1 research outputs found
Identifying potential areas of expansion for the endangered brown bear (Ursus arctos) population in the cantabrian mountains (NW Spain)
Many large carnivore populations are expanding into human-modified landscapes and the
subsequent increase in coexistence between humans and large carnivores may intensify
various types of conflicts. A proactive management approach is critical to successful mitigation
of such conflicts. The Cantabrian Mountains in Northern Spain are home to the last
remaining native brown bear (Ursus arctos) population of the Iberian Peninsula, which is
also amongst the most severely threatened European populations, with an important core
group residing in the province of Asturias. There are indications that this small population is
demographically expanding its range. The identification of the potential areas of brown bear
range expansion is crucial to facilitate proactive conservation and management strategies
towards promoting a further recovery of this small and isolated population. Here, we used a
presence-only based maximum entropy (MaxEnt) approach to model habitat suitability and
identify the areas in the Asturian portion of the Cantabrian Mountains that are likely to be
occupied in the future by this endangered brown bear population following its range expansion.
We used different spatial scales to identify brown bear range suitability according to different
environmental, topographic, climatic and human impact variables. Our models mainly
show that: (1) 4977 km2 are still available as suitable areas for bear range expansion, which
represents nearly half of the territory of Asturias; (2) most of the suitable areas in the western
part of the province are already occupied (77% of identified areas, 2820 km2), 41.4% of
them occurring inside protected areas, which leaves relatively limited good areas for further
expansion in this part of the province, although there might be more suitable areas in surrounding
provinces; and (3) in the eastern sector of the Asturian Cantabrian Mountains,
62% (2155 km2) of the land was classified as suitable, and this part of the province hosts
44.3% of the total area identified as suitable areas for range expansion. Our results further
highlight the importance of increasing: (a) the connectivity between the currently occupied
western part of Asturias and the areas of potential range expansion in the eastern parts of
the province; and (b) the protection of the eastern sector of the Cantabrian Mountains,
where most of the future population expansion may be expected.S1 Fig. Brown bear occurrence data and location of the study area in Europe.
https://doi.org/10.1371/journal.pone.0209972.s001S2 Fig.
Evaluation metrics for 130 candidate models containing different levels of complexity defined by a range of five feature type combinations including linear (L), quadratic (Q), product (P), threshold (T) and hinge (H) features, each evaluated over a range of regularization multipliers ranging from 0 to 10, for (a) the coarse and (b) fine scales of the distribution of the Cantabrian brown bear in Asturias. Evaluation metrics include delta AICc, which is the difference in AICc (Akaikes Information Criterion corrected for small sample sizes, calculated as the sum of the log transformed raw output penalized by the number of model parameters), AUC test, which is the AUC (area Under the receiving operator characteristics Curve) score for the testing data set, AUC diff, which is the difference in AUC scores between the training and testing data sets, and OR min, which is a threshold dependent statistic corresponds to the proportion of testing localities that have MaxEnt output values lower than the value associated with the training locality with the lowest value.
https://doi.org/10.1371/journal.pone.0209972.s002S3 Fig.
Jacknife evaluations of variable contributions to the (a) coarse and (b) fine scale models. The variables with the highest gain when used in isolation are slope for the coarse scale (a) and forest cover foir the fine scale model (b). These variables therefore seem to have provided the most useful information by themselves for each scale. The variables that decreased the gain most when omitted, and thus possessed the greatest amount of information not present in the other variables, were slope for the coarse scale (a) and population density for the fine scale model (b).
https://doi.org/10.1371/journal.pone.0209972.s003S4 Fig. Output of the coarse scale model with a 5 x 5 km resolution.
The map presents a clog-log transformation of the raw MaxEnt output, which can be interpreted as a probability of brown bear range occurrence.
https://doi.org/10.1371/journal.pone.0209972.s004S5 Fig.
Schematic examples of incremental range expansion (a) out of an initial core area as well as (b) a patchy range expansion were no area is occupied two consecutive years, their nestedness values as well as the association matrices used to calculate nestedness.
https://doi.org/10.1371/journal.pone.0209972.s005S6 Fig. Associations between predicted suitability estimated from the coarse scale model each of the included environmental predictors.
https://doi.org/10.1371/journal.pone.0209972.s006S7 Fig. Associations between predicted suitability estimated from the fine scale model each of the included environmental predictors.
https://doi.org/10.1371/journal.pone.0209972.s007S1 Table. Description, source and original format of the 25 environmental variables initially developed for the construction of the models.
Variables marked with * are the ones not correlated and ultimately used in the modelling.
https://doi.org/10.1371/journal.pone.0209972.s008S2 Table. Variable contribution to the construction of the coarse and fine scale models.
https://doi.org/10.1371/journal.pone.0209972.s009S3 Table. Centre coordinates of the 5 x 5 km grids classed as bear home range used as bear occurrence data in the coarse scale model.
https://doi.org/10.1371/journal.pone.0209972.s010S4 Table. Centre coordinates of the 1 x 1 km grids that contained a bear observation used as bear occurrence data in the fine scale model.
https://doi.org/10.1371/journal.pone.0209972.s011The Gobierno del Principado de Asturias (with FEDER co-financing);
the Spanish Ministry of Economy Industry and Competitiveness ((MINECO);
the Agencia Estatal de Investigación (AEI) and the Fondo Europeo de Desarrollo Regional (FEDER, EU) as well as Ramon & Cajal research contracts from the Spanish Ministry of Economy, Industry and Competitiveness.http://www.plosone.org/pm2020Mammal Research InstituteZoology and Entomolog