Simulation analysis of the ecological performance of artificial reefs using species distribution models

Introduction Artificial reefs (ARs) have been widely used to provide ecological and fisheries benefits. Quantitative evaluation of how the numbers and spatial layout of ARs affect fish responses can provide critical insights for management. Methods We used survey data from an area in the Bohai Strait (China) that has ARs deployed and developed species distribution models (SDMs) predicting the occurrence probabilities of three commercially important species ( Charybdis japonica , Sebastes schlegelii , and Hexagrammos otakii ) and one undesirable focal species ( Asterias amurensis and Asterina pectinifera combined). Three versions of SDMs were developed: a Best-fit model based on survey data, and two modified versions (Intermediate and Extreme), that incorporated increasing levels of AR-to-AR interactions, reflecting competitive and connectivity effects. A 24×24 grid with 50-m cells was populated with bottom habitat type (mud, gravel, rubble, boulder, or AR) and key environmental variables affecting occurrence probabilities. Using simulations of 10,000 randomly-generated spatial layouts of added ARs (bottom type switched to AR), we compared predicted occurrence probabilities from the Best-fit, Intermediate, and Extreme SDMs when 5, 10, 25, and 50 ARs were added to the existing ARs. Performance was evaluated using the predicted species-specific occurrence probabilities, with occurrence of the undesirable species treated inversely (i.e., lower occurrence probability indicated higher performance). Results Increasing AR numbers increased the percentage of grid cells supporting good habitat, but saturation and interference effects caused similar values for 25-50 ARs for several species, while reducing variation across spatial layouts. The three desirable species showed similar patterns with the representative layouts categorized into good and bad performing: increasing spread of good ARs on the grid with number of ARs deployed, shift of good ARs from upper to lower triangle, and an increasingly bad-performing central area with 50 ARs. The spatial overlap between high-performing cells for desirable species and elevated occurrence of undesirable Sea star illustrated an inherent trade-off that should be considered in management objectives. Discussion We conclude with a discussion of the need for AR layout-specific evaluation, consideration of AR interaction strength, broader applicability to other systems, potential pathways to expand the analysis (e.g., add hydrodynamic models), and caveats and recommendations for further development.