Sensitivity of tropical forests to climate change in the humid tropics of north Queensland

被引:86
作者
Hilbert, DW
Ostendorf, B
Hopkins, MS
机构
[1] CSIRO, Trop Forest Res Ctr, Atherton, Qld 4883, Australia
[2] Cooperat Res Ctr Trop Rainforest Ecol & Managemen, Atherton, Qld 4883, Australia
关键词
artificial neural network; Australia; global change; landscape ecology; modelling; rainforest; wet tropics;
D O I
10.1046/j.1442-9993.2001.01137.x
中图分类号
Q14 [生态学(生物生态学)];
学科分类号
071012 ; 0713 ;
摘要
An analysis using an artificial neural network model suggests that the tropical forests of north Queensland are highly sensitive to climate change within the range that is likely to occur in the next 50-100 years. The distribution and extent of environments suitable for 15 structural forest types were estimated, using the model, in 10 climate scenarios that include warming up to 1degreesC and altered precipitation from -10% to +20%. Large changes in the distribution of forest environments are predicted with even minor climate change. Increased precipitation favours some rainforest types, whereas decreased rainfall increases the area suitable for forests dominated by sclerophyllous genera such as Eucalyptus and Allocasuarina. Rainforest environments respond differentially to increased temperature. The area of lowland mesophyll vine forest environments increases with warming, whereas upland complex notophyll vine forest environments respond either positively or negatively to temperature, depending on precipitation. Highland rainforest environments (simple notophyll and simple microphyll vine fern forests and thickets), the habitat for many of the region's endemic vertebrates, decrease by 50% with only a 1degreesC warming. Estimates of the stress to present forests resulting from spatial shifts of forest environments (assuming no change in the present forest distributions) indicate that several forest types would be highly stressed by a 1degreesC warming and most are sensitive to any change in rainfall. Most forests will experience climates in the near future that are more appropriate to some other structural forest type. Thus, the propensity for ecological change in the region is high and, in the long term, significant shifts in the extent and spatial distribution of forests are likely. A detailed spatial analysis of the sensitivity to climate change indicates that the strongest effects of climate change will be experienced at boundaries between forest classes and in ecotonal communities between rainforest and open woodland.
引用
收藏
页码:590 / 603
页数:14
相关论文
共 59 条
[1]  
[Anonymous], 1996, Intergovernmental Panel on Climate Change
[2]  
*AUSLIG, 1994, TOPO 250K DAT US GUI
[3]   Stochastic models that predict trout population density or biomass on a mesohabitat scale [J].
Baran, P ;
Lek, S ;
Delacoste, M ;
Belaud, A .
HYDROBIOLOGIA, 1996, 337 (1-3) :1-9
[4]   Danish forest development during the last 3000 years reconstructed from regional pollen data [J].
Bradshaw, R ;
Holmqvist, BH .
ECOGRAPHY, 1999, 22 (01) :53-62
[5]   Supervised classification of types of glaciated landscapes using digital elevation data [J].
Brown, DG ;
Lusch, DP ;
Duda, KA .
GEOMORPHOLOGY, 1998, 21 (3-4) :233-250
[6]   A neural network method for efficient vegetation mapping [J].
Carpenter, GA ;
Gopal, S ;
Macomber, S ;
Martens, S ;
Woodcock, CE ;
Franklin, J .
REMOTE SENSING OF ENVIRONMENT, 1999, 70 (03) :326-338
[7]   A neural network model for forecasting fish stock recruitment [J].
Chen, DG ;
Ware, DM .
CANADIAN JOURNAL OF FISHERIES AND AQUATIC SCIENCES, 1999, 56 (12) :2385-2396
[8]   Changes in freshwater carbon exports from Canadian terrestrial basins to lakes and estuaries under a 2xCO2 atmospheric scenario [J].
Clair, TA ;
Ehrman, JM ;
Higuchi, K .
GLOBAL BIOGEOCHEMICAL CYCLES, 1999, 13 (04) :1091-1097
[9]  
CSIRO, 1996, CLIM CHANG SCEN AUST
[10]  
Deadman PJ, 1997, AI APPLICATIONS, V11, P107