Modeling intra-observer variation in species detections reveals diverse patterns of change over time in participatory scientists

Species observation data contain variation due to differences in observers, whether these data were collected through structured protocols by experts or opportunistically by volunteer observers. Analyses of wildlife population data generated by participatory science projects require accounting for observer effects to reduce bias and heterogeneity introduced by observer differences. Species detections may vary not only among observers, but also within individual observers over time due to learning, senescence, or adoption of new tools, for example. Existing models of observer effects in participatory science data assume that species detection rates for each individual observer either do not change or increase uniformly as the observer gains experience. Here, we develop a new index to capture inter- and intra-observer effects in species detection rates in data from the global, decades-long participatory science project eBird. We test the response of this index to simulated within-observer changes in the numbers of species reported on eBird checklists. We then compare its performance in species distribution modeling to a more restrictive method of measuring observer effects in eBird data. The index flexibly captures diverse, nonlinear inter-annual changes in species detection rates of individual observers, while conferring better predictive performance when included in occurrence models for most species. As participatory science projects expand in scope and lifespan, it is increasingly critical to deploy flexible, data-driven approaches to account for complex observer effects in the scientific use of these rich, yet nuanced, datasets.