Data from: An empirical comparison of models for the phenology of bird migration

Bird migration phenology shows strong responses to climate change. Studies of trends and patterns in phenology are typically based on annual summarizing metrics, such as means and quantiles calculated from raw daily count data. However, with irregularly sampled data and large day-to-day variation, such metrics can be biased and noisy, and may be analysed using phenological functions fitted to the data. Here we use count data of migration passage from a Finnish bird observatory to compare different models for the phenological distributions of spring migration (27 species) and autumn migration (57 species). We assess parsimony and goodness-of-fit in a set of models, with phenological functions of different complexity, optionally with covariates accounting for day-to-day variability. The covariates describe migration intensities of related species or relative migration intensities the previous day (autocovariates). We found that parametric models are often preferred over the more flexible generalized additive models with constrained degrees of freedom. Models corresponding to a mixture of two distinct passing populations were frequently preferred over simpler ones, but usually no more complex models are needed. Slightly more complex models were favoured in spring compared to autumn. Related species’ migration activity effectively improves the model by accounting for the large day-to-day variation. Autocovariates were usually not that relevant, implying that autocorrelation is generally not a major concern if phenology is modelled properly. We suggest that parametric models are relatively good for studying single-population migration phenology, or a mix of two groups with distinct phenologies, especially if daily variation in migration intensity can be controlled for. Generalized additive models may be useful when the migrating population composition is unknown. Despite these guidelines, choosing an appropriate model involves case-by-case assessment or the biological relevance and rationale for modelling phenology