Combining Geographic Distribution and Trait Information to Infer Predator-Prey Species-Level Interaction Properties

Biotic interactions are a key component of the proper functioning of ecosystems. However, information on biotic interactions is spatially and taxonomically biased and limited to several groups. The most efficient strategy to fill these gaps is to combine spatial information (species ranges) with different sources of information (functional and field data) to infer potential interactions. This approach is possible due to the fact that there is a correspondence between the traits of two trophic levels (e.g., predator and prey sizes are correlated). Therefore, our objective was to evaluate the performance of the joint use of spatial, functional and field data to infer properties of the predator-prey interaction for five neotropical cats. To do this, we used presence-absence matrices to obtain lists of potential prey species per grid-cell for each predator range. These lists were filtered according to different criteria (models), and for each model, an interaction property was estimated and compared with field observations. Our results show that the use of functional information and co-occurrence allows us to generate values similar to those observed in the field. We also observed that there were differences in model performance related to the intrinsic characteristics of the predator (body size) and the interaction property being evaluated.

Automated summary

Biotic interactions are important for ecosystems, but there is limited information available and biases towards certain groups. To fill these gaps, a combination of spatial, functional, and field data can be used to infer interactions. This approach allows for the generation of values similar to those observed in the field.