Is my sdm good enough? insights from a citizen science dataset in a point process modeling framework

Abstract

Citizen science programs, and particularly atlas schemes based on opportunistic biological records, are very important sources of data for species distribution models and conservation. Nevertheless, these data are prone to bias, particularly when they come from less popular or hard to detect/identify species, such as insects. With such biased data, it is important to evaluate the stability of the model predictions. In recent years, point process models (PPMs) have shown their strength as a unifying framework to fit presence-only species distribution models with many advantages in model implementation and interpretation; PPMs are closely connected to methods already in widespread use in ecology such as MaxEnt and to logistic regression and benefit from being more transparent about resource selection and absence handling. Moreover, there is a well-developed set of tools to fit these models and assess various features of the underlying model, including model stability. However, such tools are currently unavailable when point process models are fitted with a lasso penalty, which has been shown to improve predictive performance. Based on the French citizen science program “Stag beetle Quest”, we propose new methods to assess model stability in this context. The ultimate goal was to develop a set of functions to analyze PPM models with lasso penalties fitted with presence-only data. To assess model stability, we randomly sampled different subsets of locations with varying size from the whole dataset and used the proposed tools to compare fitted intensities and model coefficients. All the developed measures are complementary and can be used to identify at what number of point locations the model stabilizes, which will be dependent on the dataset. Our work presents a new toolbox to explore questions around model stability based on the number of locations in the context of point process models with a lasso penalty and confirms once more the use of the point process modeling framework as a flexible and unifying framework to fit presence-only species distribution models.