Shipwreck Ecology

This dataset compiles ecological and environmental parameters from five shipwrecks around Mauritius to assess their ecological performance as artificial reefs. The data include fish biomass, density, trophic group biomass and density, coral and benthic composition, and associated environmental variables (temperature, light, pH, dissolved oxygen, salinity, nitrate, phosphate, silicate, and chlorophyll-a). Measurements were collected through standardized underwater surveys and laboratory analyses in 2025. The dataset supports quantitative assessment of how environmental and structural factors influence coral and fish assemblage performance on shipwrecks. Results are expressed as site-level means (± standard error) and include derived Optimal Ecological Performance Thresholds (OEPT) based on quartile binning of environmental predictors associated with high fish biomass and coral cover. The dataset can be directly analyzed for relationships between environmental parameters and ecological performance metrics, replicated in R/Python statistical environments, or integrated into meta-analyses of artificial reef performance. Researchers can also use the OEPT thresholds as ecological benchmarks for evaluating or planning future artificial reef deployments.本数据集整合了毛里求斯周边5艘沉船的生态与环境参数，以评估其作为人工鱼礁的生态效能。数据集涵盖鱼类生物量、密度、营养类群生物量与密度、珊瑚及底栖生物组成，以及相关环境变量（温度、光照、pH值、溶解氧、盐度、硝酸盐、磷酸盐、硅酸盐与叶绿素a）。相关测量于2025年通过标准化水下调查与实验室分析完成。本数据集可用于定量评估环境与结构因素如何影响沉船区域的珊瑚与鱼类群落效能。 研究结果以样地平均值（±标准误）呈现，并基于与高鱼类生物量及珊瑚覆盖度相关的环境预测因子的四分位分箱，推导得到最优生态效能阈值（Optimal Ecological Performance Thresholds, OEPT）。 该数据集可直接用于分析环境参数与生态效能指标间的关联关系，可在R/Python统计环境中复现分析，或整合至人工鱼礁效能的元分析研究中。 研究人员还可将该最优生态效能阈值作为生态基准，用于评估或规划未来的人工鱼礁布设工作。