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Ryan M. Nielson and Hall Sawyer Estimating resource selection with count data Ecology and Evolution 3

Version of Record online: 7 JUN 2013 | DOI: 10.1002/ece3.617

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Advances in global positioning system (GPS) technology allow animal location data to be collected at fine spatio-temporal scales and have increased the size and correlation of data used in RSF analyses. We suggest a contemporary approach to modeling resource selection using such data is to model intensity of use, which can be estimated for one or more animals by relating the relative frequency of locations in a set of sampling units to the habitat characteristics of those units with count-based regression and, in particular, negative binomial (NB) regression.

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