ABSTRACT 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 ( Uria aalge ) in the German North Sea during autumn. OWF constructed by 2019 reduced common murre numbers within German waters by 18.3%. If the planned OWF priority and reservation areas outlined in the German Marine Spatial Plan are implemented, the predicted net reduction within German waters would increase to 77.7%. Importantly, these predictions do not account for additional anthropogenic activities or OWF expansion in surrounding waters, which could further affect common murre abundance beyond the scenarios considered here. By comparing predicted animal numbers and distributions under hypothetical scenarios with and without specific human pressures, our method enables the quantification and prediction of human‐induced effects on regional trends and large‐scale redistribution. The framework provides a transparent way to disentangle large‐scale redistribution and cumulative effects from local responses in spatially bounded management areas, supporting impact assessments under ongoing expansion of offshore renewables.
Mercker et al. (Sun,) studied this question.