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Non-stationarity and local approaches to modelling the distributions of wildlife

Non-stationarity and local approaches to modelling the distributions of wildlife
Non-stationarity and local approaches to modelling the distributions of wildlife
Despite a growing interest in species distribution modelling, relatively little attention has been paid to spatial autocorrelation and non-stationarity. Both spatial autocorrelation (the tendency for adjacent locations to be more similar than distant ones) and non-stationarity (the variation in modelled relationships over space) are likely to be common properties of ecological systems. This paper focuses on non-stationarity and uses two local techniques, geographically weighted regression (GWR) and varying coefficient modelling (VCM), to assess its impact on model predictions. We extend two published studies, one on the presence–absence of calandra larks in Spain and the other on bird species richness in Britain, to compare GWR and VCM with the more usual global generalized linear modelling (GLM) and generalized additive modelling (GAM). For the calandra lark data, GWR and VCM produced better-fitting models than GLM or GAM. VCM in particular gave significantly reduced spatial autocorrelation in the model residuals. GWR showed that individual predictors became stationary at different spatial scales, indicating that distributions are influenced by ecological processes operating over multiple scales. VCM was able to predict occurrence accurately on independent data from the same geographical area as the training data but not beyond, whereas the GAM produced good results on all areas. Individual predictions from the local methods often differed substantially from the global models. For the species richness data, VCM and GWR produced far better predictions than ordinary regression. Our analyses suggest that modellers interpolating data to produce maps for practical actions (e.g. conservation) should consider local methods, whereas they should not be used for extrapolation to new areas. We argue that local methods are complementary to global methods, revealing details of habitat associations and data properties which global methods average out and miss.
glm, gam, geographically weighted regression, habitat selection, spatial autocorrelation, varying coefficient modelling
1366-9516
313-323
Osborne, Patrick E.
c4d4261d-557c-4179-a24e-cdd7a98fb2b8
Foody, Giles M.
62843823-1717-4a6e-9dd6-72539e7bf44e
Suárez-Seoane, Susana
a33763ad-cd29-40bf-a813-dcb7c526c1ad
Osborne, Patrick E.
c4d4261d-557c-4179-a24e-cdd7a98fb2b8
Foody, Giles M.
62843823-1717-4a6e-9dd6-72539e7bf44e
Suárez-Seoane, Susana
a33763ad-cd29-40bf-a813-dcb7c526c1ad

Osborne, Patrick E., Foody, Giles M. and Suárez-Seoane, Susana (2007) Non-stationarity and local approaches to modelling the distributions of wildlife. Diversity and Distributions, 13 (3), 313-323. (doi:10.1111/j.1472-4642.2007.00344.x).

Record type: Article

Abstract

Despite a growing interest in species distribution modelling, relatively little attention has been paid to spatial autocorrelation and non-stationarity. Both spatial autocorrelation (the tendency for adjacent locations to be more similar than distant ones) and non-stationarity (the variation in modelled relationships over space) are likely to be common properties of ecological systems. This paper focuses on non-stationarity and uses two local techniques, geographically weighted regression (GWR) and varying coefficient modelling (VCM), to assess its impact on model predictions. We extend two published studies, one on the presence–absence of calandra larks in Spain and the other on bird species richness in Britain, to compare GWR and VCM with the more usual global generalized linear modelling (GLM) and generalized additive modelling (GAM). For the calandra lark data, GWR and VCM produced better-fitting models than GLM or GAM. VCM in particular gave significantly reduced spatial autocorrelation in the model residuals. GWR showed that individual predictors became stationary at different spatial scales, indicating that distributions are influenced by ecological processes operating over multiple scales. VCM was able to predict occurrence accurately on independent data from the same geographical area as the training data but not beyond, whereas the GAM produced good results on all areas. Individual predictions from the local methods often differed substantially from the global models. For the species richness data, VCM and GWR produced far better predictions than ordinary regression. Our analyses suggest that modellers interpolating data to produce maps for practical actions (e.g. conservation) should consider local methods, whereas they should not be used for extrapolation to new areas. We argue that local methods are complementary to global methods, revealing details of habitat associations and data properties which global methods average out and miss.

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More information

Published date: May 2007
Keywords: glm, gam, geographically weighted regression, habitat selection, spatial autocorrelation, varying coefficient modelling

Identifiers

Local EPrints ID: 52682
URI: http://eprints.soton.ac.uk/id/eprint/52682
ISSN: 1366-9516
PURE UUID: 8187d962-4b08-48ac-b33c-8243b9369740
ORCID for Patrick E. Osborne: ORCID iD orcid.org/0000-0001-8919-5710

Catalogue record

Date deposited: 11 Jul 2008
Last modified: 16 Mar 2024 03:42

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Contributors

Author: Giles M. Foody
Author: Susana Suárez-Seoane

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