Efficient statistical inference methods for assessing changes in species’ populations using citizen science data

Abstract The global decline of biodiversity, driven by habitat degradation and climate breakdown, is a significant concern. Accurate measures of change are crucial to provide reliable evidence of species’ population changes. Meanwhile citizen science data have witnessed a remarkable expansion in both quantity and sources and serve as the foundation for assessing species’ status. The growing data reservoir presents opportunities for novel and improved inference but often comes with computational costs: computational efficiency is paramount, especially as regular analysis updates are necessary. Building upon recent research, we present illustrations of computationally efficient methods for fitting new models, applied to three major citizen science data sets for butterflies. We extend a method for modelling abundance changes of seasonal organisms, firstly to accommodate multiple years of count data efficiently, and secondly for application to counts from a snapshot mass-participation survey. We also present a variational inference approach for fitting occupancy models efficiently to opportunistic citizen science data. The continuous growth of citizen science data offers unprecedented opportunities to enhance our understanding of how species respond to anthropogenic pressures. Efficient techniques in fitting new models are vital for accurately assessing species’ status, supporting policy-making, setting measurable targets, and enabling effective conservation efforts.