oSCR: a spatial capture–recapture R package for inference about spatial ecological processes

Spatial capture–recapture (SCR) methods have become widely applied in ecology. The immediate adoption of SCR is due to the fact that it resolves some major criticisms of traditional capture–recapture methods related to heterogeneity in detectabililty, and the emergence of new technologies (e.g. came...

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Veröffentlicht in:Ecography (Copenhagen) Jg. 42; H. 9; S. 1459 - 1469
Hauptverfasser: Sutherland, Chris, Royle, J. Andrew, Linden, Daniel W.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Oxford, UK Blackwell Publishing Ltd 01.09.2019
John Wiley & Sons, Inc
Schlagworte:
Accessibility
Animal populations
animals
cameras
Capture-recapture studies
capture‐recapture
computer software
density
Ecological monitoring
Ecology
Genetics
Heterogeneity
Landscape
Landscape ecology
Mathematical models
New technology
Population ecology
Population studies
SCR
SECR
Spatial data
spatial ecology
Statistical analysis
Statistical models
Workflow
ISSN:0906-7590, 1600-0587
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Abstract Spatial capture–recapture (SCR) methods have become widely applied in ecology. The immediate adoption of SCR is due to the fact that it resolves some major criticisms of traditional capture–recapture methods related to heterogeneity in detectabililty, and the emergence of new technologies (e.g. camera traps, non‐invasive genetics) that have vastly improved our ability to collection spatially explicit observation data on individuals. However, the utility of SCR methods reaches far beyond simply convenience and data availability. SCR presents a formal statistical framework that can be used to test explicit hypotheses about core elements of population and landscape ecology, and has profound implications for how we study animal populations. In this software note, we describe the technical basis and analytical workflow of oSCR, an R package for analyzing spatial encounter history data using a multi‐session sex‐structured likelihood. The impetus for developing oSCR was to create an accessible and transparent analysis tool that allows users to conveniently and intuitively formulate statistical models that map directly to fundamental processes of interest in spatial population ecology (e.g. space use, resource selection, density and connectivity). We have placed an emphasis on creating a transparent and accessible code base that is coupled with a logical workflow that we hope stimulates active participation in further technical developments.
AbstractList Spatial capture–recapture (SCR) methods have become widely applied in ecology. The immediate adoption of SCR is due to the fact that it resolves some major criticisms of traditional capture–recapture methods related to heterogeneity in detectabililty, and the emergence of new technologies (e.g. camera traps, non‐invasive genetics) that have vastly improved our ability to collection spatially explicit observation data on individuals. However, the utility of SCR methods reaches far beyond simply convenience and data availability. SCR presents a formal statistical framework that can be used to test explicit hypotheses about core elements of population and landscape ecology, and has profound implications for how we study animal populations. In this software note, we describe the technical basis and analytical workflow of oSCR, an R package for analyzing spatial encounter history data using a multi‐session sex‐structured likelihood. The impetus for developing oSCR was to create an accessible and transparent analysis tool that allows users to conveniently and intuitively formulate statistical models that map directly to fundamental processes of interest in spatial population ecology (e.g. space use, resource selection, density and connectivity). We have placed an emphasis on creating a transparent and accessible code base that is coupled with a logical workflow that we hope stimulates active participation in further technical developments.
Author Royle, J. Andrew
Linden, Daniel W.
Sutherland, Chris
Author_xml – sequence: 1
  givenname: Chris
  surname: Sutherland
  fullname: Sutherland, Chris
  email: csutherland@umass.edu
  organization: Univ. of Massachusetts
– sequence: 2
  givenname: J. Andrew
  surname: Royle
  fullname: Royle, J. Andrew
  organization: US Geological Survey Patuxent Wildlife Research Center
– sequence: 3
  givenname: Daniel W.
  surname: Linden
  fullname: Linden, Daniel W.
  organization: NOAA National Marine Fisheries Service
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Ecography © 2019 Nordic Society Oikos
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  publication-title: Spatial capture–recapture
SSID ssj0012968
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Snippet Spatial capture–recapture (SCR) methods have become widely applied in ecology. The immediate adoption of SCR is due to the fact that it resolves some major...
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StartPage 1459
SubjectTerms Accessibility
Animal populations
animals
cameras
Capture-recapture studies
capture‐recapture
computer software
density
Ecological monitoring
Ecology
Genetics
Heterogeneity
Landscape
Landscape ecology
Mathematical models
New technology
Population ecology
Population studies
SCR
SECR
Spatial data
spatial ecology
Statistical analysis
Statistical models
Workflow
Title oSCR: a spatial capture–recapture R package for inference about spatial ecological processes
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Volume 42
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