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Evaluating Bayesian spatial methods for modelling species distributions with clumped and restricted occurrence data.

Abstract:

Statistical approaches for inferring the spatial distribution of taxa (Species Distribution Models, SDMs) commonly rely on available occurrence data, which is often clumped and geographically restricted. Although available SDM methods address some of these factors, they could be more directly and accurately modelled using a spatially-explicit approach. Software to fit models with spatial autocorrelation parameters in SDMs are now widely available, but whether such approaches for inferring SDM...

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Publication status:
Published
Peer review status:
Peer reviewed
Version:
Publisher's version

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Publisher copy:
10.1371/journal.pone.0187602

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ORCID:
0000-0001-8615-1798
More by this author
Institution:
University of Oxford
Division:
Medical Sciences Division
Blackburn, TM More by this author
Ecosystem Services for Poverty Alleviation Programme More from this funder
Dynamic Drivers of Disease in Africa Consortium More from this funder
Department for International Development More from this funder
Economic and Social Research Council More from this funder
Publisher:
Public Library of Science Publisher's website
Journal:
PLoS One Journal website
Volume:
12
Issue:
11
Pages:
e0187602
Publication date:
2017-11-05
Acceptance date:
2017-10-02
DOI:
EISSN:
1932-6203
ISSN:
1932-6203
Pubs id:
pubs:810723
URN:
uri:54321f0b-2486-469d-bb4e-1192bea05e03
UUID:
uuid:54321f0b-2486-469d-bb4e-1192bea05e03
Local pid:
pubs:810723
Language:
English
Keywords:

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