Generalized functional responses for species distributions
Researchers employing resource selection functions (RSFs) and other related methods aim to detect correlates of space-use and mitigate against detrimental environmental change. However, an empirical model fit to data from one place or time is unlikely to capture species responses under different con...
Gespeichert in:
| Veröffentlicht in: | Ecology (Durham) 2011-03, Vol.92 (3), p.583-589 |
|---|---|
| Hauptverfasser: | , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 589 |
|---|---|
| container_issue | 3 |
| container_start_page | 583 |
| container_title | Ecology (Durham) |
| container_volume | 92 |
| creator | Matthiopoulos, Jason Hebblewhite, Mark Aarts, Geert Fieberg, John |
| description | Researchers employing resource selection functions (RSFs) and other related methods aim to detect correlates of space-use and mitigate against detrimental environmental change. However, an empirical model fit to data from one place or time is unlikely to capture species responses under different conditions because organisms respond nonlinearly to changes in habitat availability. This phenomenon, known as a functional response in resource selection, has been debated extensively in the RSF literature but continues to be ignored by practitioners for lack of a practical treatment. We therefore extend the RSF approach to enable it to estimate generalized functional responses (GFRs) from spatial data. GFRs employ data from several sampling instances characterized by diverse profiles of habitat availability. By modeling the regression coefficients of the underlying RSF as functions of availability, GFRs can account for environmental change and thus predict population distributions in new environments. We formulate the approach as a mixed-effects model so that it is estimable by readily available statistical software. We illustrate its application using (1) simulation and (2) wolf home-range telemetry. Our results indicate that GFRs can offer considerable improvements in estimation speed and predictive ability over existing mixed-effects approaches. |
| doi_str_mv | 10.1890/10-0751.1 |
| format | Article |
| fullrecord | <record><control><sourceid>jstor_proqu</sourceid><recordid>TN_cdi_proquest_miscellaneous_868766778</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><jstor_id>41151176</jstor_id><sourcerecordid>41151176</sourcerecordid><originalsourceid>FETCH-LOGICAL-a5743-3a4e44d87eb42a96a2e25e82aacd97a2f824b106e7e206f9869dbe574d8d66a93</originalsourceid><addsrcrecordid>eNp1kk1v1DAQhi0EokvhwA8AViCEOKTYjuOP3tCqFKRKHKAHTtYkmVReZe1gJyrLr8chywpVqg8eS_O889ozJuQ5o2dMG_qB0YKqip2xB2TFTGkKwxR9SFaUMl4YWekT8iSlLc2LCf2YnHAmqRZSrcj5JXqM0Lvf2K67yTejCx76dcQ0BJ8wrbsQ12nAxuVz69IYXT3NUHpKHnXQJ3x2iKfk-tPF983n4urr5ZfNx6sCKiXKogSBQrRaYS04GAkceYWaAzStUcA7zUXNqESFnMrOaGnaGrO01a2UYMpTcr7UvYUb9M7nzXqIjUs2gLO9qyPEvb2dovX9HIapTlbQKhfO4neLeIjh54RptDuXGux78BimZLXUSkqldCZf3yG3YYq5F38hqkqhZ-j9AjUxpBSxs0N0u9meUTvPYo7zLCzL7MtDwaneYXsk_zU_A28PAKQG-i6Cnx915AQTvJI8c-LQANfj_n5He7H5wSljhpeVLrPsxSLbpjHEo0wwVjGmZM6_WvIdBAs3MVtff8t6mX-JUcrMF3yzEDDu82-wmOA_t6Ht7PhrvI-6040_yQDJug</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>868073488</pqid></control><display><type>article</type><title>Generalized functional responses for species distributions</title><source>MEDLINE</source><source>Access via Wiley Online Library</source><source>JSTOR Archive Collection A-Z Listing</source><creator>Matthiopoulos, Jason ; Hebblewhite, Mark ; Aarts, Geert ; Fieberg, John</creator><creatorcontrib>Matthiopoulos, Jason ; Hebblewhite, Mark ; Aarts, Geert ; Fieberg, John</creatorcontrib><description>Researchers employing resource selection functions (RSFs) and other related methods aim to detect correlates of space-use and mitigate against detrimental environmental change. However, an empirical model fit to data from one place or time is unlikely to capture species responses under different conditions because organisms respond nonlinearly to changes in habitat availability. This phenomenon, known as a functional response in resource selection, has been debated extensively in the RSF literature but continues to be ignored by practitioners for lack of a practical treatment. We therefore extend the RSF approach to enable it to estimate generalized functional responses (GFRs) from spatial data. GFRs employ data from several sampling instances characterized by diverse profiles of habitat availability. By modeling the regression coefficients of the underlying RSF as functions of availability, GFRs can account for environmental change and thus predict population distributions in new environments. We formulate the approach as a mixed-effects model so that it is estimable by readily available statistical software. We illustrate its application using (1) simulation and (2) wolf home-range telemetry. Our results indicate that GFRs can offer considerable improvements in estimation speed and predictive ability over existing mixed-effects approaches.</description><identifier>ISSN: 0012-9658</identifier><identifier>EISSN: 1939-9170</identifier><identifier>DOI: 10.1890/10-0751.1</identifier><identifier>PMID: 21608467</identifier><identifier>CODEN: ECGYAQ</identifier><language>eng</language><publisher>Washington, DC: Ecological Society of America</publisher><subject>Animal and plant ecology ; Animal, plant and microbial ecology ; Animals ; Applied ecology ; availability ; Biological and medical sciences ; Biotelemetry ; Canis lupis ; climate change ; Climatology. Bioclimatology. Climate change ; computer software ; Conservation of Natural Resources ; Correlation analysis ; Demography ; distribution models ; Earth, ocean, space ; Ecological modeling ; Ecology ; Ecosystem ; Environmental space ; Exact sciences and technology ; External geophysics ; Functional responses ; Fundamental and applied biological sciences. Psychology ; General aspects ; generalized linear mixed model ; habitat preference ; Habitat preferences ; habitat selection ; Habitats ; home range ; logistic-regression ; Meteorology ; Modeling ; Models, Biological ; Natural resources ; Nonnative species ; predictive models ; preference ; relating populations ; resource selection functions ; Simulation ; simulation study ; space ; space-use ; spatial data ; spatial ecology ; species distributions ; Telemetry ; utilization distribution ; Wildlife ecology ; wolf ; Wolves ; Wolves - physiology</subject><ispartof>Ecology (Durham), 2011-03, Vol.92 (3), p.583-589</ispartof><rights>Copyright © 2011 Ecological Society of America</rights><rights>2011 by the Ecological Society of America</rights><rights>2015 INIST-CNRS</rights><rights>Copyright Ecological Society of America Mar 2011</rights><rights>Wageningen University & Research</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a5743-3a4e44d87eb42a96a2e25e82aacd97a2f824b106e7e206f9869dbe574d8d66a93</citedby><cites>FETCH-LOGICAL-a5743-3a4e44d87eb42a96a2e25e82aacd97a2f824b106e7e206f9869dbe574d8d66a93</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/41151176$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.jstor.org/stable/41151176$$EHTML$$P50$$Gjstor$$H</linktohtml><link.rule.ids>230,314,780,784,803,885,1417,27924,27925,45574,45575,58017,58250</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=24142562$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/21608467$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Matthiopoulos, Jason</creatorcontrib><creatorcontrib>Hebblewhite, Mark</creatorcontrib><creatorcontrib>Aarts, Geert</creatorcontrib><creatorcontrib>Fieberg, John</creatorcontrib><title>Generalized functional responses for species distributions</title><title>Ecology (Durham)</title><addtitle>Ecology</addtitle><description>Researchers employing resource selection functions (RSFs) and other related methods aim to detect correlates of space-use and mitigate against detrimental environmental change. However, an empirical model fit to data from one place or time is unlikely to capture species responses under different conditions because organisms respond nonlinearly to changes in habitat availability. This phenomenon, known as a functional response in resource selection, has been debated extensively in the RSF literature but continues to be ignored by practitioners for lack of a practical treatment. We therefore extend the RSF approach to enable it to estimate generalized functional responses (GFRs) from spatial data. GFRs employ data from several sampling instances characterized by diverse profiles of habitat availability. By modeling the regression coefficients of the underlying RSF as functions of availability, GFRs can account for environmental change and thus predict population distributions in new environments. We formulate the approach as a mixed-effects model so that it is estimable by readily available statistical software. We illustrate its application using (1) simulation and (2) wolf home-range telemetry. Our results indicate that GFRs can offer considerable improvements in estimation speed and predictive ability over existing mixed-effects approaches.</description><subject>Animal and plant ecology</subject><subject>Animal, plant and microbial ecology</subject><subject>Animals</subject><subject>Applied ecology</subject><subject>availability</subject><subject>Biological and medical sciences</subject><subject>Biotelemetry</subject><subject>Canis lupis</subject><subject>climate change</subject><subject>Climatology. Bioclimatology. Climate change</subject><subject>computer software</subject><subject>Conservation of Natural Resources</subject><subject>Correlation analysis</subject><subject>Demography</subject><subject>distribution models</subject><subject>Earth, ocean, space</subject><subject>Ecological modeling</subject><subject>Ecology</subject><subject>Ecosystem</subject><subject>Environmental space</subject><subject>Exact sciences and technology</subject><subject>External geophysics</subject><subject>Functional responses</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>General aspects</subject><subject>generalized linear mixed model</subject><subject>habitat preference</subject><subject>Habitat preferences</subject><subject>habitat selection</subject><subject>Habitats</subject><subject>home range</subject><subject>logistic-regression</subject><subject>Meteorology</subject><subject>Modeling</subject><subject>Models, Biological</subject><subject>Natural resources</subject><subject>Nonnative species</subject><subject>predictive models</subject><subject>preference</subject><subject>relating populations</subject><subject>resource selection functions</subject><subject>Simulation</subject><subject>simulation study</subject><subject>space</subject><subject>space-use</subject><subject>spatial data</subject><subject>spatial ecology</subject><subject>species distributions</subject><subject>Telemetry</subject><subject>utilization distribution</subject><subject>Wildlife ecology</subject><subject>wolf</subject><subject>Wolves</subject><subject>Wolves - physiology</subject><issn>0012-9658</issn><issn>1939-9170</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp1kk1v1DAQhi0EokvhwA8AViCEOKTYjuOP3tCqFKRKHKAHTtYkmVReZe1gJyrLr8chywpVqg8eS_O889ozJuQ5o2dMG_qB0YKqip2xB2TFTGkKwxR9SFaUMl4YWekT8iSlLc2LCf2YnHAmqRZSrcj5JXqM0Lvf2K67yTejCx76dcQ0BJ8wrbsQ12nAxuVz69IYXT3NUHpKHnXQJ3x2iKfk-tPF983n4urr5ZfNx6sCKiXKogSBQrRaYS04GAkceYWaAzStUcA7zUXNqESFnMrOaGnaGrO01a2UYMpTcr7UvYUb9M7nzXqIjUs2gLO9qyPEvb2dovX9HIapTlbQKhfO4neLeIjh54RptDuXGux78BimZLXUSkqldCZf3yG3YYq5F38hqkqhZ-j9AjUxpBSxs0N0u9meUTvPYo7zLCzL7MtDwaneYXsk_zU_A28PAKQG-i6Cnx915AQTvJI8c-LQANfj_n5He7H5wSljhpeVLrPsxSLbpjHEo0wwVjGmZM6_WvIdBAs3MVtff8t6mX-JUcrMF3yzEDDu82-wmOA_t6Ht7PhrvI-6040_yQDJug</recordid><startdate>201103</startdate><enddate>201103</enddate><creator>Matthiopoulos, Jason</creator><creator>Hebblewhite, Mark</creator><creator>Aarts, Geert</creator><creator>Fieberg, John</creator><general>Ecological Society of America</general><scope>FBQ</scope><scope>IQODW</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QG</scope><scope>7SN</scope><scope>7SS</scope><scope>7ST</scope><scope>7T7</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>K9.</scope><scope>P64</scope><scope>RC3</scope><scope>SOI</scope><scope>7X8</scope><scope>QVL</scope></search><sort><creationdate>201103</creationdate><title>Generalized functional responses for species distributions</title><author>Matthiopoulos, Jason ; Hebblewhite, Mark ; Aarts, Geert ; Fieberg, John</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a5743-3a4e44d87eb42a96a2e25e82aacd97a2f824b106e7e206f9869dbe574d8d66a93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Animal and plant ecology</topic><topic>Animal, plant and microbial ecology</topic><topic>Animals</topic><topic>Applied ecology</topic><topic>availability</topic><topic>Biological and medical sciences</topic><topic>Biotelemetry</topic><topic>Canis lupis</topic><topic>climate change</topic><topic>Climatology. Bioclimatology. Climate change</topic><topic>computer software</topic><topic>Conservation of Natural Resources</topic><topic>Correlation analysis</topic><topic>Demography</topic><topic>distribution models</topic><topic>Earth, ocean, space</topic><topic>Ecological modeling</topic><topic>Ecology</topic><topic>Ecosystem</topic><topic>Environmental space</topic><topic>Exact sciences and technology</topic><topic>External geophysics</topic><topic>Functional responses</topic><topic>Fundamental and applied biological sciences. Psychology</topic><topic>General aspects</topic><topic>generalized linear mixed model</topic><topic>habitat preference</topic><topic>Habitat preferences</topic><topic>habitat selection</topic><topic>Habitats</topic><topic>home range</topic><topic>logistic-regression</topic><topic>Meteorology</topic><topic>Modeling</topic><topic>Models, Biological</topic><topic>Natural resources</topic><topic>Nonnative species</topic><topic>predictive models</topic><topic>preference</topic><topic>relating populations</topic><topic>resource selection functions</topic><topic>Simulation</topic><topic>simulation study</topic><topic>space</topic><topic>space-use</topic><topic>spatial data</topic><topic>spatial ecology</topic><topic>species distributions</topic><topic>Telemetry</topic><topic>utilization distribution</topic><topic>Wildlife ecology</topic><topic>wolf</topic><topic>Wolves</topic><topic>Wolves - physiology</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Matthiopoulos, Jason</creatorcontrib><creatorcontrib>Hebblewhite, Mark</creatorcontrib><creatorcontrib>Aarts, Geert</creatorcontrib><creatorcontrib>Fieberg, John</creatorcontrib><collection>AGRIS</collection><collection>Pascal-Francis</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Animal Behavior Abstracts</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Environment Abstracts</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Genetics Abstracts</collection><collection>Environment Abstracts</collection><collection>MEDLINE - Academic</collection><collection>NARCIS:Publications</collection><jtitle>Ecology (Durham)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Matthiopoulos, Jason</au><au>Hebblewhite, Mark</au><au>Aarts, Geert</au><au>Fieberg, John</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Generalized functional responses for species distributions</atitle><jtitle>Ecology (Durham)</jtitle><addtitle>Ecology</addtitle><date>2011-03</date><risdate>2011</risdate><volume>92</volume><issue>3</issue><spage>583</spage><epage>589</epage><pages>583-589</pages><issn>0012-9658</issn><eissn>1939-9170</eissn><coden>ECGYAQ</coden><abstract>Researchers employing resource selection functions (RSFs) and other related methods aim to detect correlates of space-use and mitigate against detrimental environmental change. However, an empirical model fit to data from one place or time is unlikely to capture species responses under different conditions because organisms respond nonlinearly to changes in habitat availability. This phenomenon, known as a functional response in resource selection, has been debated extensively in the RSF literature but continues to be ignored by practitioners for lack of a practical treatment. We therefore extend the RSF approach to enable it to estimate generalized functional responses (GFRs) from spatial data. GFRs employ data from several sampling instances characterized by diverse profiles of habitat availability. By modeling the regression coefficients of the underlying RSF as functions of availability, GFRs can account for environmental change and thus predict population distributions in new environments. We formulate the approach as a mixed-effects model so that it is estimable by readily available statistical software. We illustrate its application using (1) simulation and (2) wolf home-range telemetry. Our results indicate that GFRs can offer considerable improvements in estimation speed and predictive ability over existing mixed-effects approaches.</abstract><cop>Washington, DC</cop><pub>Ecological Society of America</pub><pmid>21608467</pmid><doi>10.1890/10-0751.1</doi><tpages>7</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0012-9658 |
| ispartof | Ecology (Durham), 2011-03, Vol.92 (3), p.583-589 |
| issn | 0012-9658 1939-9170 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_868766778 |
| source | MEDLINE; Access via Wiley Online Library; JSTOR Archive Collection A-Z Listing |
| subjects | Animal and plant ecology Animal, plant and microbial ecology Animals Applied ecology availability Biological and medical sciences Biotelemetry Canis lupis climate change Climatology. Bioclimatology. Climate change computer software Conservation of Natural Resources Correlation analysis Demography distribution models Earth, ocean, space Ecological modeling Ecology Ecosystem Environmental space Exact sciences and technology External geophysics Functional responses Fundamental and applied biological sciences. Psychology General aspects generalized linear mixed model habitat preference Habitat preferences habitat selection Habitats home range logistic-regression Meteorology Modeling Models, Biological Natural resources Nonnative species predictive models preference relating populations resource selection functions Simulation simulation study space space-use spatial data spatial ecology species distributions Telemetry utilization distribution Wildlife ecology wolf Wolves Wolves - physiology |
| title | Generalized functional responses for species distributions |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-27T14%3A48%3A33IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-jstor_proqu&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Generalized%20functional%20responses%20for%20species%20distributions&rft.jtitle=Ecology%20(Durham)&rft.au=Matthiopoulos,%20Jason&rft.date=2011-03&rft.volume=92&rft.issue=3&rft.spage=583&rft.epage=589&rft.pages=583-589&rft.issn=0012-9658&rft.eissn=1939-9170&rft.coden=ECGYAQ&rft_id=info:doi/10.1890/10-0751.1&rft_dat=%3Cjstor_proqu%3E41151176%3C/jstor_proqu%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=868073488&rft_id=info:pmid/21608467&rft_jstor_id=41151176&rfr_iscdi=true |