Feral cats (Felis catus) are one of the world's worst invasive species with continuing expanding populations, particularly in urban areas. Effects of anthropogenic changing land-use, especially urbanisation, can alter distribution and behaviour of feral cats. Additionally, resource availability can influence home range and habitat use. Therefore, we investigated home range and habitat use of feral cats (n = 11) in an urban mosaic with varying degrees of urbanisation and green spaces in Pietermaritzburg, KwaZulu-Natal, South Africa. Using global positioning cellular trackers, individual feral cats were followed for a minimum of six months. Minimum convex polygons (MCP) and kernel density estimates (KDE) were used to determine their home range, core area size, and habitat use. Mean home range (± SE) for feral cats was relatively small (95% MCP 6.2 ± 4.52 ha) with no significant difference between male and female home ranges, nor core areas. There was individual variation in home ranges despite supplemental feeding in the urban mosaic. Generally supplemental resources were the primary driver of feral cat home ranges where these feeding sites were within the core areas of individuals. However, the ecological consequences of feeding feral cats can increase their survival, and reduce their home ranges and movement as found in other studies.

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Resource selection functions (RSFs) 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 RSFs 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 (SSFs) 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 SSFs) 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 RSFs. In this study, we demonstrate that this method weights each observed location in an animal's 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. Ecologists and conservation biologists can use this method to improve the quality of inferences derived from RSFs. 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.

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