Unravelling Scale Effects in Predicting Eelgrass (Zostera marina) Habitat Suitability for Coastal Restoration

Protecting and restoring marine coastal ecosystems is essential to achieving Net Zero targets and counteracting biodiversity loss. Seagrass meadows are particularly important marine nature-based solutions due to their ability to store carbon, support biodiversity, and protect coastlines from extreme weather events. In the UK, however, Zostera marina (eelgrass) has experienced significant decline. While coastal restoration projects have increased considerably in recent years, their success relies on precise, site-specific assessments to be effective. Regional assessments of Z. marina habitat suitability remain challenging due to insufficient high-resolution environmental datasets that capture comprehensive habitat and environmental heterogeneity. For restoration purposes, model transferability allows the identification of areas where the absence of surveys has led to a lack of species presence data, yet they could potentially be suitable for restoration. To address this gap, we used machine-learning and classical statistical algorithms to forecast distribution of Z. marina across the United Kingdom (UK) and Ireland (Ire) and within six case study sites that span diverse coastal topographies and environmental conditions, enabling assessment of model predictions at different scales. Our results show bathymetry and photosynthetically active radiation (PAR)—both depth-related predictors—as the most important factors determining eelgrass occurrences. This has been shown to differ where high-resolution wave energy data is available, which has been shown to be a much more effective predictor. Despite all models performing well within their respective spatial domains, model transferability was limited. The large-scale model represented by the UK and Ireland showed better transferability to smaller local sites than the reverse, likely because it encompassed broader environmental gradients. In contrast, local models, calibrated over narrower environmental ranges, were less generalizable to wider regions. The broad-scale model tended to overpredict habitat suitability in shallow coastal areas, whereas the local-scale models offered more realistic predictions. These findings highlight that while large-scale models are valuable for identifying general patterns, local-scale models are more reliable for guiding site-specific restoration and conservation efforts in the UK and Ireland.