Publications Repository - Helmholtz-Zentrum Dresden-Rossendorf

Resource selection functions are among the most commonly used statistical tools in both basic and applied animal ecology. They are typically parameterized using animal tracking data, and advances in animal tracking technology have led to increasing levels of autocorrelation between locations in such data sets. Because resource selection functions assume that data are independent and identically distributed, such autocorrelation can cause misleadingly narrow confidence intervals and biased parameter estimates. Data thinning, generalized estimating equations, and step selection functions have been suggested as techniques for mitigating the statistical problems posed by autocorrelation, but these approaches have notable limitations that include statistical inefficiency, unclear or arbitrary targets for adequate levels of statistical independence, constraints in input data, and (in the case of step selection functions) scale-dependent inference. To remedy these problems, we introduce a method for likelihood weighting of animal locations to mitigate the negative consequences of autocorrelation on resource selection functions. This method weights each observed location in an animal's movement track according to its level of autocorrelation, expanding confidence intervals to match an objective target (i.e., the effective sample size for Autocorrelated Gaussian Density Estimation) and accounting for bias that can arise when there are gaps in the movement track. In this study, we describe the mathematical principles underlying our method, demonstrate its practical advantages versus conventional approaches using simulations and empirical data on a water mongoose (\textit{Atilax paludinosus}), a caracal (\textit{Caracal caracal}), and a serval (\textit{Leptailurus serval}), and discuss pathways for further development of our method. We also provide a complete, annotated analytical workflow to help new users apply our method to their own animal tracking data using the \texttt{ctmm R} package.