Optimizing seagrass planting arrangements for animal benefits in a multi-habitat restoration seascape

Restoring lost and degraded ecosystems to enhance biodiversity and ecosystem services is a global priority, and animal responses to the restoration of habitats are a critical but undervalued component. Identifying the key drivers of animal colonization in restored habitats provides critical insights for restoration practitioners seeking to maximize ecological outcomes. When integrated into predictive frameworks and spatial decision- support tools, this knowledge becomes especially valuable for strategic planning, particularly in complex multi-habitat restoration projects where spatial configuration remains a crucial yet understudied dimension influencing ecosystem recovery trajectories. We collect and analyze animal data from one of the worlds largest multi- habitat coastal restoration systems in Denmark, comprising restored seagrass (Zostera marina), boulder reefs and mussel reefs. Using fine-scale spatial patterns in population abundances, we develop spatially explicit predictions across the seascape for various seagrass restoration scenarios and produce a series of optimizations, showing that it is practical to configure restoration to optimize biodiversity objectives, including those linked with fished species. Species-specific responses translated to variable outcomes across restoration scenarios and optimizations. While the optimal number and arrangement of restored patches varied depending on the target species or species group (e.g., fisheries species or seagrass specialists), one near-ubiquitous arrangement was patchy seagrass planting. This aligns with current practice, maximizes restoration efficiency, and highlights the importance of not homogenizing seascapes for biodiversity. Our approach provides a practical framework for incorporating animal monitoring data into restoration planning, helping practitioners design and optimize spatial planting configurations to achieve specific ecological objectives. Open Research StatementAll data and code/scripts (R language), including a README file, are freely available at: https://github.com/msievers100/DenmarkSpatial