A greedy nearest-neighbor approach to quantify site revisitation: comparing two sympatric raven species

Abstract

A central challenge in movement ecology is to describe ecologically meaningful residence sites from raw tracking data due to heterogeneous sampling frequency and uncertain site boundaries. Here, we develop a greedy nearest-neighbor clustering approach with local reassignment and polygon-based site construction that generates timestamped sequences of site visits for each tracked individual, and apply it to nine years of GPS data from two sympatric raven species in the Dead Sea region---the Fan-tailed raven (\emph{Corvus rhipidurus}) and the Brown-necked raven (\emph{C. ruficollis}). Using the resulting visitation sequences, we quantify recursion patterns using the non-Markovian individual mobility model (IMM), which captures the balance between novel-site discovery $β$ and preferential return $α$. The inferred dynamics are consistent with IMM predictions and reveal clear interspecific differences: Brown-necked ravens continue to discover new sites at a higher rate (lower $β$), whereas Fan-tailed ravens show a steeper concentration of visits among top-ranked sites. Entropy analyses further separate the species, with Brown-necked ravens exhibiting higher site entropy and higher conditional entropy of site-to-site transitions. Together, these results provide a robust framework for describing interspecific differences in movement strategies that can be used to infer memory from GPS data and suggest distinct space-use strategies in two closely related sympatric species.