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dc.contributor.authorNoonan, Michael J.
dc.contributor.authorFleming, Christen H.
dc.contributor.authorTucker, Marlee A.
dc.contributor.authorKays, Roland
dc.contributor.authorHarrison, Autumn-Lynn
dc.contributor.authorCrofoot, Margaret C.
dc.contributor.authorAbrahms, Briana
dc.contributor.authorAlberts, Susan C.
dc.contributor.authorAli, Abdullahi H.
dc.contributor.authorAltmann, Jeanne
dc.contributor.authorAntunes, Pamela Castro
dc.contributor.authorAttias, Nina
dc.contributor.authorBelant, Jerrold L.
dc.contributor.authorBeyer, Dean E.
dc.contributor.authorBidner, Laura R.
dc.contributor.authorBlaum, Niels
dc.contributor.authorBoone, Randall B.
dc.contributor.authorCaillaud, Damien
dc.contributor.authorPaula, Rogerio Cunha
dc.contributor.authorla Torre, J. Antonio
dc.contributor.authorDekker, Jasja
dc.contributor.authorDePerno, Christopher S.
dc.contributor.authorFarhadinia, Mohammad
dc.contributor.authorFennessy, Julian
dc.contributor.authorFichtel, Claudia
dc.contributor.authorFischer, Christina
dc.contributor.authorFord, Adam
dc.contributor.authorGoheen, Jacob R.
dc.contributor.authorHavmøller, Rasmus W.
dc.contributor.authorHirsch, Ben T.
dc.contributor.authorHurtado, Cindy
dc.contributor.authorIsbell, Lynne A.
dc.contributor.authorJanssen, René
dc.contributor.authorJeltsch, Florian
dc.contributor.authorKaczensky, Petra
dc.contributor.authorKaneko, Yayoi
dc.contributor.authorKappeler, Peter
dc.contributor.authorKatna, Anjan
dc.contributor.authorKauffman, Matthew
dc.contributor.authorKoch, Flavia
dc.contributor.authorKulkarni, Abhijeet
dc.contributor.authorLaPoint, Scott
dc.contributor.authorLeimgruber, Peter
dc.contributor.authorMacdonald, David W.
dc.contributor.authorMarkham, A. Catherine
dc.contributor.authorMcMahon, Laura
dc.contributor.authorMertes, Katherine
dc.contributor.authorMoorman, Christopher E.
dc.contributor.authorMorato, Ronaldo G.
dc.contributor.authorMoßbrucker, Alexander M.
dc.contributor.authorMourão, Guilherme
dc.contributor.authorO'Connor, David
dc.contributor.authorOliveira-Santos, Luiz Gustavo R.
dc.contributor.authorPastorini, Jennifer
dc.contributor.authorPatterson, Bruce D.
dc.contributor.authorRachlow, Janet
dc.contributor.authorRanglack, Dustin H.
dc.contributor.authorReid, Neil
dc.contributor.authorScantlebury, David M.
dc.contributor.authorScott, Dawn M
dc.contributor.authorSelva, Nuria
dc.contributor.authorSergiel, Agnieszka
dc.contributor.authorSonger, Melissa
dc.contributor.authorSongsasen, Nucharin
dc.contributor.authorStabach, Jared A.
dc.contributor.authorStacy-Dawes, Jenna
dc.contributor.authorSwingen, Morgan B.
dc.contributor.authorThompson, Jeffrey J
dc.contributor.authorUllmann, Wiebke
dc.contributor.authorVanak, Abi Tamim
dc.contributor.authorThaker, Maria
dc.contributor.authorWilson, John W.
dc.contributor.authorYamazaki, Koji
dc.contributor.authorYarnell, Richard W.
dc.contributor.authorZieba, Filip
dc.contributor.authorZwijacz-Kozica, Tomasz
dc.contributor.authorFagan, William F.
dc.contributor.authorMueller, Thomas
dc.contributor.authorCalabrese, Justin M.
dc.date.accessioned2020-09-04T21:11:17Z
dc.date.available2020-09-04T21:11:17Z
dc.date.issued2020
dc.identifier.issn0888-8892, 1523-1739
dc.identifier.doi10.1111/cobi.13495
dc.identifier.urihttp://hdl.handle.net/20.500.12634/673
dc.descriptionKnowing how much space (home range) a wildlife species needs is vital for effective area-based conservation. A species home range size is usually calculated utilizing tracking data from collared animals in the wild. A drawback is that there is autocorrelation inherent in tracking data. This research highlights that due to this bias inherent in traditional home range estimates that the home range sizes of small mammals (<22 pounds) were underestimated by an average of 15%, while larger animals (220 pounds) home range sizes were underestimated by 50% on average. This meta-analysis estimated home-range areas with GPS locations from 757 individual animals across 61 globally distributed terrestrial mammalian species with body masses ranging from 0.9 to 8,800 pounds. These findings are especially important because the underestimation bias is worse for large species, and their space requirements in increasingly human-dominated landscapes is larger than currently being conserved to maintain stable, interconnected populations.
dc.description.abstractAccurately quantifying species’ area requirements is a prerequisite for effective area‐based conservation. This typically involves collecting tracking data on species of interest and then conducting home‐range analyses. Problematically, autocorrelation in tracking data can result in space needs being severely underestimated. Based on the previous work, we hypothesized the magnitude of underestimation varies with body mass, a relationship that could have serious conservation implications. To evaluate this hypothesis for terrestrial mammals, we estimated home‐range areas with global positioning system (GPS) locations from 757 individuals across 61 globally distributed mammalian species with body masses ranging from 0.4 to 4000 kg. We then applied block cross‐validation to quantify bias in empirical home‐range estimates. Area requirements of mammals <10 kg were underestimated by a mean approximately15%, and species weighing approximately100 kg were underestimated by approximately50% on average. Thus, we found area estimation was subject to autocorrelation‐induced bias that was worse for large species. Combined with the fact that extinction risk increases as body mass increases, the allometric scaling of bias we observed suggests the most threatened species are also likely to be those with the least accurate home‐range estimates. As a correction, we tested whether data thinning or autocorrelation‐informed home‐range estimation minimized the scaling effect of autocorrelation on area estimates. Data thinning required an approximately93% data loss to achieve statistical independence with 95% confidence and was, therefore, not a viable solution. In contrast, autocorrelation‐informed home‐range estimation resulted in consistently accurate estimates irrespective of mass. When relating body mass to home range size, we detected that correcting for autocorrelation resulted in a scaling exponent significantly >1, meaning the scaling of the relationship changed substantially at the upper end of the mass spectrum.
dc.relation.urlhttps://onlinelibrary.wiley.com/doi/abs/10.1111/cobi.13495
dc.rights© 2020 The Authors. Conservation Biology published by Wiley Periodicals LLC on behalf of Society for Conservation Biology
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectDISTRIBUTION
dc.subjectEXPERIMENTAL METHODS
dc.subjectMAMMALS
dc.subjectPOPULATIONS
dc.subjectTRACKING
dc.titleEffects of body size on estimation of mammalian area requirements
dc.typeArticle
dc.source.journaltitleConservation Biology
dc.source.volume34
dc.source.issue4
dc.source.beginpage1017
dc.source.endpage1028
html.description.abstractAccurately quantifying species’ area requirements is a prerequisite for effective area‐based conservation. This typically involves collecting tracking data on species of interest and then conducting home‐range analyses. Problematically, autocorrelation in tracking data can result in space needs being severely underestimated. Based on the previous work, we hypothesized the magnitude of underestimation varies with body mass, a relationship that could have serious conservation implications. To evaluate this hypothesis for terrestrial mammals, we estimated home‐range areas with global positioning system (GPS) locations from 757 individuals across 61 globally distributed mammalian species with body masses ranging from 0.4 to 4000 kg. We then applied block cross‐validation to quantify bias in empirical home‐range estimates. Area requirements of mammals <10 kg were underestimated by a mean approximately15%, and species weighing approximately100 kg were underestimated by approximately50% on average. Thus, we found area estimation was subject to autocorrelation‐induced bias that was worse for large species. Combined with the fact that extinction risk increases as body mass increases, the allometric scaling of bias we observed suggests the most threatened species are also likely to be those with the least accurate home‐range estimates. As a correction, we tested whether data thinning or autocorrelation‐informed home‐range estimation minimized the scaling effect of autocorrelation on area estimates. Data thinning required an approximately93% data loss to achieve statistical independence with 95% confidence and was, therefore, not a viable solution. In contrast, autocorrelation‐informed home‐range estimation resulted in consistently accurate estimates irrespective of mass. When relating body mass to home range size, we detected that correcting for autocorrelation resulted in a scaling exponent significantly >1, meaning the scaling of the relationship changed substantially at the upper end of the mass spectrum.


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© 2020 The Authors. Conservation Biology published by Wiley Periodicals LLC on behalf of Society for Conservation Biology
Except where otherwise noted, this item's license is described as © 2020 The Authors. Conservation Biology published by Wiley Periodicals LLC on behalf of Society for Conservation Biology