Species distribution models and ecological theory: A critical assessment and some possible new approaches

被引:1071
作者
Austin, Mike [1 ]
机构
[1] CSIRO Sustainable Ecosyst, Canberra, ACT 2601, Australia
关键词
species response curves; competition; environmental gradients; generalized linear model; generalized additive model; quantile regression; structural equation modelling; geographically weighted regression;
D O I
10.1016/j.ecolmodel.2006.07.005
中图分类号
Q14 [生态学(生物生态学)];
学科分类号
071012 ; 0713 ;
摘要
Given the importance of knowledge of species distribution for conservation and climate change management, continuous and progressive evaluation of the statistical models predicting species distributions is necessary Current models are evaluated in terms of ecological theory used, the data model accepted and the statistical methods applied. Focus is restricted to Generalised Linear Models (GLM) and Generalised Additive Models (GAM). Certain currently unused regression methods are reviewed for their possible application to species modelling. A review of recent papers suggests that ecological theory is rarely explicitly considered. Current theory and results support species responses to environmental variables to be unimodal and often skewed though process-based theory is often lacking. Many studies fail to test for unimodal or skewed responses and straight-line relationships are often fitted without justification. Data resolution (size of sampling unit) determines the nature of the environmental niche models that can be fitted. A synthesis of differing ecophysiological ideas and the use of biophysical processes models could improve the selection of predictor variables. A better conceptual framework is needed for selecting variables. Comparison of statistical methods is difficult. Predictive success is insufficient and a test of ecological realism is also needed. Evaluation of methods needs artificial data, as there is no knowledge about the true relationships between variables for field data. However, use of artificial data is limited by lack of comprehensive theory. Three potentially new methods are reviewed. Quantile regression (QR) has potential and a strong theoretical justification in Liebig's law of the minimum. Structural equation modelling (SEM) has an appealing conceptual framework for testing causality but has problems with curvilinear relationships. Geographically weighted regression (GWR) intended to examine spatial non-stationarity of ecological processes requires further evaluation before being used. Synthesis and applications: explicit theory needs to be incorporated into species response models used in conservation. For example, testing for unimodal skewed responses should be a routine procedure. Clear statements of the ecological theory used, the nature of the data model and sufficient details of the statistical method are needed for current models to be evaluated. New statistical methods need to be evaluated for compatibility with ecological theory before use in applied ecology. Some recent work with artificial data suggests the combination of ecological knowledge and statistical skill is more important than the precise statistical method used. The potential exists for a synthesis of current species modelling approaches based on their differing ecological insights not their methodology. Crown Copyright (c) 2006 Published by Elsevier B.V. All rights reserved.
引用
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页码:1 / 19
页数:19
相关论文
共 163 条
[1]   Habitat predicts losses of red grouse to individual hen harriers [J].
Amar, A ;
Arroyo, B ;
Redpath, S ;
Thirgood, S .
JOURNAL OF APPLIED ECOLOGY, 2004, 41 (02) :305-314
[2]  
[Anonymous], COMMUNITY STRUCTURE, DOI DOI 10.1111/j.1529-8817.2008.00538.x
[3]  
[Anonymous], 1993, J AGR BIOL ENVIR ST
[4]  
Antoine ME, 2004, BRYOLOGIST, V107, P163, DOI 10.1639/0007-2745(2004)107[0163:CFAREN]2.0.CO
[5]  
2
[6]   Validation of species-climate impact models under climate change [J].
Araújo, MB ;
Pearson, RG ;
Thuiller, W ;
Erhard, M .
GLOBAL CHANGE BIOLOGY, 2005, 11 (09) :1504-1513
[7]   Exploring ecological patterns with structural equation modeling and Bayesian analysis [J].
Arhonditsis, GB ;
Stow, CA ;
Steinberg, LJ ;
Kenney, MA ;
Lathrop, RC ;
McBride, SJ ;
Reckhow, KH .
ECOLOGICAL MODELLING, 2006, 192 (3-4) :385-409
[8]   Evaluation of statistical models used for predicting plant species distributions: Role of artificial data and theory [J].
Austin, M. P. ;
Belbin, L. ;
Meyers, J. A. ;
Doherty, M. D. ;
Luoto, M. .
ECOLOGICAL MODELLING, 2006, 199 (02) :197-216
[9]  
Austin M.P., 2013, Vegetation Ecology, Vsecond, P71, DOI [10.1002/9781118452592.ch3, DOI 10.1002/9781118452592.CH3]
[10]  
Austin M.P., 1981, Proceedings of the Ecological Society of Australia, V11, P109