Moving beyond static species distribution models in support of conservation biogeography

被引:356
作者
Franklin, Janet [1 ,2 ]
机构
[1] Arizona State Univ, Sch Geog Sci & Urban Planning, Tempe, AZ 85287 USA
[2] Arizona State Univ, Sch Life Sci, Tempe, AZ 85287 USA
关键词
Climate change; disturbance; landscape dynamics; metapopulation model; species distribution model; species migration; CLIMATE-CHANGE; PLANT MIGRATION; HABITAT SUITABILITY; POPULATION-MODELS; RANGE EXPANSION; RISK-ASSESSMENT; CHANGE IMPACTS; LAND-USE; LANDSCAPE; FIRE;
D O I
10.1111/j.1472-4642.2010.00641.x
中图分类号
X176 [生物多样性保护];
学科分类号
090705 ;
摘要
Aim To demonstrate that multi-modelling methods have effectively been used to combine static species distribution models (SDM), predicting the geographical pattern of suitable habitat, with dynamic landscape and population models to forecast the impacts of environmental change on species' status, an important goal of conservation biogeography. Methods Three approaches were considered: (1) incorporating models of species migration to understand the ability of a species to occupy suitable habitat in new locations; (2) linking models of landscape disturbance and succession to models of habitat suitability; and (3) fully linking models of habitat suitability, habitat dynamics and spatially explicit population dynamics. Results Linking species-environment relationships, landscape dynamics and population dynamics in a multi-modelling framework allows the combined impacts of climate change (affecting species distribution and vital rates) and land cover dynamics (land use change, altered disturbance regimes) on species to be predicted. This approach is only feasible if the life history parameters and habitat requirements of the species are well understood. Main conclusions Forecasts of the impacts of global change on species may be improved by considering multiple causes. A range of methods are available to address the interactions of changing habitat suitability, habitat dynamics and population response that vary in their complexity, realism and data requirements.
引用
收藏
页码:321 / 330
页数:10
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