Making better biogeographical predictions of species' distributions

被引:331
作者
Guisan, Antoine
Lehmann, Anthony
Ferrier, Simon
Austin, Mike
Overton, Jacob Mc. C.
Aspinall, Richard
Hastie, Trevor
机构
[1] Univ Lausanne, Dept Ecol & Evolut, CH-1015 Lausanne, Switzerland
[2] Swiss Ctr Faunal Cartogr, CH-2000 Neuchatel, Switzerland
[3] New S Wales Dept Environm & Conservat, Armidale, NSW 2350, Australia
[4] CSIRO, Sustainable Ecosyst, Canberra, ACT 2601, Australia
[5] Landcare Res, Hamilton, New Zealand
[6] Arizona State Univ, Dept Geog, Tempe, AZ 85287 USA
[7] Stanford Univ, Dept Stat, Stanford, CA 94305 USA
关键词
artificial data; autocorrelation; community and diversity modelling; errors and uncertainties; generalized regressions; interactions; niche-based model;
D O I
10.1111/j.1365-2664.2006.01164.x
中图分类号
X176 [生物多样性保护];
学科分类号
090705 ;
摘要
1. Biogeographical models of species' distributions are essential tools for assessing impacts of changing environmental conditions on natural communities and ecosystems. Practitioners need more reliable predictions to integrate into conservation planning (e.g. reserve design and management). 2. Most models still largely ignore or inappropriately take into account important features of species' distributions, such as spatial autocorrelation, dispersal and migration, biotic and environmental interactions. Whether distributions of natural communities or ecosystems are better modelled by assembling individual species' predictions in a bottom-up approach or modelled as collective entities is another important issue. An international workshop was organized to address these issues. 3. We discuss more specifically six issues in a methodological framework for generalized regression: (i) links with ecological theory; (ii) optimal use of existing data and artificially generated data; (iii) incorporating spatial context; (iv) integrating ecological and environmental interactions; (v) assessing prediction errors and uncertainties; and (vi) predicting distributions of communities or collective properties of biodiversity. 4. Synthesis and applications. Better predictions of the effects of impacts on biological communities and ecosystems can emerge only from more robust species' distribution models and better documentation of the uncertainty associated with these models. An improved understanding of causes of species' distributions, especially at their range limits, as well as of ecological assembly rules and ecosystem functioning, is necessary if further progress is to be made. A better collaborative effort between theoretical and functional ecologists, ecological modellers and statisticians is required to reach these goals.
引用
收藏
页码:386 / 392
页数:7
相关论文
共 43 条
[1]   Waking the sleeping giant: The evolutionary foundations of plant function [J].
Ackerly, DD ;
Monson, RK .
INTERNATIONAL JOURNAL OF PLANT SCIENCES, 2003, 164 (03) :S1-S6
[2]   AN INDUCTIVE MODELING PROCEDURE BASED ON BAYES THEOREM FOR ANALYSIS OF PATTERN IN SPATIAL DATA [J].
ASPINALL, R .
INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SYSTEMS, 1992, 6 (02) :105-121
[3]   Spatial prediction of species distribution: an interface between ecological theory and statistical modelling [J].
Austin, MP .
ECOLOGICAL MODELLING, 2002, 157 (2-3) :101-118
[4]  
AUSTIN MP, IN PRESS ECOLOGICAL
[5]   Error and uncertainty in habitat models [J].
Barry, Simon ;
Elith, Jane .
JOURNAL OF APPLIED ECOLOGY, 2006, 43 (03) :413-423
[6]   Evaluating resource selection functions [J].
Boyce, MS ;
Vernier, PR ;
Nielsen, SE ;
Schmiegelow, FKA .
ECOLOGICAL MODELLING, 2002, 157 (2-3) :281-300
[7]   DISPERSE: A cellular automaton for predicting the distribution of species in a changed climate [J].
Carey, PD .
GLOBAL ECOLOGY AND BIOGEOGRAPHY LETTERS, 1996, 5 (4-5) :217-226
[8]   Modelling ecological niches with support vector machines [J].
Drake, John M. ;
Randin, Christophe ;
Guisan, Antoine .
JOURNAL OF APPLIED ECOLOGY, 2006, 43 (03) :424-432
[9]   Mapping epistemic uncertainties and vague concepts in predictions of species distribution [J].
Elith, J ;
Burgman, MA ;
Regan, HM .
ECOLOGICAL MODELLING, 2002, 157 (2-3) :313-329
[10]  
Ferrier S, 2004, BIOSCIENCE, V54, P1101, DOI 10.1641/0006-3568(2004)054[1101:MMOTBF]2.0.CO