Assessment of bivariate relationships between spatial patterns: Revisiting the global and local L bivariate indices for wildlife management and conservation

• It is crucial to consider meaningful connectivity matrices due to the intrinsic behavior of GL′. • GL′ and LL′ values decreased with more neighbors or with a random pattern of variables/species. • Revisited GL′ allows for the consideration of different spatial dependence for the related variables/species. • Convergence areas (positive LL′) predominate over divergence areas (negative LL′) for the studied species. • Divergence areas are more common in intergroup relationships. In wildlife management and conservation, the spatial relationship between two species ( e.g. abundance patterns) is often studied through overlapping local information or implementing clustering analysis. However, conventional approaches overlook processes operating at larger spatial scales than the observation unit. Analyses that consider not only a location but also its neighbors, such as the Global L bivariate index (GL) and its Local (LL) measure, can be more appropriate for addressing spatial relationships between two species. The current formulation considers a unique connectivity matrix to define neighbors’ structure, assuming both species exhibit the same spatial dependence even when they may differ. We revisited the Global (GL′) and Local (LL′) indices to incorporate specific connectivity matrices to enhance wildlife applicability. We studied the behavior of the revisited indices through a simulation and a case study of four wildlife species with contrasting movement capabilities. The simulation study revealed the indices’ intrinsic characteristics, including smaller GL′ values and smoother LL′ patterns as the number of neighbors increases. The case study suggested imbalanced coexistence and divergence areas for the species studied, providing meaningful scenarios for wildlife management. The application of species-specific connectivity matrices facilitates the consideration of spatial use characteristics, offering a more reliable understanding of ecological relationships, particularly when the observation unit is smaller than the species’ home range. These findings are crucial for determining accurate and ecologically meaningful connectivity matrices. Overall, the revisited indices offer high flexibility and a comprehensive description of the spatial relationships between ecological indicators.