Integrating multiple genetic detection methods to estimate population density of social and territorial carnivores

dc.contributor.authorMurphy, Sean M.en
dc.contributor.authorAugustine, Ben C.en
dc.contributor.authorAdams, Jennifer R.en
dc.contributor.authorWaits, Lisette P.en
dc.contributor.authorCox, John J.en
dc.contributor.departmentFish and Wildlife Conservationen
dc.date.accessioned2019-05-03T18:52:42Zen
dc.date.available2019-05-03T18:52:42Zen
dc.date.issued2018-10en
dc.description.abstractSpatial capture-recapture models can produce unbiased estimates of population density, but sparse detection data often plague studies of social and territorial carnivores. Integrating multiple types of detection data can improve estimation of the spatial scale parameter (sigma), activity center locations, and density. Noninvasive genetic sampling is effective for detecting carnivores, but social structure and territoriality could cause differential detectability among population cohorts for different detection methods. Using three observation models, we evaluated the integration of genetic detection data from noninvasive hair and scat sampling of the social and territorial coyote (Canis latrans). Although precision of estimated density was improved, particularly if sharing sigma between detection methods was appropriate, posterior probabilities of sigma and posterior predictive checks supported different sigma for hair and scat observation models. The resulting spatial capture-recapture model described a scenario in which scat-detected individuals lived on and around scat transects, whereas hair-detected individuals had larger sigma and mostly lived off of the detector array, leaving hair but not scat samples. A more supported interpretation is that individual heterogeneity in baseline detection rates (lambda(0)) was inconsistent between detection methods, such that each method disproportionately detected different population cohorts. These findings can be attributed to the sociality and territoriality of canids: Residents may be more likely to strategically mark territories via defecation (scat deposition), and transients may be more likely to exhibit rubbing (hair deposition) to increase mate attraction. Although this suggests that reliance on only one detection method may underestimate population density, integrating multiple sources of genetic detection data may be problematic for social and territorial carnivores. These data are typically sparse, modeling individual heterogeneity in lambda(0) and/or sigma with sparse data is difficult, and positive bias can be introduced in density estimates if individual heterogeneity in detection parameters that is inconsistent between detection methods is not appropriately modeled. Previous suggestions for assessing parameter consistency of sigma between detection methods using Bayesian model selection algorithms could be confounded by individual heterogeneity in lambda(0) in noninvasive detection data. We demonstrate the usefulness of augmenting those approaches with calibrated posterior predictive checks and plots of the posterior density of activity centers for key individuals.en
dc.description.notesThis study was funded by Cooperative Agreement Award #F15AC01292 from the U.S. Fish and Wildlife Service. Supplemental funds were provided by Louisiana Department of Wildlife and Fisheries and the University of Kentucky Department of Forestry and Natural Resources. We thank Emily Carrollo and Andrea Petrullo for assistance with data collection and Meaghan Clark and Michelle Keyes for assistance with laboratory genetic analysis. We are grateful for the support from Robert Gosnell, Leopoldo Miranda, and Billy Leonard. We also thank staff at Southwest Louisiana National Wildlife Refuge Complex, Louisiana Ecological Services Office, and Louisiana Department of Wildlife and Fisheries for providing logistical support. Author contributions: SMM conceived and designed the study, acquired funding, collected data, analyzed capture-recapture data, and led writing; BCA developed MCMC algorithms and analyzed capture-recapture data; JRA and LPW conducted laboratory genetic analysis; and JJC contributed to study design and acquired supplemental funding. All authors participated in discussion of findings, writing and reviewing of the manuscript, and provided final approval for publication. To the best of our knowledge, no conflict of interest, financial, relational, or other, exists.en
dc.description.sponsorshipU.S. Fish and Wildlife Service [F15AC01292]en
dc.description.sponsorshipLouisiana Department of Wildlife and Fisheriesen
dc.description.sponsorshipUniversity of Kentucky Department of Forestry and Natural Resourcesen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1002/ecs2.2479en
dc.identifier.eissn2150-8925en
dc.identifier.issue10en
dc.identifier.othere02479en
dc.identifier.urihttp://hdl.handle.net/10919/89352en
dc.identifier.volume9en
dc.language.isoenen
dc.publisherEcological Society of Americaen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectabundanceen
dc.subjectCanisen
dc.subjectcoyoteen
dc.subjectdata integrationen
dc.subjectdensityen
dc.subjecthair samplingen
dc.subjectindividual heterogeneityen
dc.subjectnoninvasive samplingen
dc.subjectscat samplingen
dc.subjectsocialen
dc.subjectspatial capture-recaptureen
dc.subjectterritorialen
dc.titleIntegrating multiple genetic detection methods to estimate population density of social and territorial carnivoresen
dc.title.serialEcosphereen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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