Uncertainty analysis of step-selection functions: The effect of model parameters on inferences about the relationship between animal movement and the environment

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dc.contributor.authorHolloway, Paul
dc.contributor.authorMiller, Jennifer A.
dc.contributor.funderNational Science Foundationen
dc.date.accessioned2019-11-22T09:58:11Z
dc.date.available2019-11-22T09:58:11Z
dc.date.issued2014-09
dc.date.updated2019-11-21T17:01:00Z
dc.description.abstractAs spatio-temporal movement data is becoming more widely available for analysis in GIS and related areas, new methods to analyze them have been developed. A step-selection function (SSF) is a recently developed method used to quantify the effect of environmental factors on animal movement. This method is gaining traction as an important conservation tool; however there have been no studies that have investigated the uncertainty associated with subjective model decisions. In this research we used two types of animals – oilbirds and hyenas – to examine how systematically altering user decisions of model parameters influences the main outcome of an SSF, the coefficients that quantify the movement-environment relationship. We found that user decisions strongly influence the results of step-selection functions and any subsequent inferences about animal movement and environmental interactions. Differences were found between categories for every variable used in the analysis and the results presented here can help to clarify the sources of uncertainty in SSF model decisions.en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationHolloway, P. and Miller, J. A. (2014) 'Uncertainty Analysis of Step-Selection Functions: The Effect of Model Parameters on Inferences about the Relationship between Animal Movement and the Environment', GioScience 2014, Lecture Notes in Computer Science, LCNS 8728, pp. 48-63. doi: 10.1007/978-3-319-11593-1_4en
dc.identifier.doi10.1007/978-3-319-11593-1_4en
dc.identifier.endpage63en
dc.identifier.isbn978-3-319-11592-4
dc.identifier.isbn978-3-319-11593-1
dc.identifier.issn0302-9743
dc.identifier.journaltitleLecture Notes In Computer Scienceen
dc.identifier.startpage48en
dc.identifier.urihttps://hdl.handle.net/10468/9176
dc.identifier.volume8728en
dc.language.isoenen
dc.publisherSpringeren
dc.relation.ispartof8th International Conference, GIScience 2014, Vienna, Austria, September 24-26, 2014. Proceedings
dc.relation.projectinfo:eu-repo/grantAgreement/NSF/Directorate for Social, Behavioral & Economic Sciences::Division of Social and Economic Sciences/0962198/US/Spatial Autocorrelation and Species Distribution Models: Analyzing the Effects of Spatial Structure, Sampling Strategy, Statistical Methods, and Scale Using Simulated Data/en
dc.rights© Springer International Publishing Switzerland 2014. This is a post-peer-review, pre-copyedit version of an article published in Lecture Notes in Computer Science. The final authenticated version is available online at: http://dx.doi.org/10.1007/978-3-319-11593-1_4en
dc.subjectStep-selection functionsen
dc.subjectUncertainty analysisen
dc.subjectAnimal movementen
dc.subjectConditional logistic regressionen
dc.titleUncertainty analysis of step-selection functions: The effect of model parameters on inferences about the relationship between animal movement and the environmenten
dc.typeArticle (peer-reviewed)en
dc.typeConference itemen
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