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

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126 Chapter 5 The large sensitivity to kDrT (Tab. 5.3) found in the present study is primarily a consequence of the large plausibility range of this parameter; it points to the need for further research on the drought tolerance of the tree species and their response to drought stress. RESPONSE OF PARTICULAR SPECIES TO PARAMETER CHANGES The data from this analysis (cf. Appendix V) also suggest that the parameters kNTol, kG, kL y , and kL a are most important for shaping species performance. Other parameters such as kDm, kAm, kDDMin, and kDDMax were found to be important for some species that have low abundance. However, even if the relative change of their biomass was positive and high, they did not attain considerable biomass; for example, the biomass of Tilia platyphyllos increased by 8510.6% (!) when its kDDMin parameter was lowered, yet it reached a biomass of 1.6 t/ha only. Thus, it may be concluded that the biomass estimates of minor species are not robust to parameter changes, and the simulated abundance of those species should not be interpreted quantitatively. 5.1.4 Conclusion The analysis of the sensitivity of FORCLIM to the values of its species parameters revealed the following: The species composition simulated by FORCLIM appears to be quite robust to changes of species parameters. Specifically, there are no suppressed species that attain large biomass when one of their species parameters is changed. On the other hand, in some instances the biomass of the most abundant species may decrease considerably, but they still remain characteristic of the simulated forest. The abundance of the species may vary markedly depending on the parameter values used. Thus the simulated quantity of a given species should be interpreted cautiously. This may be interpreted in the context of the scheme proposed by Levins (1966): Forest gap models may be general and realistic, but the precision of the simulated species composition is rather low. The FORCLIM model appears to be most sensitive to the values of the kNTol parameter, pointing to the need for further research on soil organic matter dynamics and nutrient
Parameter sensitivity & model validation 127 availability. Other important parameters are those shaping the maximum growth equation (kG), followed by those describing the response of the species to drought (kDrT), winter temperature (kWiT), and light availability (kL y , kL a , sType). Even though it was possible to perform a sensitivity analysis that included all speciesspecific parameters in FORCLIM, this does not mean that a generalized statement about the sensitivity of the model to the values of these parameters can be derived: First, the sensitivity of the model to the value of a certain parameter is a function of the abiotic environment and can not be stated generally from the analysis at one single site (Airolo). Second, only the effects on the steady-state species composition were evaluated. Kercher & Axelrod (1984) showed that the relative sensitivity of the SILVA model varies along the time axis; ecologically speaking, the sensitivity of a model during the transient phase may be just as important as in its steady state. Moreover, the percentage similarity coefficient used to compare the species compositions is an aggregated index which is little sensitive to the biomass of species with low abundance, thus concealing part of the effects of the changed parameters. It may be concluded that further studies on the parameter sensitivity of forest gap models would be desirable. The results from the present study agree to a large extent with those from earlier, partial sensitivity analyses (Kercher & Axelrod 1984, Dale et al. 1988, Leemans 1991), suggesting that the same ecological factors govern the dynamics in the various forest gap models. Moreover, the robustness of the species composition simulated by the FORCLIM model increases our confidence that these results are not arbitrary and that the model produces reliable hypotheses about the near-natural forest vegetation. 5.2 Choice of data and experiments for model validation The term “model validation” is used with various meanings in ecology. Swartzman & Kaluzny (1987) note that “validation” in the strict sense is a misnomer: It is impossible to assess the truth of a model. We can simply design experiments to increase our confidence that a model meets its objectives; Swartzman & Kaluzny (1987) term this “model corroboration”. However, because it is widespread, the term “validation” will be used in the present study as well with the following definition: In a validation procedure, the performance of a model is tested on its agreement with a set of observations that are
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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
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The forest model FORCLIM 69 Stress-
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The forest model FORCLIM 71 Tab. 3.
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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 115 and 116: Parameter sensitivity & model valid
Page 119: 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
Page 165 and 166: Discussion 171 Finally, the analysi
Page 167 and 168: Discussion 173 bitrarily chosen par
Page 169 and 170: 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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