Robust analysis of diel activity patterns

Research Highlight: Iannarilli, F., Gerber, B. D., Erb, J., & Fieberg, J. R. (2024). A 'how-to' guide for estimating animal diel activity using hierarchical models. Journal of Animal Ecology, https://doi.org/10.1111/1365-2656.14213. Diel activity patterns are ubiquitous in living organisms and have received considerable research attention with advances in the collection of time-stamped data and the recognition that organisms may respond to global change via behaviour timing. Iannarilli et al. (2024) provide a roadmap for analysing diel activity patterns with hierarchical models, specifically trigonometric generalized linear mixed-effect models and cyclic cubic spline generalized additive models. These methods are improvements over kernel density estimators, which for nearly two decades have been the status quo for analysing activity patterns. Kernel density estimators have several drawbacks; most notably, data are typically aggregated (e.g. across locations) to achieve sufficient sample sizes, and covariates cannot be incorporated to quantify the influence of environmental variables on activity timing. Iannarilli et al. (2024) also provide a comprehensive tutorial which demonstrates how to format data, fit models, and interpret model predictions. We believe that hierarchical models will become indispensable tools for activity-timing research and envision the development of many extensions to the approaches described by Iannarilli et al. (2024).