The potential for species distribution models to distinguish source populations from sinks
While species distribution models (SDM) are frequently used to predict species occurrences to help inform conservation management, there is limited evidence evaluating whether habitat suitability can reliably predict intrinsic growth rates or distinguish source populations from sinks. Filling this k...
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Veröffentlicht in: | The Journal of animal ecology 2024-12, Vol.93 (12), p.1924-1934 |
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container_end_page | 1934 |
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container_issue | 12 |
container_start_page | 1924 |
container_title | The Journal of animal ecology |
container_volume | 93 |
creator | Şen, Bilgecan Che‐Castaldo, Christian Akçakaya, H. Reşit |
description | While species distribution models (SDM) are frequently used to predict species occurrences to help inform conservation management, there is limited evidence evaluating whether habitat suitability can reliably predict intrinsic growth rates or distinguish source populations from sinks. Filling this knowledge gap is critical for conservation science, as applications of SDMs for management purposes ultimately depend on these typically unobserved population or metapopulation dynamics.
Using linear regression, we associated previously published population level estimates of intrinsic growth and abundance derived from a Bayesian analysis of mark‐recapture data for 17 bird species found in the contiguous United States with SDM habitat suitability estimates fitted here to opportunistic data for these same species. We then used the area under the ROC curve (AUC) to measure how well SDMs can distinguish populations categorized as sources and sinks. We built SDMs using two different approaches, boosted regression trees (BRT) and generalized linear models (GLM), and compared their source/sink predictive performance. Each SDM was built with presence points obtained from eBird (a web‐available database) and 10 environmental variables previously selected to model intrinsic growth rates and abundance for these species.
We show that SDMs built with opportunistic data are poor predictors of species demography in general; both BRT and GLM explained very little spatial variation of intrinsic growth rate and population abundance (median R2 across 17 species was close to 0.1 for both SDM methods). SDMs, however, estimated higher suitability for source populations as compared to sinks. Out of 13 species which had both source and sink populations, both BRT and GLM had AUC values greater than 0.7 for 7 species when discriminating between sources and sinks.
Habitat suitability have the potential to be a useful measure to indicate a population's ability to sustain itself as a source population; however more research on a diverse set of taxa is essential to fully explore this potential. This interpretation of habitat suitability can be particularly useful for conservation practice, and identification of explicit cases of when and how SDMs fail to match population demography can be informative for advancing ecological theory.
We demonstrate the potential of species distributions models to distinguish source/sink dynamics of animal populations. |
doi_str_mv | 10.1111/1365-2656.14201 |
format | Article |
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Using linear regression, we associated previously published population level estimates of intrinsic growth and abundance derived from a Bayesian analysis of mark‐recapture data for 17 bird species found in the contiguous United States with SDM habitat suitability estimates fitted here to opportunistic data for these same species. We then used the area under the ROC curve (AUC) to measure how well SDMs can distinguish populations categorized as sources and sinks. We built SDMs using two different approaches, boosted regression trees (BRT) and generalized linear models (GLM), and compared their source/sink predictive performance. Each SDM was built with presence points obtained from eBird (a web‐available database) and 10 environmental variables previously selected to model intrinsic growth rates and abundance for these species.
We show that SDMs built with opportunistic data are poor predictors of species demography in general; both BRT and GLM explained very little spatial variation of intrinsic growth rate and population abundance (median R2 across 17 species was close to 0.1 for both SDM methods). SDMs, however, estimated higher suitability for source populations as compared to sinks. Out of 13 species which had both source and sink populations, both BRT and GLM had AUC values greater than 0.7 for 7 species when discriminating between sources and sinks.
Habitat suitability have the potential to be a useful measure to indicate a population's ability to sustain itself as a source population; however more research on a diverse set of taxa is essential to fully explore this potential. This interpretation of habitat suitability can be particularly useful for conservation practice, and identification of explicit cases of when and how SDMs fail to match population demography can be informative for advancing ecological theory.
We demonstrate the potential of species distributions models to distinguish source/sink dynamics of animal populations.</description><identifier>ISSN: 0021-8790</identifier><identifier>ISSN: 1365-2656</identifier><identifier>EISSN: 1365-2656</identifier><identifier>DOI: 10.1111/1365-2656.14201</identifier><identifier>PMID: 39429222</identifier><language>eng</language><publisher>England: Blackwell Publishing Ltd</publisher><subject>Abundance ; Animal Distribution ; Animals ; Bayes Theorem ; Bayesian analysis ; Birds - physiology ; Conservation ; Conservation of Natural Resources ; Conservation practices ; Demographic variables ; Demography ; Ecosystem ; Estimates ; Generalized linear models ; Geographical distribution ; Growth rate ; habitat suitability ; Habitats ; intrinsic growth rate ; Linear Models ; Mathematical models ; Metapopulations ; Models, Biological ; Population ; population abundance ; population demography ; Population Dynamics ; Populations ; Regression analysis ; Sinkholes ; source‐sink dynamics ; Spatial data ; Spatial variations ; Species ; species distribution models ; Statistical models ; United States</subject><ispartof>The Journal of animal ecology, 2024-12, Vol.93 (12), p.1924-1934</ispartof><rights>2024 The Author(s). Journal of Animal Ecology © 2024 British Ecological Society. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.</rights><rights>Journal of Animal Ecology © 2024 British Ecological Society</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c2561-220432316ce2cafa7a8a3043607ebf38737ab0e57be42295aa5b7f7aee2521573</cites><orcidid>0000-0002-7393-1805 ; 0000-0002-7670-2178 ; 0000-0002-8679-5929</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1111%2F1365-2656.14201$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1111%2F1365-2656.14201$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,780,784,1417,27924,27925,45574,45575</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/39429222$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Şen, Bilgecan</creatorcontrib><creatorcontrib>Che‐Castaldo, Christian</creatorcontrib><creatorcontrib>Akçakaya, H. Reşit</creatorcontrib><title>The potential for species distribution models to distinguish source populations from sinks</title><title>The Journal of animal ecology</title><addtitle>J Anim Ecol</addtitle><description>While species distribution models (SDM) are frequently used to predict species occurrences to help inform conservation management, there is limited evidence evaluating whether habitat suitability can reliably predict intrinsic growth rates or distinguish source populations from sinks. Filling this knowledge gap is critical for conservation science, as applications of SDMs for management purposes ultimately depend on these typically unobserved population or metapopulation dynamics.
Using linear regression, we associated previously published population level estimates of intrinsic growth and abundance derived from a Bayesian analysis of mark‐recapture data for 17 bird species found in the contiguous United States with SDM habitat suitability estimates fitted here to opportunistic data for these same species. We then used the area under the ROC curve (AUC) to measure how well SDMs can distinguish populations categorized as sources and sinks. We built SDMs using two different approaches, boosted regression trees (BRT) and generalized linear models (GLM), and compared their source/sink predictive performance. Each SDM was built with presence points obtained from eBird (a web‐available database) and 10 environmental variables previously selected to model intrinsic growth rates and abundance for these species.
We show that SDMs built with opportunistic data are poor predictors of species demography in general; both BRT and GLM explained very little spatial variation of intrinsic growth rate and population abundance (median R2 across 17 species was close to 0.1 for both SDM methods). SDMs, however, estimated higher suitability for source populations as compared to sinks. Out of 13 species which had both source and sink populations, both BRT and GLM had AUC values greater than 0.7 for 7 species when discriminating between sources and sinks.
Habitat suitability have the potential to be a useful measure to indicate a population's ability to sustain itself as a source population; however more research on a diverse set of taxa is essential to fully explore this potential. This interpretation of habitat suitability can be particularly useful for conservation practice, and identification of explicit cases of when and how SDMs fail to match population demography can be informative for advancing ecological theory.
We demonstrate the potential of species distributions models to distinguish source/sink dynamics of animal populations.</description><subject>Abundance</subject><subject>Animal Distribution</subject><subject>Animals</subject><subject>Bayes Theorem</subject><subject>Bayesian analysis</subject><subject>Birds - physiology</subject><subject>Conservation</subject><subject>Conservation of Natural Resources</subject><subject>Conservation practices</subject><subject>Demographic variables</subject><subject>Demography</subject><subject>Ecosystem</subject><subject>Estimates</subject><subject>Generalized linear models</subject><subject>Geographical distribution</subject><subject>Growth rate</subject><subject>habitat suitability</subject><subject>Habitats</subject><subject>intrinsic growth rate</subject><subject>Linear Models</subject><subject>Mathematical models</subject><subject>Metapopulations</subject><subject>Models, Biological</subject><subject>Population</subject><subject>population abundance</subject><subject>population demography</subject><subject>Population Dynamics</subject><subject>Populations</subject><subject>Regression analysis</subject><subject>Sinkholes</subject><subject>source‐sink dynamics</subject><subject>Spatial data</subject><subject>Spatial variations</subject><subject>Species</subject><subject>species distribution models</subject><subject>Statistical models</subject><subject>United States</subject><issn>0021-8790</issn><issn>1365-2656</issn><issn>1365-2656</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqFkEFP3DAQRq2KqizbnrlVlrhwCdjjdZwcEVqgFWov9NKL5WQnXUMSp55EaP99nV3KgQtzsfTpzafxY-xUiguZ5lKqXGeQ6_xCrkDID2zxmhyxhRAgs8KU4pidED0KIQwI9Ykdq3IFJQAs2O-HLfIhjNiP3rW8CZHTgLVH4htPY_TVNPrQ8y5ssCU-hn3s-z-Tpy2nMMV63h-m1s0c8SaGjpPvn-gz-9i4lvDLy7tkv27WD9d32f3P22_XV_dZDTqXGYBYKVAyrxFq1zjjCqdSlAuDVaMKo4yrBGpT4Qqg1M7pyjTGIYIGqY1asvND7xDD3wlptJ2nGtvW9RgmskrKolAKjEzo2Rv0Mf2gT9clShVlCUpDoi4PVB0DUcTGDtF3Lu6sFHbWbmfJdpZs99rTxteX3qnqcPPK__ecgPwAPPsWd-_12e9XP9aH5n_CeIxi</recordid><startdate>202412</startdate><enddate>202412</enddate><creator>Şen, Bilgecan</creator><creator>Che‐Castaldo, Christian</creator><creator>Akçakaya, H. 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Reşit</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>The potential for species distribution models to distinguish source populations from sinks</atitle><jtitle>The Journal of animal ecology</jtitle><addtitle>J Anim Ecol</addtitle><date>2024-12</date><risdate>2024</risdate><volume>93</volume><issue>12</issue><spage>1924</spage><epage>1934</epage><pages>1924-1934</pages><issn>0021-8790</issn><issn>1365-2656</issn><eissn>1365-2656</eissn><abstract>While species distribution models (SDM) are frequently used to predict species occurrences to help inform conservation management, there is limited evidence evaluating whether habitat suitability can reliably predict intrinsic growth rates or distinguish source populations from sinks. Filling this knowledge gap is critical for conservation science, as applications of SDMs for management purposes ultimately depend on these typically unobserved population or metapopulation dynamics.
Using linear regression, we associated previously published population level estimates of intrinsic growth and abundance derived from a Bayesian analysis of mark‐recapture data for 17 bird species found in the contiguous United States with SDM habitat suitability estimates fitted here to opportunistic data for these same species. We then used the area under the ROC curve (AUC) to measure how well SDMs can distinguish populations categorized as sources and sinks. We built SDMs using two different approaches, boosted regression trees (BRT) and generalized linear models (GLM), and compared their source/sink predictive performance. Each SDM was built with presence points obtained from eBird (a web‐available database) and 10 environmental variables previously selected to model intrinsic growth rates and abundance for these species.
We show that SDMs built with opportunistic data are poor predictors of species demography in general; both BRT and GLM explained very little spatial variation of intrinsic growth rate and population abundance (median R2 across 17 species was close to 0.1 for both SDM methods). SDMs, however, estimated higher suitability for source populations as compared to sinks. Out of 13 species which had both source and sink populations, both BRT and GLM had AUC values greater than 0.7 for 7 species when discriminating between sources and sinks.
Habitat suitability have the potential to be a useful measure to indicate a population's ability to sustain itself as a source population; however more research on a diverse set of taxa is essential to fully explore this potential. This interpretation of habitat suitability can be particularly useful for conservation practice, and identification of explicit cases of when and how SDMs fail to match population demography can be informative for advancing ecological theory.
We demonstrate the potential of species distributions models to distinguish source/sink dynamics of animal populations.</abstract><cop>England</cop><pub>Blackwell Publishing Ltd</pub><pmid>39429222</pmid><doi>10.1111/1365-2656.14201</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0002-7393-1805</orcidid><orcidid>https://orcid.org/0000-0002-7670-2178</orcidid><orcidid>https://orcid.org/0000-0002-8679-5929</orcidid></addata></record> |
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subjects | Abundance Animal Distribution Animals Bayes Theorem Bayesian analysis Birds - physiology Conservation Conservation of Natural Resources Conservation practices Demographic variables Demography Ecosystem Estimates Generalized linear models Geographical distribution Growth rate habitat suitability Habitats intrinsic growth rate Linear Models Mathematical models Metapopulations Models, Biological Population population abundance population demography Population Dynamics Populations Regression analysis Sinkholes source‐sink dynamics Spatial data Spatial variations Species species distribution models Statistical models United States |
title | The potential for species distribution models to distinguish source populations from sinks |
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