A detection probability model for aerial surveys of mule deer
Population estimates derived from aerial surveys of ungulates are biased by imperfect detection, where probability of sighting groups is influenced by variables specific to terrain features and vegetation communities. Therefore, methods for bias-correction must be validated for the region to which t...
Gespeichert in:
| Veröffentlicht in: | The Journal of wildlife management 2016-11, Vol.80 (8), p.1379-1389 |
|---|---|
| Hauptverfasser: | , , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 1389 |
|---|---|
| container_issue | 8 |
| container_start_page | 1379 |
| container_title | The Journal of wildlife management |
| container_volume | 80 |
| creator | Zabransky, Cody J. Hewitt, David G. Deyoung, Randy W. Gray, Shawn S. Richardson, Calvin Litt, Andrea R. Deyoung, Charles A. |
| description | Population estimates derived from aerial surveys of ungulates are biased by imperfect detection, where probability of sighting groups is influenced by variables specific to terrain features and vegetation communities. Therefore, methods for bias-correction must be validated for the region to which they will be applied. Our objectives were to quantify factors affecting detection probability of mule deer (Odocoileus hemionus) during helicopter surveys in Texas, USA, rangelands, and develop a detection probability model to reduce bias in deer population estimates. We placed global positioning system (GPS) collars on 215 deer on 6 sites representative of mule deer range in the southern Great Plains and the Chihuahuan Desert during 2008-2010. We collected data during aerial surveys in January-March and fit logistic regression models to predict detection probability of mule deer based on ecological and behavioral covariates. We evaluated the model using independent estimates of population size derived from a markresight procedure. Detection of mule deer was negatively related to distance from the transect, increasing brush cover, sunlight, and increasing terrain ruggedness (P |
| doi_str_mv | 10.1002/jwmg.21143 |
| format | Article |
| fullrecord | <record><control><sourceid>jstor_proqu</sourceid><recordid>TN_cdi_proquest_miscellaneous_1837325025</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><jstor_id>44132786</jstor_id><sourcerecordid>44132786</sourcerecordid><originalsourceid>FETCH-LOGICAL-c3953-2d7feb51d351bf9ccebea299def706a8c28dc5d06ae8613d234dbc8e1a662043</originalsourceid><addsrcrecordid>eNpdkDFPwzAUhC0EEqWwsCNFYmFJ8bOTOBkYqgoKqMBSqd0sJ35BCUlT7ISSf49LUAemd9J993Q6Qi6BToBSdlvu6vcJAwj4ERlBwoXPYhDHZORM5ocBrE_JmbUlpRwgjkbkbuppbDFri2bjbU2TqrSoirb36kZj5eWN8RSaQlWe7cwX9tZrcq_uKnQxNOfkJFeVxYu_OybLh_vl7NFfvM2fZtOFn_Ek5D7TIsc0BM1DSPMkyzBFxZJEYy5opOKMxToLtZMYR8A144FOsxhBRRGjAR-Tm-GtK_jZoW1lXdgMq0ptsOmshJgLzkLKQode_0PLpjMbV25PUS4iTpmjYKB2RYW93JqiVqaXQOV-RblfUf6uKJ9XL_Nf5TJXQ6a0bWMOmSAAzkQcOd8f_MK2-H3wlfmQkeAilKvXuRTJerFa0Jlk_AeBAX-d</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1830376302</pqid></control><display><type>article</type><title>A detection probability model for aerial surveys of mule deer</title><source>Access via Wiley Online Library</source><source>JSTOR Archive Collection A-Z Listing</source><creator>Zabransky, Cody J. ; Hewitt, David G. ; Deyoung, Randy W. ; Gray, Shawn S. ; Richardson, Calvin ; Litt, Andrea R. ; Deyoung, Charles A.</creator><creatorcontrib>Zabransky, Cody J. ; Hewitt, David G. ; Deyoung, Randy W. ; Gray, Shawn S. ; Richardson, Calvin ; Litt, Andrea R. ; Deyoung, Charles A.</creatorcontrib><description>Population estimates derived from aerial surveys of ungulates are biased by imperfect detection, where probability of sighting groups is influenced by variables specific to terrain features and vegetation communities. Therefore, methods for bias-correction must be validated for the region to which they will be applied. Our objectives were to quantify factors affecting detection probability of mule deer (Odocoileus hemionus) during helicopter surveys in Texas, USA, rangelands, and develop a detection probability model to reduce bias in deer population estimates. We placed global positioning system (GPS) collars on 215 deer on 6 sites representative of mule deer range in the southern Great Plains and the Chihuahuan Desert during 2008-2010. We collected data during aerial surveys in January-March and fit logistic regression models to predict detection probability of mule deer based on ecological and behavioral covariates. We evaluated the model using independent estimates of population size derived from a markresight procedure. Detection of mule deer was negatively related to distance from the transect, increasing brush cover, sunlight, and increasing terrain ruggedness (P<0.01). Probability of detection in brush cover was greater if deer were active (P = 0.02). Population estimates corrected for visibility bias using our detection probability model or mark-resight models averaged 2.1 ± 0.49 (SD; n = 50) and 2.2 ± 0.62 times larger, respectively, than uncorrected counts. Estimates of population size derived from the detection probability model averaged 101 ± 26% of mark-resight estimates. However, the detection probability model did not improve precision of population estimates, probably because of unmodeled variation in availability of deer during surveys. Our detection probability model is a simple and effective means to reduce bias in estimates of mule deer population size in southwestern rangelands. Availability bias may be a persistent issue for surveys of mule deer in the Southwest, and appears to be a primary influence of variance of population estimates.</description><identifier>ISSN: 0022-541X</identifier><identifier>EISSN: 1937-2817</identifier><identifier>DOI: 10.1002/jwmg.21143</identifier><identifier>CODEN: JWMAA9</identifier><language>eng</language><publisher>Bethesda: Blackwell Publishing Ltd</publisher><subject>Aerial surveys ; Animal populations ; Chihuahuan Desert ; Deer ; Global positioning systems ; GPS ; Management and Conservation ; mark-resight ; Odocoileus hemionus ; population estimation ; Population number ; Rangelands ; sightability ; southern Great Plains ; Texas ; Ungulates ; visibility bias ; Wildlife ; Wildlife conservation ; Wildlife management</subject><ispartof>The Journal of wildlife management, 2016-11, Vol.80 (8), p.1379-1389</ispartof><rights>Copyright© 2016 The Wildlife Society</rights><rights>The Wildlife Society, 2016</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3953-2d7feb51d351bf9ccebea299def706a8c28dc5d06ae8613d234dbc8e1a662043</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/44132786$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.jstor.org/stable/44132786$$EHTML$$P50$$Gjstor$$H</linktohtml><link.rule.ids>315,781,785,804,1418,27925,27926,58018,58251</link.rule.ids></links><search><creatorcontrib>Zabransky, Cody J.</creatorcontrib><creatorcontrib>Hewitt, David G.</creatorcontrib><creatorcontrib>Deyoung, Randy W.</creatorcontrib><creatorcontrib>Gray, Shawn S.</creatorcontrib><creatorcontrib>Richardson, Calvin</creatorcontrib><creatorcontrib>Litt, Andrea R.</creatorcontrib><creatorcontrib>Deyoung, Charles A.</creatorcontrib><title>A detection probability model for aerial surveys of mule deer</title><title>The Journal of wildlife management</title><addtitle>Jour. Wild. Mgmt</addtitle><description>Population estimates derived from aerial surveys of ungulates are biased by imperfect detection, where probability of sighting groups is influenced by variables specific to terrain features and vegetation communities. Therefore, methods for bias-correction must be validated for the region to which they will be applied. Our objectives were to quantify factors affecting detection probability of mule deer (Odocoileus hemionus) during helicopter surveys in Texas, USA, rangelands, and develop a detection probability model to reduce bias in deer population estimates. We placed global positioning system (GPS) collars on 215 deer on 6 sites representative of mule deer range in the southern Great Plains and the Chihuahuan Desert during 2008-2010. We collected data during aerial surveys in January-March and fit logistic regression models to predict detection probability of mule deer based on ecological and behavioral covariates. We evaluated the model using independent estimates of population size derived from a markresight procedure. Detection of mule deer was negatively related to distance from the transect, increasing brush cover, sunlight, and increasing terrain ruggedness (P<0.01). Probability of detection in brush cover was greater if deer were active (P = 0.02). Population estimates corrected for visibility bias using our detection probability model or mark-resight models averaged 2.1 ± 0.49 (SD; n = 50) and 2.2 ± 0.62 times larger, respectively, than uncorrected counts. Estimates of population size derived from the detection probability model averaged 101 ± 26% of mark-resight estimates. However, the detection probability model did not improve precision of population estimates, probably because of unmodeled variation in availability of deer during surveys. Our detection probability model is a simple and effective means to reduce bias in estimates of mule deer population size in southwestern rangelands. Availability bias may be a persistent issue for surveys of mule deer in the Southwest, and appears to be a primary influence of variance of population estimates.</description><subject>Aerial surveys</subject><subject>Animal populations</subject><subject>Chihuahuan Desert</subject><subject>Deer</subject><subject>Global positioning systems</subject><subject>GPS</subject><subject>Management and Conservation</subject><subject>mark-resight</subject><subject>Odocoileus hemionus</subject><subject>population estimation</subject><subject>Population number</subject><subject>Rangelands</subject><subject>sightability</subject><subject>southern Great Plains</subject><subject>Texas</subject><subject>Ungulates</subject><subject>visibility bias</subject><subject>Wildlife</subject><subject>Wildlife conservation</subject><subject>Wildlife management</subject><issn>0022-541X</issn><issn>1937-2817</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><recordid>eNpdkDFPwzAUhC0EEqWwsCNFYmFJ8bOTOBkYqgoKqMBSqd0sJ35BCUlT7ISSf49LUAemd9J993Q6Qi6BToBSdlvu6vcJAwj4ERlBwoXPYhDHZORM5ocBrE_JmbUlpRwgjkbkbuppbDFri2bjbU2TqrSoirb36kZj5eWN8RSaQlWe7cwX9tZrcq_uKnQxNOfkJFeVxYu_OybLh_vl7NFfvM2fZtOFn_Ek5D7TIsc0BM1DSPMkyzBFxZJEYy5opOKMxToLtZMYR8A144FOsxhBRRGjAR-Tm-GtK_jZoW1lXdgMq0ptsOmshJgLzkLKQode_0PLpjMbV25PUS4iTpmjYKB2RYW93JqiVqaXQOV-RblfUf6uKJ9XL_Nf5TJXQ6a0bWMOmSAAzkQcOd8f_MK2-H3wlfmQkeAilKvXuRTJerFa0Jlk_AeBAX-d</recordid><startdate>201611</startdate><enddate>201611</enddate><creator>Zabransky, Cody J.</creator><creator>Hewitt, David G.</creator><creator>Deyoung, Randy W.</creator><creator>Gray, Shawn S.</creator><creator>Richardson, Calvin</creator><creator>Litt, Andrea R.</creator><creator>Deyoung, Charles A.</creator><general>Blackwell Publishing Ltd</general><general>Wiley Subscription Services, Inc</general><scope>BSCLL</scope><scope>7QG</scope><scope>7QL</scope><scope>7SN</scope><scope>7ST</scope><scope>7T7</scope><scope>7U6</scope><scope>7U9</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>H94</scope><scope>M7N</scope><scope>P64</scope></search><sort><creationdate>201611</creationdate><title>A detection probability model for aerial surveys of mule deer</title><author>Zabransky, Cody J. ; Hewitt, David G. ; Deyoung, Randy W. ; Gray, Shawn S. ; Richardson, Calvin ; Litt, Andrea R. ; Deyoung, Charles A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3953-2d7feb51d351bf9ccebea299def706a8c28dc5d06ae8613d234dbc8e1a662043</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Aerial surveys</topic><topic>Animal populations</topic><topic>Chihuahuan Desert</topic><topic>Deer</topic><topic>Global positioning systems</topic><topic>GPS</topic><topic>Management and Conservation</topic><topic>mark-resight</topic><topic>Odocoileus hemionus</topic><topic>population estimation</topic><topic>Population number</topic><topic>Rangelands</topic><topic>sightability</topic><topic>southern Great Plains</topic><topic>Texas</topic><topic>Ungulates</topic><topic>visibility bias</topic><topic>Wildlife</topic><topic>Wildlife conservation</topic><topic>Wildlife management</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zabransky, Cody J.</creatorcontrib><creatorcontrib>Hewitt, David G.</creatorcontrib><creatorcontrib>Deyoung, Randy W.</creatorcontrib><creatorcontrib>Gray, Shawn S.</creatorcontrib><creatorcontrib>Richardson, Calvin</creatorcontrib><creatorcontrib>Litt, Andrea R.</creatorcontrib><creatorcontrib>Deyoung, Charles A.</creatorcontrib><collection>Istex</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Ecology Abstracts</collection><collection>Environment Abstracts</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Sustainability Science Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><jtitle>The Journal of wildlife management</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zabransky, Cody J.</au><au>Hewitt, David G.</au><au>Deyoung, Randy W.</au><au>Gray, Shawn S.</au><au>Richardson, Calvin</au><au>Litt, Andrea R.</au><au>Deyoung, Charles A.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A detection probability model for aerial surveys of mule deer</atitle><jtitle>The Journal of wildlife management</jtitle><addtitle>Jour. Wild. Mgmt</addtitle><date>2016-11</date><risdate>2016</risdate><volume>80</volume><issue>8</issue><spage>1379</spage><epage>1389</epage><pages>1379-1389</pages><issn>0022-541X</issn><eissn>1937-2817</eissn><coden>JWMAA9</coden><abstract>Population estimates derived from aerial surveys of ungulates are biased by imperfect detection, where probability of sighting groups is influenced by variables specific to terrain features and vegetation communities. Therefore, methods for bias-correction must be validated for the region to which they will be applied. Our objectives were to quantify factors affecting detection probability of mule deer (Odocoileus hemionus) during helicopter surveys in Texas, USA, rangelands, and develop a detection probability model to reduce bias in deer population estimates. We placed global positioning system (GPS) collars on 215 deer on 6 sites representative of mule deer range in the southern Great Plains and the Chihuahuan Desert during 2008-2010. We collected data during aerial surveys in January-March and fit logistic regression models to predict detection probability of mule deer based on ecological and behavioral covariates. We evaluated the model using independent estimates of population size derived from a markresight procedure. Detection of mule deer was negatively related to distance from the transect, increasing brush cover, sunlight, and increasing terrain ruggedness (P<0.01). Probability of detection in brush cover was greater if deer were active (P = 0.02). Population estimates corrected for visibility bias using our detection probability model or mark-resight models averaged 2.1 ± 0.49 (SD; n = 50) and 2.2 ± 0.62 times larger, respectively, than uncorrected counts. Estimates of population size derived from the detection probability model averaged 101 ± 26% of mark-resight estimates. However, the detection probability model did not improve precision of population estimates, probably because of unmodeled variation in availability of deer during surveys. Our detection probability model is a simple and effective means to reduce bias in estimates of mule deer population size in southwestern rangelands. Availability bias may be a persistent issue for surveys of mule deer in the Southwest, and appears to be a primary influence of variance of population estimates.</abstract><cop>Bethesda</cop><pub>Blackwell Publishing Ltd</pub><doi>10.1002/jwmg.21143</doi><tpages>11</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0022-541X |
| ispartof | The Journal of wildlife management, 2016-11, Vol.80 (8), p.1379-1389 |
| issn | 0022-541X 1937-2817 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_1837325025 |
| source | Access via Wiley Online Library; JSTOR Archive Collection A-Z Listing |
| subjects | Aerial surveys Animal populations Chihuahuan Desert Deer Global positioning systems GPS Management and Conservation mark-resight Odocoileus hemionus population estimation Population number Rangelands sightability southern Great Plains Texas Ungulates visibility bias Wildlife Wildlife conservation Wildlife management |
| title | A detection probability model for aerial surveys of mule deer |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-18T14%3A07%3A41IST&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=A%20detection%20probability%20model%20for%20aerial%20surveys%20of%20mule%20deer&rft.jtitle=The%20Journal%20of%20wildlife%20management&rft.au=Zabransky,%20Cody%20J.&rft.date=2016-11&rft.volume=80&rft.issue=8&rft.spage=1379&rft.epage=1389&rft.pages=1379-1389&rft.issn=0022-541X&rft.eissn=1937-2817&rft.coden=JWMAA9&rft_id=info:doi/10.1002/jwmg.21143&rft_dat=%3Cjstor_proqu%3E44132786%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=1830376302&rft_id=info:pmid/&rft_jstor_id=44132786&rfr_iscdi=true |