10.5061/DRYAD.RFJ6Q57BC
Bautista, Carlos
0000-0003-3979-797X
Polish Academy of Sciences
Revilla, Eloy
Estación Biológica de Doñana
Berezowska-Cnota, Teresa
Polish Academy of Sciences
Fernández, Néstor
German Center for Integrative Biodiversity Research
Naves, Javier
0000-0003-3773-0288
Estación Biológica de Doñana
Selva, Nuria
Polish Academy of Sciences
Data and codes to replicate the analysis in: The spatial ecology of
conflicts: Unravelling patterns of wildlife damage at multiple scales
Dryad
dataset
2021
National Science Center
https://ror.org/03ha2q922
UMO-2013/08/M/NZ9/00469
National Science Center
https://ror.org/03ha2q922
UMO-2017/25/ N/NZ8/02861
Ministerio de Asuntos Económicos y Transformación Digital
https://ror.org/03sv46s19
CGL2017-83045-R AEI/FEDER EU
2021-09-07T00:00:00Z
2021-09-07T00:00:00Z
en
https://doi.org/10.1098/rspb.2021.1394
https://doi.org/10.5281/zenodo.5415535
51929591 bytes
7
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
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.