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dc.contributor.authorSchick, Robert Schilling
dc.contributor.authorKraus, Scott D.
dc.contributor.authorRolland, Rosalind M.
dc.contributor.authorKnowlton, Amy R.
dc.contributor.authorHamilton, Philip K.
dc.contributor.authorPettis, Heather M.
dc.contributor.authorKenney, Robert D.
dc.contributor.authorClark, James S.
dc.date.accessioned2013-07-23T10:01:01Z
dc.date.available2013-07-23T10:01:01Z
dc.date.issued2013-06
dc.identifier.citationSchick , R S , Kraus , S D , Rolland , R M , Knowlton , A R , Hamilton , P K , Pettis , H M , Kenney , R D & Clark , J S 2013 , ' Using hierarchical bayes to understand movement, health, and survival in the endangered North Atlantic right whale ' PLoS One , vol. 8 , no. 6 , e64166 . DOI: 10.1371/journal.pone.0064166en
dc.identifier.issn1932-6203
dc.identifier.otherPURE: 60298873
dc.identifier.otherPURE UUID: 7ca3a3f4-3fd1-4f36-b633-8f4b6ab28b96
dc.identifier.otherScopus: 84878800079
dc.identifier.urihttp://hdl.handle.net/10023/3860
dc.descriptionThis article was made open access through BIS OA funding.en
dc.description.abstractBody condition is an indicator of health, and it plays a key role in many vital processes for mammalian species. While evidence of individual body condition can be obtained, these observations provide just brief glimpses into the health state of the animal. An analytical framework is needed for understanding how health of animals changes over space and time.Through knowledge of individual health we can better understand the status of populations. This is particularly important in endangered species, where the consequences of disruption of critical biological functions can push groups of animals rapidly toward extinction. Here we built a state-space model that provides estimates of movement, health, and survival. We assimilated 30+ years of photographic evidence of body condition and three additional visual health parameters in individual North Atlantic right whales, together with survey data, to infer the true health status as it changes over space and time. We also included the effect of reproductive status and entanglement status on health. At the population level, we estimated differential movement patterns in males and females. At the individual level, we estimated the likely animal locations each month. We estimated the relationship between observed and latent health status. Observations of body condition, skin condition, cyamid infestation on the blowholes, and rake marks all provided measures of the true underlying health. The resulting time series of individual health highlight both normal variations in health status and how anthropogenic stressors can affect the health and, ultimately, the survival of individuals. This modeling approach provides information for monitoring of health in right whales, as well as a framework for integrating observational data at the level of individuals up through the health status of the population. This framework can be broadly applied to a variety of systems – terrestrial and marine – where sporadic observations of individuals exist.en
dc.format.extent14en
dc.language.isoeng
dc.relation.ispartofPLoS Oneen
dc.rights© 2013 Schick et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.en
dc.subjectBody conditionen
dc.subjectHealth indicatoren
dc.subjectMammalsen
dc.subjectHealth changeen
dc.subjectPopulation statusen
dc.subjectEndangered speciesen
dc.subjectState-space modelen
dc.subjectNorth Atlantic right whalesen
dc.subjectAnthropogenic stressorsen
dc.subjectQH301 Biologyen
dc.subject.lccQH301en
dc.titleUsing hierarchical bayes to understand movement, health, and survival in the endangered North Atlantic right whaleen
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews. School of Mathematics and Statisticsen
dc.contributor.institutionUniversity of St Andrews. Centre for Research into Ecological & Environmental Modellingen
dc.identifier.doihttps://doi.org/10.1371/journal.pone.0064166
dc.description.statusPeer revieweden


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