Detectability Analysis in Transect Surveys
We discuss and illustrate an analysis strategy that adjusts for the influence of variables such as weather, time of day, and observer on the detectability of animals in line transect surveys. The strategy employs ordinary least squares regression analysis followed by use of a standard estimator of e...
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Veröffentlicht in: | The Journal of wildlife management 1998-07, Vol.62 (3), p.948-957 |
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container_title | The Journal of wildlife management |
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creator | Beavers, Sallie C. Ramsey, Fred L. |
description | We discuss and illustrate an analysis strategy that adjusts for the influence of variables such as weather, time of day, and observer on the detectability of animals in line transect surveys. The strategy employs ordinary least squares regression analysis followed by use of a standard estimator of effective area. No new computer software is required because multiple linear regression is available in all statistical software packages, and a variety of suitable estimators for effective half-width are available in the program DISTANCE. The strategy is applicable to all wildlife surveys that take systematic records of detectability conditions. We apply this approach to a shipboard survey of cheloniid sea turtles in the eastern tropical Pacific Ocean. Sea state (calm seas, white-capped seas) was the primary variable influencing detection of sea turtles. Effective half-width was adjusted for each category of sea state, and effective area surveyed was calculated. Surface density of turtles in 1989-90 was 0.067$\text{turtles}/\text{km}^{2}$(95% CI = 0.053-0.084). |
doi_str_mv | 10.2307/3802547 |
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The strategy employs ordinary least squares regression analysis followed by use of a standard estimator of effective area. No new computer software is required because multiple linear regression is available in all statistical software packages, and a variety of suitable estimators for effective half-width are available in the program DISTANCE. The strategy is applicable to all wildlife surveys that take systematic records of detectability conditions. We apply this approach to a shipboard survey of cheloniid sea turtles in the eastern tropical Pacific Ocean. Sea state (calm seas, white-capped seas) was the primary variable influencing detection of sea turtles. Effective half-width was adjusted for each category of sea state, and effective area surveyed was calculated. Surface density of turtles in 1989-90 was 0.067$\text{turtles}/\text{km}^{2}$(95% CI = 0.053-0.084).</description><identifier>ISSN: 0022-541X</identifier><identifier>EISSN: 1937-2817</identifier><identifier>DOI: 10.2307/3802547</identifier><identifier>CODEN: JWMAA9</identifier><language>eng</language><publisher>Bethesda, MD: The Wildlife Society</publisher><subject>Analytical estimating ; Animal, plant and microbial ecology ; Biological and medical sciences ; Density estimation ; Fundamental and applied biological sciences. Psychology ; General aspects. Techniques ; Modeling ; Ocean fisheries ; Oceans ; Regression analysis ; Reptiles & amphibians ; Sea states ; Sea surface temperature ; Sea turtles ; Statistical analysis ; Statistical variance ; Teledetection and vegetation maps ; Turtles ; Wildlife management</subject><ispartof>The Journal of wildlife management, 1998-07, Vol.62 (3), p.948-957</ispartof><rights>Copyright 1998 The Wildlife Society</rights><rights>1998 INIST-CNRS</rights><rights>Copyright Wildlife Society Jul 1998</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c339t-6c589dd3a13ad3fb41dc1266114098f293c39c20780503117b3800a6664759d03</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/3802547$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.jstor.org/stable/3802547$$EHTML$$P50$$Gjstor$$H</linktohtml><link.rule.ids>314,777,781,800,27905,27906,57998,58231</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=2424836$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Beavers, Sallie C.</creatorcontrib><creatorcontrib>Ramsey, Fred L.</creatorcontrib><title>Detectability Analysis in Transect Surveys</title><title>The Journal of wildlife management</title><description>We discuss and illustrate an analysis strategy that adjusts for the influence of variables such as weather, time of day, and observer on the detectability of animals in line transect surveys. The strategy employs ordinary least squares regression analysis followed by use of a standard estimator of effective area. No new computer software is required because multiple linear regression is available in all statistical software packages, and a variety of suitable estimators for effective half-width are available in the program DISTANCE. The strategy is applicable to all wildlife surveys that take systematic records of detectability conditions. We apply this approach to a shipboard survey of cheloniid sea turtles in the eastern tropical Pacific Ocean. Sea state (calm seas, white-capped seas) was the primary variable influencing detection of sea turtles. Effective half-width was adjusted for each category of sea state, and effective area surveyed was calculated. Surface density of turtles in 1989-90 was 0.067$\text{turtles}/\text{km}^{2}$(95% CI = 0.053-0.084).</description><subject>Analytical estimating</subject><subject>Animal, plant and microbial ecology</subject><subject>Biological and medical sciences</subject><subject>Density estimation</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>General aspects. Techniques</subject><subject>Modeling</subject><subject>Ocean fisheries</subject><subject>Oceans</subject><subject>Regression analysis</subject><subject>Reptiles & amphibians</subject><subject>Sea states</subject><subject>Sea surface temperature</subject><subject>Sea turtles</subject><subject>Statistical analysis</subject><subject>Statistical variance</subject><subject>Teledetection and vegetation maps</subject><subject>Turtles</subject><subject>Wildlife management</subject><issn>0022-541X</issn><issn>1937-2817</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1998</creationdate><recordtype>article</recordtype><recordid>eNp1kF1LwzAUhoMoOKf4F4qIglA9yUmT5nLMTxh44QTvSpam0NG1M6cV-u_tWFEQvDoX5-HhfV_GzjncCgR9hymIROoDNuEGdSxSrg_ZBECIOJH845idEK0BkPNUTdjNvW-9a-2qrMq2j2a1rXoqKSrraBlsTcMveuvCl-_plB0VtiJ_Nt4pe398WM6f48Xr08t8togdomlj5ZLU5DlajjbHYiV57rhQinMJJi2EQYfGCdApJLsUejUkBquUkjoxOeCUXe2929B8dp7abFOS81Vla990lHGVgFJaDODFH3DddGFoQJlAKUBgYgboeg-50BAFX2TbUG5s6DMO2W6wbBxsIC9HnSVnq2Ko70r6wYUUMkX1i62pbcK_tm95O3Eh</recordid><startdate>19980701</startdate><enddate>19980701</enddate><creator>Beavers, Sallie C.</creator><creator>Ramsey, Fred L.</creator><general>The Wildlife Society</general><general>Wildlife Society</general><general>Blackwell Publishing Ltd</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</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>19980701</creationdate><title>Detectability Analysis in Transect Surveys</title><author>Beavers, Sallie C. ; Ramsey, Fred L.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c339t-6c589dd3a13ad3fb41dc1266114098f293c39c20780503117b3800a6664759d03</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1998</creationdate><topic>Analytical estimating</topic><topic>Animal, plant and microbial ecology</topic><topic>Biological and medical sciences</topic><topic>Density estimation</topic><topic>Fundamental and applied biological sciences. Psychology</topic><topic>General aspects. Techniques</topic><topic>Modeling</topic><topic>Ocean fisheries</topic><topic>Oceans</topic><topic>Regression analysis</topic><topic>Reptiles & amphibians</topic><topic>Sea states</topic><topic>Sea surface temperature</topic><topic>Sea turtles</topic><topic>Statistical analysis</topic><topic>Statistical variance</topic><topic>Teledetection and vegetation maps</topic><topic>Turtles</topic><topic>Wildlife management</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Beavers, Sallie C.</creatorcontrib><creatorcontrib>Ramsey, Fred L.</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</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>Beavers, Sallie C.</au><au>Ramsey, Fred L.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Detectability Analysis in Transect Surveys</atitle><jtitle>The Journal of wildlife management</jtitle><date>1998-07-01</date><risdate>1998</risdate><volume>62</volume><issue>3</issue><spage>948</spage><epage>957</epage><pages>948-957</pages><issn>0022-541X</issn><eissn>1937-2817</eissn><coden>JWMAA9</coden><abstract>We discuss and illustrate an analysis strategy that adjusts for the influence of variables such as weather, time of day, and observer on the detectability of animals in line transect surveys. The strategy employs ordinary least squares regression analysis followed by use of a standard estimator of effective area. No new computer software is required because multiple linear regression is available in all statistical software packages, and a variety of suitable estimators for effective half-width are available in the program DISTANCE. The strategy is applicable to all wildlife surveys that take systematic records of detectability conditions. We apply this approach to a shipboard survey of cheloniid sea turtles in the eastern tropical Pacific Ocean. Sea state (calm seas, white-capped seas) was the primary variable influencing detection of sea turtles. Effective half-width was adjusted for each category of sea state, and effective area surveyed was calculated. Surface density of turtles in 1989-90 was 0.067$\text{turtles}/\text{km}^{2}$(95% CI = 0.053-0.084).</abstract><cop>Bethesda, MD</cop><pub>The Wildlife Society</pub><doi>10.2307/3802547</doi><tpages>10</tpages></addata></record> |
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source | Jstor Complete Legacy |
subjects | Analytical estimating Animal, plant and microbial ecology Biological and medical sciences Density estimation Fundamental and applied biological sciences. Psychology General aspects. Techniques Modeling Ocean fisheries Oceans Regression analysis Reptiles & amphibians Sea states Sea surface temperature Sea turtles Statistical analysis Statistical variance Teledetection and vegetation maps Turtles Wildlife management |
title | Detectability Analysis in Transect Surveys |
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