Estimation of spatial sampling effort based on presence-only data and accessibility

Sampling bias contained in data of biological surveys is very common. Bias is clearly a function of roads, cities, rivers, or other physical features that determines accessibility of collectors, and many data sets of species are presence-only. We set out to estimate spatial sampling bias in a region, based on presence-only data, by explicitly incorporating information on these accessibility factors, and by considering a target group of species that may share a common search pattern. In order to indirectly estimate the number of individuals, we also resort to the concept of species richness. A probabilistic (multinomial) model is proposed, enabling standard likelihood inference procedures to be implemented. Simulation scenarios for exploration of the model and experimentation with the estimation procedure are included. Illustrative examples over a region of Mexico with mammals and butterflies are also reported with insightful results. Our model is able to estimate the sampling bias in a region and enhance the inferences regarding presence-only data.