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Data and codes to replicate the analysis in: The spatial ecology of conflicts: Unravelling patterns of wildlife damage at multiple scales


Bautista, Carlos et al. (2021), Data and codes to replicate the analysis in: The spatial ecology of conflicts: Unravelling patterns of wildlife damage at multiple scales, Dryad, Dataset,


Human encroachment into natural habitats is typically followed by conflicts derived from wildlife damages to agriculture and livestock. Spatial risk modelling is a useful tool to gain understanding of wildlife damage and mitigate conflicts. Although resource selection is a hierarchical process operating at multiple scales, risk models usually fail to address more than one scale, which can result in the misidentification of the underlying processes. Here, we addressed the multi-scale nature of wildlife damage occurrence by considering ecological and management correlates interacting from household to landscape scales. We studied brown bear (Ursus arctos) damage to apiaries in the North-eastern Carpathians as our model system. Using generalized additive models, we found that brown bear tendency to avoid humans and the habitat preferences of bears and beekeepers determine the risk of bear damage at multiple scales. Damage risk at fine scales increased when the broad landscape context also favoured damages. Furthermore, integrated-scale risk maps resulted in more accurate predictions than single-scale models. Our results suggest that principles of resource selection by animals can be used to understand the occurrence of damages and help mitigate conflicts in a proactive and preventive manner.


These datasets include processed data to run the analyses needed to (1) estimate the risk of bear damage to apiaries in the Eastern Polish Carpathians at different spatial scales; (2) calculate scale-integrated risk maps; (3) assess the relationship of the predicted probabilities of damage between scales. The raw data was compiled from the official databases of the organization responsible for damage compensation in the study area and from different online sources. Along with the data we provide METADATA files with the information about each variable present in each dataset. We also provided the analysis code (R script) used to generate statistics and some of the figures. For references and details about data processing, and analysis we refer to the original publication and its Electronic Supplementary Materials.


Narodowe Centrum Nauki, Award: UMO-2013/08/M/NZ9/00469

Narodowe Centrum Nauki, Award: UMO-2017/25/ N/NZ8/02861

Ministerio de Economía, Industria y Competitividad, Gobierno de España, Award: CGL2017-83045-R AEI/FEDER EU