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

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122 Chapter 5 fore was assumed to be twice as high as for the kDDMin parameter, i.e. ±40%. The uncertainty associated with the winter temperature parameter (kWiT) is also large and was assumed to be ±2 °C. Since drought gradients in the landscape are steep, the drought tolerance parameter (kDrT) is difficult to determine. Its uncertainty was assumed to be ±0.1. Please note that an absolute uncertainty was used since small kDrT values are not more precise than large ones, rather the reverse is true. Tab. 5.1 gives an overview of the plausibility range of each species parameter. Two tables with the minima and maxima for all parameters may be found in Appendix V. 5.1.2 Simulation experiments The site Airolo (Appendix III) was chosen for the sensitivity analysis because it is located in the transition zone between subalpine coniferous and mixed deciduous forests, where the simulated community may be especially sensitive to parameter changes. Each species parameter was set to the lower and the upper end of its plausibility interval, and the steady state species composition of the FORCLIM-E/P/S model was estimated for each parameter change, thus resulting in 2·420 = 840 samples. The steady states were estimated using n = 200 points and ∆t = 150 years (cf. section 4.4). Two types of analyses were performed: 1) To quantify the robustness of the simulated species composition to changes of species parameters, the simulated steady states were compared to a conjectured standard steady state calculated with the default parameter set given in Tab. 3.11 (n = 20'000 points, ∆t = 150 years). For these comparisons, the percentage similarity coefficient (PS, Eq. 2.3) was used. A sample steady state is significantly different from the standard steady state if PS < 0.871 (α = 5%, determined from an investigation for FORCLIM-E/P/S at the site Airolo similar to the ones performed in section 4.4). 2) To quantify the response of particular species to changes of their parameters, it was tested whether the average biomass (µ) of the corresponding species is significantly different from the standard biomass (µ * ) of that species. Since the sampling distribution of the mean tends to normality with increasing sample size (and here, n = 200; Zar 1984), the range µ * ±1.96·SE includes µ at a confidence level (α) of 5%, where SE is the standard error of the mean. For the species with significant differences, the percentage change was calculated.
Parameter sensitivity & model validation 123 5.1.3 Results & discussion The detailed results of all the simulation experiments are listed in Appendix V; they are summarized here, and their statistical properties are presented and discussed as well. ROBUSTNESS OF THE SIMULATED SPECIES COMPOSITION The percentage similarity coefficients between the sample steady states and the standard steady state are generally high (PS > 0.72 except for kDrt with Fagus silvatica; Tab. 5.2). The overall species composition appears to be little sensitive to changes of single parameters within the plausibility range. This is a distinct difference to the FORECE model: For example, with an ITENO indicator parameter of F. silvatica of 5 (Kienast 1987), this species attains almost 40% of the total biomass simulated by FORECE at Airolo. However, when ITENO is increased by just one class (to 6), F. silvatica disappears completely (cf. the simulations with ITENO = 6 in Kienast 1991). Similar phenomena can be observed in FORECE for other species at Airolo (e.g. Larix decidua) as well as at other sites, such as with the IMST parameter of Quercus spp. at Sion. Tab. 5.2 suggests that the FORCLIM-E/P/S model is sensitive mainly to the parameters of the most abundant species. Thus, their relative proportions are subject to considerable uncertainty: note for example that the lowest PS coefficients occur with F. silvatica, the most abundant species. On the other hand, the set of dominating species produced with the default parameter set seems to be rather robust to errors of parameter estimation, i.e. there are no species that turn up or disappear completely and alter the species composition qualitatively when their species parameters are changed within the plausibility range. WHICH SPECIES PARAMETERS ARE MOST SENSITIVE? According to Tab. 5.3 and considering the lower end of the plausibility interval of species parameters, the model appears to be most sensitive to the nitrogen tolerance parameter (kNTol), followed by the species type (sType) and the growth scaling constant (kG). For the upper end of the plausibility interval, the ranking is kNTol > kL a (shading tolerance of adult trees) > kG and kDrT (drought tolerance). The effects of the uncertainty inherent in kNTol on the simulated species composition suggest that there is a strong coupling between FORCLIM-S and FORCLIM-P, as hypothesized earlier (cf. section 4.3.4).
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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-
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 115: Parameter sensitivity & model valid
Page 119 and 120: 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
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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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