Wolf Movement Patterns: a Key to Estimation of Kill Rate?
To estimate wolf (Canis lupus) kill rates from fine-scale movement patterns, we followed adult wolves in 3 territories of the Scandinavian wolf population using Global Positioning Systems (GPS) during the winters of 2001–2003. The resulting 6 datasets of 62–84 study days gave a total of 8,747 hourly...
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creator | ZIMMERMANN, BARBARA WABAKKEN, PETTER SAND, HÅKAN PEDERSEN, HANS C LIBERG, OLOF |
description | To estimate wolf (Canis lupus) kill rates from fine-scale movement patterns, we followed adult wolves in 3 territories of the Scandinavian wolf population using Global Positioning Systems (GPS) during the winters of 2001–2003. The resulting 6 datasets of 62–84 study days gave a total of 8,747 hourly GPS positions. We visited clusters of positions in the field on average 8.8 days after positioning and found moose (Alces alces) killed by wolves during the study period on 74 (8%) of the 953 clusters. The number of positions and visits to a cluster, their interaction, and the proportion of afternoon positions were significant fixed effects in mixed logistic-regression models predicting the probability of a cluster containing a wolf-killed moose. The models, however, displayed a poor goodness-of-fit and were not a suitable tool for estimating kill rates from positioning data alone. They might be used to reduce fieldwork by excluding unlikely clusters, although the reduction was not substantial. We discuss proximate factors (i.e., human disturbance and access to prey) as well as ultimate factors (i.e., social organization, intra-guild dominance, and litter size) as potential causes of the observed high temporal and spatial variation in prey-handling. For similar future kill-rate studies, we recommend increasing field efforts and shortening positioning intervals. |
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The resulting 6 datasets of 62–84 study days gave a total of 8,747 hourly GPS positions. We visited clusters of positions in the field on average 8.8 days after positioning and found moose (Alces alces) killed by wolves during the study period on 74 (8%) of the 953 clusters. The number of positions and visits to a cluster, their interaction, and the proportion of afternoon positions were significant fixed effects in mixed logistic-regression models predicting the probability of a cluster containing a wolf-killed moose. The models, however, displayed a poor goodness-of-fit and were not a suitable tool for estimating kill rates from positioning data alone. They might be used to reduce fieldwork by excluding unlikely clusters, although the reduction was not substantial. We discuss proximate factors (i.e., human disturbance and access to prey) as well as ultimate factors (i.e., social organization, intra-guild dominance, and litter size) as potential causes of the observed high temporal and spatial variation in prey-handling. For similar future kill-rate studies, we recommend increasing field efforts and shortening positioning intervals.</description><identifier>ISSN: 0022-541X</identifier><identifier>EISSN: 1937-2817</identifier><identifier>DOI: 10.2193/2006-306</identifier><identifier>CODEN: JWMAA9</identifier><language>eng</language><publisher>Oxford, UK: The Wildlife Society</publisher><subject>Alces alces ; Canis lupus ; Conservation biology ; Datasets ; Fieldwork ; Generalized linear models ; Global Positioning System ; Global positioning systems ; GPS ; handling time ; kill rate ; Lynx ; Modeling ; Moose ; movement ; Predation ; Prey ; Research universities ; Scandinavia ; Studies ; University colleges ; Wildlife ecology ; Wildlife management ; wolf ; Wolves</subject><ispartof>The Journal of wildlife management, 2007-06, Vol.71 (4), p.1177-1182</ispartof><rights>Copyright 2007 The Wildlife Society</rights><rights>2007 The Wildlife Society</rights><rights>Copyright Alliance Communications Group, A Division of Allen Press, Inc. Jun 2007</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-b4467-a567fd8c2a3b93ef5ce726fa452e1b0bb09c8ae804c4d51508a4b0ceceb1ba013</citedby><cites>FETCH-LOGICAL-b4467-a567fd8c2a3b93ef5ce726fa452e1b0bb09c8ae804c4d51508a4b0ceceb1ba013</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/4496175$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.jstor.org/stable/4496175$$EHTML$$P50$$Gjstor$$H</linktohtml><link.rule.ids>314,776,780,799,1411,27901,27902,45550,45551,57992,58225</link.rule.ids></links><search><creatorcontrib>ZIMMERMANN, BARBARA</creatorcontrib><creatorcontrib>WABAKKEN, PETTER</creatorcontrib><creatorcontrib>SAND, HÅKAN</creatorcontrib><creatorcontrib>PEDERSEN, HANS C</creatorcontrib><creatorcontrib>LIBERG, OLOF</creatorcontrib><title>Wolf Movement Patterns: a Key to Estimation of Kill Rate?</title><title>The Journal of wildlife management</title><description>To estimate wolf (Canis lupus) kill rates from fine-scale movement patterns, we followed adult wolves in 3 territories of the Scandinavian wolf population using Global Positioning Systems (GPS) during the winters of 2001–2003. The resulting 6 datasets of 62–84 study days gave a total of 8,747 hourly GPS positions. We visited clusters of positions in the field on average 8.8 days after positioning and found moose (Alces alces) killed by wolves during the study period on 74 (8%) of the 953 clusters. The number of positions and visits to a cluster, their interaction, and the proportion of afternoon positions were significant fixed effects in mixed logistic-regression models predicting the probability of a cluster containing a wolf-killed moose. The models, however, displayed a poor goodness-of-fit and were not a suitable tool for estimating kill rates from positioning data alone. They might be used to reduce fieldwork by excluding unlikely clusters, although the reduction was not substantial. We discuss proximate factors (i.e., human disturbance and access to prey) as well as ultimate factors (i.e., social organization, intra-guild dominance, and litter size) as potential causes of the observed high temporal and spatial variation in prey-handling. For similar future kill-rate studies, we recommend increasing field efforts and shortening positioning intervals.</description><subject>Alces alces</subject><subject>Canis lupus</subject><subject>Conservation biology</subject><subject>Datasets</subject><subject>Fieldwork</subject><subject>Generalized linear models</subject><subject>Global Positioning System</subject><subject>Global positioning systems</subject><subject>GPS</subject><subject>handling time</subject><subject>kill rate</subject><subject>Lynx</subject><subject>Modeling</subject><subject>Moose</subject><subject>movement</subject><subject>Predation</subject><subject>Prey</subject><subject>Research universities</subject><subject>Scandinavia</subject><subject>Studies</subject><subject>University colleges</subject><subject>Wildlife ecology</subject><subject>Wildlife 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management</jtitle><date>2007-06</date><risdate>2007</risdate><volume>71</volume><issue>4</issue><spage>1177</spage><epage>1182</epage><pages>1177-1182</pages><issn>0022-541X</issn><eissn>1937-2817</eissn><coden>JWMAA9</coden><abstract>To estimate wolf (Canis lupus) kill rates from fine-scale movement patterns, we followed adult wolves in 3 territories of the Scandinavian wolf population using Global Positioning Systems (GPS) during the winters of 2001–2003. The resulting 6 datasets of 62–84 study days gave a total of 8,747 hourly GPS positions. We visited clusters of positions in the field on average 8.8 days after positioning and found moose (Alces alces) killed by wolves during the study period on 74 (8%) of the 953 clusters. The number of positions and visits to a cluster, their interaction, and the proportion of afternoon positions were significant fixed effects in mixed logistic-regression models predicting the probability of a cluster containing a wolf-killed moose. The models, however, displayed a poor goodness-of-fit and were not a suitable tool for estimating kill rates from positioning data alone. They might be used to reduce fieldwork by excluding unlikely clusters, although the reduction was not substantial. We discuss proximate factors (i.e., human disturbance and access to prey) as well as ultimate factors (i.e., social organization, intra-guild dominance, and litter size) as potential causes of the observed high temporal and spatial variation in prey-handling. For similar future kill-rate studies, we recommend increasing field efforts and shortening positioning intervals.</abstract><cop>Oxford, UK</cop><pub>The Wildlife Society</pub><doi>10.2193/2006-306</doi><tpages>6</tpages></addata></record> |
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subjects | Alces alces Canis lupus Conservation biology Datasets Fieldwork Generalized linear models Global Positioning System Global positioning systems GPS handling time kill rate Lynx Modeling Moose movement Predation Prey Research universities Scandinavia Studies University colleges Wildlife ecology Wildlife management wolf Wolves |
title | Wolf Movement Patterns: a Key to Estimation of Kill Rate? |
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