Mitigating pseudoreplication and bias in resource selection functions with autocorrelation-informed weighting

Abstract

O_LIResource 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. C_LIO_LIData 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. C_LIO_LIIn this study, we demonstrate that this method weights each observed location in an animals movement track according to its level of non-independence, expanding confidence intervals and reducing bias that can arise when there are missing data in the movement track. C_LIO_LIEcologists and conservation biologists can use this method to improve the quality of inferences derived from resource selection functions. We also provide a complete, annotated analytical workflow to help new users apply our method to their own animal tracking data using the ctmm R package. C_LI