Beyond Local Footprints: Disentangling Large-Scale Redistribution and Local Abundance Responses to Offshore Wind Farms.

A differentiated understanding of how regional human activities affect the spatial distribution and abundance of animals is of great ecological importance. However, estimating these effects from empirical data is challenging, as human activities influence animals in different ways and on various spatial and temporal scales. Additionally, spatio-temporal animal abundance is often shaped by intrinsic and extrinsic factors, which can confound impact assessments. To separate these influences, we combined regression and mechanistic modelling. First, we used partial differential equations to simulate potential animal redistribution patterns driven by regional human activities. These patterns were then incorporated as predictors into regression-based species distribution models, alongside other anthropogenic and environmental covariates. This allowed us to estimate and predict human-induced changes by jointly accounting for pressure-driven large-scale redistribution and local changes in expected abundance, while controlling for additional environmental influences. We applied this approach to assess the impact of offshore wind farms (OWF) on common murres (