Niche centrality and human influence predict rangewide variation in population abundance of a widespread mammal: The collared peccary ( Pecari tajacu )

Aim (1) To evaluate whether geographic variation in population abundance of a widespread mammal ( Pecari tajacu ) is related to its location with respect to the centroid of its ecological niche or to the centroid of its geographic range. (2) To assess whether the abundance–niche centrality relationship defines the maximum expected abundance at any location, rather than the realized abundance. (3) To test whether including human impacts improves the abundance–niche centrality relationship, and therefore the prediction of geographic variation in population abundance. Location The Americas. Methods We modelled the ecological niche of the species using occurrence and environmental data and created spatial models of distance to the niche centroid ( DNC ) and to the geographic centroid ( DGC ). We tested the relationships between population abundance and DNC and between abundance and DGC . We evaluated whether the rate of change in the abundance– DNC relationship was steeper near the upper boundary of quantile regressions. We tested whether the human influence index ( HII ) contributed to improve niche‐based predictions of population abundance. Finally, we generated broad‐scale predictions of collared peccary population abundances. Results We found a negative relationship between abundance and DNC and a non‐significant relationship between abundance and DGC . The abundance– DNC relationship was wedge‐shaped, steeper in the upper quantile boundary than in the median. HII also had a negative effect on abundance. The model including DNC and HII was best supported for predicting the median abundance, while DNC alone was the best to predict the upper boundary of population abundances. Main conclusions Population abundances are associated with the structure of the ecological niche, especially the maximum abundance expected in an area. Thus, the DNC approach can be useful in obtaining a spatial approximation of potential abundance patterns at biogeographic extents. To achieve a better prediction of realized abundances, it is critical to consider the human influence.