Applications of step-selection functions in ecology and conservation

Movement Ecology

Mark S Boyce²  Simone Ciuti¹  Henrik Thurfjell² 

[1] Department of Biometry and Environmental System Analysis, University of Freiburg, Freiburg 79106, Germany;Department of Biological Sciences, University of Alberta, Edmonton, Alberta T6G 2E9, Canada

关键词: Individual modelling;    Remote sensing;    Geographic Information System GIS;    Habitat selection;    Broken stick model;    State-space model;    GPS telemetry;    Resource Selection Probability Function RSPF;    Resource Selection Function RSF;    Step Selection Function SSF;   

Others  :  802490
DOI  :  10.1186/2051-3933-2-4

 received in 2013-08-15, accepted in 2014-02-03,  发布年份 2014

【 摘 要 】

Recent progress in positioning technology facilitates the collection of massive amounts of sequential spatial data on animals. This has led to new opportunities and challenges when investigating animal movement behaviour and habitat selection. Tools like Step Selection Functions (SSFs) are relatively new powerful models for studying resource selection by animals moving through the landscape. SSFs compare environmental attributes of observed steps (the linear segment between two consecutive observations of position) with alternative random steps taken from the same starting point. SSFs have been used to study habitat selection, human-wildlife interactions, movement corridors, and dispersal behaviours in animals. SSFs also have the potential to depict resource selection at multiple spatial and temporal scales. There are several aspects of SSFs where consensus has not yet been reached such as how to analyse the data, when to consider habitat covariates along linear paths between observations rather than at their endpoints, how many random steps should be considered to measure availability, and how to account for individual variation. In this review we aim to address all these issues, as well as to highlight weak features of this modelling approach that should be developed by further research. Finally, we suggest that SSFs could be integrated with state-space models to classify behavioural states when estimating SSFs.

【 授权许可】

   
2014 Thurfjell et al.; licensee BioMed Central Ltd.

【 图 表 】

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