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AbundanceR: A Novel Method for Estimating Wildlife Abundance Based on Distance Sampling and Species Distribution Models
Appropriate field survey methods and robust modeling approaches play an important role in wildlife protection and habitat management because reliable information on wildlife distribution and abundance is important for conservation planning and actions. However, accurately estimating animal abundance...
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Published in: | Land (Basel) 2022-05, Vol.11 (5), p.660 |
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description | Appropriate field survey methods and robust modeling approaches play an important role in wildlife protection and habitat management because reliable information on wildlife distribution and abundance is important for conservation planning and actions. However, accurately estimating animal abundance is challenging in most species, as usually only a small proportion of the population can be detected during surveys. Species distribution models can predict the habitat suitability index, which differs from species abundance. We designed a method to adjust the results from species distribution models to achieve better accuracy for abundance estimation. This method comprises four steps: (1) conducting distance sampling, recording species occurrences, and surveying routes; (2) performing species distribution modeling using occurrence records and predicting animal abundance in each quadrat in the study area; (3) comparing the difference between field survey results and predicted abundance in quadrats along survey routes, adjusting model prediction, and summing up to obtain total abundance in the study area; (4) calculating uncertainty from three sources, i.e., distance sampling (using detection rate), species distribution models (using R squared), and differences between the field survey and model prediction [using the standard deviation of the ratio (observation/prediction) at different zones]. We developed an R package called abundanceR to estimate wildlife abundance and provided data for the Tibetan wild ass (Equus kiang) based on field surveys at the Three-River-Source National Park, as well as 29 layers of environmental variables covering the terrestrial areas of the planet. Our method can provide accurate estimation of abundance for animals inhabiting open areas that can be easily observed during distance sampling, and whose spatial heterogeneity of animal density within the study area can be accurately predicted using species distribution models. |
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However, accurately estimating animal abundance is challenging in most species, as usually only a small proportion of the population can be detected during surveys. Species distribution models can predict the habitat suitability index, which differs from species abundance. We designed a method to adjust the results from species distribution models to achieve better accuracy for abundance estimation. This method comprises four steps: (1) conducting distance sampling, recording species occurrences, and surveying routes; (2) performing species distribution modeling using occurrence records and predicting animal abundance in each quadrat in the study area; (3) comparing the difference between field survey results and predicted abundance in quadrats along survey routes, adjusting model prediction, and summing up to obtain total abundance in the study area; (4) calculating uncertainty from three sources, i.e., distance sampling (using detection rate), species distribution models (using R squared), and differences between the field survey and model prediction [using the standard deviation of the ratio (observation/prediction) at different zones]. We developed an R package called abundanceR to estimate wildlife abundance and provided data for the Tibetan wild ass (Equus kiang) based on field surveys at the Three-River-Source National Park, as well as 29 layers of environmental variables covering the terrestrial areas of the planet. Our method can provide accurate estimation of abundance for animals inhabiting open areas that can be easily observed during distance sampling, and whose spatial heterogeneity of animal density within the study area can be accurately predicted using species distribution models.</description><identifier>ISSN: 2073-445X</identifier><identifier>EISSN: 2073-445X</identifier><identifier>DOI: 10.3390/land11050660</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Abundance ; Animals ; Cameras ; distance sampling ; Estimates ; Estimation ; Geographical distribution ; Heterogeneity ; Libraries ; Modelling ; National parks ; Polls & surveys ; population density ; Predictions ; Probability ; R package ; Sampling ; Spatial heterogeneity ; Species ; species distribution models ; Wildlife ; Wildlife conservation ; Wildlife habitats ; Wildlife management ; wildlife survey</subject><ispartof>Land (Basel), 2022-05, Vol.11 (5), p.660</ispartof><rights>2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c367t-8970f19da7e5bfe99383e32f17af5221c6c4d7f5a1f4fa29e4884712da084b7f3</citedby><cites>FETCH-LOGICAL-c367t-8970f19da7e5bfe99383e32f17af5221c6c4d7f5a1f4fa29e4884712da084b7f3</cites><orcidid>0000-0003-4870-1140 ; 0000-0003-4514-0149</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2670132663/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2670132663?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,25753,27924,27925,37012,44590,75126</link.rule.ids></links><search><creatorcontrib>Li, Xinhai</creatorcontrib><creatorcontrib>Li, Ning</creatorcontrib><creatorcontrib>Li, Baidu</creatorcontrib><creatorcontrib>Sun, Yuehua</creatorcontrib><creatorcontrib>Gao, Erhu</creatorcontrib><title>AbundanceR: A Novel Method for Estimating Wildlife Abundance Based on Distance Sampling and Species Distribution Models</title><title>Land (Basel)</title><description>Appropriate field survey methods and robust modeling approaches play an important role in wildlife protection and habitat management because reliable information on wildlife distribution and abundance is important for conservation planning and actions. However, accurately estimating animal abundance is challenging in most species, as usually only a small proportion of the population can be detected during surveys. Species distribution models can predict the habitat suitability index, which differs from species abundance. We designed a method to adjust the results from species distribution models to achieve better accuracy for abundance estimation. This method comprises four steps: (1) conducting distance sampling, recording species occurrences, and surveying routes; (2) performing species distribution modeling using occurrence records and predicting animal abundance in each quadrat in the study area; (3) comparing the difference between field survey results and predicted abundance in quadrats along survey routes, adjusting model prediction, and summing up to obtain total abundance in the study area; (4) calculating uncertainty from three sources, i.e., distance sampling (using detection rate), species distribution models (using R squared), and differences between the field survey and model prediction [using the standard deviation of the ratio (observation/prediction) at different zones]. We developed an R package called abundanceR to estimate wildlife abundance and provided data for the Tibetan wild ass (Equus kiang) based on field surveys at the Three-River-Source National Park, as well as 29 layers of environmental variables covering the terrestrial areas of the planet. Our method can provide accurate estimation of abundance for animals inhabiting open areas that can be easily observed during distance sampling, and whose spatial heterogeneity of animal density within the study area can be accurately predicted using species distribution models.</description><subject>Abundance</subject><subject>Animals</subject><subject>Cameras</subject><subject>distance sampling</subject><subject>Estimates</subject><subject>Estimation</subject><subject>Geographical distribution</subject><subject>Heterogeneity</subject><subject>Libraries</subject><subject>Modelling</subject><subject>National parks</subject><subject>Polls & surveys</subject><subject>population density</subject><subject>Predictions</subject><subject>Probability</subject><subject>R package</subject><subject>Sampling</subject><subject>Spatial heterogeneity</subject><subject>Species</subject><subject>species distribution models</subject><subject>Wildlife</subject><subject>Wildlife conservation</subject><subject>Wildlife habitats</subject><subject>Wildlife management</subject><subject>wildlife survey</subject><issn>2073-445X</issn><issn>2073-445X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNpNUdtKBDEMHURBUd_8gIKvrvY27dS39S54AS_oW8lM07XLOF3bWcW_d3ZXxLwknJycnJCi2GP0UAhDj1roHGO0pErRtWKLUy1GUpav6__qzWI35ykdwjBRyXKr-BrX885B1-DDMRmTu_iJLbnF_i064mMi57kP79CHbkJeQuva4JH8jZATyOhI7MhZyP0SeYT3WbtgD27I4wybgHnZTaGe92Gg3kaHbd4pNjy0GXd_83bxfHH-dHo1urm_vD4d34waoXQ_qoymnhkHGsvaozGiEii4Zxp8yTlrVCOd9iUwLz1wg7KqpGbcAa1krb3YLq5Xui7C1M7ScEz6thGCXQIxTSykPjQt2lIx3UApEISRntPaG8GpAlkzrxVng9b-SmuW4sccc2-ncZ66wb7lSlMmuFJiYB2sWE2KOSf0f1sZtYtP2f-fEj-uOYXn</recordid><startdate>20220501</startdate><enddate>20220501</enddate><creator>Li, Xinhai</creator><creator>Li, Ning</creator><creator>Li, Baidu</creator><creator>Sun, Yuehua</creator><creator>Gao, Erhu</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SN</scope><scope>7ST</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>PATMY</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PYCSY</scope><scope>SOI</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0003-4870-1140</orcidid><orcidid>https://orcid.org/0000-0003-4514-0149</orcidid></search><sort><creationdate>20220501</creationdate><title>AbundanceR: A Novel Method for Estimating Wildlife Abundance Based on Distance Sampling and Species Distribution Models</title><author>Li, Xinhai ; Li, Ning ; Li, Baidu ; Sun, Yuehua ; Gao, Erhu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c367t-8970f19da7e5bfe99383e32f17af5221c6c4d7f5a1f4fa29e4884712da084b7f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Abundance</topic><topic>Animals</topic><topic>Cameras</topic><topic>distance sampling</topic><topic>Estimates</topic><topic>Estimation</topic><topic>Geographical distribution</topic><topic>Heterogeneity</topic><topic>Libraries</topic><topic>Modelling</topic><topic>National parks</topic><topic>Polls & surveys</topic><topic>population density</topic><topic>Predictions</topic><topic>Probability</topic><topic>R package</topic><topic>Sampling</topic><topic>Spatial heterogeneity</topic><topic>Species</topic><topic>species distribution models</topic><topic>Wildlife</topic><topic>Wildlife conservation</topic><topic>Wildlife habitats</topic><topic>Wildlife management</topic><topic>wildlife survey</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Li, Xinhai</creatorcontrib><creatorcontrib>Li, Ning</creatorcontrib><creatorcontrib>Li, Baidu</creatorcontrib><creatorcontrib>Sun, Yuehua</creatorcontrib><creatorcontrib>Gao, Erhu</creatorcontrib><collection>CrossRef</collection><collection>Ecology Abstracts</collection><collection>Environment Abstracts</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>Environmental Science Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Environmental Science Collection</collection><collection>Environment Abstracts</collection><collection>DOAJ Open Access Journals</collection><jtitle>Land (Basel)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Li, Xinhai</au><au>Li, Ning</au><au>Li, Baidu</au><au>Sun, Yuehua</au><au>Gao, Erhu</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>AbundanceR: A Novel Method for Estimating Wildlife Abundance Based on Distance Sampling and Species Distribution Models</atitle><jtitle>Land (Basel)</jtitle><date>2022-05-01</date><risdate>2022</risdate><volume>11</volume><issue>5</issue><spage>660</spage><pages>660-</pages><issn>2073-445X</issn><eissn>2073-445X</eissn><abstract>Appropriate field survey methods and robust modeling approaches play an important role in wildlife protection and habitat management because reliable information on wildlife distribution and abundance is important for conservation planning and actions. However, accurately estimating animal abundance is challenging in most species, as usually only a small proportion of the population can be detected during surveys. Species distribution models can predict the habitat suitability index, which differs from species abundance. We designed a method to adjust the results from species distribution models to achieve better accuracy for abundance estimation. This method comprises four steps: (1) conducting distance sampling, recording species occurrences, and surveying routes; (2) performing species distribution modeling using occurrence records and predicting animal abundance in each quadrat in the study area; (3) comparing the difference between field survey results and predicted abundance in quadrats along survey routes, adjusting model prediction, and summing up to obtain total abundance in the study area; (4) calculating uncertainty from three sources, i.e., distance sampling (using detection rate), species distribution models (using R squared), and differences between the field survey and model prediction [using the standard deviation of the ratio (observation/prediction) at different zones]. We developed an R package called abundanceR to estimate wildlife abundance and provided data for the Tibetan wild ass (Equus kiang) based on field surveys at the Three-River-Source National Park, as well as 29 layers of environmental variables covering the terrestrial areas of the planet. Our method can provide accurate estimation of abundance for animals inhabiting open areas that can be easily observed during distance sampling, and whose spatial heterogeneity of animal density within the study area can be accurately predicted using species distribution models.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/land11050660</doi><orcidid>https://orcid.org/0000-0003-4870-1140</orcidid><orcidid>https://orcid.org/0000-0003-4514-0149</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Abundance Animals Cameras distance sampling Estimates Estimation Geographical distribution Heterogeneity Libraries Modelling National parks Polls & surveys population density Predictions Probability R package Sampling Spatial heterogeneity Species species distribution models Wildlife Wildlife conservation Wildlife habitats Wildlife management wildlife survey |
title | AbundanceR: A Novel Method for Estimating Wildlife Abundance Based on Distance Sampling and Species Distribution Models |
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