How to perform modeling with independent and preferential data jointly?

TL;DR

This paper explores methods for jointly modeling independent and preferential data in ecological species distribution models, proposing a mixture approach and comparing it to traditional models under various sampling conditions.

Contribution

It introduces a mixture modeling framework that combines geostatistical and preferential models to handle mixed sampling schemes in species distribution modeling.

Findings

Preferential and mixture models perform similarly in most scenarios.

Geostatistical models perform worse with high spatial complexity and limited data.

Mixture models effectively integrate different sampling schemes.

Abstract

Continuous space species distribution models (SDMs) have a long-standing history as a valuable tool in ecological statistical analysis. Geostatistical and preferential models are both common models in ecology. Geostatistical models are employed when the process under study is independent of the sampling locations, while preferential models are employed when sampling locations are dependent on the process under study. But, what if we have both types of data collectd over the same process? Can we combine them? If so, how should we combine them? This study investigated the suitability of both geostatistical and preferential models, as well as a mixture model that accounts for the different sampling schemes. Results suggest that in general the preferential and mixture models have satisfactory and close results in most cases, while the geostatistical models presents systematically worse estimates at higher spatial complexity, smaller number of samples and lower proportion of completely random samples.