Leveraging long‐term data to improve biodiversity monitoring with species distribution models

Correlative species distribution models (SDMs) are essential tools in conservation biology, global change assessment and reserve prioritization, linking species occurrences with environmental conditions. These models often rely on coarse-scale spatial and temporal predictors, overlooking fine-scale environmental conditions experienced by organisms. Additionally, SDMs commonly use short-term occurrence data to make long-term predictions, which can reduce reliability. We hypothesized that long-term and finer temporal resolution data would provide more accurate predictions by capturing population variability under microclimatic conditions. Using data from 37 bird species in the H. J. Andrews Experimental Forest (Oregon, USA), we built SDMs with a 10-year (2010–2019) dataset of breeding season observations at 184 sites. We evaluated four modelling frameworks that differed in temporal extents (short-term [1 year] vs. long-term [10 years]) and resolution (fine vs. coarse) of environmental data. Predictors included hourly microclimate temperatures beneath the forest canopy and LiDAR-derived vegetation variables. We evaluated interannual transferability and compared model performance based on temporal extent, resolution and species traits. Temporally dynamic (long-term) models with higher resolution microclimate data outperformed static and short-term models (AUC and TSS difference ~ 0.06, difference in unreliability index of ~0.04) and were more accurate and spatially consistent, particularly for migratory species. Increased temporal resolution improved performance for small-bodied species, whereas long-lived, larger species performed similarly in short- and long-term models. Synthesis and applications. To our knowledge, this is the first empirical study to demonstrate the benefits of long-term dynamic SDMs with spatially matched predictor variables. If predicting the future of biodiversity under land-use and climate change is the goal, practitioners should consider additional investment in multi-year biodiversity monitoring rather than single-year ‘snapshots’ of species distributions.