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class="social-profile-avatar-container"><img class="profile-avatar u-positionAbsolute" border="0" alt="" src="//a.academia-assets.com/images/s200_no_pic.png" /></div><div class="title-container"><h1 class="ds2-5-heading-sans-serif-sm">Jacqueline Frair</h1><div class="affiliations-container fake-truncate js-profile-affiliations"></div></div></div><div class="sidebar-cta-container"><button class="ds2-5-button hidden profile-cta-button grow js-profile-follow-button" data-broccoli-component="user-info.follow-button" data-click-track="profile-user-info-follow-button" data-follow-user-fname="Jacqueline" data-follow-user-id="33485231" data-follow-user-source="profile_button" data-has-google="false"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">add</span>Follow</button><button class="ds2-5-button hidden profile-cta-button grow js-profile-unfollow-button" data-broccoli-component="user-info.unfollow-button" data-click-track="profile-user-info-unfollow-button" 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class="js-profile-view-count"></span></p></div></span></div><div class="ri-section"><div class="ri-section-header"><span>Interests</span></div><div class="ri-tags-container"><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="33485231" href="https://www.academia.edu/Documents/in/Biological_Sciences"><div id="js-react-on-rails-context" style="display:none" data-rails-context="{"inMailer":false,"i18nLocale":"en","i18nDefaultLocale":"en","href":"https://independent.academia.edu/JacquelineFrair","location":"/JacquelineFrair","scheme":"https","host":"independent.academia.edu","port":null,"pathname":"/JacquelineFrair","search":null,"httpAcceptLanguage":null,"serverSide":false}"></div> <div class="js-react-on-rails-component" style="display:none" data-component-name="Pill" data-props="{"color":"gray","children":["Biological Sciences"]}" data-trace="false" data-dom-id="Pill-react-component-09694286-ec22-40c4-8d99-11b7664d4b6e"></div> <div id="Pill-react-component-09694286-ec22-40c4-8d99-11b7664d4b6e"></div> </a></div></div></div></div><div class="right-panel-container"><div class="user-content-wrapper"><div class="uploads-container" id="social-redesign-work-container"><div class="upload-header"><h2 class="ds2-5-heading-sans-serif-xs">Uploads</h2></div><div class="documents-container backbone-social-profile-documents" style="width: 100%;"><div class="u-taCenter"></div><div class="profile--tab_content_container js-tab-pane tab-pane active" id="all"><div class="profile--tab_heading_container js-section-heading" data-section="Papers" id="Papers"><h3 class="profile--tab_heading_container">Papers by Jacqueline Frair</h3></div><div class="js-work-strip profile--work_container" data-work-id="80386012"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/80386012/Habitat_use_as_an_indicator_of_adaptive_capacity_to_climate_change"><img alt="Research paper thumbnail of Habitat use as an indicator of adaptive capacity to climate change" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/80386012/Habitat_use_as_an_indicator_of_adaptive_capacity_to_climate_change">Habitat use as an indicator of adaptive capacity to climate change</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Aim: Populations of cold-adapted species at the trailing edges of geographic ranges are particula...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Aim: Populations of cold-adapted species at the trailing edges of geographic ranges are particularly vulnerable to the negative effects of climate change from the combination of exposure to warm temperatures and high sensitivity to heat. Many of these species are predicted to decline under future climate scenarios, but they could persist if they can adapt to warming climates either physiologically or behaviorally. We aim to understand local variation in contemporary habitat use and use this information to identify signs of adaptive capacity. We focus on moose (Alces alces), a charismatic species of conservation and public interest. Location: The northeastern United States, along the trailing edge of the moose geographic range in North America. Methods: We compiled data on occurrences and habitat use of moose from remote cameras and GPS collars across the northeastern United States. We use these data to build habitat suitability models at local and regional spatial scales, and then t...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="80386012"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="80386012"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 80386012; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=80386012]").text(description); $(".js-view-count[data-work-id=80386012]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 80386012; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='80386012']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 80386012, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=80386012]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":80386012,"title":"Habitat use as an indicator of adaptive capacity to climate change","translated_title":"","metadata":{"abstract":"Aim: Populations of cold-adapted species at the trailing edges of geographic ranges are particularly vulnerable to the negative effects of climate change from the combination of exposure to warm temperatures and high sensitivity to heat. 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During hibernation, infected little brown bats are able to initiate anti-Pd immune responses, indicating pathogen-mediated selection on the major histocompatibility complex (MHC) genes. However, such immune responses may not be protective as they interrupt torpor, elevate energy costs, and potentially lead to higher mortality rates. To assess whether WNS drives selection on MHC genes, we compared the MHC DRB gene in little brown bats pre- (Wisconsin) and post- (Michigan, New York, Vermont, and Pennsylvania) WNS (detection spanning 2014-2015). We genotyped 131 individuals and found 45 nucleotide alleles (27 amino acid alleles) indicating a maximum of 3 loci (1-5 alleles per individual). 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We observed high allelic admixture and a lack of genetic differentiation both among sampling sites a...","publisher":"Dryad","publication_date":{"day":null,"month":null,"year":2020,"errors":{}}},"translated_abstract":"White-nose syndrome (WNS), caused by the fungal pathogen Pseudogymnoascus destructans (Pd), has driven alarming declines in North American hibernating bats, such as little brown bat (Myotis lucifugus). During hibernation, infected little brown bats are able to initiate anti-Pd immune responses, indicating pathogen-mediated selection on the major histocompatibility complex (MHC) genes. However, such immune responses may not be protective as they interrupt torpor, elevate energy costs, and potentially lead to higher mortality rates. To assess whether WNS drives selection on MHC genes, we compared the MHC DRB gene in little brown bats pre- (Wisconsin) and post- (Michigan, New York, Vermont, and Pennsylvania) WNS (detection spanning 2014-2015). We genotyped 131 individuals and found 45 nucleotide alleles (27 amino acid alleles) indicating a maximum of 3 loci (1-5 alleles per individual). We observed high allelic admixture and a lack of genetic differentiation both among sampling sites a...","internal_url":"https://www.academia.edu/80386001/MHC_variation_is_similar_in_little_brown_bats_before_and_after_white_nose_syndrome_outbreak","translated_internal_url":"","created_at":"2022-05-31T10:20:26.701-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":33485231,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"MHC_variation_is_similar_in_little_brown_bats_before_and_after_white_nose_syndrome_outbreak","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":33485231,"first_name":"Jacqueline","middle_initials":null,"last_name":"Frair","page_name":"JacquelineFrair","domain_name":"independent","created_at":"2015-07-31T03:42:07.046-07:00","display_name":"Jacqueline Frair","url":"https://independent.academia.edu/JacquelineFrair"},"attachments":[],"research_interests":[],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="80386000"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/80386000/Managing_Moose_from_Home_Determining_Landscape_Carrying_Capacity_for_Alces_alces_Using_Remote_Sensing"><img alt="Research paper thumbnail of Managing Moose from Home: Determining Landscape Carrying Capacity for Alces alces Using Remote Sensing" class="work-thumbnail" src="https://attachments.academia-assets.com/86785036/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/80386000/Managing_Moose_from_Home_Determining_Landscape_Carrying_Capacity_for_Alces_alces_Using_Remote_Sensing">Managing Moose from Home: Determining Landscape Carrying Capacity for Alces alces Using Remote Sensing</a></div><div class="wp-workCard_item"><span>Forests</span><span>, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In temperate forests of the northeastern U.S., moose (Alces alces) populations are adapted for mi...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In temperate forests of the northeastern U.S., moose (Alces alces) populations are adapted for mixed-age heterogeneous landscapes that provide abundant herbaceous forage in warm months and coniferous forage during winter. Heterogeneity of forest stands is driven by management activities or natural disturbance, resulting in a multi-age forest at a landscape scale. Here, we present a method to estimate landscape carrying capacity of moose by combining remote sensing classification of forest cover class with literature or field-based estimates of class-specific forage abundance. We used Landsat imagery from 1991 to 2013 for the Allegheny National Forest and 2013–2018 for the Adirondack Park, and associated training polygons, to predict based on NDVI and SWI whether a forested landscape fit into one of three cover classes: mature forest, intermediate timber removal, or overstory timber removal. Our three-classes yielded a mean land cover prediction accuracy of 94.3% (Khat = 0.91) and 86...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="84cd0cf106c88af4111327f6a342d93d" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":86785036,"asset_id":80386000,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/86785036/download_file?st=MTczMjc0MTAwMyw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="80386000"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="80386000"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 80386000; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=80386000]").text(description); $(".js-view-count[data-work-id=80386000]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 80386000; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='80386000']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 80386000, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "84cd0cf106c88af4111327f6a342d93d" } } $('.js-work-strip[data-work-id=80386000]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":80386000,"title":"Managing Moose from Home: Determining Landscape Carrying Capacity for Alces alces Using Remote Sensing","translated_title":"","metadata":{"abstract":"In temperate forests of the northeastern U.S., moose (Alces alces) populations are adapted for mixed-age heterogeneous landscapes that provide abundant herbaceous forage in warm months and coniferous forage during winter. Heterogeneity of forest stands is driven by management activities or natural disturbance, resulting in a multi-age forest at a landscape scale. Here, we present a method to estimate landscape carrying capacity of moose by combining remote sensing classification of forest cover class with literature or field-based estimates of class-specific forage abundance. We used Landsat imagery from 1991 to 2013 for the Allegheny National Forest and 2013–2018 for the Adirondack Park, and associated training polygons, to predict based on NDVI and SWI whether a forested landscape fit into one of three cover classes: mature forest, intermediate timber removal, or overstory timber removal. 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We used Landsat imagery from 1991 to 2013 for the Allegheny National Forest and 2013–2018 for the Adirondack Park, and associated training polygons, to predict based on NDVI and SWI whether a forested landscape fit into one of three cover classes: mature forest, intermediate timber removal, or overstory timber removal. Our three-classes yielded a mean land cover prediction accuracy of 94.3% (Khat = 0.91) and 86...","internal_url":"https://www.academia.edu/80386000/Managing_Moose_from_Home_Determining_Landscape_Carrying_Capacity_for_Alces_alces_Using_Remote_Sensing","translated_internal_url":"","created_at":"2022-05-31T10:20:26.540-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":33485231,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":86785036,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/86785036/thumbnails/1.jpg","file_name":"pdf.pdf","download_url":"https://www.academia.edu/attachments/86785036/download_file?st=MTczMjc0MTAwMyw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Managing_Moose_from_Home_Determining_Lan.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/86785036/pdf-libre.pdf?1654017723=\u0026response-content-disposition=attachment%3B+filename%3DManaging_Moose_from_Home_Determining_Lan.pdf\u0026Expires=1732744603\u0026Signature=W1W7ZygwLcHZKyRW1dO3CmRuiRscn1Z~CAc8jreZrAqS9MINw2W~~~XSNbR9vi91ZUN-Z3VNv1l2MVME6NPn3ASWXNbUxHze7Lx024UiraaWcpwlO7vZ1LBk9o-wq0E3Qvpw6nSUA4lkfB5pjdiHKOE9u0jBE~RwjQTTvAxPDE0W3vVMax-7RQwKD3Xn~VfDTPaoktGj81I1HPZeE5gosGtQnWZplPUWrZ9klYRLur-r0X~-utek8~q--zdoUdC1~HwRO6UyzH5dJZEIGyD6TCRaUGUoFff7mOOuFMhGfgGKXR9VVxmdacYeLCInre2RIwgrj7ZcFUb-3TRubgv8UA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Managing_Moose_from_Home_Determining_Landscape_Carrying_Capacity_for_Alces_alces_Using_Remote_Sensing","translated_slug":"","page_count":18,"language":"en","content_type":"Work","owner":{"id":33485231,"first_name":"Jacqueline","middle_initials":null,"last_name":"Frair","page_name":"JacquelineFrair","domain_name":"independent","created_at":"2015-07-31T03:42:07.046-07:00","display_name":"Jacqueline Frair","url":"https://independent.academia.edu/JacquelineFrair"},"attachments":[{"id":86785036,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/86785036/thumbnails/1.jpg","file_name":"pdf.pdf","download_url":"https://www.academia.edu/attachments/86785036/download_file?st=MTczMjc0MTAwMyw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Managing_Moose_from_Home_Determining_Lan.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/86785036/pdf-libre.pdf?1654017723=\u0026response-content-disposition=attachment%3B+filename%3DManaging_Moose_from_Home_Determining_Lan.pdf\u0026Expires=1732744603\u0026Signature=W1W7ZygwLcHZKyRW1dO3CmRuiRscn1Z~CAc8jreZrAqS9MINw2W~~~XSNbR9vi91ZUN-Z3VNv1l2MVME6NPn3ASWXNbUxHze7Lx024UiraaWcpwlO7vZ1LBk9o-wq0E3Qvpw6nSUA4lkfB5pjdiHKOE9u0jBE~RwjQTTvAxPDE0W3vVMax-7RQwKD3Xn~VfDTPaoktGj81I1HPZeE5gosGtQnWZplPUWrZ9klYRLur-r0X~-utek8~q--zdoUdC1~HwRO6UyzH5dJZEIGyD6TCRaUGUoFff7mOOuFMhGfgGKXR9VVxmdacYeLCInre2RIwgrj7ZcFUb-3TRubgv8UA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"},{"id":86785035,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/86785035/thumbnails/1.jpg","file_name":"pdf.pdf","download_url":"https://www.academia.edu/attachments/86785035/download_file","bulk_download_file_name":"Managing_Moose_from_Home_Determining_Lan.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/86785035/pdf-libre.pdf?1654017734=\u0026response-content-disposition=attachment%3B+filename%3DManaging_Moose_from_Home_Determining_Lan.pdf\u0026Expires=1732744603\u0026Signature=cqjttdk06xhbB92Zl0V49yPufxEp7w031~6fHLvuTXGJbSh80MmfscmoBCJ5-aXOUw-hzP5YFxMeuDfUI1VEleB1MHwHgyO6p~wPb4MXsebMGxm~Hm9kSXsXwpxL8FDcCNZBgXOLvACb5p6LWNsLk599Do3gWXgRAHiwGuegeNpLJWMoxEfIBBzTy~XuhRrtoGtTGNEaeNNDhGJ0uRc49okvLi55myReMTsb6wwV~faieEna3QVjDzjGHnMCxOVafad908lQaE2kI68W0gygiLIRAtiwZFhxH58hg2T1TsHOtrL-JSs7VGFPo1E9B47l98I5JnPVpyF270ckapGj5Q__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":117033,"name":"Forests","url":"https://www.academia.edu/Documents/in/Forests"}],"urls":[{"id":20967046,"url":"https://www.mdpi.com/1999-4907/13/2/150/pdf"}]}, dispatcherData: dispatcherData }); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="80385998"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/80385998/Browse_Selection_by_Moose_in_the_Adirondack_Park_New_York"><img alt="Research paper thumbnail of Browse Selection by Moose in the Adirondack Park, New York" class="work-thumbnail" src="https://attachments.academia-assets.com/86785033/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/80385998/Browse_Selection_by_Moose_in_the_Adirondack_Park_New_York">Browse Selection by Moose in the Adirondack Park, New York</a></div><div class="wp-workCard_item"><span>Alces : A Journal Devoted to the Biology and Management of Moose</span><span>, 2020</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Moose ( Alces alces americana ), a large-bodied and cold-adapted forest herbivore, may be vulnera...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Moose ( Alces alces americana ), a large-bodied and cold-adapted forest herbivore, may be vulnerable to environmental change especially along their southern range in the northeastern United States. Better understanding of moose foraging patterns and resource constraints in this region, which moose recolonized over the past several decades, is needed to anticipate factors that may influence the long-term viability of the regional moose population. We quantified browse selection, intensity and nutritional quality, and the impact of other vegetation potentially interfering with browse availability for moose within the Adirondack Park, New York. We backtracked GPS-collared female moose (n = 23) to assess the seasonal composition of selected browse from 2016 to 2017, compared browse selection to plant nutritional quality, and modeled local browsing intensity. Moose demonstrated a generalist feeding strategy in summer, but in winter selected browse species largely in order of digestible d...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="fed9403914ad81a5805c0aadf6b25ab6" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":86785033,"asset_id":80385998,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/86785033/download_file?st=MTczMjc0MTAwMyw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="80385998"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="80385998"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 80385998; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=80385998]").text(description); $(".js-view-count[data-work-id=80385998]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 80385998; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='80385998']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 80385998, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "fed9403914ad81a5805c0aadf6b25ab6" } } $('.js-work-strip[data-work-id=80385998]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":80385998,"title":"Browse Selection by Moose in the Adirondack Park, New York","translated_title":"","metadata":{"abstract":"Moose ( Alces alces americana ), a large-bodied and cold-adapted forest herbivore, may be vulnerable to environmental change especially along their southern range in the northeastern United States. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="80385997"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/80385997/Flexible_characterization_of_animal_movement_pattern_using_net_squared_displacement_and_a_latent_state_model"><img alt="Research paper thumbnail of Flexible characterization of animal movement pattern using net squared displacement and a latent state model" class="work-thumbnail" src="https://attachments.academia-assets.com/86785058/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/80385997/Flexible_characterization_of_animal_movement_pattern_using_net_squared_displacement_and_a_latent_state_model">Flexible characterization of animal movement pattern using net squared displacement and a latent state model</a></div><div class="wp-workCard_item"><span>Movement ecology</span><span>, 2016</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Characterizing the movement patterns of animals is an important step in understanding their ecolo...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Characterizing the movement patterns of animals is an important step in understanding their ecology. Various methods have been developed for classifying animal movement at both coarse (e.g., migratory vs. sedentary behavior) and fine (e.g., resting vs. foraging) scales. A popular approach for classifying movements at coarse resolutions involves fitting time series of net-squared displacement (NSD) to models representing different conceptualizations of coarse movement strategies (i.e., migration, nomadism, sedentarism, etc.). However, the performance of this method in classifying actual (as opposed to simulated) animal movements has been mixed. Here, we develop a more flexible method that uses the same NSD input, but relies on an underlying discrete latent state model. Using simulated data, we first assess how well patterns in the number of transitions between modes of movement and the duration of time spent in a mode classify movement strategies. We then apply our approach to elucid...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="fc8a045ee28a87eb4813483b4ce0e3ea" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":86785058,"asset_id":80385997,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/86785058/download_file?st=MTczMjc0MTAwMyw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="80385997"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="80385997"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 80385997; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=80385997]").text(description); $(".js-view-count[data-work-id=80385997]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 80385997; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='80385997']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 80385997, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "fc8a045ee28a87eb4813483b4ce0e3ea" } } $('.js-work-strip[data-work-id=80385997]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":80385997,"title":"Flexible characterization of animal movement pattern using net squared displacement and a latent state model","translated_title":"","metadata":{"abstract":"Characterizing the movement patterns of animals is an important step in understanding their ecology. 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We then apply our approach to elucid...","publication_date":{"day":null,"month":null,"year":2016,"errors":{}},"publication_name":"Movement ecology"},"translated_abstract":"Characterizing the movement patterns of animals is an important step in understanding their ecology. Various methods have been developed for classifying animal movement at both coarse (e.g., migratory vs. sedentary behavior) and fine (e.g., resting vs. foraging) scales. A popular approach for classifying movements at coarse resolutions involves fitting time series of net-squared displacement (NSD) to models representing different conceptualizations of coarse movement strategies (i.e., migration, nomadism, sedentarism, etc.). However, the performance of this method in classifying actual (as opposed to simulated) animal movements has been mixed. Here, we develop a more flexible method that uses the same NSD input, but relies on an underlying discrete latent state model. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="80385982"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/80385982/Correlation_and_studies_of_habitat_selection_problem_red_herring_or_opportunity"><img alt="Research paper thumbnail of Correlation and studies of habitat selection: problem, red herring or opportunity?" class="work-thumbnail" src="https://attachments.academia-assets.com/86785007/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/80385982/Correlation_and_studies_of_habitat_selection_problem_red_herring_or_opportunity">Correlation and studies of habitat selection: problem, red herring or opportunity?</a></div><div class="wp-workCard_item"><span>Philosophical Transactions of the Royal Society B: Biological Sciences</span><span>, 2010</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">With the advent of new technologies, animal locations are being collected at ever finer spatio-te...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">With the advent of new technologies, animal locations are being collected at ever finer spatio-temporal scales. We review analytical methods for dealing with correlated data in the context of resource selection, including post hoc variance inflation techniques, ‘two-stage’ approaches based on models fit to each individual, generalized estimating equations and hierarchical mixed-effects models. These methods are applicable to a wide range of correlated data problems, but can be difficult to apply and remain especially challenging for use–availability sampling designs because the correlation structure for combinations of used and available points are not likely to follow common parametric forms. We also review emerging approaches to studying habitat selection that use fine-scale temporal data to arrive at biologically based definitions of available habitat, while naturally accounting for autocorrelation by modelling animal movement between telemetry locations. Sophisticated analyses t...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="e220d4ad3b0ecf064e5225f15d21410d" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":86785007,"asset_id":80385982,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/86785007/download_file?st=MTczMjc0MTAwMyw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="80385982"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="80385982"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 80385982; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=80385982]").text(description); $(".js-view-count[data-work-id=80385982]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 80385982; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='80385982']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 80385982, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "e220d4ad3b0ecf064e5225f15d21410d" } } $('.js-work-strip[data-work-id=80385982]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":80385982,"title":"Correlation and studies of habitat selection: problem, red herring or opportunity?","translated_title":"","metadata":{"abstract":"With the advent of new technologies, animal locations are being collected at ever finer spatio-temporal scales. 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We also review emerging approaches to studying habitat selection that use fine-scale temporal data to arrive at biologically based definitions of available habitat, while naturally accounting for autocorrelation by modelling animal movement between telemetry locations. 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src="https://attachments.academia-assets.com/86785056/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/80385981/Building_a_mechanistic_understanding_of_predation_with_GPS_based_movement_data">Building a mechanistic understanding of predation with GPS-based movement data</a></div><div class="wp-workCard_item"><span>Philosophical Transactions of the Royal Society B: Biological Sciences</span><span>, 2010</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Quantifying kill rates and sources of variation in kill rates remains an important challenge in l...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Quantifying kill rates and sources of variation in kill rates remains an important challenge in linking predators to their prey. We address current approaches to using global positioning system (GPS)-based movement data for quantifying key predation components of large carnivores. We review approaches to identify kill sites from GPS movement data as a means to estimate kill rates and address advantages of using GPS-based data over past approaches. Despite considerable progress, modelling the probability that a cluster of GPS points is a kill site is no substitute for field visits, but can guide our field efforts. Once kill sites are identified, time spent at a kill site (handling time) and time between kills (killing time) can be determined. We show how statistical models can be used to investigate the influence of factors such as animal characteristics (e.g. age, sex, group size) and landscape features on either handling time or killing efficiency. If we know the prey densities alo...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="aaa739bda0be32d8957d50b3b454ab82" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":86785056,"asset_id":80385981,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/86785056/download_file?st=MTczMjc0MTAwMyw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="80385981"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="80385981"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 80385981; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=80385981]").text(description); $(".js-view-count[data-work-id=80385981]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 80385981; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='80385981']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 80385981, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "aaa739bda0be32d8957d50b3b454ab82" } } $('.js-work-strip[data-work-id=80385981]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":80385981,"title":"Building a mechanistic understanding of predation with GPS-based movement data","translated_title":"","metadata":{"abstract":"Quantifying kill rates and sources of variation in kill rates remains an important challenge in linking predators to their prey. We address current approaches to using global positioning system (GPS)-based movement data for quantifying key predation components of large carnivores. We review approaches to identify kill sites from GPS movement data as a means to estimate kill rates and address advantages of using GPS-based data over past approaches. Despite considerable progress, modelling the probability that a cluster of GPS points is a kill site is no substitute for field visits, but can guide our field efforts. Once kill sites are identified, time spent at a kill site (handling time) and time between kills (killing time) can be determined. We show how statistical models can be used to investigate the influence of factors such as animal characteristics (e.g. age, sex, group size) and landscape features on either handling time or killing efficiency. If we know the prey densities alo...","publisher":"The Royal Society","publication_date":{"day":null,"month":null,"year":2010,"errors":{}},"publication_name":"Philosophical Transactions of the Royal Society B: Biological Sciences"},"translated_abstract":"Quantifying kill rates and sources of variation in kill rates remains an important challenge in linking predators to their prey. We address current approaches to using global positioning system (GPS)-based movement data for quantifying key predation components of large carnivores. We review approaches to identify kill sites from GPS movement data as a means to estimate kill rates and address advantages of using GPS-based data over past approaches. Despite considerable progress, modelling the probability that a cluster of GPS points is a kill site is no substitute for field visits, but can guide our field efforts. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="80385980"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/80385980/Know_Thy_Enemy_Experience_Affects_Elk_Translocation_Success_in_Risky_Landscapes"><img alt="Research paper thumbnail of Know Thy Enemy: Experience Affects Elk Translocation Success in Risky Landscapes" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/80385980/Know_Thy_Enemy_Experience_Affects_Elk_Translocation_Success_in_Risky_Landscapes">Know Thy Enemy: Experience Affects Elk Translocation Success in Risky Landscapes</a></div><div class="wp-workCard_item"><span>Journal of Wildlife Management</span><span>, 2007</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">To maximize success, reintroduction programs generally select predator-free release areas having ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">To maximize success, reintroduction programs generally select predator-free release areas having high habitat quality. Past studies provide little insight into recovery efforts where multiple, potentially novel, mortality hazards occur. The ability of translocated animals to cope with novel environments can be affected by both pre- and postrelease experiences with habitat and mortality risks. We experimentally released elk (Cervus elaphus) having</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="80385980"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="80385980"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 80385980; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=80385980]").text(description); $(".js-view-count[data-work-id=80385980]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 80385980; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='80385980']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 80385980, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=80385980]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":80385980,"title":"Know Thy Enemy: Experience Affects Elk Translocation Success in Risky Landscapes","translated_title":"","metadata":{"abstract":"To maximize success, reintroduction programs generally select predator-free release areas having high habitat quality. Past studies provide little insight into recovery efforts where multiple, potentially novel, mortality hazards occur. The ability of translocated animals to cope with novel environments can be affected by both pre- and postrelease experiences with habitat and mortality risks. We experimentally released elk (Cervus elaphus) having","publisher":"Wiley","publication_date":{"day":null,"month":null,"year":2007,"errors":{}},"publication_name":"Journal of Wildlife Management"},"translated_abstract":"To maximize success, reintroduction programs generally select predator-free release areas having high habitat quality. Past studies provide little insight into recovery efforts where multiple, potentially novel, mortality hazards occur. The ability of translocated animals to cope with novel environments can be affected by both pre- and postrelease experiences with habitat and mortality risks. We experimentally released elk (Cervus elaphus) having","internal_url":"https://www.academia.edu/80385980/Know_Thy_Enemy_Experience_Affects_Elk_Translocation_Success_in_Risky_Landscapes","translated_internal_url":"","created_at":"2022-05-31T10:19:57.576-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":33485231,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Know_Thy_Enemy_Experience_Affects_Elk_Translocation_Success_in_Risky_Landscapes","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":33485231,"first_name":"Jacqueline","middle_initials":null,"last_name":"Frair","page_name":"JacquelineFrair","domain_name":"independent","created_at":"2015-07-31T03:42:07.046-07:00","display_name":"Jacqueline Frair","url":"https://independent.academia.edu/JacquelineFrair"},"attachments":[],"research_interests":[{"id":261,"name":"Geography","url":"https://www.academia.edu/Documents/in/Geography"},{"id":47884,"name":"Biological Sciences","url":"https://www.academia.edu/Documents/in/Biological_Sciences"},{"id":58054,"name":"Environmental Sciences","url":"https://www.academia.edu/Documents/in/Environmental_Sciences"},{"id":113522,"name":"Canis lupus","url":"https://www.academia.edu/Documents/in/Canis_lupus"},{"id":133572,"name":"Wildlife Management","url":"https://www.academia.edu/Documents/in/Wildlife_Management"},{"id":169818,"name":"Second Year","url":"https://www.academia.edu/Documents/in/Second_Year"},{"id":194517,"name":"Cervus elaphus","url":"https://www.academia.edu/Documents/in/Cervus_elaphus"},{"id":230247,"name":"Translocation","url":"https://www.academia.edu/Documents/in/Translocation"},{"id":267082,"name":"Wolves","url":"https://www.academia.edu/Documents/in/Wolves"},{"id":325268,"name":"Habitat Quality","url":"https://www.academia.edu/Documents/in/Habitat_Quality"},{"id":424295,"name":"Survival Rate","url":"https://www.academia.edu/Documents/in/Survival_Rate"},{"id":442734,"name":"First Year","url":"https://www.academia.edu/Documents/in/First_Year"},{"id":1692931,"name":"Mortality Risk","url":"https://www.academia.edu/Documents/in/Mortality_Risk"},{"id":1922225,"name":"Program Generation","url":"https://www.academia.edu/Documents/in/Program_Generation"},{"id":2884295,"name":"Competing risks","url":"https://www.academia.edu/Documents/in/Competing_risks"},{"id":4062675,"name":"Ecological trap","url":"https://www.academia.edu/Documents/in/Ecological_trap"}],"urls":[{"id":20967033,"url":"https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.2193%2F2006-141"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="80385979"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/80385979/Removing_GPS_collar_bias_in_habitat_selection_studies"><img alt="Research paper thumbnail of Removing GPS collar bias in habitat selection studies" class="work-thumbnail" src="https://attachments.academia-assets.com/86785053/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/80385979/Removing_GPS_collar_bias_in_habitat_selection_studies">Removing GPS collar bias in habitat selection studies</a></div><div class="wp-workCard_item"><span>Journal of Applied Ecology</span><span>, 2004</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="5b904d0b7de423fea2367d79c7e1b525" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":86785053,"asset_id":80385979,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/86785053/download_file?st=MTczMjc0MTAwMyw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="80385979"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="80385979"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 80385979; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=80385979]").text(description); $(".js-view-count[data-work-id=80385979]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 80385979; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='80385979']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 80385979, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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Compared to traditional radio-collars, global positioning system (GPS) collars provide finer spatial resolution and collect locations across a broader range of spatial and temporal conditions. However, data from GPS collars are biased because vegetation and terrain interfere with the satellite signals necessary to acquire a location. Analyses of habitat selection generally proceed without correcting for this known sampling bias. We documented the effects of bias in resource selection functions (RSF) and compared the effectiveness of two bias-correction techniques. 2. The effects of environmental conditions on the probability of a GPS collar collecting a location were modelled for three brands of collar using data collected in 24-h trials at 194 test locations. The best-supported model was used to create GPS-biased data from unbiased animal locations. These data were used to assess the effects of bias given data losses in the range of 10-40% at both 1-and 6-h sampling intensities. We compared the sign, value and significance of coefficients derived using biased and unbiased data. 3. With 6-h locations we observed type II error rates of 30-40% given as little as a 10% data loss. Biased data also produced coefficients that were significantly more negative than unbiased estimates. Increasing the sampling intensity from 6-to 1-h locations eliminated type II errors but increased the magnitude of coefficient bias. No type I errors or changes in sign were observed. 4. We applied sample weighting and iterative simulation given a 30% data loss. For a biased vegetation type, simulation reduced more type II errors than weighting, most probably because the original sample size was re-established. However, selection for areas near trails, which was influenced by a biased vegetation type, showed fewer type II errors after weighting existing animal locations than after simulation. Both techniques corrected 100% and ≥ 80% of the biased coefficients at the 6-and 1-h sampling intensities, respectively. 5. Synthesis and applications. This study demonstrates that GPS error is predictable and biases the coefficients of resource selection models dependant upon the GPS sampling intensity and the level of data loss. We provide effective alternatives for correcting bias and discuss applying corrections under different sampling designs.","publication_date":{"day":null,"month":null,"year":2004,"errors":{}},"publication_name":"Journal of Applied 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Frair","url":"https://independent.academia.edu/JacquelineFrair"},"attachments":[{"id":86785053,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/86785053/thumbnails/1.jpg","file_name":"FrairEtAl2004_JPE.pdf","download_url":"https://www.academia.edu/attachments/86785053/download_file?st=MTczMjc0MTAwMyw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Removing_GPS_collar_bias_in_habitat_sele.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/86785053/FrairEtAl2004_JPE-libre.pdf?1654017723=\u0026response-content-disposition=attachment%3B+filename%3DRemoving_GPS_collar_bias_in_habitat_sele.pdf\u0026Expires=1732744603\u0026Signature=E552bfzxRb2xaVrguT7NqLtajsK2MnLmQ6lDoflo5QfcsgAA9m5LelHN6QVXv66ZJ3kj8-ivBvWOxefKyRCBv0IlrJjlVLpJiQzgCTIlKPgkCvpD1Q5O5sTVRKCXQjxYalBPnLrjyacxk9rgAw0HPHNFyCgIXl~~hS~X1Mk-CgbH-7sCK9O-A-hEmna-EhCoVlGJCK~wpHSbnv0PorcAhSh2UU1qDEoqDoYu3WVLg0snZXncOmwLkjSjBonVKmgmhQD4tLGJbbiXqFf5MdI7cV-mTTWuFBSpS2NS18T0Yc5HG6alhW~5IL59UVvhC-Kh9BAyL38W1GqlrPloSjDK7Q__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":300,"name":"Mathematics","url":"https://www.academia.edu/Documents/in/Mathematics"},{"id":9846,"name":"Ecology","url":"https://www.academia.edu/Documents/in/Ecology"},{"id":22456,"name":"Applied 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$a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> </div><div class="profile--tab_content_container js-tab-pane tab-pane" data-section-id="3310711" id="papers"><div class="js-work-strip profile--work_container" data-work-id="80386012"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/80386012/Habitat_use_as_an_indicator_of_adaptive_capacity_to_climate_change"><img alt="Research paper thumbnail of Habitat use as an indicator of adaptive capacity to climate change" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/80386012/Habitat_use_as_an_indicator_of_adaptive_capacity_to_climate_change">Habitat use as an indicator of adaptive capacity to climate change</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Aim: Populations of cold-adapted species at the trailing edges of geographic ranges are particula...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Aim: Populations of cold-adapted species at the trailing edges of geographic ranges are particularly vulnerable to the negative effects of climate change from the combination of exposure to warm temperatures and high sensitivity to heat. Many of these species are predicted to decline under future climate scenarios, but they could persist if they can adapt to warming climates either physiologically or behaviorally. We aim to understand local variation in contemporary habitat use and use this information to identify signs of adaptive capacity. We focus on moose (Alces alces), a charismatic species of conservation and public interest. Location: The northeastern United States, along the trailing edge of the moose geographic range in North America. Methods: We compiled data on occurrences and habitat use of moose from remote cameras and GPS collars across the northeastern United States. We use these data to build habitat suitability models at local and regional spatial scales, and then t...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="80386012"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="80386012"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 80386012; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=80386012]").text(description); $(".js-view-count[data-work-id=80386012]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 80386012; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='80386012']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 80386012, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=80386012]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":80386012,"title":"Habitat use as an indicator of adaptive capacity to climate change","translated_title":"","metadata":{"abstract":"Aim: Populations of cold-adapted species at the trailing edges of geographic ranges are particularly vulnerable to the negative effects of climate change from the combination of exposure to warm temperatures and high sensitivity to heat. Many of these species are predicted to decline under future climate scenarios, but they could persist if they can adapt to warming climates either physiologically or behaviorally. We aim to understand local variation in contemporary habitat use and use this information to identify signs of adaptive capacity. We focus on moose (Alces alces), a charismatic species of conservation and public interest. Location: The northeastern United States, along the trailing edge of the moose geographic range in North America. Methods: We compiled data on occurrences and habitat use of moose from remote cameras and GPS collars across the northeastern United States. We use these data to build habitat suitability models at local and regional spatial scales, and then t...","publisher":"Dryad","publication_date":{"day":null,"month":null,"year":2020,"errors":{}}},"translated_abstract":"Aim: Populations of cold-adapted species at the trailing edges of geographic ranges are particularly vulnerable to the negative effects of climate change from the combination of exposure to warm temperatures and high sensitivity to heat. Many of these species are predicted to decline under future climate scenarios, but they could persist if they can adapt to warming climates either physiologically or behaviorally. We aim to understand local variation in contemporary habitat use and use this information to identify signs of adaptive capacity. We focus on moose (Alces alces), a charismatic species of conservation and public interest. Location: The northeastern United States, along the trailing edge of the moose geographic range in North America. Methods: We compiled data on occurrences and habitat use of moose from remote cameras and GPS collars across the northeastern United States. 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(DOCX 20 kb)</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="80386002"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="80386002"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 80386002; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=80386002]").text(description); $(".js-view-count[data-work-id=80386002]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 80386002; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='80386002']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 80386002, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=80386002]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":80386002,"title":"Additional file 2: of Flexible characterization of animal movement pattern using net squared displacement and a latent state model","translated_title":"","metadata":{"abstract":"Scripts for clustering. (DOCX 20 kb)","publisher":"Figshare","publication_date":{"day":15,"month":12,"year":2016,"errors":{}}},"translated_abstract":"Scripts for clustering. (DOCX 20 kb)","internal_url":"https://www.academia.edu/80386002/Additional_file_2_of_Flexible_characterization_of_animal_movement_pattern_using_net_squared_displacement_and_a_latent_state_model","translated_internal_url":"","created_at":"2022-05-31T10:20:26.816-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":33485231,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Additional_file_2_of_Flexible_characterization_of_animal_movement_pattern_using_net_squared_displacement_and_a_latent_state_model","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":33485231,"first_name":"Jacqueline","middle_initials":null,"last_name":"Frair","page_name":"JacquelineFrair","domain_name":"independent","created_at":"2015-07-31T03:42:07.046-07:00","display_name":"Jacqueline Frair","url":"https://independent.academia.edu/JacquelineFrair"},"attachments":[],"research_interests":[],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="80386001"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/80386001/MHC_variation_is_similar_in_little_brown_bats_before_and_after_white_nose_syndrome_outbreak"><img alt="Research paper thumbnail of MHC variation is similar in little brown bats before and after white-nose syndrome outbreak" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/80386001/MHC_variation_is_similar_in_little_brown_bats_before_and_after_white_nose_syndrome_outbreak">MHC variation is similar in little brown bats before and after white-nose syndrome outbreak</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">White-nose syndrome (WNS), caused by the fungal pathogen Pseudogymnoascus destructans (Pd), has d...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">White-nose syndrome (WNS), caused by the fungal pathogen Pseudogymnoascus destructans (Pd), has driven alarming declines in North American hibernating bats, such as little brown bat (Myotis lucifugus). During hibernation, infected little brown bats are able to initiate anti-Pd immune responses, indicating pathogen-mediated selection on the major histocompatibility complex (MHC) genes. However, such immune responses may not be protective as they interrupt torpor, elevate energy costs, and potentially lead to higher mortality rates. To assess whether WNS drives selection on MHC genes, we compared the MHC DRB gene in little brown bats pre- (Wisconsin) and post- (Michigan, New York, Vermont, and Pennsylvania) WNS (detection spanning 2014-2015). We genotyped 131 individuals and found 45 nucleotide alleles (27 amino acid alleles) indicating a maximum of 3 loci (1-5 alleles per individual). We observed high allelic admixture and a lack of genetic differentiation both among sampling sites a...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="80386001"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="80386001"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 80386001; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=80386001]").text(description); $(".js-view-count[data-work-id=80386001]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 80386001; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='80386001']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 80386001, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=80386001]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":80386001,"title":"MHC variation is similar in little brown bats before and after white-nose syndrome outbreak","translated_title":"","metadata":{"abstract":"White-nose syndrome (WNS), caused by the fungal pathogen Pseudogymnoascus destructans (Pd), has driven alarming declines in North American hibernating bats, such as little brown bat (Myotis lucifugus). 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We observed high allelic admixture and a lack of genetic differentiation both among sampling sites a...","publisher":"Dryad","publication_date":{"day":null,"month":null,"year":2020,"errors":{}}},"translated_abstract":"White-nose syndrome (WNS), caused by the fungal pathogen Pseudogymnoascus destructans (Pd), has driven alarming declines in North American hibernating bats, such as little brown bat (Myotis lucifugus). During hibernation, infected little brown bats are able to initiate anti-Pd immune responses, indicating pathogen-mediated selection on the major histocompatibility complex (MHC) genes. However, such immune responses may not be protective as they interrupt torpor, elevate energy costs, and potentially lead to higher mortality rates. To assess whether WNS drives selection on MHC genes, we compared the MHC DRB gene in little brown bats pre- (Wisconsin) and post- (Michigan, New York, Vermont, and Pennsylvania) WNS (detection spanning 2014-2015). We genotyped 131 individuals and found 45 nucleotide alleles (27 amino acid alleles) indicating a maximum of 3 loci (1-5 alleles per individual). We observed high allelic admixture and a lack of genetic differentiation both among sampling sites a...","internal_url":"https://www.academia.edu/80386001/MHC_variation_is_similar_in_little_brown_bats_before_and_after_white_nose_syndrome_outbreak","translated_internal_url":"","created_at":"2022-05-31T10:20:26.701-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":33485231,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"MHC_variation_is_similar_in_little_brown_bats_before_and_after_white_nose_syndrome_outbreak","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":33485231,"first_name":"Jacqueline","middle_initials":null,"last_name":"Frair","page_name":"JacquelineFrair","domain_name":"independent","created_at":"2015-07-31T03:42:07.046-07:00","display_name":"Jacqueline Frair","url":"https://independent.academia.edu/JacquelineFrair"},"attachments":[],"research_interests":[],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="80386000"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/80386000/Managing_Moose_from_Home_Determining_Landscape_Carrying_Capacity_for_Alces_alces_Using_Remote_Sensing"><img alt="Research paper thumbnail of Managing Moose from Home: Determining Landscape Carrying Capacity for Alces alces Using Remote Sensing" class="work-thumbnail" src="https://attachments.academia-assets.com/86785036/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/80386000/Managing_Moose_from_Home_Determining_Landscape_Carrying_Capacity_for_Alces_alces_Using_Remote_Sensing">Managing Moose from Home: Determining Landscape Carrying Capacity for Alces alces Using Remote Sensing</a></div><div class="wp-workCard_item"><span>Forests</span><span>, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In temperate forests of the northeastern U.S., moose (Alces alces) populations are adapted for mi...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In temperate forests of the northeastern U.S., moose (Alces alces) populations are adapted for mixed-age heterogeneous landscapes that provide abundant herbaceous forage in warm months and coniferous forage during winter. Heterogeneity of forest stands is driven by management activities or natural disturbance, resulting in a multi-age forest at a landscape scale. Here, we present a method to estimate landscape carrying capacity of moose by combining remote sensing classification of forest cover class with literature or field-based estimates of class-specific forage abundance. We used Landsat imagery from 1991 to 2013 for the Allegheny National Forest and 2013–2018 for the Adirondack Park, and associated training polygons, to predict based on NDVI and SWI whether a forested landscape fit into one of three cover classes: mature forest, intermediate timber removal, or overstory timber removal. Our three-classes yielded a mean land cover prediction accuracy of 94.3% (Khat = 0.91) and 86...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="84cd0cf106c88af4111327f6a342d93d" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":86785036,"asset_id":80386000,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/86785036/download_file?st=MTczMjc0MTAwMyw4LjIyMi4yMDguMTQ2&st=MTczMjc0MTAwMyw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="80386000"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="80386000"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 80386000; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=80386000]").text(description); $(".js-view-count[data-work-id=80386000]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 80386000; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='80386000']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 80386000, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "84cd0cf106c88af4111327f6a342d93d" } } $('.js-work-strip[data-work-id=80386000]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":80386000,"title":"Managing Moose from Home: Determining Landscape Carrying Capacity for Alces alces Using Remote Sensing","translated_title":"","metadata":{"abstract":"In temperate forests of the northeastern U.S., moose (Alces alces) populations are adapted for mixed-age heterogeneous landscapes that provide abundant herbaceous forage in warm months and coniferous forage during winter. Heterogeneity of forest stands is driven by management activities or natural disturbance, resulting in a multi-age forest at a landscape scale. Here, we present a method to estimate landscape carrying capacity of moose by combining remote sensing classification of forest cover class with literature or field-based estimates of class-specific forage abundance. We used Landsat imagery from 1991 to 2013 for the Allegheny National Forest and 2013–2018 for the Adirondack Park, and associated training polygons, to predict based on NDVI and SWI whether a forested landscape fit into one of three cover classes: mature forest, intermediate timber removal, or overstory timber removal. Our three-classes yielded a mean land cover prediction accuracy of 94.3% (Khat = 0.91) and 86...","publisher":"MDPI AG","publication_date":{"day":null,"month":null,"year":2022,"errors":{}},"publication_name":"Forests"},"translated_abstract":"In temperate forests of the northeastern U.S., moose (Alces alces) populations are adapted for mixed-age heterogeneous landscapes that provide abundant herbaceous forage in warm months and coniferous forage during winter. Heterogeneity of forest stands is driven by management activities or natural disturbance, resulting in a multi-age forest at a landscape scale. Here, we present a method to estimate landscape carrying capacity of moose by combining remote sensing classification of forest cover class with literature or field-based estimates of class-specific forage abundance. We used Landsat imagery from 1991 to 2013 for the Allegheny National Forest and 2013–2018 for the Adirondack Park, and associated training polygons, to predict based on NDVI and SWI whether a forested landscape fit into one of three cover classes: mature forest, intermediate timber removal, or overstory timber removal. Our three-classes yielded a mean land cover prediction accuracy of 94.3% (Khat = 0.91) and 86...","internal_url":"https://www.academia.edu/80386000/Managing_Moose_from_Home_Determining_Landscape_Carrying_Capacity_for_Alces_alces_Using_Remote_Sensing","translated_internal_url":"","created_at":"2022-05-31T10:20:26.540-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":33485231,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":86785036,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/86785036/thumbnails/1.jpg","file_name":"pdf.pdf","download_url":"https://www.academia.edu/attachments/86785036/download_file?st=MTczMjc0MTAwMyw4LjIyMi4yMDguMTQ2&st=MTczMjc0MTAwMyw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Managing_Moose_from_Home_Determining_Lan.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/86785036/pdf-libre.pdf?1654017723=\u0026response-content-disposition=attachment%3B+filename%3DManaging_Moose_from_Home_Determining_Lan.pdf\u0026Expires=1732744603\u0026Signature=W1W7ZygwLcHZKyRW1dO3CmRuiRscn1Z~CAc8jreZrAqS9MINw2W~~~XSNbR9vi91ZUN-Z3VNv1l2MVME6NPn3ASWXNbUxHze7Lx024UiraaWcpwlO7vZ1LBk9o-wq0E3Qvpw6nSUA4lkfB5pjdiHKOE9u0jBE~RwjQTTvAxPDE0W3vVMax-7RQwKD3Xn~VfDTPaoktGj81I1HPZeE5gosGtQnWZplPUWrZ9klYRLur-r0X~-utek8~q--zdoUdC1~HwRO6UyzH5dJZEIGyD6TCRaUGUoFff7mOOuFMhGfgGKXR9VVxmdacYeLCInre2RIwgrj7ZcFUb-3TRubgv8UA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Managing_Moose_from_Home_Determining_Landscape_Carrying_Capacity_for_Alces_alces_Using_Remote_Sensing","translated_slug":"","page_count":18,"language":"en","content_type":"Work","owner":{"id":33485231,"first_name":"Jacqueline","middle_initials":null,"last_name":"Frair","page_name":"JacquelineFrair","domain_name":"independent","created_at":"2015-07-31T03:42:07.046-07:00","display_name":"Jacqueline Frair","url":"https://independent.academia.edu/JacquelineFrair"},"attachments":[{"id":86785036,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/86785036/thumbnails/1.jpg","file_name":"pdf.pdf","download_url":"https://www.academia.edu/attachments/86785036/download_file?st=MTczMjc0MTAwMyw4LjIyMi4yMDguMTQ2&st=MTczMjc0MTAwMyw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Managing_Moose_from_Home_Determining_Lan.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/86785036/pdf-libre.pdf?1654017723=\u0026response-content-disposition=attachment%3B+filename%3DManaging_Moose_from_Home_Determining_Lan.pdf\u0026Expires=1732744603\u0026Signature=W1W7ZygwLcHZKyRW1dO3CmRuiRscn1Z~CAc8jreZrAqS9MINw2W~~~XSNbR9vi91ZUN-Z3VNv1l2MVME6NPn3ASWXNbUxHze7Lx024UiraaWcpwlO7vZ1LBk9o-wq0E3Qvpw6nSUA4lkfB5pjdiHKOE9u0jBE~RwjQTTvAxPDE0W3vVMax-7RQwKD3Xn~VfDTPaoktGj81I1HPZeE5gosGtQnWZplPUWrZ9klYRLur-r0X~-utek8~q--zdoUdC1~HwRO6UyzH5dJZEIGyD6TCRaUGUoFff7mOOuFMhGfgGKXR9VVxmdacYeLCInre2RIwgrj7ZcFUb-3TRubgv8UA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"},{"id":86785035,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/86785035/thumbnails/1.jpg","file_name":"pdf.pdf","download_url":"https://www.academia.edu/attachments/86785035/download_file","bulk_download_file_name":"Managing_Moose_from_Home_Determining_Lan.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/86785035/pdf-libre.pdf?1654017734=\u0026response-content-disposition=attachment%3B+filename%3DManaging_Moose_from_Home_Determining_Lan.pdf\u0026Expires=1732744603\u0026Signature=cqjttdk06xhbB92Zl0V49yPufxEp7w031~6fHLvuTXGJbSh80MmfscmoBCJ5-aXOUw-hzP5YFxMeuDfUI1VEleB1MHwHgyO6p~wPb4MXsebMGxm~Hm9kSXsXwpxL8FDcCNZBgXOLvACb5p6LWNsLk599Do3gWXgRAHiwGuegeNpLJWMoxEfIBBzTy~XuhRrtoGtTGNEaeNNDhGJ0uRc49okvLi55myReMTsb6wwV~faieEna3QVjDzjGHnMCxOVafad908lQaE2kI68W0gygiLIRAtiwZFhxH58hg2T1TsHOtrL-JSs7VGFPo1E9B47l98I5JnPVpyF270ckapGj5Q__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":117033,"name":"Forests","url":"https://www.academia.edu/Documents/in/Forests"}],"urls":[{"id":20967046,"url":"https://www.mdpi.com/1999-4907/13/2/150/pdf"}]}, dispatcherData: dispatcherData }); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="80385998"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/80385998/Browse_Selection_by_Moose_in_the_Adirondack_Park_New_York"><img alt="Research paper thumbnail of Browse Selection by Moose in the Adirondack Park, New York" class="work-thumbnail" src="https://attachments.academia-assets.com/86785033/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/80385998/Browse_Selection_by_Moose_in_the_Adirondack_Park_New_York">Browse Selection by Moose in the Adirondack Park, New York</a></div><div class="wp-workCard_item"><span>Alces : A Journal Devoted to the Biology and Management of Moose</span><span>, 2020</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Moose ( Alces alces americana ), a large-bodied and cold-adapted forest herbivore, may be vulnera...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Moose ( Alces alces americana ), a large-bodied and cold-adapted forest herbivore, may be vulnerable to environmental change especially along their southern range in the northeastern United States. Better understanding of moose foraging patterns and resource constraints in this region, which moose recolonized over the past several decades, is needed to anticipate factors that may influence the long-term viability of the regional moose population. We quantified browse selection, intensity and nutritional quality, and the impact of other vegetation potentially interfering with browse availability for moose within the Adirondack Park, New York. We backtracked GPS-collared female moose (n = 23) to assess the seasonal composition of selected browse from 2016 to 2017, compared browse selection to plant nutritional quality, and modeled local browsing intensity. Moose demonstrated a generalist feeding strategy in summer, but in winter selected browse species largely in order of digestible d...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="fed9403914ad81a5805c0aadf6b25ab6" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":86785033,"asset_id":80385998,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/86785033/download_file?st=MTczMjc0MTAwMyw4LjIyMi4yMDguMTQ2&st=MTczMjc0MTAwMyw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="80385998"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="80385998"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 80385998; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=80385998]").text(description); $(".js-view-count[data-work-id=80385998]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 80385998; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='80385998']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 80385998, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "fed9403914ad81a5805c0aadf6b25ab6" } } $('.js-work-strip[data-work-id=80385998]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":80385998,"title":"Browse Selection by Moose in the Adirondack Park, New York","translated_title":"","metadata":{"abstract":"Moose ( Alces alces americana ), a large-bodied and cold-adapted forest herbivore, may be vulnerable to environmental change especially along their southern range in the northeastern United States. Better understanding of moose foraging patterns and resource constraints in this region, which moose recolonized over the past several decades, is needed to anticipate factors that may influence the long-term viability of the regional moose population. We quantified browse selection, intensity and nutritional quality, and the impact of other vegetation potentially interfering with browse availability for moose within the Adirondack Park, New York. We backtracked GPS-collared female moose (n = 23) to assess the seasonal composition of selected browse from 2016 to 2017, compared browse selection to plant nutritional quality, and modeled local browsing intensity. Moose demonstrated a generalist feeding strategy in summer, but in winter selected browse species largely in order of digestible d...","publication_date":{"day":null,"month":null,"year":2020,"errors":{}},"publication_name":"Alces : A Journal Devoted to the Biology and Management of Moose"},"translated_abstract":"Moose ( Alces alces americana ), a large-bodied and cold-adapted forest herbivore, may be vulnerable to environmental change especially along their southern range in the northeastern United States. Better understanding of moose foraging patterns and resource constraints in this region, which moose recolonized over the past several decades, is needed to anticipate factors that may influence the long-term viability of the regional moose population. We quantified browse selection, intensity and nutritional quality, and the impact of other vegetation potentially interfering with browse availability for moose within the Adirondack Park, New York. We backtracked GPS-collared female moose (n = 23) to assess the seasonal composition of selected browse from 2016 to 2017, compared browse selection to plant nutritional quality, and modeled local browsing intensity. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="80385997"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/80385997/Flexible_characterization_of_animal_movement_pattern_using_net_squared_displacement_and_a_latent_state_model"><img alt="Research paper thumbnail of Flexible characterization of animal movement pattern using net squared displacement and a latent state model" class="work-thumbnail" src="https://attachments.academia-assets.com/86785058/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/80385997/Flexible_characterization_of_animal_movement_pattern_using_net_squared_displacement_and_a_latent_state_model">Flexible characterization of animal movement pattern using net squared displacement and a latent state model</a></div><div class="wp-workCard_item"><span>Movement ecology</span><span>, 2016</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Characterizing the movement patterns of animals is an important step in understanding their ecolo...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Characterizing the movement patterns of animals is an important step in understanding their ecology. Various methods have been developed for classifying animal movement at both coarse (e.g., migratory vs. sedentary behavior) and fine (e.g., resting vs. foraging) scales. A popular approach for classifying movements at coarse resolutions involves fitting time series of net-squared displacement (NSD) to models representing different conceptualizations of coarse movement strategies (i.e., migration, nomadism, sedentarism, etc.). However, the performance of this method in classifying actual (as opposed to simulated) animal movements has been mixed. Here, we develop a more flexible method that uses the same NSD input, but relies on an underlying discrete latent state model. Using simulated data, we first assess how well patterns in the number of transitions between modes of movement and the duration of time spent in a mode classify movement strategies. We then apply our approach to elucid...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="fc8a045ee28a87eb4813483b4ce0e3ea" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":86785058,"asset_id":80385997,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/86785058/download_file?st=MTczMjc0MTAwMyw4LjIyMi4yMDguMTQ2&st=MTczMjc0MTAwMyw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="80385997"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="80385997"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 80385997; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=80385997]").text(description); $(".js-view-count[data-work-id=80385997]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 80385997; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='80385997']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 80385997, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "fc8a045ee28a87eb4813483b4ce0e3ea" } } $('.js-work-strip[data-work-id=80385997]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":80385997,"title":"Flexible characterization of animal movement pattern using net squared displacement and a latent state model","translated_title":"","metadata":{"abstract":"Characterizing the movement patterns of animals is an important step in understanding their ecology. Various methods have been developed for classifying animal movement at both coarse (e.g., migratory vs. sedentary behavior) and fine (e.g., resting vs. foraging) scales. A popular approach for classifying movements at coarse resolutions involves fitting time series of net-squared displacement (NSD) to models representing different conceptualizations of coarse movement strategies (i.e., migration, nomadism, sedentarism, etc.). However, the performance of this method in classifying actual (as opposed to simulated) animal movements has been mixed. Here, we develop a more flexible method that uses the same NSD input, but relies on an underlying discrete latent state model. Using simulated data, we first assess how well patterns in the number of transitions between modes of movement and the duration of time spent in a mode classify movement strategies. We then apply our approach to elucid...","publication_date":{"day":null,"month":null,"year":2016,"errors":{}},"publication_name":"Movement ecology"},"translated_abstract":"Characterizing the movement patterns of animals is an important step in understanding their ecology. Various methods have been developed for classifying animal movement at both coarse (e.g., migratory vs. sedentary behavior) and fine (e.g., resting vs. foraging) scales. A popular approach for classifying movements at coarse resolutions involves fitting time series of net-squared displacement (NSD) to models representing different conceptualizations of coarse movement strategies (i.e., migration, nomadism, sedentarism, etc.). However, the performance of this method in classifying actual (as opposed to simulated) animal movements has been mixed. Here, we develop a more flexible method that uses the same NSD input, but relies on an underlying discrete latent state model. Using simulated data, we first assess how well patterns in the number of transitions between modes of movement and the duration of time spent in a mode classify movement strategies. We then apply our approach to elucid...","internal_url":"https://www.academia.edu/80385997/Flexible_characterization_of_animal_movement_pattern_using_net_squared_displacement_and_a_latent_state_model","translated_internal_url":"","created_at":"2022-05-31T10:20:26.112-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":33485231,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":86785058,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/86785058/thumbnails/1.jpg","file_name":"WRRO_100555.pdf","download_url":"https://www.academia.edu/attachments/86785058/download_file?st=MTczMjc0MTAwMyw4LjIyMi4yMDguMTQ2&st=MTczMjc0MTAwMyw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Flexible_characterization_of_animal_move.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/86785058/WRRO_100555-libre.pdf?1654017722=\u0026response-content-disposition=attachment%3B+filename%3DFlexible_characterization_of_animal_move.pdf\u0026Expires=1732744603\u0026Signature=SMirUVsNI6-MTuUfObSpbiuQtQpOfftFdMQ1Mz2V4nV5rC2Vap7WeFcL2OEhmL5HwJ-rupNw9q7N~7IfSjDKPhKhj3iCakpJDmX7A5o-gh39gNu4U-HixlVfZEin4-ek~tzKeo9iNldRL~MoxgrV5m1-CdfgHmY~BGFxsNtB2bC7Ou4YQaQSo352PtrEm3hdMdVWnX2z7aH2Eyp66pKV1C9Fi2gPAx54sROu~xfeG5olKmjiApnXqu8AHsPyiNiCbj6hDTC0Pbh7J-b6R79r8uXCR4iO-t0vB~waMRemUXPASxgKiBtsouhEW4WDcCdSz5NPtjs3F47ot~1k8rNhOA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Flexible_characterization_of_animal_movement_pattern_using_net_squared_displacement_and_a_latent_state_model","translated_slug":"","page_count":12,"language":"en","content_type":"Work","owner":{"id":33485231,"first_name":"Jacqueline","middle_initials":null,"last_name":"Frair","page_name":"JacquelineFrair","domain_name":"independent","created_at":"2015-07-31T03:42:07.046-07:00","display_name":"Jacqueline Frair","url":"https://independent.academia.edu/JacquelineFrair"},"attachments":[{"id":86785058,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/86785058/thumbnails/1.jpg","file_name":"WRRO_100555.pdf","download_url":"https://www.academia.edu/attachments/86785058/download_file?st=MTczMjc0MTAwMyw4LjIyMi4yMDguMTQ2&st=MTczMjc0MTAwMyw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Flexible_characterization_of_animal_move.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/86785058/WRRO_100555-libre.pdf?1654017722=\u0026response-content-disposition=attachment%3B+filename%3DFlexible_characterization_of_animal_move.pdf\u0026Expires=1732744603\u0026Signature=SMirUVsNI6-MTuUfObSpbiuQtQpOfftFdMQ1Mz2V4nV5rC2Vap7WeFcL2OEhmL5HwJ-rupNw9q7N~7IfSjDKPhKhj3iCakpJDmX7A5o-gh39gNu4U-HixlVfZEin4-ek~tzKeo9iNldRL~MoxgrV5m1-CdfgHmY~BGFxsNtB2bC7Ou4YQaQSo352PtrEm3hdMdVWnX2z7aH2Eyp66pKV1C9Fi2gPAx54sROu~xfeG5olKmjiApnXqu8AHsPyiNiCbj6hDTC0Pbh7J-b6R79r8uXCR4iO-t0vB~waMRemUXPASxgKiBtsouhEW4WDcCdSz5NPtjs3F47ot~1k8rNhOA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":2982,"name":"Movement Ecology","url":"https://www.academia.edu/Documents/in/Movement_Ecology"},{"id":14483,"name":"Animal Ecology","url":"https://www.academia.edu/Documents/in/Animal_Ecology"},{"id":26327,"name":"Medicine","url":"https://www.academia.edu/Documents/in/Medicine"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="80385982"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/80385982/Correlation_and_studies_of_habitat_selection_problem_red_herring_or_opportunity"><img alt="Research paper thumbnail of Correlation and studies of habitat selection: problem, red herring or opportunity?" class="work-thumbnail" src="https://attachments.academia-assets.com/86785007/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/80385982/Correlation_and_studies_of_habitat_selection_problem_red_herring_or_opportunity">Correlation and studies of habitat selection: problem, red herring or opportunity?</a></div><div class="wp-workCard_item"><span>Philosophical Transactions of the Royal Society B: Biological Sciences</span><span>, 2010</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">With the advent of new technologies, animal locations are being collected at ever finer spatio-te...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">With the advent of new technologies, animal locations are being collected at ever finer spatio-temporal scales. We review analytical methods for dealing with correlated data in the context of resource selection, including post hoc variance inflation techniques, ‘two-stage’ approaches based on models fit to each individual, generalized estimating equations and hierarchical mixed-effects models. These methods are applicable to a wide range of correlated data problems, but can be difficult to apply and remain especially challenging for use–availability sampling designs because the correlation structure for combinations of used and available points are not likely to follow common parametric forms. We also review emerging approaches to studying habitat selection that use fine-scale temporal data to arrive at biologically based definitions of available habitat, while naturally accounting for autocorrelation by modelling animal movement between telemetry locations. Sophisticated analyses t...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="e220d4ad3b0ecf064e5225f15d21410d" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":86785007,"asset_id":80385982,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/86785007/download_file?st=MTczMjc0MTAwMyw4LjIyMi4yMDguMTQ2&st=MTczMjc0MTAwMyw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="80385982"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="80385982"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 80385982; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=80385982]").text(description); $(".js-view-count[data-work-id=80385982]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 80385982; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='80385982']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 80385982, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "e220d4ad3b0ecf064e5225f15d21410d" } } $('.js-work-strip[data-work-id=80385982]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":80385982,"title":"Correlation and studies of habitat selection: problem, red herring or opportunity?","translated_title":"","metadata":{"abstract":"With the advent of new technologies, animal locations are being collected at ever finer spatio-temporal scales. We review analytical methods for dealing with correlated data in the context of resource selection, including post hoc variance inflation techniques, ‘two-stage’ approaches based on models fit to each individual, generalized estimating equations and hierarchical mixed-effects models. These methods are applicable to a wide range of correlated data problems, but can be difficult to apply and remain especially challenging for use–availability sampling designs because the correlation structure for combinations of used and available points are not likely to follow common parametric forms. We also review emerging approaches to studying habitat selection that use fine-scale temporal data to arrive at biologically based definitions of available habitat, while naturally accounting for autocorrelation by modelling animal movement between telemetry locations. Sophisticated analyses t...","publisher":"The Royal Society","publication_date":{"day":null,"month":null,"year":2010,"errors":{}},"publication_name":"Philosophical Transactions of the Royal Society B: Biological Sciences"},"translated_abstract":"With the advent of new technologies, animal locations are being collected at ever finer spatio-temporal scales. We review analytical methods for dealing with correlated data in the context of resource selection, including post hoc variance inflation techniques, ‘two-stage’ approaches based on models fit to each individual, generalized estimating equations and hierarchical mixed-effects models. These methods are applicable to a wide range of correlated data problems, but can be difficult to apply and remain especially challenging for use–availability sampling designs because the correlation structure for combinations of used and available points are not likely to follow common parametric forms. We also review emerging approaches to studying habitat selection that use fine-scale temporal data to arrive at biologically based definitions of available habitat, while naturally accounting for autocorrelation by modelling animal movement between telemetry locations. 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src="https://attachments.academia-assets.com/86785056/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/80385981/Building_a_mechanistic_understanding_of_predation_with_GPS_based_movement_data">Building a mechanistic understanding of predation with GPS-based movement data</a></div><div class="wp-workCard_item"><span>Philosophical Transactions of the Royal Society B: Biological Sciences</span><span>, 2010</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Quantifying kill rates and sources of variation in kill rates remains an important challenge in l...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Quantifying kill rates and sources of variation in kill rates remains an important challenge in linking predators to their prey. We address current approaches to using global positioning system (GPS)-based movement data for quantifying key predation components of large carnivores. We review approaches to identify kill sites from GPS movement data as a means to estimate kill rates and address advantages of using GPS-based data over past approaches. Despite considerable progress, modelling the probability that a cluster of GPS points is a kill site is no substitute for field visits, but can guide our field efforts. Once kill sites are identified, time spent at a kill site (handling time) and time between kills (killing time) can be determined. We show how statistical models can be used to investigate the influence of factors such as animal characteristics (e.g. age, sex, group size) and landscape features on either handling time or killing efficiency. If we know the prey densities alo...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="aaa739bda0be32d8957d50b3b454ab82" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":86785056,"asset_id":80385981,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/86785056/download_file?st=MTczMjc0MTAwMyw4LjIyMi4yMDguMTQ2&st=MTczMjc0MTAwMyw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="80385981"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="80385981"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 80385981; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=80385981]").text(description); $(".js-view-count[data-work-id=80385981]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 80385981; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='80385981']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 80385981, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "aaa739bda0be32d8957d50b3b454ab82" } } $('.js-work-strip[data-work-id=80385981]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":80385981,"title":"Building a mechanistic understanding of predation with GPS-based movement data","translated_title":"","metadata":{"abstract":"Quantifying kill rates and sources of variation in kill rates remains an important challenge in linking predators to their prey. 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If we know the prey densities alo...","publisher":"The Royal Society","publication_date":{"day":null,"month":null,"year":2010,"errors":{}},"publication_name":"Philosophical Transactions of the Royal Society B: Biological Sciences"},"translated_abstract":"Quantifying kill rates and sources of variation in kill rates remains an important challenge in linking predators to their prey. We address current approaches to using global positioning system (GPS)-based movement data for quantifying key predation components of large carnivores. We review approaches to identify kill sites from GPS movement data as a means to estimate kill rates and address advantages of using GPS-based data over past approaches. Despite considerable progress, modelling the probability that a cluster of GPS points is a kill site is no substitute for field visits, but can guide our field efforts. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="80385980"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/80385980/Know_Thy_Enemy_Experience_Affects_Elk_Translocation_Success_in_Risky_Landscapes"><img alt="Research paper thumbnail of Know Thy Enemy: Experience Affects Elk Translocation Success in Risky Landscapes" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/80385980/Know_Thy_Enemy_Experience_Affects_Elk_Translocation_Success_in_Risky_Landscapes">Know Thy Enemy: Experience Affects Elk Translocation Success in Risky Landscapes</a></div><div class="wp-workCard_item"><span>Journal of Wildlife Management</span><span>, 2007</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">To maximize success, reintroduction programs generally select predator-free release areas having ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">To maximize success, reintroduction programs generally select predator-free release areas having high habitat quality. Past studies provide little insight into recovery efforts where multiple, potentially novel, mortality hazards occur. The ability of translocated animals to cope with novel environments can be affected by both pre- and postrelease experiences with habitat and mortality risks. We experimentally released elk (Cervus elaphus) having</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="80385980"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="80385980"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 80385980; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=80385980]").text(description); $(".js-view-count[data-work-id=80385980]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 80385980; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='80385980']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 80385980, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=80385980]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":80385980,"title":"Know Thy Enemy: Experience Affects Elk Translocation Success in Risky Landscapes","translated_title":"","metadata":{"abstract":"To maximize success, reintroduction programs generally select predator-free release areas having high habitat quality. 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We experimentally released elk (Cervus elaphus) having","internal_url":"https://www.academia.edu/80385980/Know_Thy_Enemy_Experience_Affects_Elk_Translocation_Success_in_Risky_Landscapes","translated_internal_url":"","created_at":"2022-05-31T10:19:57.576-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":33485231,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Know_Thy_Enemy_Experience_Affects_Elk_Translocation_Success_in_Risky_Landscapes","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":33485231,"first_name":"Jacqueline","middle_initials":null,"last_name":"Frair","page_name":"JacquelineFrair","domain_name":"independent","created_at":"2015-07-31T03:42:07.046-07:00","display_name":"Jacqueline Frair","url":"https://independent.academia.edu/JacquelineFrair"},"attachments":[],"research_interests":[{"id":261,"name":"Geography","url":"https://www.academia.edu/Documents/in/Geography"},{"id":47884,"name":"Biological Sciences","url":"https://www.academia.edu/Documents/in/Biological_Sciences"},{"id":58054,"name":"Environmental Sciences","url":"https://www.academia.edu/Documents/in/Environmental_Sciences"},{"id":113522,"name":"Canis lupus","url":"https://www.academia.edu/Documents/in/Canis_lupus"},{"id":133572,"name":"Wildlife Management","url":"https://www.academia.edu/Documents/in/Wildlife_Management"},{"id":169818,"name":"Second Year","url":"https://www.academia.edu/Documents/in/Second_Year"},{"id":194517,"name":"Cervus elaphus","url":"https://www.academia.edu/Documents/in/Cervus_elaphus"},{"id":230247,"name":"Translocation","url":"https://www.academia.edu/Documents/in/Translocation"},{"id":267082,"name":"Wolves","url":"https://www.academia.edu/Documents/in/Wolves"},{"id":325268,"name":"Habitat Quality","url":"https://www.academia.edu/Documents/in/Habitat_Quality"},{"id":424295,"name":"Survival Rate","url":"https://www.academia.edu/Documents/in/Survival_Rate"},{"id":442734,"name":"First Year","url":"https://www.academia.edu/Documents/in/First_Year"},{"id":1692931,"name":"Mortality Risk","url":"https://www.academia.edu/Documents/in/Mortality_Risk"},{"id":1922225,"name":"Program Generation","url":"https://www.academia.edu/Documents/in/Program_Generation"},{"id":2884295,"name":"Competing risks","url":"https://www.academia.edu/Documents/in/Competing_risks"},{"id":4062675,"name":"Ecological trap","url":"https://www.academia.edu/Documents/in/Ecological_trap"}],"urls":[{"id":20967033,"url":"https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.2193%2F2006-141"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="80385979"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/80385979/Removing_GPS_collar_bias_in_habitat_selection_studies"><img alt="Research paper thumbnail of Removing GPS collar bias in habitat selection studies" class="work-thumbnail" src="https://attachments.academia-assets.com/86785053/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/80385979/Removing_GPS_collar_bias_in_habitat_selection_studies">Removing GPS collar bias in habitat selection studies</a></div><div class="wp-workCard_item"><span>Journal of Applied Ecology</span><span>, 2004</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="5b904d0b7de423fea2367d79c7e1b525" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":86785053,"asset_id":80385979,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/86785053/download_file?st=MTczMjc0MTAwMyw4LjIyMi4yMDguMTQ2&st=MTczMjc0MTAwMyw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="80385979"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="80385979"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 80385979; 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Compared to traditional radio-collars, global positioning system (GPS) collars provide finer spatial resolution and collect locations across a broader range of spatial and temporal conditions. However, data from GPS collars are biased because vegetation and terrain interfere with the satellite signals necessary to acquire a location. Analyses of habitat selection generally proceed without correcting for this known sampling bias. We documented the effects of bias in resource selection functions (RSF) and compared the effectiveness of two bias-correction techniques. 2. The effects of environmental conditions on the probability of a GPS collar collecting a location were modelled for three brands of collar using data collected in 24-h trials at 194 test locations. The best-supported model was used to create GPS-biased data from unbiased animal locations. These data were used to assess the effects of bias given data losses in the range of 10-40% at both 1-and 6-h sampling intensities. We compared the sign, value and significance of coefficients derived using biased and unbiased data. 3. With 6-h locations we observed type II error rates of 30-40% given as little as a 10% data loss. Biased data also produced coefficients that were significantly more negative than unbiased estimates. Increasing the sampling intensity from 6-to 1-h locations eliminated type II errors but increased the magnitude of coefficient bias. No type I errors or changes in sign were observed. 4. We applied sample weighting and iterative simulation given a 30% data loss. For a biased vegetation type, simulation reduced more type II errors than weighting, most probably because the original sample size was re-established. However, selection for areas near trails, which was influenced by a biased vegetation type, showed fewer type II errors after weighting existing animal locations than after simulation. Both techniques corrected 100% and ≥ 80% of the biased coefficients at the 6-and 1-h sampling intensities, respectively. 5. Synthesis and applications. This study demonstrates that GPS error is predictable and biases the coefficients of resource selection models dependant upon the GPS sampling intensity and the level of data loss. 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