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.author Holloway, Paul
dc.contributor.author Miller, Jennifer A.
dc.date.accessioned 2019-11-22T09:58:11Z
dc.date.available 2019-11-22T09:58:11Z
dc.date.issued 2014-09
dc.identifier.citation Holloway, 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_4 en
dc.identifier.volume 8728 en
dc.identifier.startpage 48 en
dc.identifier.endpage 63 en
dc.identifier.isbn 978-3-319-11592-4
dc.identifier.isbn 978-3-319-11593-1
dc.identifier.issn 0302-9743
dc.identifier.uri http://hdl.handle.net/10468/9176
dc.identifier.doi 10.1007/978-3-319-11593-1_4 en
dc.description.abstract As 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.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher Springer en
dc.relation.ispartof 8th International Conference, GIScience 2014, Vienna, Austria, September 24-26, 2014. Proceedings
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_4 en
dc.subject Step-selection functions en
dc.subject Uncertainty analysis en
dc.subject Animal movement en
dc.subject Conditional logistic regression en
dc.title Uncertainty analysis of step-selection functions: The effect of model parameters on inferences about the relationship between animal movement and the environment en
dc.type Article (peer-reviewed) en
dc.type Conference item en
dc.internal.authorcontactother Paul Holloway, Geography, University College Cork, Cork, Ireland. +353-21-490-3000 Email: paul.holloway@ucc.ie en
dc.internal.availability Full text available en
dc.date.updated 2019-11-21T17:01:00Z
dc.description.version Accepted Version en
dc.internal.rssid 411665868
dc.contributor.funder National Science Foundation en
dc.description.status Peer reviewed en
dc.identifier.journaltitle Lecture Notes In Computer Science en
dc.internal.copyrightchecked No
dc.internal.licenseacceptance Yes en
dc.internal.conferencelocation Vienna, Austria en
dc.internal.IRISemailaddress paul.holloway@ucc.ie en
dc.relation.project info: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

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