Prediction of Sites with a High Probability of Wild Mammal Roadkill Using a Favourability Function

Roads are one of the main causes of loss of biodiversity, with roadkill one of the main causes of mortality. The aim of this research was to identify sites with a high probability of roadkill of medium and large mammals, and the environmental variables that would explain it. We used the favourability function (F) to build the predictive models. There were 57 explanatory variables, and we collected 685 records of 10 species of medium and large native wild mammals from the ECOBIO Uruguay databases. They were grouped into native forest and grassland species, according to the main habitat. Two models were developed, one with all the variables and one with the anthropogenic variables. For both groups, the model obtained with all the variables was the most significant according to the evaluation indices used. This made it possible to identify the hot spots of roadkill (F > 0.6) for each of the groups. The anthropic variables were the ones that best explained these hot spots. This allowed the identification of sites where the probability of roadkill is high and requires a monitoring plan to implement mitigation measures in the future.

Applying Fuzzy Logic to Comparative Distribution Modelling: A Case Study with Two Sympatric Amphibians

We modelled the distributions of two toads (Bufo bufoandEpidalea calamita) in the Iberian Peninsula using the favourability function, which makes predictions directly comparable for different species and allows fuzzy logic operations to relate different models. The fuzzy intersection between individual models, representing favourability for the presence of both species simultaneously, was compared with another favourability model built on the presences shared by both species. The fuzzy union between individual models, representing favourability for the presence of any of the two species, was compared with another favourability model based on the presences of either or both of them. The fuzzy intersections between favourability for each species and the complementary of favourability for the other (corresponding to the logical operation “A and not B”) were compared with models of exclusive presence of one species versus the exclusive presence of the other. The results of modelling combined species data were highly similar to those of fuzzy logic operations between individual models, proving fuzzy logic and the favourability function valuable for comparative distribution modelling. We highlight several advantages of fuzzy logic over other forms of combining distribution models, including the possibility to combine multiple species models for management and conservation planning.