Species distribution models and ecological theory: A critical assessment
and some possible new approaches
M. Austin. Ecological Modelling, 200 (1-2):
1-19(2007)
Abstract
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.
%0 Journal Article
%1 Austin2007
%A Austin, M.
%D 2007
%J Ecological Modelling
%K Competition; Environmental Generalized Geographically Quantile Species Structural additive curves; equation gradients; linear model; modelling regression; response weighted
%N 1-2
%P 1-19
%T Species distribution models and ecological theory: A critical assessment
and some possible new approaches
%V 200
%X 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.
@article{Austin2007,
abstract = {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.},
added-at = {2010-07-07T17:27:19.000+0200},
author = {Austin, M.},
biburl = {https://www.bibsonomy.org/bibtex/222b296f58754d898dc5b6a7e02b05ab6/pillo},
interhash = {9eb2ba51f692c2f58eb690f764829544},
intrahash = {22b296f58754d898dc5b6a7e02b05ab6},
journal = {Ecological Modelling},
keywords = {Competition; Environmental Generalized Geographically Quantile Species Structural additive curves; equation gradients; linear model; modelling regression; response weighted},
number = {1-2},
owner = {Bernd Panassiti},
pages = {1-19},
timestamp = {2010-07-07T17:27:20.000+0200},
title = {Species distribution models and ecological theory: A critical assessment
and some possible new approaches},
volume = 200,
year = 2007
}