Improving rigour and efficiency of use-availability habitat selection analyses with systematic estimation of availability

Animal habitat selection analyses often rely on comparisons of habitat use and availability to infer selection. Random locations are commonly used to assess availability despite inefficiency and potential uncertainty associated with random sampling. Herein, I propose a systematic approach to estimate habitat availability to reduce sampling error and computing time associated with GIS-based estimation of habitat availability using random locations. I used Euclidean distance analysis (EDA) as a model technique to demonstrate the sensitivity of use-availability analyses to insufficient random sampling and to evaluate the proposed systematic approach. I re-analysed data from a previous study of habitat selection of Florida panthers (Puma concolor coryi) and compared results of analyses in which distance-based habitat availability (i.e. expected distance) was estimated with a range in sample sizes of random locations, and also systematically. My results demonstrate that expected distances and statistical results of EDA based on random sampling can be unreliable with low and arbitrary numbers of random points, vary if increasing numbers of points are used, and approach results obtained systematically at greater numbers of points (i.e. with sufficient sampling). The systematic approach efficiently measures habitat availability by making calculations from all possible locations, at a specified resolution, across the scale of interest. Thus, it eliminates uncertainty due to sampling error and is considerably faster. The systematic approach improves rigour and efficiency of animal habitat selection analyses that rely on comparisons of habitat use and availability and ensures repeatability of results for practical and theoretical applications.