table 3.3). The result shows an increasing trend of branch mortality with increasing age and stem diameter, but no relation with height. The pattern of branch mortality trend in upper and lower crowns was same creating more threat to smaller plants.

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Results

Rate of branch mortality (%)

Y = 18.843 x H-0.134 R² = 0.01, p > 0.05

Rate of branch mortality (%)

Height (cm)

Y = 4.229 x D0.383 R² = 0.07, p > 0.05

Rate of branch mortality (%)

Collar diameter (mm) Y = 2.808 x A0.515 R² = 0.075, p > 0.05

Age (year)

Fig. 3.4.A Relation between rate of branch mortality (%) in the stand with height, collar diameter and age of the living inventory trees

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Rate of branch mortality (%)

Results

Y = 375.449 x H-0.539 R2 = 0.224, p < 0.05

Rate of branch mortality (%)

Height (cm)

Y = 76.759 x D-0.325 R2 = 0.052, p > 0.05

Rate of branch mortality (%)

Collar diameter (mm)

Y = 126.324 x A-0.487 R2 = 0.070, p > 0.05

Age (year)

Fig. 3.4.B Relation between rate of branch mortality (%) in upper crown with height, collar diameter and age of the living inventory trees

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Rate of branch mortality (%)

Results

Y = 17.064 x H0.108 R2 = 0.006, p > 0.05

Rate of branch mortality (%)

Height (cm)

Y = 57.616 x D-0.270 R2 = 0.034, p > 0.05

Rate of branch mortality (%)

Collar diameter (mm)

Y = 68.053 x A-0.318 R2 = 0.029, p > 0.05

Age (year)

Fig. 3.4.C Relation between rate of branch mortality (%) in middle crown with height, collar diameter and age of the living inventory trees

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Rate of branch mortality (%)

Results

Y = 165.382 x H-0.288 R2 = 0.138, p < 0.05

Rate of branch mortality (%)

Height (cm)

Y = 114.380 x D-0.347 R2 = 0.133, p = 0.05

Rate of branch mortality (%)

Collar diameter (mm)

Y = 156.577 x A-0.444 R2 = 0.125, p > 0.05

Age (year)

Fig. 3.4.D Relation between rate of branch mortality (%) in lower crown with height, collar diameter and age of the living inventory trees Albert-Ludwigs University of Freiburg

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Results

Parameters Spearman’s rho (rs) N p value -0.16 42 0.311 Height 0.22 42 0.164 Stem diameter 0.23 42 0.149 Age

Table 3.2 Correlations of rates of branch mortality (%) with tree height, stem diameter and age

Upper Middle Lower Spearman’s rho, rs = -0.5 Spearman’s rho, rs = 0.1 Spearman’s rho, rs = -0.4 N = 25, p = 0.019* N = 29, p = 0.449 N = 29, p = 0.057 Diameter Spearman’s rho, rs = -0.4 Spearman’s rho, rs = -0.2 Spearman’s rho, rs = -0.3 N = 25, p = 0.086 N = 29, p = 0.285 N = 29, p = 0.150 Spearman’s rho, rs = -0.4 Spearman’s rho, rs = -0.2 Spearman’s rho, rs = -0.3 Age N = 25, p = 0.073 N = 29, p = 0.328 N = 29, p = 0.148 Height

Table 3.3 Correlations of rate of branch mortality (%) in different crown compartments with tree height, stem diameter and age

3.5 Effect of soil drought on tree height growth The effect of soil drought on tree height growth was assessed for the inventory trees. No correlation between height and ASWSC was found (Fig. 3.5.A). Then height and length of the trees were compared. Height of the trees was found lower than the length of the trees. Paired sample t-test was performed to compare the means of the height and length of the trees. Significant difference was found between the two means (Fig. 3.5.B). Average height/length ratio of the trees was calculated both for the dry and less dry plots. The height/length ratio of the trees was 0.7 and 0.8 for the dry and less dry plots respectively. No significant difference of average height/length ratio was found between the trees on dry and less dry plots. These results show that the trees are crooked and have stunted growth in the whole stand. Though the average height/length ratio of the trees do not differ statistically between the dry and less dry plots, but the trees from the dry plots have lower average ratio than the less dry plots. Soil drought may have an adverse effect on height growth on the trees; but no clear trend was found.

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Results

Height (cm)

Y = 30.224 x ASWSC 0.205 R2 = 0.03, p > 0.05

Available soil water s torage capacity (mm)

Values (cm)

Fig. 3.5.A Relation between tree height (cm) and ASWSC (mm) of the inventory trees (Pearson’s, r = 0.16, N = 42, p = 0.31)

Fig. 3.5.B Median and range of height and length values (cm) of trees on the stand (Paired sample t-test: t(41) = -7.001, p < 0.05*)

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Results

3.6 Relation between soil drought and mean basal area increment (mBAI) The relation between soil drought (ASWSC, see page 15) and mBAI (see page 22) was assessed for the whole stand. mBAI of 47 inventory trees were plotted with their respective ASWSC in the graph (Fig. 3.6.A). The tree with highest mBAI (62.28 mm2) was found in the less dry area having ASWSC 68 mm. Mean mBAI was found 10 mm2/yr of the stand (Table 3.4). No correlation between mBAI and ASWSC was found (Fig. 3.6.B). Interpretation of the results is that soil drought might have an adverse effect on mBAI, but no clear trend was visible from the recorded data. However, very low mean mBAI shows an adverse effect of soil drought on the trees in the whole stand.

N Minimum Maximum Mean Std. Deviation 0.56 62.28 10.2449 12.12634 mean BAI (mm /yr) 47 2

ASWSC (mm) and mBAI (mm²/yr)

Table 3.4 Descriptive statistics of mBAI (mm2/yr) of the whole stand

ASWSC (mm)

mBAI (mm²/yr)

160 140 120 100 80 60 40 20 0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 Trees Fig. 3.6.A ASWSC (mm) and mBAI (mm2/yr) of the inventory trees

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Results

mBAI (mm²/yr)

Y = 28.646 x ASWSC -0.368 R2 = 0.04, p > 0.05

ASWSC (mm) Fig. 3.6.B Relation between mBAI (mm2/yr) and ASWSC (mm) of the inventory trees (Spearman’s rho, rs = -0.116, N = 47, p = 0.439)

3.7 Difference of mBAI of the trees between dry and less dry plots mBAI from both of the harvested and inventory trees from dry plots were compared with less dry plots (Fig. 3.7.A and 3.7.B). In both cases, the mean, the maximum and the standard deviation of mBAI of the trees was found higher on less dry plots than on dry plots (Fig. 3.7.C and 3.7.D; Table 3.5 and 3.6 ). Median, range and standard deviation of mBAI of dry and less dry plots are shown in the box-plots for both harvested and inventory trees (Fig. 3.7.E and 3.7.F). Paired sample t test for harvested trees and independent sample t test for inventory trees showed no significance difference between the two means of mBAI of trees on dry and less dry plots. The results show that the mBAI of dry plots in both harvested and inventory trees were lower than less dry plots but statistically they do not differ significantly.

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Results

Less dry

Dry

60.00

mBAI (mm²/yr)

50.00 40.00 30.00 20.00 10.00 0.00 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 Trees Fig. 3.7.A mBAI (mm2/yr) of the harvested trees on dry and less dry plots

Less dry

Dry

70.00

mBAI (mm²/yr)

60.00 50.00 40.00 30.00 20.00 10.00 0.00 1

3

5

7

9 11 13 15 17 19 21 23 25 27 29 31 Trees

Fig. 3.7.B mBAI (mm2/yr) of the inventory trees on dry and less dry plots

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Results

mBAI (mm²/yr)

mBAI 16.00 14.00 12.00 10.00 8.00 6.00 4.00 2.00 0.00 Less dry

Dry

Fig. 3.7.C Mean mBAI (mm2/yr) of the harvested trees on dry and less dry plots (Paired sample t-test: t(21) = -0.886, p > 0.05)

mBAI

mBAI (mm²/yr)

12.00 10.00 8.00 6.00 4.00 2.00 0.00 Less dry

Dry

Fig. 3.7.D Mean mBAI (mm2/yr) of the inventory trees on dry and less dry plots (Paired sample t-test: t(45) = -0.160, p > 0.05)

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mBAI (mm²/yr)

Results

mBAI (mm²/yr)

Fig. 3.7.E Median and range of mBAI (mm2/yr) of the harvested trees on dry and less dry plots

Fig. 3.7.F Median and range of mBAI (mm2/yr) of the inventory trees on dry and less dry plots

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Results

Area N Minimum Maximum Mean Std. Deviation 1.64 49.18 14.0841 13.93107 Less dry 22 22 1.35 37.98 11.0186 9.78065 Dry

Table 3.5 Descriptive statistics of mBAI (mm2/yr) of the harvested trees on dry and less dry plots Area N Minimum Maximum Mean Std. Deviation 0.56 62.28 10.4503 13.03060 Less dry 31 16 1.20 35.72 9.8469 10.54032 Dry

Table 3.6 Descriptive statistics of mBAI (mm2/yr) of the inventory trees on dry and less dry plots 3.8 Effect of ‘2003 summer drought’ on basal area increment The trend of mean growth of the harvested trees expressed by BAI is shown in the graph (Fig. 3.8.A). Suppression phase of the trees from the dry plots were lengthier than from the less dry plots. A sudden fall of BAI was found in 2004 (Fig. 3.8.A) in the trees on both dry and less dry plots. But in dry plots the fall is more drastic than the less dry plots. Again, in less dry plots, BAI was increasing sharply from 2005 onwards. Dry plots showed a carry-over effect and the raise of BAI increment was less sharp than less dry plots. The mean, the maximum and the standard deviation of 2003 and 2004 BAI is shown for both areas (Fig. 3.8.B and Table 3.7). Median and range of BAI of dry and less dry plots for the year of 2003 and 2004 are shown in the box-plots (Fig. 3.8.C). Paired sample t test were done to compare the means of 2003 and 2004 BAI of dry and less dry plots (Fig. 3.8.B). On less dry plots, no significant difference between 2003 and 2004 BAI was found whether on dry plots, significant difference between 2003 and 2004 BAI was found. Trees from dry plots had lower BAI in 2004 compared to less dry plots as an effect of ‘2003 summer drought’. So, the ‘2003 summer drought’ had a significantly high adverse effect on the beeches of the dry plots those were exposed to soil drought. Decreasing of plant available water causes significant depletion of increment growth in those beeches. The cumulative negative influence of soil drought and high summer drought was much higher on the beeches. Plots Year N Minimum Maximum Mean Std. Deviation 0.74 92.69 18.2644 22.33636 Less dry 2003 20 2004 20 0.49 48.94 12.9357 13.38400 2003 21 0.62 88.26 23.4967 26.64079 Dry 2004 21 0.36 66.29 15.6190 17.30569

Table 3.7 Descriptive statistics of 2003 and 2004 BAI (mm2/yr) of the harvested trees on dry and less dry plots

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Results

50.00

Dry

Less dry

45.00

BAI (mm²/yr)

40.00 35.00 30.00 25.00 20.00 15.00 10.00 5.00

1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008

0.00

Years Fig. 3.8.A Trend of growth based on BAI (mm /yr) of the harvested trees on dry and less dry plots 2

2003

2004

BAI (mm²/yr)

25.00 20.00 15.00 10.00 5.00 0.00 Dry

Less dry

Fig. 3.8.B Mean of 2003 and 2004 BAI (mm2/yr) of the harvested trees on dry and less dry plots (Dry plot: t(20) = 2.739, p < 0.05* and less dry plot: t(19) = 1.543, p > 0.05).

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BAI (mm²/yr)

Results

Fig. 3.8.C Median and range of 2003 and 2004 BAI (mm2/yr) of the harvested trees on dry and less dry plots

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Discussion

4. DISCUSSION 4.1 Soil drought effects on the beech vitality In this study, significant negative correlation between ASWSC and rate of branch mortality proved that juvenile beech vitality was highly affected by soil drought (Fig. 3.1.B and 3.1.D). Steep slope with high drainage and run-off, sandy soil having low water holding capacity and south-west aspect getting high solar exposure resulting high evaporation are the main site constraints cumulatively generating soil drought (Ellenberg, 1988; Lorenz et al., 2001; Bolte et al., 2007; Seletkovic et al., 2009). Tree vitality becomes weaker with persistence of stress like soil drought (Dobbertin, 2005). Higher numbers of living trees with comparatively lower branch mortality on less dry plots than on dry plots (Fig. 3.2.A) supports the consequences of drought which causes crown dieback, branch and leaves mortality, and those reduce tree vitality (Leuschner et al., 2001; Dobbertin, 2005). If soil drought continues, the capability of juvenile beech to overcome further stress or to survive diminishes and the vitality decreases (Pedersen, 1998; Hartmann and Messier, 2008). At a certain point irreversible damage or tree death occurs (Dobbertin, 2005). Five dead trees from the sampling plots support this argument for the study area (Fig. 3.2.A). It was shown that the juvenile trees were more affected than the understorey trees because of branch dieback due to soil drought (Fig. 3.4.A). Very weak negative correlation between branch mortality and height growth could be addressed by the stunted height growth of the trees for the whole stand (Fig. 3.5.B). Drought reduces photosynthesis (Weber and Gates, 1990; Epron and Dreyer, 1993) and thus the efficiency of light conversion into biomass production (Löf and Welander, 2000). Decreasing rate of dry biomass with age has commonly been attributed to an increase in respiring tissues in proportion to photosynthetic tissues (Kozlowski and Pallardi, 1996). In this study, highest branch mortality (42%) was found in the lower crown compartment which is not following the previous finding that upper crown was more affected than lower and middle crown (Kohler et al., 2006). An explanation could be more risk or vulnerability for the shade branches than light exposed branches as light exposed branches are less vulnerable than shade ones (Cochard et al., 1999). Drought-induced cavitation causes branch dieback that reduces transpiration demand enabling the remaining shoot to maintain a favourable water balance (Rood et al., 2000; Bréda et al., 2006). Such an interpretation could be applied to many situations of decline where crown restrictions or crown thinning resulted from branch abscission, after the onset of severe drought (Rust and Roloff, 2004). Again, plants could regulate their root and shoot biomass under drought stress and try to tune root-shoot biomass ratio to maintain a significant correlation (Schulze et al., 2005). Evenari et al. (1982) called this root-shoot regulation phenomenon “survival through dieback”, in a dry year part of the tree dies back and only a few shoots survive which are in balance with the root. Branch dieback and the consequent reduction in whole tree leaf area are usually restricted to older and lateral twigs from the last order of

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Discussion

branching; this would enable trees to adjust root-shoot ratios after drought induced decline in root system extent and efficiency; branch mortality could be an acclimation to drought stress (Rust and Roloff, 2002, 2004; Bréda et al., 2006). The pattern of branch mortality in different crown compartments shows high branch mortality threat to the small trees (Fig. 3.4.B, 3.4.C and 3.4.D) and no trees at all were found in three dry and two less dry plots. Soil drought might be impeding beech regeneration in dry plots (Gärtner et al., 2008) and with increasing soil drought, the chance of survivability of understorey beech might decrease (Fotelli et al., 2001). 4.2 Threshold of branch mortality and ASWSC to survive drought stress In this study, the threshold of branch mortality for the beech juveniles to survive drought stress was found 40 % having very low ASWSC (Fig 3.1.F). The finding of my study is that, survival probability of the plants having < 60% living branch is lower. Gärtner et al. (2008) reported ASWSC as the limiting factor for beech survivability with threshold value of≥ 68 mm which is the likely consequence of this study having 67.38 mm ASWSC of the stand. If soil drought exists over a long time, certain extreme year like ‘2003 summer drought’ might lower the level of tree vitality and symbolize no recovery for the trees (Pedersen, 1998). Five dead beech juveniles fall below a resistance threshold where irreversible damage occurs as the cumulative effect of soil drought and extreme temperature (Larcher, 2001). Based on the decline spiral model (Manion, 1981) drought can operate as a trigger (“initiating factor”) that may ultimately lead to mortality in trees that are already under stress (by “predisposing factors” such as poor site conditions) and succumb to subsequent stem and root damage by biotic agents (“contributing factors” such as wood-boring insects, fungal pathogens). Pedersen (1998) argues that “predisposing factor” and “initiating factor” alone can lead to subsequent decline of tree vitality up to tree death. In this study, drought being an “initiating factor” may ultimately lead beech juveniles to mortality which is under stress being in poor site condition. Sandy-shallow soil with high skeleton contents may act as “predisposing factor”. Eventually there may be account of a biotic factor acting as “contributing factor” in the whole stand which was not investigated in this study. 4.3 Variation in growth with soil drought In this study, I found very weak correlation between ASWSC and growth quantified by height and mean basal area increment (mBAI). The impact of drought on growth shows a considerable inter-annual variability (Leuschner et al., 2001) and mBAI being an average of yearly values might not illustrate the variability of the growth (Fig. 3.8.A). Soil drought and/or water stress can form small tree-rings in single years, but cambial activity recovers completely in the years following if beech trees are not damaged by other impacts (Felbermeier, 1994; Dittmar et al., 2003). But BAI may decline in response to a variety of stresses including competition (LeBlanc, 1990a; Duchesne et al., 2002), insect outbreaks and climatic drought (Pedersen, 1998; Hogg et al., 2002) or other atmospheric stresses (Phipps and Whiton, 1988; LeBlanc, 1990b). Dittmar Albert-Ludwigs University of Freiburg

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(2003) reported a high resistance of beech at sites where water supply is the main growth controlling factor. He argued that site factors could modify the intensity of damage symptoms, but cannot be regarded as primary causes. In this study, the comparison of mBAI between dry and less dry plots was not significant (Fig. 3.7.C, D, E and F). Beech trees from both plots of the whole stand were merely affected by soil drought (Gärtner et al., 2008). Mean mBAI of the stand was 10.24 mm2/ yr that proved lower increment growth of the whole stand (Fig. 3.6.A and Table 3.3). 4.4 Decline of growth due to ‘2003 summer drought’ BAI trend of the whole stand shows a sudden sharp fall in 2004 in this study (Fig. 3.8.A). An opinion, the cause of this fall could be attributed to the ‘2003 summer drought’ being supported by the fact that existing soil drought together with high summer drought intensity had a strong negative impact on beech stem increment in this site condition. This finding of my study was confirmed by different findings of other investigations (Eckstein et al., 1984; Leuschner et al., 2001; Czajkowski et al., 2005). High temperatures accelerate water deficiency from the soil and the influence becomes negative on the tree growth having lower tree-ring widths (Dobbertin, 2005). Czajkowski et al. (2005) documented that plant water status during July and August 2003 had a considerable effect on the relative increment of saplings during 2003 and 2004. In this study, the release phase after 2004 began much earlier in the less dry plots compared to dry plots (Fig. 3.8.A). Differences in soil water availability explain why growth of the trees on dry plots remains more suppressed than on less dry plots (Czajkowski et al., 2005). In my opinion, this sudden fall resists the release phase of understorey beech after a long suppression phase. This phenomenon is a likely consequence of high plasticity of trees; by growing slowly they can overcome the environmental stress to survive periods of poor growing conditions; normal growth usually resumes when favourable conditions return (Brubaker, 1986). So, BAI trend from dry plots shows a cumulative carry-over effect of the summer drought and soil drought on beech juvenile performance. 4.5 Soil drought effects on the establishment and survivability of beech The susceptibility of beech to high temperatures increases with decreasing soil water availability (Lebourgeois et al., 2005) which is a likely consequence of increasing temperature and altered precipitation, recorded for the Black Forest region (Mayer et al., 2005). Soil drought is particularly important in determining the southern European limit of temperate deciduous tree species like beech (Peters, 1997; Gärtner et al., 2008). Increasing periodic droughts enhance soil drought, would be necessary for beech to be out-competed by more drought-tolerant species (Lebourgeois et al., 2005). An increasing trend of branch mortality with increased stem diameter and age support the smaller chance of the survivability of the juvenile trees (Kohler et al., 2006). Major changes in understorey species (Rich et al., 2008), as well as the possible development of

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new ecosystems due to new combinations of native and invasive exotic trees depending on the climatic tolerances of seedlings (Walther et al., 2005; Millar et al., 2007; Suarez and Kitzberger, 2008) may occur. In my opinion, soil drought has an important key role in beech establishment and survivability. 4.6 Future threat for the beech Warmer temperatures in combination with site stress can increase forest water stress independent of precipitation amount and greatly accelerate drought-induced mortality (Barber et al., 2000; Gärtner et al., 2008; Adams et al., 2009). Greater chronic forest stress and mortality risk should be expected in coming decades due to the large increases in mean temperatures and significant long-term regional drying projected in some places by 2100, in addition to projected increases in the frequency of extreme events such as severe droughts, hot extremes, and heat waves (IPCC, 2007; Jentsch et al., 2007; Sterl et al., 2008). High temperatures and low precipitation are frequent limiting factors for the growth of beech across southern Europe (Peters, 1997; Jump et al., 2007). Schütt and Cowling (1985) reported beech mortality at the lower edges of elevation range due to repeated drought during 1970s and 1980s in western and central Europe. Mortality in beech was recorded in Baden-Württemberg state of Germany during 2003-2006 and for lower and middle elevation range of France, especially in Ardennes and Vosges Mountain ranges, during 1998 and 2003-2008 due to drought, caused by high temperature raise in spring and summer (Bréda et al., 2006; Landmann and Dreyer, 2006; Rouault et al., 2006; Petercord, 2008). However, Bolte et al. (2007) concluded that only depending on present information of climate scenarios and regional data, it is difficult to correlate beech distribution with climatic factors. Ammer et al. (2005) concluded that with proper silvicultural treatment and management decision, beech will grow satisfactorily also under the expected condition of climate change. So, higher temperature experienced in recent years reported for southern Europe may has a dominant effect in limiting beech growth. High summer temperature together with soil drought cumulatively could affect the survivability of the beech juveniles which might generate an alarming threat with increasing predicted summer drought.

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5. CONCLUSION In order to getting more information about the effect of soil drought on vitality and growth on juvenile and understorey beech, this study was performed in a semi-natural oak stand. Crown dieback or branch mortality was used as an indicator of tree vitality. Dry AGB was used to measure the rate of branch mortality of each beech individual. Growth was measured by tree-ring analysis and tree height. Vitality of beech trees are negatively affected due to soil drought. Very low ASWSC (67.38 mm) is one of the reasons causing soil drought. Crown dieback is the main cause that is reducing tree vitality. Both understorey and juvenile beeches were highly affected. But the rate of crown dieback was higher in juveniles. Lower crown part of the trees is highly affected with highest branch mortality. Threshold of branch mortality was found 40%. At the drought limit of beech, drought causes partial up to complete crown dieback of beech individuals. Tree growth was affected by soil drought having stunted height and lower mBAI. ‘2003 summer drought’ had high impact on basal area increment. Particularly extreme dry year is resisting the growth and survival of beech. Soil drought together with extreme summer drought impedes the establishment of beech trees in semi-natural oak forests. The interaction between different environmental and biotic stress factors and their cumulative impacts on establishment of beech trees should thoroughly be investigated. The future research on juvenile beech establishment should consider various site conditions. The effect of soil drought on above and below ground biomass relationship for the beech juveniles in natural condition should be considered in future research.

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Ponge, J.F., & Ferdy, J.B. (1997). Growth of Fagus sylvatica saplings in an old-growth forest as affected by soil and light conditions. Journal of Vegetation Science, 8, 789-796. Potocic, N., Seletkovic, I., Ugarkovic, D., Jazbec, A., & Mikac, S. (2008). The influence of climate properties on crown condition of Common beech (Fagus sylvatica L.) and Silver fir (Abies alba Mill.) on Velebit. Periodicum Biologorum, 110, 145-150. Pretzsch, H. (1999). Changes in forest growth. Forstwissenschaftliches Centralblatt, 118, 228-250. Rebetez, M., Mayer, H., Dupont, O., Schindler, D., Gartner, K., Kropp, J.P., & Menzel, A. (2006). Heat and drought 2003 in Europe: a climate synthesis. Annals of Forest Science, 63, 569-577. Regent Instruments Inc. (2009). WinDENDRO 2009a: tree ring, stem, wood density analysis and measurement. Quebec City, Canada Rich, P.M., Breshears, D.D., & White, A.B. (2008). Phenology of mixed woody-herbaceous ecosystems following extreme events: Net and differential responses. Ecology, 89, 342-352. Robson, T.M., Rodriguez-Calcerrada, J., Sanchez-Gomez, D., & Aranda, I. (2009). Summer drought impedes beech seedling performance more in a sub-Mediterranean forest understory than in small gaps. Tree Physiology, 29, 249259. Rood, S.B., Patino, S., Coombs, K., & Tyree, M.T. (2000). Branch sacrifice: cavitation-associated drought adaptation of riparian cottonwoods. Trees-Structure and Function, 14, 248-257. Rouault, G., Candau, J.N., Lieutier, F., Nageleisen, L.M., Martin, J.C., & Warzee, N. (2006). Effects of drought and heat on forest insect populations in relation to the 2003 drought in Western Europe. Annals of Forest Science, 63, 613-624. Rubner, K., & Reinhold, F. (1953). Das natürliche Waldbild Europas als Grundlage für einen europäischen Waldbau. Hamburg-Berlin, Germany Rust, S., & Roloff, A. (2002). Reduced photosynthesis in old oak (Quercus robur): the impact of crown and hydraulic architecture. Tree Physiology, 22, 597-601. Rust, S., & Roloff, A. (2004). Acclimation of crown structure to drought in Quercus robur L. - intra- and interannual variation of abscission and traits of shed twigs. Basic and Applied Ecology, 5, 283-291. Sabate, S., Gracia, C.A., & Sanchez, A. (2002). Likely effects of climate change on growth of Quercus ilex, Pinus halepensis, Pinus pinaster, Pinus sylvestris and Fagus sylvatica forests in the Mediterranean region. Forest Ecology and Management, 162, 23-37. Schack-Kirchner, H. (2001). Ein Fuzzy-Schlüssel für die Texturschätzung mit der Fingerprobe. In: Forstliche Versuchs- und Forschungsanstalt Baden Württemberg, Abteilung Bodenkunde (Ed.), Freiburger Forstliche Forschung: Chemische und physikalische Schlüsselprozesse der Speicher-, Regler- und Reaktorfunktion von Waldböden Forstwiss. Fakultät der Albert-Ludwigs-Universität und Forstliche Versuchs- und Forschungsanstalt Baden-Württemberg, 31-40. Schlichting, E., Blume, H.-P., & Stahr, K. (1995). Bodenkundliches Praktikum. Eine Einführung in pedologisches Arbeiten für Ökologen, insbesondere Land- und Forstwirte und für Geowissenschaftler. Berlin-Wien, 2nd Ed: Blackwell Wissenschafts-Verlag. Schulze, E.-D., Beck, E., & Müller-Hohenstein, K. (2005). Plant Ecology. Berlin/Heidelberg, Germany: Springer.

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Schütt, P., & Cowling, E.B. (1985). Waldsterben, a general decline of forests in Central-Europe - symptoms, development and possible causes. Plant Disease, 69, 548-558. Schütt, P., & Summerer, H. (1983). Decline symptoms on European beech. Forstwissenschaftliches Centralblatt, 102, 201-206. Seletkovic, I., Potocic, N., Ugarkovic, D., Jazbec, A., Pernar, R., Seletkovic, A., & Benko, M. (2009). Climate and relief properties influence crown condition of common beech (Fagus sylvatica L.) on the Medvednica massif. Periodicum Biologorum, 111, 435-441. Shigo, A.L. (1990). Die neue Baumbiologie. Braunschweig: Bernhard Thalacker verlag. Standortskartierung, A. (2003). Forstliche Standortsaufnahme. 6th edition. Munich: IHW-Verlag und Verlagsbuchhandlung, Eching Sterl, A., Severijns, C., Dijkstra, H., Hazeleger, W., van Oldenborgh, G.J., van den Broeke, M., Burgers, G., van den Hurk, B., van Leeuwen, P.J., & van Velthoven, P. (2008). When can we expect extremely high surface temperatures? Geophysical Research Letters, 35, 5. Suarez, M.L., & Kitzberger, T. (2008). Recruitment patterns following a severe drought: long-term compositional shifts in Patagonian forests. Canadian Journal of Forest Research-Revue Canadienne De Recherche Forestiere, 38, 3002-3010. Tinner, W., & Lotter, A.F. (2006). Holocene expansions of Fagus silvatica and Abies alba in Central Europe: where are we after eight decades of debate? Quaternary Science Reviews, 25, 526-549. Topoliantz, S., & Ponge, J.F. (2000). Influence of site conditions on the survival of Fagus sylvatica seedlings in an old-growth beech forest. Journal of Vegetation Science, 11, 369-374. vandenBerg, M., Klamt, E., vanReeuwijk, L.P., & Sombroek, W.G. (1997). Pedotransfer functions for the estimation of moisture retention characteristics of Ferralsols and related soils. Geoderma, 78, 161-180. vanHees, A.F.M. (1997). Growth and morphology of pedunculate oak (Quercus robur L) and beech (Fagus sylvatica L) seedlings in relation to shading and drought. Annales Des Sciences Forestieres, 54, 9-18. Vyskot, M. (1991). Aboveground biomass of an adult Norway spruce population. Lesnictvi (Prague), 37, 509-528. Walter, H., & Walter, E. (1953). Das gesetz der relativen Standortskonstanz: das wesen der pflanzengesellschaften. Berichte der Deutschen botanischen Gesellschaft, 66, 228-236. Walther, G.R., Berger, S., & Sykes, M.T. (2005). An ecological 'footprint' of climate change. Proceedings of the Royal Society B-Biological Sciences, 272, 1427-1432. Weber, J.A., & Gates, D.M. (1990). Gas-exchange in Quercus rubra (Northern red oak) during a drought - analysis of relations among photosynthesis, transpiration, and leaf conductance. Tree Physiology, 7, 215-225. Webster. (1992). Random House Webster's College Dictionary. Random House, New York. Yamamoto, S., Nishimura, N., & Matsui, K. (1995). Natural disturbance and tree species coexistence in an oldgrowth beech - Dwarf bamboo forest, southwestern Japan. Journal of Vegetation Science, 6, 875-886.

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Annexure

Annexure: 1

Coordinates

A

B

C

D

Lon. (Easting)

07°51'877"

07°51'922"

07°51'926"

07°51'890"

Lat. (Northing)

47°59'650"

47°59'661"

47°59'680"

47°59'700"

Alt. (m a.s.l.)

385

401

403

433

Table 1 Geographical coordinates of the four corner points of the study area

Coordinates

E

F

G

H

Lon. (Easting)

07°51'878"

07°51'916"

07°51'919"

07°51'884"

Lat. (Northing)

47°59'675"

47°59'660"

47°59'662"

47°59'690"

Alt. (m a.s.l.)

396

399

403

416

Table 2 Geographical coordinates of the four corner points of the sampling area

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Annexure

Annexure: 2

Spatial and vegetation data Plot No.

Lon. (E)

Lat. (N)

Alt. (m a.s.l.)

Aspect

Dt: Inclination (degree)

Plant species found in the plot

Remarks

Data sheet no. 1 Data sheet for collecting spatial and vegetation information from the sample plots

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Annexure

Annexure: 3

Individual tree measurement Plant No.

Spatial data for each tree Plant Plant Collar Axial distance Alt. Height length diam Lon. Lat. (m (cm) (cm) (mm) (E) (N) X Y a.s.l.)

Plot no. crown diam (cm) 1

2

3

4

Dt:

Branch measurement

Status

Diam (mm)

Dieback Dead Living compart Remarks annual annual ment shoot no. shoot no. (1, 2, 3)¥

Data sheet no. 2 Data sheet for measuring spatial, morphological and growth parameters of individual beech trees ¥ (1 = upper crown 2 = middle crown 3 = lower crown)

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Annexure

Annexure: 4

Fig. 1 Reference figure for denoting plant height measurement technique

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Annexure

Annexure: 5.1

Data sheet no. 3 Data sheet for collecting soil data from the field and calculating ASWSC in the laboratory

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Annexure

Annexure: 5.2

Fig. 2 The soil texture triangle with FAO (black) and German (grey) texture classes. In the German system S(s) = sand(y), U(u) = silt(y) and T(t)=clay(ey) (FAO, 2006; modified after Schack-Kirchner, 2001) 66 | P a g e

Annexure

Annexure: 5.3

Fig. 3 “Crowfoot” structure for accessing soil texture class

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Annexure

Annexure: 5.4 Steps for calculating ASWSC are mentioned below (Refer Data sheet no. 3 in Annex 5.1): 1. Estimation of the horizon-specific fine earth after Arbeitskreis Standortkartierung (2003) for calculating ASWSC (vol. % = mm/ 10 cm) (Texture and SOM content correction: factor a) 2. Considering the content of coarse materials - which is regarded not to contribute to the ASWSC – by subtracting the skeleton’s volume from 100 % (dimensionless) (Skeleton content: factor b) 3. Calculating the thickness factor for each horizon by dividing its vertical extension by 10 cm (10 cm) (Horizon depth: factor c) The ASWSC for each horizon is obtained by multiplying the single factors:

The profile ASWSC is the sum of the individual results: mm with individual horizons from i to n. Dry plots

Less dry plots

Plot no.

Plot ASWSC (mm)

Serial no.

Plot no.

Plot ASWSC (mm)

1

1/1

22

1

1/5

71

2

1/2

21

2

1/6

68

3

1/3

41

3

2/3

98

4

1/4

59

4

2/6

70

5

2/1

52

5

3/1

129

6

2/2

19

6

3/2

103

7

2/4

27

7

3/3

77

8

2/5

43

8

3/5

80

9

3/4

54

9

4/1

103

10

3/6

54

10

4/2

121

11

4/3

11

4/4

137

12

4/6

12

4/5

93

Serial no.

27 48

average ASWSC 39

average ASWSC 96

Table 3 ‘Dry’ and ‘less dry’ plots with their respective ASWSC

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Annexure

Annexure: 6.1

Stem and branch harvesting for biomass study Diameter (mm) of different stem and branch type

Tree identification Row no. Plot no.

Tree no.

Tree distance

2-5

5.1-8

8.1-11

11.1-18

18.1-41

Number of different annual shoot 1

2

3

Data sheet no. 4 Data sheet for biomass sample collection (1 = collected from upper crown 2 = collected from middle crown 3 = collected from lower crown)

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Annexure

Annexure: 6.2 Stem and branch weighting for biomass calculation Weight of different stem and branch type (gm)

Tree identification Row no.

Plot no.

Tree no.

2-5

5.1-8

8.1-11

11.1-18

18.1-41

Weight of different annual shoot (gm) 1

2

3

Data sheet no. 5 Data sheet for biomass calculation (1 = collected from upper crown 2 = collected from middle crown 3 = collected from lower crown)

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Annexure

Annexure: 7 Sample harvesting for tree-ring analysis Collar diameter (mm) of stem

Tree identification Row no.

Plot no.

Tree no.

Tree distance

3-7

7.1-11

11.1-15

15.1-19

19.1-27

27.1-41

Data sheet no. 6 Data sheet for harvesting trees for tree-ring analysis

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Annexure

Annexure: 8

A

D

B

C

E

F

G

Photo 1 : Different situations of the stand (A = stand in the steep slope; B = crooked canopy with a dead Scots pine; C and D = dead beech understorey having whole branch mortality; E and G = 200-250 cm heigh dead beech juvenile; F = beech understorey with partial crown dieback and dead stem axis)

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Annexure

H

J

Photo 2 : Soil profiles prepared at the stand (H = shallow soil profile; J = soil profile with high skeleton contents)

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Annexure

K

L

Photo 3 : Two stem disks harvested from the stand and prepared for tree-ring analysis (K = 33 mm diameter disk from a ‘less dry’ plot, the age of the tree was counted as 31 years; L = 41 mm diameter disk from a ‘dry’ plot, the age of the tree was counted as 42 years), more compressed radial growth increment was found in L compared than in K

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