On the Ecology of Mountainous Forests in a Changing Climate: A ...

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22 Chapter 2 500 Bern Biomass (t/ha) 400 300 200 100 0 7500 8000 8500 9000 Simulation year 9500 10000 Tilia platyphyllos Ulmus scabra Fraxinus excelsior Acer platanoides Acer pseudoplatanus Carpinus betulus Fagus silvatica Picea excelsa Abies alba Fig. 2.6: Excerpt from the time series of cumulative species-specific biomass of a single forest patch at the site Bern. A plot showing the cyclical behaviour of total tree numbers against leaf area index at the site Bern is given in Fig. 2.3. The cross-correlation function (Fig. 2.4) and the spectrum of tree numbers (Fig. 2.5) suggest that the typical cycle length in this forest is around 100-140 years, i.e. less than at Davos. This is due to the shorter average lifespan of the dominating species at the site Bern, such as Fagus silvatica. How long does the memory of species-specific biomass values and tree numbers last? At both sites, the autocorrelation functions of these variables drop below significant thresholds at lags smaller than 300 years (cf. Fig. 2.7 for an example). For the dominating species at the site Bern, the largest significant lag is 230 years, and for most species-spe- 1.0 Fagus silvatica biomass ACF 0.5 0.0 -0.5 0 100 200 Lag 300 (years) 400 500 Fig. 2.7: Autocorrelation function (ACF) of the biomass of Fagus silvatica at Bern. The dotted lines indicate the 95% confidence limits for ACF = 0.
Analysis of existing forest gap models 23 cific biomass variables it is less than 200 years. It should also be noted that the largest significant lag of tree numbers is always smaller than the largest significant lag of the corresponding biomass. In conclusion, the Davos and Bern simulations represent two examples of the various patterns possible under the gap-phase dynamics hypothesis: A species-poor, slowly growing forest whose pattern is dominated by one self-replacing species on the one hand, and a species-rich, fast growing forest with a diverse array of species replacement and competition on the other hand. In this sense, the FORECE model can be viewed as a valid computer based description of the gap dynamics hypothesis (Watt 1947, Shugart 1984). The length of a gap dynamics cycle in the simulated unmanaged forests, i.e. ≈200 and ≈150 years at Davos and Bern, respectively, is higher than the rotation length in managed forests of the area (e.g. Dengler et al. 1990). However, the idea that extremely old trees are abundant in “virgin” forests is supported neither by the present simulation study nor by field data (e.g. Leibundgut 1993). We may conclude that two samples taken from the same forest patch in the model can be considered to be independent from each other if the lag between them is at least 200 years. 2.2.2 Statistical sampling of the stochastic process For many applications of gap models, the behaviour of a single patch is of little interest because the major emphasis is on the dynamics of a larger area of forested land. Thus, the results from several patches may be averaged to obtain the dynamics on larger scales. Immediately there raises a question: How many patches do we have to simulate if we want their average to be reliable? How fast does this average converge towards the expected value? Are 5 patches sufficient, does it take 50, or even 500 (cf. Tab. 2.1)? MATERIAL & METHODS Species biomass and the numbers of trees originating from multiple simulation runs are rarely normally distributed at a given point in time (Fig. 2.8). Therefore, simple measures of convergence like the coefficient of variation (Zar 1984) do not provide robust estimates of model convergence. For highly skewed distributions, a more robust statistical measure is the interval between the 10% and the 90% percentile (p 10 , p 90 ) for the range
Page 1: Diss. ETH No. 10638 On the Ecology
Page 4 and 5: ii Table of contents A BSTRACT.....
Page 6 and 7: iv APPENDIX .......................
Page 8 and 9: vi Harald BUGMANN, 1994: On the eco
Page 10 and 11: viii Harald BUGMANN, 1994: Aspekte
Page 13 and 14: 1 1 . Introduction 1.1 Climatic cha
Page 15 and 16: Introduction 3 1.2 Methods for the
Page 17 and 18: Introduction 5 in a changing climat
Page 19 and 20: Introduction 7 Their integrative ca
Page 21 and 22: Introduction 9 (1984) provides a mo
Page 23 and 24: Introduction 11 The main advantage
Page 25 and 26: 13 2 . Analysis of existing forest
Page 27 and 28: Analysis of existing forest gap mod
Page 33: Analysis of existing forest gap mod
Page 39 and 40: The forest model FORCLIM 45 carbon
Page 41 and 42: The forest model FORCLIM 47 TREE GR
Page 43 and 44: The forest model FORCLIM 49 gBFlag
Page 45 and 46: The forest model FORCLIM 51 Disturb
Page 47 and 48: The forest model FORCLIM 53 decay o
Page 49 and 50: The forest model FORCLIM 55 the est
Page 51 and 52: The forest model FORCLIM 57 3.3 Mod
Page 53 and 54: The forest model FORCLIM 59 Light a
Page 55 and 56: The forest model FORCLIM 61 Overall
Page 57 and 58: The forest model FORCLIM 63 D (cm)
Page 59 and 60: The forest model FORCLIM 65 a) b) c
Page 61 and 62: The forest model FORCLIM 67 Growth
Page 63 and 64: The forest model FORCLIM 69 Stress-
Page 65 and 66: The forest model FORCLIM 71 Tab. 3.
Page 67 and 68: The forest model FORCLIM 73 3.3.2 F
Page 69 and 70: The forest model FORCLIM 75 where k
Page 71 and 72: The forest model FORCLIM 77 NITROGE
Page 73 and 74: The forest model FORCLIM 79 peratur
Page 75 and 76: The forest model FORCLIM 81 of degr
Page 77 and 78: The forest model FORCLIM 83 microcl
Page 79 and 80: The forest model FORCLIM 85 Tab. 3.
Page 81 and 82: The forest model FORCLIM 87 3.4.3 F
Page 83 and 84: The forest model FORCLIM 89 All the
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The forest model FORCLIM 91 The mas
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The forest model FORCLIM 93 FORCLIM
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Behaviour of FORCLIM along a transe
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Parameter sensitivity & model valid
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153 6 . Model applications Climatic
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Model applications 155 The simulate
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Model applications 157 6.2 Possible
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Model applications 159 simulation s
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Model applications 161 Simulation e
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Model applications 163 At the site
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Model applications 165 Bever Biomas
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Model applications 167 tainty inher
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Model applications 169 scenario cho
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Discussion 171 Finally, the analysi
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Discussion 173 bitrarily chosen par
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Discussion 175 comparably small imp
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Discussion 177 end of the 21st cent
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Conclusions 179 Ecological factors
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Conclusions 181 species composition
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References 183 Begon, M., Harper, J
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References 185 Burger, H., 1951. Ho
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References 187 Faber, P.J., 1991. A
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References 189 Huntley, B. & Birks,
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References 191 Leemans, R. & Prenti
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References 193 Olson, J.S., 1963. E
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References 195 Rudloff, W., 1981. W
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References 197 Smith, T.M., Leemans
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References 199 Whittaker, R.H., 195
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Appendix 201 II. Derivation of para
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Appendix 203 Tab. A-4: Tree species
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Appendix 205 Tab. A-7: Values for m
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Appendix 207 The minimum winter tem
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Appendix 209 Tab. A-11: Shade toler
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Appendix 211 Tab. A-14: Climatic pa
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Appendix 213 IV. Source code of the
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Appendix 215 FROM SimBase IMPORT De
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Appendix 217 END; (* IF *) prevDay
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Appendix 219 PROCEDURE InitializeFW
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Appendix 221 InstallSeparator( fMen
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Appendix 223 FROM FCPFileIO IMPORT
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Appendix 225 modelSp^.p.kHm := sens
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Appendix 227 DeclMV( meanLitt[leafS
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Appendix 229 Swiss Federal Institut
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Appendix 231 (*********************
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Appendix 233 BEGIN WITH sp^ DO d :=
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Appendix 235 Definition module FCPB
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Appendix 237 Purpose Simulation mod
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Appendix 239 END Immobilization; PR
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Appendix 241 CONST lem = 3; VAR ef:
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Appendix 243 Purpose Provides the b
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Appendix 245 Example of a text file
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Appendix 247 Tab. A-15: Lower end o
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Appendix 249 Tab. A-17: Percentage
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Appendix 251 Tab. A-19: Significant
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Appendix 253 VI. Derivation of para
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Appendix 255 Tab. A-21: Species-spe
Page 251 and 252:
Appendix 257 Tab. A-22 (continued)
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