Although many large mammals currently face significant threats that could lead to their extinction, resources for conservation are often scarce, resulting in the need to develop efficient plans to prioritize conservation actions. We combined several methods in spatial ecology to identify the distribution of the endangered Baird's tapir across its range from southern Mexico to northern Colombia. Twenty-eight habitat patches covering 23% of the study area were identified, harboring potentially 62% or more of the total population for this flagship species. Roughly half of the total area is under some form of protection, while most of the remaining habitat (~70%) occurs in indigenous/local communities. The network with maximum connectivity created from these patches contains at least one complete break (in Mexico between Selva El Ocote and Selva Lacandona) even when considering the most generous dispersal scenario. The connectivity analysis also highlighted a probable break at the Panama Canal and high habitat fragmentation in Honduras. In light of these findings, we recommend the following actions to facilitate the conservation of Baird's tapir: 1) protect existing habitat by strengthening enforcement in areas already under protection, 2) work with indigenous territories to preserve and enforce their land rights, and help local communities maintain traditional practices; 3) re-establish connections between habitat patches that will allow for connectivity across the species' distribution; 4) conduct additional noninvasive surveys in patches with little or no species data; and 5) collect more telemetry and genetic data on the species to estimate home range size, dispersal capabilities, and meta-population structure.

Aim: We test a new species distribution modelling (SDM) framework, while comparing results to more common distribution modelling techniques. This framework allows for the combination of presence-only (PO) and presence-absence (PA) data and accounts for imperfect detection and spatial bias in presence data. The new framework tested here is based on a Poisson point process model, which allows for predictions of population size. We compared these estimates to those provided by experts on the species. Species and Location: Presence data on Baird’s tapir (Tapirus bairdii) throughout its range from southern México to northern Colombia were used in this research, primarily from the years 2000 to 2016. Methods: Four SDM frameworks are compared as follows: (1) Maxent, (2) a presence-only (PO) SDM based on a Poisson point process model (PPM), (3) a presence-absence (PA) SDM also based on a PPM and (4) an Integrated framework which combines the previous two models. Model averaging was used to produce a single set of coefficient estimates and predictive maps for each model framework. A hotspot analysis (Gi*) was used to identify habitat cores from the predicted intensity of the Integrated model framework. Results: Important variables to model the distribution of Baird’s tapir included land cover, human pressure and topography. Accounting for spatial bias in the presence data affected which variables were important in the model. Maxent and the Integrated model produced predictive maps with similar patterns and were considered to be more in agreement with expert knowledge compared to the PO and PA models.

Main conclusions: Total abundance as predicted by the model was higher than expert opinion on the species, but local density estimates from our model were similar to available independent assessments. We suggest that these results warrant further validation and testing through collection of independent test data, development of more precise predictor layers and improvements to the model framework.