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Generalized functional responses for species distributions
Matthiopoulos, J.; Hebblewhite, M.; Aarts, G.M; Fieberg, J. (2011). Generalized functional responses for species distributions. Ecology 92(3): 583-589. dx.doi.org/10.1890/10-0751.1
In: Ecology. Ecological Society of America: Brooklyn, NY. ISSN 0012-9658; e-ISSN 1939-9170, meer
Peer reviewed article  
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Trefwoord
    Canis lupis
Author keywords
    Canis lupis; climate change; generalized linear mixed model; habitatpreference; home range; predictive models; simulation study; space-use;spatial ecology; species distributions; utilization distribution; wolf
Auteurs  Top 
  • Matthiopoulos, J.
  • Hebblewhite, M.
  • Aarts, G.M, meer
  • Fieberg, J.
Abstract
    Researchers employing resource selection functions (RSFs) and other related methods aim to detect correlates of space-use and mitigate against detrimental environmental change. However, an empirical model fit to data from one place or time is unlikely to capture species responses under different conditions because organisms respond nonlinearly to changes in habitat availability. This phenomenon, known as a functional response in resource selection, has been debated extensively in the RSF literature but continues to be ignored by practitioners for lack of a practical treatment. We therefore extend the RSF approach to enable it to estimate generalized functional responses (GFRs) from spatial data. GFRs employ data from several sampling instances characterized by diverse profiles of habitat availability. By modeling the regression coefficients of the underlying RSF as functions of availability, GFRs can account for environmental change and thus predict population distributions in new environments. We formulate the approach as a mixed-effects model so that it is estimable by readily available statistical software. We illustrate its application using (1) simulation and (2) wolf home-range telemetry. Our results indicate that GFRs can offer considerable improvements in estimation speed and predictive ability over existing mixed-effects approaches.
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