OSCR: a spatial capture–recapture R package for inference about spatial ecological processes. Issue 9 (3rd July 2019)

• Record Type: Journal Article
• Authors: Sutherland, Chris; Royle, J. Andrew; Linden, Daniel W.
• Is Part Of: Ecography. Volume 42:Issue 9(2019)
• Page Start: 1459
• Page End: 1469
• Publication Date: 2019-07-03
• Subjects: capture‐recapture -- density -- landscape ecology -- SCR -- SECR -- spatial ecology; Ecology -- Periodicals; Biodiversity -- Periodicals; 574.5
• DOI: 10.1111/ecog.04551
• Languages: English
• ISSNs: 0906-7590

Abstract

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.