Uncovering hidden spatial structure in species communities with spatially explicit joint species distribution models
Summary Modern species distribution models account for spatial autocorrelation in order to obtain unbiased statistical inference on the effects of covariates, to improve the model's predictive ability through spatial interpolation and to gain insight in the spatial processes shaping the data. S...
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Veröffentlicht in: | Methods in ecology and evolution 2016-04, Vol.7 (4), p.428-436 |
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container_title | Methods in ecology and evolution |
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creator | Ovaskainen, Otso Roy, David B. Fox, Richard Anderson, Barbara J. Orme, David |
description | Summary
Modern species distribution models account for spatial autocorrelation in order to obtain unbiased statistical inference on the effects of covariates, to improve the model's predictive ability through spatial interpolation and to gain insight in the spatial processes shaping the data. Somewhat analogously, hierarchical approaches to community‐level data have been developed to gain insights into community‐level processes and to improve species‐level inference by borrowing information from other species that are either ecologically or phylogenetically related to the focal species.
We unify spatial and community‐level structures by developing spatially explicit joint species distribution models. The models utilize spatially structured latent factors to model missing covariates as well as species‐to‐species associations in a statistically and computationally effective manner.
We illustrate that the inclusion of the spatial latent factors greatly increases the predictive performance of the modelling approach with a case study of 55 species of butterfly recorded on a 10 km × 10 km grid in Great Britain consisting of 2609 grid cells. |
doi_str_mv | 10.1111/2041-210X.12502 |
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Modern species distribution models account for spatial autocorrelation in order to obtain unbiased statistical inference on the effects of covariates, to improve the model's predictive ability through spatial interpolation and to gain insight in the spatial processes shaping the data. Somewhat analogously, hierarchical approaches to community‐level data have been developed to gain insights into community‐level processes and to improve species‐level inference by borrowing information from other species that are either ecologically or phylogenetically related to the focal species.
We unify spatial and community‐level structures by developing spatially explicit joint species distribution models. The models utilize spatially structured latent factors to model missing covariates as well as species‐to‐species associations in a statistically and computationally effective manner.
We illustrate that the inclusion of the spatial latent factors greatly increases the predictive performance of the modelling approach with a case study of 55 species of butterfly recorded on a 10 km × 10 km grid in Great Britain consisting of 2609 grid cells.</description><identifier>ISSN: 2041-210X</identifier><identifier>EISSN: 2041-210X</identifier><identifier>DOI: 10.1111/2041-210X.12502</identifier><language>eng</language><publisher>London: John Wiley & Sons, Inc</publisher><subject>Butterflies & moths ; community models ; Geographical distribution ; Interpolation ; joint species distribution models ; latent factors ; Performance prediction ; Phylogeny ; Spatial data ; spatial models ; Species ; Statistical analysis ; Statistical inference</subject><ispartof>Methods in ecology and evolution, 2016-04, Vol.7 (4), p.428-436</ispartof><rights>2015 The Authors. Methods in Ecology and Evolution © 2015 British Ecological Society</rights><rights>Methods in Ecology and Evolution © 2016 British Ecological Society</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4182-b0e77d9efb894bd78ae0e00ee7bf38a92309addfae4c4fc765a92e3304177ffd3</citedby><cites>FETCH-LOGICAL-c4182-b0e77d9efb894bd78ae0e00ee7bf38a92309addfae4c4fc765a92e3304177ffd3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1111%2F2041-210X.12502$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1111%2F2041-210X.12502$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,780,784,1417,27924,27925,45574,45575</link.rule.ids></links><search><contributor>Orme, David</contributor><creatorcontrib>Ovaskainen, Otso</creatorcontrib><creatorcontrib>Roy, David B.</creatorcontrib><creatorcontrib>Fox, Richard</creatorcontrib><creatorcontrib>Anderson, Barbara J.</creatorcontrib><creatorcontrib>Orme, David</creatorcontrib><title>Uncovering hidden spatial structure in species communities with spatially explicit joint species distribution models</title><title>Methods in ecology and evolution</title><description>Summary
Modern species distribution models account for spatial autocorrelation in order to obtain unbiased statistical inference on the effects of covariates, to improve the model's predictive ability through spatial interpolation and to gain insight in the spatial processes shaping the data. Somewhat analogously, hierarchical approaches to community‐level data have been developed to gain insights into community‐level processes and to improve species‐level inference by borrowing information from other species that are either ecologically or phylogenetically related to the focal species.
We unify spatial and community‐level structures by developing spatially explicit joint species distribution models. The models utilize spatially structured latent factors to model missing covariates as well as species‐to‐species associations in a statistically and computationally effective manner.
We illustrate that the inclusion of the spatial latent factors greatly increases the predictive performance of the modelling approach with a case study of 55 species of butterfly recorded on a 10 km × 10 km grid in Great Britain consisting of 2609 grid cells.</description><subject>Butterflies & moths</subject><subject>community models</subject><subject>Geographical distribution</subject><subject>Interpolation</subject><subject>joint species distribution models</subject><subject>latent factors</subject><subject>Performance prediction</subject><subject>Phylogeny</subject><subject>Spatial data</subject><subject>spatial models</subject><subject>Species</subject><subject>Statistical analysis</subject><subject>Statistical inference</subject><issn>2041-210X</issn><issn>2041-210X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><recordid>eNqFkb1PwzAQxSMEEhV0Zo3EwpLWdj6cjKgqH1IRC5XYLMe-UFdJHGyH0v8eh0CFGOAWn55-73S-FwQXGM2wrzlBCY4IRs8zTFJEjoLJQTn-0Z8GU2u3yFecF4gkk8CtW6HfwKj2JdwoKaENbced4nVonemF6w2EahBBKLCh0E3Tt8oN_U65zTdd70N472ollAu3WrXu4JDKD1Jl75Ruw0ZLqO15cFLx2sL06z0L1jfLp8VdtHq8vV9cryKR4JxEJQJKZQFVmRdJKWnOAQFCALSs4pwXJEYFl7LikIikEjRLvQZx7H9LaVXJ-Cy4Gud2Rr_2YB1rlBVQ17wF3VuGaZ6SJMtS7NHLX-hW96b12zESk4ziBGfpX5Sf5c-a--t7aj5SwmhrDVSsM6rhZs8wYkNcbAiEDYGwz7i8IxsdO1XD_j-cPSyX8Wj8AGtRmUQ</recordid><startdate>201604</startdate><enddate>201604</enddate><creator>Ovaskainen, Otso</creator><creator>Roy, David B.</creator><creator>Fox, Richard</creator><creator>Anderson, Barbara J.</creator><creator>Orme, David</creator><general>John Wiley & Sons, Inc</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QG</scope><scope>7SN</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>P64</scope><scope>RC3</scope><scope>7ST</scope><scope>7U6</scope></search><sort><creationdate>201604</creationdate><title>Uncovering hidden spatial structure in species communities with spatially explicit joint species distribution models</title><author>Ovaskainen, Otso ; Roy, David B. ; Fox, Richard ; Anderson, Barbara J. ; Orme, David</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4182-b0e77d9efb894bd78ae0e00ee7bf38a92309addfae4c4fc765a92e3304177ffd3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Butterflies & moths</topic><topic>community models</topic><topic>Geographical distribution</topic><topic>Interpolation</topic><topic>joint species distribution models</topic><topic>latent factors</topic><topic>Performance prediction</topic><topic>Phylogeny</topic><topic>Spatial data</topic><topic>spatial models</topic><topic>Species</topic><topic>Statistical analysis</topic><topic>Statistical inference</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ovaskainen, Otso</creatorcontrib><creatorcontrib>Roy, David B.</creatorcontrib><creatorcontrib>Fox, Richard</creatorcontrib><creatorcontrib>Anderson, Barbara J.</creatorcontrib><creatorcontrib>Orme, David</creatorcontrib><collection>CrossRef</collection><collection>Animal Behavior Abstracts</collection><collection>Ecology Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Genetics Abstracts</collection><collection>Environment Abstracts</collection><collection>Sustainability Science Abstracts</collection><jtitle>Methods in ecology and evolution</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ovaskainen, Otso</au><au>Roy, David B.</au><au>Fox, Richard</au><au>Anderson, Barbara J.</au><au>Orme, David</au><au>Orme, David</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Uncovering hidden spatial structure in species communities with spatially explicit joint species distribution models</atitle><jtitle>Methods in ecology and evolution</jtitle><date>2016-04</date><risdate>2016</risdate><volume>7</volume><issue>4</issue><spage>428</spage><epage>436</epage><pages>428-436</pages><issn>2041-210X</issn><eissn>2041-210X</eissn><abstract>Summary
Modern species distribution models account for spatial autocorrelation in order to obtain unbiased statistical inference on the effects of covariates, to improve the model's predictive ability through spatial interpolation and to gain insight in the spatial processes shaping the data. Somewhat analogously, hierarchical approaches to community‐level data have been developed to gain insights into community‐level processes and to improve species‐level inference by borrowing information from other species that are either ecologically or phylogenetically related to the focal species.
We unify spatial and community‐level structures by developing spatially explicit joint species distribution models. The models utilize spatially structured latent factors to model missing covariates as well as species‐to‐species associations in a statistically and computationally effective manner.
We illustrate that the inclusion of the spatial latent factors greatly increases the predictive performance of the modelling approach with a case study of 55 species of butterfly recorded on a 10 km × 10 km grid in Great Britain consisting of 2609 grid cells.</abstract><cop>London</cop><pub>John Wiley & Sons, Inc</pub><doi>10.1111/2041-210X.12502</doi><tpages>9</tpages><oa>free_for_read</oa></addata></record> |
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source | Wiley Online Library - AutoHoldings Journals; Alma/SFX Local Collection |
subjects | Butterflies & moths community models Geographical distribution Interpolation joint species distribution models latent factors Performance prediction Phylogeny Spatial data spatial models Species Statistical analysis Statistical inference |
title | Uncovering hidden spatial structure in species communities with spatially explicit joint species distribution models |
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