Carbon Pools of European Beech Forests (Fagus Sylvatica) Under Different Silvicultural Management

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: 02. 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 non- forested 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 semi- natural 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”) (meta- analysis 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 (UN- ECE/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 well- defined 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 old- growth 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 “group- shelterwood 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 2 Terminology of this study

“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 2 Terminology of this study

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 -1 substrate (e.g. gC gsoil , 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 2 Terminology of this study 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.

10 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 Material and methods

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 Hainich- Dün (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 3 Material and methods 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

515202510 30 km

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

13 3 Material and methods

Location Location 10°21´19´É 10°21´19´É 10°22´04´É 10°18´20´É 10°19´11´É 10°18´25´É 10°22´07´É 10°22´07´É 10°18´36´É 10°18´36´É 10°19´13´É 10°19´13´É 10°22´07´É 10°22´07´É 10°22´07´É 10°22´07´É 51°19´48´Ń 51°19´48´Ń 51°19´41´Ń 51°11´41´Ń 51°11´53´Ń 51°11´24´Ń 51°20´02´Ń 51°20´02´Ń 51°11´37´Ń 51°11´37´Ń 51°11´31´Ń 51°11´31´Ń 51°20´13´Ń 51°20´13´Ń 51°20´02´Ń 51°20´02´Ń

------Klein: Fl.2 Klein: Fl.2 study plots plots study Code of adjacent of adjacent Code FORCAST Lei-62 FORCAST Lei-30 FORCAST Lei-111 = = Lei-111 FORCAST Tower site Leinefelde CARBOEUROFLUX: CARBOEUROFLUX: FORCAST Lei-153+16 Lei-153+16 FORCAST Revier Geney Geney Revier Geney Revier Revier Geney Geney Revier Revier Geney Geney Revier Revier Geney Geney Revier FoA Leinefelde, FoA Leinefelde, FoA Leinefelde, FoA Leinefelde, FoA Leinefelde, Forestry district Stadt Mühlhausen, Mühlhausen, Stadt Mühlhausen, Stadt Mühlhausen, Stadt Stadt Mühlhausen, Mühlhausen, Stadt Stadt Mühlhausen, Mühlhausen, Stadt Revier Eigenrieden Eigenrieden Revier Revier Eigenrieden Eigenrieden Revier Revier Eigenrieden Eigenrieden Revier Eigenrieden Revier Eigenrieden Revier (1) Source:Bascietto 2003, (2) Forestry records, (3) Estimated age of the oldest “Mühlhausen”, “Mühlhausen”, “Mühlhausen”, “Mühlhausen”, “Mühlhausen”, Chronosequence Chronosequence Chronosequence Chronosequence Chronosequence Chronosequence Chronosequence Chronosequence Chronosequence Chronosequence Chronosequence Chronosequence Chronosequence Chronosequence Chronosequence Chronosequence northern Hainich Hainich northern Hainich northern northern Hainich Hainich northern northern Hainich Hainich northern “Leinefelde”, Dün “Leinefelde”, Dün “Leinefelde”, Dün “Leinefelde”, Dün “Leinefelde”, Dün Study site/Region site/Region Study

2 1

1 2 2 2 1 1 62 141 141 38 85 171 + 10 10 + 171 in 2000 (years) Stand or tree age system system Shelterwood Shelterwood Shelterwood Shelterwood Shelterwood Shelterwood Silvicultural Silvicultural Mühl-38 Mühl-55 Shelterwood 55 Mühl-85 Lei-30M Shelterwood Shelterwood Lei-30M 30 Lei-62M Mühl-102 Shelterwood Shelterwood Mühl-102 102 Lei-111M Shelterwood Shelterwood Lei-111M 111 Lei-141M Study plot plot Study Mühl-171+10 Lei-153+16M Shelterwood 153 + 16 Shelterwood Lei-153+16M + 153 Table 3.1: Study sites at the Hainich-Dün region, Germany. Germany. region, Hainich-Dün the at sites Study 3.1: Table = Forstamt. plot. FoA inventory per trees of all age estimated mean trees/ largest 20% the of age estimated mean trees/

14 3 Material and methods

Location 51°04´45Ń 10°20´16´É 10°22´16´É 10°27´45´É 10°27´07´É 10°27´14´É 10°22´14´É 51°10´33´Ń 51°10´33´Ń 51°08´33´Ń 51°08´33´Ń 51°04´48´Ń 51°04´42´Ń 51°04´42´Ń 51°07´44´Ń 51°07´44´Ń ------site: Hai-T Hai-T site: study plots plots study Gerold: Parzelle I I Parzelle Gerold: Code of adjacent adjacent of Code Tower site Hainich Hainich site Tower Gerold: Parzelle III III Parzelle Gerold: close to FORCAST FORCAST to close CARBOEUROFLUX: CARBOEUROFLUX: Hainich, Hainich, Hainich, Hainich, Hainich, National Park Park National Park National National Park Park National Revier Langula Langula Revier Revier Langula Langula Revier Revier Langula Langula Revier Forestry district FoA Mühlhausen, Mühlhausen, FoA FoA Mühlhausen, Mühlhausen, FoA FoA Mühlhausen, Mühlhausen, FoA Weberstedter Holz Holz Weberstedter Holz Weberstedter Weberstedter Holz Weberstedter "Langula", "Langula", "Langula", "Langula", "Langula", "Langula", Hainich NP, NP, Hainich NP, Hainich Hainich NP, NP, Hainich central Hainich central Hainich central Hainich Selection system system Selection Selection system system Selection Selection system system Selection southern Hainich Hainich southern southern Hainich Hainich southern Hainich southern Study site/Region site/Region Study

3 3 3 3 3 3

uneven-aged uneven-aged uneven-aged uneven-aged uneven-aged uneven-aged 178 / 168/ 87 87 / 168/ 178 180 / 123 / 39 / 39 / 123 180 / 51 / 147 230 178 / 131 / 48 / 48 / 131 178 190 / 122 / 45 / 45 / 122 190 202 / 153 / 74 / / 153 202 in 2000 (years) Stand or tree age age tree or Stand cutting cutting cutting cutting cutting cutting system system Selection Selection Selection Selection Selection Selection Unmanaged Unmanaged Silvicultural Silvicultural Continued. Continued. Hai-I Hai-II Unmanaged Lang-I Hai-III Lang-II Lang-III Study plot plot Study Table 3.1:

15 3 Material and methods

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 3 Material and methods

Hai-III

Hai-II "Hainich NP" Hai-I

Unmanaged forest

Lang-III

Lang-II "Langula" Lang-I Selection system

Mühl-171+10

Mühl-102

Mühl-85

Regular shelterwood Mühl-55 Chronosequence "Mühlhausen" Mühl-38

Lei-141M

Lei-111M

Lei-62M Regular shelterwood Regular

Chronosequence "Leinefelde" Lei-30M Lei-153+16M

Factors Silviculture site Study plot Study Figure 3.2: The hierarchical design of the present study. 17 3 Material and methods

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 Inventory plot 100 x 100 m 50 x 50 m litter trap

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 3 Material and methods

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 3 Material and methods

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 sickle- shaped 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) “Hainich- Dün”, which in turn belongs to the forest growing region (Wuchsgebiet) “Mitteldeutsches Trias- Berg- und Hügelland”. The “Hainich-Dün” encompasses a total area of about 65 000 ha, of which 39 % are forested (TLWF 1997).

20 3 Material and methods

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 Precipitation (mm) Leinefelde Mühlhausen Hainich NP

2000 8791 n.d. 9261 / 9492 2001 8041 10711 8241 / 7222 2002 11071 12851 9131 / 9452 2003 6834 n.d. 6893

21 3 Material and methods

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 3 Material and methods

• 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 3 Material and methods

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 (“Stamm- Standortsformengruppen”) “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 (ISSS- ISRIC-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 3 Material and methods

Table 3.3: Overview of characteristics, which were used to evaluate and select the study plots. (1) mo/mm/mu = upper/middle/lower Triassic limestone. (2) Soil and site classification system of Thuringia, for explanation see Appendix Table 1, or in detail VEB Forstprojektierung (1974); Sources: Forestry site maps. Forestry records and site maps were provided by the Forstamt Mühlhausen, Forstamt Leinefelde, Amt für Forst und Landespflege Stadt Mühlhausen, Nationalparkverwaltung Bad Langensalza, Bundesforstverwaltung. (3) G = Galio odorati-Fagetum = Melico-Fagetum; H = Hordelymo-Fagetum (nomenclature according to Oberdorfer (1994)), a = in spring domination of Allium ursinum, f = in summer domination of ferns.

2 2

1 (°) Slope (m a.s.l) Site code Elevation Geology Exposition Vegetation Soil classification Nutritional status

Lei-30M 440 <1 mu Sf.L-5 K2 G Lei-62M 440 1 N mm Ta.T-5 R2 H Lei-111M 450 1 SW mo Wü.L-5 K2 G Lei-141M 420 <1 mm Ta.T-5 K2 G

Lei-153+16M 440 <1 mm Ta.T-5 R2 H

Mühl-38 460 1 SSE mu Sf.L-5/Dd.T-5 K2/R2 G Mühl-55 460 2 SSE mu Dd.T-5 R2 H (a) Mühl-85 460 2 NNE mm Ta.T-5 R2 H Mühl-102 460 <1 mm Kr..L-5 K2 G Mühl-171+10 460 <1 mu Sf.L-5/Dd.T-5 K2/R2 G (f) Lang-I 430 2 N mm Ta.T-5 R2 H 6 Lang-II 400 NW mm/mo Ta.T-5/Le.K-5 R2 G/H (partly 8) Ta.T-5/Fe.LL- Lang-III 420 <1 mm/mo R2 (K2) G (f) 5 (Kr.L-5) Hai-I 430 1 N mo Fa.T-5 R2 H Hai-II 440 <1 mo Fa.T-5 R2 H (a) Hai-III 440 <1 mo Fa.T-5 R2 H

25 3 Material and methods

The soil texture varied between clay loam and loamy clay. The soils of this region are usually rich in cations and they offer very favourable growing conditions for broadleaved deciduous forests. However, the soils are also very sensitive to siltation and encrustation. The risk of siltation and encrustation increases particularly when the soil is not covered by plants and subsequently exposed directly to rain and insolation.

Table 3.4: Soil types of the study plots. The distribution of soil types is based on 15 random samples with an auger and one soil pit per study plot. Classification according to AG Boden 1994. Many samples, which were classified as Parabraunerde or Terra fusca-Braunerde, showed hydromorphic characteristics (Pseudovergleyung).

(Braunerde-) Rendzina-Braunerde, Braunerde Parabraunerde Terra fusca, Braunerde-Terra fusca, (on Terra (on Terra Rendzina Terra fusca-Braunerde fusca) fusca)

Study plot Percentage of all soil samples per study plot (%)

Lei-30M 18.8 0 50.0 31.3 Lei-62M 6.3 18.8 0.0 75.0 Lei-111M 0 0 18.8 81.3 Lei-141M 0 0 0 100.0 Lei-153+16M 6.3 31.3 31.3 31.3 Mühl-38 0.0 18.8 12.5 68.8 Mühl-55 62.5 25.0 12.5 0 Mühl-85 6.3 12.5 0.0 81.3 Mühl-102 0 6.3 0.0 93.8 Mühl-171+10 0 25.0 18.8 56.3 Lang-I 12.5 75.0 12.5 0.0 Lang-II 6.3 12.5 6.3 75.0 Lang-III 0 0 6.3 93.8 Hai-I 12.5 12.5 12.5 62.5 Hai-II 38.9 55.6 5.6 0 Hai-III 81.3 12.5 6.3 0

26 3 Material and methods

3.4.5 Vegetation

The current vegetation of the study sites belonged to the plant sociological formation Fagion sylvaticae and the sub-formation Galio odorati-Fagenion. Except for the detailed proportion of the different tree species, the vegetation was similar to a “natural vegetation” of the sites. The tree canopy is clearly dominated by European beech (Fagus sylvatica), and, depending on soil depth, soil moisture and fertility, additional species contributed to the canopy: Common ash (Fraxinus excelsior), Sycamore (Acer pseudoplatanus), Norway maple (A. platanoides), European hornbeam (Carpinus betulus), elm (Ulmus glabra), poplar (Populus spec.) and oak (Quercus petraea, Q. robur) are co-dominant or intermediate. Also single trees of wild cherry (Prunus avium), lime (Tilia cordata, T. platyphyllos), field maple (Acer campestre) and wild service-tree (Sorbus torminalis) were found. Depending on the water availability and acidity of the upper soil, the ground vegetation of the study sites was typical for the plant sociological association Galio odorati-Fagetum (= Melico-Fagetum) or Hordelymo-Fagetum (nomenclature according to Oberdorfer 1994) (Table 3.3). At some sites Allium ursinum forms a dense mono- species ground layer in spring.

The dormant season of the vegetation lasts about 6 months, from November to March. Bud break of tree leaves started in the year 2000 in the middle of April and in 2001 at the beginning of May (Knohl et al. 2003). The main leaf fall is in October.

3.4.6 Recent silviculture and stand structure

The following descriptions of recent silviculture are based on personal communications with local foresters and joint visits with forest managers to the study sites at the beginning of this study.

3.4.6.1 Even-aged stands of the regular shelterwood systems

The chronosequences “Leinefelde” and “Mühlhausen” represent a typical regular shelterwood system that was managed with the objective to maximise the annual increment of stem biomass and to get a uniform, high yield of beech timber. At both chronosequences the stands are thinned regularly every 5 to 10 years to reduce crowding within the main crown canopy and to increase the light around foliage of the residual trees (crown thinning, Hochdurchforstung). The thinning intensity was generally higher at the chronosequence “Leinefelde” than at the chronosequence “Mühlhausen”.

27 3 Material and methods

The youngest stands, Lei-30M and Mühl-38, represent the pole stage (Nyland 1996), characterised by tree diameters between 5 and 15 cm, high stand densities and a relatively high proportion of ash and maple trees. The naturally regenerated ash and maple trees take advantage of the high light availability after seed- and final cutting and grow faster than the young beech trees at the beginning of a new rotation period. The shelter by the fast growing ash and maple trees shadows the smaller beech trees and provides site conditions that are favourable for growth and timber quality of regenerating beech. Furthermore, at the sapling stage game grazes preferably on ash and maple trees, and thus neglects the young beech trees. Later on at the pole stage, precommercial thinning promotes the growth of beech and reduces successively the number of ash and maple trees. Corresponding to this general scheme, both stands (Lei-30M and Mühl-38) were thinned in 1998/1999.

The pole to sawtimber stage (tree diameter varied between 15 and 30 cm) was represented by the 55- (Mühl-55), the 62- (Lei-62M), and the 85- (Mühl-85) year-old stand. Ash and maple trees represented 15% of total tree number at the 55-year-old stand, while the 62- and 85-year-old stands were almost monotypic beech stands (proportion of other species < 2%). Slash (branches and twigs) remaining from the last thinning (2-5 years ago) was found in all of these stands.

The 102- and 111-year-old stand (Mühl-102 and Lei-111M) represented the bole phase. At the 102-year-old stand there were few ash, maple, elm and poplar trees still present. The 111-year-old stand had some oak trees. These specific features of the stands are very likely a relict of historical forest use (see section 3.4.7). The 102-year-old stand is part of permanent study site of the Fachhochschule Weihenstephan, Prof. Dr. Klein.

The 141-year-old stand (Lei-141M) represented the stage directly after opening the canopy for regeneration. Under an open canopy of big old beech trees a dense understory of various shrubs (e.g. Sambucus nigra, S. racemosa, Lonicera xylosteum, Daphne mezereum) and grasses were growing. Single old spruce trees (Picea abies) originated from planting in the middle of the 19th century, when natural regeneration was missing or too low (section 3.4.7).

The oldest stands had two distinct canopies built up by two cohorts of trees: a shelter (overstory) of 153 or 171 years old beech trees, and a dense canopy of saplings and poles (understory of ash, maple and beech), that regenerated naturally within the last 10 or 16 years. The residual shelter will be cut in the near future (final cutting).

28 3 Material and methods

3.4.6.2 Uneven-aged stands of the selection system

The selection forests are the private property of local forest cooperation (“Laubgenossenschaften”). The “Laubgenossenschaften” (LG) at the Hainich are lawful associations of local landowners who manage their property of forested land jointly. The property of each member of the LG is quantified but their land area is not defined locally in the forest (“ideal shares”). The LG hold all rights as landowners but forest management and silvicultural treatments, in particular, are executed by the forestry administration of the Thüringer Forstamt Mühlhausen (for details on forest history see section 3.4.7).

The study sites were managed according to modern understanding of selection systems since the 1930s. From the middle of the 18th century to the 1930s the sites were selectively cut and the forested area was still divided into “cutting areas” at which the harvest was carried out every 12 years (D. Fritzlar, head of the Forstamt Mühlhausen, pers. comm.). The modern selection cutting of the stands executes the final harvest, tending, and regeneration cutting at the same time and the same area (see also chapter 2). The selection cutting cycle is about 5-10 years (D. Fritzlar, head of the Forstamt Mühlhausen, pers. comm., Gerold 2002).

The stand structure of the studied selection forest obviously differs from that of selection forests dominated by conifers. At broadleaved forests the selection cutting includes single trees as well as small groups of trees (group-selection cutting). This modification of the original idea of selection systems, which was originally based on (sub-) montane forests dominated by conifers, is needed to provide enough space and light for less shade-tolerant tree species than beech, in particular, for valuable broadleaved species such as ash, maple, wild service-tree or wild cherry.

The stand Lang-I is the property of the LG Oppershausen. The site is identical with the permanent study site of the University of Dresden, Tharandt, and it is often visited by excursions of national and international groups of forestry students and foresters. The diameter distribution as well as the vertical structure represents nearly the ideal structure of selection beech forests. However, because of a high game population there is presently the risk that damages caused by game suppresses the natural regeneration of the stand. Thus, the study plot was fenced in 2001.

The stand Lang-II (property of the LG Langula) was characterised by relatively large gaps in which groups of very well growing young trees formed a distinct canopy. This plot is also a permanent study site of the University of Dresden, Tharandt.

29 3 Material and methods

The stand Lang-III (property of the LG Oberdorla) had a quite dense canopy of large old trees and a lack of well established regeneration.

The canopies of all study plots of the selection system were clearly dominated by beech, but they also include some large valuable ash and maple trees. The most important challenge of the future silvicultural treatment of the selection system is to sustain or improve the stand structure towards a stable and balanced diameter distribution and to promote the regeneration and growth of valuable broadleaved species other than Fagus (Acer pseudoplatanus, Fraxinus excelsior, Prunus avium) (D. Fritzlar, head of the Forstamt Mühlhausen, pers. comm.).

3.4.6.3 Uneven-aged and unmanaged stands of the Hainich Nationalpark

The three study plots of the study site Hainich NP are located at the most protected area (“core zone”, “Schutzzone I Weberstedter Holz”, about 600 ha) of the Hainich Nationalpark (7600 ha). Within the “core zone” all plots belong to the former forestry district called “Weberstedter Holz” (261 ha). The entire forest of the Weberstedter Holz can be characterized as an old-growth, uneven-aged (0-250 years) mixed deciduous forest. European beech (Fagus sylvatica) dominates the canopy. European ash (Fraxinus excelsior) and sycamore (Acer pseudoplatanus) are co-dominant tree species. Single trees of hornbeam (Carpinus betulus), Norway maple (Acer platanoides), elm (Ulmus minor, Ulmus laevis), lime (Tilia cordata, T. platyphyllos), wild cherry (Prunus avium), oak (Quercus petraea, Q. robur) and service tree (Sorbus torminalis) are mixed into the Fagus canopy or form individual groups.

The study site at the Hainich Nationalpark represented the unmanaged “reference site” in this study. Nevertheless, all forests in central Europe (except those with very extreme site conditions) are influenced by forest use and management in the past. Thus, it has to be kept in mind that the unmanaged sites do not represent a primary forest. The stands have not been harvested, thinned or used in any way since 1997. The previous 32 years the forest was part of a military training area and during this period there was no regular forest management, but a few single trees of high value were cut (section 3.4.7). The “Weberstedter Holz” was a protection zone for a shooting range and was never used as a training area for tanks. Presently the forest is at an “advanced” stage of development, i.e. the old and tall trees start to top over or even break down, multiple canopies exists and gaps are rapidly filled by fast growing regeneration. Dead trees and wood from the past 35 years remain at the site so that relatively large amounts of standing dead

30 3 Material and methods wood and coarse woody debris reflect the unmanaged character as well as the “advanced” stage of development.

The stand Hai-I was clearly dominated by old growth beech trees forming a close canopy. Trees of small sawtimber size were underrepresented, but natural regeneration was not completely suppressed and there was a second distinct canopy of beech saplings and young poles. Single hornbeam and oak trees had the size of sawtimber but they were suppressed by the beech canopy or were dead.

At the stand Hai-II large amounts of dead wood, gaps in the canopy and patches of natural regeneration and saplings caused the impression of a “typical old-growth forest”. However, contrasting former theories of the development of natural beech forests (see Leibundgut 1982, Ellenberg 1996, Jenssen and Hofmann 1996), the gaps were quite small (about 10 m in diameter) and the vertical and horizontal structure was very diverse. Beech dominated all canopy layers but ash and sycamore were co-dominant and there were also some vital Norway maple, oak and hornbeam trees.

The stand Hai-III had a very diverse vertical structure and was obviously dominated by large (> 60 cm in diameter), very vital beech, ash and sycamore trees. However, there were also relatively large amounts of coarse woody debris and some standing dead trees.

At all study plots of the Hainich Nationalpark there was an obvious lack of non-beech trees at the pole stage despite high tree species diversity at the seedling and sapling stage.

3.4.7 Land use history

Knowledge of the land-use and forest history of the study sites is essential for the understanding and interpretation of this study. Therefore, an intensive investigation of land-use and forest history was carried out on the basis of old maps, diploma theses of the “Thüringer Fachhochschule für Forstwirtschaft”, Schwarzburg, old regional publications on regional forestry and personal communications from local foresters and forest scientists. The investigation is being continued by the administration of the Hainich Nationalpark and will be expanded to sources presently stored at the historical archives in Gotha, Weimar, Dresden and Mainz.

The present investigation focused (1) on all historical silvicultural practices and types of forest use that could have had major impacts on the forest carbon budget and (2) on descriptions of forests that reveal stand structure, forest productivity, species diversity and the conditions of

31 3 Material and methods soils. One of the major limitations of this investigation was that nearly all available information refers to forest districts or large forested regions, such as the “Weberstedter Holz”, or “Vogtei” or “Hainich”, and not to our specific study plots. Furthermore, much information on forest history is based on forestry records and reports which primarily focused on forestry planning and economic or administrative aspects of forest management. These aspects of forest management provide many clues to the conditions of forests. These are interpretations, however, of available data, and not assured facts. For example, instructions in a forestry record of the 19th century requested that a certain forest district be transformed into a selection system. It can be concluded from this order that the forest was a coppice with standards at that time, but it is not clear if and when the order was implemented. The particular interests of forest landowners and frequent changes of forest ownerships caused further limitations to the interpretation of historical data and information. In the 12th to 18th century the power of noble landowners and authorities increased, and they had an increasing interest in reducing forest use by local people. Thus, it cannot be excluded that historical reports on forest conditions were highly influenced by the authorities, i.e. the forest conditions were described as being worse than they really were just to give the higher authorities the legitimation to reduce the rights of local people to use the forest for their own needs. Many historical changes in silvicultural practices or the intensity of forest use do not reflect demands of “good forest practices” at specific sites, but the development and progress in agriculture and industry (e.g. increase of crop yields, availability of hard coal), or changes in European policy and subsequent changes of the landownership, or just changes in forestry policy and doctrines.

The most important historical facts are presented as keywords in a chronological order in Table 3.5. From the available historical data it can be concluded that all study plots have definitely been forested with broadleaved, deciduous forests since the early 1500s. Except for some plots at the Dün, it is very likely that the study plots were never ploughed (Table 3.5A).

Summarizing Table 3.5 there are two factors which determined forest use and silviculture at the Hainich-Dün: (1) the landownership of forested land and (2) the accessibility of forests or the distance of forests from settlements. The combination of these factors had the effect that the study site “Mühlhausen” in the northern Hainich was extensively (little exploitation) used between the 16th and the 18th century, while the current selection forests in the central Hainich and maybe also the unmanaged forest in the southern Hainich were the most intensively and partly destructively-used forests in this region. The forest at “Leinefelde” (Dün) was moderately used between 1500 and 1900.

32 3 Material and methods

A further, general result of this investigation was that the harvest and collection of fire wood was the most common forest use at the Hainich-Dün. Until the 19th century (and later on during the first and second world war) it was much more important than timber use. In some regions excessive theft of fire wood was a very serious problem for sustainable forest management in the 19th century (Staubesand 1937, Winkler 2003). Moreover, forest grazing was very common and partly very destructive, while litter raking played a minor role in the Hainich-Dün region (Thomas 1995, Winkler 2003). Except for the last century or the southern Hainich (Staubesand 1937, Forestry record LG Weberstedt 1938) substantial damages caused by game were not reported officially (Thomas 1995, Winkler 2003).

The present tree species diversity results from the silvicultural history (Table 3.5) and varied from stand to stand. In general, the following combinations of species were reported for the former silvicultural systems (Thomas 1995, Winkler 2003, Oettelt 1785):

• coppice with standards: coppice trees: lime (Tilia cordata, T. platyphyllos), hazelnut (Corylus avellana), hornbeam (Carpinus betulus), European mountain-ash (Sorbus aucuparia), poplar (Populus spec.), beech (Fagus sylvatica), hawthorn (Crataegus spec.), dogwood (Cornus sanguinea), and many other shrubs, sometimes oak (Quercus petraea), ash (Fraxinus excelsior), and maple (Acer pseudoplatanus or A. platanoides); standards: oak (Quercus petraea), poplar (Populus spec.), beech (Fagus sylvatica), lime (Tilia cordata, T. platyphyllos), ash (Fraxinus excelsior) and sycamore (Acer pseudoplatanus), single trees of hornbeam (Carpinus betulus), wild cherry (Prunus avium), and wild service-tree (Sorbus torminalis). The proportion of beech trees increased with increasing density of the standards (e.g. 19th century at the Revier Geney and the Stadtwald Mühlhausen).

• Selectively cut forests and selection forests: dominance of European beech (Fagus sylvatica), and co-dominance of common ash (Fraxinus excelsior) and sycamore (Acer pseudoplatanus), single trees of Norway maple (A. platanoides), European hornbeam (Carpinus betulus), elm (Ulmus glabra), poplar (Populus spec.), oak (Quercus petraea), wild cherry (Prunus avium), lime (Tilia cordata, T. platyphyllos) and wild service-tree (Sorbus torminalis).

33 3 Material and methods

At some locations of the Hainich-Dün Populus was very common, in particular at the northern Hainich. The relatively fast growing poplar was part of the standards in coppice with standards systems and substituted oak as the main source for timber (Staubesand 1937). Indeed, most of the old timber framework houses of the city Mühlhausen are made of poplar wood (Thomas 1995). At the Hainich Nationalpark the largest tree individuals still originate from coppice shoots or standards. Some large ash and maple trees, for example, are still growing on large hollow stumps, which built up their base more than a hundred years ago. The oldest trees at the Hainich NP germinated and partly grew up in a coppice with standards system. At Leinefelde the oldest trees represent the first tree generation that did not grow up under a coppice with standards system.

In contrast to many other large forested areas in central Europe, the impacts of intensive use of forests seemed to be buffered by the fertile and calcic soils of the Hainich-Dün region. From the very beginning of human settlements and forest use in the Hainich-Dün region the favourable site conditions supported the recovery of the forests and sustained their high productivity despite periodically very intensive use. However, the soils are very sensitive to siltation and encrustation (section 3.4.4). The local foresters were aware of this problem and recommended “a permanent cover by trees or shrubs to protect the soil against devastation” (forestry records “Genossenschaftswald Weberstedt” 1939, 1951/1960; Castendyck 1906, Staubesand 1937, Winkler 2003).

Agricultural use of the study sites before 1500 cannot be excluded, but it seems to be very unlikely at the Hainich NP, Langula and Mühlhausen because of their location at the upper mountain range of the Hainich (poor accessibility) and a high clay content of the soil. It is very difficult to plough the soil without modern machines and the bedrock of limestone also contains a high risk of summer drought for crops. The combination of a relatively thick loess layer and the vicinity to the monastery “Reifenstein” (beneficence in 1162) at the study site “Leinefelde” may have led to temporary cropping at some plots (Lei-111M and Lei-141M) before 1500.

34 3 Material and methods

y d

iews b stuhl an

ühhausen) 3) Staubesand 3) Staubesand m.), (6) Mantel

creasing distancecreasing from

excluded, but it is not likely likely not it is but excluded,

(12) Beneke 2002, (13) Rock (12) Beneke ing of lumber in semi-natural forests, in forests, lumber semi-natural of ing in forests, lumber semi-natural of ing Fritzlar (head of the Forstamt M (head Fritzlar to high grazing pressure. Soil was partly t and the sacral buildings, of construction t and the sacral buildings, of construction thorities had the rights of landowners had the rights thorities thorities had the rights of landowners had the rights thorities

wald Weberstedt" 1951/1960, (19) Großmann 1951/1960, (19) Großmann wald Weberstedt" horses (ranking with in

st stands were st standscharacterised were by of high, old one canopy Overexploitation very was common.

. Overexploitation cannot be g. It is assumed that this extensive use had no major impact no use had this that extensive assumed is It g. ssible with available tools for cutting, skidding, loading or loading skidding, with available tools for cutting, ssible ssible with available tools for cutting, skidding, loading or loading skidding, with available tools for cutting, ssible orks), increased forest use. orks), forestedLarge areas were orks), increased forest use. orks), forestedLarge areas were

to harvest the periodfire wood. Rotation (5; 3). 11-15 years chaftswaldes 1884, Weberstedt" (17) Forestry record map and 1910, (11) Castendyck 1906, (11) Castendyck 1910, Forest use fire wood. Typical for forests close to settlements. Size of trees was trees was of Size settlements. to close wood. Typicalfire forests for nt were typical for forests at the Hainich-Dün: nt were typical for forests at the Hainich-Dün: ., (5) Oettelt 1785 (translated by Prof. Dr. Wittecke, pers. com Wittecke, byDr. Prof. (translated 1785 Oettelt ., (5) elde) perselde) and comm., (15) Biehl hule Schwarzburg) pers. comm., (2) Klaus and Reisinger 1995, ( Reisinger and comm., (2) Klaus pers. hule Schwarzburg) , selective cutting): irregular selective selective cutt irregular selective cutting): , , selective cutting): irregular selective selective cutt irregular selective cutting): , generation or shrubs could not alive due of oak and beech nuts). Fore rests for their own needs, while noble au rests for their own needs, while noble au ) (2). urban developmen ) population, Increasing ) (2). urban developmen ) population, Increasing wood, bark, wood, twigs and fruits. 6) (1; Forestry map and record Forestry "Genossenschafts “): grazing in forests by goats, sheep, cows and twald Mühlhausen. (2; 3; 4; 8) ) Winkler Matthes 2003, (10) LG = Laubgenossenschaften. = Laubgenossenschaften. LG Eigentum Eigentum irtschaftskarte des Laubgenossens “ “ “: regular and organized harvest of Hutewald ungeregelte Plenterung ungeregelte Plenterung („ “( “( evierleiter Geney, Forstamt Leinef Blünderwald Blünderwald Coppice systems Coppice Forest pasture deutsch-rechtliches deutsch-rechtliches („ which was associated with a high production of glass (glassw overexploited. manageme of forest types following the general, In 1. “ which were far away from settlements or which were hardly acce mainly sawtimber restrictions small technical of Because transport. 6) (1; harvested. was for is most likely the use of forest type This Stadtwald Mühlhausen Irregular selective cutting in natural forests and extensive grazin forests cutting in and extensive selective natural Irregular the had thefo right people to use Local on forest structureon forest and biogeochemical cycles. (1) („ which was associated with a high production of glass (glassw overexploited. manageme of forest types following the general, In 1. “ which were far away from settlements or which were hardly acce mainly sawtimber restrictions small technical of Because transport. 6) (1; harvested. was for is most likely the use of forest type This Stadtwald Mühlhausen Local people had the right to use the the had thefo right people to use Local 3. kept very small, so that older people and children could help Collection of nearly all pieces of 2. “ for the study sites at the Stad the village) and fatting of pigs (rich offoot fatting pigs village) the and trees, dominated by oak and beech. Tree re

th century -15 th th ers. comm., (16) Forestry map comm., (16) Forestry "W ers. A) Forest history of the of the Hainich-Dün history A) Forest region. century Before the 12 Time period Time 12 Störzner 1999, (14) Meyer (R Störzner 1999, (14) Meyer Röhling). "Genossenschaftswald Weberstedt" 1939, (18) Weberstedt" "Genossenschaftswald 1937, (4) Willner (head of the Stadtwald Mühlhausen) pers. comm pers. Mühlhausen) Stadtwald the (head of Willner (4) 1937, Robisch 1994, (8) Thomas 1995, (9 1990, (7) Table 3.5: Overview of forest use history. use history. forest of Table 3.5: Overview p Sources are given in brackets: (1) Prof. Dr. Wittecke (Fachhochsc Dr. Wittecke Prof. (1) in brackets: are given Sources (scientific coordinator of the Hainich Nationalpark) pers. comm., (20) Former forestry workers (pers. comm. according to interv according comm. (pers. workers forestry (20) Former pers. comm., Nationalpark) Hainich the of coordinator (scientific

35 3 Material and methods ("Vitzthumbrecht", (2;5; 7; 9). of forest products of , rotation length of the coppice about 15 15 of the coppice length , rotation about and probably the Dün (1; 7; 9; 12) (1; 7; 9; 12) sions of forests to croplands are generally not very and the vicinity to the monastery “Reifenstein” monastery to the vicinity the and quite offered Forest use the Hainich southern . Overexploitation very was common. 5;(1; 6) to regulateto forest use and paymentthe “coppice with standards system” “coppice with standards consequence of wars and diseases. (1; 6) orks and charcoal, mainly in the neighbourhood of cities or monasteries. (1; 6) al. Overexploitation was very common. (1; 6) common. was very Overexploitation al. with forest (1; 7; 12).grazing with broadleaved deciduous forests! forests! deciduous broadleaved with with broadleaved deciduous forests! forests! deciduous broadleaved with : Origin of the: Origin first forestry law forestry first century th 16 16 for cropping. century the century th or changesor between cropland and coppiceConversystems: or 14 or th 13 Combination of and forest system coppice pasture Cropland Cropland Local people still collected nearly all pieces of wood. (1) of wood. pieces nearly still all people collected Local production of potash for glassw Increasing Onset of litter raking, but it was of minor importance importance the Hainich-Dün minor for entire of was but it raking, litter of Onset (8). Forest vegetation recovered temporarily in temporarily recovered vegetation Forest Beginning of the Beginning of the years combination often in 3), (11; • • • • "Vitztumbuche") was enacted by the by Erzbistumthe enacted was "Vitztumbuche") Dün the at sites the which to Mainz Hainich central the and to (2;7) belonged All sites were definitely forested forested definitely were All sites • unprotected by any plants or organic materi Hainich, the for central likely is most use intensive This forested definitely were All sites 4. • likely at the upper mountain range of the Hainich-Dün, but it cannot be excluded for the study sites at the Dün. The The Dün. the sites at study for the be excluded it cannot but Hainich-Dün, the of range mountain upper the at likely moderate climate, layer, loess thick of a relatively combination conditions suitable In the 5. Continued. th th -15 -18 th th 16 12 Table 3.5 A: A: 3.5 Table century (continued) century Time period Time

36 3 Material and methods coppice coppice details are details are intensive intensive Southern Hainich Southern It is assumed that forest It is assumed that forest Governance of the likelyVery Governance of the likelyVery LG WeberstedterLG Holz (study site "Hainich(study NP") • Kurfürsten von Sachsen (Kursachsen), missing (13) • forest conditions and use were very similar at the Vogtei and the Weberstedter Holz • management as a standardswith and grazing forest • Kurfürsten von Sachsen (Kursachsen), missing (13) • forest conditions and use were very similar at the Vogtei and the Weberstedter Holz • management as a standardswith and grazing forest coppice with century : stipulation, that that : stipulation, th intensive Central Hainich Central : second forestry law forestry to : second century LG Oppershausen (study site "Langula") (study th Local people Local many had rights 1569 18 Since theSince 14 LG Langula, LG Oberdorla, Oberdorla, LG Langula, LG Langula Oberdorla and frequent changes of the governance (Erzbistum Mainz, Herren von Treffurt,Fürsten or Fürsten Sachsen, von von Hessen). • to use the forest. Due to the proper the about confusion governance and struggles for was there hegemony the on or no control practically restriction for forest use. (7) likely Very management as a grazing forest and standards • the of overexploitation reduce intensive very generally forests; forest use (7) • Langula Oberdorla and only citizensonly of the village, who had some property (house or village the theat in land) (Vogtei): • forest district ", all citizens of : most districts of Güldenes Holz 1700 : first law on forest use to on litter (8): ban raking Stadtwald Mühlhausen Stadtwald (study site "Mühlhausen") Forest is property of the Very likely management as a 1565 successful reduction of forest in the forest was fattening pig there was a well general, in 1602 about Freie Reichsstadt the Stadtwaldthe Mühlhausen were managed as coppicewith the but standards, "Güldenes was definitely Holz" • " Mühlhausen Mühlhausen city the the had or to collect defined right a cut wood volume of (8) • selectively cut highforest • reduce the overexploitation of forest the • grazing (8) • harmfulcommon but not the for (8) ecosystem • organized forestry administration and effective the council by city control (3; 8) • • (9) to which the study sites belonged to. to which to. studybelonged sites the th high high century th Dün coppice with definitely Revier Geney Erzbistum Mainz property and governance governance and property before the 18 compared to other at the end of the the end at 18 (study site "Leinefelde")(study • of the (9) • century management as a coppice system, standards with characterised by a number of standards • coppice with standards at the Hainich-Dün the forest district was characterised by a quite sustainable use forest (9) very likelyvery and system standards grazing forest • th -18 th Time Time period 16 B) Forest use of the forest districts century

37 3 Material and methods Southern Hainich Southern LG WeberstedterLG Holz ) (7) ) (7) Central Hainich Central : agreement between use intensive on report : of the : modifications : compromise between : agreement between use intensive on report : of the : modifications : compromise between LG Oppershausen LG 1773 1785 1786 1792 1773 1785 1786 1792 LG Langula, LG Oberdorla, Oberdorla, Langula, LG LG given time, are beneficiaries of of beneficiaries are time, given the forests use right to the Kreises("Schließung" des der Nutzungsberechtigten • Erzbistum MainzFürsten and and von Sachsen, enactment of a new forestry law(7) "absolutistic" new the to owing beneficiaries the law forestry the use forests to the right of joined their forcesand founded the lawful "Laubgenossen- (7) (LG) schaften" • as coppice with standards combined intensive with very forest grazing, generally very forests bad of the conditions (5) • forestry law from1773 after protests (7) of the LG strong • the governance: the and LG the (rights property forest got LG given time, are beneficiaries of of beneficiaries are time, given the forests use right to the Kreises("Schließung" des der Nutzungsberechtigten • Erzbistum MainzFürsten and and von Sachsen, enactment of a new forestry law(7) "absolutistic" new the to owing beneficiaries the law forestry the use forests to the right of joined their forcesand founded the lawful "Laubgenossen- (7) (LG) schaften" • as coppice with standards combined intensive with very forest grazing, generally very forests bad of the conditions (5) • forestry law from1773 after protests (7) of the LG strong • the governance: the and LG the (rights property forest got LG Güldenes Holz : ban on wood : ban on wood Stadtwald Mühlhausen Stadtwald 1735 1735 selectively cut highforest selectively cut highforest a was cycle cutting The (3;8). about 30 years. (8) • a was cycle cutting The (3;8). about 30 years. (8) • collection by local people local people by collection banand on forest grazing (8) collection by local people local people by collection banand on forest grazing (8) Dün Revier Geney Continued. th -18 th Table 3.5 B: B: 3.5 Table century (continued) Time Time period 16 16

38 3 Material and methods th , probably probably , selectively cut adjacent forest end of the 19 : the Weberstedter : the 1884 at the • Holz was definitely a coppice with standards, main tree species were beech,maple, ash, oak, (16) elm. and hornbeam, • century stands were high forests even "modern" selection also Thus, (10). systems was likely site a the study selectivelyhigh cut forest in a at least was or transition phase from a coppice with standards to system a high forest. details are details are However, it is Kurfürsten von Sachsen Governance and property of could assert theirThe LG Langula Oberdorla and • the (Kursachsen) • rights and forest property. The administrationforestry but not could intended implementsignificant a of irregular forest reduction the to With respect use. private the of interests the of the landowner LG administrationforestry the transitionpromote of coppice with standards to forests high cut selectively as not to forests even-aged and public the in done it was (7) forests. missing (9). assumed that forest use assumed and were very conditions forest Vogtei to the similar (15) of use and landownership) on and the one hand, admitted the the other on laws forestry hand (7) Oppershausen: shelterwood by clear-cutting and and clear-cutting by : very high mast: very of : The Hainich-Dün was assigned to the Königreich Preussen. (8;9) The city MühlhausenThe city lostits Forestry administration 1803 beech; large parts of forests high cut selectively were ha) converted (200 to even-aged systems subsequent regeneration from mast. (3;8) the • the forest and sovereignty the became property of Königreich Preussen (Preussischer Staatsforst) (8) • forestry new many initiated up net built a of and laws roads.forest (1; 8) • 1803/1813/14 , rotation lengths lengths , rotation : Initiation of : Initiation Revier was "Geney" administration Forestry 1838 • the Königreich of property Preussen (Preussischer Staatsforst) • reduced irregular forest use, new forestry many initiated of net up a built and laws forest roads. Gradual the of transformation coppice with standards to high forests. (9) system • “regular shelterwood systems" maple ash beech, of and 120 Tree years. 140 oak years, Continued. th -18 th 16 Table 3.5 B: B: 3.5 Table century (continued) 1800-1900

39 3 Material and methods Southern Hainich Southern LG WeberstedterLG Holz , or gap : forestry : forestry selection systems new lawnew to reduce or Central Hainich Central : still intensive and "old- : still intensive : ending of forest forest of : ending LG Oppershausen 1843 1819 and 1829 and 1819 1844 1872: LG Langula, LG Oberdorla, Oberdorla, LG Langula, LG administration complained theabout bad conditions of the of mainly because forests, irregular high harvest of wood, timing of unfavourable harvesting, cutting of branches (7) grazing intensive and • forests was use of fashioned" by locala forester. mentioned However, there was a trend to higher density of standards and a gradual changetowards (7) forests high cut selectively forbid non-timber use and to transfer the coppice with standards to the the at beginning but were similarmore to cuttings cuttings selective cuttings (7, 15) (7, cuttings • • grazing (7) • very good condition Güldenes Holz Stadtwald Mühlhausen Stadtwald 1873 • of the forest district "Güldenes was Holz" reported (8) Picea ") (9) Dün : moderate Triften Revier Geney : more intensive ) on bare patches, which which patches, bare on ) 1839-1854 forest wasgrazing well spruce ( of planting 1870s abies intensivewere by caused when and past, the in grazing was regeneration natural (9) low too was or missing single(e.g. spruce trees in the stand). 141-year-old species composition: mainly mainly composition: • species maple, some oak, ash, beech, lime trees. poplar (9) and • was total thinning thinning, than it had been lower recommended by forestry deciduous valuable high plans; they when disappeared trees were not promoted by thinning activities; excessive theft of fire wood (9) • on allowed only regulated and trailings (" • • promotion of valuable treebroadleaved species by (9) cuttings gap some Continued. Time Time period 1800-1900 (continued) Table 3.5 B: B: 3.5 Table

40 3 Material and methods timber 3 details are details selection : : : management (17) -1 of 2.1 m of 2.1 selection system, selection system, year -1 1900-1926 1926-1939 1938/1939 • • • ha • as a iscanopy dominated by maple beech, and ash; average age of beech trees about 100 (range 1toyears timber standing 220 years), missing cutting selection : Management since 1930s since • system according to the modern modern the to according of understanding a ") (9) ") - final : further increase of : Tree species species Tree : : theft of fire wood wood of fire : theft Bodenverwundung Lichtungshiebe at some stands probably very 1890s shelter- of the improvement 1894 1904/05 cutting when tree height of height tree when cutting was 0.6-1.2 regeneration m; thinning, of intensity increased • of the scarification soil slight (" • beech, mainly composition: some ash and maple trees, trees of hornbeamsingle and (9) oak • preparatorywood system: several seed cutting, cutting, cuttings successive (" • decreased (9) • thinning according to the to according thinning recommendations by the forestry scientist Hartig: at 10-25 stands (about young years old) intensive precommercial thinning, then, of 60 age years, stand a until every 5 moderate years stands from below", "thinning 60 than older years were that Continued. Table 3.5 B: 1800-1900 (continued) 1900-1948

41 3 Material and methods 3 . on -1 -1 ha 3 ha 3 : Government : Government , annual , annual 1939-1958 -1 -1 ha : Canopy is : Canopy 3 . Instruction to . Instruction year -1 Southern Hainich Southern -1 1949-1965 1950 LG WeberstedterLG Holz volume on average 238 m average 3.67 m of the GDR and prohibition of private Forestry landownership. administration in Mühlhausen managed the former private land and selection the continued cuttings. (15) year • • dominated by beech, mapleplannedash; and timber harvest for the m 7.8 1951-1960: period ha increase gap cuttings to cuttings gap increase promote tree regeneration, to instruction and substitute the selection cuttings by grouped shelterwood (17) cuttings Planned timber harvest for Planned the period increment 3.52 m : Government of Central Hainich Central LG Oppershausen LG 1949-1990 LG Langula, LG Oberdorla, Langula, Oberdorla, LG LG the GDR prohibition and of private landownership. The administrationforestry in Mühlhausen managed the former private land and selection the continued the During GDR (15) cuttings. time the foresters had to take care that after harvesting all pieces of wood were collected ("Saubere floor the forest from Waldwirtschaft") • Government of of Government : ongoing : ongoing Güldenes Holz Stadtwald Mühlhausen Stadtwald 1949 –1990: –1990: 1949 -1997 1900 management as regular shelter wood system (4) • the GDR. Ongoing management as a regular shelterwood system. (4) time the the GDR During foresters had to take care that pieces harvesting of all after wood were collected from the floor ("Saubereforest Wald- wirtschaft") • : Government : Government Dün Revier Geney 1949 –1990 1949 were intensively thinned thinned intensively were every 10 years "from above" (9) • time the GDR the • During take to had care that foresters harvestingafter all pieces of of the GDR. Ongoing management as a regular shelterwood system. However, because of the of the situation economic GDR partly very high commercial thinning and partly very low precommercial thinning (14). Continued. Time Time period 1900-1948 1900-1948 (continued) 1949 -2003 1949 Table 3.5 B: B: 3.5 Table

42 3 Material and methods 3 , (district of selectively -1 Property of Property : The Weber- : The year were -1 -1 ha 3 year . (19). the In 1980s: -1 1990-1997: 1990-1997: 1965-1990 Acer pseudoplatanus the Germanthe government and the by administration veneer timber) (20); of broken removal 1987/88: (windthrow) (20) timber • stedter Holz part was of a area military training large is It of the GDR. ha) (5700 assumed that about 2 m plot Hai-II, Hai-III). Hai-III). plot Hai-II, In- struction to return to a get to cuttings selection standing stem higher same the at and, volume time,higherof a yield of high promotion timber; broadleaved treevaluable species (18) • cut single, of cutting selective valuablevery maple trees ( (district of plot Hai-I) of Hai-I) and plot (district m 6.5 ha (district of plot Hai-I) of Hai-I) and plot (district m 6.5 : The gotLG 1990-2003 • back their former rights as continued and landowners the selection cuttings. The timber of volume harvest the than lower is and was annual increment. Thus, there is a general trend to a higher standing stem volume compared to the past. (15) the city city the : gradual since 1997 since 19.6.1991: Mühlhausenback got all rights of the landowner the as "Stadtwald Mühlhausen", management under forest council city of the direction • systems to selection transition (4) • 1985 1985 : : irregular : irregular : regular, : regular, Saubere "). untill the 1970s the 1980s since wood were collected from the forest floor (" • harvesting; and thinning no partly or intensive, partly particular in at thinning, low very stand the 62-year-old (14) thinning low • thinning high relatively 141-year-old stand: 1994 2000 1999 and seed cutting, to cuttings successive the light availability increase • 153-year-old stand successive 1992 cutting, seed 2003 2002 and cutting, the of final cutting beginning at the southern edge of the stand. Waldwirtschaft Continued. 1949 -2003 -2003 1949 (continued) Table 3.5 B: B: 3.5 Table

43 3 Material and methods (12) -1 ha regarding regarding 3 -1 : Formation of : Formation year -1 : at district 8 (Hai- Southern Hainich Southern LG WeberstedterLG Holz 31.12.1997 1992 “Bundesforstverwaltung"; “Bundesforstverwaltung"; about of cutting selective m³3.6 ha • the “Nationalpark Hainich”. Weber- the date this Since stedter Holz is highly totally and protected (19) unused. “Bundesforstverwaltung"; “Bundesforstverwaltung"; about of cutting selective m³3.6 ha to the entire area of the the of area entire the to Nationalpark - it clear is not was tree harvest if the carried out at the Weber- stedter Holz (19); • valuable of cutting II,III) timber, ca. 20 m Central Hainich LG Oppershausen LG Langula, LG Oberdorla, Oberdorla, Langula, LG LG Güldenes Holz Güldenes Stadtwald Mühlhausen Stadtwald Dün Revier Geney Continued. Time Time period 1949 -2003 1949 (continued) Table 3.5 B: B: 3.5 Table

44 3 Material and methods

3.5 Cooperation with other research projects

Independent of the present study several other research projects, which focused on different ecosystem aspects, were carried out at the study sites (Table 3.1). For the benefit of all projects there was a close cooperation and data exchange among the projects. In particular, the investigations on dead wood and litter fall by Francesca Cotrufo, on leaf litter decomposition by Bernd Zeller, Etienne Dambrine and Francesca Cotrufo, on soil organic carbon by Tryggve Persson and Ingolf Schöning, on stem growth by Marco Bascietto (EU-project FORCAST) and on net ecosystem fluxes by Alexander Knohl and Peter Anthoni (EU-project CARBOEUROFLUX) were considered in the present study. The study plots and the soil pits of the FORCAST project at Leinefelde and at the Hainich NP were very close to the study plots and soil pits of this study (distance was less than 100 m) but they were not identical. To distinguish the plots of this study from the adjacent FORCAST plots, the plot codes end with the letter “M” for Mund.

Three study plots were also part of permanent study sites of the University of Applied Science Weihenstephan, Fachbereich für Wald und Forstwirtschaft, Prof. E. Klein, or the Technical University of Dresden in Tharandt, Lehrstuhl für Waldwachstums- und Holzmesskunde der Fachrichtung Forstwissenschaften, Dr. D. Gerold (Table 3.1). For these sites long term data on stand structure and forest growth were available.

45 3 Material and methods

46 4 Stand structure and biomass

4 Stand structure and biomass

Photosynthesis followed by plant growth is the primary process that imports organic carbon into terrestrial ecosystems. In natural, unmanaged forested ecosystems tree mortality (including herbivory), mineralization or stabilization of organic carbon in the soil are the consecutive processes that affect the release of carbon from the ecosystem or the storage of carbon within the ecosystem. In managed forests the most obvious impacts of biomass export due to tree harvesting are changes of the stand density and the reduction of living and dead tree biomass.

In this chapter the forest structure and the carbon pools in tree biomass of the shelterwood systems and the selection system are compared with each other and with the unmanaged stands of the Hainich Nationalpark. The analysis of stand structure is not directly linked to forest carbon pools, but it reveals information about stand productivity, recent forest harvesting or thinning and the stage of development of the study plots.

4.1 Methods

4.1.1 Forest inventory

At each study stand a squared inventory plot of variable size, depending on tree size and stand heterogeneity (Table 4.1), was established and tree girth at breast height (1.3 m above ground) and tree height of all trees (tree height > 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.

Size of inventory plot Stand/Plot (size of subplot) (m2)

Lei-30M 625 Lei-62M 625 Lei-111M 2500 Lei-141M 2500 Lei-153+16M 2500 (500) Mühl-38 625 Mühl-55 500 Mühl-85 1250 Mühl-102 2038 Mühl-171+10 2500 (25) Lang-I 10000 Lang-II 10000 Lang-III 2500 Hai-I 3000 Hai-II 2500 Hai-III 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 4 Stand structure and biomass

General stand characteristics included the arithmetic mean of tree diameters and heights ( D

N and H ) and the quadratic mean of tree diameters (Dg = ∑ 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

N 2 the 20% largest trees per stand (Do = ∑ dbho N ) (WEISEsche Oberhöhe; Kramer and Akça i=1 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.

c y = a * ⎡1− −b*x ⎤ (Equation 4.1) ⎣⎢ e ⎦⎥ Chapman-Function (= Mitscherlich-Function) with y: tree height (m) x: dbh (m) a, b, c: empirical model parameters

49 4 Stand structure and biomass

45 Chronosequence "Leinefelde" 40 A

Tree heightTree (m) 15 Lei-30M Lei-62M Lei-111M 10 Lei-141M Lei-153M 5 (16-year-old regeneration is not presented) 0 0.00.10.20.30.40.50.60.70.80.91.0 dbh (m) 45 Chronosequence "Mühlhausen" 40 B

20 Mühl-38

Tree (m) height 15 Mühl-55 Mühl-85 10 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 Waldwachstums- und Holzmesskunde (pers. comm.), respectively.

50 4 Stand structure and biomass

45 Selection system "Langula" 40 C

Tree height (m) height Tree 15

10 Lang-I 5 Lang-II Lang-III 0 0.00.10.20.30.40.50.60.70.80.91.0 dbh (m) 45 Unmanaged forest "Hainich NP" 40 D

Tree height (m) height Tree 15

10 Hai-I 5 Hai-II Hai-III 0 0.00.10.20.30.40.50.60.70.80.91.0 dbh (m)

51 4 Stand structure and biomass

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 Study plot a b c P R2 n function Lei-30M 13.561 36.491 2.513 <0.001 0.841 298 Chapman Lei-62M 29.039 14.591 3.692 <0.001 0.835 39 (equation 4.1) Lei-111M 37.803 8.355 2.212 <0.001 0.956 56 Lei-141M 32.749 8.144 - 0.017 0.224 25 Linear Lei-153+16M 29.56 14.469 - 0.007 0.417 16 Mühl-38 12.715 32.889 2.107 <0.001 0.830 217 Mühl-55 Chapman 25.879 13.914 1.711 <0.001 0.913 75 Mühl-85 (equation 4.1) 30.704 10.543 2.120 <0.001 0.904 70 Mühl-102 36.878 7.189 1.710 <0.001 0.943 124 Mühl-171+10 Linear 34.7 n.s. - 0.945 - 21 Lang-I 39.531 3.697 1.003 <0.001 0.973 651 Chapman Lang-II 41.475 3.264 0.996 <0.001 0.973 677 (equation 4.1) Lang-III 38.068 5.397 1.272 <0.001 0.974 68 Hai-I 39.531 3.497 0.944 <0.001 0.979 168 Chapman Hai-II 37.948 2.803 0.837 <0.001 0.956 186 (equation 4.1) Hai-III 38.764 3.721 0.971 <0.001 0.949 147

The sum of the cross-sectional area at 1.3 m above ground of all stems per subplot (basal area of trees) extrapolated to one hectare resulted in the basal area of the stand.

Stand parameters such as the diameter and tree height distribution or the dominant stand height were used to compare stand structure and growth of the studied stands, but these parameters were interpreted considering differences in the thinning regime or tree harvesting. Yield tables and corresponding site indices (expected height at a given reference age) were not used in this study, because they are based on even-aged stands that are managed according to a defined management regime, so that they do not represent the stand structure and growth of uneven-aged and differently managed stands. In addition, most available yield tables do not reflect changes in forest growth due to recent changes in forest management practices and

52 4 Stand structure and biomass

environmental conditions (increased nitrogen deposition and CO2 concentration) (Franz et al. 1993, Spiecker et al. 1996, Mund et al. 2002).

The stem volume over bark of each tree within a subplot was estimated by the product of tree height, basal area and a form factor, which resulted from regressions given by Mette and Korell

(1986) for the respective tree species. The timber volume VD (Derbholzvolumen) included the merchantable stem and branches with a diameter of at least 7 cm. The stem volume VS (Schaftholzvolumen), which is not restricted by a thin-end diameter but which excludes branches and twigs, is equivalent to the stem biomass resulting from a multiple regression model given by Wirth et. al. (2003) (Appendix Table A.3), and thus it is not presented separately. The sum of timber volumes per inventory plot was extrapolated to the timber volume per one hectare.

The estimates of stem, branch, twig and coarse root (> 2 cm in diameter) biomass at the tree level resulted from multiple regression models for beech given by Wirth et al. (2003) (Appendix Table A.3). Comprehensive regression models for the biomass of Fraxinus excelsior, Acer pseudoplatanus, Carpinus betulus or other non-beech tree species of the study sites were not available. Fine root biomass was derived from Claus (2003) or was assumed to be equal to leaf biomass (average ratio between leaf biomass and fine root biomass of the FORCAST and CANIF-data set; Claus 2003, Scarascia-Mugnozza et al. 2000). The biomass at stand level resulted from the sum of tree biomass within each inventory plot extrapolated to one hectare.

The integrated mean of one rotation period of the even-aged stands was calculated by the mathematical procedure “calculation of area” of the software “Xact” (XactPro Version 7.22, SciLab GmbH). For this calculation the starting point was set to stand age 12 years (Leinefelde) and 18 years (Mühlhausen) and the end point to 155 or 173 years, respectively. These borders derived from the fact, that the final cuttings of the oldest stands were carried out 2 years after the field work for this study and thus, when the understory of the study plots Lei-153+16M and Mühl-171+10 was 12 or 18 years old and the overstory 155 or 173 years old, respectively. The biomass at the beginning and the end of the rotation period was linearly extrapolated from the youngest and the oldest stands of the chronosequences.

Leaf biomass resulted from litter sampling over two years (1999/2000 or 2000/2001). The litter was collected every two weeks from October to November and every two months over the rest of the year. For more details on litter sampling and processing see chapter 5.

53 4 Stand structure and biomass

A multiple regression model for leaf biomass was also provided by Wirth et al. (2003). However, a comparison of leaf biomass with independent measurements of the effective LAI (leaf area index, LAI-2000 instruments, Li-Cor, USA) confirms the litter sampling approach for the older even-aged stands (Figure 4.2). An only slight decrease of leaf biomass in relation to a rapid decrease of the LAI can be explained by a rapid decrease of the SLA (specific leaf area; m2 kg-1). A decreasing SLA is associated with a larger amount of sun leaves in thinned stands or after canopy opening for regeneration. The amount of leaf litter in relation to the measured LAI corresponded with a decrease of the SLA from 21.2 at the 30-year-old stand to 13.6 m2 kg-1 at the 153+16-year-old stand. The regression model would result in a SLA of 21.6 and 10.2 m2 kg-1, respectively. With respect to the SLA reported for other European beech forests (Scarascia- Mugnozza et al. 2000; range 16-24 m2 kg-1) the estimate for the SLA of 13.6 m2 kg-1 that resulted from the leaf litter fall seemed to be more reasonable. Consequently, in this study the measured annual leaf litter fall was used for an estimate of total leaf biomass instead of the multiple regression model.

2.0 Leaf biomass = 1.510+ 0.024* LAI 2 1.8 R = 0.255; P = 0.385 ) -1 1.6 Lei-30 Estimated from annual 1.4 Hai-T leaf litter fall Multiple regression 1.2 Lei-111 Lei-62 model Lei-153 1.0 Leaf biomass (tC ha

0.8 Leaf biomass = 0.783+ 0.127* LAI R2 = 0.982; P = 0.001 0.6 2345678

2 -2 LAI (m m )

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.). Annual leaf litter was sampled over two years (1999/2000 or 2000/2001). The multiple regression model for leaf biomass of beech was taken from Wirth et al. (2003).

For the conversion of woody biomass into carbon pools we assumed an average carbon concentration of 50% of dry weight (Wirth et al. 2003). The carbon concentration of beech leaf

54 4 Stand structure and biomass litter was 0.494 g g-1 and that of non-beech leaf litter 0.463 g g-1 (for details see chapter 5). For the fine roots a carbon concentration of 0.408 g g-1, measured by Claus (2003) at the FORCAST plots in “Leinefelde” and the “Hainich NP”, was used.

4.1.2 Coarse woody debris and large dead wood (snags and logs)

The coarse woody debris (CWD), defined as lying dead wood with a diameter of 5-20 cm and a length of more than 10 cm, was determined by the “line intersect method” (Baily 1970, Ringvall and Ståhl 1999, Marshall et al. 2000). At the study plots (100 m x 100 m) six parallel lines, each 100 m long, were marked by a string (line transect). The diameter of all pieces of CWD, whose central axis cross the line transect, was measured at the point the line was crossed. Corrections for the angle of the wooden pieces from the horizontal were not needed, because all pieces either lay on the ground or the angle from the horizontal was smaller than 15 degrees (Marshall et al. 2000).

The volume of coarse woody debris resulted from Equation 4.2:

2 n π 2 (Equation 4.2) Vi = × ∑dij 8× L j=1 with 3 -1 Vi: total volume of CWD (m ha ) that is based on the line transect i L: length (m) of the line transect d: diameter (m) of the wooden piece at the point it is crossed by the line transect n: number of pieces that are crossed by the line transect i

In order to get an average biomass of CWD that also considered the volume of different decay classes, the decay class and the length of all pieces were also determined. The volume per piece of wood that was crossed by the line transect was calculated by Equation 4.3, and the biomass resulted from Equation 4.4.

2 ⎛ d ⎞ v ⎜ ij ⎟ h (Equation 4.3) ij = π ×⎜ ⎟ × ij ⎝ 2 ⎠ with 3 vij: volume (m ) of the wooden piece j of the line transect i dij: diameter (m) of the wooden piece j of the line transect i hij: length (m) of the wooden piece j of the line transect i n: number of pieces that are crossed by the line transect i

55 4 Stand structure and biomass

(Equation 4.4) wijz = vijz × BWD z with wijz: dry weight (kg) of the wooden piece j of the line transect i and the decay class z 3 vijz: volume (m ) of the wooden piece j of the line transect i and the decay class z BWDz: basic wood density (kg m-3) of the decay class z (Table 4.3)

The classification into five decay classes was based on a combination of several descriptive and quantitative parameters such as remaining proportion of bark, infection by fungi and number and condition of fruit bodies, resistance to a knife blade that is pushed into the wood, texture (hard, soft, friable), shape and colour of the decaying wood (modified from Graham and Cromack 1982, McGee et al. 1999, Pyle and Brown 1999). The parameters were collected for all pieces of dead wood and compared with each other resulting in a relative scale of wood decay from fresh dead wood (decay class 1) to highly decayed dead wood (decay class 5). Large logs and snags often decay from their centre outwards to the youngest tree rings, resulting in hollow cylinders with a narrow shell of nearly un-decayed wood. To detect this “pseudo fresh dead wood”, logs were knocked on in order to hear if inner parts were already decayed. Hollow logs were generally classified as decay class 4.

There are no available data on changes of wood density with a progressive decay of dead wood from European beech, ash or maple trees in situ. Thus, it was assumed that the basic density of dead wood declines linearly and quite rapidly with increasing decay class (Table 4.3). This assumption was based on several studies dealing with the decay of dead hardwood (e.g. Arthur et al. 1993, Stewart and Burrows 1994, Duvall and Grigal 1999, Weber 2001, Clinton et al. 2002, Mackensen et al. 2003), and on some specific properties of beech wood, which affect its decay rates (e.g. relatively low concentrations of tannin agents and the deposit of them in the cell lumen instead of the cell wall, sensitivity of beech wood to infection by white-root fungi, Kučera 1991, Hammel 1997).

In contrast to nitrogen concentrations, carbon concentrations of dead wood do not change significantly with the progressive decay process. Therefore, the carbon concentration was assumed to be 50% of dry dead wood (Stewart and Burrows 1994, Weber 2001, Clinton et al. 2002, Kahl 2003).

56 4 Stand structure and biomass

Table 4.3: Basic wood densities for different decay classes. The wood densities are rough estimates that are derived from the average basic wood density of living dead wood (558 kg m-3, Mette and Korell 1989) and a linear decrease with increasing decay class that fits the mean values reported for temperate hardwood forests (Arthur et al. 1993, Stewart and Burrows 1994, Duvall and Grigal 1999, Weber 2001, Clinton et al. 2002, Mackensen et al. 2003). Decay classes: 1 (= fresh dead wood) to 5 (= highly decayed dead wood).

Decay class Basic wood density (kg m-3)

1 558 2 434 3 310 4 186 5 62

The biomass of CWD per area was calculated by the volume of CWD and the ratio of biomass and volume of the individual pieces of wood that were crossed by the line transect (Equation 4.5). The average pool of CWD per study plot resulted from the average of the six line transects per study plot.

n ∑ wijz j=1 −1 CWDi = Vi × n ×1000 (Equation 4.5) ∑vijz j=1 with CWDi: biomass of CWD (t ha-1) of the line transect i 3 -1 Vi: total volume of CWD (m ha ) that based on the line transect i wijz: dry weight (kg) of the wooden piece j of the line transect i and the decay class z 3 vijz: volume (m ) of the wooden piece j of the line transect i and the decay class z

Lying dead wood (logs) with a diameter of more than 20 cm and standing dead wood (snags; including stumps) with a dbh ≥ 7 cm were determined on a plot basis, which means that all logs and snags within the study plots (100 m x 100 m) were measured. The average diameter of a log resulted from the diameter in the middle and at both ends of the log. Snags were measured in the same way as living trees (dbh and height). The volume of logs and snags was calculated by Equation 4.6 and 4.7, respectively. The conversion from volume to biomass was based on the decay class and the respective basic density given in Table 4.3. Total biomass of large dead wood per hectare resulted from the sum of all snags and logs.

57 4 Stand structure and biomass

2 ⎛ (dt + d m + db ) ⎞ VL = ⎜ ⎟ ×π × h (Equation 4.6) ⎝ 3× 2 ⎠ with 3 VL = log volume (m ) d = diameter (m) at the small-end (t), in the middle (m) and at the large-end (b) of the log h = length (m) of the log

2 ⎛ dbh ⎞ VS = ⎜ ⎟ ×π × h × f (Equation 4.7) ⎝ 2 ⎠ with 3 VS = snag volume (m ) dbh = diameter at breast height (m) h = height (m) of the snag f = form factor (0.627)

The fine woody debris (FWD), defined as lying dead wood smaller than 5 cm in diameter and 10 cm length, was assumed to be part of the organic layer. The results are presented together with the leaves of the organic layer in chapter 5.

4.2 Results

4.2.1 Forest inventory

4.2.1.1 Diameter distribution

The frequency diameter distributions of the chronosequences showed a transfer of the tree cohorts from small diameter size classes to larger size classes (Figure 4.3). At the chronosequence “Leinefelde” the diameter distribution of the youngest stand was skewed to right while the tree diameters of the older stands were nearly normally distributed. At the youngest stand of the chronosequence “Mühlhausen” the diameter distribution was nearly a normal distribution. The older stands Mühl-85 and Mühl-102 showed a bimodal diameter distribution. The shelter of old trees of the stand Mühl-171+10 showed a diameter distribution that was slightly skewed to left. Stand densities of the chronosequence “Mühlhausen” were generally higher than those of the chronosequence “Leinefelde”.

The bimodal distribution of tree diameters is typical for stands that were thinned regularly to reduce crowding within the main crown canopy (crown thinning, von Gadow and Hui 1999,

58 4 Stand structure and biomass

Hessenmöller and von Gadow 2001). The higher stand densities at “Mühlhausen” suggest that the stands were thinned only moderately with the objective to maximise total timber production per stand area, while the timber volume per tree played only a second role. In contrast, at the chronosequence “Leinefelde” more trees were cut at several thinning operations and in addition to some dominant trees of the main canopy nearly all overtopped and intermediate trees were removed to support the growth of the remaining stands. It is very likely that the stands at “Leinefelde” were thinned in the past according to an instruction given by the forest scientist Hartig at the end of the 18th century (Winkler 2003). This instruction was noted in the forestry record of the “Oberförsterei Reifenstein” (to which the study sites belonged to) in 1904/05. The instruction included the following thinning regime: young beech stands (10- 25 years old) should be cleaned from broken or badly formed trees and trees that top over. Between a stand age of 25 to 60 years the stand should be thinned moderately every 5 years, which means that dead, weak, infected or badly formed boles had to be cut and that the crowns of the well formed and vigorous trees were released from competition. Then the stands should be thinned intensively every 10 years removing some of the co-dominant and dominant trees to favor the growth of the remaining trees of upper-crown positions.

59 4 Stand structure and biomass

Chronosequence "Leinefelde" Chronosequence "Mühlhausen"

2100 2100 -1 Lei-30M Mühl-38 -1 1800 1800 1500 1500 1200 1200 900 900 600 600

Number of trees ha 300 300 Number of trees ha 0 0 600 600 -1 Lei-62M Mühl-55 -1 500 500 400 400 300 300 200 200 100 100 Number of treesha Number of trees ha 0 0 160 240 -1 140 Lei-111M Mühl-85 -1 200 120 100 160 80 120 60 80 40 40 Number ha of trees 20 Number of treesha 0 600 40 160 -1 -1 Lei-141M Mühl-102 500 35 140 30 120 400 25 100 Understory 300 Overstory 20 80 200 15 60 Understory: 10 40 100 Number of trees ha Number of trees ha 5 20 0 0 0

24000 40 24000 40 -1 -1 Lei-153+16M 35 Mühl-171+10 35 20000 20000 30 30 16000 25 16000 25 Overstory Understory Overstory Understory 12000 20 12000 20

15 15 Overstory:

Understory: Understory: 8000 8000 10 10 4000 Number ha of trees Number of trees ha 5 4000 5 0 0 0 0 <=5 152535 45 55 65 75 85 >90 <=5 152535 45 55 65 75 85 >90 102030 40 50 60 70 80 90 102030 40 50 60 70 80 90 Diameter class (cm) Diameter class (cm) (upper boundaries) Beech (upper boundaries) Others

Figure 4.3: Frequency diameter distribution of the study stands. Trees with a tree height below 1.3 m were not considered. The data of plot Lang-I and Lang-II were provided by Gleichmar (1996) and the University of Dresden, Lehrstuhl für Waldwachstums- und Holzmesskunde (pers. comm.), respectively. The intended structure of the selection forest is based on Equation 4.8 (Gerold and Biehl 1992). N = number of living trees

60 4 Stand structure and biomass

Selection system Unmanaged forest

120 up to 303 N/ha 120 -1 -1 up to 288 N/ha Lang-I Hai-I 100 100

80 80

60 60

40 40

20 20 Number of trees ha Number of trees ha 0 0 120 up to 312 N/ha 120 -1 -1 up to 396 N/ha Lang-II Hai-II 100 and 188 N/ha 100

80 80

60 60

40 40

20 20 Number of trees ha Number of trees ha 0 0 120 120 -1 -1 Lang-III Hai-III 100 100

80 80

60 60

40 40

20 20 Number of trees ha Number of trees ha 0 0 <=5 152535 45 55 65 75 85 >90 <=5 152535 45 55 65 75 85 >90 102030 40 50 60 70 80 90 102030 40 50 60 70 80 90 Diameter class (cm) Diameter class (cm) (upper boundaries) (upper boundaries)

Beech Others Intended structure according to Gerold and Biehl (1992)

61 4 Stand structure and biomass

The study stands Lang-I and Lang-II had a diameter distribution that more or less followed a reverse-J curve (Figure 4.3). The plot Lang-I, in particular, was very close to the intended “ideal Plenterstructure” (“balanced” or “equilibrium distribution”, Plentergleichgewicht) as it was recommended by Gerold and Biehl (1992) for very productive selection beech forests (Equation 4.8). However, from the silvicultural point of view at both stands the number of trees with a diameter between 10 and 40 cm was too low, while the number of trees larger than 65 cm in diameter was too high. At the stand Lang-III there was even a clear lack of the smallest diameter classes and the stand consisted nearly of two distinct canopies (Figure 4.3).

−0.055*x (Equation 4.8) y = 160*e with y: number of trees per area x: dbh (cm) Parameters are valid only for a diameter distribution with following restrictions: (1) diameter class size: 5 cm, (2) smallest tree diameter accounted for in the regression: 7 cm, (3) economically defined target diameter for the largest fraction of timber harvest: 65 cm (Gerold 2002).

The trend towards a higher proportion of large trees and a lack of intermediate trees in selection forests (“over-aging of forest”) and its consequences for stand development and the economic yield are discussed intensively by forest scientists (Schütz 2001a, Gerold 2002) and by local foresters and landowners (Biehl, former head of the Forstamt Mühlhausen, pers. comm.).

From a more ecological point of view the plots of the selection system confirmed a pattern that was described for old uneven-aged or old-growth uneven-aged hardwood forests in the United States (Nyland 1996). Plotted at a semi-log graph the diameter distribution resembles a changing rate in the difference of tree numbers between progressive diameter classes (Figure 4.4). According to Nyland (1996) the diameter distribution forms three segments of different slopes that could be interpret as temporal phases of stand development. For the present selection system it follows that the large number of small trees (understory of saplings) reflected the high level of recruitment following tree harvest. The number of saplings is reduced rapidly due to competition (high mortality). The following group of tree diameters represented the “ideal Plenterstructure” that was characterised by vigorous trees and a moderate, successive removal of trees. The third group of diameters reflected single residual trees of extra large size.

62 4 Stand structure and biomass

6 Selection system 5

) 4 -1 3

ln (N ln (N ha 2

0 <=5 152535 45 55 65 75 85 >90 10 2030 40 50 60 70 80 90 Diameter class (cm) (upper boundaries)

6 Unmanaged forest 5

) 4 -1 3

ln (N ha ln (N 2

0 <=5 152535 45 55 65 75 85 >90 10 2030 40 50 60 70 80 90 Diameter class (cm) (upper boundaries)

Schematic pattern of changing regressions with progressive diameter classes

Figure 4.4: Semi-log graphs of the mean frequency diameter distribution of the selection system and the unmanaged forest. Trees with a tree height below 1.3 m were not considered. N = number of living trees

The diameter distributions of the unmanaged stands were quite different from an “ideal Plenterstructure”. At the stands Hai-I and Hai-II the number of trees per diameter class decreased very rapidly from the first and second diameter class to the third class. Along the following wide range of diameters classes (20 to >90 cm) the number of trees per size class only varied between 3 and 25 trees (Figure 4.3; the largest dbh of 92 cm was found at plot Hai-I). This pattern of the

63 4 Stand structure and biomass diameter distribution was also described for primary beech forests at the western Carpathians (Korpeľ 1995). The diameter distribution of plot Hai-III was close to an “ideal Plenterstructure” at the lower diameter classes (5-30 cm). At larger diameter classes the number of trees was relatively high compared to the plots of the selection system. In comparison to Albanian and Slovakian primary beech forests the unmanaged forest at the Hainich NP showed a lack of trees larger than 90 cm in diameter (Korpeľ 1995, Meyer et al. 2003). The linear regressions of the semi-log graph (Figure 4.4) and its temporal interpretation according to Nyland (1996) revealed that the mortality within the intermediate diameter classes (dbh 20-60 cm) was relatively low, resulting in a relatively high number of large trees (dbh > 40 cm). Trees with a diameter above 60 cm represented the senescent overstory characterised by a relatively high mortality but also by large individual trees (dbh > 80 cm).

With respect to the previous analysis the selection system can be characterised as an old, uneven-aged stand in transition towards an old-growth uneven-aged stand, and the unmanaged forest as an old-growth uneven-aged stand with a trend towards primary beech forests.

The diameter distribution, separated for “beech” and “non-beech” tree species, showed that all managed stands were dominated by beech (Figure 4.3). At the shelterwood systems the high number of non-beech tree species in the first and second diameter class indicated the high regeneration potential of these tree species under the open canopy of old beech trees. Their number will rapidly be reduced by thinning within the next 20-30 years. At the selection forest “Langula” only single non-beech trees were found. In contrast, a high proportion of other tree species than beech were found at the medium and partly also the upper diameter classes of the unmanaged plots at the Hainich NP. At the plot Hai-I and Hai-II non-beech trees even dominated some medium diameter classes, and at the plot Hai-III non-beech and beech trees were equally distributed at the upper diameter classes. The lower diameter classes were occupied only by beech trees. It is very likely that the high proportion of non-beech tree species at the upper diameter classes is a relict of former forest use. The largest ash and maple trees were growing on hollow stumps that originated from the beginning of the 19th century when the forest was used as a coppice with standards system (chapter 3). In conclusion, the diameter distribution at the Hainich NP reflected similarities to old-growth forests or primary forests, while the species distribution demonstrated the strong influence of historical forest use on current stand composition.

64 4 Stand structure and biomass

4.2.1.2 General forest stand characteristics

The most important stand characteristics are summarised in Table 4.4. At the even-aged stand the stand characteristics reflect the different stages of stand development after canopy opening for stand regeneration and the temporally and functional defined cuttings that are typical for regular shelterwood systems. For example, after several years of relatively constant basal area at the stands Lei-62M, Lei-111M, Mühl-55, Mühl-85 and Mühl-102 (“Grundflächenhaltung”) the basal area of the stands Lei-141M, Lei-153+16M and Mühl-171+10 rapidly decreased due to seed cutting and following successive cuttings to provide light for regeneration.

Stand densities of the chronosequence “Mühlhausen” were about two times higher than those of the corresponding stands of the chronosequence “Leinefelde” (Table 4.4). Consequently, mean tree diameters of the middle-aged stands at “Mühlhausen” were lower than those of the middle-aged stands at “Leinfelde”. The basal area of the middle-aged stands did not differ between the two sites. At “Mühlhausen” a dominant stand diameter of about 70 cm was reached after about 170 years. At “Leinfelde” this tree size was already reached after about 150 years. The differences in stand density, single tree size and rotation length confirmed differences in the thinning regime of the two chronosequences, which were already mentioned above (section 4.2.1.1). The stands at “Leinfelde” seemed to be thinned with the priority objective to support individual stem growth and size, while at “Mühlhausen” total timber production per stand area may have been the main objective of former thinning activities.

The stand characteristics of the unmanaged stands showed a generally larger tree size, tree age and a higher basal area compared to the selection system. The stands of the selection system had relatively low basal areas even in comparison to the even-aged stands (excluding the youngest stands). In Table 4.4 data of a forest inventory of the main “footprint” area of the Eddy- Covariance tower at the Hainich NP are also added (transect of 14 inventory plots, radius of each plot: 15 m, length of transect: 420 m, total area of all plots: 9896 m2; measurement of the dbh of all tress ≥ 7 cm in diameter; Mund et al. in prep. b). The stand characteristics of the footprint area were similar to those of study plot Hai-II.

4 Stand structure and biomass species All o

H (m) o species All D ht of the 20% (m)

stem diameters at species All g Lang-I and Lang-II

H (m) species All g

D (m) Total Total

)

-1 Others Others the Hainich (Table NP 4.5).

(m² ha

Basal area 7cm. for the density Theis also stand given (N) Beech

≥ Total Total 0.107 11.9 0.137 12.4 6 15.47 3.11 18.58 0.174 22.1 0.263 24.7 6 24.96 6.44 31.40 0.586 34.7 0.682 7 21.46 1.16 22.63

12.4 0.103 12.8 0.117 13.1 9.45 4.42 13.87 Others

(m) pecific height curve for Beech Beech

n, Lehrstuhl für Waldwachstums-Holzmesskunde und (pers.comm.), Total Total

ht calculated for Do). (1): The inventory data of the stands Others (m)

. Data are given for treesall with dbh given are a Data .

Beech Beech

all species species all Understory Understory

) -1 Total

N (in prep. b). *: Estimate that is based on the site-s Others sticsstands of the study (Trees ha

et al. Beech Beech 64 0 64 0.596 16680 0 0.596 38.2 0 38.2 18.17 0 0.601 38.3 0.706 39.8 18.17 80 34.9 30.4 34. 0.580 4 0.608 0.579 84 23100 976 688 1664 3104 0.108 0.089 0.100 12.3 12.5 0.100 0.089 0.108 976 3104 688 1664 624 0 216 624 8 0 100 224 0.249 0 0 0 0.436 34.4 0.249 24.2 0.366 100 0.447 35.8 0.536 36.9 32.8 0.438 34.3 34.38 0.85 35.23 564 0 0.570 0 24.2 34.01 0.570 37.5 0 0.263 26.8 0.367 28.5 34.01 0 37.5 24.25 0 0.556 37.3 0.676 38.3 552 24.25 8 470 560 42 0 512 0.240 23.0 0.250 0.267 26.9 0.406 29.8 28.2 0.240 23.1 30.09 1.31 31.40 0 0.285 25.5 0.409 0.314 30.5 0.459 34.6 31.3 0.273 26.0 33.92 5.78 39.72 1616 432 2048 1440 0.106 0.094 0.104 11.5 11.9 11. 0.104 0.094 0.106 1440 1616 432 2048 19.0 22.5 19. 0.160 0.193 1120 200 1320 180 0.154

Study plot Lei-30M Lei-62M Lei-111M Lei-141M Lei-153+16M Mühl-38 Mühl-55 Mühl-85 Mühl-102 Mühl-171+10 A) Even-aged stands. Table characteri 4.4: Stand understory thatincludes smallertressthan7 cmin dbh all and above 1.3 m tree height. N: stand density, Dg: quadratic mean of breast height. Hg: tree height calculated for Dg. Do: dominant stand diameter (= quadratic mean of stem diameters at breast heig subplot). Ho: dominant stand height heig tree per trees (= largest were provided by Gleichmar (1996) and the University of Dresde respectively.Mund (2):

4 Stand structure and biomass species All o

H (m) species All o

D (m) species All g

H (m) species All g

D (m) Total Total

)

-1 Others Others

(m² ha

Basal area Beech Beech Total Total 1* 11.70 34.15 0.361 28.0* 0.598 22.46 34.0*

Others Others

(m)

Beech Beech

Total Total Others

(m)

Beech Beech

all species species all Understory Understory

) -1 Total

N Others Others

(Trees ha (Trees 269 0.307 23.6 27.26 269 0.307 23.6 27.26 293 0.350 24.5 36.69 Beech Beech 312 17 329 323 0.255 20.5 32.6 0.240 0.536 0.311 27.0 0.462 32.4 21.2 3.97 24.99 21.01 271 63 334 189 0.272 30.3* 23. 0.461 0.308 21.4* 246 17 263 414 0.301 22.9 27.9 0.296 0.357 0.348 28.2 0.463 32.4 23.3 1.93 25.00 23.07 212 4 216 56 0.365 26.3 150 25.7 0.366 0.307 0.433 33.4 0.683 36.9 26.3 0.30 31.79 31.49 37 187 220 373 88 0.427 28.0 257 29.1 0.442 0.368 0.481 32.6 0.592 34.8 28.2 4.06 34.00 29.94 308 87 440 344 0.274 0.428 0.318 150 19.3 27.3 0.288 21.6 0.512 0.345 22.2 13.62 34.94 0.380 21.31 26.7 33.2 0.522 30.4 25.0 19.28 43.67 0.402 24.39 30.3 0.623 35.1 continued

1 2

Study plot Lang-I Lang-II Lang-III Average “Lanugla” Hai-I Hai-II Hai-III Hai-T Average “Hainich NP” Table 4.4: stands. B) Uneven-aged

67 4 Stand structure and biomass

Comparing the mean stand heights (Hg) or the dominant stand heights (Ho) (Table 4.4) it appears as if the even-aged stands (except the stand Mühl-171+10) had reached higher tree heights and consequently would grow at better sites than the uneven-aged stands. This conclusion is not justified, because the mean stand height Hg is highly affected by the thinning regime (Wenk et al. 1990, Kramer and Akça 1995). The dominant stand height Ho is less influenced by a thinning regime that is restricted to overtopped and intermediate trees than the mean stand height Hg. However, at the chronosequences that were studied, both dominant and codominant trees had been cut (crown thinning); thus the dominant stand height Ho (Table 4.4) was also affected by thinning. At the stand Mühl-171+10 trees with large diameters (above 55 cm) but relatively low heights were left for seed production. At the stand Lei-153+16M trees with relatively low heights were cut and the highest trees were left for seed production and further timber production (for the detailed height distributions see Figure 4.1). In contrast, at the selection system the largest and longest well shaped stems, which will provide valuable timber, are preferred for cutting. The height curve of the unmanaged forest was influenced by many overtopped trees (Figure 4.1), and may be by a selective cutting for veneer wood in the 1980s and in 1992 (Table 3.5).

The strong effect of the thinning regime on the tree height distribution is also demonstrated by the height curves of the study sites (“site-specific” height curves, Figure 4.5). Trees, which were smaller than 10 cm in diameter, had similar tree heights at all study sites. First, the differences in tree height of the uneven-aged and the even-aged stands increased with increasing diameter. Then, the tree heights of trees larger than 30 or 40 cm in diameter differed between the two chronosequences and between the selection system and the unmanaged forest.

68 4 Stand structure and biomass

45 Chronosequence 40 "Leinfelde" Chronosequence "Mühlhausen" 35 Selection system 30 "Langula" Unmanaged forest "Hainich NP" 25

Tree height (m) height Tree 15

0 0.00.10.20.30.40.50.60.70.80.91.0

dbh (m)

Figure 4.5: Height curves of the study sites. The tree heights in relation to the tree diameters are predicted by the Chapman-Function (Equation 4.1) that was parameterized on the basis of all trees per inventory plot and study site. For parameters and statistics see Table 4.5.

Table 4.5: Parameters and statistics of the site-specific height curves. The height curves of the study sites are predicted by the Chapman-Function (Equation 4.1) that was parameterized on the basis of all trees per inventory plot and study site. The parameter “a” represents the “maximum tree height” (asymptotic height) that theoretically can be reached at the study sites. n = total number of tree height measurements.

Unmanaged Chronosequence Chronosequence Selection system Study site forest “Leinefelde” “Mühlhausen” “Langula” “Hainich NP”

a 46.728 39.426 40.239 39.079 b 2.756 4.268 3.568 3.272 c 0.878 1.050 1.005 0.912 P <0.001 <0.001 <0.001 <0.001 R2 0.968 0.929 0.973 0.961 n 434 507 1396 501

69 4 Stand structure and biomass

It can not be excluded that higher soil depths and higher proportions of silt in the soil at “Leinefelde” are associated with higher water availability and thus higher forest growth at “Leinefelde” compared to “Langula” and the “Hanich NP”. However, soil types and depths at the selection forest Lang-III and at the 111-year-old stand at “Leinefelde” are very similar (see chapter 3 and 6), but tree height as a function of stem diameter is lower at Lang-III than at the 111-year-old stand (Figure 4.1). It has also to be considered that height/diameter curves do not represent annual height growth such as height/age curves do. The relationship between tree height and diameter can be used as a predictor for site productivity, but it represents primarily the stem form of a stand (“site form”, Vanclay 1992). The “site form” often correlates with stem growth, but it is also sensitive to recent cuttings (Vanclay 1992). Consequently, it is assumed that the regular thinning of the study stands shifted the site-specific height curves sequentially upwards from the unmanaged forest to the selection system to the chronosequence “Mühlhausen” to the chronosequence “Leinefelde” (Figure 4.5). Potential differences in growth conditions of the study sites are likely superimposed by management.

For some of the study stands data on annual stem growth (dbh ≥ 7 cm) were available (Table 4.6). The data for the chronosequence “Leinefelde” were determined by tree ring analysis of harvested trees (Bascietto 2003). The data for the permanent study plots Lang-I and –II were derived from repeated tree measurements (measurement of dbh and tree height of all trees per study plot in 1956 and in 2002, Gerold 2002). The annual growth rates of the even-aged stand Mühl-102 were based on measurements of the dbh in 1998 and in 2002 (E. Klein, Fachbereich für Wald und Forstwirtschaft, University of Applied Sciences Weihenstephan, pers. comm.). The even-aged stands showed on average similar annual stem growth as the uneven-aged stands Lang-I and Lang-II (3.2 tC ha-1 year-1 and 3.0 tC ha-1 year-1, respectively; Table 4.6). The relatively low annual timber increment of the 111-year-old stand in Leinfelde in 2000 is very likely related to the mast of beech nuts in that year (chapter 5). A reduction of timber increment in favour of an extraordinary high fruit production in older beech stands (sawtimber stage) was observed in many other studies of beech forests (e.g. Hartig 1889, von Jazewitsch 1953, Pellinen 1986).

The data on annual stem growth of the selected study stands are based on different methods and refer to different growth periods. However, they supported the assumption that the observed differences in stand height did not indicate substantial differences in the site productivity, but in the thinning regime of the study sites.

70 4 Stand structure and biomass

Table 4.6: Annual stem growth (stem and branches ≥ 7 cm in diameter) of selected study plots. Data of the volume increment were converted to carbon fluxes assuming a mean basic wood density of 558 kg m-3 (Mette and Korell 1989) and a carbon concentration of 50% of dry weight.

Annual timber Time Plot Source Method growth period (tC ha-1 year-1) Stem analysis, Lei-30 Bascietto 2003 (FORCAST) 2000 3.2 10 sample trees Lei-62 Bascietto 2003 (FORCAST) 2000 -"- 2.9 Lei-111 Bascietto 2003 (FORCAST) 2000 -"- 2.1 -"- -"- 1999 -"- 3.9 Stem analysis, Lei-153+16 Bascietto 2003 (FORCAST) 2001 3.5 8 sample trees E. Klein, University of Repeated Mühl-102 Applied Sciences 1998-2001 3.4 inventory Weihenstephan, pers. comm. Average 3.2

Repeated Lang-I Gerold 2002 1956/1996 2.9 inventory Repeated Lang-II Gerold 2002 1956/2002 3.1 inventory

71 4 Stand structure and biomass

4.2.2 Carbon pools in living tree biomass

Tree biomass carbon pools of the two chronosequences showed a typical management driven optimum curve (Figure 4.6). The integrated averages of one rotation period of the two chronosequences differed only by 6% (158 tC ha-1 at Leinefelde and 149 tC ha-1 at Mühlhausen, Table 4.7). The maximum value of 230 tC ha-1 at a stand age of about 100 years was 10 to 80 tC ha-1 higher than the biomass carbon pools of the selection system. However, the average of the chronosequences (154 tC ha-1) and the average of the selection system (176 tC ha-1, Table 4.7) did not differ significantly (ANOVA, P > 0.5). The mean tree biomass of the selection system was similar to the mean biomass of beech stands resulting from a literature review by -1 -1 Jacobsen et al. (2003) (347 tdw ha ~ 174 tC ha ).

Carbon pools in tree biomass of the unmanaged forest (247 tC ha-1) were not significantly higher than those of the selection system, but they differed significantly from mean biomass carbon pools of the chronosequences (ANOVA; P = 0.013; Figure 4.6). Biomass carbon pools within the main “footprint” of the Hainich-tower (213 tC ha-1) were similar to those of the study plot Hai-II.

72 4 Stand structure and biomass

ce Different letters indicate significant differences Hai-T represents the main footprint area of the Eddy-covarian . in prep. b). et al etween the silvicultural systems (ANOVA, P < plot 0.5). The study Figure 4.6: Carbon pools in living tree biomass ofdifferent silvicultural systems. b tower at the Hainich NP (Mund NP (Mund Hainich the at tower

73 4 Stand structure and biomass

Table 4.7: Timber volume and carbon pools in living tree biomass of the study plots. The timber volume includes the volume of stems and branches ≥ 7cm in diameter. In general, the living tree biomass includes all trees with a dbh ≥ 7cm. Leaf biomass equals the annual leaf litter fall. Fine root biomass was derived from studies by Claus (2003, FORCAST) or it was assumed that it is equal to leaf biomass (Scarascia-Mugnozza et al. 2000). *: Integrated average over one rotation period. (1): Claus (2003, FORCAST); mean of two sampling occasions in spring and fall 2002, (2): Mund et al. (in prep. b), (3): 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, (4): Cotrufo (2003, FORCAST).

A) Even-aged stands.

Timber Timber volume carbon Carbon pools in living tree biomass 3 -1 -1 VD (m ha ) pools (tC ha ) (tC ha-1)

Study plot Stem roots Total Total Total Beech Others Leaves Coarse Branches and twigs Fine roots

Lei-30M 50.5 20.9 71.4 19.9 26.18 6.41 1.74 6.23 2.621 43.18 Lei-62M 455.1 0 455.1 127.0 121.51 23.55 1.46 25.17 1.031 172.72 Lei-111M 633.8 14.4 648.2 180.8 160.68 34.23 1.41 32.96 1.221 230.50 Lei-141M 466.9 0 466.9 130.3 112.85 30.87 1.11 25.19 1.11 171.12 Lei-153+16M 360.1 0 360.1 100.5 86.23 24.78 1.21 19.53 2.061 133.81 Integrated 425 119 160 average* Mühl-38 76.9 14.6 91.4 25.5 32.93 9.78 1.59 8.56 1.59 54.45 Mühl-55 250.9 69.7 320.6 89.4 98.18 16.29 1.46 19.43 1.46 136.83 Mühl-85 411.8 5.5 417.4 116.5 108.99 22.74 1.40 23.17 1.40 157.71 Mühl-102 518.5 95.4 613.9 171.3 164.20 32.52 1.37 33.31 1.37 232.77 Mühl-171+10 385.1 18.1 403.2 112.5 97.29 32.94 1.48 24.02 1.48 157.21 Integrated 376 105 149 average*

74 4 Stand structure and biomass

Table 4.7: continued B) Uneven-aged stands.

Timber Timber volume carbon Carbon pools in living tree biomass 3 -1 -1 VD (m ha ) pools (tC ha ) (tC ha-1)

Study plot roots Stem Total Total Total Beech Others Leaves Coarse Branches and twigs Fine roots

Lang-I3 326.6 66.5 393.0 109.65 98.19 29.44 1.68 23.27 1.68 154.26

Lang-II3 367.4 31.1 398.5 111.18 99.57 28.01 1.56 23.17 1.56 153.87

Lang-III 565.9 3.8 569.7 158.95 137.92 45.04 1.58 33.23 1.58 219.35 Average 454 127 176

(± SD) (100) (28) (38) Hai- I 532.8 61.9 594.7 165.92 143.53 52.69 1.49 36.22 1.49 235.42

Hai- II 327.0 203.2 530.1 147.90 130.72 50.18 1.81 34.65 1.81 219.18

Hai- III 389.1 339.6 728.7 203.31 178.54 59.38 1.60 43.82 1.60 284.95

Hai- T 344.92 194.82 539.72 150.582 133.972 42.372 1.904 32.682 1.781 212.7 Average 598 167 238

(± SD) (91) (26) (33)

75 4 Stand structure and biomass

Leaf biomass (estimated from annual leaf litter fall) and fine root biomass accounted for less than 3% of total tree biomass at the older stands. Consequently, the carbon pools in total tree biomass are closely correlated with the timber volume (Figure 4.7) and resulted in a mean ratio of total timber volume to total carbon pools of 0.386. (The leaf biomass and its relation to stand age or silvicultural management are presented in detail in chapter 5.)

300

) Slope of the regression: 0.386 -1 R2 = 0.975 (Intercept was set to zero) 250

200

150

100 Chronosequence "Leinefelde" Chronosequence "Mühlhausen" 50 Selection system "Langula" Unmanaged forest "Hainich NP" Carbon pools in living treebiomassCarbon pools inliving (tC ha 0 0 100 200 300 400 500 600 700 800 Timber volume (m3 ha-1)

Figure 4.7: Carbon pools in living tree biomass as a function of the timber volume.

There are no biomass regressions for the non-beech tree species of the study plots. Thus, it was not possible to separate the biomass carbon pools into species-specific carbon pools. However, the proportion of non-beech timber to total timber can be assumed to be similar to the proportion of non-beech living biomass to total living biomass. At the study plots Hai-II and Hai-III the volume of non-beech timber accounted for 38 and 47% of total timber volume, respectively. At all other stands the proportion of non-beech timber was less than 30% of total timber volume.

76 4 Stand structure and biomass

4.2.3 Carbon pools in dead wood biomass (snags, logs and CWD)

At managed forests the amount of CWD fluctuates periodically depending on thinning or harvesting and on the local or regional economic situation. The economic situation typically determines if CWD debris is sampled by local people for fire wood, or if it is just collected and then left or burned on site, or if it remains as it was fallen down after cutting. During the GDR regime foresters had to take care that each piece of wood was sampled after harvest (“Saubere Waldwirtschaft”), and regionally it was also common that local people sampled fire wood. Consequently, the managed study plots had generally very low pools of CWD. Therefore, the measurement of CWD was restricted to the Hainich NP and two plots of the selection forest (Lang-I and Lang-II), which were harvested two years ago (in 2000) and which were assumed to represent the long-term average of CWD pools of managed forests, excluding the effect of fire wood sampling.

At the unmanaged study plots carbon pools in snags, logs and CWD ranged between 3 and 9 tC ha-1 (Table 4.8, Figure 4.8). An inventory at an adjacent permanent study plot of the University of Freiburg (30 ha) resulted in a mean dead wood volume of 62 m3 ha-1 (~9.6 tC ha-1, assuming a mean basic wood density of 310 kg m-3) and a coefficient of variation of 65% (Benecke 2002). The high amount of dead wood and the high variability was caused by subplots that contained a high proportion of dead elm trees, which were infected by Ophiostoma novo- ulimi. Excluding these “hot spots” of dead wood the mean dead wood pools (50 m3 ha-1 ~7.8 tC ha-1) were similar to those found in this study. Considering the results of all studies the dead wood carbon pools at the Hainich (~ 7 tC ha-1) were similar to those reported for a recently unmanaged beech stand at the Solling, Germany (in 1994: 28 m3 ha-1 (~4.3 tC ha-1) and in 2000: 50 m3 ha-1 (~7.8 tC ha-1), stand age: 160 years, unmanaged since 1967; Müller-Using and Bartsch 2003).

Dead wood carbon pools of primary beech forests in the western Carpathian and Albania (about 14 tC ha-1, assuming a basic wood density of 310 kg m3 and a carbon concentration of 50% of dry weight; Korpeľ 1995, Meyer et al. 2003) were about two times higher than the dead wood carbon pools found at the Hainich NP. The relatively low dead wood pools at the Hainich NP may reflect differences in the disturbance regime and decay rates compared to the sites in Slovakia and Albania (partly higher elevation and partly higher precipitation), but most likely they are the result of former forest use (chapter 3).

77 4 Stand structure and biomass

Total dead wood carbon pools at the selection forest reached on average 1.5 tC ha-1 (Figure 4.8). This is less than 50% of the pools at the unmanaged forest. Carbon pools in snags and logs at the study plot Lang-I resulted from one large broken dead tree.

The spatial variability of CWD at the unmanaged forest and the selection forest was very high (coefficient of variation ranged between 40 and 80%), so that differences between the sites were obviously not significant

At the unmanaged plots Hai-I, Hai-II and Hai-III about 66, 48 and 42%, respectively, of large dead wood originated from non-beech tree species (Figure 4.8). This proportion of non-beech tree species was relatively high compared to the ratio of living non-beech timber biomass and total living timber biomass (10, 38 and 47%, respectively, Table 4.7). This finding indicates that the non-beech tree species may partly be displaced by beech trees.

Table 4.8: Carbon pools in dead wood biomass of selected study plots. CWD: coarse woody debris. SD: Standard deviation. (1): Cotrufo (2003, FORCAST).

Total Study plot CWD ± SD Snags and logs dead wood (tC ha-1)

Lang- I 1.05 ± 0.08 1.41 2.47

Lang- II 0.91 ± 0.03 0.06 0.97

Lang- III n.d. n.d. n.d.

Average ± SD 1.47 ± 0.86

Hai- I 1.23 ± 0.05 4.41 5.65

Hai- II 2.16 ± 0.16 5.26 7.42

Hai- III 1.06 ± 0.05 2.01 3.07 9.01 ± 7.7 0.27 Hai- T1 9.28 (incl. logs) (stumps only) Average ± SD 6.36 ± 2.64

78 4 Stand structure and biomass

At the selection forest most pieces of dead wood (60%) belonged to the decay class 2, while the decay classes of dead wood at the unmanaged stands were nearly normally distributed and about 40% of pieces were classified as decay class 3. The weighted (by volume) average decay class of dead wood was 2 in the plots Lang-I and II and 3 in the unmanaged plots. Highly decayed dead wood (class 5) was not found at the unmanaged stands. This finding may indicate that the production and decay of dead wood at the Hainich NP have not reached a steady state and thus, that the structure of the Hainich NP is still far away from that of a natural, primary forest. However, the descriptive system of decay classes does not consider differences in the residence time of dead wood within each decay class. If the residence time of dead wood in decay class 5 is shorter than in other decay classes, the probability to find dead wood of decay class 5 at a distinct time is also lower than the probability of finding dead wood at earlier stages of decay (Kruys et al. 2002). Detailed data on the distribution of coarse woody debris within the plots and of the mean decay classes of dead wood are given in the Appendix, Table A.4.

)

-1 10 Unmanaged forest 9

8 Legend 7 CWD 6 Non-beech trees 5 Beech 4 Selection system Snags and logs 3 Non-beech trees 2 1 Beech Dead wood carbon pools (tC ha Dead wood 0 Lang-I Hai-I Hai-II Hai-III Hai-T Lang-II *

Study plots Figure 4.8: Carbon pools of different compartments of dead wood. CWD: coarse woody debris. For details see text. *: Cotrufo (2003, FORCAST)

79 4 Stand structure and biomass

80 5 Aboveground litter

5 Litter fall, aboveground litter decomposition and carbon pools in the organic layer

The annual litter production represents the primary carbon source for soil organic carbon, and it is assumed that the annual litter input (aboveground and belowground) is one of the most important parameters to describe and predict the organic carbon dynamic of the upper mineral soil (Liski et al. 2002, Hahn 2003). Deciduous hardwood forests on fertile soils are characterised by a relatively thin organic layer (mull or F-mull), that forms a transient pool of organic carbon from aboveground biomass to organic carbon in the mineral soil.

In the following, the input of carbon to the organic layer via aboveground litter fall, carbon pools in the organic layer, aboveground litter decomposition and the mean residence time of litter will be quantified and related to stand age, the study sites and the silvicultural systems. The annual root litter production and root litter decomposition was not investigated in the framework of this study.

5.1 Methods

5.1.1 Litter fall

In contrast to coniferous needles, leaf litter of broadleaved forests is evenly spread over large areas (Rothe and Binkley 2001), so that the position of traps relative to individual trees is of minor importance. In contrast, the spatial variability of wooden litter fall (branches, twigs and nuts) is very high and closely linked to the position relative to individual trees and the size and condition of the surrounding trees. In addition, litter fall of branches is influenced by strong wind events, which may increase the interannual variability, and large branches can damage standing litter traps. The following flexible design of litter traps was used considering the characteristics of broadleaved leaf litter fall and the constraints from wooden litter fall as well as the large range of different stand structures investigated in this study. The number of traps per study plot (3 to 7 traps) and the size of each trap (46 cm x 46 cm to 1.5 m x 2 m) depended on stand density, tree size and the heterogeneity of the canopy (Table 5.1). For example, many small traps (e.g. 7 traps, each 46 cm x 46 cm) were exposed at the very dense young stands or at dense patches of saplings, which form the understory below the shelter of large, old trees (plot Lei- 153+16M and Mühl-171+10). Three large traps (1.5 m x 2 m) were exposed at older stands, which were characterised by a relatively homogeneous, closed canopy (e.g. Lei-111M), and five

81 5 Aboveground litter traps at more heterogeneous ones (e.g. Hai-II). In general, the traps were randomly distributed within the inventory plots (chapter 3). All traps consisted of a net (mesh size 4 x 4 mm) that was fixed directly on the forest floor to reduce damages and data loss by falling large branches or wild boars, and to catch the leaves of saplings. The litter was sampled from the traps every two weeks from October to November and every two months over the rest of the year. The sampling period and the total sampling area of the traps are given in Table 5.1 for each study plot.

Table 5.1: Overview of the period of litter fall sampling at the study plots, number of litter traps and total sampling area.

Number of Study plot Sampling period Total sampling area (m²) traps

Lei-30M spring 2000 to winter 2001 7 1.5 Lei-62M fall 1999 to summer 2001 5 15 Lei-111M fall 1999 to summer 2001 3 9 Lei-141M fall 1999 to summer 2001 5 15 Lei-153+16M fall 1999 to summer 2001 9 15 Mühl-38 spring 2000 to winter 2001 7 1.5 Mühl-55 spring 2000 to winter 2001 5 12 Mühl-85 fall 1999 to summer 2001 3 9 Mühl-102 fall 1999 to summer 2001 5 15 Mühl-171+10 fall 1999 to summer 2001 6 3.8 Lang-I fall 1999 to summer 2001 5 15 Lang-II fall 1999 to summer 2001 4 9 Lang-III fall 1999 to summer 2001 3 9 Hai.I fall 1999 to summer 2001 3 9 Hai-II fall 1999 to summer 2001 5 15 Hai-III fall 1999 to summer 2001 3 9

Wet litter was air dried in the laboratory, and separated into leaves, twigs and branches, and a remaining “rest”, that included mainly beech nuts and a small fraction of other fruits and buds. All litter fractions were dried at 70 °C for three days. Only in the first year of litter sampling also the tree species of the leaves were determined and separated. When the litter in the traps was already air-dry it was separated and weighed in the field and a representative subsample was taken, separated and dried at 70 °C to determine the remaining water content and to calculate the absolute dry weight of the whole litter sample. Mixed subsamples of the fractions “beech leaves”, “leaves of non-beech tree species”, “wooden compartments (twigs, branches and shells of the beech nuts)” were taken for C/N analysis (total combustion, elemental analyzer

82 5 Aboveground litter

“VarioEL II”, 1998, Elementar Analyse GmbH, Hanau, Germany). The resulting carbon concentrations were 0.494 g g-1 for beech leaves, 0.463 g g-1 for other tree species and 0.497 g g-1 for the wooden fractions.

5.1.2 Organic layer

The organic layer was sampled at the end of September 2001 (18.-23.09.2001), a week before litter fall began. At this time of the year the remaining organic layer represents the net accumulation of carbon in the organic layer over the past years and there is no double counting of litter from the previous year and litter fall of the current year.

The organic layer was sampled at 15 random points within the study plots (100 m x 100 m) of each study site. The sampling points were chosen by pairs of random numbers that defined the X and Y coordinates (unit: 1 m) of a Cartesian coordinate grid, built up by two edges (100 m x 100 m) of the study plots. (The sampling points were the same as those for the soil cores, chapter 6). The sampling area of each sample was defined by a wooden frame with an inner size of 50 cm x 50 cm, resulting in a total sampling area of 3.75 m2 per study site. In order to separate organic material that is decomposed relatively fast (leaf litter) from organic material that is decomposed more slowly (wooden litter) and to homogenize the organic layer material for the determination of carbon pools, the air-dried samples of the organic layer were separated into (1) “fine woody debris (FWD)” (not as well as partly decomposed twigs and branches smaller than 50 mm in diameter, and shells of the beech nuts (= cupulae)), (2) “coarse leaf litter” (weakly decomposed leaves and partly decomposed leaves > 2 mm) and (3) “partly decomposed leaf litter” (fragmented and partly fermented leaves ≤ 2 mm). It could not be excluded that the latter fraction also included small amounts of fragmented, partly decomposed or well decomposed, amorphous material originating from woody material. All fractions were dried at 70 °C and weighed. The C/N analyses of mixed subsamples of each fraction resulted in carbon concentrations of 0.486 g g-1 for the fine woody material, 0.479 g g-1 for “coarse leaf litter” and 0.302 g g-1 for “partly decomposed leaf litter” (total combustion, elemental analyzer “varioEL II”, 1998, Elementar Analyse GmbH, Hanau, Germany). The low carbon concentration of the latter fraction resulted from mineral particles admixed with the well decomposed litter. After weighing and C/N-analysis the coarse leaf litter and the partly decomposed leaf litter were pooled to the fraction “leaf litter”.

83 5 Aboveground litter

5.1.3 Mean residence time of leaf litter and fine woody debris (FWD)

The estimate of the mean residence time (MRT) of leaf litter was based on two methods: (1)

Incubation of leaf litter bags in the field (MRTleaves-bags), and (2) the ratio of leaf litter fall and the mass of remaining leaf litter at the end of the growing season (MRTleaves-ratio). The MRT of fine woody debris (FWD) was estimated only on the ratio of the mass of remaining FWD at the end of the growing season and annual litter fall of twigs, beech nuts and small branches (< 5 cm in diameter).

5.1.3.1 Incubation of leaf litter bags

The leaves for the litter bags were collected via litter traps (section 5.1.1) in October 1999. The nylon litter bags with a size of 18 cm x 18 cm were filled with 3-4 g air-dried beech leaf litter (in total 448 bags) or mixed leaf litter of ash and maple trees (in total 36 bags). The mesh size of the bags was 1 mm x 1 mm. At each study site 15-25 bags with beech leaves were deposed close to the litter traps. Litter bags filled with leaves of ash and maple trees were deposited at the study sites Hai-II and Hai-III, which were characterised by a high proportion of ash and maple trees in the upper canopy (chapter 4).

In general, every two months 3 to 5 bags were sampled, cleaned, dried at 70 °C and weighed. The last sampling period of the beech litter bags was prolonged to four months. Thus the total incubation period for beech leaf litter was 16 months and for leaf litter from ash and maple trees 12 months. Unfortunately, some litter bags were damaged or got lost due to wild boars or mice so that the number of replicates was reduced at some sampling dates and sites.

The weight of leaf litter in the bags before the incubation was corrected for the remaining water content (air-dried leaf litter compared to absolute dry leaf litter (70 °C)), and the differences in absolute dry weight of leaves before and after the incubation resulted in the loss of leaf litter over time. Changes of carbon concentrations in the remaining leaf litter over time were not considered. However, when the remaining leaf material was contaminated with mineral particles then the weight of the samples was corrected for the amount of mineral particles by “loss-on-ignition”. The average decrease of leaf biomass in the bags was plotted against time and fitted with an exponential function (Equation 5.1). The exponent k represents the decay rate constant and the reciprocal of k equals the mean residence time of leaves in the organic layer

(MRTleaves-bags = 1/k). Assuming an exponential decay the MRT is the time that is needed to decompose 63% of the initial amount of organic matter, where “t63” = ln(0.37)/-k ≈ 1/k. The

84 5 Aboveground litter

“half lifetime”, which is the time that is needed to decompose 50% of the initial amount, results from the expression “t50” = ln(0.50)/-k. The “lifetime” represents the time after which 95% of the initial amount of leaves is decomposed (“t95” = ln(0.05)/-k) (Harmon et al. 1986). The MRT per study plot was weighted by the total amount of leaf litter from beech or non-beech tree species.

−kt (Equation 5.1) yt = y0 * e with yt: quantity of leaf biomass left at time t (%) y0: initial quantity of leaf biomass (100 %) k: decay rate constant (day-1) t: time of incubation (days)

Within the FORCAST project two studies on beech leaf litter decomposition were carried out. The study by B. Zeller (INRA-Nancy, France, pers. comm.) was based on litter bags with a mesh size of 5 mm x 5 mm and 6 replicates per study plot and sampling period. Cotrufo (2003) used 10 litter bags with a mesh size of 0.77 mm x 0.27 mm per study plot and sampling period.

5.1.3.2 The “ratio-approach”

If the mass of organic matter in the organic layer is in steady-state on an annual basis (annual input to the organic layer equals the annual output), then the MRT of organic matter in the organic layer will result from the ratio of the mass of organic matter and the annual input (litter fall) or output (litter decomposition rate) (Equation 5.2, Gosz et al. 1976, Sollins 1982, Harmon et al. 1986). This approach to estimate the MRT of organic matter was used for the total organic layer and its fractions leaf litter and FWD.

M (Equation 5.2) MRT = F with MRT: mean residence time of organic matter (years) M: total mass of organic matter (tC ha-1) F: input of organic matter via litter fall (tC ha-1 year-1)

The MRT of CWD was not calculated in this study because the input of CWD via tree harvest or natural dieback of trees could not be measured in the timeframe of this study.

85 5 Aboveground litter

5.2 Results

5.2.1 Litter fall

The total litter fall of the study stands varied between 1.8 and 3.5 tC ha-1 year-1 (Table 5.2). The means of the study sites were relatively similar, varying only between 2.1 and 2.8 tC ha-1 year-1. The higher variability within individual study stands compared to the variability between the stands was also reflected by the coefficients of variation, which ranged between 4 and 36% at the stand level and between 9 and 21% at the site level (Table 5.2). Neither the “stand age” nor the “study site” or “silvicultural system” showed a significant effect on total litter fall (Figure 5.1A).

At the even-aged stands the relationship between mean leaf litter fall and stand age did not differ significantly between the two chronosequences (ANCOVA, P > 0.05). Thus, the results of the two chronosequences were pooled and presented as one time series (Figure 5.1B). The mean leaf litter fall of the even-aged stands showed a clear trend in relation to stand age: From the youngest stands to the 141-year-old stand leaf biomass decreased with increasing stand age (y = 1.780-0.0043*x, R2 = 0.847, P = 0.0012). From the 141-year-old stand to the 153- and 171- year-old stand it increased and reached similar amounts as the youngest stands (y = -0.678+0.013*x, R2 = 0.979, P = 0.039, Figure 5.1B).

It is important to mention that the standard deviations of the means per study plot indicate that the homogeneity of variance was violated and that the number of litter traps or the total sampling area per study plot may have not been sufficient to represent the means per study plot. Furthermore, the regression for leaf litter fall in stands older than 140 years was only based on three stands. Consequently, the observed pattern of leaf biomass in relation to stand age should be interpreted as a trend that needs further investigation.

The successive decrease of stand density due to regular thinning (from about 2000 to 200 trees ha-1), and the opening of the canopy for regeneration that resulted in 100 residual old trees ha-1 (Figure 5.2), supported the observed decrease of leaf biomass from the youngest even-aged stand to the 141-year-old stand. After canopy opening leaf production of the residual old trees (overstory) increased very likely due to higher light availability. At the same time the recruitment of a dense vigorous understory of saplings and poles contributed to total leaf production of the oldest stands. The relationship between leaf biomass and thinning or stand

86 5 Aboveground litter density indicates that the decrease of leaf biomass with increasing stand age is not an effect of the senescence of trees.

The mean leaf biomass of the uneven-aged stands, which varied between 1.49 and 1.89 tC ha-1, was not related to stand density or the mean age of dominant trees (20% largest trees per stand). The higher standard deviation of leaf litter fall at the Hainich-Tower site compared to the other study plots at the Hainich NP may be caused by differences in the sampling design. Cotrufo (2003) exposed 25 litter traps within the footprint of the Eddy-tower, and each trap had a sampling area of 0.5 m2. In this study less (n = 3-5) but larger litter traps (each 3 m2) were used, which corresponds to a “pooling” of smaller traps prior to analysis.

The mean leaf biomass of the unmanaged forest and the selection forest did not differ significantly, but the mean leaf biomass of all uneven-aged stands (1.66 ± 0.14 tC ha-1, n = 7) was significantly higher than that of the even-aged stands (1.42 ± 0.18 tC ha-1, n = 10; ANOVA, P = 0.011).

At most study stands the proportion of leaf litter fall from non-beech tree species varied between 1 and 10% of total leaf litter fall (Table 5.2). An exception was the leaf litter fall of the youngest even-aged stands Lei-30M and Mühl-38, which included about 20% non-beech leaves, and at the unmanaged stands Hai-II and Hai-III non-beech leaves accounted for 35 and 39%, respectively, of total leaf litter fall. The proportions of non-beech leaves were similar to the proportions of non-beech timber volume in comparison to total timber volume (Table 4.7).

87 5 Aboveground litter

Table 5.2: Annual litter fall. Data represent the means of two years (1999 and 2000 or 2000 and 2001) and all litter traps per study stand (± standard deviation). The proportion of litter from different tree species was determined in the first year of litter sampling. Different letters indicate significant differences between the study sites (ANOVA, P < 0.05, followed by the post hoc Newman-Keuls Test). CV: coefficient of variation. n.d.: not determined (1) Data from Cotrufo (2003, FORCAST; n = 25, surface area of each litter trap: 0.5 m2, sampling period: 2001 and 2002).

A) Amount of annual litter fall.

Twigs & Fruits & CV of Study plot Leaves Total branches buds the total tC ha-1 year-1 (%) Lei-30M 1.74 ± 0.13 0.21 ± 0.27 0.09 ± 0.03 2.04 ± 0.26 12.6 Lei-62M 1.46 ± 0.19 0.61 ± 0.28 0.27 ± 0.03 2.34 ± 0.37 15.8 Lei-111M 1.41 ± 0.12 0.50 ± 0.57 0.50 ± 0.15 2.41 ± 0.66 27.4 Lei-141M 1.11 ± 0.23 0.26 ± 0.09 0.94 ± 0.36 2.31 ± 0.57 24.7 Lei-153+16M 1.21 ± 0.33 0.25 ± 0.15 0.43 ± 0.07 1.89 ± 0.34 18.0 Average 1.39 ± 0.24 a 2.2 ± 0.2 a 9.1 “Leinefelde” Mühl-38 1.59 ± 0.24 0.09 ± 0.06 0.09 ± 0.02 1.77 ± 0.29 16.4 Müh-55 1.46 ± 0.18 0.59 ± 0.29 0.17 ± 0.12 2.22 ± 0.31 14.0 Mühl-85 1.40 ± 0.36 0.37 ± 0.18 0.43 ± 0.17 2.21 ± 0.13 5.9 Mühl-102 1.37 ± 0.25 0.08 ± 0.04 0.52 ± 0.21 1.98 ± 0.43 21.8 Mühl-171+10 1.48 ± 0.27 0.18 ± 0.15 0.85 ± 0.39 2.51 ± 0.56 22.3 Average 1.46 ± 0.08 a 2.1 ± 0.3 a 14.3 “Mühlhausen” Lang-I 1.68 ± 0.16 0.45 ± 0.44 0.54 ± 0.44 2.68 ± 0.66 24.7 Lang-II 1.56 ± 0.27 0.21 ± 0.03 0.42 ± 0.17 2.18 ± 0.22 9.9 Lang-III 1.58 ± 0.34 0.23 ± 0.08 1.65 ± 0.28 3.46 ± 0.65 18.7 Average 1.61 ± 0.07 a 2.8 ± 0.6 a 21.4 “Langula” Hai-I 1.49 ± 0.10 0.31 ± 0.11 0.33 ± 0.11 2.12 ± 0.09 4.4 Hai-II 1.81 ± 0.31 0.63 ± 0.76 0.30 ± 0.16 2.74 ± 0.99 36.0 Hai-III 1.60 ± 0.10 0.46 ± 0.17 0.46 ± 0.07 2.52 ± 0.27 10.7 Hai-T1 1.84 ± 0.42 n.d. n.d. n.d. n.d. Average 1.70 ± 0.18 a 2.5 ± 0.3 a 12.0 “Hainich NP”

88 5 Aboveground litter

B) Species composition of annual leaf litter fall.

Study plot Acer minor Fagus Ulmus betulus petraea tremula Populus Quercus sylvatica sylvatica platanus excelsior excelsior Carpinus Fraxinus platanoides Acer pseudo-

% of total leaf litter fall Lei-30M 82.3 16.9 0.8 0 0 0 0 0 Lei-62M 89.5 10.5 0 0 0 0 0 0 Lei-111M 98.4 0 0 0 0 0 1.6 0 Lei-141M 97.6 0.1 2.2 0 0 0 0.1 0 Lei-153+16M 88.6 4.7 6.7 0 0 0 0 0 Mühl-38 79.4 20.6 0 0 0 0 0 0 Müh-55 92.3 7.4 0 0.3 0 0 0 0 Mühl-85 96.5 3.1 0.4 0 0 0 0 0 Mühl-102 88.9 9.9 0.3 0.2 0 0 0 0.7 Mühl-171+10 96.4 1.6 2.0 0 0 0 0 0 Lang-I 89.0 10.3 0.5 0.1 0 0 0 0 Lang-II 99.9 0.1 0 0 0 0 0 0 Lang-III 99.7 0 0.1 0 0.1 0.1 0 0 Hai-I 94.9 2.2 0.7 0 2.0 0 0 0 Hai-II 65.2 14.3 8.1 11.2 0.2 0.1 0.8 0 Hai-III 60.8 29.6 9.3 0 0.1 0 0.1 0 Hai-T1 n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d.

89 5 Aboveground litter

Even-aged stands Uneven-aged stands 4.5 4.5 Total litter fall A ) -1 ) 4.0 4.0 -1

3.5 3.5 year -1 year

-1 3.0 3.0 2.5 2.5 2.0 2.0 1.5 1.5

1.0 1.0 ha C Litter fall (t Litter fall (t C ha fall (t C Litter 0.5 0.5 0.0 0.0 0 20 40 60 80 100 120 140 160 180 IIIIII IIIIII

2.5 2.5 B ) -1 ) -1 2.0 2.0 year -1 year -1 1.5 1.5

1.0 1.0

0.5 0.5 Litterfall (t C ha Litter Litter fall (t C ha Leaves 0.0 0.0 0 20 40 60 80 100 120 140 160 180 IIIIII I II III Hai-T Stand age (years) Shelterwood system forest system Selection Unmanaged

Chronosequence "Leinefelde" Chronosequence "Mühlhausen" Selection system "Langula" Unmanaged forest "Hainich NP"

Data from Cotrufo, FORCAST 2003 (Hainich-Tower site)

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. Data from Cotrufo (2003, FORCAST) represent the leaf litter fall in the “main footprint” of the Eddy-Tower at the Hainich NP (n = 25, surface area of each litter trap: 0.5 m2, sampling period: 2001 and 2002). The shadowed area in Figure B indicates the amount of leaf litter that was very likely produced by the understory of the older stands.

90 5 Aboveground litter

Even-aged stands Uneven-aged stands 1.5 1.5

C ) -1

) Branches and twigs -1 year -1 year

-1 1.0 1.0

0.5 0.5 Litterha fallC (t Litter fall (t C ha fall (t C Litter

0.0 0.0 0 20 40 60 80 100 120 140 160 180 IIIIII I II III 2.0 2.0

D ) -1 ) Fruits and buds -1

1.5 1.5 year -1 year -1

1.0 1.0

0.5 0.5 Litter fall (t C ha fall (t C Litter Litter fall (t C ha fall (t C Litter

0.0 0.0 0 20 40 60 80 100 120 140 160 180 I II III I II III

Stand age (years) Shelterwood system system forest Selection Unmanaged

91 5 Aboveground litter

Even-aged stands Uneven-aged stands 24000 24000 16000 16000 ) ) -1 -1 3500 3500 3000 Understory 3000 2500 2500 2000 2000 1500 1500 Overstory 1000 1000 Stand density (TreesStand density ha Stand density (TreesStand density ha 500 500 0 0 0 20 40 60 80 100 120 140 160 180 120 140 160 180 Stand age (years) Age of dominant trees (years)

Understory: dbh < 7 cm, tree height > 1.3 m

Overstory: dbh >= 7 cm

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).

The wooden fraction of total litter fall was 2-5 times smaller at the stands Lei-30M and Mühl-38 than at the older stands or the uneven-aged stands (Figure 5.1C). However, this difference in woody litter fall was nearly equilibrated by higher leaf litter fall at the two youngest stands. The large amount of fruits in the older stands and the uneven-aged stands, in particular in Lei-141M and Lang-II, reflected the mast of beech in the year 2000 (Table 5.2). The large amount of nuts at the older even-aged stands reduced the differences in leaf and branch litter fall in relation to stand age, so that total litter fall did not show any “stand age-effect” (Figure 5.1A).

The largest interannual variability was found for beech nuts, reflecting the mast of beech in the year 2000, and for branches and twigs. Also the leaf litter fall of the stands varied between the two sampling years, but at most stands the variability within a stand was higher than the interannual variability, and there was no general trend towards higher or lower leaf litter fall in the first or second year. Therefore, only the averages of the sampling years are presented and analysed here. The clear trend of decreasing annual leaf litter fall from 2000 to 2002 reported by Cotrufo (2003) for the Hainich-Tower site (2.72, 1.97 and 1.67 tC ha-1 in 2000, 2001 and 2002, respectively) was not observed on the plots of this study. The extraordinary high leaf litter fall measured in 2000 at the Hainich-Tower site (2.72 tC ha-1) was about 57% higher than the maximum value measured in 2000 in this study (1.73 tC ha-1, Lang-I) and may be influenced by

92 5 Aboveground litter the sorting procedure used in 2000 (Cotrufo 2003). The averaged annual litter fall of the years 2001 and 2002 for the Hainich-Tower site equals the litter fall in the plot Hai-II of this study (Figure 5.1B).

5.2.2 Carbon pools in the organic layer

Before the carbon pools in the organic layer will be related to stand age and the silvicultural systems, it is important to mention some general findings with respect to the spatial variability of the organic layer. In chapter 3 it was already noticed that the samples per study plot were taken to improve the estimates for the individual study plots, but that they do not represent replicates of the independent variables “stand age”, “basal area of the stand”, “study site” or “silvicultural systems”. Therefore, the means (± standard deviation) 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 carbon pools of the “study sites”. However, at the plot level the ANOVA assumptions “homogeneity of variance” and “normal distribution of data” were violated, so that the ANOVA was restricted to a comparison of the means per study site. “Extreme values” or “outliers”, identified via box-plot-analysis, were not excluded from the analysis, because there was no justification to exclude single samples. Furthermore, the exclusion of single samples, even if they were clearly identified as “extreme values” (e.g. the two highest values in the study plot Mühl-171+10, Figure 5.3) did not substantially affect the linear regression analysis or the comparison of means at the site level. This robustness of the data set against single extreme values is also reflected by the fact that the medians and the arithmetic means per study plot were similar (Figure 5.3). However, it can not be excluded that the number of samples or the sampling area per study plot (15 sampling points, covering a total area of 3.75 m2) was not sufficient to represent the means per study plot.

93 5 Aboveground litter

Even-aged stands Uneven-aged stands 6 Leaf litter * 6 ) ) -1

-1 5 5 * 4 4 * 3 * 3 * * 2 2 Carbon pools (t C ha pools (t Carbon

Carbon pools (t C ha Carbon pools (t 1 1

0 0

0 20 40 60 80 100 120 140 160 180 IIIIII IIIIII

Stand age (years) Shelterwood system system forest Selection

Arithmetic mean Unmanaged (+/- standard deviation)

Median

Figure 5.3: Carbon pools of leaf litter resting in the organic layer at the end of the growing season (n = 15 per study plot). The asterisks mark the outliers that were identified by box-plot- analysis. However, these values were not excluded from further analysis.

Carbon pools in the organic layer of the study plots varied between 2.3 tC ha-1 (Lei-141M and Hai-II) and 4.6 tC ha-1 (Lei-30M) (Table 5.3). This relatively small amount of organic layer carbon pools is typical for temperate hardwood forests on fertile soils (humus type: mull to F- mull) (BMELF 1997). The mean carbon pools in the organic layer of the study sites ranged only between 3.0 tC ha-1 (“Hainich NP”) and 4.1 tC ha-1 (“Langula”). The differences between the sites were not significant (ANOVA, P > 0.05, Table 5.3). Total carbon pools in the organic layer (Table 5.3) showed a much higher variability within each plot than the total litter fall (Table 5.2). However, at the even-aged stands the general pattern of decreasing carbon pools with increasing stand age until a stand age of about 140 years and then an increase with increasing stand age was observed for the entire organic layer as well as for its leaf fraction (Figure 5.4).

94 5 Aboveground litter

Table 5.3: Carbon pools in the organic layer at the end of the growing season (means ± standard deviation, n = 15 per study plot). FWD: Fine woody debris including twigs and branches < 5 cm in diameter and the shells of the beech nuts. Different letters indicate significant differences between the study sites (ANOVA, P < 0.05, followed by the post hoc Newman-Keuls Test). n.d.: not determined. Data from Persson (2003, FORCAST) represented 9 random samples per study plot (sampling area: 0.0625 m2) that were pooled to 4 samples prior analysis, and included fruits, twigs and branches < 1 cm in diameter. (1) Data from Cotrufo (2003, FORCAST). They were based on 9 random samples per study plot (sampling area: 1 m2) and included woody material < 2 cm in diameter.

Leaf litter FWD Total organic layer

Partly Persson Study plot Coarse Total This study decomposed (2003)

tC ha-1 Lei-30M 1.86 ± 0.59 0.23 ± 0.10 2.09 ± 0.64 2.50 ± 2.05 4.59 ± 2.13 3.79 ± 0.16 Lei-62M 1.23 ± 0.51 0.17 ± 0.10 1.40 ± 0.55 2.51 ± 2.40 3.92 ± 2.32 3.69 ± 0.58 Lei-111M 1.19 ± 0.29 0.19 ± 0.10 1.38 ± 0.37 1.28 ± 0.94 2.66 ± 1.18 3.09 ± 0.59 Lei-141M 0.86 ± 0.45 0.09 ± 0.07 0.95 ± 0.50 1.37 ± 0.80 2.32 ± 1.10 n.d. Lei-153+16M 1.76 ± 0.67 0.18 ± 0.08 1.94 ± 0.71 1.26 ± 0.78 3.20 ± 1.16 3.68 ± 1.06 Average 1.55 ± 0.46 ab 3.4 ± 0.7 a “Leinefelde” Mühl-38 1.58 ± 0.92 0.29 ± 0.18 1.87 ± 1.08 2.51 ± 1.91 4.38 ± 2.70 Müh-55 1.47 ± 0.58 0.21 ± 0.19 1.67 ± 0.73 2.05 ± 0.93 3.72 ± 1.14 Mühl-85 1.35 ± 0.67 0.29 ± 0.13 1.65 ± 0.78 2.49 ± 1.60 4.14 ± 1.81 Mühl-102 1.00 ± 0.77 0.12 ± 0.12 1.12 ± 0.88 2.18 ± 1.93 3.30 ± 2.10 Mühl-171+ 10 2.15 ± 1.06 0.48 ± 0.31 2.64 ± 1.35 1.61 ± 0.84 4.25 ± 1.86 Average 1.79 ± 0.55 ab 4.0 ± 0.4 a “Mühlhausen” Lang-I 2.02 ± 0.61 0.31 ± 0.16 2.33 ± 0.68 1.89 ± 0.89 4.22 ± 1.26 Lang-II 2.51 ± 0.62 0.29 ± 0.14 2.79 ± 0.70 1.31 ± 0.60 4.1 ± 1.12 Lang-III 1.69 ± 0.81 0.21 ± 0.17 1.9 ± 0.97 2.26 ± 1.07 4.16 ± 1.73 Average 2.34 ± 0.45 a 4.1 ± 0.1 a “Langula” Hai-I 1.40 ± 0.62 0.14 ± 0.05 1.55 ± 0.66 2.27 ± 1.45 3.81 ± 1.86 Hai-II 0.67 ± 0.34 0.09 ± 0.05 0.76 ± 0.38 1.59 ± 1.07 2.34 ± 1.19 Hai-III 0.86 ± 0.45 0.11 ± 0.14 0.96 ± 0.46 1.98 ± 1.15 2.94 ± 1.23 Hai-T n.d. n.d. 1.32 ± 0.571 2.95 ± 0.26 Average 1.09 ± 0.41 b 3.0 ± 0.6 a “Hainich NP”

95 5 Aboveground litter

Even-aged stands Uneven-aged stands 8 8 Total organic layer A

7 7 ) ) -1 -1 6 6

5 5

4 4

3 3

2 2 C ha (t Carbon pools Carbon pools (t C ha (t pools Carbon 1 1

0 0 0 20 40 60 80 100 120 140 160 180 I II III I II III Hai-T 4.5 4.5 Leaf litter 4.0 B 4.0 ) ) -1

-1 3.5 3.5 3.0 3.0 2.5 2.5 2.0 2.0 1.5 1.5 Carbon pools (t Cha (t pools Carbon

Carbon pools (t C ha (t pools Carbon 1.0 1.0 0.5 0.5 0.0 0.0 0 20 40 60 80 100 120 140 160 180 I II III I II III

Stand age (years) Shelterwood system system forest Selection Unmanaged Chronosequence "Leinefelde" Chronosequence "Mühlhausen" Selection system "Langula" Unmanaged forest "Hainich NP"

Data from Persson, FORCAST 2003

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. The data represent the means ± standard deviation of 15 random samples per study plot. Data from Persson (2003, FORCAST) are based on 9 random samples per study plot, which were pooled to 4 samples per study plot prior analysis. The shadowed area in Figure A and B indicates the amount of litter that was likely produced by the understory of the older stands.

96 5 Aboveground litter

In the leaf litter about 0.8 tC ha-1 (Hai-II) to 2.8 tC ha-1 (Lang-II) were stored. Mean leaf litter carbon pools at “Langula” (2.3 tC ha-1) were significantly higher than those at the “Hainich NP” (1.09 tC ha-1) (Table 5.3). In “Leinefelde” and “Mühlhausen” intermediate pools of 1.6 tC ha-1and 1.8 tC ha-1, respectively, were found.

The leaf litter carbon pools were positively related to the litter fall of beech leaves and negatively related to the basal area of the stands (Figure 5.5). Leaf litter from ash and maple trees and other non-beech species was already decomposed or at least removed from the organic layer at the end of the growing season, and thus did not contribute to the carbon pools in the organic layer. It is well known that ash and maple leaves decompose much faster than beech leaves (e.g. Wittich 1961, Cornelissen 1996, Scott and Binkley 1997, Wardle and Lavelle 1997, Neirynck et al. 2000, Berg and McClaugherty 2003). Particularly the low carbon pools in leaves of the organic layer at the unmanaged stands Hai-II and Hai-III (0.76 and 0.97 tC ha-1, respectively; Table 5.3) were related to the relatively high proportion of leaf litter fall from ash and maple trees in these two plots (Table 5.2). The plots in the selection forest showed the reverse situation. They had a relatively small proportion of non-beech leaves (on average less than 4%) and stored on average higher amounts of carbon in the leaf litter than the unmanaged stands (Figure 5.4).

97 5 Aboveground litter

3.0 Linear regression: 2 y = -1.427+2.363*x, R = 0.490 2.5 ) -1 2.0

1.5 Leaf litter (tC ha litter (tC Leaf 1.0

A 0.5 0.8 1.0 1.2 1.4 1.6

Litter fall of beech leaves (tC ha-1 year-1)

3.0 Linear regression: 2 y = 2.993-0.045*x, R = 0.393 2.5 ) -1 2.0

1.5 Leaf litter (tC ha (tC litter Leaf 1.0

B 0.5 10 20 30 40 50

2 -1 Basal area (m ha )

Chronosequence "Leinefelde" Chronosequence "Mühlhausen" Selection system "Langula" Unmanaged forest "Hainich NP"

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 5 Aboveground litter

The multiple linear regression analysis (Table 5.4) showed that the litter fall of beech-leaves was the strongest predictor for carbon pools of leaf litter and of total carbon pools in the organic layer compared to all other aboveground variables measured in this study (e.g. basal area, tree density, stand age etc). Excluding the effect of leaf litter fall, the remaining variance of leaf litter carbon pools could significantly be explained by the basal area. In contrast to the hypothesis of this study, carbon pools in the organic layer decreased with an increasing basal area (Table 5.4, Figure 5.5). An inverse relationship was expected, because the light availability at the forest floor often increases with a decreasing basal area, and an increasing light availability was assumed to be associated with an increase in soil temperature and thus higher decomposition rates. For example, the percentage of total radiation that reaches the forest floor of closed beech stands varies between 4 and 10% and it often decreases with increasing basal area (Mitscherlich 1981, Ellenberg et al. 1986, Geiger et al. 1995, Mund et al. in prep. b). After canopy opening for regeneration (141-year-old stand at “Leinefelde”) the proportion of total radiation that reached the forest floor increased to about 30% of total incoming radiation (Mund et al. in prep. b). It may be possible that stands with a higher basal area provide a more constant and humid microclimate than stands with lower basal area (Mitscherlich 1981), and this may support, in particular, the activity of larger soil fauna (Schaefer 1991a, 1991b, Wardle and Lavelle 1997, Wachendorf et al. 1997,).

When only the even-aged stands were taken into account, then the annual leaf litter fall and the stand age were strong predictors for carbon pools in the leaf litter (Table 5.4C). Excluding the effects of leaf litter fall and stand age a small, insignificant fraction of the remaining variance of leaf litter could be explained by the basal area.

99 5 Aboveground litter

Table 5.4: Summary of the multiple regressions (forward stepwise regression) for carbon pools in the organic layer. The regression analysis was based on the means per study plot (n = 16).

A) Leaf litter of the organic layer. All study plots.

-1 Model: Leaf litter (tC ha ) = ß0+ ß1*x1+ ß2* x2 adj. R2=0.670, P = 0.0003 Coefficients SE BETA P

Intercept ß0 0.106 0.821 0.899 Beech leaf litter fall ß 1.979 0.518 0.582 0.002 (tC ha-1 year-1) 1 2 -1 Basal area (m ha ) ß2 -0.035 0.011 -0.493 0.007

B) Total organic layer. All study plots.

-1 Model: Total organic layer (tC ha ) = ß0+ ß1*x1+ ß2* x2 2 adj. R =0.487, P = 0.0052 Coefficients SE BETA P

Intercept ß0 1.088 1.272 0.408 Beech leaf litter fall (tC ha-1 year-1) ß1 2.553 0.803 0.604 0.007 2 -1 Basal area (m ha ) ß2 -0.028 0.017 -0.319 0.117

C) Leaf litter of the organic layer. Even-aged stands (chronosequence “Leinefelde” and “Mühlhausen”).

-1 Model: Leaf litter (tC ha ) = ß0+ ß1*x1+ ß2* x2 + ß3* x3 adj. R2=0.732, P = 0.012 Coefficients SE BETA P

Intercept ß0 -2.555 1.353 0.108 -1 -1 Leaf litter fall (tC ha year ) ß1 0.009 0.003 0.808 0.018

Stand age (years) ß2 2.834 0.735 1.004 0.008 2 -1 Basal area (m ha ) ß3 -0.022 0.011 -0.374 0.088

Carbon pools in fine woody debris (FWD; including branches, twigs and beech nuts) accounted for 40 to 70% of total carbon pools in the organic layer. Thus, this fraction was similar or even higher than the leaf fraction (Table 5.3). At the older stands and the uneven-aged stands, between 20 and 50% of the FWD fraction was contributed by beech nuts, while beech nuts were not found at the two youngest even-aged stands (data are not presented in detail). However, the amount of fruits in the organic layer of the older stands did not equilibrate the lower amount of

100 5 Aboveground litter leaf litter in these stands compared to the younger stands (Figure 5.4) as it was found for the litter fall of fruits and leaves (Figure 5.1). This can be explained by the fact that the fruits germinated in spring or were eaten by mice and other animals. At the end of the growing season the fruits in the organic layer consist only of shells from the beech nuts.

At the managed forests carbon pools in FWD (Table 5.3) were similar or even higher than carbon pools in CWD (Table 4.8). Mean carbon pools in the FWD at the study plot Hai-II (1.59 tC ha -1) were similar to those reported for the main footprint of the Eddy-Tower in the Nationalpark (1.32 tC ha -1, Cotrufo 2003).

5.2.3 Mean residence time of leaf litter and FWD in the organic layer

The “mean residence time” of leaf litter or FWD is the average period of time that organic matter deriving from leaf litter or FWD remains in the compartment “organic layer”. From this approach it cannot be concluded how much carbon from leaf litter or FWD is respired and how much carbon is transferred to the mineral soil. With respect to the rich soils and the thin organic layer of the study plots it is very likely that a large proportion of plant litter is transported to the mineral soil and mixed with the mineral soil by larger soil fauna (“bioturbation”, Schaefer 1991a, 1991b, Beck 1993, Cornelissen 1996, Scheu 1996, Bradford et al. 2002). Thus, it can be assumed that the exclusion of soil fauna (macrofauna 1-20 mm and megafauna > 20 mm; Schachtschabel et al. 1992) from leaf litter decomposition by litter bags leads to an overestimation of the MRT of leaf litter (Bradford et al. 2002). However, the term “decomposition” of plant litter is often defined as the loss of mass from plant litter due to microbial decomposition or leaching of water soluble substances (Berg and McClaughtery 2003). According to this definition the removal of leaf litter by larger soil fauna results in an overestimation of the decomposition rate of leaf litter. Consequently, it is assumed that the “litter bag-approach” (MRTleaves-bags) represents the most reasonable approach to estimate the decomposition of leaf litter, but may overestimate the MRT of leaf litter that is driven by the activity of larger soil fauna. The “ratio-approach” (MRTleaves-ratio) was assumed to reflect the undisturbed conditions on the mineral soil, and thus the loss of carbon from the leaf fraction in the organic layer under most natural conditions. Therefore, this approach may provide the best estimate for the MRT of leaf litter in the organic layer. The differences between the rates of leaf litter loss resulting from the “litter bag-approach” and the “ratio-approach” were assumed to

101 5 Aboveground litter approximate the proportion of leaf litter that was removed by soil fauna larger than the mesh size of the litter bags.

Two examples for the decrease of leaf litter in the litter bags over time and the exponential regressions that were used to describe the decrease over time are shown in Figure 5.6 (the decay constant k for each study plot is given in Table A.5 of the Appendix). The exponential regression predicted the amount of beech leaf litter in the middle (after about 250 days) and at the end of the incubation period quite well. At the beginning of the incubation there was a clear “transition period” with lower decay rates than predicted by the exponential regression. At the second half of the incubation period the loss of leaf litter was higher than it could be described by one exponential regression. Nevertheless, the main objective of this study was to quantify the average MRT of leaf litter at different silvicultural systems. For this objective the simple fit with one exponential regression was sufficient. The rapid decrease of leaf litter from ash and maple trees confirms the well-known strong effect of litter quality on litter decomposition. At the unmanaged plots Hai-II and Hai-III the MRTleaves-bags of non-beech leaves was 70-80% lower than the MRTleave-bags of beech leaves (Table 5.5).

102 5 Aboveground litter

Unmanaged forest "Hainich NP" 100 Beech 90 Beech Ash and maple Hai-I 80 Hai-I Hai-II Hai-II 70 Hai-II Hai-III Hai-III 60 Hai-III 50 40 Ash and maple

(% of weight) initial dry 30 Hai-III

Remaining mass of leaf litter 20 10 Hai-II A 0 0 50 100 150 200 250 300 350 400 450 500 Days of incubation Start of incubation 08.12.1999

Chronosequence "Leinefelde"

100 Beech 90 Lei-30M Lei-30M Lei-62M 80 Lei-62M Lei-111M Lei-111M Lei-141M 70 Lei-153+16M

60 Lei-141M 50 (% of weight) initial dry Lei-153+16M

Remaining mass of leaf litter 40

10 B 0 0 50 100 150 200 250 300 350 400 450 500 550 Days of incubation Start of incubation 24.12.1999

Figure 5.6: Loss of leaf litter from litter bags over time. A) Unmanaged forest at the “Hainich NP”. B) Chronosequence “Leinefelde”. The decrease of remaining leaf litter was fitted with an exponential function (Equation 5.1).

103 5 Aboveground litter

The MRTleaves-bags of leaf litter in the organic layer of the study plots varied between 1.66 years (Hai-II) and 2.91 years (Hai-I), while the MRTleaves-ratio ranged between 0.42 years (Hai-II) and 1.79 years (Lang-II) (Table 5.5). The difference between the two methods of about one year was also apparent when the means per study sites were compared: The average MRTleaves-bags of the study sites ranged between 2.1 years (“Mühlhausen”) and 2.5 years (“Langula”), and the

MRTleaves-ratio between 0.7 years (“Hainich NP”) and 1.5 years (“Langula”) (see also Figure 5.7). The MRT of leaves at the study sites did not differ significantly, independently of the method that was used (Table 5.5).

The MRT of FWD ranged between 1.2 years at the study plot Lang-III and 14.4 years at the study plot Mühl-38. The MRT of FWD at the managed stands (in particular at the youngest even-aged stands) is biased, because the flux of FWD included only the annual litter fall of FWD, while the pool of FWD in the organic layer included FWD from regular annual litter fall and from thinning activities in previous years. However, the mean of all study plots of about 3 years is supposed to be a rough but reasonable estimate for the MRT of FWD. In general, branches and twigs in deciduous forests are decomposed within 10 to 30 years, which equals a

MRT of about 3-10 years (“t63”; Rayner and Boddy 1988, Stewart and Burrows 1994, Krankina et al. 2002).

The differences between the MRTleaves-bags of beech leaves measured in this study compared to the FORCAST studies (Table 5.5) can be explained by differences in the mesh size of the litter bags that were incubated in the field. Bernd Zeller (INRA-Nancy, France, pers. comm., raw data were recalculated according to the exponential regression analysis of this study) used the largest mesh size (5 x 5 mm) and found at “Leinefelde” the lowest MRTleaves-bags (1.71 to 2.74 years). In this study, where a mesh size of 1 x 1 mm was used, a MRTleaves-bags of 2.28 to 3.04 years was calculated for “Leinefelde”. The litter bags incubated at the Hainich-tower site by Cotrufo (2003) had only a mesh size of 0.77 x 0.27 mm and resulted in a MRT of 3.75 years (MRTleaves-bags of this study at the study plot “Hainich NP”: 2.28 to 3.04 years).

104 5 Aboveground litter

Table 5.5: Mean residence time (MRT) of leaf litter and fine woody debris (FWD) in the organic layer. The mesh sizes of the litter bags were: This study: 1 x 1 mm, B. Zeller (FORCAST, pers. comm.): 5 x 5 mm, Cotrufo (2003, FORCAST): 0.77 x 0.27 mm. SE: Standard error, including error propagation. SD: Standard deviation. n.d.: not determined. Different letters indicate significant differences (ANOVA, P < 0.05, followed by the post hoc Newman-Keuls Test). (1) The mean MRTleaves-bags for all tree species was derived from beech litter bags at each plot and from non-beech leaf litter bags incubated at Hai-II and Hai-III weighted by the proportion of beech and non-beech leaf litter (Table 5.2).

A) Method: Leaf litter bags (MRTleaves-bags)

Beech, Ash & maple Beech All species 1 B. Zeller Study plot leaves (± SE) (± SE) (pers. comm.) (± SE) (± SE) MRT (years) Lei-30M 2.90 ± 0.27 n.d. 2.48 ± 0.43 2.48 ± 0.20 Lei-62M 2.95 ± 0.24 n.d. 2.70 ± 0.44 2.48 ± 0.25 Lei-111M 2.87 ± 0.17 n.d. 2.84 ± 0.40 1.70 ± 0.13 Lei-141M 2.54 ± 0.36 n.d. 2.50 ± 0.58 n.d. Lei-153+16M 2.27 ± 0.17 n.d. 2.07 ± 0.37 2.87 ± 0.24 Average ± SD 2.5 ± 0.3 a “Leinefelde” Mühl-38 2.48 ± 0.21 n.d. 2.08 ± 0.37 Mühl-55 2.48 ± 0.26 n.d. 2.33 ± 0.48 Mühl-85 2.42 ± 0.15 n.d. 2.35 ± 0.38 Mühl-102 2.32 ± 0.31 n.d. 2.12 ± 0.49 Mühl-171+10 1.88 ± 0.40 n.d. 1.83 ± 0.61 Average ± SD 2.1 ± 0.2 a “Mühlhausen” Lang-I 2.50 ± 0.40 n.d. 2.28 ± 0.56 Lang-II 2.82 ± 0.22 n.d. 2.82 ± 0.46 Lang-III 2.48 ± 0.28 n.d. 2.47 ± 0.52 Average ± SD 2.5 ± 0.3 a “Langula” Hai-I 3.34 ± 0.44 n.d. 3.20 ± 0.63 Hai-II 2.29 ± 0.46 0.62 ± 0.09 1.69 ± 0.45 2.94 ± 0.46 Hai-III 0.49 ± 0.06 2.00 ± 0.43

Hai-T (Cotrufo 2003) 3.76 ± 0.20 n.d. n.d. Average ± SD 2.3 ± 0.8 a “Hainich NP”

105 5 Aboveground litter

Table 5.5: Continued.

B) Method: “Ratio –approach” (MRTleaves-ratio)

Leaves Total organic FWD Study plot - all species - layer (± SE) (± SE) (± SE)

MRT (years) Lei-30M 1.20 ± 0.10 8.27 ± 3.34 2.25 ± 0.30 Lei-62M 0.96 ± 0.11 2.84 ± 0.81 1.67 ± 0.28 Lei-111M 0.98 ± 0.08 1.29 ± 0.50 1.11 ± 0.22 Lei-141M 0.86 ± 0.14 1.15 ± 0.23 1.01 ± 0.17 Lei-153+16M 1.60 ± 0.21 1.87 ± 0.34 1.69 ± 0.19 Average ± SD 1.1 ± 0.3 a 3.1 ± 3.0 1.6 ± 0.5 “Leinefelde” Mühl-38 1.18 ± 0.19 14.38 ± 0.39 2.48 ± 0.42 Mühl-55 1.14 ± 0.14 2.68 ± 0.58 1.67 ± 0.17 Mühl-85 1.17 ± 0.23 3.09 ± 0.76 1.87 ± 0.22 Mühl-102 0.82 ± 0.18 3.58 ± 1.00 1.66 ± 0.32 Mühl-171+10 1.78 ± 0.27 1.56 ± 0.33 1.69 ± 0.25 Average ± SD 1.2 ± 0.4 a 5.1 ± 5.3 1.9 ± 0.3 “Mühlhausen” Lang-I 1.38 ± 0.12 1.90 ± 0.55 1.57 ± 0.21 Lang-II 1.79 ± 0.19 2.10 ± 0.38 1.88 ± 0.16 Lang-III 1.21 ± 0.22 1.20 ± 0.18 1.20 ± 0.18 Average ± SD 1.5 ± 0.3 a 1.7 ± 0.5 1.6 ± 0.3 “Langula” Hai-I 1.04 ± 0.12 3.58 ± 0.79 1.80 ± 0.24 Hai-II 0.42 ± 0.06 1.70 ± 0.69 0.85 ± 0.17 Hai-III 0.60 ± 0.08 2.15 ± 0.41 1.17 ± 0.15 Hai-T (Cotrufo 2003) n.d n.d. n.d. Average ± SD 0.7 ± 0.3 a 2.5 ± 0.9 1.3 ± 0.5 “Hainich NP”

106 5 Aboveground litter

The MRT of leaf litter was not related to stand age or silvicultural system, independently of the method that was used to determine the MRT (Figure 5.7). The close negative relation of the

MRTleaves-ratio with the basal area of the stands and with the proportion of non-beech leaves (not presented separately) just reflects the dependency of the MRTleaves-ratio from the leaf litter of the 2 organic layer. The MRTleaves-bags was not related to the basal area (R < 0.000, P = 0.982) or other aboveground variables.

Even-aged stands Uneven-aged stands 4.0 4.0

3.5 Litter bags 3.5 Litter bags 3.0 3.0

2.5 2.5

2.0 2.0

1.5 1.5 MRT (years) MRT (years)

1.0 1.0 Ratio-approach Ratio-approach 0.5 0.5

0.0 0.0 20 40 60 80 100 120 140 160 180 I II III I II III Stand age (years) Selection Unmanaged system forest Shelterwood systems

Litter bags (mesh size: 1x1 mm) Ratio-approach

Figure 5.7: Mean residence times (MRT) of leaf litter in relation to stand age, study site and silvicultural system (means ± standard error, including error propagation). The Figure shows the MRTs that result from the “litter bag-approach” and the “ratio approach”.

It is assumed that the leaf litter remaining in the litter bags after the “lifetime” (“t95”) of leaf litter calculated from the “ratio-approach” equals the amount of leaf litter that was removed by soil fauna larger than 1 mm (Equation 5.3). Figure 5.8 illustrates this approach.

107 5 Aboveground litter

−kbags*T −kratio*T (Equation 5.3) y = (y0 * e ) − (y0 * e ) with y: quantity of leaf biomass removed by the soil fauna larger than 1x1 mm (tC ha-1) -1 y0: initial amount of fresh leaf litter (tC ha ) kbags: decay rate constant resulting from the “litter bag-approach” kratio: decay rate constant resulting from the “ratio-approach” T: “lifetime” (t95) of leaf litter according the “ratio-approach”, (kleaves-ratio = 1/MRTleaves-ratio)

1.6 1.5 1.4 1.3 1.2 )

-1 1.1 " L a i 1.0 p tt p e r r " o b 0.9 R a a a c g h t - i " 0.8 o - a Removal by soil fauna > 1 x 1 mm: p 0.7 p -1 -1 r 0.43 tC ha year o a 0.6 c (= 29% of annual leaf litter fall) h 0.5 " Carbon pools (tC ha pools Carbon 0.4 0.3 0.2 Leaf litter fall in 2000 0.1 0.0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Year Figure 5.8: Decrease of carbon pools in leaf litter at the study plot Lei-62M. The “litter bag- approach” is based on the loss of leaf litter from litter bags over time and the resulting decay rate constant (Equation 5.1). The “ratio-approach” is based on the ratio of annual leaf litter fall and the total amount of leaf litter in the organic layer (Equation 5.2). The carbon pools that remain in leaf litter bags after the “lifetime” of leaf litter (“t95”= time to decompose 95% of the initial amount of leaf litter) according to the “ratio-approach2 represents the fraction of carbon that is removed by soil fauna larger than 1 x 1 mm. The Figure includes only the leaf litter fall of the year 2000.

According to this estimate between 0.4% (Mühl-171+10) and 43% (Hai-II) of the annual leaf litter fall, which is equivalent to 0.01 tC ha-1 year-1 or 0.77 tC ha-1year-1, respectively, were removed by larger soil fauna (> 1 mm) (Table 5.6). The two oldest, even-aged stands Lei- 153+16M and Mühl-171+10 showed the lowest rates of leaf litter removal by larger soil fauna compared to all other study plots. At the level of the study sites the significantly highest rate of

108 5 Aboveground litter leaf litter removal were estimated for the unmanaged forest (0.61 tC ha-1year-1 or 37% of leaf litter fall), while the means of the other study sites did not differ significantly (average of the managed sites: 0.25 tC ha-1year-1 or 17% of leaf litter fall, Table 5.6).

Table 5.6: Estimates of the amount of leaf litter that was removed from the organic layer by larger soil fauna (>1 mm). Different letters indicate significant differences between the study sites (ANOVA, P < 0.05, followed by the post hoc Newman-Keuls Test). n.d.: not determined

Proportion of annual leaf Total amount of carbon Study plot litter fall that was removed that was removed by by larger soil fauna larger soil fauna % tC ha-1 year-1 Lei-30M 18.5 0.32 Lei-62M 29.3 0.43 Lei-111M 30.6 0.43 Lei-141M 30.8 0.34 Lei-153+16M 4.9 0.06 Average ± SD “Leinefelde” 22.8 ± 11.2 a 0.32 ± 0.15 a Mühl-38 13.4 0.21 Mühl-55 18.0 0.26 Mühl-85 17.4 0.24 Mühl-102 26.7 0.37 Mühl-171+10 0.4 0.01 Average ± SD “Mühlhausen” 15.2 ± 9.6 a 0.22 ± 0.13 a Lang-I 11.2 0.19 Lang-II 9.9 0.15 Lang-III 18.2 0.29 Average ± SD “Langula” 13.1 ± 4.4 a 0.21 ± 0.07 a Hai-I 32.9 0.49 Hai-II 42.6 0.77 Hai-III 35.6 0.57 Hai-T n.d. n.d. Average ± SD “Hainich NP” 37.0 ± 5.0 b 0.61 ± 0.14 b

109 5 Aboveground litter

110 6 Soil organic carbon pools

6 Soil organic carbon pools

The objectives of the following chapter are (1) to quantify SOC pools of differently managed forests and (2) to separate the effects of soil-specific parameters on SOC pools from the influence of management.

6.1 Methods

6.1.1 Soil pits

6.1.1.1 Sampling

At each study plot one soil pit was dug. The locations of the pits were chosen within the inventory plots based on some excluding criteria. Extreme positions such as topographic depressions or forest tracks were avoided and the pits had to have a minimum distance (about 2 m) to larger trees (dbh > 10 cm). Within the remaining plot area the soil pits were chosen randomly. The soil pits were dug and sampled from September 1999 to September 2000. The organic layer at the soil pits was not sampled. The organic layer samples were taken separately in an extra, short term sampling campaign at a larger number of sampling points (see chapter 5).

At the undisturbed wall of each soil pit a cuboid with an area of 30 x 30 cm and a thickness of 5 cm (0 to 30 cm depth, with “0” being the top of the mineral soil), 10 cm (30 to 50 cm depth) or 20 cm (below 50 cm depth) were sampled (“excavation method”). To avoid a mixture of different pedogenic soil horizons, a sampling depth should be divided also according to soil horizons. In the present study this modification of the sampling by depth was not necessary due to the small sampling steps in the upper soil (5 cm) and due to continuous transitions between consecutive soil horizons.

The whole sample of each sampling depth was well mixed and all stones and roots larger than 5 mm in diameter were picked out in the field. Following this homogenization procedure the fresh weights of the whole soil sample, the roots and stones were determined, and a subsample of the mineral soil of about 900 to 1500 g was taken and weighed again. Some stones were taken to determine their specific gravity (g per cm³) in the laboratory.

111 6 Soil organic carbon pools

6.1.1.2 Soil processing and chemical analysis

The subsamples were dried at the laboratory at 35 °C to weight constancy (by definition the “air-dried sample”). Dead and living roots, plant and animal residues (> 1mm) were picked out of the air dried subsamples, and then the soil samples were sieved to 2 mm with a sieving machine that forces also very hard clumps of clayish soil through the 2-mm mesh (“Siebmaschine für Bodenproben”, Jehmlich GmbH, Nossen, Germany, 2001). The fraction that was larger than 2 mm in diameter (stones and roots) was weighed and added to the fraction of large stones, which was removed in the field already.

Five subsamples of each fine soil sample (≤ 2 mm) were taken with an automatic subsampler (Labor-Probenteiler Typ PT 100, Retsch GmbH & Co.KG, Haan, Germany, 1998) to determine: (1) the residual water content of the air-dried samples (drying at 105 °C), (2) the C and N concentrations, (3) pH values (extraction with KCl and demineralised water), (4) exchangeable cations and (5) the particle size distribution. The samples for C and N analysis were ground with a mixer mill (type MM 200, 1998, Retsch, Haan, Germany), and then the element concentrations were determined via total combustion by the elemental analyzer VarioEL II (1998) or VarioMax (2000) (“Elementar Analysen Systeme GmbH”, Hanau). Total inorganic carbon was quantified by ignition of the air dried and grounded soil samples at 450 °C for at least 8 h to oxidize all organic matter, followed by a determination of the inorganic C with the elemental analyzer VarioMax, 2000 (“Elementar Analysen Systeme GmbH”, Hanau, Germany). The organic carbon was calculated from the difference of total organic carbon and inorganic carbon. All elemental analyses were corrected for the residual water content (difference between air-dried samples and samples dried at 105 °C). To determine the exchangeable cations the cations were extracted from the soil with 1 M NH4OAc (batch procedure, SSSA and ASA 1996, Lüer and Böhmer 2000) and the resulting cation concentrations in the extracts were determined by the ICP-AES (Optima 3300 DV, PerkinElmer, Connecticut, USA). The particle size distribution was determined by the AUA GmbH, Jena, using the combined “sieving and sedimentation method” according to the DIN ISO 11277 norm (“Pipette method”; sand: 63 µm-2mm, silt: 2µm-63 µm, clay: < 2 µm).

Considering all subsamples the absolute dry weight (at 105 °C) of fine soil per area and depth were calculated. Total SOC pools resulted from Equation 6.1:

112 6 Soil organic carbon pools

n (Equation 6.1) SOCtotal pool = ∑ FSM j *Cconc. j /10 j=1 with -1 -1 SOCtotal pool: total soil organic carbon pools (tC ha total soil depth ) FSMj: fine soil (≤ 2mm) mass per sampling depth j (0-5 cm, 5-10 cm, 10-15 cm etc.) and sampling area (g cm-1cm-2) -1 Cconcj: concentration of soil organic carbon at sampling depth j (mg g dw soil)

The fine soil bulk density (fBD, g cm-3) is the absolutely dry weight of fine soil mass (≤ 2 mm) per unit of fine soil volume (fine soil volume = total soil volume - stone volume - root volume; the root volume was considered when it comprised more than 5% of the sampling volume).

6.1.1.3 Soil classification

The soil pits were classified in the field according to the German handbook for soil classification (“Bodenkundliche Kartieranleitung“, (AG Boden 1994)). The classification or exact borders of the soil horizons “T” and “Bt” and “cCv” were partly modified according to the particle size distribution and the inorganic carbon content after measuring in the laboratory.

6.1.2 Soil cores (0-15 cm soil depth)

6.1.2.1 Sampling

Within the study plot (1 ha) of each study site 15 random soil samples were taken from December 2000 to October 2001. The sampling points were chosen by pairs of random numbers that define the X and Y coordinates (unit 1 m) within a Cartesian coordinate grid. The Cartesian coordinate grid was built up by two edges (100 m x 100 m) of the study plots. (The sampling points were the same as those for the organic layer, chapter 5). The soil samples were taken with PE-tubes that were 20 cm high and had an inner diameter of 10.5 cm. For the sampling procedure the tubes were equipped with a metal header and at the bottom with a sharp metal ring. The tubes were 5 cm longer than the required sampling depth (0-15 cm). This oversize was important to prevent any soil compaction from the top (space of 1-2 cm between the soil surface in the tube and the metal header), and to remove the metal ring at the bottom without disturbing the lower part of the soil sample. The construction of the tubes based on ideas by Harrison, A.F.

113 6 Soil organic carbon pools

(Centre of Ecology and Hydrology, Cumbria, UK, pers. comm.) and was further improved and adopted to site conditions of the Hainich-Dün by the mechanical workshop of the Max Planck Institute for Biogeochemistry, Jena. The tubes are in particular suitable for samples of the upper mineral soil and for soils with high clay content.

The tubes were pressed 17-19 cm into the soil with a few, strong strokes to avoid any disturbances of the soil samples due to moving roots, stones or compact soil layers. Then the tubes, including the soil cores, were dug out with a spade, and the metal header and the metal bottom ring were removed. Because of the relatively large diameter of the tube (105 mm), the sharp metal ring that cut even coarse roots easily and the fact, that the samples were dug out (and not pulled out) this sampling method did not cause any soil compaction. The length of the soil core and the height of the hole in the mineral soil were always the same indicating that the soil core within the tube was not compacted. However, this method works only in soils without large stones.

6.1.2.2 Soil processing and chemical analysis

Each tube that included an undisturbed soil core (0-15 cm) was stored in the laboratory and air dried until a crack appeared between the wall of the tube and the soil core, so that the entire core could be pushed out of the tube without any compaction. The soil core was divided by depth into three fractions: 0-5 cm, 5-10 cm and 10-15 cm. Dead and living roots, plant and animal residues (> 1 mm) and large stones were picked out. The stones were weighed to calculate the bulk soil density on the basis of the specific gravity of the stones, the fine soil mass, the sampling volume and the stone mass. The resulting soil sample was dried at 35 °C until weight constancy and sieved to 2 mm with a sieving machine (“Siebmaschine für Bodenproben”, Jehmlich GmbH, Nossen, Germany, 2001). The fraction that was larger than 2 mm in diameter was weighed and added to the larger stones that were picked out already.

Two subsamples of each fine soil sample (≤ 2 mm) were taken with an automatic subsampler (Labor-Probenteiler Typ PT 100, Retsch GmbH & Co.KG, Haan, Germany, 1998) to determine: (1) the residual water content of the air-dried samples (drying at 105°C) and (2) the C and N concentrations. The determination of C and N concentrations followed the same procedure as described for the samples of the soil pits (6.1.1.2).

114 6 Soil organic carbon pools

6.1.3 Determination of total soil depth and soil type

Close to each of the 15 random sampling points per study plot (see above) an auger was hammered into the soil down to the transition zone to the bedrock (C-horizon). The resulting sample was used to determine “total soil depth” and to classify the soil (according to the German handbook “Bodenkundliche Kartieranleitung”; AG Boden, 1994). Total soil depth was defined as the depth from the top of the mineral soil down to the transition zone to the bedrock (C-horizon).

6.2 Results

The total soil solum (A- and B-horizon of variable depth depending on pedogenesis and thickness of the loess layer) was investigated to quantify total SOC pools and to analyse potential effects of edaphic properties on SOC pools and their interaction with soil depth. An analysis of the soil pit data set with respect to silvicultural effects was not reliable as the soil profiles did not cover the spatial heterogeneity of soil texture and SOC pools (see below).

The samples taken by an auger (n = 15 per plot) provided information about the spatial variability of total soil depth and soil types. These samples were not used for chemical analysis.

It is assumed that SOC pools of the upper mineral soil (0-15 cm) are more susceptible to disturbances and less affected by total soil depth than deeper soil layers. Furthermore, a restriction to the upper soil offered the possibility to analyse a relatively large number of soil samples per study plot within the framework of this study. Consequently, the upper mineral soil (0-15 cm) was analysed to separate potential effects of soil properties or other “plot-specific” characteristics from potential effects of the silvicultural management on SOC pools of the upper mineral soil (SOC0-15 pools). All data of the core samples (n = 15 per study plot) and of the soil pit samples (n = 1 per study plot, 0-15 cm) were included in the analysis, because there was no systematic difference between SOC pools deriving from these two data sets. Carbon and nitrogen concentrations, fine soil mass and SOC pools were separately determined for the sampling depths 0-5, 5- 10 and 10-15 cm. The average SOC concentrations and C:N ratios of the upper 15 cm of the soil were weighted by the soil mass per sampling depth. (The SOC pools of the soil depths 0-5, 5-10 and 10-15 cm are given in Table A.6 of the Appendix).

115 6 Soil organic carbon pools

6.2.1 Total SOC pools

Total SOC pools of this study varied between 69 tC ha-1 (Lei-141M) and 122 tC ha-1 (Hai-III) (Figure 6.1, Table 6.1). Schöning (2003) reported for the 111-year-old stand total SOC pools of 57 tC ha-1. The averages of the study sites, including the results of the FORCAST project, were less variable and ranged between 75 tC ha-1 at “Leinefelde” and 105 tC ha-1 at the “Hainich NP” (Table 6.1). The averaged total SOC pools at the study site “Mühlhausen” were about 23 tC ha-1 higher than those at the study site “Leinefelde”. However, the differences between the sites were not significant (ANOVA; P > 0.05) (Table 6.1). At the even-aged stands no relationship between total SOC pools and stand age was found (Figure 6.1). The total SOC pools of the study sites were similar to those found at the “Göttinger Wald” (74.5 - 114.4 tC ha-1, 120-year-old beech stand, limestone covered with loess, Meiwes and Beese 1988).

140 140 ) ) -1 -1 120 120

100 100

80 80

60 60

40 40

20 20 Total SOC poolsTotal ha (tC SOC Total SOC pools (tC ha pools (tC Total SOC 0 0 0 20 40 60 80 100 120 140 160 180 I II III IIIIII Stand age (years)

Shelterwood systems system forest Selection Unmanaged

Chronosequence "Leinefelde" Chronosequence "Mühlhausen" Selection system "Langula" Unmanaged forest "Hainich NP"

Data from Schöning, FORCAST 2003

Figure 6.1: Total SOC pools of the study plots depending on stand age, study site and the silvicultural system. The data include the SOC of the A- and B-horizon of variable depth. For total soil depth see Table 6.1. The data from Schöning (2003) derived from soil pits in adjacent FORCAST plots.

116 6 Soil organic carbon pools

The lowest total soil depth of 10 cm was found in the study plot Lang-II and the highest total soil depth of 94 cm in the study plots Lei-62M and Hai-II. The variation of total soil depth within individual study plots was partly relatively low (e.g. Mühl-102: coefficient of variation 8.9%) and partly relatively high (e.g. Mühl-55: coefficient of variation 35%). In four study plots (Lei-62M, Lei-111M, Lang-I, Hai-I) the total depth of the soil pits was much lower than the mean total soil depth of the 15 sampling points of the soil cores (Table 6.1). Total SOC pools of the soil pits did not correlate significantly with total soil depth (r =0.288).

A comparison of total SOC pools of this study with results from the FORCAST project shows the large spatial variability of total SOC pools within individual study plots. Total SOC pools deriving from two soil pits in the FORCAST plot Lei-30 differed by 18 tC ha-1 (about 20%) (Schöning 2003) (Table 6.1). In this study total SOC pools of 103 tC ha-1 were found at the 153-year-old stand. Schöning (2003) reported for the same stand 66 tC ha-1. The large difference in total SOC pools reflects the spatial heterogeneity of soil types, and thus of many soil parameters such as clay content, C:N ratio, soil acidity and total soil depth within individual forest stands. For example, the soil of the FORCAST plot was classified as a “Pseudovergleyte Parabraunerde” (Stagnic Luvisol, Schöning 2003) with a total depth of 55 cm. The pit of this study at the 153-year-old stand reached a total depth of 70 cm and was classified as a deeply developed “Rendzina-Braunerde” (Cambisol) (Appendix Figure A.1).

117 6 Soil organic carbon pools

Table 6.1: Total soil organic carbon (SOC) pools (A) and total soil depth (B). Total soil depth was defined as the depth from the top of the mineral soil down to the transition zone to the bedrock (C-horizon), and was determined at each soil pit and at 15 random sampling points per study plot (auger samples). The mean (± standard deviation) of SOC pools in the upper mineral soil was based on all soil core samples per study plot (for details see section 6.2.3) and the soil pits. SOC pools of the deeper soil layers (> 15 cm depth) included only the SOC pools that were measured at the soil pits. Different letters indicate significant differences between the study sites (ANOVA, P < 0.05, post hoc comparison with the Newman-Keuls Test).

A) SOC pools.

SOC pools (tC ha-1) 0-15 cm soil depth > 15 cm Total Total Study plot (mean ± SD) soil depth -this study- -Schöning 2003- Lei-30M 46.05 ± 7.60 31.88 77.93 68.87 / 86.89 Lei-62M 39.44 ± 10.31 42.93 82.37 64.01 Lei-111M 30.02 ± 3.56 42.20 72.21 56.94 Lei-141M 31.84 ± 6.35 37.65 69.49 Lei-153+16M 45.45 ± 7.08 57.85 103.30 65.60 Average ± SD 74.8 ± 13.4a “Leinefelde” Mühl-38 36.85 ± 6.95 51.96 88.81 Mühl-55 50.70 ± 8.25 53.59 104.31 Mühl-85 42.76 ± 8.11 45.41 88.17 Mühl-102 41.43 ± 8.08 67.71 109.14 Mühl-171+10 40.50 ± 6.58 59.22 99.72 Average ± SD 98.0 ± 9.3a “Mühlhausen” Lang-I 45.70 ± 7.36 29.25 74.95 Lang-II 37.21 ± 5.13 44.43 81.64 Lang-III 35.25 ± 6.26 61.75 97.00 Average ± SD 84.5 ± 11.3a “Langula” Hai-I 51.38 ± 5.83 38.77 90.15 Hai-II 51.19 ± 7.25 51.26 102.45 Hai-III 54.90 ± 8.56 67.30 122.20 Average ± SD 104.9 ± 16.2a “Hainich NP”

118 6 Soil organic carbon pools

Table 6.1: continued

B) Soil depth.

Total soil depth (cm) Soil pit Auger samples Schöning Study plot This study Mean Minimum Maximum SD CV (%) (2003) Lei-30M 50 43 / 52 50.8 15 65 12.9 25.4 Lei-62M 40 35 77.0 40 94 16.1 21.0 Lei-111M 82 40 82.3 66 92 8.5 10.4 Lei-141M 44 70.0 44 84 12.4 17.7 Lei-153+16M 70 55 65.1 47 86 10.6 16.2 Average ± SD 51 ± 14 69 ± 12 “Leinefelde” Mühl-38 58 58.6 27 72 14.1 24.0 Mühl-55 39 47.9 28 82 16.8 35.0 Mühl-85 51 48.3 33 64 9.7 20.1 Mühl-102 70 64.0 52 75 5.7 8.9 Mühl-171+10 60 59.2 34 75 9.5 16.0 Average ± SD 56 ± 12 56 ± 7 “Mühlhausen” Lang-I 41 67.9 41 82 10.5 15.5 Lang-II 67 51.9 10 74 17.8 34.3 Lang-III 78 75.8 57 87 9.0 11.8 Average ± SD 62 ± 19 65 ± 12 “Langula” Hai-I 36 63.1 36 83 10.9 17.2 Hai-II 49 69.1 44 94 12.8 18.6 Hai-III 50 57.3 48 71 6.9 12.1 Average ± SD 45 ± 8 63 ± 6 “Hainich NP”

119 6 Soil organic carbon pools

In the following linear multiple regression analysis the SOC pools “per sampling depth” or the “total SOC pools” of the soil pits were included as the dependent variable. The parameters

“sampling depth” or “total soil depth”, “clay content”, “pHKCl-value”, “C:N ratio” and “stone volume” of the soil pits were considered as independent variables (for details about these variables along the soil profiles see Figure A.1 in the Appendix). The variable “exchangeable cations” was not taken in the multiple regression analysis, because in cation rich soils on limestone this variable strongly depends on soil organic matter and clay particles that provide the exchanging surfaces of soils (Sollins et al. 1996). (The FORCAST data could not be considered in the following analysis, because in the FORCAST project the soil pits were sampled by soil horizon and not by soil depth.)

SOC pools in different soil depths could be predicted significantly by the soil depth

(ln-transformed), the clay content, the pHKCl-value and the stone volume (Table 6.2A). The C:N ratio had no significant effect on SOC pools in different soil depths.

The variation of total SOC pools was explained weakly but significantly by the mean (weighted) C:N ratios of the entire soil profiles (Table 6.2B). The higher the C:N ratio was the lower was the total SOC pool.

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).

A) SOC pools in different soil depths. The “soil depth” is the mean of each sampling step: 2.5 cm (for 0-5 cm), 7.5 cm (for 5-10 cm),..., 35 cm (for 30-40 cm), 45 cm (for 40-50 cm) etc.. The soil pools were normalised to a soil layer of 1 cm thickness prior to analysis.

-1 -1 Model: SOC pools in different soil depths (tC ha cm ) = ß0+ ß1*x1+ ß2* x2+ ß3* x3 + ß4* x4 adj. R2=0.765, P<0.0001 Coefficients SE BETA P Intercept ß0 3.631 0.203 0.000 ln (soil depth) (cm) ß1 -1.045 0.057 -0.920 0.000 Clay content (%) ß2 0.020 0.004 0.249 0.000 Stone volume (%) ß3 -0.027 0.006 -0.220 0.000 pHKCl ß4 0.120 0.046 0.152 0.011

120 6 Soil organic carbon pools

B) Total SOC pools (The mean C:N ratio per soil pit was weighted by the fine soil mass of each sampling depth).

-1 Model: Total SOC pools (tC ha ) = ß0+ ß1*x1 adj.R2=0.227, P=0.036 Coefficients SE BETA P

Intercept ß0 149.706 25.253 0.000 -1 C:N ratio (g g ) ß1 -5.318 2.287 -0.528 0.036

The clay content and its changes with soil depth are one of the most important soil characteristics on which the German soil classification is based (AG Boden 1994). As the soil depth and the clay content were strong predictors for SOC pools in different soil depths (Table 6.2A), it was expected that total SOC pools were also related to the soil type. Total SOC pools decreased from the soil type “Braunerde-Terra fusca, Terra fusca-Braunerde or Rendzina- Braunerde” (99 tC ha-1) to the soil type “Parabraunerde” (81 tC ha-1, Table 6.3), but the differences were statistically not significant (ANOVA, P = 0.068).

Table 6.3: Mean total SOC pools (± standard deviation) of the different soil types of the soil pits. The differences between the means of the soil types were statistically not significant (ANOVA, P = 0.068).

Soil type Total SOC pools Number soil pits (tC ha-1)

(Braunerde-) Terra fusca or Rendzina - 0 Braunerde-Terra fusca, Terra fusca- 99.3 ± 14.5 8 Braunerde or Rendzina-Braunerde Braunerde (on Terra fusca) 88.8 ± 10.9 3 Parabraunerde (on Terra fusca) 80.5 ± 10.8 5

In contrast to the weak effect of the soil type on total SOC pools, the effect of the soil type on the distribution of SOC pools within the soil profile was relatively strong. When the significant effects of “soil depth” and “clay content” were excluded, the “soil type” explained the remaining variance of SOC pools significantly (ANCOVA, Table 6.4). For a soil depth of 15.5 cm and a clay content of 37.8% SOC pools of 1.7 tC ha-1cm-1 were predicted in a “Parabraunerde” and 2.3 tC ha-1 cm-1 in a “Braunerde-Terra fusca, Terra fusca-Braunerde or Rendzina-Braunerde”. It

121 6 Soil organic carbon pools may be possible that the effect of the “soil type” on SOC pools in different soil depths represented the effects of the variables “C:N ratio”, “pHKCl” and “stone volume” (see Table 6.2). These variables were not taken in this analysis, because the assumption of “homogeneity of variance” for the factor “soil type” was crucially violated by these variables and could not be reached by mathematical transformation. A relationship between aboveground parameters such as tree density, basal area or litter fall and total SOC pools was not found.

Table 6.4: Effect of the factor “soil type” on SOC pools in different soil depths (ANCOVA). The “soil depth” is the mean of each sampling step: 2.5 cm (for 0-5 cm), 7.5 cm (for 5-10 cm),..., 35 cm (for 30-40 cm), 45 cm ( for 40-50 cm) etc.. The SOC pools were normalised to a soil layer of 1 cm thickness prior to analysis.

A) Statistics of the ANCOVA. Factor “soil type”.

Dependent variable: SOC pools in different soils depths (tC ha-1cm-1) SS DF MS F P Intercept 146.341 1 146.341 676.996 0.000 ln (soil depth) (cm) 70.312 1 70.312 325.273 0.000 Clay content (%) 1.203 1 1.203 5.567 0.020 Soil type 6.728 2 3.364 15.562 0.000 Error 25.723 119 0.216

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%. n = number of soil samples

Soil type SOC pools (tC ha-1cm-1) SE -95.00% +95.00% n

(Braunerde-) Terra fusca or Rendzina - - - - - Braunerde-Terra fusca, Terra fusca- 2.281 0.066 2.151 2.412 61 Braunerde or Rendzina-Braunerde Braunerde (on Terra fusca) 1.869 0.123 1.625 2.113 16 Parabraunerde (on Terra fusca) 1.706 0.071 1.565 1.848 47

A serious restriction of the soil pit data set for further analysis focusing on “silvicultural effects” is the low number of pits per study site and, in particular, that the soil pits were not representative for the study plots (see above).

122 6 Soil organic carbon pools

6.2.2 Overview of SOC concentrations and fine soil bulk density in the upper mineral soil (0-15 cm) of the study plots

The SOC concentrations and fine soil bulk densities (fBD) will be separately presented in this section, but then the analysis is focused on SOC pools as the depending variable.

-1 Average SOC0-15 concentrations of the study plots varied between 20 and 50 mg g soil. The lowest values were found at the study plots Lei-111M and Lei-141M and the highest value at the study plot Mühl-55M. The variation of SOC0-15 concentrations within a plot was sometimes very high (e.g. coefficient of variation at Lei-62M: 42%, Table 6.5). After logarithmic (ln) transformation the ANOVA procedure followed by a post hoc comparison (Newman-Keuls test) identified some significant differences between the study plots (Table 6.5). Outliers of SOC0-15 concentrations per study plot identified via a box-plot analysis (in total six outliers) were not excluded, because there was no justification by errors due to the sampling procedure or the processing of the samples. Furthermore, there was a close negative relationship between fine soil bulk density (fBD) and SOC0-15 concentrations (Figure 6.2), as it was already reported by many other soil studies (e.g. Lal and Kimble 2001, Schöning 2003, Wirth et al. 2003). Samples with a very high SOC0-15 concentration had a very low fBD and vice versa. Consequently, samples identified as outliers for SOC0-15 concentrations were not identical with those identified for

SOC0-15 pools (section 6.2.3).

123 6 Soil organic carbon pools

Table 6.5: SOC concentrations (A), fine soil bulk density and C:N ratios (B) of the upper mineral soil (0-15 cm). Different letters indicate significant differences (ANOVA, P <0.05, post hoc Newman-Keuls Test). For the comparison of the study plots SOC concentrations were ln- transformed prior to analysis. Number of samples per study plot: 16 (plot Hai-II: 18). The C:N ratios per study plot resulted from linear regressions passing the origin. Data from Persson (2003, FORCAST) were calculated from SOC concentrations of the upper 20 cm of the soil (SOC0-15 = SOC0-10 + SOC10-20/2) and represented 9 random samples per study plot that were pooled to 4 samples prior to analysis (surface area of soil cylinder: 32.17 cm2). SD: standard deviation, CV: coefficient of variation

A) SOC0-15 concentrations.

-1 SOC0-15 conc. (mg gsoil ) This study Study plot Median Mean ± SD CV (%) Lei-30M 30.9 35.4 ± 14.9 def 42.2 Lei-62M 25.7 25.9 ± 9.9 bc 38.3 Lei-111M 19.7 20.2 ± 2.0 ab 9.8 Lei-141M 19.5 20.5 ± 5.7 a 28.0 Lei-153+16M 30.3 31.5 ± 7.3 cde 23.0 Lei-30 (Persson 2003) 28.5 28.2 ± 2.4 8.6 Lei-62 (Persson 2003) 22.8 22.8 ± 4.7 19.6 Lei-111 (Persson 2003) 22.1 21.0 ± 5.4 25.7 Lei-141 (Persson 2003) Lei-153+16 (Persson 2003) 25.2 24.7 ± 3.5 14.0 Average ± SD “Leinefelde” 25.8 ± 5.2a Mühl-38 30.9 29.7 ± 7.8 cde 26.4 Mühl-55 46.1 49.0 ± 10.7 g 21.9 Mühl-85 29.6 30.2 ± 7.0 cde 23.1 Mühl-102 28.7 27.3 ± 7.5 cd 27.4 Mühl-171+10 32.1 31.6 ± 5.6 cde 17.7 Average ± SD “Mühlhausen” 33.5 ± 8.8a Lang-I 33.7 34.0 ± 5.7 def 16.9 Lang-II 24.9 26.1 ± 7.5 c 28.9 Lang-III 21.0 24.6 ± 5.8 bc 23.7 Average ± SD “Langula” 28.2 ± 5.1a Hai-I 34.3 35.3 ± 4.8 ef 13.7 Hai-II 35.2 35.7 ± 7.4 ef 20.6 Hai-III 40.8 41.3 ± 9.2 f 22.2 Hai-T (Persson 2003) 39.8 42.8 ± 7.9 18.5 Average ± SD “Hainich NP” 37.4 ± 3.4a

124 6 Soil organic carbon pools

Table 6.5: continued

B) Fine soil bulk density (fBD) and C:N ratios of the upper mineral soil (0-15 cm soil depth).

C:N ratio fBD (g cm-3) (g g-1) Persson (2003) This study This study (0-20 cm) Study plot Mean ± SD CV (%) Mean Mean Lei-30M 0.956 ± 0.135 bcd 14.1 12.7 12.8 Lei-62M 1.064 ± 0.174 f 16.4 12.7 12.9 Lei-111M 1.028 ± 0.124 cd 12.1 13.8 13.9 Lei-141M 1.049 ± 0.130 cdef 12.4 13.3 Lei-153+16M 0.978 ±00.123 bcd 12.6 12.6 13.2 Average ± SD 1.008 ± 0.048a 12.9 “Leinefelde” Mühl-38 0.864 ± 0.130 b 15 13.1 Mühl-55 0.705 ± 0.114 a 16.2 12.3 Mühl-85 0.956 ± 0.109 bcd 11.4 11.7 Mühl-102 1.042 ± 0.148 cde 14.2 11.9 Mühl-171+10 0.871 ± 0.157 b 18 12.6 Average ± SD 0.888 ± 0.125b 12.3 “Mühlhausen” Lang-I 0.902 ± 0.084 bc 9.3 11.2 Lang-II 0.988 ± 0.174 bcd 17.6 13.4 Lang-III 0.972 ± 0.115 bcd 11.8 12.3 Average ± SD 0.953 ± 0.047c 11.9 “Langula” Hai-I 0.973 ± 0.069 bcd 7.1 11.5 Hai-II 0.959 ± 0.102 cd 10.6 11.7 Hai-III 0.923 ± 0.108 bcd 11.7 11.9 Hai-T 12.4 Average ± SD 0.950 ± 0.026c 11.7 “Hainich NP”

125 6 Soil organic carbon pools

1.6

) 1.4

-3 y = 2.072-0.331*x R2 = 0.526; P < 0.001 cm

soil 1.2

1.0

0.8

Fine soil bulk density (g density soil bulk Fine 0.6

0.4 2.2 2.4 2.6 2.8 3.0 3.2 3.4 3.6 3.8 4.0 4.2 4.4 4.6

-1 ln (SOC0-15 (mg gsoil ))

Chronosequence "Leinefelde" Chronosequence "Mühlhausen" Selection forest "Langula" Unmanaged forest "Hanich NP"

Figure 6.2: Linear relation between fine soil bulk density and SOC0-15 concentrations (ln-transformed).

-1 The mean SOC0-15 concentrations of the chronosequence “Leinefelde” (22.58 mg g ) and the -1 selection system “Langula” (28.2 mg g ) were lower than the mean SOC0-15 concentrations of the chronosequence “Mühlhausen” (33.5 mg g-1) and the unmanaged forest “Hainich NP” (37.5 mg g-1), but the differences were statistically not significant (Table 6.5).

The variation of fine soil bulk density (fBD) within a plot was up to three times lower than the variation of SOC0-15 concentrations (coefficient of variation: 7 to 18% for fBD compared to

10 to 42% for SOC0-15 concentrations). Differences between the mean fBD of the study plots were relatively small and only a few significant differences were found (Table 6.5, no transformation was needed). The plot Mühl-55 had an extraordinarily low fBD (0.705 g cm-3)

126 6 Soil organic carbon pools compared to all other study plots. The highest value of 1.064 g cm-3 was found in the plot Lei-62M.

The mean fBDs of the study sites differed significantly (Table 6.5, ANOVA, P < 0.05, Newman-Keuls test), but there was no signficant effect of the silvicultural treatments on fBD. The fBD at the shelterwood system “Leinefelde” was significantly higher than the fBD at the unmanaged forest “Hainich-NP” and the selection system “Langula”. At the shelterwood system “Mühlhausen” the lowest fBD was found.

The linear relationship between SOC and nitrogen concentrations at the upper mineral soil of the study plots was very strong (at all study plots R2 above 0.840). Only at two plots were the intercepts significantly different from zero (Lei-111M, P = 0.005; Lei-153+16M, P = 0.01). Thus, this relation was simplified to the “C:N ratio” (Table 6.5), which equals the slope of the regression line when the intercept was set to zero. The C:N ratios of the study plots varied between 13.8 (Lei-111M) and 11.2 (Lang-I), a range that is typical for nutrient rich soils on limestone covered with loess (Meiwes and Beese 1988, Rehfuess 1990).

6.2.3 Overview of SOC pools in the upper mineral soil (0-15 cm) of the study plots

The data of the upper mineral soil (0-15 cm) were analysed in three steps: (1) The “study plot” was assumed to be the main independent factor that controls for the effects of soil-specific covariates (e.g. clay content or C:N ratio) on SOC0-15 pools, (2) it was assumed that the “study site” or the “silvicultural system” was the main independent factor, and (3) aboveground parameters (e.g. stand age or litter fall) were included in the regression analyses as potential covariates.

Prior to the ANOVA the SOC0-15 pools of the study plots and the study sites were transformed with y = x1.7 to meet the “homogeneity of variance”. An exclusion of outliers within individual study plots identified via box-plot analysis (Figure 6.3) did not improve the further analysis and was not justified by errors due to the sampling procedure. Therefore, all available SOC data were included in the following statistical analyses (15 core samples and 1 sample from the soil pit; except for Hai-II with 18 samples).

127 6 Soil organic carbon pools

75 75 70 * 70 65 65 ) )

-1 60 60 -1 55 55 50 * 50 45 45

pools (tC ha (tC pools 40 40 ha (tC pools

0-15 35 35 0-15 30 30 SOC SOC 25 25 20 * 20 15 15 0 20 40 60 80 100 120 140 160 180 IIIIII I II III Stand age (yrs) Shelterwood systems system forest Selection Selection Unmanaged

Soil pits (one soil pit per study plot)

Cores (15 samples per study plot)

Chronosequence "Leinefelde" Chronosequence "Mühlhausen" Selection system "Langula" Unmanaged forest "Hainich NP"

Figure 6.3: SOC0-15 pools of all soil samples in relation to stand age, study site and silvicultural system. *: “Outliers” that were identified via a box-plot analysis. However, these samples were not excluded from further statistical analysis (for explanation see text).

-1 The highest mean SOC0-15 pool of 55 tC ha was found at the study plots Hai-III and Hai-T, and the lowest value of 30 tC ha-1 was found at the study plot Lei-111M. Significant differences between individual study plots are marked in Table 6.6. The coefficients of variation of the

SOC0-15 pools at the study plots ranged between 11% and 26%. Thus, the variation of SOC0-15 pools was lower than the variation of SOC0-15 concentrations (coefficients of variation: 10% to

42%, Table 6.5). The variability of SOC0-15 pools within an individual study plot was similar to the variability between the study sites or silvicultural systems.

128 6 Soil organic carbon pools

Figure 6.4 shows the mean SOC0-15 pools of the study plots in relation to stand age, study site and the silvicultural system. The SOC0-15 pools were not related significantly to stand age, neither with respect to the single chronosequences (“Leinefelde”: R2 = 0.130, P = 0.551; “Mühlhausen”: R2 = 0.026, P = 0.796) nor to the joined data set of both chronosequences (R2 = 0.085, P = 0.414).

The lack of a “stand age-effect” on SOC0-15 pools in the even-aged stands at Leinefelde was also reported by Persson (2003) (Figure 6.4, linear regression including all available data: R2 = 0.032, P = 0.540). The data from Persson were based on four mixed soil samples representing 9 random sampling points per study plot (FORCAST study plots; 100 x 100 m) and comprised

SOC pools of the upper 5, 10 and 20 cm of the mineral soil. The re-calculation of SOC0-15 pools

(SOC0-15 = SOC0-10 + SOC10-20/2) caused a small systematic underestimation of SOC pools from Persson compared to those of this study. At the 62- and 111-year-old stand (“Leinefelde”) and at the tower site of the “Hainich NP”, both studies found similar SOC0-15 pools. At the 30- and 153-year-old stand the estimates from Persson (2003) were about 10 tC ha-1 lower than those of the present study. A comparison of soil standards within the FORCAST project showed that the SOC measurements were not affected by a different soil processing and elemental analysis (Philip Rowland, Centre for Ecology and Hydrology, CEH Merlewood, pers. comm.). Therefore, it is very likely that the differences in SOC0-15 pools at the 30- and 153-year-old stand reflected the large spatial variability of SOC0-15 pools within the even-aged stands. Consequently, it may be possible that the number of soil samples taken per study plot (this study: n = 16, Persson

2003: n = 9) was too small to represent mean SOC0-15 pools of the forest stands. The lower standard deviations of Persson´s samples reflected the pooling of samples prior to sampling analysis (see above).

Schöning (University of Munich, pers. comm.) studied the spatial variability of SOC pools of the upper 0-12 cm at the tower site in “Leinefelde” (111-year-old stand; 54 samples distributed -1 over the FORCAST study plot). He found a range of SOC0-12 pools of 31 tC ha (minimum: -1 -1 18 tC ha and maximum: 49 tC ha ). This range was two times larger than the range of SOC0-15 pools within the adjacent plot of this study (Lei-111M: range: 13 tC ha-1, minimum: 22 tC ha-1, maximum: 35 tC ha-1, Table 6.6). Compared to the other plots of this study the plot Lei-111M was characterised by a relatively low variation of SOC0-15 pools (Table 6.6). However, the -1 median of SOC0-12 pools (28 tC ha ) observed by Schöning was similar to the median or mean found in this study and by Persson (2003).

129 6 Soil organic carbon pools

Table 6.6: SOC pools in the upper mineral soil (0-15 cm). Different letters indicate significant differences between the study plots or study sites (ANOVA, P <0.05, post hoc Newman-Keuls Test). SOC pools were transformed with y= x1.7 prior to analysis. Number of samples per study plot was 16 (plot Hai-II: 18). Data from Persson (2003) were recalculated from SOC pools of the upper 20 cm of the soil (SOC0-15 = SOC0-10 + SOC10-20/2) and represented 9 random samples per study plot that were pooled to 4 samples prior to analysis (surface area of soil cylinder: 32.17 cm2). SD: standard deviation, CV: coefficient of variation

-1 SOC0-15 pools (tC ha ) Study plot Mean ± SD CV (%) Median Minimum Maximum Lei-30M 46.05 ± 7.60de 16.5 45.91 31.31 58.77 Lei-62M 39.44 ± 10.31bcd 26.2 38.5 27.11 60.75 Lei-111M 30.02 ± 3.56a 11.9 29.47 21.73 35.40 Lei-141M 31.84 ± 6.35a 19.9 32.74 22.05 47.89 Lei-153+16M 45.45 ± 7.08de 15.6 47.42 32.81 58.14 Lei-30 (Persson 2003) 34.28 ± 3.15 9.2 34.69 Lei-62 (Persson 2003) 33.76 ± 3.80 11.3 35.80 Lei-111 (Persson 2003) 30.88 ± 2.56 8.3 30.37 Lei-141 (Persson 2003) Lei-153+16 (Persson 2003) 35.13 ± 5.71 16.3 34.61 Average ± SD “Leinefelde” 36.3 ± 6.0a Mühl-38 36.85 ± 6.95bc 18.9 36.17 25.14 47.87 Mühl-55 50.70 ± 8.251ef 16.3 50.55 37.77 62.40 Mühl-85 42.76 ± 8.11cd 19.0 41.4 30.79 57.03 Mühl-102 41.43 ± 8.08bcd 19.5 43.05 25.50 52.82 Mühl-171+10 40.50 ± 6.58bcd 16.3 40.77 32.00 55.79 Average ± SD “Mühlhausen” 42.5 ± 5.1b Lang-I 45.70 ± 7.36de 16.1 43.48 33.21 62.10 Lang-II 37.21 ± 5.13bc 13.8 36.17 30.57 49.21 Lang-III 35.25 ± 6.26ab 17.8 33.69 26.16 49.63 Average ± SD “Langula” 39.4 ± 5.6a Hai-I 51.38 ± 5.83ef 11.4 53.23 39.99 59.80 Hai-II 51.19 ± 7.25ef 14.2 50.70 40.50 67.80 Hai-III 54.90 ± 8.56f 15.6 54.07 40.38 67.81 Hai-T (Persson 2003) 55.75 ± 7.18 12.9 53.19 Average ± SD “Hainich” 53.3 ± 2.4c

130 6 Soil organic carbon pools

70 70

60 60 ) ) -1 50 50 -1

40 40

30 30 pools (tC ha pools (tC ha 0-15 0-15 20 20 Linear regression SOC R2 = 0.032, P = 0.540 SOC 10 10

0 0 0 20406080100120140160180 IIIIII I II III

Stand age (years) Shelterwood systems system forest Selection Selection Unmanaged

Chronosequence "Leinefelde" Chronosequence "Mühlhausen" Selection system "Langula" Unmanaged forest "Hanich NP"

Data from Persson, FORCAST 2003

Figure 6.4: SOC0-15 pools of the study plots depending on stand age, study site and the silvicultural system. Data from Persson (2003) were calculated from SOC pools of the upper 20 cm of the soil (SOC0-15 = SOC0-10 + SOC10-20/2) and represented 9 random samples per study plot that were pooled to 4 samples prior to analysis (surface area of soil cylinder: 32.17 cm2).

-1 The averaged SOC0-15 pools of the chronosequence “Leinefelde” (36 tC ha ) differed -1 significantly from the averaged SOC0-15 pools of the chronosequence “Mühlhausen” (43 tC ha ), but not from those of the selection system “Langula” (39 tC ha-1) (transformation with y = x1.7,

ANOVA, Newman-Keuls Test; Table 6.6). At the “Hanich NP” the significantly highest SOC0-15 pools (53 tC ha-1) were found. This result should not immediately be interpreted as a net carbon loss of 10-14 tC ha-1 due to forest management. In the following section it will be shown that there were additional effects of and interactions with edaphic variables.

131 6 Soil organic carbon pools

6.2.4 Soil-specific effects and effects of silvicultural treatments on SOC pools in the upper mineral soil (0-15 cm)

It is well known that soil texture has a key function for carbon stabilization and storage in mineral soils (Christensen 1992, Sollins et al. 1996, Christensen 2001, Six et al. 2002). Within the scope of this study it was not possible to determine the particle size distribution of all core samples. However, the soil pit data set revealed that the water content remaining in the soil samples after air drying was a very strong predictor for the clay content of the soil samples (Figure 6.5). The residual water was removed from subsamples when drying the samples at 105 °C, and it is given in percent of the air-dried soil samples. The residual water content was available for all soil samples (cores and pits, excluding eight outliers), and it is used in the following in place of the clay content. The estimates of the mean clay content of the upper mineral soil (0-15 cm) of each study plot are summarized in Table 6.7.

The FORCAST data on SOC pools were not included in the following statistical analysis, because the variables “residual water content” or “clay content” and the “soil type” were not available for all soil samples of the FORCAST study plots.

70 Chronosequence "Leinefelde" Chronosequence "Mühlhausen" 60 Selection system "Langula" 50 Unmanaged forest "Hainich NP"

30 Clay content (%) content Clay

20 y = 12.170 + 10.307*x R2 = 0.666; P < 0.0001 10

0 01234567

Residual water content (%)

Figure 6.5: The clay content of the soil as a function of the residual water content. The data set includes all samples of the soil pits. At some soil pits 2 to 3 samples of consecutive soil depths and similar soil texture were mixed prior to soil texture analysis (total number of samples: 75).

132 6 Soil organic carbon pools

Table 6.7: Estimates of the mean clay content of the upper mineral soil (0-15 cm) of the study plots. The clay content was predicted from the linear regression between the clay content and the residual water content (Figure 6.4). The means (± standard deviation) of the residual water content include all individual soil samples (cores and soil pits, 0-15 cm) except for eight outliers identified by the box-plot analysis. The standard error of the predictions was calculated according to Draper and Smith (1998).

Mean clay content Mean residual water content Study plot (prediction ± standard error of (mean ± SD) prediction) % of air-dried soil % of absolutely dry soil Lei-30M 2.42 ± 0.61 37.1 ± 7.0 Lei-62M 1.98 ± 0.62 32.6 ± 7.0 Lei-111M 1.23 ± 0.20 24.9 ± 7.0 Lei-141M 1.31 ± 0.39 25.7 ± 7.0 Lei-153+16M 2.29 ± 0.60 35.8 ± 7.0 Mühl-38 1.60 ± 0.35 28.6 ± 7.0 Mühl-55 3.30 ± 0.60 46.2 ± 7.0 Mühl-85 2.45 ± 0.62 37.4 ± 7.0 Mühl-102 1.80 ± 0.39 30.7 ± 7.0 Mühl-171+10 1.85 ± 0.39 31.3 ± 7.0 Lang-I 2.65 ± 0.46 39.5 ± 7.0 Lang-II 2.09 ± 0.65 33.7 ± 7.0 Lang-III 1.67 ± 0.28 29.4 ± 7.0 Hai-I 2.58 ± 0.37 38.8 ± 7.0 Hai-II 2.57 ± 0.58 38.6 ± 7.0 Hai-III 2.98 ± 0.49 42.9 ± 7.0

A restriction for the statistical analysis of the effects of silviculture on SOC0-15 pools was (1) the dependency between several variables, as they are reflected in the correlation matrix (Table 6.8), and (2) the relatively high number of independent, discrete variables (factors) such as the “soil type”, “study plot”, “study site” and “silvicultural system”. Therefore, in the following the set of variables is reduced step-by-step to a set of variables that showed the most significant effects on SOC0-15 pools.

133 6 Soil organic carbon pools

Total Total depth -0.369* -0.369* -0.310* -0.310* -0.249* -0.249* The correlations are on), measured with an -0.652* -0.652* -0.582* -0.582* -0.596* -0.596* inorganic carbon. fBD: carbon. inorganic 0.351* 0.351* 0.160* -0.224* -0.210* -0.226* -0.226* -0.210* -0.224* -0.202* -0.202* -0.260* -0.260* C:N ratio C:N Soil type -0.426* -0.426* water 0.561* 0.795* Residual Residual

0-15 pools 0.795* 0.795* -0.596* -0.652* -0.652* -0.596* SOC fBD fBD -0.694* 0.865* 0.865* -0.694* 0.838* -0.480* -0.480*

0-15 mineral soil down to the transition zone to the bedrock (C-horiz bedrock the to zone transition to the soil down mineral conc. conc. 0.482* -0.288* -0.288* 0.482* TIC -0.210* 0.254* 0.254* -0.210*

* -0.256* -0.288* -0.480* 0.051 0.254* 0.203* 0.203* 0.254* 0.051 -0.480* -0.288* -0.256* * 0-15 conc. -0.582 -0.582 0.865* 0.561* 0.561* 0.865* 0.857* 0.857* -0.694 SOC

upper mineral soil mineral *: soil cores pits). cm, P highly and upper (0-15 < 0.05. Bold numbers mark significant < 0.001) (P

0.147* 0.196* 0.158 -0.282* 0.071 0.100 -0.216* -0.004 -0.325* -0.325* -0.004 -0.216* 0.100 0.071 -0.282* 0.158 0.196* 0.147* 0.100 -0.091 -0.091 0.100 0.158 0.015 0.490* 0.490* 0.015 0.158 0.482* -0.256* 0.071 0.015 0.015 0.071 -0.004 -0.031 0.196* -0.084 -0.084 0.196* 0.490* 0.147* -0.084 0.015 0.121 0.015 -0.091 -0.140* -0.031 0.198* 0.198* 0.147* -0.031 -0.140* -0.091 0.015 0.121 0.015 -0.084 -0.282* 0.121 0.121 -0.282* -0.224* -0.202* -0.140* -0.216* 0.051 -0.426* -0.260* -0.369* -0.249* -0.226* 0.203* 0.053 -0.310* -0.325* 0.198* 0.160* 0.351* 0.053 conc. pools

conc. conc. 0-15 0-15 0-15 coefficients above 0.500. Total N = 225 after case wise deletion of missing data/outliers. SOC: soil organic carbon, TIC: total TIC: carbon, SOC: soil organic data/outliers. missing of case deletion wise after 225 N = 0.500. Total above coefficients Fine soil bulk density, total depth: depth from the top of the the top of the from depth total depth: density, soil bulk Fine point core. soil sampling of the the at auger based on all soil samples of the of soil samples on all based Table 6.8: Pearson correlation coefficients of all variables that were considered for the following statistical analysis. Study site Tree age Study site Tree age SOC TIC fBD SOC Residual water C:N ratio Soil type depth Total

134 6 Soil organic carbon pools

The residual water content was highly correlated with all variables except for the factor “study plot” and the continuous variable “stand age/tree age”. For a regression analysis it is reasonable to reduce the number of independent variables and to focus on the strongest predictors for SOC pools. In Figure 6.2 the strong relationship between fine soil bulk density

(fBD) and SOC0-15 concentrations was already shown. In section 6.2.2 it was also mentioned that there was no clear trend towards soil compaction (higher fBD) due to management. Thus, the list of variables could be reduced by the fBD without loss of relevant information. The C:N ratio may affect SOC storage independently of or in addition to the clay content, because the correlation of the C:N ratio with the residual water content was relatively weak compared to the other variables (Table 6.8), so that both variables, residual water content and C:N ratio, were included in the regression analyses as soil-specific parameters.

The soil type was expected to be one of the most important factors influencing SOC0-15 pools.

Indeed, there was a clear relationship between SOC0-15 pools and soil type (Figure 6.6A). Considering the large spatial variability of soil types within a single study plot (Table 3.4), these differences are expected to affect SOC0-15 pools substantially. There was also a strong relationship between the soil type and the residual water content or the C:N ratio (Figure 6.6 B and C). With respect to the soil type as the independent factor the C:N ratios did not meet the assumption of homogeneity of variance, and this assumption could not be reached by mathematical transformation. Thus, the mean C:N ratios of the soil types were not compared statistically.

The soil type alone would be a significant predictor for SOC0-15 pools of each individual sample (R2 = 0.353, P < 0.001, n = 258). However, the residual water content was a much stronger predictor (R2 = 0.615, P < 0.001, excluding eight outliers, n = 250), and it is a continuous variable, which improves the options for further detailed regression analysis. Consequently, the covariates C:N ratio and the residual water will replace the factor “soil type” in the following analysis of SOC0-15 pools.

135 6 Soil organic carbon pools

65 4.0 SOC0-15 pools Residual water content 60 a ) 3.5 (~clay content) -1 n=22 a (%) 55 3.0 n=23 b 50 2.5 b n=63 45 c n=66 c 2.0

pools (tC ha pools n=32 d 40 n=33 d 1.5 n=133 0-15 35 n=136 1.0 SOC 30 A content water Residual 0.5 B 25 0.0 IIIIIIIV I II III IV Factor " soil type" Factor " soil type"

15.0 14.5 C:N ratio 14.0 13.5 Soil types 13.0 I: (Braunerde-) Terra fusca or Rendzina n=32 12.5 n=133 II: Braunerde-Terra fusca, Terra fusca-Braunerde or Rendzina-Braunerde

C:N ratio 12.0 n=23 11.5 III: Braunerde (on Terra fusca) n=63 IV: Parabraunerde (on Terra fusca) 11.0 10.5 C 10.0 IIIIIIIV Factor " soil type"

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). The mean and the standard deviation are given for each soil type. Different letters mark significant differences between the soil types (P < 0.05, ANOVA and Newman-Keuls Test). n = number of samples

When the highly significant effects of the “residual water content” and the “C:N ratio” on

SOC0-15 pools were statistically excluded via the SSM analysis, the factor “study plot” did not explain the remaining variance of SOC0-15 pools significantly (Table 6.9, Figure 6.7). Two plots,

Lei-111M and Lang-II, showed relatively low SOC0-15 pools when the effects of the C:N ratio and the residual water content were taken into account. The high variation of the plot specific regression (given as the 95% limits) at Lei-111M was caused mainly due to a lack of samples with a residual water that was close to the mean of this covariate. Furthermore, at the plots Lei-

111M and Lang-II there was no significant relationship between SOC0-15 pools and the residual water content (Lei-111M: R² = 0.016, P = 0.638, Lang-II: R² = 0.011, P = 0.708), while this relationship was generally relatively strong for all other study plots. Figure 6.8 demonstrates that this lack of a significant relation was not caused by single samples or “outliers”. The data set of

136 6 Soil organic carbon pools plot Lei-153+16M gives an example how strong this relationship was at the other study plots (R2 = 0.570, P = 0.0007).

Table 6.9: Summary of the “separate-slopes 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”.

-1 Depending variable: SOC0-15 pools (tC ha ) Effect SS DF MS F P Intercept 31.886 1 31.886 1.197 0.275 Plot*residual water content 5821.434 16 363.840 13.661 0.000 (%) Plot*C:N ratio (g g-1) 1111.385 16 69.462 2.608 0.001 Plot 439.774 15 29.318 1.101 0.358 Error 5379.967 202 26.634

137 6 Soil organic carbon pools

Covariate means: Residual water content: 2.18 % C:N ratio: 12.47

Factor "study plot": P = 0.358 65 Chronosequence Chronosequence Selection system Unmanaged forest "Leinefelde" "Mühlhausen" "Langula" "Hainich" 60

55 )

-1 50

40 pools (tC ha pools (tC 35 0-15

30 SOC

15 Hai-I Hai-II Hai-III Lang-I Lang-II Lang-III Lei-30M Lei-62M Mühl-38 Mühl-55 Mühl-85 Lei-111M Lei-141M Mühl-102 Lei-153+16M Mühl-171+10 Study plots

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”. The least squares means of the SOC0-15 pools resulted from a SSM analysis and are given for the covariates at their means (± 95% limits of the regression). Thus the given SOC0-15 pools represent an estimate of SOC0-15 pools of the study plots for the case that the soils of the study plots had the same residual water content (2.18%) and the same C:N ratio (12.47). The remaining variation of SOC0-15 pools is not significantly explained by the factor “study plot” (P = 0.358, for details see Table 6.9).

138 6 Soil organic carbon pools

Lei-153+16M 50 ) -1 45

40 Lang-II

pools (tC ha (tC pools 35

0-15 Lei-111M 30 SOC 25

15 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0

Residual water content (%)

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. (Lang-II: R² = 0.011, P = 0.708; Lei-111M: R² = 0.016, P = 0.638; Lei-153+16M: R2 = 0.570, P = 0.0007).

For the next step of analysis the “study site” was assumed to be the independent factor -1 influencing SOC0-15 pools. In the upper mineral soil of the “Hainich NP” about 48 tC ha would have been found if the soil had the same residual water content and the same C:N ratio as the other study sites (Figure 6.9). At the managed forests “Leinfelde”, “Mühlhausen” and “Langula” about 40, 43 and 42 tC ha-1, respectively, would have been stored in the upper mineral soil

(Figure 6.9). The mean SOC0-15 pools of the study sites, corrected for the effects of the residual water content and the C:N ratio (“corrected” SOC0-15), did not differ significantly, but there was a clear trend towards higher “corrected” SOC0-15 pools in the unmanaged forest compared to the shelterwood systems and the selection system (ANOVA, P = 0.07, data were transformed with y = x3.5 prior to analysis, Figure 6.9). This means that not the type of silvicultural management

(shelterwood or selection system) affected SOC0-15 pools but that forest use and management in general may reduce SOC0-15 pools. In other words, it may be possible that higher SOC0-15 pools are associated with the cessation of timber use. Assuming that a higher net accumulation of

SOC0-15 was induced by the cessation of regular timber use about 35 years ago (chapter 3), about 0.17 tC ha-1 year-1 (= 6 tC ha-1 / 35 years) have been accumulated in the mineral soil of the unmanaged site. It has to be pointed out that this estimate of SOC0-15 accumulation is based on

139 6 Soil organic carbon pools differences between SOC pools of managed and unmanaged forests that were measured at the same time. Consequently, this estimate does not include SOC that may have been accumulated at all study sites independently of the type of management. A further analysis about the effect of management is not reliable because of the small number of unmanaged plots (n = 3 + Hai-T).

50 47.90a )

-1 48 (± 2.95) 46 43.22a a 44 (± 1.94) 41.68 (± 6.62) 42

pools (tC ha pools (tC 40 40.31a Average of managed

"Corrected" forests: 41.7 0-15 38 (± 4.66) 36 SOC 34 32 "Leinefelde" "Langula" "Mühlhausen" "Hainich NP" "Study site"

Figure 6.9: Mean “corrected” SOC0-15 pools (± standard deviation) of the study sites. The "corrected" SOC0-15 pools represent the mean SOC0-15 pools for the case that all study sites had the same residual water content (2.18%) and the same C:N ratio (12.47) (SSM analysis). The letter “a” indicates that the means per study site did not differ significantly (ANOVA, P = 0.07, data were transformed with y = x3.5 prior to analysis).

In the following the effects of all aboveground variables (Table 6.10) on the “corrected”

SOC0-15 pools are analysed. All variables that had no significant effect on SOC0-15 pools or did not improve the multiple linear models to predict SOC0-15 pools substantially were excluded via the “forward stepwise” procedure.

140 6 Soil organic carbon pools

)

-1 0-15 pool

SOC corrected corrected

leaves -ratio MRT ) (years) (tC ha ) (years) (tC -1 (for details about the details aboveground(for layer Total Total organic ) (tC ha ) (tC -1 layer of the organic Leaf litter ) (tC ha ) (tC -1 year -1 Non-beech Non-beech leaf litter fall litter leaf multiple linear regression analysis regression analysis linear multiple ) (tC ha ) (tC -1 year -1 litter fall Beech leaf leaf Beech ) (tC ha ) (tC -1 Stand Stand density density ) (trees ha ) (trees -1 s that were taken into account for account into taken were that s ha 2 Basal area Basal area

trees age of age dominant dominant Stand age/ age/ Stand (years) (m (years) Lei-30M 30 13.9 1664 1.43 Lei-30M 43.63 30 13.9 0.31 1.20 2.1 4.6 Lei-62M 62 34.0 624 1.30 0.15 1.4 3.9 0.96 40.92 0.96 3.9 1.30 1.4 0.15 624 34.0 62 Lei-62M 224 1.39 35.2 Lei-111M 111 47.63 32.21 0.02 100 47.35 1.08 0.98 24.2 41.61 Lei-141M 141 1.4 2.7 41.77 0.03 0.86 Lei-153+16M 153 1.17 1.0 2.31.04 1.38 2048 1.26 Mühl-38 18.2 4.1 43.73 50.98 1.35 1.6 0.05 38 18.6 4.2 0.33 34.40 560 1.18 1320 1.35 3.8Mühl-55 64 1.9 41.12 4.4 2.3 55 31.4 30.5 0.11 1.14 0.18 0.42 1.79 Mühl-85 85 1.50 1.7 3.7 1.5 1.08 512 329 1.22 31.6 45.10 4.1 Mühl-102 102 0.08 45.98 2.3 0.15 43.292.8 0.82 0.00 1.56 1.42 1.1 25.0 Mühl-171+10 171 3.3 263 0.60 0.14 0.8 1.21 187 22.6 0.63 122 Lang-I 25.0 4.2 1.571.18 2.9 0.00 216 1.9 84 1.9 123 Lang-II 308 1.0 31.8 34.0 0.63 0.97 168 Lang-III 3.2 1.43 344 34.9 1.60 147 Hai-I 43.07 43.7 131 Hai-II 0.05 153 Hai-III 2.6 4.3 1.78 43.67 Study plot plot Study Table 6.10: Overview of the variable variables see chapter 4 and 5).

141 6 Soil organic carbon pools

When the strong influence of the residual water content (~ clay content) and the C:N ratio was excluded, the remaining variation of SOC0-15 pools was positively correlated with the litter fall of non-beech leaves (Table 6.11). This effect of non-beech leaf litter fall on “corrected” SOC0-15 pools was significant (P = 0.023), but the distribution of the data indicates that the regression analysis was dominated by the high amount of non-beech leaf litter in the plots Hai-II and

Hai-III (Figure 6.10). The study plot Hai-I showed similar “corrected” SOC0-15 pools as the other study plots at the “Hainich NP”, even though the amount of non-beech leaf litter in this plot was very low (0.08 tC ha-1 year-1 or 5% of total leaf litter fall). Thus, it may be possible that the effect of litter quality just reflected the trend of higher SOC pools due to the cessation of timber use at the “Hainich NP”. On the basis of the available data set it is not possible to separate the effects of these two variables on SOC0-15 pools. An effect of stand age on “corrected” SOC0-15 pools in the even-aged stands was not found (Table 6.11, Figure 6.10).

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. Only the independent variables that contributed to the “best” regression model are presented. (List of all variables that were included in the multiple linear regression analysis is given in Table 6.10.)

A) All study plots.

-1 Model: “corrected” SOC0-15 pools (tC ha ) = ß0+ ß1*x1 adj. R2=0.268, P = 0.023 Coefficients SE BETA P Intercept ß0 40.580 1.346 0.000 -1 Non-beech leaf litter fall (tC ha ) ß1 12.958 5.091 0.562 0.023

B) Even-aged stands.

-1 Model: “corrected” SOC0-15 pools (tC ha ) = ß0+ ß1*x1+ ß2*x2 adj. R2=0.199, P = 0.191 Coefficients SE BETA P Intercept ß0 33.959 4.629 0.000 -1 Non-beech leaf litter fall (tC ha ) ß1 29.092 14.176 0.856 0.079 Stand age (years) ß2 0.041 0.031 0.548 0.230

142 6 Soil organic carbon pools

) A

-1 Hai-II 50 Hai-I Lang-I

102 Hai-III 45 Lang-III 171+10 pools (tC ha 153+16 38 30 0-15 141 62 40 85 55

35 Lang-II Linear regression: y = 40.58+12.96*x, 111

"Corrected" SOC 2 adj. R = 0.268, P = 0.023 30 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

Non-beech leaf litter fall (tC ha-1 year-1)

) B -1

45 pools (tC ha

0-15 40

35 "Corrected" SOC 30 0 20 40 60 80 100 120 140 160 180

Stand age (years)

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). The “corrected” SOC0-15 pools represent the predicted SOC0-15 pools for the case that all study plots had the same residual water content of 2.18% and the same C:N ratio of 12.47 (SSM analysis, see Figure 6.7). The numbers in Figure A indicate the stand age of the study plots.

143 6 Soil organic carbon pools

144 7 Total carbon budgets of the silvicultural systems

7 Total carbon budgets of the silvicultural systems

Table 7.1 summarizes the carbon pools of the study sites and silvicultural systems. The selection system “Langula” contained on average about 8% more carbon than the shelterwood systems “Leinefelde” and “Mühlhausen”, but the difference was statistically not significant. Total carbon pools in the unmanaged forest were 33-43% higher than in the managed forests. The differences between the managed forests and the unmanaged forest were clearly dominated by differences in the living tree biomass of the study sites. The tree biomass accounted for about 65% of total carbon pools in the forest ecosystems. The higher total SOC pools at the “Hainich NP” were mainly affected by soil-specific properties, in particular by a higher clay content of the soils in the “Hainich NP” compared to the other study sites. SOC pools of the upper mineral soil (0-15 cm) accounted for about 48% of total SOC pools. When the effects of the clay content and the C:N ratio on SOC0-15 pools were -1 excluded, there remained a trend of higher SOC0-15 pools (about 6 tC ha ) at the “Hainich NP”. In conclusion, the unmanaged forest contained higher amounts of carbon than the managed forests, and a cessation of timber use may result in a higher long-term carbon accumulation in the mineral soil. However, the uncertainties with respect to the effect of current and former forest management on SOC pools are large.

Table 7.1: Summary of carbon pools of the silvicultural systems. Minimum and maximum values are given in round brackets. (1) Estimate that was derived from the selection system.

Shelterwood systems Selection system Unmanaged forest

tC ha-1 Living tree biomass 154.5 175.8 238.1 (149-160) (153.9-219.4) (212.7-285.0) Aboveground dead wood 1.5(1) 1.5 (0-2.5) 6.4 (3.1-9.3) Organic layer 3.7 (3.3-4.0) 4.1 (4.1-4.2) 3.0 (2.3-3.8)

SOC0-15 pools 40.5 (33.6-42.5) 39.4 (35.3-45.7) 52.5 (51.2-54.9) SOC pools 45.9 (36.2-55.5) 45.1 (29.3-61.7) 52.5 (38.8-67.3) > 15 cm soil depth Total mineral soil 86.4 84.5 105.0

Total 246.1 265.9 352.5

“Corrected” 41.8 (40.3-43.2) 41.7 (34.4-47.6) 47.9 (45.1-51.0) SOC0-15 pools

145

146 8 Discussion

8 Discussion

Considering site-specific characteristics and the main dependent variables of this study, “carbon pools” and “silvicultural treatment”, the number of comparable, published studies is very limited. Most available data on forest biomass or soil organic carbon pools focus on single aspects of forest ecosystems such as different climates or elevations, different vegetation types or different soil types (e.g. BMELF 1997, Baritz 1998, Wirth et al. 2003) and different types and intensities of cuttings (e.g. “whole tree harvesting” or “sawlog harvesting”, partial cuttings or clear-cuttings, Covington 1981, Edwards and Ross-Todd 1983, Johnson et al. 1995, Johnson and Todd 1998, Bauhus 1994, Johnson and Curtis 2001).

Thus, the results of this study are discussed in the following way: First, studies on beech or temperate broadleaved forests that include at least rough information about climate, silvicultural treatments or stand age, soil properties, and living and dead aboveground biomass were selected. The sites of these studies were classified as “even-aged managed forests”, “uneven-aged managed forests”, “unmanaged/protected forests”, “primary forests (disturbed and undisturbed)” and forests growing under “favourable conditions” or “less favourable conditions”. Due to the lack of detailed information about silvicultural treatments and site conditions in many publications, this classification is very rough and represents only a scheme to improve the transparency of available data. Hardwood forests containing a substantial proportion of coniferous trees are explicitly excluded from this literature review, because these mixed forests often grow at higher elevations and showed a clear tendency to higher standing biomass and dead wood biomass (see below) than pure hardwood forests.

After the comparison of biomass carbon pools the discussion will focus on processes and interactions between aboveground parameters, soil properties and soil organic carbon (SOC). Then the annual carbon fluxes of selected study sites will be estimated using the data of this study and those of other projects carried out at the same study sites. The results will be discussed with respect to the methodological approaches that were used. Finally, the potential to increase carbon pools in forest ecosystems due to a cessation of timber use is estimated.

147 8 Discussion

8.1 Silvicultural effects and site-specific effects on carbon pools in forest biomass

The main hypothesis of this study was that carbon pools in differently-managed forest ecosystems increase sequentially from the regular shelterwood system to the selection system to the unmanaged forest. This hypothesis could be confirmed only partly: the unmanaged forest had indeed the highest carbon pools, while the carbon pools of the selection system and the shelterwood system were similar. The carbon pools of the study sites were clearly dominated by forest biomass (chapter 7) and this carbon reservoir was the most sensitive one with respect to silvicultural management. In the following, this result is analysed in the context of other studies on living and dead tree biomass in deciduous forests. A scenario for the future development of forest biomass at the study sites is discussed at the end of this section.

8.1.1 Carbon pools in living tree biomass

In Figure 8.1A carbon pools in living tree biomass of deciduous forests representing different growth conditions and forest management are presented (case studies only). The carbon pools in living tree biomass of all sites of this study are at the upper range of carbon pools found in other beech forests or temperate hardwood forests. This confirms the favourable growing conditions of the study sites (chapter 3).

Compared to other case studies, the 102- and 111-year-old stand of this study showed the highest carbon pools in living tree biomass (Figure 8.1A). Mean carbon pools in living tree biomass of the chronosequences “Leinefelde” and “Mühlhausen” were about 20-30 tC ha-1 higher than the average reported for beech stands in Thuringia (131 tC ha-1, Wirth et al. 2003) or of even-aged beech stands in western Germany (~129 tC ha-1, resulting from 309 m3 ha-1 (BMELF 1990) multiplied by 0.42 (expansion/conversion factor for 101 to 120-year-old stands, Wirth et al. 2003)).

148 8 Discussion

A) Carbon pools in living tree biomass

300 Even-aged managed stands Uneven-aged forests 300 ) )

250 250 -1 -1

200 200

150 150

100 100 Carbon pools (tC ha Carbon pools (tC ha 50 50

0 0 0 20 40 60 80 100 120 140 160 180 Managed Unmanaged stands Primary forests uneven-aged stands Stand age (years)

Less favourable site conditions Favourable site conditions This study

B) Leaf biomass carbon pools 3.0 3.0 Even-aged managed stands Uneven-aged forests

2.5 2.5 ) ) -1 -1

2.0 2.0

1.5 1.5

1.0 1.0 Carbon pools (tC ha Carbon pools (tC ha 0.5 0.5

0.0 0.0 0 20 40 60 80 100 120 140 160 180 Stand age (years)

Figure 8.1: Carbon pools in living tree biomass (A) and in leaf biomass (B) of differently managed forests under different growing conditions. The unmanaged beech forests include very old forest reserves as well as recently protected forests. The primary beech forests can naturally be disturbed. The sources of the data are given in detail in Table A.7 of the Appendix. To convert the timber volume per hectare that was reported in most studies, the conversion- expansion factors by Wirth et al. 2003 were used.

The selection forest “Langula” had higher carbon pools in living tree biomass than some other managed uneven-aged stands or unmanaged stands (Figure 8.1A). However, the tree biomass of the selection forest “Langula” (154-219 tC ha-1) was typical for selection forests on nutrient rich soils in Thuringia. For example, 148 and 213 tC ha-1 were stored in tree biomass at two stands of the selection forest “Keula” (Michl and Licht 2002). In the selection forest “Bleicherode” about

149 8 Discussion

162 tC ha-1 were accumulated in living tree biomass (Tabaku 1999). A mean timber volume of 391 m3 ha-1, which equals about 152 tC ha-1, was reported for the entire forestry district “Langula” (1227 ha) that includes “ideal” selection forests as well as nearly even-aged stands and stands on less fertile sites (Winterhoff and Storch 1994). Furthermore, all these estimates for carbon pools in living tree biomass are similar to the total average of beech stands resulting from -1 -1 a literature review by Jacobsen et al. (2003) (347 tdw ha ~ 174 tC ha ). Wirth et al. (2003) reported for selection forests in Thuringia mean carbon pools of 115 tC ha-1. This relatively low value can be explained by the fact, that this study averaged selection forests on fertile and poor soils, and that it is based on forestry records of the former GDR, which may have underestimated the timber stocks due to the procedures that were used to estimate standing stem volume of uneven-aged stands (D. Gerold, University of Dresden, pers. comm.).

Timber carbon pools in unmanaged, protected forests (Figure 8.1A) are very variable and do not show a clear relation to general site conditions. This lack of a clear relationship between site conditions and tree biomass may reflect the large heterogeneity of former forest use of many recently protected stands (von Lochow 1987). Timber carbon pools in the Hainich NP were already close to those reported for primary beech stands under favourable conditions in Albania and Slovakia (Korpeľ 1995, Meyer et al. 2003). Timber carbon pools in primary Nothofagus pumilio stands on poor soils in New Zealand (maximum 206 tC ha-1, Weber 2001) were exceeded by the carbon pools of the Hainich NP.

The analysis of leaf biomass or leaf litter production in this study showed a relatively high sensitivity to changes in stand density and basal area. Therefore, this fraction of tree biomass is discussed in the following separately. Leaf biomass of managed hardwood forests can vary between 0.6 and 2.6 tC ha-1, on average it is about 1.6 ± 0.4 tC ha-1 (Figure 8.1B). The extraordinary high leaf biomass of some even-aged stands can be related to very high tree densities. For example, a 61-year-old stand studied by Gerighausen (2002) had a leaf biomass of 2.6 tC ha-1 and a tree density of 928 trees ha-1. Garelkov (1973) reported for a 100-year-old stand a leaf biomass of 2.4 tC ha-1 and a stand density of 1200 trees ha-1. Excluding the extraordinary high values a trend of decreasing leaf biomass with increasing stand age can be observed for the meta-data set. It is very likely that this trend reflects the decrease of stand density by regular thinning and not the senescence of trees. A recent literature study by Jacobsen et al. 2003 -1 -1 resulted in a mean leaf biomass of beech stands of 3.95 ± 1.28 tdw ha (~ 1.9 tC ha , n = 10).

150 8 Discussion

A relatively high constancy of leaf biomass production of beech forests with respect to climatic conditions is indicated by a relatively low interannual variability of leaf biomass. For example, beech stands at the “Solling” (Germany, recently unmanaged, coloured sandstone) annual leaf litter fall varied over a time period of 16 years between 1.3 and 1.7 tC ha-1 (120/130- year-old stand) or 1.5 and 1.8 tC ha-1 (78/88-year-old stand; Ellenberg et al. 1986, Beese et al. 1991). In managed beech stands at the "Göttinger Wald" (about 120 years old, limestone covered with loess) 5 years of leaf litter sampling showed a minimum value of about 1.2 tC ha-1 (in 1983) and a maximum value of about 1.7 tC ha-1 (in 1982) (Pellinen 1986, Beese et al. 1991). Along an elevation gradient from 550 to 790 m a.s.l. annual leaf litter fall of hardwood forests in an undisturbed watershed of the Hubbard Brook forest decreased only from 1.5 to 1.1 tC ha-1 (sampling period October 1968 to October 1969, primary species: Acer saccharum, Fagus grandifolia, Betula allegheniensis, Gosz et al. 1972). In conclusion, the leaf litter production of mature beech forests or hardwood forests can vary from year to year between 1.1 and 1.8 tC ha-1 year-1. This range is similar to the range of leaf litter fall that was found at the chronosequence “Leinefelde”, and that was induced by changes in tree density due to regular thinning and canopy opening for regeneration.

8.1.2 Dead wood carbon pools

For coniferous primary forests, dead wood pools (snags and logs) of about 15 to 250 tC ha-1 were reported (Grier and Logan 1977, Harmon et al. 1986, Harmon et al. 1990, Kirby et al. 1998, Leibundgut 1993, Duvall and Grigal 1999, Krankina et al. 2002, Pedlar et al. 2002). These dead wood pools are eight times higher than mean dead wood pools in undisturbed primary beech forests (5-34 tC ha-1, median 13 tC ha-1, Table 8.1). The generally lower dead wood pools of temperate hardwood forests correspond with the shorter lifetime of dead wood from deciduous tree species compared to dead wood from conifers. Wood from conifers contains higher concentrations of secondary metabolic products (e.g. phenolic compounds), which reduce wood decomposition, than wood from deciduous trees. Wood from hardwood trees is predominantly infected by white-rot fungi that decompose cellulose and lignin. Coniferous wood is predominantly decomposed by brown-rot fungi that remove cellulose and modify lignin only, which results in lower decomposition rates compared to the decomposition by white-rot fungi (Rayner and Boddy 1988, Hammel 1997, Jurgensen et al. 1997, Schwarze et al. 1999). Consequently, the “lifetime” of snags and logs from deciduous trees varies between 27 and 40 years (MRT = “t63” ~ 9-13 years), and the branches and twigs are decomposed within 10 to 30 years (MRT ~ 3-10 years). The decomposition of snags and logs from conifers takes between 46

151 8 Discussion and 111 years (MRT = ~ 15-37 years) (Rayner and Boddy 1988, Stewart and Burrows 1994, Krankina et al. 2002, Müller-Using and Bartsch 2003, Wirth et al. 2003).

In managed beech stands dead wood pools did not exceed 5 tC ha-1. The largest amount of dead wood (more than 20 tC ha-1) were found in unmanaged stands after large disturbances (e. g. natural succession after clear-cutting or windthrow) (Table 8.1). Dead wood pools of forest reserves in Germany (1.4-12.2 tC ha-1) are very similar to those found in the Hainich NP. Only for the forest reserve “Heilige Hallen” extraordinary high dead wood pools (30.8 tC ha-1) were reported, which may be related to the history of forest use at this site (Tabaku 1999).

In this study dead wood carbon pools differed more between the managed and the unmanaged sites than all other carbon pools except for the stem and branch biomass. However, the average amount of carbon stored in dead wood was very low (1.5-6.4 tC ha-1, Table 4.8). Even at the Hainich NP dead wood carbon pools accounted only for 4.4% of carbon pools in living timber biomass and only 1.8% of total carbon pools in the forest. In the late 1980s an unknown amount of dead wood was removed from the unmanaged stands (chapter 3) and this may affect current dead wood pools. However, with respect to the short "lifetime" of dead wood from deciduous tress and the low dead wood pools found in other forest reserves and in primary beech forests it is not likely that dead wood carbon pools at the Hainich NP will exceed an average of about 13 tC ha-1.

Apart from the role as a “carbon reservoir” dead wood may affect the carbon budget of unmanaged forests by working as a source for SOC. This potentially important role of dead wood for long-term carbon storage in the mineral soil will be discussed in more detail in section 8.2.2.

152 8 Discussion

Table 8.1: Dead wood carbon pools of temperate hardwood forests. Dead wood volume was converted to carbon pools assuming a mean basic wood density of 310 kg m-3 (~ decay class 3, chapter 4). * Ratio of dead wood biomass to living timber biomass. Dominant tree species: 1 Abies alba, 2 Acer platanoides, 3 Acer pseudoplatanus, 5 Acer saccharum, 6 Betula alba, 7 Betula lutea, 9 Carpinus betulus, 11 Fagus grandifolia, 12 Fagus sylvatica, 13 Fraxinus excelsior, 14 Nothofagus fusca, 15 Nothofagus menziesii, 16 Nothofagus pumilio, 17 Nothofagus solandri, 18 Nothofagus truncata, 19 Quercus montana, 20 Quercus petraea, 24 Tilia cordata. Sources: (1) Erdmann and Wilke 1997, (2) Ohland 2000, (3) Bascietto 2003 FORCAST, Cotrufo 2003 FORCAST, (4) Granier et al. 2000, Cotrufo 2003 FORCAST, (5) Bobiec 2002, (6) Struck 2001 in Wirth et al. 2003, (7) Meyer 1999, (8) Bobiec 2002, (9) Harmon et al. 1986, (10) Muller 2003, (11) Whittacker et al. 1979; Bormann and Likens 1979, (12) Hart et al. 2003, (13) Stewart and Burrows 1994, (14) Weber 2001, (15) Davis et al. 2003, (16) Meyer et al. 2003, (17) Korpeľ 1995, (18) Tabaku 1999, (19) Müller-Using and Bartsch 2003.

Site Dominant Management Timber Dead Ratio* Source tree species wood tC ha-1 tC ha-1 %

Managed stands Schiefergebirge, 12 11 managed 95 1.4 1.3 1 Hessen, Germany beech stands Mühlhausen, 12 Selection system 0.8 2 Thuringia, Germany Bleicherode, 12 (3, 2) Selection system 116.4 0.4 0.3 18 Thuringia, Germany Soroe, Denmark 12 Natural 73.8 4.9 6.6 3 regeneration Hesse, France 12 Natural 26.9 2.8 10.4 4 (20, 24, 6) regeneration Białowieza, 9, 24, 2 etc. Selection cutting 64.7 0.5 0.8 Poland -"- 9, 24, 2 etc. Selection cutting 70 0.5 0.7 5 Median 0.8 1.1

Unmanaged stands and old-growth forests Brandesbachtal, 12 Forest reserve 4.1 6 Thuringia, Germany Northwestern 12, 20 Forest reserve 104.6 1.4 1.4 7 Germany -"- 12 Forest reserve 123.6 4.2 3.4 7 -"- 12, 20 Forest reserve 170.7 12.2 7.1 7 -"- 12 Forest reserve 191.7 6.4 3.3 7 Solling, Germany 12 Forest reserve, 139.6 4.4 3.2 19 1994 -"- 12 Forest reserve, 136.1 7.9 5.8 19 2000

153 8 Discussion

Table 8.1: continued

Site Dominant Management Timber Dead Ratio* Source tree species wood tC ha-1 tC ha-1 %

Unmanaged stands and old-growth forests (continued) "Heilige Hallen", 12 Forest reserve 143.4 30.8 21.5 18 Germany Limker Strang, 12 Forest reserve 141.6 2.3 1.6 18 Germany Białowieza, 9, 24, 2 etc. Forest reserve 104.7 15.4 14.7 8 Poland -"- 9, 24, 2 etc. Forest reserve 136.7 21.9 16.0 8 Hardwood 5, 11 etc. not reported not 10.4 not 9 forests, northern reported reported America -"- 5, 11 etc. not reported not 11.6 not 9 reported reported -"- 5, 11 etc. not reported not 24.7 not 9 reported reported -"- 11, 5, 19 not reported not 14.5 not 9 etc. reported reported Southeastern 11, 5, 19 not reported 117.3 14.8 12.6 10 Kentucky, USA Hubbard Brook 5, 11, 7 unmanaged 41 14 34.1 11 Forest, USA natural succession after clear cutting Big Bush, 18, 17, 15 not reported 100.5 27.9 27.8 12 Nelson, New Zealand Station Creek, 15, 14 undisturbed old- not 65.6 not 13 New Zealand growth forest reported reported Fergies Bush, 15, 14 undisturbed old- not 148.9 not 13 New Zealand growth forest reported reported Rough Creek, 15, 14 undisturbed old- not 73.2 not 13 New Zealand growth forest reported reported Tierra del Fuego, 16 natural succession 131 37.9 29.0 14 Argentina after windthrow -"- 16 -"- 112 31.3 27.9 14 -"- 16 -"- 122 25.4 20.8 14 -"- 16 -"- 39 28.7 73.4 14 -"- 16 -"- 31 35.4 115.2 14 -"- 16 natural succession 45.4 60.5 133.3 14 after clear cutting without slash removal

154 8 Discussion

Table 8.1: continued

Site Dominant Management Timber Dead Ratio* Source tree species wood tC ha-1 tC ha-1 %

Unmanaged stands and old-growth forests (continued) Central South 17 natural succession 122.6 11.9 9.7 15 Island, New after wind throw Zealand Median 15.1 15.4

Undisturbed primary forests Tierra del Fuego, 16 primary forest 128 34.4 26.9 14 Argentina Mirdita, 12, 3 primary forest 156 6.2 4.0 15 Munellagebirge, Albania Puka, 12 (1) primary forest 217.6 4.7 2.2 16 Munellagebirge, Albania Rajca, Eastern 12, 24, 6 primary forest 225.3 13.2 5.9 16 Albania SNr Vihorlat, 12 (3, 13) primary forest 135 10.0 7.4 17 Western Carpathians, Slovakia SNr Rozok, 12 primary forest 206 29.4 14.1 17 Western Carpathians, Slovakia SNr Havesova, 12 primary forest 196 21.9 11.2 17 Western Carpathians, Slovakia Median 13.2 7.4

155 8 Discussion

8.1.3 Scenarios of future changes

It is a declared intention of forestry policy today to increase the proportion of beech forests and to increase the structural heterogeneity of forests in Germany. Hence, it can be expected that an increasing amount of even-aged stands will be transformed to more heterogeneous, uneven- aged stands via different kinds of partial-cuttings (e.g. selection cutting, grouped-shelterwood cutting, target diameter harvesting or gap feeling) (Kenk and Gühne 2001, O'Hara 2001, Schütz 2001b, Sterba and Zingg 2001). If large areas of even-aged stands will be transformed to selection systems, the small, insignificant difference in biomass carbon pools of these two silvicultural systems (about 20 tC ha-1) may affect forest carbon pools at the regional scale.

However, it is assumed that the observed carbon pools in living tree biomass of 176 tC ha-1 represent the upper limit in selection forests and that a reduction to 117-140 tC ha-1 can be expected in the future. This assumption is based on the following findings: Gerold (2002) reported an annual increment of the permanent study plots Lang-I and Lang-II of 10.3 and 11.0 m3 ha-1 year-1, respectively. The annual timber harvest during the time period from 1956-1996 was on average 7.5 and 7.1 m3 ha-1 year-1. Consequently, there was a net accumulation of timber volume of about 100 and 160 m3 ha-1, respectively (~ 39-62 tC ha-1 in total tree biomass), over the last 40 years. The consequences of the resulting high biomass stocks for the structure and sustainability of selection forests (“over-aging of stands”) are intensively discussed by forest scientists (Schütz 2001a, Gerold 2002) as well as by local foresters and the landowners of the selection forests at the Hainich (Biehl pers. comm., Fritzlar pers. comm.). Gerold and Biehl (1992) recommended for beech stands on fertile sites (dominant tree height 32-38 m) a timber volume of 300-360 m3 ha-1 (~ 117-140 tC ha-1 in total tree biomass). For less fertile soils they recommended a timber volume of 240-300 m3 ha-1 (~ 94-117 tC ha-1, dominant tree height 26-32 m) and 180-240 m3 ha-1 (~70-94 tC ha-1, dominant tree height below 26 m). Other authors suggested even lower average timber stocks for selection systems (e.g. 280-330 m3 ha-1, Matthes 1910; 200-250 m3 ha-1, Schilling 1949 in Mayer 1992; 200-300 m3 ha-1, Landbeck 1952 in Mayer 1992; 220 m3 ha-1, Schütz 2001a).

A general increase of biomass carbon pools due to a longer rotation period or higher stand densities seems to be unlikely and not reasonable. The stands already have a very high biomass and a further increase of biomass may conflict with the economic objective to produce high amounts of valuable timber. The same conclusion was drawn with respect to the entire German

156 8 Discussion forestry by the WBGU (2003). The release of dead wood and harvest residues in managed forests may be a more reasonable option to increase carbon pools in managed forests (section 8.2.2).

With respect to the future carbon sink capacity of the Hainich NP the most important question is, if the large carbon pools will sustain or even increase in the long-run. A large scaled disintegration of living tree biomass in the Hainich NP, as it could be assumed from former ideas (see Leibundgut 1982, Ellenberg 1996), is very unlikely. Korpeľ (1995) showed a temporal and spatial overlap of the disintegration and the regeneration phase in Slovakian primary beech forests, resulting in a stand structure that is characterised by a small-scale mosaic of tree patches at different successional phases. Meyer et al. (2003) confirmed this result by studies in primary beech stands in Albania. Gaps were formed mainly by the death of single trees. The size of canopy gaps in these forests only varied between 60.6 and 74.1 m2. The sum of gaps ranged between 3.3 and 6.6% of total plot area. Results of a recent study on gap dynamics in the Hainich NP correspond with these results from “true” primary forests (study plot of 28 ha, close to the study plot Hai-III and the footprint of the Eddy-Tower; Manning 2003). Canopy gaps in the Hainich NP had on average a size of 144 m2 (in 2002) and the sum of gaps per hectare reached 3.5% of total plot area. Large scaled natural damages by windthrows and snow are untypical for the entire Hainich-Dün region.

The most drastic change that may happen in the Hainich NP in the nearest future is the reduction of non-beech tree species in favour of beech trees. This succession seems to be very likely because of the lack of non-beech species in lower diameter classes (chapter 4) and the predominance of small gaps that may not provide enough light for the regrowth of less shade tolerant tree species (Manning 2003). However, besides the dominance of small gaps, single large gaps can also be found in European primary forests (Tabaku and Meyer 1999, Drößler and von Lüpke in press.), so that at least a small proportion of non-beech trees can co-exist in primary beech forests. A cyclic successional replacement between Fagus sylvatica and Fraxinus excelsior is proposed by Emborg et al. (2000) for a mixed deciduous forest in Denmark (Suserup Skov). They assume that Fraxinus established before Fagus in gaps, and then Fagus takes over canopy control, either due to a shorter lifetime of Fraxinus (~130 years) compared to Fagus (~ 250 years) or due to the ability of Fagus to grow through the canopy of Fraxinus.

Potential climatic changes are another aspect of future stand development. Fagus sylvatica naturally dominates at lower and medium altitudes and on not too dry and too wet soils (anaerobic conditions) in central Europe (Puhe and Ulrich 2001). However, if the climate is

157 8 Discussion getting warmer and/or dryer Fraxinus excelsior, Acer spec., Tilia spec, Carpinus betulus and other tree species may become more competitive (Puhe and Ulrich 2001).

8.2 Soil-specific effects and silvicultural effects on SOC pools of forests

The following section focuses on soil-specific and silvicultural effects on SOC pools, which are directly linked to the hypotheses of this study (chapter 1).

8.2.1 Soil-specific effects on SOC pools and their interactions with former forest use

All sites of the present study were located within the relatively small forested region “Hainich-Dün” (about 2.5 km2) characterised by similar climatic and edaphic conditions. Based on these facts it was assumed that differences in carbon pools between the differently managed forest sites were not affected by different site-specific characteristics. The edaphic conditions and growing conditions for beech forests were indeed very similar at a regional scale. What did vary were soil-specific properties influencing carbon stabilization and accumulation at the local scale. The large small-scale variability of soil properties reduced the strength of the statistical analysis, but it provided some hints about potential processes involved in the carbon accumulation of forest soils.

It is well known that many factors and processes are involved in the decomposition of dead organic matter, its transformation to less as well as to more stabilized organic compounds, and the stabilization of a portion of the organic matter in the mineral soil. However, it is still unclear to which extent the individual processes are responsible for the total amount of accumulated SOC and how the relative importance of these processes will change with changing environmental conditions or different land-use types (Christensen 1992, Sollins et al. 1996, Six et al. 2002).

The most important mechanisms for soil organic matter (SOM) stabilization and storage in the mineral soil can be summarized briefly as in the following (e.g. Christensen 1992; Sollins et al. 1996; Christensen 2001, Gleixner et al. 2001, Six et al. 2002, Kaiser and Guggenberger 2003):

• Physical stabilization (including physical and chemical sorption)

• due to micro-aggregates that reduce the accessibility of SOM

• due to adsorption to clay and silt surfaces (organomineral complexes)

• due to adsorption to sesquioxides

158 8 Discussion

• Biochemical stabilization

• as an inherent property of the plant material (intrinsic chemical recalcitrance)

• due to condensation and complexation of decomposition residuals during the decomposition process

• Biological stabilization within living biomass in the soil due to re-synthesis of molecules and microbial biomass from already decomposed molecules.

It is assumed that the physical stabilization within micro-aggregates and the adsorption to and in between clay particles is the quantitatively most relevant process leading to the observed SOC pools in the Hainich-Dün region. This assumption is confirmed by the highly significant correlation between the proportion of clay (or its predictor “residual water content”) and SOC pools. Furthermore, calcareous soils like those of the study sites are characterized by the clay mineral Smectite, which has a high capacity to adsorb organic carbon (Schachtschabel et al. 1992, BMELF 1997).

The soil texture is closely related to many soil properties such as soil acidity, exchangeable cations, water and nitrogen availability. For example, a lower nitrogen availability in the mineral soil of the study plot Lei-111 (relatively low clay content), compared to the tower site of the Hainich NP (high clay content) was indicated by a higher C:N ratio (chapter 6), lower inorganic nitrogen concentrations of the mineral soil (Persson 2003) and lower nitrogen concentrations in fine roots (Claus 2003). The soil characteristics influence the biological activity in soils. A high biological activity, in particular of larger soil fauna, is associated with higher litter decomposition rates (e.g. Scheu and Wolters 1991, Beck 1993, Cornelissen 1996, Wardle and Lavelle 1997, Wachendorf et al. 1997, Tiunov and Scheu 2000, Bradford et al. 2002, Berg and McClaughtery 2003). The stimulation of litter decomposition by larger soil fauna is mainly an indirect effect. Earthworms, for example, respire and use for biomass production less than 5% of ingested litter substrate (Schaefer 1990), but they transport litter from relatively dry aboveground layers to the deeper, relatively moist soil layers, which provide more favourable conditions for bacteria and fungi. Furthermore, microbial decomposition of organic matter in the egested faeces is stimulated during gut passage (Schaefer 1991a). Only about 11% of heterotrophic respiration is related to litter consumption by soil animals. However, at the same time many soil animals (e.g. collembola, earthworms, ants) promote soil aggregation by forming faecal pellets and excreting binding agents. This process transforms the organic matter into chemically more recalcitrant forms and improves the association with clay particles. Fungal hyphae support the

159 8 Discussion formation of larger aggregates by binding solid grains and aggregates together and bacteria attach to soil particles and thus form bridges between particles. Both microbial groups release diverse polysaccharides that serve as binding agents (Zech and Kögel-Knabner 1994, Sollins et al. 1996).

It may be possible that some soil parameters are also affected by forest management and former land use. This means, for example, that the low N availability and low pH value at the study plot Lei-111M may have been affected by the collection of nearly all branches and twigs for fire wood in the past (chapter 3). Twigs and small branches contain a relatively high proportion of cations and nitrogen (Jacobsen et al. 2003), so that their export seriously influences the cation and nitrogen balance of forest soils. Forest grazing that was very common at the entire Hainich-Dün region about 150 years ago (chapter 3) was associated with an export of nitrogen and cations from large areas and an accumulation of these elements at a few, small locations (transport of faeces). At the more calcareous soils of the Hainich NP or at the study plot Lang-I (Appendix Figure A.1) the export of biomass was likely of minor importance. However, considering the fact that the soils of the study sites are generally very fertile and productive soils, it can be assumed that they have a higher resilience to disturbances than most other forested soils in Germany (BMELF 1997). In contrast to many other forested regions in Germany, litter raking was of minor relevance at the study sites (chapter 3). Deforestation and cropland use have a large impact on soils, and it is likely that such a land use change in the past would influence current soil processes and properties. For certain study plots at “Leinefelde”" (Lei-111M and Lei-141M) an agricultural use before the 16th century may be possible since they were in the vicinity of a monastery (“Reifenstein”), and because they have relatively silty soils and a moderate climate that offer quite suitable conditions for cropping (chapter 3).

An agricultural use of the study plot Lei-111M could have caused a strong depletion of carbon that has not refilled yet completely (Bonde et al. 1992, Balesdent et al. 1998). This mechanism would explain the weak relationship between SOC pools and the clay content (or its predictor the residual water content) (chapter 6). This interpretation is not valid, however, for the situation at the plot Lang-II, which also showed extraordinarily low SOC pools in relation to the clay content (chapter 6) and also a weak correlation between SOC and clay. This stand was used in the past very intensively, including timber harvest, fire wood sampling and forest pasture (Chapter 3), but an agricultural use is very unlikely (hardly accessible). It seems to be more likely that the combination of relatively low amounts of exchangeable cations (in particular Ca2+), low pH

160 8 Discussion values, high C:N ratios and maybe a different composition of clay minerals (more Illite instead of Smectite) have caused the low SOC pools and the weak relationship between SOC pools and the clay content. These soil properties may also be responsible for the lack of a significant relationship between SOC pools and the clay content reported for many other forest soils (e.g. spruce forests on acid soils on granite in Thuringia (Wirth et al. 2003), beech forests on coloured sandstone partly covered with loess in the Eichsfeld (Gerighausen 2002), beech forests on sandy soils in Poland and France (Schaaf 2003), spruce forests on (sandy-) loamy soils on granite at the Fichtelgebirge (Mund et al. in prep. a).

In conclusion, the clay content had a “key function” for SOC accumulation at the study sites, and most of the complex interactions between chemical soil properties, biological processes and carbon accumulation were associated with the clay content. This implies: when the “clay-effect” on SOC pools was excluded, as it was done in this study (chapter 6), also the effects that are associated with the clay content are excluded. The influence of soil properties and processes that are not associated with the clay content may be reflected by the significant effect of the C:N ratio on SOC pools (chapter 6). Hence, the observed trend to higher “corrected” SOC0-15 pools in the unmanaged forest represents a “true” management-effect. However, 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 this study did not reflect the potential for carbons storage in the mineral soil due to a cessation of regular timber use.

161 8 Discussion

8.2.2 How are SOC pools linked to silvicultural activities?

In contrast to the hypothesis (chapter 1), SOC pools were not related to total litter fall, basal area or stand age. Only the amount of non-beech leaf litter fall showed a weak influence on

SOC0-15 pools, when the effects of the clay content and the C:N ratio of the soil were excluded.

The lack of a clear relationship between SOC pools and litter fall may be influenced by the fact that the litter fall is only a part of total litter production. However, at least the fine root litter production is similar (Scarascia-Mugnozza et al. 2000, Claus 2003) or even smaller than leaf litter production (Wu 2000). Thus, it seems to be unlikely that the inclusion of root litter production would lead to a significant relationship between current litter production and SOC pools.

The close relationship of the organic layer to aboveground parameters (chapter 5) corresponds with the fact that carbon pools in the organic layer are much more susceptible to disturbances than SOC pools (Mattson and Smith 1993, Black and Harden 1995, Vesterdal et al. 1995, BMELF 1997, Meiwes et al. 2002, Prescott 2002, Thuille 2003). The lack of a significant relationship between the organic layer (or the MRT of litter) and soil-specific properties, including SOC pools, leads to the conclusion that the organic layer belongs eco-physiologically more to the aboveground compartments than to the mineral soil. This conclusion is confirmed by recent experimental studies, which show that the decomposition of nutrient rich litter is more or less independent from soil-specific conditions (Flessa et al. 2002, Horváth and Beese, University of Göttingen, pers. comm.).

The lack of a “stand age-effect” at the even-aged stands may be related to the fact that the disturbances due to canopy opening for regeneration (shelterwood-cuttings) did not last long enough and were too small to induce significant changes in SOC pools. There are many studies that showed substantial effects of partial cuttings on air and soil temperature (Mitscherlich 1981, Liechty et al. 1992, Bauhus and Bartsch 1995, Brumme 1995, Chen et al. 1995, Reynolds et al. 1997, Fleming et al. 1998, Barg and Edmonds 1999, Gray et al. 2002, Laporte et al. 2003) and on mineralization rates and cation losses (Clayton and Kennedy 1985, Bauhus 1994, Vesterdal et al. 1995, Messina et al. 1997, Bradley et al. 2001, Prescott 2002). However, most of these studies did not consider the duration of these changes. The fast growing regeneration under the shelter of the remaining old trees covers the forest soil within a few years after canopy opening, and thus reduces the time span of potential carbon losses due to tree harvesting. Furthermore, it is possible that SOC losses due to increased decomposition after thinning and harvesting are

162 8 Discussion balanced by a translocation of carbon from decomposing branches, twigs and pieces of broken timber (harvest residues) to the mineral soil (Bormann and Likens 1979, Mattson et al. 1987, Huntington and Ryan 1990, Mattson and Smith 1993, Johnson 1995, Johnson et al. 1995, Olsson et al. 1996, Dai et al. 2001). The role of decomposing woody residues was also indicated by 14C measurements of respired CO2 in the mineral soil (Hahn 2003). Significant “stand age-effects” on SOC pools were mainly reported for forest stands that developed after much larger disturbance than those induced by shelterwood cuttings (afforestation of grasslands or croplands, e.g. Thuille et al. 2000, Turner and Lambert 2000, Berthold and Beese 2002, Guo and Gifford 2002, Paul et al. 2002, Vesterdal et al. 2002; forest stands regrowing after stand replacing fires, e.g. Bhatti et al. 2002, Wirth et al. 2002, and forest stands regrowing after clear-cutting, e.g. Bormann and Likens 1979, Covington 1981, Heinsdorf 1986, Black and Harden 1995).

The effect of non-beech leaf litter on SOC pools, observed in this study may be associated with the higher litter quality of ash, maple and most of the other non-beech tree species that grow at the study sites (Wittich 1961, Cornelissen 1996, Scott and Binkley 1997, Wardle and Lavelle 1997, Neirynck et al. 2000, Berg and McClaugherty 2003). The non-beech leaf litter is preferably transported into the mineral soil and eaten by the soil fauna (chapter 5), which in turn may lead to a higher proportion of stabilized organic matter from non-beech leaves compared to organic matter from beech leaves (see section 8.2.1).

However, the effect of non-beech leaf litter on SOC pools was dominated by the study plots

Hai-II and Hai-III. The plot Hai-I had “corrected” SOC0-15 pools similar to the other two plots of the Hainich NP, but it had a much lower input of non-beech leaf litter (chapter 6). Thus, it is assumed that the regression analysis, which indicated a significant influence of non-beech leaf litter fall on SOC0-15 pools, was biased and that the differences in "corrected" SOC0-15 pools are related to the cessation of regular timber use about 35 years ago. This assumption is supported by a recent literature review about the interactions of forest biodiversity, silviculture and SOC balance (Mund and Schulze 2004). Most effects of tree species on biogeochemical cycles of forest soils reported in the literature are related to cations and the nitrogen cycle and to leaf litter decomposition in the organic layer, but not to SOC pools. In many studies an effect of the "vegetation type" rather than of different species was observed.

When SOC pools are not or only weakly linked to stand characteristics, the question remains:

Which are the processes or mechanisms that could have caused higher “corrected” SOC0-15 at the unmanaged forest? It is hypothesized that the trend to higher SOC0-15 pools at the unmanaged

163 8 Discussion forest is the result of (1) differences in the microclimate and the disturbance regime and (2) higher carbon inputs to the mineral soil from decaying dead wood compared to the managed stands. The effects of these two factors cumulate over longer time periods.

The permanently dense canopy of the unmanaged forest, which is opened only for a short period of time when single senescent trees break down, may provide a stable and humid microclimate that in turn may promote the incorporation and stabilization of organic matter by the soil fauna. This hypothesis corresponds with the relationship between carbon pools in the organic layer and the basal area and with the high activity of larger soil fauna at the Hainich NP (chapter 5).

In the unmanaged forest large dead wood is an additional carbon pool that was not available or at least only in very small amounts since man has colonized the Hainich-Dün region. Additionally, during the GDR time foresters had to take care that, after harvesting, all pieces of wood were collected from the forest floor (“Saubere Waldwirtschaft”), while at the same time most of the dead wood decomposed on site in the unmanaged forest. The removal of some dead wood after a strong storm in the late 1980s at the Hainich NP (chapter 3), could not reduce this additional carbon source at the unmanaged forest substantially.

There are many studies about decomposition rates of wood, the chemical processes taking place during fungal decay of wood, or about the role of dead wood as a source or reserve for cations and nitrogen (e.g. Swift et al. 1972, Harmon et al. 1986, Rayner and Boddy 1988, Arthur et al. 1993, Stewart and Burrows 1994, Hammel 1997, Duvall and Grigal 1999, Clinton et al. 2002, Krankina et al. 2002, Mackensen et al. 2003, Müller-Using and Bartsch 2003). In contrast, investigations about the contribution of dead wood, probably in chemically altered form, to long- term carbon storage of the mineral soil are rare. Some authors assumed that wood is a poor source for SOC only (Rayner and Boddy 1988), and a long-term storage of dead wood in the organic layer, as it was reported for coniferous forests ("soil wood", McFee and Stone 1966, Harvey et al. 1981), is very likely of minor importance in temperate hardwood forests on fertile soils without a mor or row humus layer. Nevertheless, Busse (1994) found significantly larger microbial biomass in the surface mineral soil (0-4 cm) beneath decaying wood in a Pinus contorta forest. Mattson et al. (1987) reported that 39% of total carbon loss during woody debris decomposition within 6 years after clear cutting of a mixed hardwood forests was redistributed to the mineral soil (9.3% via fragmented bark, 18.5% via fragmented FWD, 5.6% via solution; in total 10 tC ha-1). In central Amazonian forests it was estimated that 76% of carbon from dead

164 8 Discussion wood was lost to the atmosphere and that 24% was translocated to the soil (Chambers et al. 2001). Assuming a mean dead wood litter production at the Hainich NP that equals the annual wood increment of a primary beech forest (about 2.6 tC ha-1 year-1, Korpeľ 1995), a contribution of 30% of carbon from decaying dead wood to the mineral soil would result in an additional carbon input to the mineral soil of about 0.8 tC ha-1 year-1. How much of this carbon will remain in the mineral soil over longer time periods (more than decades) is an open question.

8.3 Estimates of net carbon fluxes by different methodological approaches

In combination with the results of other projects working at “Leinefelde” and the “Hainich NP”, a first estimate of mean annual carbon fluxes can be made. The approach of different levels of productivity is adopted from Schulze et al. (2000) (Figure 8.2). The NEP (net ecosystem productivity) is defined as the difference between carbon assimilation (NPP, net primary productivity) and all heterotrophic respiratory processes. The NBP (net biome productivity) equals the NEP minus the export of carbon due to “non-respiratory” processes such as fire and tree harvesting.

Autotrophic Heterotrophic Carbon losses due to respiration respiration tree harvest or fire

GPP NPP NEP NBP

~ Photosynthesis ~Forest growth ~ Accumulation of living ~Accumulation of dead organic and dead tree biomass matter in the mineral soil over and of dead organic matter long time periods (several on and in the mineral soil rotations or tree generations) Figure 8.2: Scheme of the different levels of productivity in forest ecosystems (simplified after Schulze et al. 2000). GPP: Gross primary productivity. NPP: Net primary productivity. NEP: Net ecosystem productivity. NBP: Net biome productivity.

In the following, the NEP was estimated by the difference between total tree NPP (Bascietto 2003, Claus 2003) and heterotrophic respiration rates, resulting from incubation studies of the organic layer and the mineral soil (0-30 cm) (Persson 2003). The net ecosystem exchange (NEE) measured by the “eddy towers” (eddy covariance technique) was assumed to equal the NEP.

The NBP was approximated in three different ways: (1) The NBP resulted from the difference between NEP and biomass removal due to tree harvesting. (2) The NBP was the difference between carbon inputs to the soil and carbon outputs via heterotrophic respiration (organic layer

165 8 Discussion and mineral soil (0-30 cm), Persson 2003) and DOC efflux (Paces 2003). (3) The NBP was derived from changes of carbon pools over time in the organic layer and mineral soil. The results of these estimates for NEP and NBP are given in Table 8.2.

Table 8.2: Estimates of carbon fluxes of differently managed beech forests. (1) Bascietto 2003, FORCAST, (2) Claus 2003, FORCAST, (3) Korpeľ 1995 (4) Persson 2003, FORCAST (heterotrophic respiration including the organic layer and the mineral soil (0-30 cm), (5) Anthoni et al. (submitted), (6) Knohl et al. 2003, (7) Paces 2003, FORCAST.

A) NEP estimates (tC ha-1 year-1)

Shelterwood system Unmanaged forest Approaches Lei-111 "Leinefelde" "Hanich NP"

8.95 Net C assimilation 7.59 6.02 -9.74 = NPP stem and branches = 6.32(1) + = 4.31(1) + 1.39(chap. 5) = 2.60(3) (to 6.32(1)) + NPP leaves + NPP fine 1.41(chap. 5) + + 1.89(2) + 1.64(chap. 5) + 1.78(3) roots 1.22(2) Heterotrophic respiration -3.09(4) -2.95(4) -3.12(4) (organic layer + mineral soil, 0-30 cm) NEP = C assimilation - 5.86 4.64 2.90 - 6.62 heterotrophic respiration NEE (Eddy covariance 4.71-5.41(5) 4.55-4.84(6) technique)

B) NBP estimates (tC ha-1 year-1)

Shelterwood system Unmanaged forest Approaches Lei-111 "Leinefelde" "Hanich NP"

1. approach: NEP - tree -0.46 0.33 3.12 - 6.62 harvest (~NPP stem and branches) 2. approach: litter fall 0.04 0.69 0.76 (excluding fruits and buds) (chap. 5) (chap. 5) (2) (chap. 5) (3) = 1.91 + = 1.75 + 1.89 = 2.10 + 1.78 + fine root litter input - (2) (4) (4) (7) (4) (7) 1.22 -3.09 - -2.95 - 0 -3.12 - 0 heterotrophic respiration - (7) 0 DOC 3. approach: changes of C 0.02 ~0 0.17 pools in the organic layer (chap. 5) (chap. 5) (chap. = 0.02 + 0 = 0 + 0.17 and mineral soil (0-15 cm) (chap. 6) 6)

166 8 Discussion

A major constraint for the NEP estimate of the “Hainich NP” was the lack of available NPP data (destructive methods are not allowed at the Nationalpark and stem-growth measurements are in process). Thus, the NPP for the Hainich NP was based on two assumptions, which represent the “minimum” and the “maximum” reasonable estimate for forest growth. The stem and branch NPP of 2.6 tC ha-1 year-1 was derived from primary beech forests in Slovakia, growing under similar climatic conditions (Appendix Table A.7) as the beech forests of the Hainich-Dün (Korpeľ 1995). The timber volume increment (wood ≥ 7 cm in diameter) reported by Korpeľ 1995 was converted to the NPP of stems, branches and twigs assuming a basic wood density of 558 kg m-3, a carbon concentration of 50% of total dry weight, and a proportion of timber volume to stem, branch and twig volume of 85% (Table 4.7). The “maximum” value of 6.3 tC ha-1 year-1 equals the NPP of the 111-year-old stand in Leinefelde. The mean annual increment (MAI) of the unmanaged forest, estimated by the ratio of the stem and branch biomass (198 tC ha-1, Table 4.7) and the estimated average tree age of the stands (59 years, Table 3.1), was about 3.4 tC ha-1 year-1.

The NEP for the 111-year-old stand in “Leinefelde” of 5.86 tC ha-1 year-1 (Table 8.2A) seems to be very reliable compared to the results of the tower based NEE measurements of 4.7-5.4 tC ha-1 year-1 (Anthoni et al. submitted) and other comparable tower sites in Europe (Valentini et al. 1996, Granier et al. 2002). The large range of NPP estimates for the unmanaged forest “Hainich NP” caused a large range of NEP estimates between 3.1 and 6.6 tC ha-1 year-1. However, the mean NEP of 4.8 tC ha-1 year-1 for the unmanaged forest was similar to the NEE (4.6-4.8 tC ha-1 year-1, Knohl et al. 2003).

The NBP estimates for the regular shelterwood system over several rotations varied between 0 and 0.69 tC ha-1 year-1 (Table 8.2B). If only the 111-year old stand was considered, the first approach (NEP minus tree harvest) resulted in a net loss of carbon from the ecosystem, while the other approaches led to an NBP of 0 to 0.02 tC ha-1 year-1. For the unmanaged forest a NBP between 0.17 and 6.6 tC ha-1 year-1 was estimated. The high NBP estimate of 6.6 tC ha-1 year-1 reflects an increase of tree biomass at the Hainich NP, which may continue until the forest biomass will reach a “steady state”. In the long run it seems to be likely that the NBP is determined only by carbon accumulation in the mineral soil, which may vary between 0.17 and 0.76 tC ha-1 year-1 (Table 8.2B). The lower estimate of carbon fluxes to and from the mineral soil is similar to that one reported for the “Göttinger Wald” (carbon input: 2.9 tC ha-1 year-1, carbon output: 2.7 tC ha-1 year-1, balance: 0.2 tC ha-1 year-1, Brumme 1986).

167 8 Discussion

The “flux based” NBP´s (approach 1 and 2, Table 8.2B) are higher than the estimates that were based on changes in SOC0-15 pools along the chronosequences or of differently managed forests (approach 3, Table 8.2B). It may be possible that the “flux based” NBP estimates are overestimated due to an underestimation of the heterotrophic respiration rates, which may not represent the high decomposition rates of fresh non-beech leaf litter in autumn (Knohl et al. 2003; sampling for soil incubation in June 2000). If the cumulative “flux based” NBP estimates are compared with carbon pools, they appear to be quite high. For example, with an NBP of 0.76 tC ha-1 year-1 at the Hainich NP current SOC pools at the upper 30 cm (soil depth that was incubated) of about 85 tC ha-1 would be exceeded after about 112 years. However, it is possible that a large portion of carbon is transported to deeper soil horizons (> 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.):

2 S 2 (Equation 8.1) n = *(t + t ) MDD2 1−α ,υ 1−β ,υ 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 Kyoto- Protocol, 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 Unmanaged forest at in the currently the "Hanich NP" managed forests

1. Maximum carbon pools in biomass (living trees and aboveground dead wood) 283 (224- 328) (1) 283 (224- 328) (1) of primary beech forests (tC ha-1) 2. Current carbon pools in biomass (living trees and aboveground dead wood) 163 (150-220) 252 (226-288) of the studied forests (tC ha-1) 3. Potential for carbon accumulation in 120 31 biomass (=1-2) (tC ha-1) 4. NEP (tC ha-1year-1) (Table 8.2) 4.6 4.9 5. Duration of net biomass accumulation 26 6.3 (years) (= 3:4) 6. Net accumulation of SOC following the cessation of regular timber use 0.17 0.17 (tC ha-1year-1) (chapter 6)

Total induced carbon sink after 26 or 6 122.4 31.7 years, respectively (tC ha-1) (=3+5*6)

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 management- related 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 -1 -1 48% of total SOC pools. SOC0-15 pools in “Leinefelde” (36 tC ha ) and in “Langula” (39 tC ha ) -1 were significantly lower than the SOC0-15 pools in “Mühlhausen” (42 tC ha ). The highest -1 SOC0-15 pools were found in the “Hainich NP” with 53 tC ha .

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 -1 “Langula” “corrected” SOC0-15 pools of about 42 tC ha were predicted. For the “Hainich NP” -1 “corrected” SOC0-15 pools of 48 tC ha 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 -1 -1 36 t C ha bzw. 39 t C ha 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 -1 Tongehalt und C:N-Verhältnis „korrigierte“ Boden0-15-C-Vorräte von 42 t C ha . Im „NP -1 Hainich“ wurden „korrigierte“ Boden0-15-C-Vorräte von 48 t C ha 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 Nicht-

Buchenblättern 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 Kohlenstoff- Flussmessungen und mit wiederholten Messungen der Boden-C-Vorräte am selben Standort kombiniert werden, um die verschiedenen zeitlichen Skalen der Kohlenstoffdynamik im Boden zu erfassen.

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198 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

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

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 Tree no. dbh (m) Tree height (m) g*h (m3) Age (years) Fagus sylvatica 13/4 0.729 40.1 16.737 196 Fagus sylvatica 21/1 0.670 35 12.321 180 Fagus sylvatica 25/6 0.434 33.6 4.971 114 Fagus sylvatica 28/2 0.383 31.7 3.643 120 Fagus sylvatica 30/2 0.608 34.3 9.942 186 Fagus sylvatica 34/3 0.591 34 9.311 131 Fagus sylvatica 37/5 0.371 31.2 3.364 104 Acer platanoides 38/2 0.438 32.4 4.871 118 Fagus sylvatica 50/1 0.766 32.4 14.931 180 Fagus sylvatica 63/3 0.456 34.6 5.638 116 Fagus sylvatica 70/6 0.520 30 6.371 121 Fagus sylvatica 73/5 0.319 30.4 2.422 116 Fraxinus excelsior 75/6 0.430 33.2 4.810 120 Fagus sylvatica 77/5 0.363 30.2 3.125 126 Fagus sylvatica 81/1 0.632 35.6 11.150 150 Fagus sylvatica 84/3 0.794 35.3 17.457 195 Fagus sylvatica 87/2 0.805 35.2 17.893 181 Fraxinus excelsior 98/2 0.358 27.7 2.788 118 Fagus sylvatica 99/4 0.631 33.2 10.382 118

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 dbh (m) Tree height (m) g*h (m3) Age (years)

Lei-153M, underwood 0.040 5.5 0.00004 5 (estimate) Lei-153M, underwood 0.014 2.2 0.00031 10 (estimate) Lei-153M, underwood 0.020 4.5 0.00141 15 (estimate) Lei-153M, underwood 0.006 1.7 0.00691 20 (estimate) Mühl-171, underwood 0.011 1.5 0.00013 5 (estimate) Mühl-171, underwood 0.023 3.0 0.00125 10 (estimate) Lei-30M 0.108 12.3 0.11234 30 (stand age) Lei-62M 0.249 24.2 1.18192 62 (stand age) Mühl-38 0.106 11.5 0.10245 38 (stand age) Mühl-55 0.154 19.0 0.35603 55 (stand age) Mühl-85 0.240 23.0 1.03976 85 (stand age) Mühl-102 0.275 27.3 1.62845 102 (stand age) Lang-I; 58/2 0.121 18.6 0.21388 72 (harvested tree) Lang-I, 93/3 0.132 19.2 0.26076 55 (harvested tree)

206 C) ANOVA table for trees with g*h > 2 m3.

Model: Tree age = a + b*(g*h) R2=0.790, P<0.001

Coefficients SE P a 94.690 6.832 <0.001 b 5.483 0.685 <0.001

D) ANOVA table for trees with g*h ≤ 2 m3.

Model: Tree age = a * x^b R2=0.962, P<0.001

Coefficients SE P a 76.669 1.085 <0.001 b 0.277 0.016 <0.001

Table A.3: Allometric functions and coefficients for tree biomass of beech trees (Fagus sylvatica) given by Wirth et al. (2003). dbh: diameter at breast height (cm); h: tree height (m)

2 2 Model: biomass = ß0+ ß1*ln dbh+ ß2* (ln dbh) + ß3* ln h + ß4*(ln h) + ß5*ln age

ß0 ß1 ß2 ß3 ß4 ß5 Stem -3,47197 1,90119 0,98218 Branches and twigs -0,92263 2,68122 0,09993 -1,91638 0,14018 Coarse roots -3,88751 2,51218 Leaves 1,05970 2,06365 0,06577 -2,52730 0,31601 -0,46808

207 Table A.4: Dead wood of the study plots.

A) Carbon pools in CWD (coarse woody debris) of the transect lines. Study plot Line tC ha-1 Hai-I 1 0.275 Hai-I 2 1.472 Hai-I 3 1.859 Hai-I 4 1.782 Hai-I 5 1.126 Hai-I 6 0.890 Hai-II 1 5.018 Hai-II 2 3.425 Hai-II 3 0.830 Hai-II 4 1.624 Hai-II 5 0.713 Hai-II 6 1.368 Hai-III 1 1.150 Hai-III 2 0.871 Hai-III 3 1.115 Hai-III 4 0.589 Hai-III 5 0.604 Hai-III 6 2.001 Lang-I 1 1.426 Lang-I 2 0.132 Lang-I 3 0.779 Lang-I 4 0.860 Lang-I 5 2.649 Lang-I 6 0.475 Lang-II 1 0.775 Lang-II 2 0.643 Lang-II 3 1.401 Lang-II 4 1.389 Lang-II 5 0.624 Lang-II 6 0.650

B) Average decay classes per study plot (weighted by the volume of the wooden pieces). SD: standard deviation

Decay class SD CWD Hai-I 3.0 1.0 Hai-II 3.0 0.8 Hai-III 3.2 0.7 Lang-I 2.4 0.7 Lang-II 2.6 0.7 Snags and logs Hai-I 2.9 0.9 Hai-II 2.7 0.7 Hai-III 3.6 0.6 Lang-I 2.1 0.4 Lang-II 2 -

208 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). SE: standard error, n.d.: not determined

k (year-1) ± SE Study Plot Beech Ash and maple Lei-30M 0.345 ± 0.032 Lei-62M 0.339 ± 0.028 Lei-111M 0.348 ± 0.020 Lei-141M 0.393 ± 0.055 Lei-153+16M 0.441 ± 0.033 Lei-30 (B. Zeller pers. comm.) 0.403 ± 0.032 Lei-62 (B. Zeller pers. comm.) 0.404 ± 0.041 Lei-111 (B. Zeller pers. comm.) 0.589 ± 0.044 Lei-141 (B. Zeller pers. comm.) n.d. Lei-153+16 (B. Zeller pers. comm.) 0.348 ± 0.029 Mühl-38 0.404 ± 0.035 Mühl-55 0.403 ± 0.043 Mühl-85 0.414 ± 0.026 Mühl-102 0.431 ± 0.057 Mühl-171+10 0.532 ± 0.113 Lang-I 0.400 ± 0.064 Lang-II 0.354 ± 0.027 Lang-III 0.404 ± 0.045 Hai-I 0.299 ± 0.039 Hai-II 0.436 ± 0.087 1.610 ± 0.226 Hai-III 0.342 ± 0.054 2.046 ± 0.240 Hai-T (Cotrufo 2003) 0.266 ± 0.014

209 Table A6: SOC pools of the upper mineral soil. SD: standard deviation, CV: coefficient of variation.

A) Soil depth 0-5 cm and 5-10 cm.

0-5 cm 5-10 cm Study plot Average SD CV (%) Average SD CV (%) (tC ha-1) Lei-30M 21.13 3.15 14.92 14.24 3.99 27.99 Lei-62M 16.57 3.85 23.23 12.68 4.74 37.35 Lei-111M 14.73 2.98 20.23 8.65 1.16 13.42 Lei-141M 13.68 4.06 29.69 9.64 2.45 25.44 Lei-153+16M 19.14 3.05 15.95 14.21 3.44 24.19 Mühl-38 16.08 3.84 23.9 10.79 2.45 22.75 Mühl-55 19.83 4.13 20.8 16.7 3.7 22.14 Mühl-85 17.99 4.79 26.62 13.76 2.77 20.11 Mühl-102 17.37 3.85 22.14 12.96 3.3 25.47 Mühl-171+10 17.53 4.08 23.29 12.92 2.86 22.13 Lang-I 18.77 3.16 16.82 14.82 3.08 20.77 Lang-II 15.89 3.52 22.15 11.91 2.72 22.82 Lang-III 15.21 2.41 15.85 11.01 2.95 26.84 Hai-I 22.26 2.66 11.93 16.07 2.9 18.02 Hai-II 23.02 4.63 20.14 15.2 3.45 22.72 Hai-III 23.3 3.47 14.88 17.34 4.43 25.58

B) Soil depth 10-15 cm.

10-15 cm Study plot Average SD CV (%) (tC ha-1) Lei-30M 10.68 3.9 36.46 Lei-62M 10.19 3.14 30.82 Lei-111M 6.64 1.02 15.36 Lei-141M 8.52 3.12 36.67 Lei-153+16M 12.1 3.78 31.22 Mühl-38 9.99 3.15 31.56 Mühl-55 14.18 2.55 17.98 Mühl-85 11.02 2.23 20.28 Mühl-102 11.1 3.03 27.3 Mühl-171+10 10.05 2.58 25.68 Lang-I 12.11 2.32 19.13 Lang-II 9.41 3.34 35.49 Lang-III 9.03 2.57 28.5 Hai-I 13.05 1.92 14.71 Hai-II 12.97 2.1 16.17 Hai-III 14.27 2.81 19.67

210 Table A.7: Carbon pools in living tree biomass and leaf biomass of differently managed temperate hardwood forests. The data are presented graphically in Figure 8.1. Dominant tree species: 1 Abies alba, 2 Acer platanoides, 3 Acer pseudoplatanus, 4 Acer rubrum, 5 Acer saccharum, 6 Betula alba, 7 Betula lutea, 8 Betula papyrifera, 9 Carpinus betulus, 10 Fagus crenata, 11 Fagus grandifolia, 12 Fagus sylvatica, 13 Fraxinus excelsior, 14 Nothofagus fusca, 15 Nothofagus menziesii, 16 Nothofagus pumilio, 17 Nothofagus solandri, 18 Nothofagus truncata, 19 Quercus montana, 20 Quercus petraea, 21 Quercus robur, 22 Sorbus torminalis, 23 Taxus bacata, 24 Tilia cordata. Sources: (1) Pellinen 1986, Meiwes and Beese 1988, (2) Bascietto 2003, FORCAST, Schöning 2003, FORCAST, Cotrufo 2003, FORCAST, (3) Gerighausen 2002, (4) Ellenberg et al. 1986, (5) DeAngelis et al. 1981, (6) Granier et al. 2000, Granier et al. 2002, Schöning 2003, FORCAST, Cotrufo 2003, FORCAST, (7) Kestemont, 1975 in Röhrig 1991, (8) Duvigneaud, 1971 in Röhrig 1991, (9) Garelkov 1973, (10) Bartelink 1997, (11) Persson et al. 2000, Scarascia-Mugnozza et al. 2000, (12) Weber 2001, (13) Bormann and Likens 1979, Whittaker et al. 1979, (14) Crowell and Freedman 1994, (15) Erdmann and Wilke 1997, (16) Michl and Licht 2002, (17) Bobiec 2002, Schaaf 2003, (18) Meyer 1999, (19) von Lochow 1987, (20) Davis et al. 2003, (21) Muller 2003, (22) Kakubari 1983, Kakubari 1991, (23) Hart et al. 2003, (24) Meyer et al. 2003, (25) Réh 1993, (26) Korpeľ 1995. Site Tree Elevation Temperature / Parent material / Management Stand age Living Leaves Source species Precipitation Soil biomass m a.s.l. °C / mm years tC ha-1 tC ha-1 Even-aged managed stands under favourable conditions Göttinger Wald 12 400 8.6 / 613 limestone, loess; likely shelterwood 100-115 209.58 1.5 1 Rendzina, Terra fusca, system Braunerde Collelongo, 12 1560 6.3 / 1180 limstone; Rendzic very extensive, selective 104 155.95 1.7 2 central Italy Leptosol harvesting, forest grazing; in the past coppice system Eichsfeld, 12 425 7.5 / 700 coloured sandstone, regular shelterwood 22 41.27 1.4 3 Germany loess; pseudovergleyte system Braunerde Eichsfeld, 12 425 7.5 / 700 Braunerde regular shelterwood 61 151.19 2.6 3 Germany system -"- -"- -"- -"- pseudovergleyte -"- 170 183.43 2.8 3 Parabraunerde -"- -"- -"- -"- pseudovergleyte -"- 180+10 79.94 1.4 3 Parabraunerde

211

Site Tree Elevation Temperature / Parent material / Management Stand age Living Leaves Source species Precipitation Soil biomass m a.s.l. °C / mm years tC ha-1 tC ha-1 Even-aged managed stands under favourable conditions (continued) Solling, 12 503 6.1 / 1032 coloured sand-stone, shelterwood system 130 178.76 1.5 4 Germany (B1) loess; clay silt, loamy silt, acid Braunerde Solling, 12 470 -"- -"- -"- 88 138.16 1.7 4 Germany (B3) Solling, 12 435 -"- -"- -"- 67 139.98 1.6 4 Germany (B4) Soroe, 12 40 8.1 / 510 stagnic Phaeozem not reported 60 97.95 1.8 2 Denmark Denmark 12 11-28 7.1 / 660 Parabraunerde not reported 85-90 100.45 1.3 5 Hesse, France 12 (20, 300 9.2 / 885 stagnic Luvisol not reported 38 52.02 1.2 6 24, 6) Belgium 12 350 not reported not reported not reported 144 224.00 1.5 7 Belgium 12 350 not reported not reported not reported 130 204.00 1.5 8 Bulgaria 12 1400-1600 not reported dark brown soil, north not reported 100 207.35 2.4 9 faced (Fagetum asperulosum) France 12 135 10.2 / 674 Sable Eolien not reported mature 138.39 1.7 5 Quaternaire Sur (~160) Calcaire Oligocene; Sol Lessive

212

Site Tree Elevation Temperature / Parent material / Soil Management Stand age Living Leaves Source species Precipitation biomass m a.s.l. °C / mm years tC ha-1 tC ha-1 Even-aged managed stands under less favourable conditions central 12 not reported not reported preglacial deposits; acid not reported 8 8.46 1.5 10 Netherlands brown, podzolic soils -"- -"- -"- -"- -"- -"- 11 8.69 0.6 10 -"- -"- -"- -"- -"- -"- 20 46.71 1.8 10 -"- -"- -"- -"- -"- -"- 21/22 47.56 1.5 10 -"- -"- -"- -"- -"-. -"- 38/40/41 96.97 2.1 10 -"- -"- -"- -"- s.o. -"- 58/59 98.28 2.0 10 Aubure, France 12 1000 5.4 / 1192 haplic Podzol not reported 161 149.10 1.4 11 Gribskov, 12 45 7.5 / 632 fine sand; Arenosol not reported 118 194.40 2.4 11 Denmark Sweden 12 60 7 / 650 silurian slate overlaid by not reported 80-100 144.43 1.4 5 clay and slate and primary rock moraine; brown soil with gley horizon Sweden 12 150 6 / 900 Cambrian sandstone, not reported 80-120 97.86 1.2 5 overlaid by primary rock moraine, dominated by sand; Podzol Fichtel- 12 850 5.5 / 890 granite; dystric Cambisol shelterwood system 120 157.90 1.8 11 gebirge, Germany Jezeri, Czech 12 750 5.9 / 935 dystric Cambisol not reported 79 122.60 1.8 11 Republic Japan 10 680 11.3 / 2788 brown soil not reported 150 115.90 1.7 5 Tierra del 16 140-270 5.6 / 550-705 Palaeozoic; podzolic natural succession 32 65.30 12 Fuego, brown soil after clear-cutting Argentina and slash removal

213

Site Tree Elevation Temperature / Parent material / Soil Management Stand age Living Leaves Source species Precipitation biomass m a.s.l. °C / mm years tC ha-1 tC ha-1 Even-aged managed stands under less favourable conditions (continued) Bulgaria 12 1400-1600 3-4 / 1079-1150 light brown soil, sunny not reported 100 126.35 1.9 9 exposure (Fagetum myrtillosum) Bulgaria 12 1400-1600 3-4 / 1079-1150 transition soil, east- not reported 100 175.75 1.5 9 western to south-western exposure (Fagetum festucosum) Hubbard 5, 11, 23 546-791 ? / 1300 bouldery glacial till, natural succession after ~55 73.22 1.4 13 Brook Forest, Littleton gneiss; Podzol clear-cutting USA

central Nova 4, 5, 8 not reported not reported stony-sandy till natural succession after 11 15.66 1.2 14 Scotia, Canada etc. composed of granitic clear-cutting minerals; shallow Gibraltar soil -"- -"- -"- -"- -"- -"- 12 18.02 1.3 14 -"- -"- -"- -"- -"- -"- 13 21.13 1.4 14 -"- -"- -"- -"- -"- -"- 20 44.75 1.5 14 -"- -"- -"- -"- -"- -"- 30 70.17 1.7 14 -"- -"- -"- -"- -"- -"- 40 65.70 1.5 14 -"- -"- -"- -"- -"- -"- 50 82.59 1.7 14 -"- -"- -"- -"- -"- -"- 60 72.45 1.8 14 central Nova 4, 5, 8 -"- -"- stony-sandy till natural succession after 63 95.38 1.8 14 Scotia, Canada etc. composed of granitic clear-cutting minerals; shallow Gibraltar soil -"- -"- -"- -"- -"- -"- 75 72.54 1.5 14

214

Site Tree Elevation Temperature / Parent material / Soil Management Stand age Living Leaves Source species Precipitation biomass m a.s.l. °C / mm years tC ha-1 tC ha-1 Even-aged managed stands under less favourable conditions (continued) Schieferge- 12 variable variable schist not reported 138 150.80 15 birge, Hessen, Germany -"- -"- -"- -"- -"- -"- 133 186.80 15 -"- -"- -"- -"- -"- -"- 138 204.00 15 -"- -"- -"- -"- -"- -"- 183 91.20 15 -"- -"- -"- -"- -"- -"- 143 115.44 15 -"- -"- -"- -"- -"- -"- 138 144.80 15 -"- -"- -"- -"- -"- -"- 148 133.38 15 -"- -"- -"- -"- -"- -"- 163 106.02 15 -"- -"- -"- -"- -"- -"- 193 78.28 15 -"- -"- -"- -"- -"- -"- 160 118.95 15 -"- -"- -"- -"- -"- -"- 154 133.77 15 Managed uneven-aged stands under favourable conditions Dün (Keula), 12 420-520 6.8 / 750-800 limestone and loess; selection system uneven-aged 148.20 16 Germany Rendzina to Parabraunerde Dün (Keula), 12 420-520 6.8 / 750-800 limestone and loess; selection system uneven-aged 212.94 16 Germany Rendzina to Parabraunerde

215

Site Tree Elevation Temperature / Parent material / Soil Management Stand age Living Leaves Source species Precipitation biomass m a.s.l. °C / mm years tC ha-1 tC ha-1 Managed uneven-aged stands under less favourable conditions Białowieza, 9, 24, 2 134-200 6.8 / 640 ablation and ground selection cutting uneven-aged 95.16 17 Poland etc. moraine, sand, loamy sand, clay or gravel deposits; Podzoluvisol to Luvisol -"- -"- -"- -"- -"- -"- uneven-aged 102.96 17 Sweden 12 120 7 / 880 Cambrian shales and not reported 45-130 130.28 1.8 5 sandstone, stony-sandy moraine; acid brown soil Recently unmanaged (national parks or forest reserves) under favourable conditions North-western 12, 20 not reported not reported brown soils forest reserves max. 85-87 263.16 18 Germany -"- 12 -"- -"- brown soils forest reserves max 116 288.54 18

-"- 12 -"- -"- Rendzina forest reserves max. 109 241.92 18 Niedersachsen, 12 180-550 variable variable recently unmanaged uneven-aged 187.20 19 Germany (13, 9) (Melico Fagetum) -"- 12 200-320 variable limestone; variable recently unmanaged uneven-aged 116.22 19 (13, 20) soils (Lathyro Fagetum) -"- 12 250-360 variable limestone; variable recently unmanaged uneven-aged 74.88 19 (23, 22) soils (Carici Fagetum) -"- 12 320-420 variable limestone; variable recently unmanaged uneven-aged 161.46 19 (13, 3) soils (Aceri Fraxinetum)

216

Site Tree Elevation Temperature / Parent material / Management Stand age Living Leaves Source species Precipitation Soil biomass m a.s.l. °C / mm years tC ha-1 tC ha-1 Recently unmanaged (national parks or forest reserves) under favourable conditions (continued) central South 17 650-1400 8 / 1447 greywacke, loess and likely unmanaged ~150+ 153.25 20 Island, New colluvium allophanic Zealand brown soils Recently unmanaged (national parks or forest reserves) under less favourable conditions Northwestern 12, 20 not reported not reported podzolic brown soils forest reserves max. 157 146.25 18 Germany -"- 12 not reported not reported podzolic brown soils forest reserves max. 133 177.20 18 Southeastern 11, 5, 19 mesophytic likely unmanaged old-growth 279.76 21 Kentucky, USA Białowieza, 9, 24, 2 134-200 6.8 / 640 ablation and ground forest reserves uneven-aged 154.05 17 Poland etc. moraine, sand, loamy sand, clay or gravel deposits; Podzo- luvisol to Luvisol -"- -"- -"- -"- -"- forest reserves uneven-aged 201.24 17

Niedersachsen, 12 (21) 15-105 variable variable recently unmanaged uneven-aged 131.04 19 Germany (Deschampsio flexuosae Fagetum) Naebas, Japan 10 550 11.4 (317 m) / andesite, basalt, primary forest 150 171.18 1.3 22 2800 (608 m) brown soil (probably very extensive selective cutting) -"- -"- 700 -"- -"- -"- 150 178.23 1.1 22

217

Site Tree Elevation Temperature / Parent material / Management Stand age Living Leaves Source species Precipitation Soil biomass m a.s.l. °C / mm years tC ha-1 tC ha-1 Recently unmanaged (national parks or forest reserves) under less favourable conditions (continued) Naebas, Japan 10 700 11.4 (317 m) / andesite, basalt, primary forest 120 186.00 1.1 22 2800 (608 m) brown soil (probably very extensive selective cutting) Big Bush, 18, 17, 600 10.5 / 1307 stony regolith or likely unmanaged, uneven-aged 212.60 4.6 23 Nelson, New 15 deposit regolith; mature (ever- Zealand Podzols and acid green !) brown soils Primary forest under favourable conditions Mirdita, 12, 3 1300-1500 6 / ~2200 serpentine, gabbro, primary forest, single uneven-aged 218.13 24 Munellage- dolomite; rich brown trees could have been birge, Albania soil harvested Puka, 12 (1) -"- -"- serpentine, gabbro, -"- uneven-aged 304.47 24 Munellage- dolomite; rich brown birge, Albania soil Rajca, Eastern 12, 3 (1) -"- -"- serpentine, gabbro, -"- uneven-aged 314.89 24 Albania dolomite; rich brown soil Popriĕný, 12 -"- -"- rich brown soil -"- uneven-aged 273.00 25 Eastern Slovakia SNr Vihorlat, 12 700-820 6 / 750-800 andesit, sandy loam; primary forest uneven-aged 185.03 25+26 Western (3, 13) brown soil Carpathians, Slovakia

218

Site Tree Elevation Temperature / Parent material / Management Stand age Living Leaves Source species Precipitation Soil biomass m a.s.l. °C / mm years tC ha-1 tC ha-1 Primary forest under favourable conditions (continued) SNr Rozok, 12 500-790 7 / 780 sandstone (partly with primary forest uneven-aged 279.15 26 Western carbonates), partly Carpathians, clay schist; brown Slovakia soil, sandy loam SNr Havesova, 12 500-560 7 / 700-800 flysch sandstone; primary forest uneven-aged 273.78 26 Western brown or grey soils Carpathians, Slovakia SNr Stuzica, 12 (1) 650-900 5.5 / 850-1000 flysch sandstone; primary forest uneven-aged 221.91 26 Western brown soil Carpathians, Slovakia Primary forest under less favourable conditions Tierra del 16 140-270 5.6 / 550-700 podzolic brown natural uneven-aged 202.80 12 Fuego, soils Argentina -"- -"- -"- -"- -"- natural succession after 160 206.70 12 windthrow -"- -"- -"- -"- -"- natural succession after 140 177.50 12 windthrow -"- -"- -"- -"- -"- natural succession after 100 193.00 12 windthrow

219 Table A.8: Parameters of the soil pits. RWC: residual water content, TOC: total organic carbon, TIC: Total inorganic carbon, TN: Total nitrogen, fBD: fine soil bulk density, SV: stone volume, SOC: soil organic carbon, n.d.: not determined, m: missing sample, italics: estimate.

Soil RWC TOC TIC TN C/N SOC fBD SV pH pH Sand Silt Clay Study site depth

[cm] [%] [%] [%] [%] [g/g] [tC/ha] [g/cm³] [%] KCl H2O [%] [%] [%] Lei-30M 0-5 2.43 4.66 0.03 0.32 14.72 20.46 0.879 0.1 4.35 5.31 3 70 27 Lei-30M 5-10 1.69 1.90 0.00 0.12 16.08 11.65 1.225 0.0 3.38 4.52 3 70 27 Lei-30M 10-15 1.63 1.45 0.00 0.10 15.15 10.13 1.395 0.0 3.47 4.69 3 72 25 Lei-30M 15-20 1.68 1.18 0.00 0.08 13.98 9.71 1.645 0.1 3.59 4.89 3 72 25 Lei-30M 20-25 2.05 1.02 0.00 0.08 13.03 7.86 1.666 7.2 3.91 5.55 7 56 37 Lei-30M 25-30 2.45 0.93 0.39 0.07 12.49 5.01 1.275 15.9 6.75 7.28 7 56 37 Lei-30M 30-40 3.02 0.85 1.00 0.08 10.48 6.46 1.070 29.0 6.77 7.59 7 56 37 Lei-30M 40-50 3.20 0.88 2.36 0.08 11.03 2.83 0.563 42.7 6.97 7.86 7 56 37 Lei-62M 0-5 1.90 3.10 0.04 0.21 14.67 19.53 1.261 0.0 4.98 5.9 4 71 25 Lei-62M 5-10 1.60 1.59 0.00 0.12 13.71 12.54 1.576 0.0 4.1 5.55 3 73 24 Lei-62M 10-15 1.67 1.25 0.00 0.10 12.44 9.62 1.548 0.4 4.42 5.89 3 72 25 Lei-62M 15-20 1.91 1.10 0.00 0.10 11.48 8.29 1.505 0.0 4.79 6.38 3 64 33 Lei-62M 20-25 2.60 1.15 0.00 0.10 11.19 8.79 1.527 0.3 5.2 6.73 5 54 41 Lei-62M 25-30 2.72 1.27 0.00 0.12 10.52 8.78 1.385 0.0 5.21 6.64 2 46 52 Lei-62M 30-40 2.73 1.19 1.80 0.12 10.29 17.08 1.440 0.1 6.63 7.64 1 47 52 Lei-111M 0-5 1.47 2.88 0.00 0.18 15.76 14.16 0.985 0.0 4.06 5.12 3 80 17 Lei-111M 5-10 0.93 1.28 0.00 0.07 17.35 7.09 1.112 0.0 3.51 4.53 3 80 17 Lei-111M 10-15 0.89 1.00 0.00 0.06 16.68 7.14 1.428 0.0 3.6 4.45 4 80 16 Lei-111M 15-20 1.04 0.85 0.00 0.05 15.57 5.29 1.244 0.0 3.63 4.5 4 80 16 Lei-111M 20-25 1.02 0.75 0.00 0.05 15.54 5.36 1.435 0.0 3.7 4.57 3 81 16 Lei-111M 25-30 1.01 0.57 0.00 0.04 14.03 3.62 1.282 0.0 3.68 4.69 5 62 33 Lei-111M 30-40 2.40 0.51 0.00 0.05 10.22 7.29 1.433 0.0 3.66 5.12 5 62 33 Lei-111M 40-51 3.29 0.72 0.15 0.08 9.35 7.02 1.326 33.6 6.15 7.08 5 62 33 Lei-111M 51-70 2.58 0.70 1.49 0.06 11.59 8.89 0.994 33.2 7 7.46 17 39 44 Lei-111M 70-82 m m m m m 4.73 m m m m m m m Lei-141M 0-7 1.97 3.33 0.00 0.24 14.13 23.30 1.000 0.0 4.25 5.26 3 72 25 Lei-141M 7-10 1.53 1.60 0.00 0.12 13.81 3.97 0.829 0.0 3.91 5.19 3 73 24 Lei-141M 10-15 1.51 1.38 0.00 0.12 11.70 8.74 1.268 0.0 4.14 5.52 6 71 23

220 Table A.8: continued

Soil RWC TOC TIC TN C/N SOC fBD SV pH pH Sand Silt Clay Study site depth

[cm] [%] [%] [%] [%] [g/g] [tC/ha] [g/cm³] [%] KCl H2O [%] [%] [%] Lei-141M 15-20 1.50 1.08 0.00 0.10 11.33 6.55 1.214 0.0 4.79 6.23 4 72 24 Lei-141M 20-25 1.74 0.88 0.00 0.08 11.50 6.00 1.358 0.0 4.04 6.36 6 67 27 Lei-141M 25-30 2.55 0.89 0.00 0.08 11.89 7.05 1.577 0.0 5.06 6.47 4 56 40 Lei-141M 30-40 3.89 1.00 0.05 0.11 9.24 13.19 1.331 0.6 6.35 7.49 6 35 59 Lei-141M 40-44 m m m m m 4.86 m m m m m m m Lei-153+16M 0-5 3.99 6.45 0.28 0.52 12.31 22.16 0.687 0.0 6.78 7.38 4 58 38 Lei-153+16M 5-10 3.34 4.49 0.13 0.38 11.70 18.79 0.838 0.0 6.7 7.42 4 58 38 Lei-153+16M 10-15 3.15 3.24 0.11 0.30 10.86 17.18 1.062 0.1 6.77 7.47 4 58 38 Lei-153+16M 15-20 2.94 2.35 0.34 0.24 9.94 16.07 1.371 0.1 6.9 7.7 3 63 34 Lei-153+16M 20-25 2.79 1.79 0.68 0.19 9.37 12.07 1.350 0.2 7.02 7.44 3 63 34 Lei-153+16M 25-30 3.00 1.35 0.76 0.15 9.07 9.60 1.420 0.0 m 7.66 2 59 39 Lei-153+16M 30-35 2.61 1.08 2.29 0.11 9.54 6.92 1.287 0.1 7.13 7.78 2 59 39 Lei-153+16M 35-40 1.79 0.76 6.05 0.08 9.28 3.85 1.098 7.6 7.26 8.08 2 59 39 Lei-153+16M 40-50 1.16 0.40 7.94 0.03 11.51 4.23 1.406 24.2 7.29 8.26 6 75 19 Lei-153+16M 50-60 0.88 0.26 8.55 0.02 16.82 2.27 1.175 25.0 7.35 8.37 6 75 19 Lei-153+16M 60-70 0.94 0.22 8.42 0.02 13.57 2.84 0.739 13.1 7.48 8.36 9 76 15 Mühl-38 0-5 1.62 4.15 0.00 0.32 12.85 17.75 0.855 0.0 3.69 4.52 3 75 22 Mühl-38 5-10 1.34 1.97 0.00 0.15 12.76 11.95 1.211 0.0 3.56 4.5 3 75 22 Mühl-38 10-15 1.28 1.46 0.00 0.11 13.39 7.30 1.003 0.0 3.61 4.52 3 75 22 Mühl-38 15-20 1.23 1.14 0.00 0.09 12.98 7.40 1.297 0.0 3.6 4.52 3 75 22 Mühl-38 20-25 1.29 0.85 0.00 0.07 11.42 5.35 1.265 0.0 3.63 4.51 3 75 22 Mühl-38 25-30 1.51 0.73 0.00 0.07 10.50 5.34 1.460 0.0 3.61 4.56 3 75 22 Mühl-38 30-40 4.29 0.91 0.00 0.09 10.05 11.53 1.270 0.0 3.52 4.67 3 46 51 Mühl-38 40-48.5 3.36 1.31 0.00 0.13 10.43 10.81 0.968 0.1 3.85 5.27 1 31 68 Mühl-38 48.5-58 m m m m m 11.52 m m m m m m m Mühl-55 0-5 3.08 5.75 0.03 0.46 12.57 22.39 0.778 0.0 3.69 4.63 2 54 44 Mühl-55 5-10 2.50 3.41 0.03 0.28 11.98 14.17 0.832 0.0 3.58 4.6 2 54 44 Mühl-55 10-15 4.06 2.78 0.03 0.24 11.43 11.67 0.841 0.0 3.63 4.85 3 44 53 Mühl-55 15-20 2.80 2.64 0.00 0.23 11.27 12.32 0.963 3.2 4.02 5.26 3 44 53 Mühl-55 20-25 3.12 2.36 0.00 0.21 10.96 11.12 0.944 0.0 4.18 5.69 3 44 53 Mühl-55 25-30 4.21 2.11 0.00 0.20 10.54 10.86 1.031 0.0 4.49 6.03 3 44 53 Mühl-55 30-39 3.40 2.28 0.06 0.21 10.68 19.29 0.949 1.1 6.29 7.38 3 44 53

221 Table A.8: continued

Soil RWC TOC TIC TN C/N SOC fBD SV pH pH Sand Silt Clay Study site depth

[cm] [%] [%] [%] [%] [g/g] [tC/ha] [g/cm³] [%] KCl H2O [%] [%] [%] Mühl-85 0-5 2.81 3.85 0.03 0.31 12.38 14.97 0.777 0.0 3.81 4.78 5 66 29 Mühl-85 5-10 2.33 2.75 0.03 0.23 12.09 14.04 1.021 0.0 3.5 4.63 5 66 29 Mühl-85 10-15 1.93 1.93 0.00 0.16 11.84 10.70 1.107 0.0 3.55 4.63 5 66 29 Mühl-85 15-20 1.72 1.62 0.00 0.15 11.12 7.32 0.905 0.0 3.62 4.83 3 68 29 Mühl-85 20-25 1.48 1.43 0.00 0.16 9.10 11.06 1.543 0.0 3.65 5.01 3 68 29 Mühl-85 25-30 1.99 1.07 0.00 0.13 8.13 7.93 1.480 0.0 3.98 5.49 2 57 41 Mühl-85 30-40 2.55 0.86 0.11 0.11 7.80 9.78 1.371 17.2 6.33 7.44 6 41 53 Mühl-85 40-51 2.73 0.86 1.59 0.11 8.05 9.32 1.402 29.9 6.96 8 6 41 53 Mühl-102 0-5 1.76 3.86 0.00 0.31 12.49 21.77 1.128 0.1 3.96 4.96 4 66 30 Mühl-102 5-10 1.46 1.98 0.03 0.20 9.92 12.63 1.277 0.0 3.66 4.77 5 66 29 Mühl-102 10-15 1.38 1.51 0.00 0.15 9.78 10.16 1.341 0.0 3.68 4.85 5 67 28 Mühl-102 15-20 1.43 1.51 0.00 0.15 9.78 9.17 1.216 0.0 3.76 4.98 4 67 29 Mühl-102 20-25 1.63 1.39 0.00 0.15 9.04 9.72 1.398 0.0 3.89 5.32 3 63 34 Mühl-102 25-30 2.19 1.07 0.00 0.13 8.24 8.22 1.530 0.0 4.04 5.61 5 46 49 Mühl-102 30-40 2.72 0.94 0.03 0.11 8.26 12.45 1.341 0.8 5.94 7.2 6 44 50 Mühl-102 40-52 3.93 0.78 0.21 0.10 8.16 12.40 1.477 10.8 6.88 7.76 5 46 49 Mühl-102 52-70 2.66 0.75 0.07 0.10 7.84 15.76 0.937 0.5 6.66 7.67 3 49 48 Mühl-171+10 0-5 2.29 5.85 0.04 0.43 13.65 10.53 0.360 0.0 4.33 5.32 7 70 23 Mühl-171+10 5-10 1.83 4.16 0.02 0.31 13.27 15.06 0.725 0.0 3.79 4.65 7 70 23 Mühl-171+10 10-15 1.29 2.20 0.02 0.18 12.46 9.51 0.865 0.0 3.69 4.75 7 70 23 Mühl-171+10 15-20 1.58 1.56 0.00 0.13 12.00 10.21 1.306 0.0 3.71 4.67 3 76 21 Mühl-171+10 20-25 1.26 1.33 0.00 0.10 12.83 9.24 1.386 0.0 3.84 4.67 3 76 21 Mühl-171+10 25-30 1.23 0.96 0.00 0.08 12.46 5.84 1.220 0.0 3.95 5.06 3 73 24 Mühl-171+10 30-40 1.43 0.79 0.00 0.07 11.76 11.46 1.446 0.0 3.77 4.86 3 73 24 Mühl-171+10 40-50 3.11 1.15 0.24 0.11 10.19 12.93 1.138 0.8 6.47 7.17 15 40 45 Mühl-171+10 50-60 1.86 0.61 6.06 0.06 9.89 9.55 1.669 6.6 7.21 8.17 15 40 45 Lang-I 0-5 3.48 5.12 0.06 0.43 11.79 17.69 0.691 0.0 4 4.95 4 59 37 Lang-I 5-10 2.78 2.38 0.04 0.23 10.49 14.36 1.209 0.0 3.71 4.92 3 58 39 Lang-I 10-15 3.29 1.76 0.04 0.18 9.60 9.81 1.114 0.0 3.91 5.32 4 51 45 Lang-I 15-20 3.91 1.46 0.04 0.17 8.79 8.88 1.228 0.8 5.45 6.59 3 39 58 Lang-I 20-25 4.21 1.18 0.69 0.13 8.85 7.43 1.276 1.0 6.91 7.74 4 42 54

222 Table A.8: continued

Soil RWC TOC TIC TN C/N SOC fBD SV pH pH Sand Silt Clay Study site depth

[cm] [%] [%] [%] [%] [g/g] [tC/ha] [g/cm³] [%] KCl H2O [%] [%] [%] Lang-I 25-30 3.47 0.83 2.43 0.09 9.04 5.70 1.486 7.3 7.09 8 11 46 43 Lang-I 30-41 2.83 0.59 4.28 0.07 9.13 7.24 1.340 17.3 7.23 8.13 18 45 37 Lang-II 0-5 2.53 3.80 0.03 0.27 14.09 10.58 0.559 0.2 3.92 5.01 5 66 29 Lang-II 5-10 1.95 2.19 0.04 0.16 13.65 13.77 1.262 0.3 3.6 4.78 5 66 29 Lang-II 10-15 2.02 1.41 0.00 0.11 12.41 11.40 1.620 0.3 3.53 4.91 5 66 29 Lang-II 15-20 2.37 1.15 0.00 0.11 10.51 10.85 1.885 0.1 3.83 5.41 5 56 39 Lang-II 20-25 4.37 1.13 0.00 0.12 9.42 6.19 1.177 6.5 4.28 5.84 5 56 39 Lang-II 25-30 5.33 1.13 0.00 0.12 9.08 3.39 0.671 10.3 5.19 6.77 5 56 39 Lang-II 30-35 6.07 1.15 0.00 0.12 9.21 5.31 1.103 16.0 6.3 7.53 5 29 66 Lang-II 35-40 6.33 1.06 0.10 0.12 8.64 3.76 0.956 26.1 6.74 7.67 5 29 66 Lang-II 40-50 5.78 0.92 0.55 0.11 8.45 6.08 1.023 35.3 6.98 8.04 5 29 66 Lang-II 50-67.5 3.66 0.60 3.77 0.08 7.21 8.85 2.672 68.2 7.05 8.14 n.d. n.d. n.d. Lang-III 0-5 2.72 4.49 0.05 0.35 12.89 15.02 0.991 32.5 4.91 5.42 3 69 28 Lang-III 5-10 2.00 1.97 0.00 0.17 11.45 10.44 1.059 0.0 3.67 4.82 3 69 28 Lang-III 10-15 2.28 1.45 0.00 0.15 9.75 8.46 1.167 0.0 3.64 4.65 3 69 28 Lang-III 15-20 2.22 1.35 0.00 0.14 9.46 6.57 0.971 0.0 3.72 4.83 3 63 34 Lang-III 20-25 2.56 1.05 0.00 0.12 8.60 7.33 1.395 0.0 3.95 5.48 3 63 34 Lang-III 25-30 2.91 0.88 0.00 0.11 8.36 6.65 1.514 0.0 5.25 6.34 3 63 34 Lang-III 30-40 2.80 0.85 0.53 0.10 8.68 10.29 1.215 0.7 6.67 7.28 3 63 34 Lang-III 40-50 2.53 0.73 1.67 0.08 9.32 9.73 1.374 3.6 7.03 7.72 3 63 34 Lang-III 50-70 2.88 0.64 1.65 0.08 8.33 17.68 1.429 2.8 7.01 6.93 3 63 34 Lang-III 70-78 1.32 0.32 8.67 0.03 10.72 3.50 1.738 20.8 n.d. n.d. n.d. n.d. n.d. Hai-I 0-5 3.58 5.44 0.05 0.48 11.26 22.05 0.810 0.0 4.26 5.21 3 43 54 Hai-I 5-10 3.29 3.40 0.03 0.33 10.45 19.89 1.214 3.8 4.04 5.26 3 43 54 Hai-I 10-15 3.19 2.58 0.00 0.25 10.27 14.26 1.184 6.7 5.3 6.57 3 43 54 Hai-I 15-20 3.12 2.01 0.00 0.20 9.93 13.69 1.514 10.0 6.24 7.42 3 43 54 Hai-I 20-25 2.84 1.42 0.00 0.16 9.19 8.61 1.265 4.4 6.23 7.55 3 37 60 Hai-I 25-30 3.27 1.25 0.00 0.14 8.89 7.15 1.243 7.8 6.3 7.76 3 37 60 Hai-I 30-36 3.80 1.15 0.04 0.14 8.33 9.32 1.467 7.7 6.39 7.525 3 37 60 Hai-II 0-5 3.54 4.53 0.02 0.37 12.39 31.97 0.942 0.0 4.95 6.07 3 57 40

223 Table A.8: continued

Soil RWC TOC TIC TN C/N SOC fBD SV pH pH Sand Silt Clay Study site depth

[cm] [%] [%] [%] [%] [g/g] [tC/ha] [g/cm³] [%] KCl H2O [%] [%] [%] Hai-II 5-10 3.87 3.86 0.02 0.32 12.26 15.79 0.817 0.0 5.06 6.18 2 53 45 Hai-II 10-15 3.50 3.05 0.02 0.27 11.44 13.09 0.857 0.0 5.2 6.46 3 58 39 Hai-II 15-20 3.39 2.02 0.01 0.19 10.43 12.75 1.264 0.1 5.39 6.79 5 57 38 Hai-II 20-25 3.37 1.67 0.02 0.16 10.37 9.22 1.103 0.1 5.52 7.03 6 46 48 Hai-II 25-30 3.39 1.33 0.01 0.14 9.72 10.12 1.527 0.2 5.61 7.2 6 47 47 Hai-II 30-40 4.04 0.78 0.01 0.09 9.09 11.81 1.523 0.0 5.61 7.23 8 39 53 Hai-II 40-49 4.96 0.61 0.05 0.07 8.21 7.37 1.410 4.2 6.73 7.76 7 27 66 Hai-III 0-5 2.77 6.05 0.04 0.51 11.93 19.01 0.662 0.0 4.87 5.67 4 53 43 Hai-III 5-10 2.43 4.45 0.03 0.41 10.85 21.55 0.968 0.0 4.57 5.57 4 55 41 Hai-III 10-15 2.24 2.99 0.03 0.31 9.68 16.20 1.084 0.0 4.61 5.69 3 54 43 Hai-III 15-22 2.43 2.89 0.03 0.28 10.44 18.92 0.934 0.0 5.1 6.19 3 56 41 Hai-III 22-25 2.24 2.37 0.03 0.24 10.03 8.40 1.401 0.0 5.5 6.43 6 50 44 Hai-III 25-30 2.75 1.92 0.03 0.21 9.25 9.50 1.165 0.0 5.89 6.93 5 54 41 Hai-III 30-40 2.26 1.49 0.00 0.16 9.28 17.42 1.170 0.1 5.94 7.19 4 53 43 Hai-III 40-50 2.88 0.98 0.08 0.12 8.26 13.06 1.429 7.1 6.67 7.93 5 38 57

224

Table A.9: Parameters of the soil cores. RWC: residual water content, TOC: total organic carbon, TIC: Total inorganic carbon, TN: Total nitrogen, fBD: fine soil bulk density, SOC: soil organic carbon, max.: maximum, SC: Soil category, 1 = (Braunerde-) Terra fusca, Rendzina, 2 = Rendzina-Braunerde, Braunerde-Terra fusca, Terra fusca-Braunerde, 3 = Braunerde (on Terra fusca), 4 = Parabraunerde (on Terra fusca); ds: disturbed sample, m: missing sample, ev: extreme value, italics: estimate

Soil Max. Site No Date Core No. RWC TOC TIC TN C/N fBD SOC SC depth depth [cm] [%] [%] [%] [%] [g/g] [g/cm³] [tC/ha] [cm] Lei-30M 23.10.2001 K 16 0-5 2.53 4.10 0.02 0.31 13.170.95 19.42 42 3 Lei-30M 23.10.2001 K 16 5-10 2.06 1.94 0.03 0.17 11.651.12 10.85 42 3 Lei-30M 23.10.2001 K 16 10-15 2.14 1.42 0.01 0.13 10.93 1.27 9.02 42 3 Lei-30M 23.10.2001 K 20 0-5 3.69 6.96 0.05 0.53 13.170.66 23.05 45 2 Lei-30M 23.10.2001 K 20 5-10 3.78 5.19 0.03 0.43 12.050.74 19.15 45 2 Lei-30M 23.10.2001 K 20 10-15 3.58 3.98 0.03 0.32 12.240.83 16.57 45 2 Lei-30M 23.10.2001 K 24 0-5 2.23 3.84 0.02 0.31 12.530.93 17.84 54 4 Lei-30M 23.10.2001 K 24 5-10 1.72 1.93 0.02 0.15 12.69 0.98 9.44 54 4 Lei-30M 23.10.2001 K 24 10-15 1.73 1.50 0.01 0.11 13.84 1.16 8.73 54 4 Lei-30M 23.10.2001 K 28 0-5 4.04 6.68 0.06 0.54 12.380.66 21.82 65 2 Lei-30M 23.10.2001 K 28 5-10 3.06 3.78 0.05 0.32 11.700.96 18.07 65 2 Lei-30M 23.10.2001 K 28 10-15 2.91 3.25 0.03 0.28 11.490.82 13.26 65 2 Lei-30M 23.10.2001 K 30 0-5 3.60 6.99 0.03 0.46 15.330.67 23.41 48 3 Lei-30M 23.10.2001 K 30 5-10 3.19 3.32 0.02 0.28 11.960.88 14.58 48 3 Lei-30M 23.10.2001 K 30 10-15 3.11 2.32 0.01 0.20 11.361.01 11.68 48 3 Lei-30M 23.10.2001 1 0-5 2.61 4.93 0.03 0.36 13.520.76 18.76 63 4 Lei-30M 23.10.2001 1 5-10 2.09 2.12 0.02 0.18 11.750.96 10.20 63 4 Lei-30M 23.10.2001 1 10-15 1.72 1.78 0.02 0.16 11.161.41 12.61 63 4 Lei-30M 23.10.2001 2 0-5 3.21 5.76 0.03 0.42 13.590.74 21.42 35 4 Lei-30M 23.10.2001 2 5-10 2.13 2.37 0.02 0.20 11.880.89 10.49 35 4 Lei-30M 23.10.2001 2 10-15 2.29 1.88 0.02 0.17 11.231.12 10.49 35 4 Lei-30M 23.10.2001 3 0-5 2.81 5.40 0.03 0.41 13.040.84 22.57 60 3 Lei-30M 23.10.2001 3 5-10 2.75 3.36 0.02 0.29 11.621.10 18.44 60 3 Lei-30M 23.10.2001 3 10-15 2.74 2.59 0.02 0.24 10.841.01 13.11 60 3 Lei-30M 23.10.2001 4 0-5 2.19 4.60 0.02 0.34 13.600.74 16.89 59 3 Lei-30M 23.10.2001 4 5-10 1.94 2.54 0.02 0.21 11.930.97 12.29 59 3 Lei-30M 23.10.2001 4 10-15 1.80 1.67 0.02 0.15 11.041.26 10.56 59 3 Lei-30M 23.10.2001 5 0-5 2.76 5.53 0.03 0.42 13.170.91 25.08 51 3 Lei-30M 23.10.2001 5 5-10 2.47 3.51 0.03 0.29 11.970.83 14.50 51 3 Lei-30M 23.10.2001 5 10-15 2.39 2.66 0.02 0.24 11.221.07 14.23 51 3 Lei-30M 23.10.2001 6 0-5 2.07 3.82 0.03 0.26 14.830.87 16.67 59 4 Lei-30M 23.10.2001 6 5-10 1.53 1.49 0.02 0.10 14.74 1.05 7.80 59 4 Lei-30M 23.10.2001 6 10-15 1.49 1.33 0.01 0.09 14.06 1.03 6.84 59 4 Lei-30M 23.10.2001 7 0-5 2.27 4.89 0.03 0.36 13.580.89 21.86 59 3 Lei-30M 23.10.2001 7 5-10 2.24 2.82 0.03 0.22 13.011.05 14.72 59 3 Lei-30M 23.10.2001 7 10-15 1.92 1.51 0.02 0.13 11.78 1.24 9.37 59 3 Lei-30M 23.10.2001 8 0-5 5.39 10.35 0.08 0.77 13.48 0.56 28.93 34 1 Lei-30M 23.10.2001 8 5-10 4.67 6.12 0.09 0.50 12.120.78 20.89 15 1 Lei-30M 23.10.2001 8 10-15 ds ds ds ds ds ds ds ds 1 Lei-30M 23.10.2001 9 0-5 2.12 3.74 0.02 0.28 13.281.08 20.23 63 4 Lei-30M 23.10.2001 9 5-10 2.17 3.11 0.03 0.23 13.391.08 16.73 63 4 Lei-30M 23.10.2001 9 10-15 1.28 1.43 0.01 0.11 13.50 1.24 8.92 63 4 Lei-30M 23.10.2001 10 0-5 3.03 6.78 0.03 0.47 14.330.58 19.72 44 3 Lei-30M 23.10.2001 10 5-10 2.73 4.21 0.03 0.34 12.410.86 18.01 44 3 Lei-30M 23.10.2001 10 10-15 2.44 2.88 0.02 0.24 12.171.07 15.42 44 3

225 Table A.9: continued

Soil Max. Site No Date Core No. RWC TOC TIC TN C/N fBD SOC SC depth depth [cm] [%] [%] [%] [%] [g/g] [g/cm³] [tC/ha] [cm] Lei-62M 17.07.2001 A 0-5 1.78 4.25 0.03 0.29 14.640.71 15.02 88 4 Lei-62M 17.07.2001 A 5-10 2.26 1.52 0.02 0.11 13.58 1.14 8.67 88 4 Lei-62M 17.07.2001 A 10-15 2.22 1.10 0.02 0.10 11.40 1.23 6.73 88 4 Lei-62M 17.07.2001 B 0-5 4.40 6.63 0.44 0.51 13.110.46 15.22 78 1 Lei-62M 17.07.2001 B 5-10 3.46 4.79 0.49 0.41 11.610.96 23.04 78 1 Lei-62M 17.07.2001 B 10-15 3.08 3.72 0.55 0.34 10.810.81 15.11 78 1 Lei-62M 17.07.2001 C 0-5 2.58 3.36 0.04 0.26 12.980.91 15.20 80 4 Lei-62M 17.07.2001 C 5-10 1.89 2.12 0.02 0.18 11.83 0.90 9.57 80 4 Lei-62M 17.07.2001 C 10-15 1.94 1.44 0.02 0.14 10.51 1.24 8.94 80 4 Lei-62M 17.07.2001 D 0-5 1.86 3.55 0.02 0.29 12.240.83 14.74 80 2 Lei-62M 17.07.2001 D 5-10 1.84 2.71 0.02 0.22 12.191.02 13.76 80 2 Lei-62M 17.07.2001 D 10-15 1.56 1.78 0.02 0.15 11.791.14 10.19 80 2 Lei-62M 17.07.2001 E 0-5 2.24 3.38 0.02 0.26 12.910.87 14.65 72 4 Lei-62M 17.07.2001 E 5-10 2.08 2.50 0.02 0.20 12.641.08 13.53 72 4 Lei-62M 17.07.2001 E 10-15 2.05 2.06 0.01 0.17 11.841.24 12.79 72 4 Lei-62M 17.07.2001 1 0-5 2.60 4.19 0.08 0.33 12.650.71 14.77 92 4 Lei-62M 17.07.2001 1 5-10 2.10 2.65 0.02 0.24 11.100.83 11.01 92 4 Lei-62M 17.07.2001 1 10-15 2.11 2.31 0.02 0.21 10.981.09 12.53 92 4 Lei-62M 17.07.2001 2 0-5 2.27 4.39 0.03 0.32 13.600.95 20.76 86 4 Lei-62M 17.07.2001 2 5-10 1.89 2.47 0.03 0.21 11.890.96 11.90 86 4 Lei-62M 17.07.2001 2 10-15 2.03 1.58 0.02 0.15 10.39 1.23 9.69 86 4 Lei-62M 17.07.2001 3 0-5 2.71 4.65 0.03 0.34 13.560.76 17.65 55 2 Lei-62M 17.07.2001 3 5-10 2.35 3.17 0.02 0.24 13.300.82 13.08 55 2 Lei-62M 17.07.2001 3 10-15 2.28 2.44 0.02 0.20 12.281.18 14.39 55 2 Lei-62M 17.07.2001 4 0-5 1.81 2.43 0.02 0.20 11.871.11 13.49 81 4 Lei-62M 17.07.2001 4 5-10 1.34 1.81 0.02 0.16 11.431.23 11.11 81 4 Lei-62M 17.07.2001 4 10-15 1.33 1.28 0.02 0.12 10.31 1.46 9.35 81 4 Lei-62M 17.07.2001 5 0-5 1.45 2.60 0.03 0.19 13.381.05 13.69 84 4 Lei-62M 17.07.2001 5 5-10 1.10 1.12 0.02 0.09 13.17 1.31 7.36 84 4 Lei-62M 17.07.2001 5 10-15 1.06 0.91 0.02 0.08 11.45 1.33 6.06 84 4 Lei-62M 17.07.2001 7 0-5 1.82 3.27 0.02 0.23 14.110.86 14.01 91 4 Lei-62M 17.07.2001 7 5-10 1.44 1.94 0.02 0.15 13.071.07 10.41 91 4 Lei-62M 17.07.2001 7 10-15 1.40 1.25 0.02 0.10 12.73 1.30 8.12 91 4 Lei-62M 17.07.2001 9 0-5 1.48 2.66 0.02 0.20 13.421.04 13.85 46 4 Lei-62M 17.07.2001 9 5-10 1.27 1.09 0.01 0.09 12.53 1.39 7.58 46 4 Lei-62M 17.07.2001 9 10-15 1.48 0.87 0.01 0.08 10.82 1.40 6.05 46 4 Lei-62M 17.07.2001 10 0-5 2.63 5.15 0.03 0.36 14.480.86 22.16 94 2 Lei-62M 17.07.2001 10 5-10 2.66 4.38 0.03 0.31 14.220.91 19.91 94 2 Lei-62M 17.07.2001 10 10-15 2.92 2.64 0.03 0.23 11.661.08 14.24 94 2 Lei-62M 17.07.2001 11 0-5 1.45 3.34 0.02 0.23 14.370.81 13.47 85 4 Lei-62M 17.07.2001 11 5-10 1.07 1.44 0.02 0.09 15.32 1.19 8.56 85 4 Lei-62M 17.07.2001 11 10-15 0.92 0.97 0.01 0.07 13.95 1.27 6.18 85 4 Lei-62M 17.07.2001 12 0-5 2.83 5.76 0.05 0.36 16.030.93 26.85 80 4 Lei-62M 17.07.2001 12 5-10 2.50 4.16 0.04 0.28 14.691.00 20.85 80 4 Lei-62M 17.07.2001 12 10-15 1.60 2.49 0.02 0.19 13.311.05 13.05 80 4

226

Table A.9: continued

Soil Max. Site No Date Core No. RWC TOC TIC TN C/N fBD SOC SC depth depth [cm] [%] [%] [%] [%] [g/g] [g/cm³] [tC/ha] [cm] Lei-111M 26.07.2001 A 0-5 1.33 5.36 0.03 0.36 15.02 0.73 19.56 91 4 Lei-111M 26.07.2001 A 5-10 2.18 1.76 0.01 0.14 12.96 1.27 11.23 91 4 Lei-111M 26.07.2001 A 10-15 1.01 0.76 0.01 0.07 11.59 1.22 4.62 91 4 Lei-111M 26.07.2001 B 0-5 1.74 4.22 0.03 0.29 14.68 0.87 18.40 81 4 Lei-111M 26.07.2001 B 5-10 0.98 1.59 0.02 0.12 12.86 1.12 8.93 81 4 Lei-111M 26.07.2001 B 10-15 0.92 1.11 0.01 0.08 13.78 1.27 7.01 81 4 Lei-111M 18.07.2001 1 0-5 1.50 3.48 0.02 0.26 13.23 0.88 15.30 80 4 Lei-111M 18.07.2001 1 5-10 1.14 1.56 0.01 0.13 12.30 1.13 8.80 80 4 Lei-111M 18.07.2001 1 10-15 1.43 1.35 0.02 0.12 11.47 1.38 9.33 80 4 Lei-111M 18.07.2001 2 0-5 1.47 3.34 0.03 0.23 14.65 0.96 16.11 85 3 Lei-111M 18.07.2001 2 5-10 1.24 1.54 0.01 0.10 15.02 1.17 8.99 85 3 Lei-111M 18.07.2001 2 10-15 1.25 1.30 0.01 0.09 14.62 1.16 7.53 85 3 Lei-111M 18.07.2001 3 0-5 1.75 3.40 0.03 0.27 12.58 0.71 12.02 69 4 Lei-111M 18.07.2001 3 5-10 1.39 1.48 0.01 0.11 13.36 0.94 6.92 69 4 Lei-111M 18.07.2001 3 10-15 1.27 1.11 0.01 0.08 13.73 1.16 6.41 69 4 Lei-111M 18.07.2001 4 0-5 1.52 4.58 0.02 0.33 13.74 0.53 12.15 79 3 Lei-111M 18.07.2001 4 5-10 1.04 1.93 0.01 0.14 14.06 1.05 10.13 79 3 Lei-111M 18.07.2001 4 10-15 0.93 1.36 0.01 0.10 14.32 1.04 7.11 79 3 Lei-111M 18.07.2001 5 0-5 1.72 3.93 0.02 0.28 13.86 0.76 14.90 89 4 Lei-111M 18.07.2001 5 5-10 1.13 1.50 0.01 0.11 13.48 1.11 8.35 89 4 Lei-111M 18.07.2001 5 10-15 1.06 1.06 0.01 0.08 12.84 1.31 6.94 89 4 Lei-111M 18.07.2001 6 0-5 1.40 3.61 0.11 0.26 14.08 1.09 19.67 88 4 Lei-111M 18.07.2001 6 5-10 1.05 1.45 0.03 0.11 13.28 1.16 8.43 88 4 Lei-111M 18.07.2001 6 10-15 0.87 0.81 0.01 0.07 12.16 1.44 5.88 88 4 Lei-111M 18.07.2001 7 0-5 1.24 3.88 0.02 0.27 14.39 0.85 16.42 66 4 Lei-111M 18.07.2001 7 5-10 0.76 1.34 0.02 0.13 9.95 1.27 8.53 66 4 Lei-111M 18.07.2001 7 10-15 0.87 0.98 0.01 0.07 13.94 1.38 6.81 66 4 Lei-111M 18.07.2001 8 0-5 1.94 3.79 0.02 0.28 13.46 0.68 12.80 86 4 Lei-111M 18.07.2001 8 5-10 1.55 2.07 0.01 0.16 12.62 0.96 9.91 86 4 Lei-111M 18.07.2001 8 10-15 1.46 1.49 0.01 0.12 12.09 0.86 6.39 86 4 Lei-111M 18.07.2001 9 0-5 1.43 3.88 0.04 0.27 14.18 0.69 13.45 86 4 Lei-111M 18.07.2001 9 5-10 1.18 1.70 0.02 0.12 14.03 1.07 9.06 86 4 Lei-111M 18.07.2001 9 10-15 1.08 1.27 0.01 0.09 13.75 1.00 6.33 86 4 Lei-111M 18.07.2001 10 0-5 1.91 4.12 0.03 0.29 14.20 0.80 16.56 85 4 Lei-111M 18.07.2001 10 5-10 1.11 1.42 0.01 0.10 14.18 1.03 7.32 85 4 Lei-111M 18.07.2001 10 10-15 1.03 0.96 0.01 0.07 13.12 1.18 5.67 85 4 Lei-111M 18.07.2001 11 0-5 1.17 2.85 0.02 0.19 14.64 0.84 12.02 92 4 Lei-111M 18.07.2001 11 5-10 0.96 1.45 0.01 0.10 14.66 1.19 8.58 92 4 Lei-111M 18.07.2001 11 10-15 0.80 1.11 0.01 0.08 13.44 1.19 6.59 92 4 Lei-111M 18.07.2001 12 0-5 1.82 4.44 0.14 0.32 14.04 0.60 13.29 91 4 Lei-111M 18.07.2001 12 5-10 1.24 2.08 0.01 0.14 14.49 0.86 8.90 91 4 Lei-111M 18.07.2001 12 10-15 1.05 1.24 0.01 0.08 14.68 1.10 6.83 91 4 Lei-111M 26.07.2001 13 0-5 1.68 4.12 0.03 0.28 14.70 0.43 8.86 66 3 Lei-111M 26.07.2001 13 5-10 1.27 1.27 0.00 0.09 13.40 1.14 7.24 66 3 Lei-111M 26.07.2001 13 10-15 0.85 0.93 0.02 0.06 14.60 1.21 5.63 66 3 Lei-141M 02.08.2001 A 0-5 1.34 4.38 0.02 0.29 15.08 0.37 8.14 70 4 Lei-141M 02.08.2001 A 5-10 0.98 2.38 0.02 0.15 15.66 1.08 12.81 70 4 Lei-141M 02.08.2001 A 10-15 0.83 1.16 0.01 0.07 15.70 1.12 6.54 70 4

227 Table A.9: continued

Soil Max. Site No Date Core No. RWC TOC TIC TN C/N fBD SOC SC depth depth [cm] [%] [%] [%] [%] [g/g] [g/cm³] [tC/ha] [cm] Lei-141M 02.08.2001 B 0-5 1.20 2.50 0.02 0.18 13.52 1.09 13.58 71 4 Lei-141M 02.08.2001 B 5-10 1.03 1.35 0.02 0.10 14.00 1.23 8.31 71 4 Lei-141M 02.08.2001 B 10-15 0.95 0.92 0.01 0.07 12.98 1.26 5.76 71 4 Lei-141M 02.08.2001 C 0-5 1.60 3.88 0.04 0.27 14.31 0.53 10.24 75 4 Lei-141M 02.08.2001 C 5-10 1.10 1.73 0.02 0.14 12.82 1.07 9.25 75 4 Lei-141M 02.08.2001 C 10-15 1.12 1.31 0.01 0.11 11.84 1.25 8.17 75 4 Lei-141M 02.08.2001 D 0-5 1.61 5.37 0.02 0.42 12.91 0.56 15.15 63 4 Lei-141M 02.08.2001 D 5-10 0.98 1.74 0.02 0.13 13.93 1.12 9.77 63 4 Lei-141M 02.08.2001 D 10-15 ds ds ds ds ds ds 8.52 63 4 Lei-141M 02.08.2001 E 0-5 1.30 2.16 0.02 0.17 12.82 0.78 8.47 84 4 Lei-141M 02.08.2001 E 5-10 1.21 1.87 0.01 0.15 12.88 0.97 9.06 84 4 Lei-141M 02.08.2001 E 10-15 1.05 1.32 0.01 0.09 13.96 1.11 7.31 84 4 Lei-141M 02.08.2001 1 0-5 2.15 5.51 0.06 0.35 15.89 0.60 16.41 84 4 Lei-141M 02.08.2001 1 5-10 1.84 3.49 0.03 0.25 14.16 0.87 15.17 84 4 Lei-141M 02.08.2001 1 10-15 1.48 2.47 0.02 0.18 13.48 1.32 16.31 84 4 Lei-141M 02.08.2001 2 0-5 2.00 4.44 0.03 0.33 13.52 0.54 11.91 84 4 Lei-141M 02.08.2001 2 5-10 1.72 2.38 0.02 0.20 11.89 0.89 10.58 84 4 Lei-141M 02.08.2001 2 10-15 1.66 2.09 0.02 0.19 11.24 1.24 12.96 84 4 Lei-141M 02.08.2001 3 0-5 1.04 3.06 0.02 0.22 14.17 0.70 10.67 81 4 Lei-141M 02.08.2001 3 5-10 0.95 1.38 0.01 0.12 11.74 1.33 9.11 81 4 Lei-141M 02.08.2001 3 10-15 0.90 1.06 0.02 0.08 13.06 1.31 6.93 81 4 Lei-141M 02.08.2001 4 0-5 1.40 3.67 0.02 0.24 15.00 0.79 14.44 59 4 Lei-141M 02.08.2001 4 5-10 1.14 1.86 0.01 0.14 13.24 1.25 11.64 59 4 Lei-141M 02.08.2001 4 10-15 1.12 0.98 0.01 0.07 13.31 1.24 5.96 59 4 Lei-141M 02.08.2001 5 0-5 1.15 3.06 0.02 0.22 13.77 0.78 11.93 79 4 Lei-141M 02.08.2001 5 5-10 0.84 1.41 0.01 0.11 13.02 1.10 7.75 79 4 Lei-141M 02.08.2001 5 10-15 0.89 1.02 0.01 0.07 13.95 1.40 7.13 79 4 Lei-141M 02.08.2001 6 0-5 1.98 1.76 0.03 0.15 11.77 1.07 9.44 60 4 Lei-141M 02.08.2001 6 5-10 0.97 1.03 0.01 0.08 12.29 1.57 8.10 60 4 Lei-141M 02.08.2001 6 10-15 0.94 0.72 0.02 0.06 11.52 1.26 4.51 60 4 Lei-141M 02.08.2001 7 0-5 1.96 4.18 0.03 0.31 13.67 0.67 14.06 68 4 Lei-141M 02.08.2001 7 5-10 m m m m m m 9.64 68 4 Lei-141M 02.08.2001 7 10-15 1.71 1.82 0.02 0.16 11.44 1.29 11.74 68 4 Lei-141M 02.08.2001 8 0-5 1.70 4.42 0.02 0.33 13.48 0.72 15.97 65 4 Lei-141M 02.08.2001 8 5-10 1.15 1.69 0.01 0.12 13.58 1.17 9.92 65 4 Lei-141M 02.08.2001 8 10-15 0.78 1.29 0.01 0.09 14.12 1.26 8.15 65 4 Lei-141M 02.08.2001 9 0-5 2.80 4.81 0.08 0.36 13.42 0.65 15.75 51 4 Lei-141M 02.08.2001 9 5-10 2.19 1.89 0.02 0.17 11.08 1.17 11.04 51 4 Lei-141M 02.08.2001 9 10-15 1.97 2.02 0.02 0.17 11.57 1.15 11.55 51 4 Lei-141M 02.08.2001 10 0-5 1.65 3.84 0.04 0.26 14.68 1.01 19.34 82 4 Lei-141M 02.08.2001 10 5-10 0.92 1.33 0.01 0.10 13.49 1.23 8.19 82 4 Lei-141M 02.08.2001 10 10-15 0.89 0.88 0.01 0.07 12.56 1.38 6.06 82 4 Lei-153+16M 23.10.2001 A 0-5 3.37 6.14 0.06 0.44 13.91 0.62 18.90 52 4 Lei-153+16M 23.10.2001 A 5-10 2.41 2.50 0.03 0.20 12.23 0.67 8.41 52 4 Lei-153+16M 23.10.2001 A 10-15 2.38 2.10 0.02 0.18 11.49 0.89 9.37 52 4 Lei-153+16M 23.10.2001 B 0-5 2.67 5.88 0.07 0.38 15.31 0.75 22.12 73 4 Lei-153+16M 23.10.2001 B 5-10 1.76 2.63 0.04 0.19 13.67 1.03 13.48 73 4 Lei-153+16M 23.10.2001 B 10-15 1.90 1.39 0.03 0.12 11.50 1.62 11.32 73 4

228

Table A.9: continued

Soil Max. Site No Date Core No. RWC TOC TIC TN C/N fBD SOC SC depth depth [cm] [%] [%] [%] [%] [g/g] [g/cm³] [tC/ha] [cm] Lei-153+16M 23.10.2001 C 0-5 2.55 5.51 0.04 0.35 15.80 0.61 16.74 61 4 Lei-153+16M 23.10.2001 C 5-10 2.12 2.81 0.03 0.21 13.35 0.80 11.27 61 4 Lei-153+16M 23.10.2001 C 10-15 2.12 2.88 0.02 0.22 12.93 1.49 21.53 61 4 Lei-153+16M 23.10.2001 D 0-5 2.98 4.96 0.04 0.37 13.43 0.83 20.69 47 2 Lei-153+16M 23.10.2001 D 5-10 2.13 3.79 0.03 0.31 12.02 0.80 15.11 47 2 Lei-153+16M 23.10.2001 D 10-15 ds ds ds ds ds ds 12.10 47 2 Lei-153+16M 23.10.2001 E 0-5 2.76 5.53 0.05 0.42 13.17 0.62 17.21 86 2 Lei-153+16M 23.10.2001 E 5-10 2.54 3.48 0.04 0.29 11.95 1.06 18.45 86 2 Lei-153+16M 23.10.2001 E 10-15 2.29 2.29 0.03 0.21 11.09 1.14 13.11 86 2 Lei-153+16M 23.10.2001 1 0-5 1.78 4.46 0.04 0.38 11.79 0.69 15.49 72 2 Lei-153+16M 23.10.2001 1 5-10 1.88 3.34 0.03 0.24 13.92 0.98 16.41 72 2 Lei-153+16M 23.10.2001 1 10-15 1.54 1.74 0.02 0.14 12.25 1.40 12.22 72 2 Lei-153+16M 23.10.2001 2 0-5 1.59 4.24 0.03 0.29 14.83 0.78 16.60 52 4 Lei-153+16M 23.10.2001 2 5-10 1.25 1.92 0.02 0.15 12.86 1.08 10.33 53 4 Lei-153+16M 23.10.2001 2 10-15 1.14 1.13 0.02 0.09 11.93 1.30 7.33 54 4 Lei-153+16M 23.10.2001 3 0-5 3.30 6.79 0.17 0.51 13.23 0.43 14.47 64 1 Lei-153+16M 23.10.2001 3 5-10 3.19 4.76 0.12 0.40 11.86 0.73 17.44 65 1 Lei-153+16M 23.10.2001 3 10-15 2.97 3.12 0.04 0.28 11.19 1.03 16.07 66 1 Lei-153+16M 23.10.2001 4 0-5 2.76 5.00 0.16 0.34 14.88 0.86 21.59 53 3 Lei-153+16M 23.10.2001 4 5-10 2.28 3.14 0.18 0.23 13.61 0.89 14.01 54 3 Lei-153+16M 23.10.2001 4 10-15 1.59 1.57 0.02 0.12 13.18 1.31 10.27 55 3 Lei-153+16M 23.10.2001 5 0-5 2.99 6.96 0.07 0.46 15.09 0.65 22.78 72 3 Lei-153+16M 23.10.2001 5 5-10 1.83 2.53 0.03 0.19 13.00 1.01 12.79 73 3 Lei-153+16M 23.10.2001 5 10-15 1.97 2.00 0.03 0.17 11.79 1.26 12.60 74 3 Lei-153+16M 23.10.2001 6 0-5 3.09 5.21 0.16 0.39 13.26 0.79 20.54 53 3 Lei-153+16M 23.10.2001 6 5-10 2.38 3.07 0.16 0.25 12.04 1.05 16.04 54 3 Lei-153+16M 23.10.2001 6 10-15 2.21 1.49 0.11 0.15 9.99 1.22 9.08 55 3 Lei-153+16M 23.10.2001 7 0-5 3.07 5.01 0.05 0.37 13.70 0.94 23.59 56 3 Lei-153+16M 23.10.2001 7 5-10 2.54 2.59 0.03 0.21 12.07 1.04 13.46 57 3 Lei-153+16M 23.10.2001 7 10-15 2.64 2.02 0.02 0.19 10.55 1.26 12.70 58 3 Lei-153+16M 16.10.2001 8 0-5 2.09 4.01 0.11 0.29 13.99 0.83 16.56 78 4 Lei-153+16M 16.10.2001 8 5-10 1.38 1.52 0.02 0.13 12.04 1.26 9.59 78 4 Lei-153+16M 16.10.2001 8 10-15 1.30 1.08 0.02 0.09 11.44 1.23 6.66 78 4 Lei-153+16M 23.10.2001 9 0-5 3.15 6.05 0.09 0.44 13.89 0.71 21.48 65 2 Lei-153+16M 23.10.2001 9 5-10 2.65 3.56 0.03 0.28 12.79 1.12 19.84 65 2 Lei-153+16M 23.10.2001 9 10-15 2.60 2.42 0.03 0.21 11.62 1.06 12.88 65 2 Lei-153+16M 16.10.2001 10 0-5 2.25 4.45 0.05 0.31 14.37 0.69 15.28 69 3 Lei-153+16M 16.10.2001 10 5-10 1.55 2.04 0.02 0.16 12.76 1.16 11.90 69 3 Lei-153+16M 16.10.2001 10 10-15 1.38 1.37 0.02 0.12 11.65 1.34 9.19 69 3 Mühl-38 12.05.2001 1 0-5 1.85 6.39 0.02 0.38 16.840.80 25.41 62 3 Mühl-38 12.05.2001 1 5-10 1.05 2.03 0.01 0.13 16.211.07 10.86 62 3 Mühl-38 12.05.2001 1 10-15 1.30 1.72 0.01 0.10 16.84 1.09 9.36 62 3 Mühl-38 12.05.2001 2 0-5 1.93 4.85 0.02 0.38 12.73 0.38 9.10 63 4 Mühl-38 12.05.2001 2 5-10 1.62 4.50 0.03 0.36 12.590.60 13.54 63 4 Mühl-38 12.05.2001 2 10-15 1.20 1.99 0.02 0.16 12.45 0.94 9.34 63 4 Mühl-38 12.05.2001 3 0-5 2.32 8.38 0.02 0.63 13.370.39 16.30 59 4 Mühl-38 12.05.2001 3 5-10 1.53 4.17 0.02 0.31 13.330.72 15.07 59 4 Mühl-38 12.05.2001 3 10-15 2.70 3.69 0.03 0.33 11.340.89 16.49 59 4

229 Table A.9: continued

Soil Max. Site No Date Core No. RWC TOC TIC TN C/N fBD SOC SC depth depth [cm] [%] [%] [%] [%] [g/g] [g/cm³] [tC/ha] [cm] Mühl-38 12.05.2001 4 0-5 2.71 4.37 0.03 0.35 12.580.68 14.82 27 2 Mühl-38 12.05.2001 4 5-10 2.35 2.38 0.02 0.21 11.36 0.77 9.15 27 2 Mühl-38 12.05.2001 4 10-15 ds ds ds ds ds ds 9.99 27 2 Mühl-38 12.05.2001 5 0-5 1.48 3.85 0.02 0.28 13.680.70 13.58 58 2 Mühl-38 12.05.2001 5 5-10 1.17 1.45 0.01 0.12 11.96 1.11 8.02 58 2 Mühl-38 12.05.2001 5 10-15 1.02 1.00 0.01 0.08 12.63 1.40 7.03 58 2 Mühl-38 12.05.2001 6 0-5 2.58 4.87 0.03 0.40 12.270.68 16.68 27 2 Mühl-38 12.05.2001 6 5-10 2.21 2.24 0.02 0.20 11.201.06 11.85 27 2 Mühl-38 12.05.2001 6 10-15 2.06 2.73 0.02 0.21 13.141.14 15.61 27 2 Mühl-38 12.05.2001 7 0-5 1.81 3.35 0.02 0.27 12.210.80 13.35 70 4 Mühl-38 12.05.2001 7 5-10 1.48 1.18 0.01 0.12 9.85 1.26 7.43 70 4 Mühl-38 12.05.2001 7 10-15 ds ds ds ds ds ds 9.99 70 4 Mühl-38 12.05.2001 8 0-5 3.02 6.68 0.04 0.53 12.520.49 16.51 67 4 Mühl-38 12.05.2001 8 5-10 1.86 3.75 0.02 0.32 11.810.64 12.08 67 4 Mühl-38 12.05.2001 8 10-15 2.05 1.87 0.02 0.16 11.52 0.99 9.31 67 4 Mühl-38 12.05.2001 9 0-5 1.98 6.78 0.02 0.51 13.190.56 19.08 44 4 Mühl-38 12.05.2001 9 5-10 1.53 3.37 0.01 0.17 19.790.92 15.46 44 4 Mühl-38 12.05.2001 9 10-15 1.23 1.97 0.01 0.12 15.80 0.88 8.65 44 4 Mühl-38 12.05.2001 10 0-5 1.92 3.99 0.02 0.30 13.360.53 10.52 72 4 Mühl-38 12.05.2001 10 5-10 1.23 1.84 0.01 0.15 11.98 1.06 9.73 72 4 Mühl-38 12.05.2001 10 10-15 1.14 0.96 0.01 0.08 12.50 1.02 4.89 72 4 Mühl-38 12.05.2001 11 0-5 1.86 5.80 0.03 0.42 13.790.47 13.66 70 4 Mühl-38 12.05.2001 11 5-10 1.68 1.93 0.02 0.15 12.52 0.85 8.20 70 4 Mühl-38 12.05.2001 11 10-15 1.34 1.25 0.01 0.10 12.60 1.14 7.15 70 4 Mühl-38 12.05.2001 12 0-5 2.11 4.33 0.03 0.30 14.240.76 16.39 61 4 Mühl-38 12.05.2001 12 5-10 1.21 2.28 0.02 0.17 13.79 0.72 8.16 61 4 Mühl-38 12.05.2001 12 10-15 1.18 1.84 0.01 0.13 14.251.17 10.80 61 4 Mühl-38 13.05.2001 Kasten 9 0-5 2.24 6.90 0.02 0.40 17.26 0.55 18.86 63 4 Mühl-38 13.05.2001 Kasten 9 5-10 1.26 2.52 0.01 0.18 13.92 0.83 10.45 63 4 Mühl-38 13.05.2001 Kasten 9 10-15 1.16 2.01 0.01 0.13 15.95 1.16 11.59 63 4 Mühl-38 12.05.2001 Kasten 10 0-5 1.68 5.48 0.02 0.42 13.130.72 19.70 67 4 Mühl-38 12.05.2001 Kasten 10 5-10 1.64 3.65 0.02 0.31 11.920.61 11.22 67 4 Mühl-38 12.05.2001 Kasten 10 10-15 1.49 1.96 0.01 0.17 11.261.43 13.99 67 4 Mühl-38 12.05.2001 Kasten 12 0-5 1.70 3.93 0.02 0.31 12.740.79 15.54 70 4 Mühl-38 12.05.2001 Kasten 12 5-10 1.49 2.25 0.02 0.19 11.67 0.84 9.47 70 4 Mühl-38 12.05.2001 Kasten 12 10-15 1.19 1.47 0.01 0.13 11.73 1.13 8.32 70 4 Mühl-55 28.02.2001 B 0-5 4.65 9.01 0.03 0.68 13.250.50 22.47 38 1 Mühl-55 28.02.2001 B 5-10 3.70 4.09 0.02 0.35 11.870.94 19.31 38 1 Mühl-55 28.02.2001 B 10-15 3.63 2.93 0.02 0.25 11.671.01 14.83 38 1 Mühl-55 28.02.2001 C 0-5 3.06 8.21 0.03 0.57 14.310.68 27.80 52 1 Mühl-55 28.02.2001 C 5-10 3.12 3.22 0.02 0.27 11.860.94 15.12 52 1 Mühl-55 28.02.2001 C 10-15 2.95 2.20 0.01 0.19 11.361.24 13.67 52 1 Mühl-55 28.02.2001 D 0-5 4.42 8.45 0.03 0.60 14.030.48 20.45 65 1 Mühl-55 28.02.2001 D 5-10 3.39 3.57 0.02 0.31 11.390.86 15.36 65 1 Mühl-55 28.02.2001 D 10-15 3.20 2.71 0.02 0.24 11.350.80 10.88 65 1 Mühl-55 28.02.2001 E 0-5 3.06 6.40 0.03 0.49 13.110.39 12.55 50 2 Mühl-55 28.02.2001 E 5-10 2.63 3.75 0.02 0.30 12.450.75 14.06 50 2 Mühl-55 28.02.2001 E 10-15 2.46 3.23 0.02 0.25 12.810.77 12.35 50 2

230

Table A.9: continued

Soil Max. Site No Date Core No. RWC TOC TIC TN C/N fBD SOC SC depth depth [cm] [%] [%] [%] [%] [g/g] [g/cm³] [tC/ha] [cm] Mühl-55 28.02.2001 1 0-5 5.04 8.72 0.05 0.67 13.010.53 23.05 54 1 Mühl-55 28.02.2001 1 5-10 3.30 5.50 0.04 0.45 12.110.78 21.50 54 1 Mühl-55 28.02.2001 1 10-15 3.48 4.14 0.02 0.36 11.570.76 15.76 54 1 Mühl-55 28.02.2001 2 0-5 2.54 4.79 0.03 0.36 13.360.79 18.93 39 3 Mühl-55 28.02.2001 2 5-10 2.35 4.70 0.03 0.33 14.270.76 17.87 39 3 Mühl-55 28.02.2001 2 10-15 2.23 3.69 0.03 0.26 14.000.87 16.07 39 3 Mühl-55 28.02.2001 3 0-5 4.34 7.16 0.04 0.56 12.860.49 17.42 37 1 Mühl-55 28.02.2001 3 5-10 3.79 5.01 0.03 0.41 12.240.49 12.15 37 1 Mühl-55 28.02.2001 3 10-15 3.83 4.25 0.02 0.35 12.050.78 16.50 37 1 Mühl-55 28.02.2001 4 0-5 4.16 8.18 0.06 0.69 11.790.45 18.60 40 1 Mühl-55 28.02.2001 4 5-10 2.98 3.54 0.02 0.30 11.780.76 13.40 40 1 Mühl-55 28.02.2001 4 10-15 3.12 2.81 0.02 0.24 11.470.87 12.15 40 1 Mühl-55 28.02.2001 5 0-5 3.39 8.96 0.06 0.61 14.640.30 13.26 28 3 Mühl-55 28.02.2001 5 5-10 2.97 4.58 0.03 0.37 12.290.52 11.92 28 3 Mühl-55 28.02.2001 5 10-15 2.95 3.46 0.02 0.28 12.320.73 12.59 28 3 Mühl-55 28.02.2001 6 0-5 2.58 4.65 0.03 0.37 12.650.73 16.88 82 2 Mühl-55 28.02.2001 6 5-10 2.21 3.53 0.02 0.31 11.340.77 13.64 82 2 Mühl-55 28.02.2001 6 10-15 1.98 2.25 0.02 0.20 11.05 0.86 9.65 82 2 Mühl-55 28.02.2001 7 0-5 1.68 5.47 0.18 0.45 12.070.55 15.05 60 2 Mühl-55 28.02.2001 7 5-10 2.95 4.60 0.16 0.40 11.520.63 14.41 60 2 Mühl-55 28.02.2001 7 10-15 3.05 3.36 0.02 0.31 10.810.93 15.68 60 2 Mühl-55 28.02.2001 8 0-5 4.38 10.19 0.11 0.77 13.29 0.43 21.81 30 1 Mühl-55 28.02.2001 8 5-10 3.87 6.03 0.04 0.53 11.480.71 21.47 30 1 Mühl-55 28.02.2001 8 10-15 3.49 4.76 0.02 0.43 11.090.62 14.73 30 1 Mühl-55 28.02.2001 9 0-5 4.38 10.77 0.10 0.87 12.42 0.46 24.85 29 1 Mühl-55 28.02.2001 9 5-10 3.81 7.25 0.04 0.61 11.860.64 23.26 29 1 Mühl-55 28.02.2001 9 10-15 4.54 4.92 0.02 0.44 11.200.58 14.29 29 1 Mühl-55 28.02.2001 10 0-5 3.54 6.77 0.21 0.54 12.610.61 20.64 44 1 Mühl-55 28.02.2001 10 5-10 3.30 5.78 0.11 0.48 12.080.72 20.92 44 1 Mühl-55 28.02.2001 10 10-15 4.56 4.45 0.09 0.40 11.010.74 16.42 44 1 Mühl-55 28.02.2001 11 0-5 3.53 6.62 0.05 0.50 13.270.64 21.22 80 1 Mühl-55 28.02.2001 11 5-10 3.35 5.60 0.03 0.44 12.620.66 18.58 80 1 Mühl-55 28.02.2001 11 10-15 2.95 4.57 0.02 0.37 12.230.86 19.67 80 1 Mühl-85 14.12.2000 A 0-5 3.51 4.98 0.04 0.42 11.840.67 16.72 54 4 Mühl-85 14.12.2000 A 5-10 3.01 2.72 0.04 0.25 10.910.99 13.39 54 4 Mühl-85 14.12.2000 A 10-15 2.11 2.17 0.04 0.21 10.131.17 12.75 54 4 Mühl-85 14.12.2000 C 0-5 2.41 4.43 0.03 0.36 12.180.66 14.52 62 4 Mühl-85 14.12.2000 C 5-10 1.42 2.42 0.03 0.21 11.440.88 10.70 62 4 Mühl-85 14.12.2000 C 10-15 1.92 1.82 0.02 0.16 11.221.21 10.99 62 4 Mühl-85 14.12.2000 1 0-5 1.66 3.68 0.03 0.28 13.330.78 14.32 64 4 Mühl-85 14.12.2000 1 5-10 1.78 1.92 0.03 0.16 12.321.32 12.72 64 4 Mühl-85 14.12.2000 1 10-15 1.73 1.21 0.02 0.12 9.98 1.25 7.56 64 4 Mühl-85 01.12.2000 2 0-5 2.22 3.06 0.03 0.27 11.31 0.56 8.58 54 4 Mühl-85 01.12.2000 2 5-10 1.57 2.49 0.03 0.23 10.770.89 11.06 54 4 Mühl-85 01.12.2000 2 10-15 2.05 2.27 0.03 0.21 10.640.98 11.15 54 4 Mühl-85 01.12.2000 3 0-5 2.29 5.35 0.06 0.44 12.300.92 24.74 54 4 Mühl-85 01.12.2000 3 5-10 2.47 2.53 0.03 0.23 10.981.09 13.81 54 4 Mühl-85 01.12.2000 3 10-15 2.35 1.81 0.02 0.17 10.411.33 12.08 54 4

231 Table A.9: continued

Soil Max. Site No Date Core No. RWC TOC TIC TN C/N fBD SOC SC depth depth [cm] [%] [%] [%] [%] [g/g] [g/cm³] [tC/ha] [cm] Mühl-85 01.12.2000 4 0-5 2.94 6.24 0.04 0.45 13.800.56 17.45 45 4 Mühl-85 01.12.2000 4 5-10 1.39 2.45 0.02 0.21 11.691.13 13.83 45 4 Mühl-85 01.12.2000 4 10-15 1.87 1.71 0.03 0.16 10.89 1.01 8.66 45 4 Mühl-85 01.12.2000 5 0-5 3.49 5.03 0.05 0.39 12.770.73 18.30 33 1 Mühl-85 01.12.2000 5 5-10 3.02 3.08 0.03 0.27 11.271.03 15.87 33 1 Mühl-85 01.12.2000 5 10-15 2.70 1.98 0.04 0.20 9.98 1.17 11.55 33 1 Mühl-85 01.12.2000 6 0-5 3.29 7.04 0.07 0.53 13.180.71 24.95 40 4 Mühl-85 01.12.2000 6 5-10 3.63 3.89 0.06 0.34 11.471.07 20.72 40 4 Mühl-85 01.12.2000 6 10-15 3.38 2.66 0.02 0.24 11.110.91 11.36 40 4 Mühl-85 01.12.2000 7 0-5 3.50 8.08 0.08 0.62 12.930.55 22.31 40 4 Mühl-85 01.12.2000 7 5-10 3.94 4.53 0.04 0.38 11.810.80 18.14 40 4 Mühl-85 01.12.2000 7 10-15 3.67 3.22 0.03 0.29 10.941.03 16.57 40 4 Mühl-85 01.12.2000 8 0-5 3.47 5.53 0.06 0.43 12.910.87 24.14 60 4 Mühl-85 01.12.2000 8 5-10 3.11 3.50 0.04 0.29 12.130.74 12.86 60 4 Mühl-85 01.12.2000 8 10-15 2.91 1.84 0.03 0.16 11.34 1.02 9.35 60 4 Mühl-85 01.12.2000 9 0-5 2.16 5.13 0.03 0.41 12.450.65 16.76 45 4 Mühl-85 01.12.2000 9 5-10 1.58 2.21 0.03 0.20 11.251.00 11.10 45 4 Mühl-85 01.12.2000 9 10-15 2.51 1.42 0.03 0.15 9.67 1.20 8.56 45 4 Mühl-85 01.12.2000 10 0-5 2.41 3.47 0.04 0.28 12.370.89 15.49 40 4 Mühl-85 01.12.2000 10 5-10 2.18 1.94 0.03 0.18 11.021.06 10.27 40 4 Mühl-85 01.12.2000 10 10-15 1.50 1.94 0.03 0.18 11.06 0.87 8.42 40 4 Mühl-85 01.12.2000 11 0-5 2.87 4.71 0.03 0.34 13.970.94 22.24 42 2 Mühl-85 01.12.2000 11 5-10 2.26 2.41 0.02 0.21 11.361.24 14.86 42 2 Mühl-85 01.12.2000 11 10-15 1.46 1.67 0.02 0.16 10.521.36 11.37 42 2 Mühl-85 01.12.2000 12 0-5 1.87 4.58 0.04 0.35 12.900.52 11.94 34 4 Mühl-85 01.12.2000 12 5-10 2.01 2.71 0.03 0.22 12.270.89 12.10 34 4 Mühl-85 01.12.2000 12 10-15 1.84 1.89 0.03 0.16 11.821.25 11.81 34 4 Mühl-85 01.12.2000 13 0-5 2.91 4.57 0.03 0.39 11.690.89 20.38 54 4 Mühl-85 01.12.2000 13 5-10 2.73 2.77 0.04 0.26 10.651.06 14.64 54 4 Mühl-85 01.12.2000 13 10-15 2.77 2.29 0.02 0.22 10.211.17 13.39 54 4 Mühl-102 15.12.2000 A 0-5 2.60 6.17 0.05 0.44 13.99 0.69 21.26 66 4 Mühl-102 15.12.2000 A 5-10 ev 1.74 0.02 0.17 10.13 0.99 8.61 66 4 Mühl-102 15.12.2000 A 10-15 2.12 2.63 0.03 0.23 11.21 0.95 12.55 66 4 Mühl-102 15.12.2000 C 0-5 2.11 4.01 0.02 0.34 11.88 1.05 20.98 64 4 Mühl-102 15.12.2000 C 5-10 1.87 2.79 0.03 0.27 10.36 1.05 14.72 64 4 Mühl-102 15.12.2000 C 10-15 1.79 2.21 0.03 0.22 10.05 1.21 13.39 64 4 Mühl-102 15.12.2000 D 0-5 1.76 2.87 0.03 0.25 11.29 1.01 14.49 52 4 Mühl-102 15.12.2000 D 5-10 ev 3.36 0.03 0.29 11.58 1.09 18.26 52 4 Mühl-102 15.12.2000 D 10-15 1.47 1.34 0.02 0.13 10.03 1.44 9.63 52 4 Mühl-102 15.12.2000 E 0-5 1.19 2.26 0.03 0.19 12.06 1.01 11.46 57 4 Mühl-102 15.12.2000 E 5-10 1.02 1.15 0.02 0.10 12.06 1.46 8.41 57 4 Mühl-102 15.12.2000 E 10-15 2.22 0.91 0.03 0.09 10.33 1.24 5.62 57 4 Mühl-102 15.12.2000 1 0-5 1.81 4.97 0.04 0.36 13.74 0.71 17.61 60 4 Mühl-102 15.12.2000 1 5-10 2.25 3.15 0.03 0.25 12.52 0.95 14.95 60 4 Mühl-102 15.12.2000 1 10-15 2.19 3.16 0.03 0.25 12.87 0.85 13.38 60 4 Mühl-102 15.12.2000 2 0-5 2.40 4.04 0.03 0.31 13.18 0.62 12.62 63 4 Mühl-102 15.12.2000 2 5-10 2.08 2.54 0.02 0.23 11.09 1.17 14.86 63 4 Mühl-102 15.12.2000 2 10-15 1.88 1.68 0.02 0.16 10.24 1.23 10.29 63 4

232

Table A.9: continued

Soil Max. Site No Date Core No. RWC TOC TIC TN C/N fBD SOC SC depth depth [cm] [%] [%] [%] [%] [g/g] [g/cm³] [tC/ha] [cm] Mühl-102 15.12.2000 3 0-5 1.99 6.11 0.03 0.40 15.40 0.66 20.07 64 4 Mühl-102 15.12.2000 3 5-10 2.02 2.58 0.02 0.22 11.76 0.93 11.94 64 4 Mühl-102 15.12.2000 3 10-15 2.06 2.17 0.02 0.19 11.63 1.10 11.98 64 4 Mühl-102 15.12.2000 4 0-5 2.23 5.14 0.03 0.38 13.46 0.71 18.30 64 4 Mühl-102 15.12.2000 4 5-10 1.82 2.64 0.02 0.24 10.86 1.10 14.47 64 4 Mühl-102 15.12.2000 4 10-15 1.66 1.78 0.02 0.18 9.67 1.23 10.91 64 4 Mühl-102 15.12.2000 5 0-5 1.95 3.26 0.03 0.28 11.43 0.90 14.61 69 4 Mühl-102 15.12.2000 5 5-10 1.83 2.17 0.02 0.22 9.69 0.96 10.46 69 4 Mühl-102 15.12.2000 5 10-15 1.75 1.66 0.02 0.18 9.29 1.19 9.89 69 4 Mühl-102 15.12.2000 6 0-5 2.42 5.36 0.04 0.38 14.01 0.50 13.48 64 4 Mühl-102 15.12.2000 6 5-10 1.56 2.33 0.02 0.20 11.70 0.93 10.80 64 4 Mühl-102 15.12.2000 6 10-15 1.39 1.68 0.02 0.15 11.23 1.54 12.93 64 4 Mühl-102 15.12.2000 7 0-5 2.98 5.94 0.10 0.41 14.49 0.61 18.14 63 4 Mühl-102 15.12.2000 7 5-10 2.61 4.31 0.05 0.34 12.85 0.79 17.00 63 4 Mühl-102 15.12.2000 7 10-15 2.09 2.59 0.02 0.23 11.25 1.37 17.67 63 4 Mühl-102 15.12.2000 8 0-5 1.86 3.28 0.03 0.22 14.96 1.02 16.78 75 4 Mühl-102 15.12.2000 8 5-10 1.12 1.23 0.02 0.10 12.08 1.33 8.19 75 4 Mühl-102 15.12.2000 8 10-15 1.35 0.79 0.02 0.08 9.93 1.53 6.00 75 4 Mühl-102 15.12.2000 9 0-5 2.42 5.30 0.04 0.38 13.97 0.92 24.42 70 4 Mühl-102 15.12.2000 9 5-10 1.76 2.72 0.03 0.24 11.31 1.16 15.75 70 4 Mühl-102 15.12.2000 9 10-15 1.35 1.77 0.03 0.17 10.25 1.39 12.28 70 4 Mühl-102 15.12.2000 10 0-5 1.95 2.79 0.03 0.23 11.95 0.90 12.55 59 4 Mühl-102 15.12.2000 10 5-10 1.67 1.73 0.03 0.16 10.76 1.12 9.69 59 4 Mühl-102 15.12.2000 10 10-15 1.28 1.37 0.02 0.14 10.09 1.13 7.76 59 4 Mühl-102 15.12.2000 13 0-5 2.95 5.39 0.03 0.41 13.02 0.72 19.31 nd 4 Mühl-102 15.12.2000 13 5-10 2.60 4.23 0.02 0.35 12.14 0.79 16.61 nd 4 Mühl-102 15.12.2000 13 10-15 2.31 2.56 0.03 0.24 10.59 1.03 13.16 nd 4 Mühl-171+10 16.10.2001 A 0-5 1.65 4.32 0.08 0.35 12.28 0.82 17.69 34 4 Mühl-171+10 16.10.2001 A 5-10 1.25 1.16 0.01 0.09 12.27 1.42 8.27 34 4 Mühl-171+10 16.10.2001 A 10-15 1.15 0.97 0.01 0.08 12.30 1.39 6.70 34 4 Mühl-171+10 16.10.2001 B 0-5 2.23 7.88 0.02 0.51 15.31 0.33 12.87 65 4 Mühl-171+10 16.10.2001 B 5-10 1.54 3.39 0.02 0.25 13.66 0.72 12.12 65 4 Mühl-171+10 16.10.2001 B 10-15 1.65 1.69 0.01 0.13 12.64 0.96 8.15 65 4 Mühl-171+10 16.10.2001 D 0-5 2.88 6.22 0.04 0.46 13.45 0.76 23.69 52 2 Mühl-171+10 16.10.2001 D 5-10 2.65 3.62 0.04 0.32 11.33 0.86 15.65 52 2 Mühl-171+10 16.10.2001 D 10-15 2.54 2.95 0.02 0.27 11.15 1.11 16.45 52 2 Mühl-171+10 16.10.2001 E 0-5 2.53 5.31 0.04 0.42 12.69 0.59 15.69 63 4 Mühl-171+10 16.10.2001 E 5-10 2.01 3.22 0.02 0.25 12.63 0.43 6.98 63 4 Mühl-171+10 16.10.2001 E 10-15 1.90 1.71 0.01 0.15 11.20 1.09 9.33 63 4 Mühl-171+10 16.10.2001 F 0-5 1.84 5.98 0.03 0.43 14.05 0.67 19.99 52 3 Mühl-171+10 16.10.2001 F 5-10 2.04 2.18 0.02 0.19 11.35 1.17 12.82 52 3 Mühl-171+10 16.10.2001 F 10-15 2.37 1.53 0.02 0.14 10.90 1.13 8.64 52 3 Mühl-171+10 16.10.2001 1 0-5 2.64 4.64 0.04 0.37 12.54 0.70 16.15 69 3 Mühl-171+10 16.10.2001 1 5-10 2.88 4.29 0.06 0.35 12.12 0.69 14.88 69 3 Mühl-171+10 16.10.2001 1 10-15 1.65 2.98 0.03 0.27 11.22 0.89 13.24 69 3 Mühl-171+10 16.10.2001 2 0-5 2.48 5.29 0.03 0.38 13.98 0.70 18.64 69 4 Mühl-171+10 16.10.2001 2 5-10 2.08 2.71 0.02 0.22 12.09 0.78 10.59 69 4 Mühl-171+10 16.10.2001 2 10-15 2.04 1.89 0.02 0.17 10.81 1.04 9.86 69 4

233 Table A.9: continued

Soil Max. Site No Date Core No. RWC TOC TIC TN C/N fBD SOC SC depth depth [cm] [%] [%] [%] [%] [g/g] [g/cm³] [tC/ha] [cm] Mühl-171+10 16.10.2001 3 0-5 2.27 6.71 0.03 0.49 13.78 0.44 14.83 52 4 Mühl-171+10 16.10.2001 3 5-10 1.51 3.42 0.02 0.26 13.27 0.91 15.59 52 4 Mühl-171+10 16.10.2001 3 10-15 1.15 1.89 0.02 0.14 13.34 1.31 12.36 52 4 Mühl-171+10 16.10.2001 4 0-5 2.07 4.68 0.00 0.40 11.74 0.77 17.98 53 2 Mühl-171+10 16.10.2001 4 5-10 1.90 2.95 0.02 0.25 11.94 0.90 13.25 53 2 Mühl-171+10 16.10.2001 4 10-15 1.55 1.63 0.01 0.15 11.12 1.32 10.75 53 2 Mühl-171+10 16.10.2001 5 0-5 2.21 7.28 0.02 0.45 16.10 0.66 23.99 59 3 Mühl-171+10 16.10.2001 5 5-10 1.49 2.50 0.01 0.20 12.75 1.05 13.21 59 3 Mühl-171+10 16.10.2001 5 10-15 1.31 1.55 0.02 0.13 12.09 1.09 8.43 59 3 Mühl-171+10 15.10.2001 6 0-5 2.38 5.89 0.02 0.44 13.52 0.41 12.19 75 4 Mühl-171+10 15.10.2001 6 5-10 1.76 3.13 0.02 0.26 12.04 0.69 10.82 75 4 Mühl-171+10 15.10.2001 6 10-15 1.69 2.69 0.02 0.22 12.03 0.81 10.93 75 4 Mühl-171+10 15.10.2001 7 0-5 2.72 6.46 0.07 0.47 13.69 0.59 19.19 60 2 Mühl-171+10 15.10.2001 7 5-10 2.16 3.76 0.03 0.32 11.93 0.92 17.39 60 2 Mühl-171+10 15.10.2001 7 10-15 2.19 2.12 0.02 0.20 10.74 1.19 12.62 60 2 Mühl-171+10 15.10.2001 8 0-5 2.02 5.34 0.02 0.41 12.98 0.52 13.88 63 4 Mühl-171+10 15.10.2001 8 5-10 1.46 2.88 0.02 0.24 12.13 1.02 14.67 63 4 Mühl-171+10 15.10.2001 8 10-15 1.09 1.43 0.02 0.11 12.52 1.13 8.08 63 4 Mühl-171+10 15.10.2001 9 0-5 2.19 5.94 0.02 0.44 13.64 0.69 20.57 61 4 Mühl-171+10 15.10.2001 9 5-10 1.79 3.27 0.02 0.25 12.87 0.91 14.82 61 4 Mühl-171+10 15.10.2001 9 10-15 1.47 1.63 0.01 0.14 11.65 1.09 8.86 61 4 Mühl-171+10 15.10.2001 10 0-5 2.28 5.64 0.16 0.38 14.83 0.80 22.60 60 4 Mühl-171+10 15.10.2001 10 5-10 1.20 1.86 0.01 0.14 12.96 1.14 10.61 60 4 Mühl-171+10 15.10.2001 10 10-15 0.99 1.14 0.01 0.09 12.29 1.21 6.90 60 4 Lang-I 06.03.2001 12 0-5 3.35 7.50 0.06 0.60 12.500.69 25.77 67 2 Lang-I 06.03.2001 12 5-10 2.84 4.72 0.07 0.42 11.150.84 19.72 67 2 Lang-I 06.03.2001 12 10-15 4.19 3.08 0.08 0.30 10.411.08 16.61 67 2 Lang-I 06.03.2001 15 0-5 2.61 6.01 0.04 0.44 13.630.48 14.37 71 3 Lang-I 06.03.2001 15 5-10 2.23 2.44 0.03 0.20 11.930.92 11.29 71 3 Lang-I 06.03.2001 15 10-15 2.32 1.65 0.02 0.16 10.52 0.93 7.55 71 3 Lang-I 06.03.2001 D 0-5 2.88 4.16 0.03 0.36 11.630.92 19.06 58 3 Lang-I 06.03.2001 D 5-10 2.43 2.37 0.03 0.24 10.030.92 10.91 58 3 Lang-I 06.03.2001 D 10-15 4.96 2.02 0.03 0.21 9.63 0.99 9.95 58 3 Lang-I 06.03.2001 1 0-5 2.97 6.31 0.04 0.49 12.980.46 14.48 60 2 Lang-I 06.03.2001 1 5-10 2.47 3.35 0.03 0.30 11.250.93 15.65 60 2 Lang-I 06.03.2001 1 10-15 2.17 2.34 0.04 0.24 9.87 0.91 10.64 60 2 Lang-I 06.03.2001 2 0-5 3.09 5.20 0.03 0.43 12.060.76 19.76 72 2 Lang-I 06.03.2001 2 5-10 2.55 3.30 0.03 0.29 11.561.10 18.16 72 2 Lang-I 06.03.2001 2 10-15 2.36 2.41 0.03 0.23 10.520.97 11.66 72 2 Lang-I 06.03.2001 3 0-5 3.13 7.63 0.02 0.62 12.250.52 19.68 76 2 Lang-I 06.03.2001 3 5-10 2.22 4.30 0.02 0.36 11.780.81 17.37 76 2 Lang-I 06.03.2001 3 10-15 1.96 2.57 0.02 0.24 10.731.17 15.02 76 2 Lang-I 06.03.2001 4 0-5 4.00 3.67 0.03 0.33 11.140.84 15.49 66 2 Lang-I 06.03.2001 4 5-10 2.50 3.28 0.03 0.31 10.640.70 11.48 66 2 Lang-I 06.03.2001 4 10-15 2.25 2.53 0.04 0.25 9.96 1.10 13.92 66 2 Lang-I 06.03.2001 5 0-5 2.89 6.61 0.04 0.53 12.410.55 18.34 81 2 Lang-I 06.03.2001 5 5-10 1.84 2.88 0.03 0.27 10.800.89 12.73 81 2 Lang-I 06.03.2001 5 10-15 1.69 2.21 0.03 0.20 10.841.05 11.58 81 2

234

Table A.9: continued

Soil Max. Site No Date Core No. RWC TOC TIC TN C/N fBD SOC SC depth depth [cm] [%] [%] [%] [%] [g/g] [g/cm³] [tC/ha] [cm] Lang-I 06.03.2001 6 0-5 3.00 6.29 0.06 0.51 12.400.73 22.92 80 2 Lang-I 06.03.2001 6 5-10 2.61 3.97 0.03 0.36 11.061.02 20.20 80 2 Lang-I 06.03.2001 6 10-15 2.41 2.83 0.02 0.27 10.351.07 15.19 80 2 Lang-I 06.03.2001 7 0-5 2.27 4.75 0.04 0.42 11.330.67 15.99 66 2 Lang-I 06.03.2001 7 5-10 2.08 3.24 0.03 0.31 10.450.81 13.08 66 2 Lang-I 06.03.2001 7 10-15 2.06 2.48 0.03 0.23 10.630.91 11.28 66 2 Lang-I 06.03.2001 8 0-5 2.47 5.54 0.03 0.52 10.740.76 21.03 77 2 Lang-I 06.03.2001 8 5-10 3.18 3.80 0.03 0.34 11.060.90 17.16 77 2 Lang-I 06.03.2001 8 10-15 2.99 2.46 0.03 0.24 10.171.06 12.93 77 2 Lang-I 06.03.2001 9 0-5 3.31 5.41 0.05 0.44 12.290.60 16.24 82 2 Lang-I 06.03.2001 9 5-10 3.00 3.17 0.03 0.28 11.161.05 16.57 82 2 Lang-I 06.03.2001 9 10-15 2.81 2.18 0.03 0.21 10.341.25 13.65 82 2 Lang-I 06.03.2001 10 0-5 2.31 4.66 0.04 0.39 12.050.77 18.04 63 2 Lang-I 06.03.2001 10 5-10 2.75 3.05 0.04 0.28 10.730.94 14.31 63 2 Lang-I 06.03.2001 10 10-15 2.70 2.18 0.03 0.21 10.251.01 11.05 63 2 Lang-I 06.03.2001 11 0-5 2.73 5.94 0.04 0.48 12.260.75 22.15 61 2 Lang-I 06.03.2001 11 5-10 2.34 2.45 0.03 0.22 10.920.94 11.50 61 2 Lang-I 06.03.2001 11 10-15 2.14 1.77 0.03 0.18 9.75 1.28 11.32 61 2 Lang-I 06.03.2001 13 0-5 2.36 4.32 0.02 0.35 12.230.89 19.27 66 2 Lang-I 06.03.2001 13 5-10 2.07 2.44 0.02 0.22 11.051.04 12.64 66 2 Lang-I 06.03.2001 13 10-15 2.07 1.87 0.03 0.17 11.111.25 11.65 66 2 Lang-II 03.07.2001 A 0-5 2.52 5.63 0.04 0.37 15.320.57 16.17 31 4 Lang-II 03.07.2001 A 5-10 1.96 2.43 0.04 0.18 13.481.03 12.49 31 4 Lang-II 03.07.2001 A 10-15 1.83 1.91 0.02 0.16 11.981.26 11.94 31 4 Lang-II 03.07.2001 B 0-5 1.45 3.28 0.03 0.20 16.311.03 16.84 70 4 Lang-II 03.07.2001 B 5-10 1.77 1.55 0.02 0.10 15.44 1.25 9.68 70 4 Lang-II 03.07.2001 B 10-15 1.30 0.97 0.02 0.08 12.77 1.62 7.87 70 4 Lang-II 03.07.2001 C2 0-5 2.12 5.86 0.04 0.36 16.240.69 20.35 40 4 Lang-II 03.07.2001 C2 5-10 1.81 2.84 0.03 0.18 16.011.10 15.58 40 4 Lang-II 03.07.2001 C2 10-15 1.59 1.42 0.02 0.11 13.03 1.41 9.94 40 4 Lang-II 03.07.2001 1 0-5 2.02 3.53 0.04 0.26 13.510.76 13.39 70 2 Lang-II 03.07.2001 1 5-10 1.79 1.84 0.03 0.16 11.141.19 10.93 70 2 Lang-II 03.07.2001 1 10-15 1.86 1.41 0.02 0.14 10.24 1.21 8.53 70 2 Lang-II 03.07.2001 2 0-5 2.70 6.70 0.04 0.46 14.520.52 17.34 65 4 Lang-II 03.07.2001 2 5-10 2.17 3.93 0.04 0.27 14.400.80 15.67 65 4 Lang-II 03.07.2001 2 10-15 2.22 3.20 0.03 0.23 13.711.01 16.21 65 4 Lang-II 03.07.2001 3 0-5 2.29 4.70 0.04 0.30 15.760.88 20.56 43 4 Lang-II 03.07.2001 3 5-10 1.64 1.95 0.03 0.14 13.841.03 10.04 43 4 Lang-II 03.07.2001 3 10-15 1.56 1.36 0.03 0.12 11.20 1.21 8.24 43 4 Lang-II 03.07.2001 4 0-5 2.55 3.96 0.03 0.28 14.190.73 14.45 30 4 Lang-II 03.07.2001 4 5-10 2.17 1.84 0.04 0.14 13.381.14 10.42 30 4 Lang-II 03.07.2001 4 10-15 2.54 1.52 0.02 0.14 10.54 1.17 8.87 30 4 Lang-II 03.07.2001 5 0-5 1.93 3.51 0.05 0.24 14.470.96 16.90 53 3 Lang-II 03.07.2001 5 5-10 1.79 1.57 0.04 0.12 13.30 1.12 8.79 53 3 Lang-II 03.07.2001 5 10-15 3.25 1.32 0.04 0.12 10.99 1.20 7.91 53 3 Lang-II 03.07.2001 6 0-5 1.86 4.62 0.04 0.32 14.230.57 13.18 45 4 Lang-II 03.07.2001 6 5-10 1.55 3.14 0.03 0.22 14.020.77 12.12 45 4 Lang-II 03.07.2001 6 10-15 1.22 1.54 0.01 0.12 12.751.32 10.17 45 4

235 Table A.9: continued

Soil Max. Site No Date Core No. RWC TOC TIC TN C/N fBD SOC SC depth depth [cm] [%] [%] [%] [%] [g/g] [g/cm³] [tC/ha] [cm] Lang-II 03.07.2001 7 0-5 2.94 5.07 0.04 0.36 14.000.53 13.35 54 4 Lang-II 03.07.2001 7 5-10 1.75 2.54 0.03 0.19 13.511.13 14.36 54 4 Lang-II 03.07.2001 7 10-15 1.57 1.88 0.03 0.16 11.941.21 11.35 54 4 Lang-II 03.07.2001 8 0-5 2.09 5.32 0.05 0.39 13.500.65 17.14 60 4 Lang-II 03.07.2001 8 5-10 1.47 2.78 0.03 0.20 13.710.96 13.27 60 4 Lang-II 03.07.2001 8 10-15 1.40 1.61 0.02 0.13 12.261.31 10.50 60 4 Lang-II 03.07.2001 9 0-5 4.54 6.07 0.08 0.44 13.690.69 20.84 10 1 Lang-II 03.07.2001 9 5-10 4.04 3.28 2.41 0.26 12.471.00 16.44 10 1 Lang-II 03.07.2001 9 10-15 0 0 0 0 0 0 0 10 1 Lang-II 03.07.2001 10 0-5 2.18 3.86 0.02 0.27 14.210.98 18.90 74 2 Lang-II 03.07.2001 10 5-10 1.44 1.32 0.02 0.12 11.24 1.38 9.14 74 2 Lang-II 03.07.2001 10 10-15 1.85 1.19 0.01 0.12 10.09 1.44 8.55 74 2 Lang-II 03.07.2001 11 0-5 2.78 3.57 0.04 0.28 12.93 0.48 8.50 65 4 Lang-II 03.07.2001 11 5-10 1.71 2.07 0.03 0.16 12.811.01 10.46 65 4 Lang-II 03.07.2001 11 10-15 2.62 2.01 0.03 0.17 12.091.16 11.61 65 4 Lang-II 03.07.2001 12 0-5 2.24 4.90 0.05 0.32 15.090.64 15.73 53 4 Lang-II 03.07.2001 12 5-10 1.74 1.44 0.04 0.12 12.33 1.04 7.46 53 4 Lang-II 03.07.2001 12 10-15 2.27 1.31 0.03 0.12 11.15 1.14 7.49 53 4 Lang-III 02.07.2001 A 0-5 2.11 5.13 0.02 0.40 12.800.74 18.97 72 4 Lang-III 02.07.2001 A 5-10 1.91 2.64 0.03 0.23 11.550.95 12.60 72 4 Lang-III 02.07.2001 A 10-15 1.57 1.41 0.02 0.12 11.87 1.07 7.54 72 4 Lang-III 02.07.2001 C 0-5 1.78 4.41 0.03 0.33 13.520.65 14.37 68 4 Lang-III 02.07.2001 C 5-10 2.14 2.92 0.02 0.26 11.160.72 10.46 68 4 Lang-III 02.07.2001 C 10-15 1.11 1.61 0.01 0.13 12.32 1.07 8.64 68 4 Lang-III 02.07.2001 1 0-5 2.01 3.72 0.02 0.26 14.490.96 17.82 57 4 Lang-III 02.07.2001 1 5-10 1.25 1.42 0.00 0.11 12.40 1.21 8.61 57 4 Lang-III 02.07.2001 1 10-15 1.42 1.20 0.01 0.11 11.19 1.13 6.78 57 4 Lang-III 02.07.2001 2 0-5 2.04 2.98 0.08 0.23 12.720.97 14.45 78 4 Lang-III 02.07.2001 2 5-10 1.62 1.68 0.02 0.16 10.83 1.15 9.71 78 4 Lang-III 02.07.2001 2 10-15 1.54 1.51 0.01 0.14 10.54 1.31 9.92 78 4 Lang-III 02.07.2001 3 0-5 1.96 4.21 0.04 0.32 13.050.64 13.52 84 4 Lang-III 02.07.2001 3 5-10 1.91 3.48 0.02 0.28 12.630.80 14.01 84 4 Lang-III 02.07.2001 3 10-15 1.81 2.22 0.01 0.19 11.501.10 12.27 84 4 Lang-III 02.07.2001 4 0-5 1.93 3.64 0.01 0.30 12.110.79 14.30 75 4 Lang-III 02.07.2001 4 5-10 1.36 1.49 0.02 0.14 10.78 1.22 9.06 75 4 Lang-III 02.07.2001 4 10-15 1.17 1.36 0.01 0.13 10.45 1.16 7.92 75 4 Lang-III 02.07.2001 5 0-5 2.98 8.96 0.03 0.58 15.420.31 14.09 58 4 Lang-III 02.07.2001 5 5-10 1.62 3.50 0.02 0.24 14.500.89 15.64 58 4 Lang-III 02.07.2001 5 10-15 1.52 1.27 0.01 0.12 10.71 1.29 8.16 58 4 Lang-III 02.07.2001 6 0-5 2.37 4.75 0.12 0.35 13.560.89 21.04 83 4 Lang-III 02.07.2001 6 5-10 2.02 2.79 0.04 0.23 12.381.03 14.30 83 4 Lang-III 02.07.2001 6 10-15 1.91 2.20 0.02 0.20 11.041.06 11.63 83 4 Lang-III 02.07.2001 7 0-5 1.63 3.21 0.02 0.28 11.550.89 14.27 82 4 Lang-III 02.07.2001 7 5-10 1.62 1.79 0.02 0.18 10.16 0.98 8.72 82 4 Lang-III 02.07.2001 7 10-15 1.55 1.19 0.01 0.13 9.10 1.31 7.80 82 4 Lang-III 02.07.2001 8 0-5 1.81 3.56 0.02 0.26 13.560.84 14.98 87 4 Lang-III 02.07.2001 8 5-10 1.27 1.67 0.01 0.13 12.65 1.05 8.81 87 4 Lang-III 02.07.2001 8 10-15 1.21 1.39 0.01 0.11 12.27 1.32 9.18 87 4

236

Table A.9: continued

Soil Max. Site No Date Core No. RWC TOC TIC TN C/N fBD SOC SC depth depth [cm] [%] [%] [%] [%] [g/g] [g/cm³] [tC/ha] [cm] Lang-III 02.07.2001 9 0-5 2.00 4.23 0.05 0.31 13.540.77 16.19 80 4 Lang-III 02.07.2001 9 5-10 2.40 3.68 0.02 0.28 13.250.96 17.66 80 4 Lang-III 02.07.2001 9 10-15 1.63 2.94 0.03 0.23 12.891.07 15.77 80 4 Lang-III 02.07.2001 10 0-5 2.05 4.62 0.03 0.34 13.460.59 13.61 80 4 Lang-III 02.07.2001 10 5-10 1.21 1.23 0.01 0.11 10.81 1.22 7.51 80 4 Lang-III 02.07.2001 10 10-15 1.13 0.81 0.01 0.09 9.35 1.25 5.04 80 4 Lang-III 02.07.2001 11 0-5 1.91 5.31 0.03 0.41 13.080.46 12.24 83 4 Lang-III 02.07.2001 11 5-10 1.58 2.90 0.01 0.24 11.910.72 10.45 83 4 Lang-III 02.07.2001 11 10-15 1.26 2.02 0.02 0.17 11.821.06 10.72 83 4 Lang-III 02.07.2001 12 0-5 m 4.41 0.00 0.33 13.340.75 16.59 80 3 Lang-III 02.07.2001 12 5-10 1.90 1.64 0.01 0.16 10.35 1.19 9.74 80 3 Lang-III 02.07.2001 12 10-15 1.64 1.18 0.01 0.12 9.73 1.29 7.62 80 3 Lang-III 02.07.2001 13 0-5 1.94 4.06 0.02 0.31 13.260.59 11.91 68 4 Lang-III 02.07.2001 13 5-10 1.39 1.60 0.01 0.14 11.54 1.05 8.41 68 4 Lang-III 02.07.2001 13 10-15 1.31 1.09 0.01 0.10 10.95 1.29 7.06 68 4 Hai-I 07.06.2001 13 0-5 3.40 6.90 0.05 0.53 13.020.83 28.77 54 1 Hai-I 07.06.2001 13 5-10 2.82 4.18 0.03 0.37 11.390.93 19.18 54 1 Hai-I 07.06.2001 13 10-15 2.58 2.41 0.02 0.25 9.81 0.98 11.84 54 1 Hai-I 07.06.2001 14 0-5 2.67 4.88 0.03 0.37 13.240.71 17.25 59 4 Hai-I 07.06.2001 14 5-10 2.02 2.43 0.02 0.23 10.630.97 11.75 59 4 Hai-I 07.06.2001 14 10-15 2.00 2.19 0.02 0.21 10.351.00 10.98 59 4 Hai-I 07.06.2001 15 0-5 2.55 5.47 0.03 0.46 12.000.81 21.99 67 4 Hai-I 07.06.2001 15 5-10 2.40 3.72 0.02 0.32 11.671.02 19.08 67 4 Hai-I 07.06.2001 15 10-15 2.13 2.40 0.02 0.22 10.781.14 13.72 67 4 Hai-I 07.06.2001 1 0-5 3.08 5.69 0.03 0.42 13.440.83 23.58 67 3 Hai-I 07.06.2001 1 5-10 2.39 3.55 0.03 0.29 12.081.07 19.00 67 3 Hai-I 07.06.2001 1 10-15 ds ds ds ds ds ds 13.05 67 3 Hai-I 07.06.2001 2 0-5 2.87 5.29 0.02 0.43 12.200.82 21.63 61 2 Hai-I 07.06.2001 2 5-10 2.72 3.30 0.03 0.30 10.810.95 15.69 61 2 Hai-I 07.06.2001 2 10-15 2.73 2.24 0.02 0.24 9.42 1.41 15.76 61 2 Hai-I 07.06.2001 3 0-5 2.53 4.12 0.02 0.35 11.860.94 19.42 65 4 Hai-I 07.06.2001 3 5-10 2.37 2.59 0.02 0.23 11.091.02 13.23 65 4 Hai-I 07.06.2001 3 10-15 2.50 2.17 0.02 0.21 10.351.16 12.66 65 4 Hai-I 07.06.2001 4 0-5 2.85 5.84 0.03 0.48 12.110.81 23.55 83 4 Hai-I 07.06.2001 4 5-10 2.92 4.04 0.03 0.37 10.920.99 19.90 83 4 Hai-I 07.06.2001 4 10-15 2.66 2.88 0.02 0.29 10.071.00 14.41 83 4 Hai-I 07.06.2001 5 0-5 2.57 4.18 0.03 0.36 11.610.92 19.22 69 3 Hai-I 07.06.2001 5 5-10 2.35 3.25 0.02 0.30 10.780.89 14.42 69 3 Hai-I 07.06.2001 5 10-15 2.48 2.33 0.02 0.22 10.590.86 10.05 69 3 Hai-I 07.06.2001 6 0-5 2.56 4.64 0.03 0.32 14.430.92 21.44 75 4 Hai-I 07.06.2001 6 5-10 2.12 3.25 0.03 0.24 13.710.98 15.93 75 4 Hai-I 07.06.2001 6 10-15 1.91 1.86 0.02 0.15 12.181.28 11.97 75 4 Hai-I 07.06.2001 7 0-5 2.93 5.74 0.04 0.43 13.400.77 21.99 60 4 Hai-I 07.06.2001 7 5-10 2.33 3.08 0.02 0.28 11.200.91 13.96 60 4 Hai-I 07.06.2001 7 10-15 2.34 2.38 0.02 0.22 10.681.17 13.92 60 4 Hai-I 07.06.2001 8 0-5 2.71 5.56 0.03 0.39 14.300.77 21.40 71 4 Hai-I 07.06.2001 8 5-10 1.75 2.27 0.01 0.19 11.851.02 11.63 71 4 Hai-I 07.06.2001 8 10-15 1.83 1.74 0.02 0.16 10.881.36 11.89 71 4

237 Table A.9: continued

Soil Max. Site No Date Core No. RWC TOC TIC TN C/N fBD SOC SC depth depth [cm] [%] [%] [%] [%] [g/g] [g/cm³] [tC/ha] [cm] Hai-I 07.06.2001 9 0-5 3.19 4.56 0.02 0.39 11.750.95 21.62 50 1 Hai-I 07.06.2001 9 5-10 2.99 3.93 0.03 0.36 10.940.96 18.47 50 1 Hai-I 07.06.2001 9 10-15 3.00 3.17 0.03 0.31 10.401.09 17.34 50 1 Hai-I 07.06.2001 10 0-5 3.38 6.84 0.03 0.50 13.600.68 23.24 60 4 Hai-I 07.06.2001 10 5-10 2.69 4.16 0.03 0.35 11.860.77 16.02 60 4 Hai-I 07.06.2001 10 10-15 2.63 2.60 0.03 0.25 10.521.09 14.13 60 4 Hai-I 07.06.2001 11 0-5 2.39 6.12 0.03 0.45 13.700.85 25.95 61 4 Hai-I 07.06.2001 11 5-10 1.95 3.48 0.02 0.30 11.700.92 15.94 61 4 Hai-I 07.06.2001 11 10-15 2.60 2.15 0.02 0.20 10.531.12 12.02 61 4 Hai-I 07.06.2001 12 0-5 2.76 5.83 0.04 0.44 13.380.79 23.13 71 4 Hai-I 07.06.2001 12 5-10 2.06 2.52 0.02 0.25 10.011.03 12.97 71 4 Hai-I 07.06.2001 12 10-15 2.77 2.09 0.02 0.21 9.85 1.03 10.73 71 4 Hai-II 15.05.2001 A 0-5 2.48 4.05 0.03 0.33 12.280.93 18.78 59 2 Hai-II 15.05.2001 A 5-10 2.47 3.33 0.02 0.29 11.621.07 17.75 60 2 Hai-II 15.05.2001 A 10-15 2.42 2.88 0.03 0.25 11.620.94 13.55 61 2 Hai-II 15.05.2001 B 0-5 3.28 6.08 0.05 0.48 12.530.62 18.85 80 1 Hai-II 15.05.2001 B 5-10 3.07 3.92 0.03 0.34 11.561.04 20.40 80 1 Hai-II 15.05.2001 B 10-15 2.82 2.30 0.02 0.24 9.77 1.15 13.26 80 1 Hai-II 15.05.2001 C 0-5 2.34 5.08 0.02 0.43 11.930.75 19.16 67 3 Hai-II 15.05.2001 C 5-10 1.98 2.61 0.02 0.22 11.600.88 11.52 67 3 Hai-II 15.05.2001 C 10-15 1.89 2.27 0.02 0.21 10.961.19 13.48 67 3 Hai-II 15.05.2001 D 0-5 4.45 11.09 0.14 0.73 15.11 0.55 30.37 58 2 Hai-II 15.05.2001 D 5-10 3.30 5.06 0.05 0.42 12.130.82 20.62 58 2 Hai-II 15.05.2001 D 10-15 2.85 3.06 0.03 0.28 10.881.10 16.82 58 2 Hai-II 15.05.2001 E 0-5 2.86 6.42 0.03 0.50 12.820.70 22.55 70 2 Hai-II 15.05.2001 E 5-10 2.71 4.30 0.02 0.38 11.310.64 13.75 70 2 Hai-II 15.05.2001 E 10-15 2.59 3.12 0.02 0.28 11.050.94 14.65 70 2 Hai-II 15.05.2001 1 0-5 3.01 5.68 0.03 0.46 12.400.87 24.71 80 2 Hai-II 15.05.2001 1 5-10 2.67 3.52 0.02 0.31 11.270.88 15.40 80 2 Hai-II 15.05.2001 1 10-15 2.61 2.32 0.02 0.22 10.311.25 14.44 80 2 Hai-II 15.05.2001 2 0-5 2.69 4.22 0.02 0.37 11.570.80 16.87 80 2 Hai-II 15.05.2001 2 5-10 2.59 3.09 0.02 0.29 10.561.04 16.07 80 2 Hai-II 15.05.2001 2 10-15 2.45 2.54 0.02 0.25 10.191.14 14.46 80 2 Hai-II 15.05.2001 3 0-5 2.81 4.98 0.04 0.39 12.890.89 22.18 71 2 Hai-II 15.05.2001 3 5-10 1.30 2.25 0.01 0.17 12.890.99 11.17 71 2 Hai-II 15.05.2001 3 10-15 2.45 2.51 0.02 0.24 10.580.98 12.24 71 2 Hai-II 15.05.2001 4 0-5 3.04 6.02 0.03 0.44 13.830.84 25.30 58 2 Hai-II 15.05.2001 4 5-10 2.85 4.71 0.04 0.37 12.740.98 23.11 58 2 Hai-II 15.05.2001 4 10-15 2.31 3.24 0.03 0.28 11.560.95 15.42 58 2 Hai-II 15.05.2001 5 0-5 2.72 5.50 0.03 0.44 12.550.79 21.84 67 2 Hai-II 15.05.2001 5 5-10 2.46 3.43 0.02 0.32 10.740.87 14.87 67 2 Hai-II 15.05.2001 5 10-15 2.47 2.54 0.02 0.25 10.331.05 13.32 67 2 Hai-II 15.05.2001 6 0-5 3.22 5.91 0.03 0.49 12.050.88 26.07 65 2 Hai-II 15.05.2001 6 5-10 2.53 3.59 0.03 0.33 10.780.67 12.03 65 2 Hai-II 15.05.2001 6 10-15 2.73 2.60 0.02 0.26 10.041.01 13.10 65 2 Hai-II 15.05.2001 7 0-5 3.23 6.89 0.06 0.48 14.460.81 27.97 44 2 Hai-II 15.05.2001 7 5-10 1.93 2.15 0.02 0.19 11.161.32 14.23 44 2 Hai-II 15.05.2001 7 10-15 2.14 1.15 0.01 0.13 9.16 1.44 8.25 44 2

238

Table A.9: continued

Soil Max. Site No Date Core No. RWC TOC TIC TN C/N fBD SOC SC depth depth [cm] [%] [%] [%] [%] [g/g] [g/cm³] [tC/ha] [cm] Hai-II 15.05.2001 8 0-5 2.55 5.38 0.03 0.43 12.450.83 22.45 94 3 Hai-II 15.05.2001 8 5-10 1.86 2.47 0.02 0.22 11.130.92 11.34 94 3 Hai-II 15.05.2001 8 10-15 1.91 2.07 0.02 0.20 10.391.15 11.91 94 3 Hai-II 15.05.2001 9 0-5 3.40 6.85 0.03 0.53 12.880.84 28.84 60 1 Hai-II 15.05.2001 9 5-10 2.77 2.73 0.02 0.26 10.331.05 14.29 60 1 Hai-II 15.05.2001 9 10-15 2.89 1.82 0.01 0.20 9.29 1.15 10.44 60 1 Hai-II 15.05.2001 10 0-5 2.42 4.83 0.02 0.36 13.410.80 19.42 77 2 Hai-II 15.05.2001 10 5-10 1.67 1.96 0.01 0.18 10.821.18 11.56 77 2 Hai-II 15.05.2001 10 10-15 1.64 1.42 0.01 0.15 9.73 1.34 9.52 77 2 Hai-II 15.05.2001 11 0-5 2.01 4.04 0.02 0.35 11.630.80 16.20 86 2 Hai-II 15.05.2001 11 5-10 1.91 2.38 0.02 0.24 9.96 1.13 13.43 86 2 Hai-II 15.05.2001 11 10-15 2.04 2.15 0.01 0.22 9.66 1.06 11.44 86 2 Hai-II 15.05.2001 12 0-5 2.43 4.32 0.02 0.36 12.000.96 20.76 75 2 Hai-II 15.05.2001 12 5-10 2.16 3.12 0.02 0.30 10.521.04 16.20 75 2 Hai-II 15.05.2001 12 10-15 2.14 2.44 0.02 0.24 10.031.16 14.15 75 2 Hai-III 27.07.2001 A 0-5 3.85 8.07 0.04 0.65 12.500.71 28.52 61 1 Hai-III 27.07.2001 A 5-10 3.50 5.52 0.04 0.48 11.550.81 22.41 61 1 Hai-III 27.07.2001 A 10-15 3.23 3.78 0.03 0.34 11.020.85 16.17 61 1 Hai-III 27.07.2001 B 0-5 3.24 6.30 0.04 0.51 12.260.67 21.19 50 2 Hai-III 27.07.2001 B 5-10 2.56 3.37 0.02 0.32 10.560.88 14.89 50 2 Hai-III 27.07.2001 B 10-15 2.57 2.45 0.02 0.25 9.82 1.16 14.29 50 2 Hai-III 27.07.2001 C 0-5 2.26 4.74 0.02 0.37 12.691.03 24.35 56 3 Hai-III 27.07.2001 C 5-10 2.44 2.33 0.01 0.21 10.971.11 12.91 56 3 Hai-III 27.07.2001 C 10-15 1.84 1.26 0.01 0.09 13.98 1.36 8.58 56 3 Hai-III 27.07.2001 1 0-5 2.65 3.25 0.02 0.23 13.951.15 18.75 51 2 Hai-III 27.07.2001 1 5-10 2.75 2.15 0.02 0.18 12.08 0.81 8.67 51 2 Hai-III 27.07.2001 1 10-15 ds ds ds ds ds ds 12.96 51 2 Hai-III 27.07.2001 2 0-5 4.50 8.41 0.07 0.64 13.080.69 28.95 49 1 Hai-III 27.07.2001 2 5-10 3.64 4.77 0.03 0.42 11.350.79 18.78 49 1 Hai-III 27.07.2001 2 10-15 3.86 3.71 0.02 0.28 13.381.08 20.07 49 1 Hai-III 27.07.2001 3 0-5 2.62 5.90 0.03 0.47 12.530.69 20.21 55 2 Hai-III 27.07.2001 3 5-10 2.33 3.22 0.02 0.30 10.811.08 17.47 55 2 Hai-III 27.07.2001 3 10-15 2.27 2.91 0.02 0.26 11.160.94 13.69 55 2 Hai-III 27.07.2001 4 0-5 3.07 6.16 0.02 0.46 13.460.79 24.23 59 2 Hai-III 27.07.2001 4 5-10 2.79 2.86 0.02 0.23 12.540.82 11.68 59 2 Hai-III 27.07.2001 4 10-15 2.72 2.12 0.02 0.19 11.301.19 12.62 59 2 Hai-III 27.07.2001 5 0-5 2.92 4.46 0.02 0.36 12.280.97 21.70 66 2 Hai-III 27.07.2001 5 5-10 2.83 2.47 0.02 0.24 10.481.21 14.95 66 2 Hai-III 27.07.2001 5 10-15 2.87 1.81 0.02 0.19 9.55 1.32 11.95 66 2 Hai-III 27.07.2001 6 0-5 2.69 6.07 0.05 0.45 13.580.66 20.01 60 2 Hai-III 27.07.2001 6 5-10 2.27 2.39 0.03 0.21 11.281.04 12.40 60 2 Hai-III 27.07.2001 6 10-15 2.17 2.05 0.02 0.19 10.811.17 12.01 60 2 Hai-III 27.07.2001 7 0-5 2.44 4.89 0.02 0.37 13.270.79 19.23 71 2 Hai-III 27.07.2001 7 5-10 2.95 3.97 0.02 0.32 12.460.84 16.75 71 2 Hai-III 27.07.2001 7 10-15 2.28 2.99 0.01 0.26 11.690.97 14.53 71 2 Hai-III 27.07.2001 8 0-5 3.50 7.59 0.05 0.60 12.630.67 25.43 55 2 Hai-III 27.07.2001 8 5-10 3.07 4.12 0.02 0.37 11.180.93 18.56 55 2 Hai-III 27.07.2001 8 10-15 2.58 3.22 0.02 0.29 11.250.93 14.95 55 2

239 Table A.9: continued

Soil Max. Site No Date Core No. RWC TOC TIC TN C/N fBD SOC SC depth depth [cm] [%] [%] [%] [%] [g/g] [g/cm³] [tC/ha] [cm] Hai-III 27.07.2001 9 0-5 3.91 7.50 0.06 0.53 14.200.59 22.22 58 1 Hai-III 27.07.2001 9 5-10 3.87 5.31 0.05 0.42 12.580.88 23.46 58 1 Hai-III 27.07.2001 9 10-15 3.82 3.57 0.03 0.30 11.950.99 17.59 58 1 Hai-III 27.07.2001 10 0-5 3.60 6.95 0.04 0.56 12.510.72 24.99 68 2 Hai-III 27.07.2001 10 5-10 4.06 4.64 0.03 0.41 11.210.98 22.83 68 2 Hai-III 27.07.2001 10 10-15 3.33 3.64 0.03 0.34 10.850.97 17.64 68 2 Hai-III 27.07.2001 11 0-5 3.64 7.37 0.04 0.55 13.430.72 26.67 48 1 Hai-III 27.07.2001 11 5-10 3.08 4.57 0.02 0.39 11.750.93 21.16 48 1 Hai-III 27.07.2001 11 10-15 2.68 2.40 0.02 0.23 10.421.08 12.98 48 1 Hai-III 27.07.2001 12 0-5 3.67 6.08 0.03 0.50 12.130.90 27.32 60 2 Hai-III 27.07.2001 12 5-10 3.28 4.14 0.02 0.36 11.380.91 18.90 60 2 Hai-III 27.07.2001 12 10-15 3.12 2.62 0.03 0.25 10.670.92 12.04 60 2

240

Figure A.1: Overview of parameters of the soil pits. (Exch. cations = exchangeable cations)

Leinefelde, 30-year-old Lei-30M

SOC concentrations Fine soil bulk density Texture Soil classification and stone volume Exch. cations and pHKCl and cumulative pools 0 0 Ah -10 -10 Bv -20 -20

-30 -30 (II)T-Bv -40 -40 Exch. cations (II)T- (II)cCv -50 -50 Stone Silt Bulk pH Sand C conc. cum. -60 density volume Clay -60

pools Soil depth (cm)

Soil depth (cm) depth Soil Braunerde -70 -70 über Terra fusca -80 -80

-90 -90

-100 -100 0.0 0.5 1.0 1.5 2.0 2.5 0 200 400 600 800 0 2040608010002468 -3 -1 SOC (%) Bulk density (g cm ) Exch. cations (mmolc kg ) Fractions (%)

0 10203040506070 02468 0 20406080100120 pH -1 Stone volume (%) KCl SOC pools (tC ha )

241 Leinefelde, 62-year-old Lei-62M

Fine soil bulk density SOC concentrations Exch. cations and pH Texture Soil classification and stone volume KCl and cumulative pools 0 0 Ah -10 -10 Al -20 -20 Bt -30 -30 (II)T -Bt Bulk cum. (II)cCv- (II)T-Bt -40 Stone Sand Clay C conc. -40 volume density Exch. cations pH Silt pools -50 -50

-60 -60 Parabraunerde über Soil depth (cm)Soil Soil depth (cm)Soil -70 -70 Terra fusca

-80 -80

-90 -90

-100 -100 0.0 0.5 1.0 1.5 2.0 2.5 0 200 400 600 800 0 2040608010002468 -3 -1 SOC (%) Bulk density (g cm ) Exch. cations (mmolc kg ) Fractions (%)

0 10203040506070 02468 0 20 40 60 80 100120 -1 Stone volume (%) pHKCl SOC pools (tC ha )

242

Leinefelde, 111-year-old Lei-111M

SOC concentrations Fine soil bulk density Texture Soil classification and stone volume Exch. cations and pHKCl and cumulative pools 0 0 Ah -10 -10 Al -20 -20

-30 -30 Bt

-40 -40 Sw-Bt -50 -50

-60 -60 Stone Exch. cations depthSoil (cm) Sd-(II)T Soil depthSoil (cm) Bulk -70 volume Clay -70 density cum. pseudovergleyte pH Sand Silt pools -80 C conc. -80 Parabraunerde über Terra fusca -90 -90

-100 -100 0.0 0.5 1.0 1.5 2.0 2.5 0 200 400 600 800 0 2040608010002468 -3 -1 SOC (%) Bulk density (g cm ) Exch. cations (mmolc kg ) Fractions (%)

0 10203040506070 02468 0 20406080100120 -1 pH SOC pools (tC ha ) Stone volume (%) KCl

243 Leinefelde, 141-year-old Lei-141M

Fine soil bulk density SOC concentrations Exch. cations and pH Texture Soil classification and stone volume KCl and cumulative pools 0 0 Ah -10 -10 Al -20 -20

-30 -30 Bt Exch. cations Silt (II)T-Bt -40 Stone Bulk pH Clay -40 Sand cum. volume density -50 C conc. pools -50 Parabraunerde über Terra fusca -60 -60 Soil depth(cm) Soil Soil depth (cm)Soil -70 -70

-80 -80

-90 -90

-100 -100 0.00.51.01.52.02.5 0 200 400 600 800 0 2040608010002468 -3 -1 SOC (%) Bulk density (g cm ) Exch. cations (mmolc kg ) Fractions (%)

0 10203040506070 02468 0 20 40 60 80 100120 -1 Stone volume (%) pHKCl SOC pools (tC ha )

244

Leinefelde, 153+16-year-old Lei-153+16M

SOC concentrations Fine soil bulk density Texture Soil classification and stone volume Exch. cations and pHKCl and cumulative pools 0 0 Ah -10 -10

-20 -20 Ah-Bv

-30 -30 Bv Bv-(II)cCv -40 -40

-50 Stone -50 (II)cCv volume -60 Clay Silt -60 Rendzina-Braunerde C conc. cum. depth (cm) Soil Soil depth (cm) Soil pH Bulk pools -70 density -70 Exch. cations Sand -80 -80

-90 -90

-100 -100 0.0 0.5 1.0 1.5 2.0 2.5 0 200 400 600 800 0 2040608010002468 -3 -1 SOC (%) Bulk density (g cm ) Exch. cations (mmolc kg ) Fractions (%)

0 10203040506070 02468 0 20 40 60 80 100120 -1 Stone volume (%) pHKCl SOC pools (tC ha )

245 Mühlhausen, 38-year-old Mühl-38

SOC concentrations Fine soil bulk density Texture Soil classification and stone volume Exch. cations and pHKCl and cumulative pools 0 0 Ah -10 -10 Bv -20 -20 Sw-Bv -30 -30 Stone pH (II)Sd-Bv-(II)T -40 volume -40 Sand (II)T -50 Bulk -50 density Exch. cations Silt Clay pseudovergleyte -60 -60 C conc. cum. depthSoil (cm) Braunerde Soil depthSoil (cm) pools über Terra fusca -70 -70

-80 -80

-90 -90

-100 -100 0.00.51.01.52.02.5 0 200 400 600 800 0 2040608010002468 -3 -1 SOC (%) Bulk density (g cm ) Exch. cations (mmolc kg ) Fractions (%)

0 10203040506070 02468 0 20406080100120 -1 Stone volume (%) pHKCl SOC pools (tC ha )

246

Mühlhausen, 55-year-old Mühl-55

Fine soil bulk density SOC concentrations Exch. cations and pH Texture Soil classification and stone volume KCl and cumulative pools 0 0 Ah -10 -10

-20 -20 Ah-Bv-T

-30 -30 Bulk Silt -40 Clay -40 density Exch. cations Stone pH Sand C conc. cum. -50 volume pools -50 Braunerde-Terra fusca -60 -60 Soil depth(cm) Soil depthSoil (cm) -70 -70

-80 -80

-90 -90

-100 -100 0.00.51.01.52.02.5 0 200 400 600 800 0 2040608010002468 -3 -1 SOC (%) Bulk density (g cm ) Exch. cations (mmolc kg ) Fractions (%)

0 10203040506070 02468 020406080100120 -1 Stone volume (%) pHKCl SOC pools (tC ha )

247 Mühlhausen, 85-year-old Mühl-85

SOC concentrations Fine soil bulk density Texture Soil classification and stone volume Exch. cations and pHKCl and cumulative pools 0 0 Ah Ah-Bv -10 -10 Bv -20 -20 (II)T-Bv -30 -30 pH -40 -40 (II)cCv-(II)T-Bv Stone Bulk Exch. cations -50 volume density Clay -50 Silt Sand C conc. cum. -60 -60

pools depthSoil (cm) Soil depthSoil (cm) -70 -70 Terra fusca-Braunerde

-80 -80

-90 -90

-100 -100 0.00.51.01.52.02.5 0 200 400 600 800 0 2040608010002468 -3 -1 SOC (%) Bulk density (g cm ) Exch. cations (mmolc kg ) Fractions (%)

0 10203040506070 02468 0 20406080100120 -1 Stone volume (%) pHKCl SOC pools (tC ha )

248

Mühlhausen, 102-year-old Mühl-102

Fine soil bulk density SOC concentrations Soil classification Exch. cations and pH Texture and stone volume KCl and cumulative pools 0 0 Ah -10 -10 Ah-Bv Bv -20 -20 (II)T-Bv -30 Stone -30 volume Silt Clay Bv-(II)T -40 -40

-50 -50

-60 -60 Bulk C conc. Soil depth (cm) Soil depth (cm) depth Soil Sand cum. -70 density pools -70 Exch. cations pH Terra fusca-Braunerde -80 -80

-90 -90

-100 -100 0.0 0.5 1.0 1.5 2.0 2.5 0 200 400 600 800 0 2040608010002468 -3 -1 SOC (%) Bulk density (g cm ) Exch. cations (mmolc kg ) Fractions (%)

0 10203040506070 02468 0 20406080100120 -1 Stone volume (%) pHKCl SOC pools (tC ha )

249 Mühlhausen, 171+10-year-old Mühl-171+10

SOC concentrations Fine soil bulk density Texture Soil classification and stone volume Exch. cations and pHKCl and cumulative pools 0 0 (M) Ah -10 -10 M -20 -20 (II)Bv

-30 -30 pH (II) Sw-(II)Bv -40 -40 (II) Sd-(II)Bv-(III)T -50 -50 Stone Bulk Silt -60 Exch. cations -60 (III)T-(III)cCv

density depth (cm) Soil volume Clay C conc. cum. Soil depth Soil (cm) Sand -70 pools -70

-80 -80 pseudovergleyte -90 -90 Braunerde über Terra fusca

-100 -100 (M = vermutlich 0.0 0.5 1.0 1.5 2.0 2.5 0 200 400 600 800 0 2040608010002468 Umlagerungsmaterial) -3 -1 SOC (%) Bulk density (g cm ) Exch. cations (mmolc kg ) Fractions (%)

0 10203040506070 02468 0 20406080100120 pH -1 Stone volume (%) KCl SOC pools (tC ha )

250

Langula, Selection system Lang-I

SOC concentrations Fine soil bulk density Texture Soil classification and stone volume Exch. cations and pHKCl and cumulative pools 0 0 Ah -10 -10 Ah-Bv Bv-(II)T -20 -20 (II) T -30 -30 (II)T-(II)cCv pH -40 Bulk Silt C conc. cum. -40 density Exch. cations Clay pools -50 Stone Sand -50 volume -60 -60 Soil depthSoil (cm) Soil depth (cm)Soil -70 -70 Braunerde-Terra fusca -80 -80

-90 -90

-100 -100 0.0 0.5 1.0 1.5 2.0 2.5 0 200 400 600 800 0 20 40 60 80 100 02468 -3 -1 SOC (%) Bulk density (g cm ) Exch. cations (mmolc kg ) Fractions (%)

0 10203040506070 02468 0 20 40 60 80 100120 -1 Stone volume (%) pHKCl SOC pools (tC ha )

251 Langula, Selection system Lang-II

SOC concentrations Fine soil bulk density Texture Soil classification and stone volume Exch. cations and pHKCl and cumulative pools 0 0 Ah -10 Ah-Al -10 Al -20 Bulk pH Sand -20 Bt density -30 -30 Bt-(II)T -40 -40 Bt-(II)cCv-(II)T -50 -50 Stone Silt Clay cum. (II)T-(II)cCv -60 volume C conc. -60 Exch. cations pools depthSoil (cm) Soil depth (cm) depth Soil -70 -70 Parabraunerde -80 -80 über Terra fusca -90 -90

-100 -100 0.0 0.5 1.0 1.5 2.0 2.5 0 200 400 600 800 0 2040608010002468 -3 -1 SOC (%) Bulk density (g cm ) Exch. cations (mmolc kg ) Fractions (%)

0 10203040506070 02468 0 20 40 60 80 100120 -1 Stone volume (%) pHKCl SOC pools (tC ha )

252

Langula, Selection system Lang-III

SOC concentrations Fine soil bulk density Texture Soil classification and stone volume Exch. cations and pHKCl and cumulative pools 0 0 Ah -10 -10 (Al)-Bv

-20 -20 (Bt)-Bv -30 -30

-40 -40

-50 -50 (II)cCv-Bv -60 -60 Sand Soil depth (cm) pH Soil depth (cm) -70 -70 Clay Silt C conc. Bv-(II)cCv -80 -80 Stone Bulk Exch. cations cum. volume pools -90 density -90 (Para) -Braunerde

-100 -100 0.0 0.5 1.0 1.5 2.0 2.5 0 200 400 600 800 0 2040608010002468 -3 -1 SOC (%) Bulk density (g cm ) Exch. cations (mmolc kg ) Fractions (%)

0 10203040506070 02468 0 20406080100120 -1 Stone volume (%) pHKCl SOC pools (tC ha )

253 Hainich NP, unmanaged Hai-I

SOC concentrations Fine soil bulk density Texture Soil classification and stone volume Exch. cations and pHKCl and cumulative pools 0 0 Ah -10 -10 Ah-Bv -20 -20 Bv-(II)T -30 Sand -30 Stone Bulk pH -40 volume density -40 Exch. cations Silt Clay C conc. cum. pools Braunerde-Terra fusca -50 -50

-60 -60 Soil depth Soil (cm) Soil depth (cm) Soil -70 -70

-80 -80

-90 -90

-100 -100 0.00.51.01.52.02.5 0 200 400 600 800 0 2040608010002468 -3 -1 SOC (%) Bulk density (g cm ) Exch. cations (mmolc kg ) Fractions (%)

0 10203040506070 02468 0 20 40 60 80 100120 -1 Stone volume (%) pH SOC pools (tC ha ) KCl

254

Hainich NP, unmanaged Hai-II

SOC concentrations Fine soil bulk density Texture Soil classification and stone volume Exch. cations and pHKCl and cumulative pools 0 0 Ah -10 -10 Ah-Bv -20 -20 Sand -30 -30 Bv-(II)T -40 -40 Bulk pH C conc. cum. (II)T -50 -50 Stone density Exch. cations Silt Clay pools -60 volume -60 Soil depth (cm) depth Soil Soil depth (cm) depth Soil -70 -70 Braunerde-Terra fusca -80 -80

-90 -90

-100 -100 0.00.51.01.52.02.5 0 200 400 600 800 0 2040608010002468 -3 -1 SOC (%) Bulk density (g cm ) Exch. cations (mmolc kg ) Fractions (%)

0 10203040506070 02468 0 20406080100120 -1 Stone volume (%) pH SOC pools (tC ha ) KCl

255 Hainich NP, unmanaged Hai-III

SOC concentrations Fine soil bulk density Texture Soil classification and stone volume Exch. cations and pHKCl and cumulative pools 0 0 Ah -10 -10 -20 -20 Ah-Bv Sand -30 -30 Bv -40 -40 pH cum. -50 C conc. -50 Bv-(II)T Bulk Silt Clay pools Stone density Exch. cations -60 volume -60 Soil depth (cm) depth Soil Soil depth (cm) depth Soil -70 -70 Terra fusca-Braunerde -80 -80

-90 -90

-100 -100 0.0 0.5 1.0 1.5 2.0 2.5 0 200 400 600 800 0 2040608010002468 -3 -1 SOC (%) Bulk density (g cm ) Exch. cations (mmolc kg ) Fractions (%)

0 10203040506070 02468 0 20 40 60 80 100120 -1 Stone volume (%) pHKCl SOC pools (tC ha )

256