Carbon pools of European beech forests (Fagus ...

Carbon pools of European beech forests (Fagus sylvatica) under different silvicultural management

Dissertation zur Erlangung des Doktorgrades der Fakultät für Forstwissenschaften und Waldökologie der Georg-August-Universität Göttingen

Martina Mund geboren am 27. Oktober 1969 in Arnsberg

Published in: Berichte des Forschungszentrums Waldökosysteme Reihe A, Band 189, 256 pp. ISSN 0939-1347

D7 1.

Berichterstatter und Prüfer: Prof. Dr. Friedrich Beese

2.

Berichterstatter und Prüfer: Prof. Dr. Burghard von Lüpke

3.

Berichterstatter und Prüfer: Prof. Dr. Ernst-Detlef Schulze

4.

Prüfer: Prof. Dr. Klaus von Gadow

Eingereicht: Februar 2004 Tag der mündlichen Prüfung: 03. April 2004

Acknowledgments Many people have supported my Ph.D. study in many different ways. I want to thank especially my advisors who were always interested in my studies and stimulated the progress by many encouraging scientific discussions. I thank Prof. Dr. Ernst-Detlef Schulze for offering me the opportunity to work at the Max Planck Institute for Biogeochemistry, Jena. I thank him particularly for his critical but always motivating discussions. I am very grateful to Prof. Dr. Friedrich Beese who “adopted” me as a student of the University of Göttingen and who was always willing to discuss critical questions of forest management and soil science.

Many thanks go to my friends and colleagues at the Max Planck Institute for Biogeochemistry: Agnes Fastnacht and Olaf Kolle for their never-ending support during field work and in solving technical problems, Iris Kuhlmann, Ines Hilke, Katarina Schenk and Antje Seckerdieck for their assistance with the large amount of laboratory work, Jens Schumacher for teaching me many “secrets” of statistical analysis, Christian Wirth, Alexander Knohl, Astrid Søe and Reiner Zimmermann for many fruitful discussions and good cooperation, Tiemo Kahl for his support in the field to measure snags and logs, Andrew Manning, Vicky Temperton, Annette Freibauer and Axel Don for helpful comments on the manuscript, Annett Börner for her assistance in graphical presentations. Many thanks go to the people from the workshop, namely Bernd Schlöffel and Reimo Leppert, the computer department, the library and the administration. I thank in particular the colleagues of the FORCAST-project for fruitful discussions and for providing their data: Tryggve Persson, Giorgio Matteucci, Francesca Cotrufo, Ingo Schöning, Marco Bascietto, Bernd Zeller, Alberto Masci, Massimiliano Hajny, Harmke van Oene and Volker Hahn.

I thank the forestry administration of Thuringia, namely Mr. Weller (TLWJF), Mr. Weber (TLWJF) and Mr. Eckardt (TMLNU) for supporting my research and for valuable information about forest management in Thuringia. I thank Manfred Grossmann, Karola Marbach and Thomas Möhlich and the administration of the Hainich National Park for their support of my research in the Park and for providing much information about the Hainich National Park and its history. I thank the local foresters Mr. Fritzlar, Mr. Biehl, Mr. Willner (sen.), Mr. Willner (jun.), Mr. Trümper, Mr. Posselt, Mr. Meyer, Mr. Fahrig and Mr. Kohlstedt for providing forestry maps and data and much unwritten valuable information about former and current management of the study sites. For valuable discussions about soil classification I thank Mr. Burse (TLWJF), Wolfgang Brandner (TLUG) and Dr. Philipp Jaesche (TU München). I thank Prof. Dr. Wittecke (FH Schwarzburg) for all the information and data about forest history. I thank Dr. Siegfried Klaus (TLUG) for his help to get to know the Hainich NP. Special thanks go to the private landowners of the “Laubgenossenschaften” Langula, Oberdorla and Oppershausen. I gratefully acknowledge all friends and colleagues at the Max Planck Institute: Lina Mercado, Claudia Czimczik, Waldemar Ziegler, Gerd Gleixner, Anna Ekberg, Antje Weitz, Michael Scherer-Lorenzen, Angelika Thuille, Stephanie Nöllert, Corinna Rebmann, Constanze Schaaf, Armin Jordan, Hannes Böttcher and Peter Anthoni for helpful discussions and good cooperation. I thank my parents, Gabriele and Friedhelm Mund, and my sisters Raphaela and Veronika, for their encouraging support, their love and trust in me throughout my life. In particular I thank Ralf Schindek for his never-ending love, support and patience during the last 11 years.

Content 1. INTRODUCTION...................................................................................................................... 1 1.1 Forest ecosystems and the global carbon budget ............................................................ 1 1.2 Impacts of forest management on the carbon budget of forests...................................... 1 1.3 Forest management and the Kyoto Protocol ................................................................... 3 1.4 Main objectives and hypotheses of this study................................................................. 4 2 TERMINOLOGY OF THIS STUDY ............................................................................................... 7 3 MATERIAL AND METHODS .................................................................................................... 11 3.1 General approaches ....................................................................................................... 11 3.2 Regional distribution of the study sites ......................................................................... 12 3.3 Statistical design and analysis ....................................................................................... 16 3.3.1 Data sampling and replicates.................................................................................. 16 3.3.2 Statistical analysis and software............................................................................. 19 3.4 The study sites and plots ............................................................................................... 20 3.4.1 Geography .............................................................................................................. 20 3.4.2 Climate ................................................................................................................... 21 3.4.3 Selection of the study plots .................................................................................... 22 3.4.4 Geology and general soil characteristics................................................................ 23 3.4.5 Vegetation .............................................................................................................. 27 3.4.6 Recent silviculture and stand structure................................................................... 27 3.4.6.1 Even-aged stands of the regular shelterwood systems .................................... 27 3.4.6.2 Uneven-aged stands of the selection system ................................................... 29 3.4.6.3 Uneven-aged and unmanaged stands of the Hainich Nationalpark................. 30 3.4.7 Land use history ..................................................................................................... 31 3.5 Cooperation with other research projects...................................................................... 45 4 STAND STRUCTURE AND BIOMASS ........................................................................................ 47 4.1 Methods......................................................................................................................... 47 4.1.1 Forest inventory...................................................................................................... 47 4.1.2 Coarse woody debris and large dead wood (snags and logs)................................. 55 4.2 Results ........................................................................................................................... 58 4.2.1 Forest inventory...................................................................................................... 58 4.2.1.1 Diameter distribution....................................................................................... 58 4.2.1.2 General forest stand characteristics................................................................. 65 4.2.2 Carbon pools in living tree biomass....................................................................... 72

4.2.3 Carbon pools in dead wood biomass (snags, logs and CWD) ................................77 5 LITTER FALL, ABOVEGROUND LITTER DECOMPOSITION AND CARBON POOLS IN THE ORGANIC LAYER ...................................................................................................................81 5. 1 Methods.........................................................................................................................81 5.1.1 Litter fall .................................................................................................................81 5.1.2 Organic layer...........................................................................................................83 5.1.3 Mean residence time of leaf litter and fine woody debris (FWD) ..........................84 5.1.3.1 Incubation of leaf litter bags .............................................................................84 5.1.3.2 The "ratio-approach".........................................................................................85 5.2 Results............................................................................................................................86 5.2.1 Litter fall .................................................................................................................86 5.2.2 Carbon pools in the organic layer ...........................................................................93 5.2.3 Mean residence time of leaf litter and FWD in the organic layer.........................101 6 SOIL ORGANIC CARBON POOLS ............................................................................................111 6.1 Methods........................................................................................................................111 6.1.1 Soil pits .................................................................................................................111 6.1.1.1 Sampling ........................................................................................................111 6.1.1.2 Soil processing and chemical analysis ...........................................................112 6.1.1.3 Soil classification ...........................................................................................113 6.1.2 Soil cores (0-15 cm soil depth) .............................................................................113 6.1.2.1 Sampling ........................................................................................................113 6.1.2.2 Soil processing and chemical analysis ...........................................................113 6.1.3 Determination of total soil depth and soil type .....................................................115 6.2 Results..........................................................................................................................115 6.2.1 Total SOC pools....................................................................................................116 6.2.2 Overview of SOC concentrations and fine soil bulk density in the upper mineral soil (0-15 cm) of the study plots ...........................................................................123 6.2.3 Overview of SOC pools in the upper mineral soil (0-15 cm) of the study plots ..127 6.2.4 Soil-specific effects and effects of silvicultural treatments on SOC pools in the upper mineral soil (0-15 cm).................................................................................132 7 TOTAL CARBON BUDGETS OF THE SILVICULTURAL SYSTEMS...............................................145 8 DISCUSSION ........................................................................................................................147 8.1 Silvicultural effects and site-specific effects on carbon pools in forest biomass.........148 8.1.1 Carbon pools in living tree biomass......................................................................148 8.1.2 Dead wood carbon pools.......................................................................................151

8.1.3 Scenarios of future changes.................................................................................. 156 8.2 Soil-specific effects and silvicultural effects on SOC pools of forests ........................ 158 8.2.1 Soil-specific effects on SOC pools and their interactions with former forest use 158 8.2.2 How are SOC pools linked to silvicultural activities? ......................................... 162 8.3 Estimates of net carbon fluxes by different methodological approaches.................... 165 8.4 How large is the potential for increasing carbon pools in formerly managed forests due to a cessation of timber use?................................................................................. 170 9 CONCLUSIONS .................................................................................................................... 173 10 SUMMARY ......................................................................................................................... 175 11 ZUSAMMENFASSUNG ......................................................................................................... 179 12 REFERENCES ...................................................................................................................... 183 13 LIST OF ABBREVIATIONS ................................................................................................... 199 14 LIST OF FIGURES ................................................................................................................ 201 15 LIST OF TABLES ................................................................................................................. 203 16 APPENDIX .......................................................................................................................... 205

1 Introduction

1. Introduction 1.1 Forest ecosystems and the global carbon budget Fossil fuel combustion and land use changes have resulted in a drastic increase of atmospheric carbon dioxide (CO2) concentrations. The globally averaged CO2 concentration increased from about 280 ppm in 1850 to 367 ppm in 1999 (IPCC 2001a). Carbon dioxide (CO2) is the most important greenhouse gas contributing to ongoing climate change. Since the 1850´s an average of about 40% of anthropogenic CO2 emissions have accumulated in the atmosphere. The remaining 60% have been absorbed by the land and oceans in roughly equal proportions (IPCC 2001a). Forest ecosystems play a particularly important role in the global carbon budget, because almost 46% of terrestrial organic carbon is stored in tree biomass (359 Gt C) and forest soils (787 Gt C) (WBGU 1998). Consequently, changes of net carbon release or uptake by forest ecosystems due to a conversion to other land use types or due to changes in forest use and management can have a considerable impact on atmospheric CO2 concentrations (WBGU 1998, IPCC 2000). In general, it is expected that forest ecosystems are a sink for atmospheric CO2 (e.g. IPCC 2000, Puhe and Ulrich 2001). However, high spatial heterogeneity and temporal variability of terrestrial carbon pools and fluxes as well as natural and anthropogenic disturbances and environmental changes (e.g. N and CO2 fertilization) lead to large uncertainties in estimates (e.g. WBGU 1998, Schulze et al. 1999, IPCC 2001b, Wang and Hsieh 2002, Janssens et al. 2003, Körner, 2003). The largest contributions to the terrestrial sink capacity for carbon are expected from afforestation of agricultural land and the protection of forests against conversion to nonforested lands (IPCC 2000). The role played by different forest management practices or unmanaged, old growth forests and primary forests to the global carbon budget is unclear, particularly with respect to long-term carbon storage in the mineral soil.

1.2 Impacts of forest management on the carbon budget of forests The conversion of primary forests or old-growth forests to plantations or managed seminatural forests leads to a significant reduction of carbon pools in the living tree biomass and in the dead aboveground biomass. Depending on the thinning regime, rotation period, cutting methods, tree species, climate and site productivity, the average living and dead aboveground 1

1 Introduction

biomass of managed forests reaches only 20-60% of that present in the original primary forest (e.g. Harmon et al. 1990, Cannell et al. 1992, Karjalainen 1996, Fleming and Freedman 1998, Trofymow and Blackwell 1998, Weber 2001, Crow et al. 2002). The impact of forest use and management on organic carbon pools in the mineral soil depends on many site-specific factors (e.g. forest type, climate, and edaphic conditions) and is often overridden by the high spatial variability of soil organic carbon (SOC) pools in forest soils. Consequently, general effects on SOC pools are evident only with the most intensive practices. For example, N-fertilization leads to higher SOC concentrations in the upper mineral soil (Johnson and Curtis 2001). Clear-cuttings combined with intensive soil preparation (such as scalping or bedding), herbicide treatments and/or prescribed fires cause soil erosion, soil compaction, and significant losses of SOC and cations (e.g. Bormann and Likens 1979, Covington 1981, Heinsdorf 1986, Mattson and Smith 1993, Black and Harden 1995, Johnson and Henderson 1995, Apps and Price 1996, Jurgensen et al. 1997, Worrell and Hampson 1997, Rollinger et al. 1998, Prescott et al. 2000, Quesnel and Curran 2000, Johnson and Curtis 2001, Block et al. 2002, Laiho et al. 2003). In contrast to high losses of carbon pools in living and dead wood biomass due to the conversion of primary forests to managed forests, significant changes in SOC pools (excluding the organic matter resting on the mineral soil (= organic layer)) have not yet been found (Fleming and Freedman 1998, Weber 2001) or were restricted to regions receiving annual precipitation above 1500 mm, or to coniferous plantations or young plantations (below 40 years old, Guo and Gifford 2002). It is evident that increased decomposition of dead organic matter after clear-cutting results in a net loss or a zero carbon balance of a forest ecosystem for about 5-6 years after clear-cutting, even when successful regeneration occurs (Pypker and Fredeen 2002, Rannik et al. 2002). This time period of net carbon loss may extend to 14-20 years if growth of the regenerating stands is reduced or if large amounts of dead wood remain on site (e. g. Cohen et al. 1996, Schulze et al. 1999). It is not always clear to what extent these carbon losses result from decomposition of harvest residues, organic layer material and the mineral soil. There is some evidence that increased decomposition of organic matter on the mineral surface is associated with an increased transport of organic matter (by soil fauna or DOC) into the mineral soil or that organic matter is mechanically incorporated into the mineral soils by harvesting machines. Both processes result in a net increase of SOC in the upper mineral soil (Bormann and Likens 1979, Mattson et al. 1987, Huntington and Ryan 1990, Mattson and Smith 1993, Johnson et al. 1995, Olsson et al. 1996, Dai et al. 2001, Laiho et al. 2003). Such transport processes are also thought to cause the increase observed in SOC concentrations (on average 18%) in the A-horizon of many coniferous 2

1 Introduction

forests after clear-cutting when the residues were left on site (“sawlog harvesting”) (metaanalysis by Johnson and Curtis 2001). In contrast, clear-cuttings of coniferous forests in combination with a removal of all residues ("whole-tree harvesting") reduced SOC pools in the A-horizon by 6% compared to undisturbed sites. How long the positive effects of “sawlog harvesting” in coniferous forests will last is still unknown. In hardwoods, which are generally characterised by lower amounts of harvesting residues and a thinner organic layer, “sawlog harvesting” resulted in a small negative effect on SOC pools, and in mixed stands “sawlog harvesting” had no effect on SOC pools at all (Johnson and Curtis 2001). Forests and woodlands cover about 30% of European land area. Except for some protected or inaccessible areas, all of these forests (about 97% of the forested area) are used by man (UNECE/FAO 2000). As a consequence of historical forest use and management, only about 14% of the forested area in Germany is covered by European beech (Fagus sylvatica) (BMVEL 2003; in Thuringia 24%, Wirth et al. 2003), even though this tree species would dominate the vegetation across central Europe under natural conditions (Ellenberg 1996, Leuschner 1998, Puhe and Ulrich 2001). It is a declared intention of German and Thuringian forestry policy today to increase the proportion of beech forests (e.g. BMVEL 2002, TMLNU 2002), so that beech forests and their management will get much higher priority in forest management, forest research and regional carbon budgets in the future.

1.3 Forest management and the Kyoto Protocol It is evident that the loss or gain of carbon due to “deforestation”, “afforestation” and “reforestation” practices is of major relevance to the global carbon balance (IPCC 2000), and such direct human-induced activities are declared explicitly in Article 3.3 of the Kyoto Protocol (UNFCCC 1997) as “accountable activities” in considering the national commitments to reduce net greenhouse gas emissions. In contrast, "additional human-induced activities" related to agricultural soils and forestry, mentioned in Article 3.4 of the Kyoto Protocol, are less welldefined and less obvious with respect to their impact on the global carbon budget. For forestry and forest science, the following fundamental questions have risen from Article 3.4: (1) What management practices have the largest and most sustainable influence on the carbon sink capacity of managed forests? (2) Does the present-day common practice of forest use and management in Europe support or reduce carbon sequestration and storage in forest ecosystems compared to unmanaged or primary forests? (3) Shall the cessation of timber use be interpreted as an “additional human-induced activity”? 3

1 Introduction

Particularly the latter question has resulted in some controversy. In the past it was generally assumed that old-growth forests or primary forests are at a “steady state” with respect to carbon balance (Jarvis 1989, Melillo et al. 1996). However, more recently Schulze et al. (2000) and Carey et al. (2001) postulated a substantial carbon sink capacity in unmanaged forests and oldgrowth forests. The interpretation of Article 3.4 may have considerable impacts on political intentions to conserve primary forests or to replace them by young managed forests. Even if primary forests would not serve as a substantial carbon sink, measured according to the Kyoto Protocol as changes of carbon stocks with time, their conversion to managed forests or agricultural land will definitely induce large carbon losses. In conclusion, the quantification of carbon pools in tree biomass, the organic layer and the mineral soil of differently-managed beech forests will provide essential information about the capacity of forest ecosystems to act as a carbon sink.

1.4 Main objectives and hypotheses of this study The overall objectives of this study are: (1) to quantify the carbon pools in differently managed European beech (Fagus sylvatica) forests. The silvicultural treatments are: regular shelterwood system, selection system, and unmanaged forest. (2) to enhance the understanding of ecosystem processes that link the effects of silvicultural activities on stand characteristics with changes in soil organic carbon pools. The carbon storage in wood products is explicitly excluded from this study. This work does not focus on short-term effects of single harvesting events and harvesting methods, but rather on long-term effects of forest management on the carbon budget of European beech forests. This approach includes the possibility of detecting effects of forest management that have accumulated over several years to decades and that may override the spatial variability of forest soils. This study is restricted to well-growing beech forests on nutrient-rich soils. Hence the carbon pools, measured in this study, will not be representative for average carbon pools of forests in Germany or Europe, because most German or European forests are growing under less favourable conditions. For example, in western Germany and in the eastern state of Thuringia 4

1 Introduction

only about 22% or 35%, respectively, (BMELF 1997, Wirth et al. 2003) of forested soils are similar to those of the presented study sites (soils on limestone and limestone covered with loess). However, if this study is able to reveal the main processes or mechanisms that affect carbon storage in forest ecosystems and that are induced by forest management, then an extrapolation to less fertile sites may be reasonable. In a managed forest a large proportion of wood biomass is regularly removed from the ecosystem and each cutting is associated with soil disturbances and canopy openings of variable size, all of which may affect the litter production and the organic matter decomposition in the remaining stand. When defining the degree of forest disturbance by the size of canopy openings resulting from cuttings and the maximum amount of wood that is removed by a single action for tree harvesting, the degree of disturbance

decreases

along

the

following

series:

clear cutting > shelterwood system > selection system > unmanaged / primary forest (see also Grigal 2000 and Marshall 2000). (When the number of operational actions per unit of time is considered, the selection system represents the most intensive silvicultural system (Röhrig and Gussone 1990)). With respect to beech forests growing under favourable conditions, this study is based on the following hypotheses: (1) Carbon pools in differently-managed forest ecosystems increase sequentially from the regular shelterwood system to the selection system to the unmanaged forest. (2) Different silvicultural treatments give rise to changes in biomass carbon pools as well as to changes in soil organic carbon pools. (3) Soil organic carbon pools are positively correlated with the annual litter fall and the basal area of a forest stand. (4) Soil organic carbon pools of even-aged stands increase with increasing stand age. A saturation of soil organic carbon pools within a single rotation is not expected.

5

1 Introduction

6

2 Terminology of this study

2 Terminology of this study A number of terms of forest science and soil science are inconsistently used in forest ecology. These terms are defined or explained in view of the following comparison. In central Europe the most common silvicultural system for European beech forests is the regular (uniform) shelterwood system (in German “(Groß-) Schirmschlagbetrieb”). It is a silvicultural system where the regeneration is initiated and supported by the removal of the harvestable (“mature”) trees in two or more successive steps of cutting (e.g. preparatory- and seed cutting, several successive cuttings to increase the light availability for the regeneration, and final-cutting). The temporarily remaining old trees (overstory, shelter) provide seeds and protect the (natural) regeneration from climatic extremes. The higher light available due to these cuttings also promotes the growth of the remaining tress. Shelterwood cutting and later thinning produces an even-aged stand with a homogenous vertical and horizontal structure (by convention the age of even-aged tree communities does not differ by more than 20% of the intended rotation, Nyland 1996). Only at the regeneration stage, when the shelter of mature trees covers the seedlings and saplings, is the shelterwood system characterized by two, clear canopies. Another common system for beech forests is the group-shelterwood system (in German “Femelschlagbetrieb”). Depending on the duration of the regeneration stage, this system may lead to an even-aged or an uneven-aged stand. The selection system (in German “Plenterwaldbetrieb” or “plenterartige Bewirtschaftung”) is a less common silvicultural system in central Europe that results in uneven-aged stands. The stand structure of forest stands established by “group-selection cutting” (in German “Gruppenplenterung”) or “groupshelterwood cutting” are similar so that these terms are sometimes used synonymously (e.g. Gayer 1898 in Röhrig and Gussone 1990). However, these two systems differ conceptually. In general, the group-shelterwood system passes through a cycle of regeneration, growth and harvest, similar to other silvicultural systems that are based on distinct temporally and functionally defined cuttings on a larger area. The time period that is needed to reach a specific stage of maturity defines the rotation. In contrast, at a selection system individual trees or small groups of trees are cut periodically to obtain the yield, to improve the forest structure and growth and to support (but not to force) the regeneration at the same time and at the same area. There are no defined “cutting areas” that are managed (e.g. thinned or harvested) at a specific time. The selection cutting shall result in (1) a multi-cohort (uneven-aged) stand, (2) a reverse-J shape diameter distribution or “balanced” diameter distribution (“equilibrium distribution”, in German 7


“Plentergleichgewicht”), (3) a continuous vertical distribution of foliage, (4) a permanent, closed tree canopy, and (5) an equilibrium of tree harvest and regrowth of trees on a small spatial scale (Burschel and Huss 1987, Röhrig and Gussone 1990, Mayer 1992, Nyland 1996, Schütz 2001a). Dohrenbusch (2001) used the term “group-selection system” as an equivalent to the German expression “Femelschlagbetrieb” and distinguished it from the “plenter-forest”. Selection cuttings should be separated from selective cuttings (in German “ungeregelte Plenterung”) that are exploitive cuttings, which remove only the largest, most valuable trees and do not ensure a balanced diameter distribution and an adequate regeneration, and they do not promote stand growth and timber quality. Theoretically, the term “soil” or “solum” includes the mineral soil as well as the organic layer on the surface of the mineral soil, excluding plant material that have not begun to decompose (Schachtschabel et al. 1992, Soil Survey Staff 1999). However, historically most studies on the biogeochemistry of soils and in particular on soil organic matter (SOM) or soil organic carbon (SOC) pools were carried out on croplands, which are not covered by a well defined layer of dead organic matter. Consequently, the terms soil organic matter (SOM) and soil organic carbon (SOC) often include only the dead organic matter or the organic carbon, respectively, of the mineral soil. In forest ecosystems the terms are used sometimes for the dead organic matter resting on the surface of the mineral soil and in the mineral soil, and sometimes only for dead organic matter in the mineral soil. In the latter case, the organic layer on the surface of the mineral soil is neglected or mentioned separately as “organic layer” or “forest floor”. Both terms refer to all organic matter resting on, but not mixed with, the mineral soil surface (Pritchett and Fisher 1979). The term “forest floor” may also include the herbaceous ground vegetation and mosses. The term “humus layer” is often used for the decomposing organic material beneath the litter layer (L-layer) as well as the Ah soil horizon. To avoid confusion both carbon pools are discussed separately in the following terms: •

“Organic layer” = all dead organic matter smaller than 5 cm in diameter on the surface of the mineral soil that derived from litter fall and all kinds of disturbances (e.g. tree harvesting, windthrow), including dead leaves, twigs, branches, fruits and roots, and small material (< 1 mm) of dead animals, fungi or bacteria. The term “organic layer” is not used as a synonym for the German expression “Auflagehumus” (AG Boden 1994). The term “Auflagehumus” specified types of humus layers (“moder humus” to “raw humus” (“mor humus”)) that include a L-layer (L-horizon = litter layer; not and only

8


weakly decomposed organic matter), a F-layer (= Of-horizon; fragmented, and partly decomposed organic matter that is sufficiently well preserved to permit identification as origin) and a well developed H-layer (Oh-horizon; largely well-decomposed, amorphous organic matter) that is more than 5 mm thick. In contrast, the term “organic layer” is not restricted to specific types of humus layers (humus forms). At the study sites of this work the “organic layer” consists of a L-layer only ((L-) mull) or a L-layer and a thin (< 2 cm), weakly developed F-layer that partly included some mineral particles (F-mull). “Organic layer carbon” is the carbon content in the organic layer. •

“Soil organic matter (SOM)” = all dead organic matter in the mineral soil that derived from litter input, including leaves, twigs, branches, woody debris, fruits and roots, and small material (< 1 mm) of dead animals, fungi or bacteria in the mineral soil. “Soil organic carbon (SOC)” is the carbon content of the soil organic matter.

All soil samples were analysed for total organic carbon (TOC) and total inorganic carbon (TIC). The sum of total organic carbon and total inorganic carbon results in the total carbon of the mineral soil (TC = TOC + TIC). Total inorganic carbon was measured but it was excluded from the “carbon budget” of this study. The biomass of living microorganisms in the mineral soil or the organic layer was not quantified separately. Thus, about 1-3% of the presented SOC pools originated from living microorganisms (Wardle 1992, Carter et al. 1998, Kögel-Knabner 2002). A separation between living and dead roots is very time consuming and the proportion of dead root carbon pools can be assumed to be less than 2% of SOC pools (Stober et al. 2000). Consequently, all roots were picked out from the soil samples before analysis. The concentration of carbon denotes the mass of carbon per mass of an absolutely dry substrate (e.g. gC gsoil-1, or in % of the substrate). The term “carbon pool” is equivalent to the term “carbon stock” and represents the mass of carbon per unit area (e.g. tC ha-1), while carbon fluxes indicate the rate of exchange of carbon between carbon reservoirs and are given in the mass of carbon per unit area and time (e.g. tC ha-1 year-1). In the present study the “carbon budget” includes the sum of organic carbon pools in the organic layer, the mineral soil, living tree biomass and aboveground dead wood biomass within the forest ecosystem. It represents the long-term differences between carbon inputs and carbon outputs of the ecosystem. Organic carbon in ground vegetation and animals and outside the

9


forest ecosystem in primary and secondary forest products is not included in the analysis. The term “carbon balance” is used particularly for the short-term balance of carbon fluxes. The study site is defined by the regional location and includes several study plots representing different age classes of the shelterwood system or different stand structures of the uneven-aged stands (see section 3.4.6). The term “stand” is used synonymously to “study plot”, when forest structure, age or tree biomass are analysed.

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3 Material and methods

3 Material and methods 3.1 General approaches Forest ecosystems are characterised by the long lifetimes of trees and, compared to other terrestrial ecosystems, high pools of living biomass, a well-defined microclimate and a relatively close internal cycling of nutrients. Therefore, silvicultural activities (as disturbances) can affect the carbon budget of forest ecosystems for years to decades. Furthermore, the extent and duration of impacts on the carbon budget depend on the stage of development of the affected forest ecosystem. If stand age plays any role in the carbon budget of forest ecosystems (“stand age-effect”) the entire rotation of even-aged stands has to be analysed and the average of at least one rotation has to be compared with uneven-aged stands. To quantify effects of silviculture on the carbon budget of beech forests, the present study is based on two general approaches: •

The comparison of stands of different age classes, representing the stages of forest development (chronosequence approach).

•

The comparison of beech forests that were managed according to different silvicultural systems or intensities.

The first approach implies the independent, continuous variable “stand age” and the second approach the co-factor “silvicultural system”. The main dependent variable is the “organic carbon pool” in tree biomass, the organic layer or the mineral soil. The combination of these two approaches should allow investigation of long term processes within the time span of a thesis, but they also involve major constraints: (1) all study sites must have similar climate and edaphic conditions over time, (2) tree growth and litter decomposition should not be limited by specific, extreme site factors that could dominate or modify any potential effect of silviculture (e.g. drought, water logging, extreme topography), and (3) the processes and interactions investigated in this study are working on different time scales and partly on a scale that is longer than the given rotation period or range of tree age.

11


3.2 Regional distribution of the study sites The constraints regarding to site-specific factors are satisfied at the Hainich-Dün region in central Thuringia (Germany, Figure 3.1). Here we could find the unique situation that there are presently unmanaged deciduous forests (Hainich Nationalpark) as well as differently managed deciduous forests within the same climatic region, at the same elevation and on the same bedrock. Climate and soil conditions of the region provide optimum growing conditions for beech forests (TLWF 1997). The pedogenesis can be expected to be uniform in the same regional climate and topography of the sites. Only a loess layer, which contributes to the favourable growing conditions in this region, can vary significantly and increases the spatial heterogeneity of the mineral soil even within the study plots (chapter 6). With respect to the unmanaged forest, the situation is constrained by the fact that there are no “true” primary forests left in Germany and that the protected forest of the Hainich Nationalpark is also influenced by forest use and management of the past (section 3.4.7). Initially, it was assumed that the land use history of all study sites was similar, independent of their recent management, because of the relatively small size of the entire Hainich-Dün region and its homogeneous site conditions. However, investigations about the land use history of the study sites revealed that the type and intensity of historical forest use and silviculture were highly influenced by different land ownerships, which in turn were affected by political events in history (section 3.4.7). Furthermore, the forest use history affected the current geographical grouping of the silvicultural systems along the Hainich-Dün. The chronosequences or shelterwood systems were located in the northern part of the Hainich-Dün, the selection system at the central Hainich and the unmanaged forest at the south of the Hainich (Figure 3.1). A random selection of the silvicultural systems over the Hainich-Dün region or a reverse arrangement of sites was not possible. All study plots that belonged to the same study site were located within a very small, homogeneous area. The age classes of each chronosequence, for example, were located within an area of 2.5 km2. Even the distance between the study sites in the south (“Hainich NP”) and the study sites at the Dün (Chronosequence “Leinefelde”) was only about 30 km. However, it cannot be excluded that there is any “regional factor” or “gradient from the north to south” that may result from historical land use. Therefore, forest use history or the location within the HainichDün (Dün, northern, central, southern Hainich, Figure 3.1; Table 3.1) is considered carefully when interpreting the results of the present study. A gradient of precipitation, air temperature or 12


nitrogen depositions from the Dün to the southern Hainich were not found by local measurements (section 3.4.2, Mund et al. in prep. b).

Shelterwood system Chronosequence “Leinefelde” Revier Geney (FA Leinefelde), 5 study plots of different stand age

Shelterwood system Chronosequence “Mühlhausen” Revier Eigenrieden, Stadtwald Mühlhausen, 5 study plots of different stand age

Selection system Reviere Langula and Oberdorla (FA Mühlhausen), 3 study plots of different stand structure

Unmanaged forest “Hanich NP”, 3 study plots of different stand structure

5

10

15

20

25

30

km

Figure 3.1: Overview of the locations of the study sites. Source of air photograph: Nationalparkverwaltung Hainich 1999.

13

14

Silvicultural system

Shelterwood

Shelterwood

Shelterwood

Shelterwood

Shelterwood

Shelterwood

Shelterwood

Shelterwood

Shelterwood

Shelterwood

Study plot

Lei-30M

Lei-62M

Lei-111M

Lei-141M

Lei-153+16M

Mühl-38

Mühl-55

Mühl-85

Mühl-102

Mühl-171+10

171 + 10 2

102 2

85 2

55 2

38 2

153 + 161

FORCAST Lei-153+16

------

---------

Stadt Mühlhausen, Revier Eigenrieden

Klein: Fl.2 Stadt Mühlhausen, Revier Eigenrieden


-----

FoA Leinefelde, Revier Geney FoA Leinefelde, Revier Geney

Chronosequence “Leinefelde”, Dün Chronosequence “Leinefelde”, Dün Chronosequence “Mühlhausen”, northern Hainich Chronosequence “Mühlhausen”, northern Hainich Chronosequence “Mühlhausen”, northern Hainich Chronosequence “Mühlhausen”, northern Hainich Chronosequence “Mühlhausen”,

51°11´24´Ń 10°18´25´É

51°11´31´Ń 10°19´13´É

51°11´53´Ń 10°19´11´É

51°11´37´Ń 10°18´36´É

51°11´41´Ń 10°18´20´É

51°19´41´Ń 10°22´04´É 51°20´02´Ń 10°22´07´É

51°20´02´Ń 10°22´07´É

FORCAST Lei-111 = CARBOEUROFLUX: Tower site Leinefelde


FoA Leinefelde, Revier Geney

Chronosequence “Leinefelde”, Dün

1111

51°19´48´Ń 10°21´19´É

51°20´13´Ń 10°22´07´É

Location

FORCAST Lei-62

-----



621

FORCAST Lei-30

Code of adjacent study plots




301

141

Forestry district

Study site/Region

Stand or tree age in 2000 (years)

Table 3.1: Study sites at the Hainich-Dün region, Germany. (1) Source: Bascietto 2003, (2) Forestry records, (3) Estimated age of the oldest trees/ mean estimated age of the 20% largest trees/ mean estimated age of all trees per inventory plot. FoA = Forstamt.


Unmanaged

Hai-II

Hai-III

Hainich NP, southern Hainich

Unmanaged

Hai-I

uneven-aged 202 / 153 / 74 3

Unmanaged

Lang-III

Hainich NP, southern Hainich

uneven-aged 178 / 168/ 87 3

Selection cutting

Lang-II

uneven-aged 178 / 131 / 48 3

uneven-aged 180 / 123 / 39 3

Selection cutting

Lang-I

Selection system "Langula", central Hainich Selection system "Langula", central Hainich Selection system "Langula", central Hainich Hainich NP, southern Hainich

uneven-aged 190 / 122 / 45 3

Selection cutting

Study site/Region

uneven-aged 230 / 147 / 51 3

Stand or tree age in 2000 (years)

Silvicultural system

Study plot

Table 3.1: Continued.

National Park Hainich, Weberstedter Holz

National Park Hainich, Weberstedter Holz National Park Hainich, Weberstedter Holz

FoA Mühlhausen, Revier Langula



Forestry district

51°04´42´Ń 10°27´14´É 51°04´45Ń 10°27´07´É

close to FORCAST site: Hai-T CARBOEUROFLUX: Tower site Hainich

51°04´48´Ń 10°27´45´É

51°10´33´Ń 10°20´16´É

51°08´33´Ń 10°22´16´É

51°07´44´Ń 10°22´14´É

Location

-----

-----

-----

Gerold: Parzelle I

Gerold: Parzelle III

Code of adjacent study plots


15


3.3 Statistical design and analysis 3.3.1 Data sampling and replicates In the framework of this thesis it was not possible to measure several types of silvicultural systems at different regions and thus, to analyse “true” replicates of the factor or treatment “silvicultural system”. However, to represent at least the variability of forests structure and carbon pools within a single silvicultural system, the following hierarchical design was chosen (Figure 3.2): At the selection system and the unmanaged forest three study plots (each 100 x 100 m) that represented the range of different stand structures on similar soils were established (for details see section 3.4.6). For the shelterwood system differences in stand age or the stage of development after seed- and final cutting and differences between stands of similar stand age or development stage had to be considered. Therefore, two chronosequences each with five study plots (100 x 100 m) of different stand age were selected. The stand ages or stages of development of the study plots in turn should be evenly distributed over the entire rotation. Thus, gaps of stand ages along the rotation period of the first chronosequence were filled by the other chronosequence whenever possible. As the two chronosequences of this study were located at different sites, managed by different foresters, they represented the spatial variability within the shelterwood system and they were “true” replicates of the silvicultural treatment “shelterwood system”. The study plots did not represent replicates of the silvicultural systems (“pseudo-replicates”) but they reflect spatial differences in stand structure and maybe of soil conditions that were not known before the presented detailed studies.

16

Lei-111M Lei-153+16M Lei-30M Lei-141M Lei-62M

Chronosequence "Leinefelde"

Regular shelterwood

Figure 3.2: The hierarchical design of the present study.

Study plot

Study site

Silviculture

Factors

"Langula"

Hai-I Hai-II

Hai-III

"Hainich NP"

Selection system Unmanaged forest

Mühl-55 Mühl-102 Lang-I Lang-III Mühl-85 Mühl-38 Mühl-171+10 Lang-II

Chronosequence "Mühlhausen"

Regular shelterwood


17


Because of the very high spatial variability to be expected for carbon pools of the upper mineral soil and the organic layer, even within a study plot of one hectare, the sampling of the organic layer and the upper mineral soil was carried out randomly within the entire study plot (Figure 3.3). The measurements of tree height and diameter, the collection of annual litter fall and the soil pits were restricted to a subplot called “inventory plot” (Figure 3.3).

Study plot 100 x 100 m

Inventory plot litter trap

50 x 50 m

soil pit random samples: organic layer upper mineral soil auger

Total sampling: * 15 random samples of the organic layer and of the upper mineral soil (0-15 cm depth) * 15 random samples with an auger (soil classification and total soil depth) * 1 soil pit * 3-7 litter traps

Figure 3.3: Schematic overview about the sampling design at each study plot. (Graphic: A. Börner)

18


The relatively high number of samples of the organic layer and the upper mineral soil per study plot was needed to cover the high spatial variability of the mineral soil within an individual study plot. The individual samples of a study plot do not represent replicates of the independent variables “stand age”, “basal area of the stand”, “study site” or “silvicultural system”. Therefore, the means per study plot and not the individual samples of each study plot were plotted against the independent variables for linear regression analysis or were taken to compare the mean carbon pools of the “study sites”. For example, mean carbon pools in the organic layer of each study plot were based on 15 samples. Thus, the mean per study plot is the average of 15 samples. The mean of a study site is the average of the means of all study plots per study site (n = 3 (“Langula” and “Hainich NP”) or n = 5 (“Leinefelde” and “Mühlhausen”)). 3.3.2 Statistical analysis and software Prior to the main statistical analysis of the data, extreme values and outliers were identified via box-plot-analysis. However, outliers were excluded from further analysis only if they could be functionally justified (e.g. soil samples that were not totally air-dry). The homogeneity of variance was tested via the Bartlett Chi-square statistics and visually by the “plot of means versus standard deviations”. The normal distribution of the data was inspected visually by normal probability plots. The main statistical analysis was based on a generalization of the linear regression model (General Linear Model) that included procedures to test for effects of categorical and continuous predictor variables. The most relevant procedures were the analysis of variance (ANOVA), single and multiple regression analysis, and the “separate-slopes model” (SSM) analysis. The ANOVA is used to compare the means of the study plots with each other (“plot effect”) or to compare the study sites and silvicultural systems (“site effect” and “silvicultural effect”). For the post hoc comparison of means, following the ANOVA, the Newman-Keuls test was used. The effects of different continuous variables on SOC pools were analysed via single and multiple regression analysis. The normal distribution of residuals was inspected visually via the “normal plot of residues”. Significant predictors were identified via the “forward stepwise” procedure of multiple regression analysis.

19


The SSM calculates the effects of continuous and categorical predictor variables when there are interactions between the predictors. The analysis via the SSM is similar to the “Analysis of Covariance” (ANCOVA) that statistically excludes the effects of covariates due to a comparison of the factor-specific regressions at the means of the covariates. For the ANCOVA it is assumed that all factor-specific regression lines have the same slope (no interaction between the covariates and the factor). In contrast to the ANCOVA, the SSM also considers the effects of different slopes of the factor-specific regressions. “Different slopes” or “interactions between the factor and the covariates” indicate that the continuous, independent variables had different effects on the dependent variable at different levels of the categorical, independent variable. In other words, the SSM tests if the linear regressions of the covariates and the dependent variable (e.g. SOC pools) have significantly different intercepts and different slopes depending on the factor (e.g. study plot). All these statistical procedures were provided by the Visual GLM module of the Software STATISTICA for Windows, StatSoft, Inc. 2000. Non-linear regressions and graphical presentations were carried out with SigmaPlot for Windows 2000 (version 6.0). For data management and simple mathematical operations Microsoft Excel 2002 was used.

3.4 The study sites and plots 3.4.1 Geography The entire study region “Hainich-Dün” is located in central Thuringia, Germany, close to the cities Eisenach (to the south-west) and Mühlhausen (to the east). The name “Hainich-Dün” specifies the lower mountain ranges “Hainich” and “Dün”, which form the north-western sickleshaped corner of the “Thuringian basin”. The “Hainich” and the “Dün” are separated geographically by the Unstrut valley, but because of the same climate and natural growing conditions for forests, they are integrated to the forest growing district (Wuchsbezirk) “HainichDün”, which in turn belongs to the forest growing region (Wuchsgebiet) “Mitteldeutsches TriasBerg- und Hügelland”. The “Hainich-Dün” encompasses a total area of about 65 000 ha, of which 39 % are forested (TLWF 1997).

20


3.4.2 Climate The suboceanic-submontane climate of the plateau of the Hainich-Dün is characterised by an annual precipitation of 750-800 mm (TLWF 1997). During the growing season the precipitation is 320-370 mm. The average annual air temperature is 6.8 °C. According to the climate classification system of Thuringia, all study sites belong to the climate class (Klimastufe) “Vff” (very humid lower mountain range; forestry site maps from 1988). Precipitation data of the last two to four years showed a trend towards higher precipitation at Mühlhausen (Table 3.2), but there was not a gradient of precipitation from north (Leinefelde) to south (Hainich NP). Significant differences of bulk nitrogen depositions (on average 12.8 ± 3.3 kg N ha-1 Jahr-1) between the study sites were not found (Mund et al. in prep. b). Table 3.2: Mean annual precipitation at the study sites. (1) Mund et al. (in prep. b), (2) Knohl et al. (2003) (3) Kolle, pers. comm., (4) Anthoni, pers. comm.. Data are given for the “hydrological year”: 1st October to 30th September. n.d. = not determined. Year Leinefelde 2000 2001 2002 2003

8791 8041 11071 6834

Precipitation (mm) Mühlhausen n.d. 10711 12851 n.d.

Hainich NP 9261 / 9492 8241 / 7222 9131 / 9452 6893

21


3.4.3 Selection of the study plots The selection of the study sites was based on the general approaches mentioned above. In detail, the following criteria for site conditions and specific characteristics of silvicultural systems or unmanaged, natural forests were taken into account for the selection of the study plots: •

Location: within the “Hainich-Dün” region

•

Bedrock/ parent material: Triassic limestone, covered with Pleistocene loess deposits

•

Topography: plateau of the Hainich-Dün, elevation 400-460 m a.s.l.

•

Tree canopy: dominated by beech (except for the regeneration phase of the even-aged stands and some parts of the unmanaged forest)

•

Silviculture: 1. The managed study plots had to be part of a shelterwood or a selection system for at least 140 years (equivalent to a rotation period). 2. All silvicultural treatments carried out within the last 140 years, including the timing of activities, were typical for the specific silvicultural system and for beech forests on fertile soils. This criterion implies that the range of age of the even-aged shelterwood stands were less than ± 10% of the intended rotation, except for the phase when the shelter of mature trees covers the regeneration.

•

Forest growth: All study plots had to provide optimal conditions for beech growth (site index II or better).

•

Size of the study plots: Each study plot had to be part of a forested area that was characterized by a defined stand structure and that comprised more than 3 ha. The study plot itself had a size of 1 ha.

22


•

Stand structure: 1. At even-aged stands the structure had to represent a typical stage of development within the forest rotation. 2. The selection forests should have a “balanced” or “reverse J-curve” diameter distribution. In reality this ideal structure is often not realized within an area of a few hectares. Thus, the entire set of the three study plots had to represent the typical range of stand structures of selection forests. 3. The “typical structure” of unmanaged, natural beech forests in central Europe is not defined generally, and it depends on many factors such as the natural disturbance regime, the mosaic of tree species and tree size, tree growth and tree mortality, etc.. Furthermore, it is very unlikely that a stand of a few hectares could represent all structural elements of an unmanaged, nearly natural forest in an ideal form. In this study we defined an unmanaged study plot as an area that is characterised by a relatively homogeneous stand structure compared to the surrounding forest, and the combination of three unmanaged study plots should represent the typical range of stand structures within the totally protected forest (“Kernzone”) of the Hainich Nationalpark.

For the selection of the study plots, local foresters provided much information about the silvicultural treatments that are typical for the entire region and, in particular, that were carried out in single stands (section 3.4.6). They also made forest site maps available that included information on soil properties and climate (Table 3.3). 3.4.4 Geology and general soil characteristics The Hainich-Dün forms the north western corner of the “Thuringian basin”. Tectonic movement caused an enhancement of the Hainich-Dün region and a relative subsidence of the Thuringian basin (Seidel 1995). Different resistance of the rocks to weathering and erosion of the exposed surface resulted in a typical vertical sequence of escarpments (slight inclination of the layers to the east). The lower, middle and upper Triassic limestone and the Keuper form a horizontal sequence from the top of the Hainich-Dün to the central Thuringian basin. The lower Triassic limestone is characterized by a compact marly limestone that is relatively resistant to weathering. The middle Triassic limestone mainly consists of alternating layers of hard dolomite, marl and limestone that weathers relatively fast compared to the upper and lower limestone. The upper Triassic limestone consists of alternating layers of limestone and marl (Seidel 1995).

23


All study sites are covered by a Pleistocene loess layer of variable thickness (ca. 10-50 cm; see also Greitzke 1989). The local soil forms (“Lokalbodenformen”) derived from soils maps of the study sites (soil classification system of Thuringia) are listed in Table 3.3. Integrating climate, soil properties and topography the study sites belonged to the site classes (“StammStandortsformengruppen”) “Vff-R2” or “Vff-K2”, which means “moderately moist soils with a high base saturation at the very humid lower mountain range” (site classification system of Thuringia; see also Appendix Table A.1). The humus form (type of the organic layer) varied between mull and F-mull (German classification, AG Boden 1994). The loess layer increases the site fertility but also the spatial heterogeneity of soil formation processes and, subsequently, of soil types. Depending on periglacial redistribution and the soil formation processes, the borders between soil layers of different origin are often diffuse and difficult to identify in the field. Soil formation resulted in Rendzina or Terra fusca (German classification, AG Boden 1994; Rendzic Leptisols to Cambisols according to ISSS-ISRIC-FAO classification 1998) on sites without or with a very thin cover of loess. With an increasing proportion of loess, the soil formation resulted in various brown soils (Braunerden and Parabraunerden (German classification, AG Boden 1994) or Cambisols to Luvisols (ISSSISRIC-FAO classification 1998)). Table 3.4 gives an overview of the distribution of soil types within the study plots (for details of the sampling procedure see chapter 6). Schöning (2003) reported the following soil types for adjacent study plots at “Leinefelde” (one or two soil pits per study plot, ISSS-ISRIC-FAO classification 1998): Lei-30: Rendzic Leptisol and Stagnic Luvisol, Lei-62: Stagnic Luvisol, Lei-111: Haplic Luvisol, Lei-153+16: Stagnic Luvisol. The influence of different soil types on SOC pools of the study plots or silvicultural systems was excluded by statistical analysis (chapter 6).

24


Soil classification 2

Nutritional status 2

Vegetation 3 G

N

mm

Ta.T-5

R2

H

SW

mo

Wü.L-5

K2

G

1.3 m) within the inventory plot were measured. Tree height was measured with an optical height meter (Suunto PM-5/1520P). At the oldest even-aged stands, which were characterised by two canopies built up by the residual shelter of old trees and the understory of saplings and poles, an additional small subplot was fixed within the inventory plot (Table 4.1, see also Figure 3.3) to get an estimate on tree number and size of the very dense understory. All saplings (tree height > 0.2 m, dbh 0-0.05 m) and poles (dbh 0.05-0.15 m) within a subplot were grouped into five diameter and height classes, and the number of trees per size class was counted. At the study plot Mühl-171+10 the tree regeneration did not form a regular, closed understory but there were alternating, very dense groups of saplings, which cover about 55% of total stand area. Seedlings (tree height < 0.2 m) were neglected at all stands.

47

4 Stand structure and biomass

Table 4.1: Overview of the size of the inventory plots.

Stand/Plot Lei-30M Lei-62M Lei-111M Lei-141M Lei-153+16M Mühl-38 Mühl-55 Mühl-85 Mühl-102 Mühl-171+10 Lang-I Lang-II Lang-III Hai-I Hai-II Hai-III

Size of inventory plot (size of subplot) (m2) 625 625 2500 2500 2500 (500) 625 500 1250 2038 2500 (25) 10000 10000 2500 3000 2500 3000

Except for the plots Lang-I and Lang-II all stand inventories were carried out in winter 1999/2000. The inventory data (digital raw data) of the plots Lang-I and Lang-II were provided by W. Gleichmar (inventory in 1995, Gleichmar 1996) and the Technical University of Dresden in Tharandt, Lehrstuhl für Waldwachstums- und Holzmesskunde der Fachrichtung Forstwissenschaften (inventory in 1997, unpublished), respectively. Both inventories covered a forest area of one hectare (permanent study plots) and included all trees above 1.3 m tree height. In selection forests the dbh of single trees is highly correlated with tree age (Schütz 2001a). Consequently, tree age can be substituted by stem diameter or vice versa. For the present study we took advantage of a regular selection cutting at the study plot Lang-I in 2000 to determine the tree age of all harvested trees (counting of tree rings of the base disk with a magnifying lens; error of tree age about ± 5 years). The close linear relationship between the product of basal area (g) and tree height (h) and the tree age (Appendix Table A.2) was used to estimate tree age of all trees within the inventory plots of the uneven-aged stands. The age of small trees (g*h ≤ 2 m3) was estimated on the basis of comparable trees of the even-aged stands and the harvested population of study plot Lang-I (Appendix Table A.2).

48


General stand characteristics included the arithmetic mean of tree diameters and heights ( D and H ) and the quadratic mean of tree diameters (Dg =

N

∑ dbh

2

N ) and its corresponding

i =1

tree height (Hg). The dominant stand height (Ho) is the predicted height of the quadratic mean of diameters of the 20% largest trees per stand (Do =

N

∑ dbh i =1

2

o

N ) (WEISEsche Oberhöhe; Kramer and Akça

1995). The height of the quadratic mean diameter was calculated by plot-specific regressions, describing the tree height in relation to tree diameter (Figure 4.1). At the oldest even-aged stands (Lei-141M, Lei-153+16M, Mühl-171+10), which were already partly cut for stand regeneration, this relation could be described by a simple linear regression. At the other stands the 3-parameter asymptotic Chapman-Function (Equation 4.1) offered the best fit compared to other, less flexible functions that are often used to describe height curves of forest stands (e.g. Logarithmic-, Korsun-, Gompertz-, Petterson-, Freese- and Michailoff-Function). The Chapman-Function provided the best model to predict the wide tree height distribution of the unmanaged stands and to predict the tree height of the lower and the upper diameter classes of the managed stands. At some uneven-aged stands the 4-parameter Richard-Chapman-Function fitted the tree height distribution of the middle diameter classes (20-40 cm) better than the Chapman-Function. However, to ensure the comparability between the stand heights the Chapman-Function was preferred (see also Kramer and Akça 1995). The parameter “a” of the Chapman-Function represents the asymptotic height (maximum tree height of the stand). The other parameters describe the shape of the curve (von Gadow and Hui 1999). The coefficients and statistics of all stand-specific regressions are given in Table 4.2.

(Equation 4.1)

y = a * ⎡1 − e−b* x ⎤ ⎢⎣ ⎥⎦

c

Chapman-Function (= Mitscherlich-Function)

with y: tree height (m) x: dbh (m) a, b, c: empirical model parameters

49


45

Chronosequence "Leinefelde"

A

40

Tree height (m)

35 30 25 20

Lei-30M Lei-62M Lei-111M Lei-141M Lei-153M

15 10 5

(16-year-old regeneration is not presented)

0 0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

dbh (m) 45

Chronosequence "Mühlhausen"

B

40

Tree height (m)

35 30 25 20

Mühl-38

15

Mühl-55

10

Mühl-85 Mühl-102 Mühl-171

5

(10 -year-old regeneration is not presented)

0 0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

dbh (m)

Figure 4.1: Stand-specific height curves of the silvicultural systems. Allometric functions, parameters and statistics of the height curves are given in Table 4.2. (The figures include all trees of the inventory plots with a dbh ≥ 7cm). The data of the study plots Lang-I and Lang-II were provided by W. Gleichmar (1996) and the University of Dresden, Lehrstuhl für Waldwachstumsund Holzmesskunde (pers. comm.), respectively. 50


45

Selection system "Langula"

C

40

Tree height (m)

35 30 25 20 15 10

Lang-I Lang-II Lang-III

5 0 0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

dbh (m) 45

Unmanaged forest "Hainich NP"

D

40

Tree height (m)

35 30 25 20 15 10

Hai-I Hai-II Hai-III

5 0 0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

dbh (m)

51


Table 4.2: Allometric functions of the stand-specific height curves (including all trees of the inventory plots with a dbh ≥ 7cm). n.s.= not significant. The inventory data of the stands Lang-I and Lang-II were provided by Gleichmar (1996) and the University of Dresden, Lehrstuhl für Waldwachstums- und Holzmesskunde (pers. comm.), respectively. n = Number of tree height measurements. Allometric function

a

b

c

P

R2

n

13.561

36.491

2.513

30 cm soil depths). In contrast, the “chronosequence approach” and the “comparison of unmanaged and managed forests”, which are based on differences in SOC pools, may underestimate the NBP. These approaches do not indicate a net accumulation of SOC that may occur independently and in addition to management induced SOC accumulation at all study plots and at the same time (“background SOC accumulation”). Another restriction is that the variable “stand age” reflects the time after canopy opening for regeneration (or at clear-cutting systems after clear cutting) and not an absolute time scale. Thus a net accumulation of SOC pools with increasing stand age represents a re-accumulation of carbon that was lost due to canopy opening (or clear-cutting), but there is no information about the amount of re-accumulated carbon that will be lost again at the end of the rotation. It can be only assumed, for example, that after final cutting the oldest stands would reach the SOC pools of the youngest stands, so that over several rotations managed forests are at a “steady state”. In conclusion, the presented estimates of NEP and NBP are assumed to indicate the upper and lower limits for European beech forests on fertile soils. Another methodological approach to approximate the NBP is the measurement of changes in carbon pools of the organic layer and the mineral soil due to repeated measurements of the same plot after a distinct period of time. The number of samples that is needed to detect a certain change of carbon pools over time depends on the variance of carbon pools and the method that is used to re-sample the organic layer or mineral soil. Generally, the number of samples that is needed can vary between 10 to several 1000 per study plot (Schaaf 2003, Wirth et al. 2003, Yanai et al. 2003, Conen et al. in prep.). To minimize the number of samples Yanai et al. (2003) suggest in particular a paired re-sampling design, and Conen et al. (in prep.) pointed out that 168

8 Discussion

carbon concentration and soil mass needs to be measured on the same sample. Carbon pools must be calculated for each individual sample before averaging (procedure of this study). However, even if these suggestions will be considered for a repeated sampling at the plots of this study, a very large number of samples would be needed to detect significant changes in SOC pools. For example, accepting a probability for falsely rejecting the null-hypothesis of 5% (α = 0.05, Type I error) and for falsely accepting the null-hypothesis of 10% (β = 0.10, Type II error), the number of samples that would be needed to detect a defined change in SOC pools can be estimated in a one-sample t-test from the following Equation (Zar 1999 in Conen et al. in prep.):

S2 * (t1−α ,υ + t1− β ,υ ) 2 (Equation 8.1) n = 2 MDD with: n: number of samples s: (estimated) standard deviation of the population MDD: minimum detectable difference tα ,υ and t β ,υ : critical t-values for the specified values of α and β with n-1 degrees of freedom (υ)

According to these settings 176, 272 and 379 samples need to be taken in 10 years at the study plots Hai-I, Hai-II, and Hai-III, respectively, to verify the estimated SOC0-15 accumulation of 0.17 tC ha-1 year-1 induced by the cessation of timber use (chapter 6). Considering the large spatial variability of SOC pools, its close interactions with soil-specific properties and the fact that SOC pools integrate over very long time scales, it seems to be reasonable to combine different methodological approaches in the future. The comparison of SOC pools in differently managed forests (this study) could be combined with flux measurements and with repeated SOC measurements at selected study sites.

169

8 Discussion

8.4 How large is the potential for increasing carbon pools in formerly managed forests due to a cessation of timber use? On the basis of a detailed statistical analysis it was shown that the unmanaged forest contained more organic carbon in the ecosystem than the managed forests, even though it is obvious that the Hainich NP is still affected by former forest use and that it is not a “true” primary forest. With respect to the ongoing discussion about “human induced” forest carbon sinks and the potential to account for “the cessation of forest use” within the scope of the KyotoProtocol, two questions arise from this study: (1) How much carbon will the protected, unmanaged forest accumulate in the future and how long will it be able to keep-up this accumulation rate? (2) Which amount of additional forest carbon sinks would be generated due to the cessation of timber use in currently managed forests? To calculate the potential carbon accumulation of already unmanaged forests and of managed forests that will not be managed in the future the following assumptions were made: •

The mean living tree biomass and aboveground dead wood biomass of the primary pure beech forests of Slovakia and Albania, which grow under similar climatic conditions as the beech forests of this study (Korpeľ 1995, Meyer et al. 2003), represent the "final or climax stage" of forest stand development after total protection.

•

The timber volume per hectare of the primary forests was converted to total carbon pools of living tree biomass (including roots) by a conversion-expansion factor of 0.39, resulting from this study (chapter 4).

•

The NEP resulted from the NPP minus heterotrophic respiration (Table 8.2).

•

A potential net accumulation of SOC0-15 pools of managed stands was neglected as it is not part of an induced carbon sink due to the cessation of timber use.

•

After the cessation of timber use the NBP of formerly managed forests increases to a NBP that was estimated for the “Hainich NP”, neglecting any potential soil-specific effect on this flux.

170

8 Discussion

Table 8.3: Estimate of net carbon accumulation in the managed forests and the unmanaged forest for the case that the forests would not be managed or would remain unmanaged, respectively, in the future. For explanation see text. (1): Korpeľ 1995, Meyer et al. 2003. Cessation of timber use in the currently managed forests

Unmanaged forest at the "Hanich NP"

1. Maximum carbon pools in biomass (living trees and aboveground dead wood) of primary beech forests (tC ha-1)

283 (224- 328) (1)

283 (224- 328) (1)

2. Current carbon pools in biomass (living trees and aboveground dead wood) of the studied forests (tC ha-1)

163 (150-220)

252 (226-288)

3. Potential for carbon accumulation in biomass (=1-2) (tC ha-1)

120

31

4. NEP (tC ha-1year-1) (Table 8.2)

4.6

4.9

5. Duration of net biomass accumulation (years) (= 3:4)

26

6.3

6. Net accumulation of SOC following the cessation of regular timber use (tC ha-1year-1) (chapter 6)

0.17

0.17

Total induced carbon sink after 26 or 6 years, respectively (tC ha-1) (=3+5*6)

122.4

31.7

According to this carbon budget the cessation of timber use would initiate net carbon storage in forest ecosystems of 122.4 tC ha-1 that would be reached in 26 years (Table 8.3). The already unmanaged forest will continue to accumulate carbon for 6 years. Thus, it will store 31.7 t ha-1 additional carbon. It is assumed that carbon pools in living and dead tree biomass will reach a maximum, but the net accumulation of SOC pools will continue for a longer time period that cannot be quantified yet. This carbon budget does not include the carbon stored in or released from wood products. It is obvious that this “external part” of managed forests ecosystems is needed for a complete carbon budget.

171

8 Discussion

172

9 Conclusions

9 Conclusions On the basis of this study the following conclusions can be drawn: •

The regular shelterwood system and the selection system store on average similar amounts of carbon. The impacts of shelterwood cuttings and selection cuttings on the forest carbon budget are lower than those reported for clear cuttings.

•

Differences in soil organic carbon pools of forest ecosystems are the result of cumulative differences in carbon input (litter production), carbon stabilization and carbon output (litter decomposition) over decades to centuries. Therefore, it is not possible to increase soil organic carbon pools within a few years by different silvicultural treatments. A sustainable increase of carbon storage in forest soils will be a long-term, continuous process that exceeds the time frames of several commitment periods defined by the Kyoto Protocol. In contrast, the loss of carbon due to disturbances is a very rapid process.

•

To reduce carbon losses due to harvesting, canopy openings should be as small as possible and harvest residues should remain on site. This suggestion does not mean that forest management should “maximise standing biomass”, but that an optimal balance is needed between high biomass, stand stability and gaps which allow for stand regeneration.

•

If the economic situation does not allow for the production and use of predominantly long-living wood products, it would be reasonable to protect a certain proportion of forest ecosystems, so that high amounts of living and dead biomass, and in the long-run also of SOC, can accumulate in these forests. To increase the incentives for forest protection the accumulation of carbon and the protection of carbon pools due to a cessation of forest use should be interpreted as “additional induced human activities” in the scope of Article 3.4 of the Kyoto Protocol.

•

The release of dead wood and harvest residues in managed forests will increase aboveground carbon pools and very likely also SOC pools. Additionally, high amounts of dead wood will provide many other benefits with respect to biogeochemical cycles and the preservation and increase of biodiversity of forested ecosystems (e.g. Harmon et al. 1986, Albrecht 1991). It is obvious that higher amounts of dead wood will conflict with some other objectives of forest management such as pest management, safety of work and

173

9 Conclusions

harvesting costs. However, the benefits of higher dead wood pools in managed forests justify the search for compromises.

174

10 Summary

10 Summary In this study the influence of forest management on the carbon budget of European beech forests (Fagus sylvatica L.) in the region “Hainich-Dün” (Thuringia, Germany) was investigated. The overall objectives were to quantify the carbon pools of managed beech forests subject to different silvicultural practices and to enhance the understanding of ecosystem processes that link forest management with changes in the soil organic carbon pools. The following sites and silvicultural treatments were studied: •

Study site “Leinefelde”, shelterwood system

•

Study site “Mühlhausen”, shelterwood system

•

Study site “Langula”, selection system

•

Study site “Hainich National Park” (“Hanich NP”), unmanaged forest.

The study sites “Leinefelde” and “Mühhausen” were represented by two chronosequences, each consisting of five even-aged stands of different stand age (“Leinefelde”: 30-, 62, - 111-, 141-, and 153(+16)-year-old stands; “Mühlhausen”: 38,- 55-, 85-, 102-, and 171(+10)-year-old stand). In “Langula” and in the “Hainich NP” three uneven-aged stands were studied. In total 16 forest stands (or study plots) were investigated in this study. The geological substrate of all study plots was limestone covered with loess. At each study plot the carbon pools in total living and dead tree biomass, in the organic layer (dead plant material resting on the mineral soil) and in the mineral soil were quantified. In addition, the annual litter fall and the mean residence time (MRT) of litter in the organic layer were determined. At the study sites “Leinefelde” and “Mühlhausen” mean total carbon pools were 240 and 253 tC ha-1, respectively. At the site “Langula” 266 tC ha-1 and at the “Hainich NP” 353 tC ha-1 were stored. The living tree biomass of the sites “Leinefelde” and “Mühlhausen” amounted to 160 and 149 tC ha-1, respectively, while in “Langula” 176 tC ha-1 and in the “Hainich NP” 238 tC ha-1 were stored in the living tree biomass. On average the carbon pools in tree biomass accounted for 65% of total carbon pools in the forest ecosystems. Dead wood carbon pools ranged between 1.5 tC ha-1 in the managed forests and 6.4 tC ha-1 in the unmanaged forest. Thus dead wood carbon pools accounted for 0.6 and 2%, respectively, of total carbon pools.

175

10 Summary

The mean annual litter fall of the study sites “Leinefelde” and “Mühlhausen” (2.2 and 2.2 tC ha-1 year-1) was lower than that of the site “Langula” (2.8 tC ha-1 year-1) and the “Hainich NP” (2.5 tC ha-1 year-1), but the differences were statistically not significant. Age-related differences in leaf litter fall were found along the chronosequences that were associated with changes in the stand density due to regular thinning. Average total carbon pools in the organic layer of the study sites varied between 3.0 tC ha-1 in the “Hainich NP” and 4.1 tC ha-1 in “Langula”. Carbon pools in the leaf litter, which ranged from 1.1 tC ha-1 to 2.3 tC ha-1, were positively correlated with the litter fall of beech leaves and negatively correlated with the basal area of the stands. The negative relationship between the leaf litter and the basal area of the stands may reflect higher decomposition rates due to a more constant and humid microclimate in stands with a higher basal area compared to stands with a lower basal area. The mean residence time (MRT) of leaf litter in the organic layer was on average 1.1 years. When the larger soil fauna (> 1mm) was excluded from litter decomposition, the MRT was prolonged to 2.4 years. Significant differences in the MRT of leaf litter were not found. The MRT of fine woody debris (twigs, branches (< 5 cm in diameter) and beech nuts) in the organic layer was on average about 3 years. Total soil organic carbon pools (total SOC pools) of 75 and 98 tC ha-1 were found in “Leinefelde” and “Mühlhausen”, respectively. In “Langula” about 85 tC ha-1 and in the “Hanich NP” about 105 tC ha-1 were stored in the mineral soil. The mean C:N ratio was the only significant predictor and correlated positively with SOC pools. Soil-specific and managementrelated effects on total SOC pools could not be separated on the basis of available data (1 soil pit per study plot). Organic carbon pools in the upper mineral soil (0-15 cm, SOC0-15 pools) accounted for about 48% of total SOC pools. SOC0-15 pools in “Leinefelde” (36 tC ha-1) and in “Langula” (39 tC ha-1) were significantly lower than the SOC0-15 pools in “Mühlhausen” (42 tC ha-1). The highest SOC0-15 pools were found in the “Hainich NP” with 53 tC ha-1. The effects of soil-specific properties and forest management on SOC0-15 pools could be separated via statistical analysis that was based on 16 soil samples per study plot. The SOC0-15 pools were significantly controlled by the clay content (estimated by the residual water content of the air-dried soil samples) and the C:N ratio of the soil. Excluding the effects of the clay 176

10 Summary

content and the C:N ratio on SOC0-15 pools ("corrected" SOC0-15 pools), the differences between the SOC0-15 pools of the study sites decreased. For the sites “Leinefelde”, “Mühlhausen” and “Langula” “corrected” SOC0-15 pools of about 42 tC ha-1 were predicted. For the “Hainich NP” “corrected” SOC0-15 pools of 48 tC ha-1 were calculated. Thus, excluding the effects of the clay content and the C:N ratio, there remained a trend of a difference in SOC0-15 pools between the managed forests and the unmanaged forest of about 6 tC ha-1. A significant relationship between stand density, basal area or living tree biomass on the “corrected” SOC0-15 pools was not found. Only a small proportion of the variance of the “corrected” SOC0-15 pools was explained by the amount of leaf litter fall from Fraxinus excelsior, Acer pseudoplatanus, A. platanoides and other non-beech tree species growing at the study sites. The higher the leaf litter fall from non-beech tree species the higher was the “corrected” SOC0-15 pool. This influence of non-beech leaf litter on SOC pools may reflect the higher quality of the non-beech leaf litter, which is associated with higher decomposition rates and an intensive incorporation of leaf litter in the mineral soil by larger soil fauna, in particular earthworms. These processes in turn may lead to a higher stabilization of dead organic matter in the mineral soil. However, it is important to mention, that the regression analysis, that indicated a significant influence of non-beech leaf litter fall on the "corrected" SOC0-15 pools, was dominated by two plots at the “Hainich NP”. Thus, it seems to be likely that the cessation of regular timber use about 35 years ago has induced higher SOC0-15 pools in the ”Hainich NP”. The permanent, dense canopy of the unmanaged forest provides a stable and humid microclimate that may promote the incorporation and stabilization of organic matter in the mineral soil by the soil fauna. In addition, the production and decomposition of dead wood at the unmanaged site may substantially contribute to carbon inputs to the mineral soil. In conclusion, the carbon storage in the shelterwood systems and the selection system did not differ substantially. In contrast, the cessation of timber use resulted in an increase of carbon pools in beech forests. Except for a potential effect of non-beech leaf litter on SOC pools there was no significant relationship between changes in stand characteristics due to forest management and SOC pools. This lack of a relationship between current stand characteristics and SOC pools indicated that SOC pools result from carbon inputs and outputs over several decades to centuries and that the current stand characteristics represent only a snap shot of forest development. It may be possible that the SOC pools of the “Hainich NP” are still affected by former forest use, so that the differences between the managed and the unmanaged sites found in

177

10 Summary

this study did not reflect the potential for carbon storage in the mineral soil due to a cessation of regular timber use. This study also showed that the large spatial variability of soil-specific properties and their strong influence on SOC pools reduces the possibility to identify significant effects of forest management. It seems to be reasonable to combine different methodological approaches to detect and verify impacts of forest management on SOC pools in future. For example, the comparison of SOC pools in differently managed forests could be combined with flux measurements and with repeated SOC measurements at selected study sites to enhance the understanding of the SOC dynamic at different time scales.

178

11 Zusammenfassung

11 Zusammenfassung In der vorliegenden Arbeit wurde der Einfluss der forstlichen Bewirtschaftung auf den Kohlenstoffhaushalt von Rotbuchenwäldern (Fagus sylvatica L.) im Hainich-Dün Gebiet (Thüringen, Deutschland) untersucht. Das Ziel dieser Arbeit war, die Kohlenstoffvorräte unterschiedlich

bewirtschafteter

Rotbuchenwälder

zu

quantifizieren

und

mögliche

Zusammenhänge zwischen Änderungen der Bestandeseigenschaften infolge forstlicher Bewirtschaftung und Änderungen der Bodenkohlenstoffvorräte aufzuzeigen. Es wurden folgende Standorte und waldbauliche Behandlungsformen untersucht: •

Standort „Leinefelde“, Schirmschlagbetrieb

•

Standort „Mühlhausen“, Schirmschlagbetrieb

•

Standort „Langula“, Plenterbetrieb

•

Standort „Nationalpark (NP) Hainich“, unbewirtschafteter Wald.

Die Standorte „Leinefelde“ und „Mühlhausen“ wurden jeweils durch eine Chronosequenz („unechte Zeitreihe“) von fünf unterschiedlich alten Beständen repräsentiert („Leinefelde“: 30-, 62-, 111-, 141- und 153+16- jähriger Bestand; „Mühlhausen“: 38-, 55-, 85-, 102- und 171+10- jähriger Bestand). In „Langula“ und im „NP Hainich“ wurden jeweils drei ungleichaltrige Bestände untersucht. Insgesamt basierte die vorliegende Arbeit somit auf 16 Beständen (bzw. Untersuchungsflächen). Das geologische Ausgangsmaterial der Böden aller Standorte besteht aus Muschelkalk, der mit einer unterschiedlich mächtigen Lössdecke bedeckt ist. Auf allen Untersuchungsflächen wurden die Kohlenstoffvorräte in der lebenden ober- und unterirdischen Baumbiomasse (Dendromasse), im Totholz, in der organischen Auflage des Bodens (abgestorbenes organisches Material, das dem Mineralboden aufliegt) und im Mineralboden quantifiziert. Zudem wurden der jährliche Streufall und die mittlere Verweildauer (engl.: mean residence time) der Streu in der organischen Auflage bestimmt. Die mittleren Gesamtkohlenstoffvorräte der Standorte „Leinefelde“ und „Mühlhausen“ lagen bei 240 bzw. 253 t C ha-1. In „Langula“ waren rund 266 t C ha-1 und im „NP Hainich“ rund 353 t C ha-1 gespeichert. In der lebenden Dendromasse waren in „Leinefelde“ 160 t C ha-1 und in „Mühlhausen“ 149 t C ha-1 festgelegt. In „Langula“ und im „NP Hainich“ waren in der lebenden Dendromasse 176 t C ha-1 bzw. 238 t C ha-1 gespeichert. Nur die mittleren Vorräte in der Dendromasse der Schirmschlagbetriebe und des unbewirtschafteten Waldes unterschieden 179

11 Zusammenfassung sich signifikant voneinander. Gemittelt über alle Untersuchungsstandorte waren in der lebenden Dendromasse rund 65% des gesamten organischen Kohlenstoffvorrates gespeichert. Die Kohlenstoffvorräte im Totholz betrugen 1.5 t C ha-1 in den bewirtschafteten Wäldern „Leinefelde“, „Mühlhausen“ und „Langula“ und 6.4 t C ha-1 im „NP Hainich“. Damit waren im Totholz nur 0.6 bis 2% der gesamten Kohlenstoffvorräte zu finden. Der mittlere Streufall war in „Leinefelde“ und „Mühlhausen“ (2.2 bzw. 2.1 t C ha-1 Jahr-1) geringer als in „Langula“ (2.8 t C ha-1 Jahr-1) und im „NP Hainich“ (2.5 t C ha-1 Jahr-1), die Unterschiede waren jedoch statistisch nicht signifikant. In den gleichaltrigen Beständen der Chronosequenzen zeigte sich ein Trend zur Änderung des Blattstreufalls in Abhängigkeit vom Bestandesalter bzw. der Bestandesdichte. Die Kohlenstoffvorräte in der organischen Auflage variierten zwischen 3.0 t C ha-1 im „NP Hainich“ und 4.1 t C ha-1 in „Langula“. Die Kohlenstoffvorräte in der Blattstreu, die zwischen 1.1 t C ha-1 („NP Hainich“) und 2.3 t C ha-1 („Langula“) schwankten, wurden signifikant durch den jährlichen Streufall von Buchenblättern und die Bestandesgrundfläche beeinflusst. Je höher der jährliche Eintrag von Buchenblättern war, desto höher waren die Kohlenstoffvorräte in der Blattstreu. In Abhängigkeit von einer steigenden Bestandesgrundfläche nahmen die Kohlenstoffvorräte in der Blattstreu jedoch ab. Möglicherweise wurde der Abbau der Streu durch ein feuchteres und konstanteres Mikroklima in den Beständen mit hoher Bestandesgrundfläche im Vergleich zu den Beständen mit geringer Bestandesgrundfläche gefördert. Die mittlere Verweildauer der Blattstreu in der organischen Auflage lag im Mittel aller Standorte bei 1.1 Jahren. Wenn größere Bodentiere (> 1 mm) durch die Verwendung von Streusäckchen vom Abbau der Streu ausgeschlossen wurden, verlängerte sich die mittlere Verweildauer der Blattstreu auf 2.4 Jahre. Signifikante Unterschiede zwischen den Standorten wurden nicht festgestellt. Die mittlere Verweildauer von Zweigen, kleinen Ästen (< 5 cm im Durchmesser) und Bucheckern in der organischen Auflage betrug im Mittel über alle Standorte etwa 3 Jahre. Die organischen Kohlenstoffvorräte im gesamten Mineralboden (Gesamtboden-C-Vorräte) erreichten in „Leinefelde“ und „Mühlhausen“ rund 75 bzw. 98 t C ha-1. In „Langula“ waren 85 t C ha-1 und im „NP Hainich“ 105 t C ha-1 im gesamten Mineralboden gespeichert. Die Gesamtboden-C-Vorräte

der

Untersuchungsflächen

konnten

nur

mit

dem

mittleren

C:N-Verhältnis des Bodens in einen signifikanten Zusammenhang gebracht werden. Je höher das 180

11 Zusammenfassung C:N-Verhältnis war, desto geringer waren die Gesamtboden-C-Vorräte. Eine Trennung des Einflusses der forstlichen Bewirtschaftung von Einflüssen bodenspezifischer Eigenschaften auf die Gesamtboden-C-Vorräte war anhand des vorliegenden Datenmaterials (1 Bodenprofil pro Untersuchungsfläche) nicht möglich. In den oberen 15 cm des Mineralbodens waren, gemittelt über alle Standorte, rund 48% der Gesamtboden-C-Vorräte gespeichert. Die Kohlenstoffvorräte in den oberen 15 cm des Mineralbodens (Boden0-15-C-Vorräte) der Standorte „Leinfelde“ und „Langula“ waren mit 36 t C ha-1 bzw. 39 t C ha-1 signifikant geringer als die Boden0-15-C-Vorräte des Standortes „Mühlhausen“ mit 42 t C ha-1. Im „NP Hainich“ wurden mit 53 t C ha-1 die signifikant höchsten mittleren Boden0-15-C-Vorräte gefunden. Da für den oberen Mineralboden 16 Proben pro Untersuchungsfläche vorlagen, war es möglich, über statistische Verfahren pedogene Effekte auf die Boden0-15-C-Vorräte von möglichen Effekten der forstlichen Bewirtschaftung zu trennen. Die Boden0-15-C-Vorräte wurden signifikant durch den Tongehalt (abgeschätzt über den Wassergehalt des luftgetrockneten Bodens) und das mittlere C:N-Verhältnis des Bodens beeinflusst. Nachdem die Effekte des Tongehaltes und des C:N-Verhältnisses auf die Boden0-15-C-Vorräte mittels statistischer Analysen eliminiert worden waren, verringerten sich die Unterschiede zwischen den Standorten. Für „Leinfelde“, „Mühlhausen“ und „Langula“ ergaben sich mittlere, um den Effekt von Tongehalt und C:N-Verhältnis „korrigierte“ Boden0-15-C-Vorräte von 42 t C ha-1. Im „NP Hainich“ wurden „korrigierte“ Boden0-15-C-Vorräte von 48 t C ha-1 ermittelt. Damit verblieb ein Trend zu höheren Boden0-15-C-Vorräten im „NP Hainich“ im Vergleich zu den bewirtschafteten Wäldern von durchschnittlich 6 t C ha-1. Ein signifikanter Einfluss der Bestandesdichte, Bestandesgrundfläche oder Dendromasse auf die "korrigierten" Boden0-15-C-Vorräte wurde nicht gefunden. Ein geringer Anteil der Variabilität der „korrigierten“ Boden0-15-C-Vorräte konnte durch den Blattstreufall von Fraxinus excelsior, Acer pseudoplatanus, A. platanoides und anderer, in den Untersuchungsbeständen vorkommender Laubbaumarten außer Buche, erklärt werden. Je höher der Streufall von NichtBuchenblättern war, desto höher waren die Boden0-15-C-Vorräte. Dieser mögliche Einfluss der Nicht-Buchenstreu auf die Boden0-15-C-Vorräte könnte mit der besseren Abbaubarkeit (höhere „Streuqualität“) und einer intensiveren Einarbeitung dieser Streu in den Boden durch Bodentiere, insbesondere Regenwürmer, und damit verbunden einer Stabilisierung des organischen Materials im

Boden

zusammenhängen.

Es

muss

jedoch

berücksichtigt

werden,

dass

der 181

11 Zusammenfassung regressionsanalytisch ermittelte Einfluss der Streuqualität vor allem durch zwei der drei Untersuchungsbestände im „NP Hainich“ bedingt ist. Daher erscheint es wahrscheinlicher, dass die höheren „korrigierten“ Boden0-15-C-Vorräte im „NP Hainich“ mit der Aufgabe der forstlichen Nutzung des Waldes vor rund 35 Jahren im Zusammenhang stehen. Im Vergleich zu den bewirtschafteten Wäldern könnte das dauerhaft geschlossene Kronendach im „NP Hainich“, welches wahrscheinlich nur durch ein altersbedingtes Absterben einzelner Bäume vorübergehend unterbrochen wurde, zu einem konstanteren und feuchteren Bestandesklima und damit zu einer höheren Akkumulation von Bodenkohlenstoff geführt haben. Darüberhinaus könnte die Produktion und der Verbleib von Totholz in den unbewirtschafteten Beständen mit einem höheren Eintrag von Kohlenstoff in den Mineralboden einhergehen. Zusammenfassend läßt sich festhalten, dass sich die hier untersuchten waldbaulichen Verfahren, Schirmschlagbetrieb und Plenterbetrieb, im Bezug auf die Kohlenstoffspeicherung im Ökosystem nicht wesentlich unterscheiden. Der Verzicht auf forstliche Nutzung führt hingegen zu einer Zunahme der Kohlenstoffvorräte in Buchenwäldern. Außer einem möglichen Einfluss der Baumartenzusammensetzung auf die Speicherung von Kohlenstoff im Mineralboden wurde kein direkter Zusammenhang zwischen Änderungen der Eigenschaften des Baumbestandes durch forstliche Bewirtschaftung und den Kohlenstoffvorräten im Mineralboden gefunden. Hier zeigt sich deutlich, dass die Kohlenstoffvorräte im Mineralboden die Bilanz von Kohlenstoffeinträgen und -austrägen über viele Jahrzehnte und Jahrhunderte darstellen, und dass die aktuellen Bestandeseigenschaften nur einen relativ kleinen zeitlichen Ausschnitt der Entwicklung von Waldökosystemen repräsentieren. Es ist daher auch möglich, dass die Bodenkohlenstoffvorräte im „NP Hainich“ noch zu sehr von der forstlichen Nutzung in der Vergangenheit beinflusst sind, als dass sich deutlichere Unterschiede im Vergleich zu den bewirtschafteten Wäldern hätten ergeben können. Die vorliegende Arbeit zeigt zudem, dass die hohe räumliche Variabilität pedogener Eigenschaften, die die Kohlenstoffspeicherung im Boden maßgeblich bestimmen, den Nachweis signifikanter Effekte der forstlichen Nutzung auf die Boden-C-Vorräte sehr erschwert. Für den Nachweis nutzungsbedingter Änderungen von Boden-C-Vorräten in Wäldern scheint eine Kombination verschiedener wissenschaftlicher Ansätze notwendig zu sein. So könnte die Bestimmung von Kohlenstoffvorräten unterschiedlich genutzter Standorte mit KohlenstoffFlussmessungen und mit wiederholten Messungen der Boden-C-Vorräte am selben Standort kombiniert werden, um die verschiedenen zeitlichen Skalen der Kohlenstoffdynamik im Boden zu erfassen.

182

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13 List of Abbreviations

13 List of Abbreviations Dg

quadratic mean diameter; quadratic mean of tree diameters at breast height of a forest stand

H

average stand height, arithmetic mean of tree heights of a stand

D

average stand diameter, arithmetic mean of tree diameters at breast height of a stand

ANCOVA

analysis of covariance

ANOVA

analysis of variance

B

basal area of a stand

CV

coeffecient of variation

CWD

coarse woody debris

dbh

diameter at breast height of an individual tree

Do

quadratic mean of diameters at breast height of the 20% largest trees per stand

fBD

fine soil bulk density

FORCAST

EU-project; Forest Carbon-Nitrogen Trajectories

FSM

fine soil mass

FWD

fine woody debris

g

basal area of an individual tree

h

height of an individual tree

Hg

tree height predicted for the quadratic mean diameter Dg

Ho

dominant stand height; tree height predicted for Do

LAI

leaf area index

n

number of samples

n.d.

n.d. not determined

GPP

gross primary productivity

NPP

net primary productivity

NEP

net ecosystem productivity

NBP

net biome productivity

SD

standard deviation

SE

standard error

SLA

specific leaf area

SOC

soil organic carbon

SOC0-15

soil organic carbon in the upper 15 cm of the mineral soil

SOM

soil organic matter 199

13 List of Abbreviations

SSM

separate-slopes model

VD

timber volume of a stand; volume of stems and branches with a diameter ≥ 7 cm

VS

stem volume of a stand

200

14 List of Figures

14 List of Figures Figure 3.1: Overview of the locations of the study sites.......................................................... 13 Figure 3.2: The hierarchical design of the present study. ........................................................ 17 Figure 3.3: Schematic overview about the sampling design at each study plot....................... 18 Figure 4.1: Stand-specific height curves of the silvicultural systems. .................................... 50 Figure 4.2: Comparison of different estimates of total leaf biomass per stand in relation to the effective leaf area index (LAI), measured by G. Matteucci in July 2001 (G. Matteucci, pers. comm.).................................................................................. 54 Figure 4.3: Frequency diameter distribution of the study stands. ........................................... 60 Figure 4.4: Semi-log graphs of the mean frequency diameter distribution of the selection system and the unmanaged forest. ........................................................................ 63 Figure 4.5: Height curves of the study sites. ........................................................................... 69 Figure 4.6: Carbon pools in living tree biomass of different silvicultural systems. ............... 73 Figure 4.7: Carbon pools in living tree biomass as a function of the timber volume. ............ 76 Figure 4.8: Carbon pools of different compartments of dead wood. ...................................... 79 Figure 5.1: Total litter fall (A), leaf litter fall (B), litter fall of branches and twigs (C), and litter fall of fruits and buds (D) (means ± standard deviation) as a function of stand age, study site and silvicultural system. ................................................. 90 Figure 5.2: Tree density in relation to stand age (even-aged stands) or estimated age of dominant trees (20% largest trees per stand; uneven-aged stands). ..................... 92 Figure 5.3: Carbon pools of leaf litter resting in the organic layer at the end of the growing season. .................................................................................................................. 94 Figure 5.4: Carbon pools in the total organic layer (A) and in the leaf litter (B) in relation to stand age and silvicultural system. ................................................................... 96 Figure 5.5: Leaf litter in the organic layer at the end of the growing season as a function of the litter fall of beech leaves (A) and of the basal area of the study stands (B). .. 98 Figure 5.6: Loss of leaf litter from litter bags over time. A) Unmanaged forest at the ”Hainich NP”. B) Chronosequence “Leinefelde”. .............................................. 103 Figure 5.7: Mean residence times (MRT) of leaf litter in relation to stand age, study site and silvicultural system. ..................................................................................... 107 Figure 5.8: Decrease of carbon pools in leaf litter at the study plot Lei-62M. ..................... 108 Figure 6.1: Total SOC pools of the study plots depending on stand age, study site and the silvicultural system. ............................................................................................ 116 Figure 6.2: Linear relation between fine soil bulk density and SOC0-15 concentrations (ln-transformed). ................................................................................................. 126 Figure 6.3: SOC0-15 pools of all soil samples in relation to stand age, study site and silvicultural system. ............................................................................................ 128 Figure 6.4: SOC0-15 pools of the study plots depending on stand age, study site and the silvicultural system. ............................................................................................ 131 201

14 List of Figures

Figure 6.5: The clay content of the soil as a function of the residual water content. ............132 Figure 6.6: Effect of the soil type on SOC0-15 pools (A), the residual water content (~ clay content) (B) and the C:N ratio (C) of the upper mineral soil (0-15 cm). 136 Figure 6.7: SOC0-15 pools of the study plots corrected for the effects of the covariates “residual water content” (~ clay content) and “C:N ratio”. .................................138 Figure 6.8: SOC0-15 pools as a function of the residual water content (~ clay content) of the study plots Lang-II, Lei-111M and Lei-153+16M. ..................................139 Figure 6.9: Mean “corrected” SOC0-15 pools (± standard deviation) of the study sites. ........140 Figure 6.10: “Corrected” SOC0-15 pools of the study plots in relation to non-beech leaf litter fall (A), and in relation to stand age (only even-aged stands) (B). .............143 Figure 8.1: Carbon pools in living tree biomass (A) and in leaf biomass (B) of differently managed forests under different growth conditions. ...........................................149 Figure 8.2: Scheme of the different levels of productivity in forest ecosystems (simplified after Schulze et al. 2000). ...................................................................................165

202

15 List of Tables

15 List of Tables Table 3.1: Study sites at the Hainich-Dün region, Germany.................................................... 14 Table 3.2: Mean annual precipitation at the study sites. .......................................................... 21 Table 3.3: Overview of characteristics, which were used to evaluate and select the study plots. ........................................................................................................................ 25 Table 3.4: Soil types of the study plots. ................................................................................... 26 Table 3.5: Overview of forest use history. A) Forest history of the Hainich-Dün region. B) Forest use of the forest districts to which the study sites belonged to ............... 35 Table 4.1: Overview of the size of the inventory plots. ........................................................... 48 Table 4.2: Allometric functions of the stand-specific height curves (including all trees of the inventory plots with a dbh ≥ 7cm). ............................................................... 52 Table 4.3: Basic wood densities for different decay classes. ................................................... 57 Table 4.4: Stand characteristics of the study stands. A) Even-aged stands. B) Uneven-aged stands. ...................................................................................................................... 66 Table 4.5: Parameters and statistics of the site-specific height curves..................................... 69 Table 4.6: Annual stem growth (stem and branches ≥ 7 cm in diameter) of selected study plots. ....................................................................................................................... 71 Table 4.7: Timber volume and carbon pools in living tree biomass of the study plots. A) Even-aged stands. B) Uneven-aged stands. ...................................................... 74 Table 4.8: Carbon pools in dead wood biomass of selected study plots. ................................ 78 Table 5.1: Overview of the period of litter fall sampling at the study plots, number of litter traps and total sampling area. .................................................................................. 82 Table 5.2: Annual litter fall. A) Amount of annual litter fall. B) Species composition of annual leaf litter fall. .......................................................................................................... 88 Table 5.3: Carbon pools in the organic layer at the end of the growing season....................... 95 Table 5.4: Summary of the multiple regressions (forward stepwise regression) for carbon pools in the organic layer. A) Leaf litter of the organic layer. All study plots. B) Total organic layer. All study plots. C) Leaf litter of the organic layer. Even-aged stands (chronosequence “Leinefelde” and “Mühlhausen”).............................................. 100 Table 5.5: Mean residence time (MRT) of leaf litter and fine woody debris (FWD) in the organic layer. A) Method: Leaf litter bags (MRTleaves-bags). B) Method: “Ratio – approach” (MRTleaves-ratio). .................................................................................... 105 Table 5.6: Estimates of the amount of leaf litter that was removed from the organic layer by larger soil fauna (>1 mm)...................................................................................... 109 Table 6.1: Total soil organic carbon (SOC) pools (A) and total soil depth (B). .................... 118 Table 6.2: Summary of the multiple regression analysis (forward stepwise procedure) for SOC pools in different soil depths (A) and for total SOC pools (B)..................... 120 Table 6.3: Mean total SOC pools (± standard deviation) of the different soil types of the soil pits. ................................................................................................................. 121 203

15 List of Tables

Table 6.4: Effect of the factor “soil type” on SOC pools in different soil depths (ANCOVA). A) Statistics of the ANCOVA. Factor “soil type”. B) SOC pools of the soil types calculated for a mean sampling depth of 15.4 cm and a mean clay content of 37.8%. ............................................................................................................................... 122 Table 6.5: SOC concentrations (A), fine soil bulk density and C:N ratios (B) of the upper mineral soil (0-15 cm)........................................................................................... 124 Table 6.6: SOC pools in the upper mineral soil (0-15 cm). ................................................... 130 Table 6.7: Estimates of the mean clay content of the upper mineral soil (0-15 cm) of the study plots. ...................................................................................................................... 133 Table 6.8: Pearson correlation coefficients of all variables that were considered for the following statistical analysis. ................................................................................ 134 Table 6.9: Summary of the “separate slope model (SSM)” analysis for the effect of the factor “study plot” on SOC0-15 pools and the interactions between the factor “study plot” and the covariates “residual water content” and “C:N ratio”................................ 137 Table 6.10: Overview of the variables that were taken into account for multiple linear regression analysis. ..................................................................................... 141 Table 6.11: Summary of multiple linear regression analysis (forward stepwise procedure) for SOC0-15 pools corrected for the effects of the residual water content (~ clay content) and the C:N ratio. A) All study plots. B) Even-aged stands. .................. 142 Table 7.1: Summary of carbon pools of the silvicultural systems. ....................................... 145 Table 8.1: Dead wood carbon pools of temperate hardwood forests. .................................... 153 Table 8.2: Estimates of carbon fluxes of differently managed beech forests. A) NEP estimates. B) NBP estimates. .................................................................. 166 Table 8.3: Estimate of net carbon accumulation in the managed forests and the unmanaged forest for the case that the forests would not be managed or would remain unmanaged, respectively, in the future. ................................................................ 171

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16 Appendix Table A.1: Terms and abbreviations of the soil and site classification system of Thuringia that were used in the present study. .......................................................................................................... Table A.2: Tree age estimates that were based on harvested trees at the selection forest and on the young even-aged stands. g: basal area (m²); h: tree height (m). A) Size and age of harvested, large trees (g*h > 2 m3) at the permanent study site Lang-I, February 2002. B) Tree age classes that were used to get a relationship between tree size and tree age of small trees (g*h ≤ 2m3). C) ANOVA table for trees with g*h > 2 m3. D) ANOVA table for trees with g*h ≤ 2 m3. ................................................................................................................................ Table A.3: Allometric functions and coefficients for tree biomass of beech trees (Fagus sylvatica) given by Wirth et al. (2003)................................................................................................................ Table A.4: Dead wood of the study plots. A) Carbon pools in CWD (coarse woody debris) of the transect lines. B) Averages of the decay classes per study plot. ............................................... Table A.5: Decay rate constant “k” for leaf litter. The decay rate constant “k” is the exponent of the exponential function that describes the loss of leaf litter from litter bags over time (Equation 5.1). .......................................................................................................................... Table A.6: SOC pools of the upper mineral soil: A) Soil depth 0-5 cm and 5-10 cm. B) Soil depth 10-15 cm. .................................................................................................................................. Table A.7: Carbon pools in living tree biomass and leaf biomass of differently managed temperate hardwood forests. ...................................................................................................................... Table A.8: Parameters of the soil pits......................................................................................................... Table A.9: Parameters of the soil cores. ..................................................................................................... Figure A.1: Overview of parameters of the soil pits. ................................................................................

Table A.1: Terms and abbreviations of the soil and site classification system of Thuringia that were used in the present study (VEB Forstprojektierung 1974). Term

Abbreviation

Meaning (in German)

Wasserhaushaltsstufe

5

mäßig frisch

Stammfeuchtestufe

2

mittelfrische normal bewirtschaftbare Standorte

Lokalbodenformen

Fe.LL

Felchtaer Löß-Braunfahlerde

Sf.L

Stiefelburg-Decklößlehm-Braunlessive

Wü.L

Wüllerslebener Decklößlehm-Braunlessive

Kr..L

Kranichfelder Decklößlehm-Braunlessive

Fa.T

Falkener Deckton-Braunerde

Ta.T

Taubentaler Deckton-Braunerde

Dd.T

Dosdorfer Flachdeckton-Braunerde

Le.K

Legefelder Kalkstein-Braunrendzina

K

„Kräftig“; Bodenformen mittlerer Sättigungsverhältnisse; in den Kammlagen und höheren Berglagen enthält die Nährkraftstufe auch die reichen und carbonatischen Bodenformen „Reich“; Bodenformen mit den Bodentypen höherer Basensättigung in den unteren Lagen und den mittleren Berglagen

Stamm-Nährkraftstufe

R

205

Table A.2: Tree age estimates that were based on harvested trees at the selection forest and on the young even-aged stands. g: basal area (m²); h: tree height (m) A) Size and age of harvested, large trees (g*h > 2 m3) at the permanent study site Lang-I, February 2002. Tree species Fagus sylvatica Fagus sylvatica Fagus sylvatica Fagus sylvatica Fagus sylvatica Fagus sylvatica Fagus sylvatica Acer platanoides Fagus sylvatica Fagus sylvatica Fagus sylvatica Fagus sylvatica Fraxinus excelsior Fagus sylvatica Fagus sylvatica Fagus sylvatica Fagus sylvatica Fraxinus excelsior Fagus sylvatica

Tree no. 13/4 21/1 25/6 28/2 30/2 34/3 37/5 38/2 50/1 63/3 70/6 73/5 75/6 77/5 81/1 84/3 87/2 98/2 99/4

dbh (m) 0.729 0.670 0.434 0.383 0.608 0.591 0.371 0.438 0.766 0.456 0.520 0.319 0.430 0.363 0.632 0.794 0.805 0.358 0.631

Tree height (m) 40.1 35 33.6 31.7 34.3 34 31.2 32.4 32.4 34.6 30 30.4 33.2 30.2 35.6 35.3 35.2 27.7 33.2

g*h (m3) 16.737 12.321 4.971 3.643 9.942 9.311 3.364 4.871 14.931 5.638 6.371 2.422 4.810 3.125 11.150 17.457 17.893 2.788 10.382

Age (years) 196 180 114 120 186 131 104 118 180 116 121 116 120 126 150 195 181 118 118

B) Tree age classes that were used to get a relationship between tree size and tree age of small trees (g*h ≤ 2 m3). Study plot / tree no Lei-153M, underwood Lei-153M, underwood Lei-153M, underwood Lei-153M, underwood Mühl-171, underwood Mühl-171, underwood Lei-30M Lei-62M Mühl-38 Mühl-55 Mühl-85 Mühl-102 Lang-I; 58/2 Lang-I, 93/3

206

dbh (m)

Tree height (m)

g*h (m3)

Age (years)

0.040 0.014 0.020 0.006 0.011 0.023 0.108 0.249 0.106 0.154 0.240 0.275 0.121 0.132

5.5 2.2 4.5 1.7 1.5 3.0 12.3 24.2 11.5 19.0 23.0 27.3 18.6 19.2

0.00004 0.00031 0.00141 0.00691 0.00013 0.00125 0.11234 1.18192 0.10245 0.35603 1.03976 1.62845 0.21388 0.26076

5 (estimate) 10 (estimate) 15 (estimate) 20 (estimate) 5 (estimate) 10 (estimate) 30 (stand age) 62 (stand age) 38 (stand age) 55 (stand age) 85 (stand age) 102 (stand age) 72 (harvested tree) 55 (harvested tree)

C) ANOVA table for trees with g*h > 2 m3. Model: Tree age = a + b*(g*h) R2=0.790, P