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

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120 5 . Parameter sensitivity & model validation 5.1 Sensitivity of species parameters in FORCLIM There are various aims of a sensitivity analysis: It may be used for model corroboration, to provide guidelines for future research, or even for parameter estimation (Swartzman & Kaluzny 1987, p. 217). The former two aspects are especially important in the present study: First, little confidence can be placed in the predictions from a model that is extremely sensitive to parameter changes unless the real system has a similar sensitivity to these parameters. Second, since the values of most parameters in ecological models can not be determined with sufficient certainty, it is important to indicate which of them have a large influence on model behaviour; these findings then can provide guidelines for further research. So far, only few sensitivity studies have been conducted with forest gap models (Kercher & Axelrod 1984, Dale et al. 1988, Leemans 1991). Due to the large parameter space of these models and the long simulation time required to run them, such analyses were restricted to a limited number of parameters (Kercher & Axelrod 1984, Dale et al. 1988), or they dealt with species-poor forests (Leemans 1991). The FORCLIM model has a comparably small parameter space (420 species parameters, cf. chapter 3), and it is apt for performing large-scale simulation studies (cf. chapter 4). Thus with FORCLIM it becomes possible to evaluate the sensitivity of all 420 species parameters. Two major questions shall be addressed in the present sensitivity analysis: 1) How sensitive is the simulated species composition to the uncertainty inherent in the species parameters? Would the abundance of the dominating species change strongly if their parameters were altered? Would new, previously suppressed species become abundant if they had different parameter values?
Parameter sensitivity & model validation 121 2) Which species parameters are most important for determining the successional properties of the simulated forests? What are the most urgent research needs for narrowing the plausibility range of these parameters? To answer these questions it is necessary to determine the plausibility range of each species parameter. In the following, both the response of the species composition as well as the simulated biomass of selected species will be evaluated. 5.1.1 Range of plausibility for species parameters The uncertainty inherent in the estimation of the parameters describing maximum tree diameter (kDm), maximum height (kHm), and maximum age (kAm) was quantified from the data assembled during parameter estimation (Appendix II). For the parameters denoting the tolerance of the species on a nominal scale in the range [1…3] or [1…5], such as nitrogen (kNTol) and browsing tolerance (kBrow), as well as for the parameters describing the species type (sType) and its leaf litter quality (kLQ), the uncertainty was assumed to be ±1. Correspondingly, the uncertainty of the shade tolerance parameters (kL a , kL y ), which are in the range [1…9], was assumed to be ±2 classes. The growth scaling constant (kG), which had turned out to be difficult to determine (Appendix II), was assigned the relatively large uncertainty of ±30%. The uncertainty of the parameters determining the response to climate was set as follows: While the lower limit of the degree-day range (kDDMin) may be determined with adequate precision (assumed to be ±20%), the determination of the upper limit (kDDMax) has been shown to be more difficult (Prentice & Helmisaari 1991); its uncertainty there- Tab. 5.1: Uncertainty inherent in the estimation of the species parameters of the FORCLIM-P model. Parameter Uncertainty Parameter Uncertainty sType ±1, C/D not varied kWiT ±2 °C kDm 1) kDrT ±0.1 kHm 1) kNTol ±1 kAm 1) kBrow ±1 kG ±30% kL y ±2 kDDMin ±20% kL a ±2 kDDMax ±40% kLQ ±1 1) according to the range limits from the literature review (Appendix II).
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Diss. ETH No. 10638 On the Ecology
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ii Table of contents A BSTRACT.....
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iv APPENDIX .......................
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vi Harald BUGMANN, 1994: On the eco
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viii Harald BUGMANN, 1994: Aspekte
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1 1 . Introduction 1.1 Climatic cha
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Introduction 3 1.2 Methods for the
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Introduction 5 in a changing climat
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Introduction 7 Their integrative ca
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Introduction 9 (1984) provides a mo
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Introduction 11 The main advantage
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13 2 . Analysis of existing forest
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Analysis of existing forest gap mod
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The forest model FORCLIM 45 carbon
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The forest model FORCLIM 47 TREE GR
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The forest model FORCLIM 49 gBFlag
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The forest model FORCLIM 51 Disturb
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The forest model FORCLIM 53 decay o
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The forest model FORCLIM 55 the est
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The forest model FORCLIM 57 3.3 Mod
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The forest model FORCLIM 59 Light a
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The forest model FORCLIM 61 Overall
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The forest model FORCLIM 63 D (cm)
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The forest model FORCLIM 65 a) b) c
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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
Page 85 and 86: The forest model FORCLIM 91 The mas
Page 87 and 88: The forest model FORCLIM 93 FORCLIM
Page 89 and 90: Behaviour of FORCLIM along a transe
Page 113: Behaviour of FORCLIM along a transe
Page 117 and 118: Parameter sensitivity & model valid
Page 147 and 148: 153 6 . Model applications Climatic
Page 149 and 150: Model applications 155 The simulate
Page 151 and 152: Model applications 157 6.2 Possible
Page 153 and 154: Model applications 159 simulation s
Page 155 and 156: Model applications 161 Simulation e
Page 157 and 158: Model applications 163 At the site
Page 159 and 160: Model applications 165 Bever Biomas
Page 161 and 162: Model applications 167 tainty inher
Page 163 and 164: 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
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Appendix 257 Tab. A-22 (continued)
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