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

More documents

Recommendations

Info

118 Chapter 4 Tab. 4.6: Statistics of the distribution of the PS coefficients at the three test sites. CI denotes the lower and upper 95% confidence interval of PS. The arcsine transformation is according to Zar (1984). Bern Airolo Davos arcsine transformed arcsine transformed untransformed untransformed untransformed arcsine transformed CI upper 0.955 0.950 0.963 0.957 0.989 0.983 µ(PS) 0.902 0.903 0.907 0.909 0.954 0.956 CI lower 0.849 0.844 0.850 0.844 0.920 0.916 It is evident from Tab. 4.7 & 24 that the coefficient of variation tends to increase with decreasing species-specific biomass (e.g. P. excelsa vs. L. decidua at Davos, Tab. 4.7). Moreover, the species role (Shugart 1984) is important as well: For example, L. decidua and P. cembra have similar biomass at Davos; yet, since P. cembra is shade tolerant and usually present with low biomass, its CV is considerably smaller than that of L. decidua, which is usually absent except after the formation of a large gap, when it can establish and grow to a considerable size; afterwards, it disappears again. Similar reasoning can be applied to the results from the site Bern: The biomass estimates of the two dominant species, F. silvatica and A. alba, have small coefficients of variation (Tab. 4.8). On the other hand, species with similar and low biomass such as Acer pseudoplatanus and Quercus petraea have coefficients of variation that differ considerably, again due to their different roles: Q. petraea is much less shade tolerant than A. pseudoplatanus. Obviously the species “roles” defined by Shugart (1984) provide a useful framework for this analysis. Thus it may be concluded that the statistical properties of the PS coefficient between two independent estimates of the same steady state can not be stated generally. They depend Tab. 4.7: Averages (µ) and coefficients of variation (CV) of 400 species-specific steady-state biomass estimates at the site Davos (n = 200, ∆t = 150). Variable µ [t/ha] CV total biomass 340.8 2.4% Picea excelsa 269.7 3.8% Abies alba 34.5 16.2% Populus nigra 13.8 16.6% Larix decidua 8.9 61.3% Pinus cembra 6.5 36.3%
Behaviour of FORCLIM along a transect in the European Alps 119 both on the number of species participating in the succession as well as the specific role of those species (Shugart 1984). The forest simulated by FORCLIM-E/P at the site Bern is among those with the highest species diversity (Fig. 4.12). Since the average value of the PS coefficients tends to increase with decreasing diversity, this site may be considered as a “worst case” of the statistical properties of the PS coefficients. Hence, two steady states of low-elevation, species-rich forests that are estimated using n = 200 and ∆t = 150 years are significantly (95%) different from each other if PS < 0.85 (Tab. 4.6). The less species are present, the more the lower confidence limit increases; for species-poor forests (typically at higher elevations such as Davos), the lower end of the confidence interval (95%) increases to ≈0.92 (Tab. 4.6). Thus, also with respect to the size of this confidence limit the design of simulation experiments with n = 200 points and ∆t = 150 years appears to be appropriate. The simulation time required on a Macintosh Quadra 700 computer for simulating 200 patches of 1200 years each with the FORCLIM-E/P model is 133 minutes. On the other hand, estimating the steady state with n = 200 and ∆t = 150 years and discarding the first 1000 years of the simulation requires to simulate one patch during 31'000 years, which corresponds to 17 minutes of simulation time, or 12.8% of the time necessary for the transient experiment. Thus, the method presented above is quite efficient if one desires to estimate the steady-state species composition of forest gap models. Tab. 4.8: Averages (µ) and coefficients of variation (CV) of 400 species-specific steady-state biomass estimates at the site Bern (n = 200, ∆t = 150). Variable µ [t/ha] CV total biomass 392.6 2.4% Fagus silvatica 151.1 8.8% Abies alba 128.9 9.5% Acer pseudoplatanus 17.8 26.3% Ulmus scabra 17.3 24.1% Quercus petraea 17.3 59.8% Picea excelsa 11.3 35.1% Acer platanoides 10.4 20.0% Populus nigra 9.6 26.4% Fraxinus excelsior 6.5 30.3% Quercus robur 5.6 122.7%
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 29 and 30:
Page 31 and 32:
Page 33 and 34:
Page 35 and 36:
Page 37 and 38:
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
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 111: Behaviour of FORCLIM along a transe
Page 115 and 116: 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
Page 171 and 172:
Discussion 177 end of the 21st cent
Page 173 and 174:
Conclusions 179 Ecological factors
Page 175 and 176:
Conclusions 181 species composition
Page 177 and 178:
References 183 Begon, M., Harper, J
Page 179 and 180:
References 185 Burger, H., 1951. Ho
Page 181 and 182:
References 187 Faber, P.J., 1991. A
Page 183 and 184:
References 189 Huntley, B. & Birks,
Page 185 and 186:
References 191 Leemans, R. & Prenti
Page 187 and 188:
References 193 Olson, J.S., 1963. E
Page 189 and 190:
References 195 Rudloff, W., 1981. W
Page 191 and 192:
References 197 Smith, T.M., Leemans
Page 193 and 194:
References 199 Whittaker, R.H., 195
Page 195 and 196:
Appendix 201 II. Derivation of para
Page 197 and 198:
Appendix 203 Tab. A-4: Tree species
Page 199 and 200:
Appendix 205 Tab. A-7: Values for m
Page 201 and 202:
Appendix 207 The minimum winter tem
Page 203 and 204:
Appendix 209 Tab. A-11: Shade toler
Page 205 and 206:
Appendix 211 Tab. A-14: Climatic pa
Page 207 and 208:
Appendix 213 IV. Source code of the
Page 209 and 210:
Appendix 215 FROM SimBase IMPORT De
Page 211 and 212:
Appendix 217 END; (* IF *) prevDay
Page 213 and 214:
Appendix 219 PROCEDURE InitializeFW
Page 215 and 216:
Appendix 221 InstallSeparator( fMen
Page 217 and 218:
Appendix 223 FROM FCPFileIO IMPORT
Page 219 and 220:
Appendix 225 modelSp^.p.kHm := sens
Page 221 and 222:
Appendix 227 DeclMV( meanLitt[leafS
Page 223 and 224:
Appendix 229 Swiss Federal Institut
Page 225 and 226:
Appendix 231 (*********************
Page 227 and 228:
Appendix 233 BEGIN WITH sp^ DO d :=
Page 229 and 230:
Appendix 235 Definition module FCPB
Page 231 and 232:
Appendix 237 Purpose Simulation mod
Page 233 and 234:
Appendix 239 END Immobilization; PR
Page 235 and 236:
Appendix 241 CONST lem = 3; VAR ef:
Page 237 and 238:
Appendix 243 Purpose Provides the b
Page 239 and 240:
Appendix 245 Example of a text file
Page 241 and 242:
Appendix 247 Tab. A-15: Lower end o
Page 243 and 244:
Appendix 249 Tab. A-17: Percentage
Page 245 and 246:
Appendix 251 Tab. A-19: Significant
Page 247 and 248:
Appendix 253 VI. Derivation of para
Page 249 and 250:
Appendix 255 Tab. A-21: Species-spe
Page 251 and 252:
Appendix 257 Tab. A-22 (continued)
show all

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

Create successful ePaper yourself

Delete template?

Save as template?