The time of acquisition of multispectral predictors matters: the role of seasonality in bird species distribution models

Species distribution models (SDMs) analyse the relationships between species occurrences and environmental predictors. Their efficacy largely depends on the selection of ecologically relevant predictors, with remote sensing (RS) data being one of the most commonly used sources. The usability of multispectral predictors is influenced by temporal changes in vegetation and environmental conditions. However, the impact of seasonality is often overlooked, despite its potential to affect model accuracy. This study aims to assess the influence of seasonality in RS predictors on SDM performance for bird species. The study was conducted for an area of the Czech Republic, using presence–absence data from the Breeding Bird Survey (2018–2021) covering 147 survey squares and 104 bird species. We used Sentinel‐2 satellite imagery to derive monthly and full‐season composites of vegetation indices and reflectance bands from March to September (hereafter ‘periods'). Precipitation, terrain, and vegetation structure were also included. SDMs were constructed using Lasso‐regularized logistic regression, and model performance was assessed through area under the ROC curve (AUC) and R². Linear mixed‐effects models were employed to evaluate model performance, temporal prediction stability, and predictor importance stability across all species. Our results show that model performance depends on the period from which the predictors were derived. This dependence varies significantly among species and is partially associated with habitat preferences and prevalence, with forest species exhibiting greater stability. Differences in model performance across periods aligned with shifts in predictor importance, causing different RS predictors to become significant with seasonal changes. In conclusion, seasonal changes in vegetation, as reflected in the temporal variability of RS predictors, significantly affect SDM performance and predictor selection. Although species' ecological characteristics played a role, the effects remained species‐dependent, making it difficult to develop universal recommendations. Nevertheless, accounting for seasonal variations in RS predictors can enhance model accuracy for many species.