Potential distribution of seagrass meadows based on MaxEnt model in Chinese coastal waters

Seagrass meadows are generally diverse in China and have the same essential ecosystem services as elsewhere. However, an evaluation of seagrass distribution across China is still lacking, and the magnitude and direction of changes in seagrass meadows remains unclear. Our primary objective was to provide a nationwide seagrass distribution map, and to explore the dynamic changes of seagrass population under global climate change. We use simulation studies within the modelling software MaxEnt with 58961 occurrence records and 27 marine environmental variables, to simulate the potential distribution of seagrasses and calculate the area. 7 environmental variables were deleted before the modelling processes based on a correlation analysis to ensure predicted suitability. The predicted area was 790.09 km2, which is much larger than the known seagrass distribution in China, and would be increased to 923.62 km2 by the year 2100. However, the suitable habitat of almost all seagrass will shift northwest in the future. The sum of individual family will under-predict the national distribution of seagrass, showed a downward trend consistently in the future. Out of all environmental variables, the physical ones (e.g. depth, land distance and sea surface temperature) had the greatest contribution in predicting seagrass distributions, and nutrients (e.g. nitrate, phosphate) ranked among the key influential predictors for habitat suitability in our focal area. As this is a first effort to fill a gap in our understanding of the distribution of seagrass in China, further studies are necessary using both modeling and biological/ecological approaches. Methods This dataset contains input data for the maxent model, including seagrass occurrence data and marine environment data. There are 4 families, Cymodoceaceae, Hydrocharitaceae, Ruppiaceae, and Zosteraceae, with a total of 22 species of seagrasses distributed in Chinese coastal waters. Hence, this study focused on the species belonging to these families. Seagrass occurrence data were extracted from the Global Biodiversity Information Facility (GBIF, 2017) and Ocean Biogeographic Information System (OBIS, 2017). 27 abiotic variables related to the distribution of seagrasses were chosen as modelling parameters from the Global Marine Environment Datasets (GMED), which are the publicly available climatic, biological, and geophysical environmental layers featuring present, past, and future environmental conditions.中国海草床整体物种多样性丰富，且具备与全球其他区域海草床一致的核心生态系统服务功能。然而，目前仍缺乏针对中国全域海草分布的系统评估，海草床变化的幅度与方向尚不明确。本研究的核心目标是绘制中国全域海草分布地图，并探究全球气候变化背景下海草种群的动态变化。本研究依托最大熵模型（MaxEnt），使用58961条物种出现记录与27项海洋环境变量开展模拟研究，以推演海草的潜在分布范围并测算其面积。建模前期，本研究通过相关性分析剔除了7项冗余环境变量，以保障预测结果的生境适生性可靠性。本次模拟得到的海草潜在分布总面积为790.09平方千米，远大于中国当前已知的海草分布范围；至2100年，该潜在面积将增至923.62平方千米。但未来几乎所有海草的适宜生境均将向西北方向迁移。若按单科海草分别统计，则会低估全国海草的总分布范围，且未来该统计方式下的分布面积将持续呈缩减趋势。在所有环境变量中，物理因子（如水深、离岸距离与海表温度）对海草分布预测的贡献度最高；营养盐因子（如硝酸盐、磷酸盐）则是本研究区域内影响生境适生性的关键预测变量之一。鉴于本研究是填补中国海草分布认知空白的首次尝试，后续需结合建模与生物/生态学方法开展进一步研究。 方法 本数据集包含MaxEnt模型的输入数据，涵盖海草物种出现记录与海洋环境数据集。中国近岸海域共分布有丝粉藻科（Cymodoceaceae）、水鳖科（Hydrocharitaceae）、川蔓藻科（Ruppiaceae）和大叶藻科（Zosteraceae）4个科的海草，总计22种。因此本研究聚焦于上述4个科的海草物种。海草物种出现记录提取自全球生物多样性信息设施（Global Biodiversity Information Facility, GBIF, 2017）与海洋生物地理信息系统（Ocean Biogeographic Information System, OBIS, 2017）。本研究从全球海洋环境数据集（Global Marine Environment Datasets, GMED）中选取了27项与海草分布相关的非生物变量作为建模参数；该数据集为公开可用的气候、生物与地球物理环境图层，涵盖当前、历史与未来的环境条件数据。