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SPECTRUM OF SELECTION: NEW APPROACHES TO DETECTING THE SCALE‐DEPENDENT RESPONSE TO HABITAT
Author(s) -
Mayor S. J.,
Schaefer J. A.,
Schneider D. C.,
Mahoney S. P.
Publication year - 2007
Publication title -
ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.144
H-Index - 294
eISSN - 1939-9170
pISSN - 0012-9658
DOI - 10.1890/06-1672.1
Subject(s) - habitat , ecology , spatial ecology , woodland caribou , selection (genetic algorithm) , variogram , scale (ratio) , snow , environmental science , geography , biology , cartography , statistics , computer science , mathematics , kriging , meteorology , artificial intelligence
Detecting habitat selection depends on the spatial scale of analysis, but multi‐scale studies have been limited by the use of a few, spatially variable, hierarchical levels. We developed spatially explicit approaches to quantify selection along a continuum of scales using spatial (coarse‐graining) and geostatistical (variogram) pattern analyses at multiple levels of habitat use (seasonal range, travel routes, feeding areas, and microsites). We illustrate these continuum‐based approaches by applying them to winter habitat selection by woodland caribou ( Rangifer tarandus caribou ) using two key habitat components, Cladina lichens and snow depth. We quantified selection as the reduction in variance in used relative to available sites, thus avoiding reliance on correlations between organism and habitat, for which interpretation can be impeded by cross‐scale correlations. By consistently selecting favorable habitat features, caribou experienced reduced variance in these features. The degree to which selection was accounted for by the travel route, feeding area, or microsite levels varied across the scale continuum. Caribou selected for Cladina within a 13‐km scale domain and selected shallower snow at all scales. Caribou responded most strongly at the dominant scales of patchiness, implicating habitat heterogeneity as an underlying cause of multi‐scale habitat selection. These novel approaches enable a spatial understanding of resource selection behavior.