Species Distribution Modelling For Combined Data Sources

Increasingly, multiple sources of species occurrence data are available for a particular species, collected through different protocols. For single-source models, a variety of methods have been developed: point process models for presence-only data, logistic regression for presence-absence data obtained through single-visit systematic surveys, and occupancy modelling for detection/non-detection data obtained through repeat-visit surveys. In situations for which multiple sources of data are available to model a species, these sources may be combined via a joint likelihood expression. Nonetheless, there are questions about how to interpret the output from such a combined model and how to diagnose potential violations of model assumptions such as the assumption of spatial independence among points.
In this presentation, I will explore questions of interpretation of the output from these combined approaches, as well as propose extensions to current practice through the introduction of a LASSO penalty, source weights to account for differing quality of data, and models which account for spatial dependence among points. This approach will be demonstrated by modelling the distribution of the Eurasian lynx in eastern France.