Abstract During the last fifty years, there has been a dramatic increase in the development of anthropogenic activities, and this is particularly threatening to marine coastal ecosystems. The management of these multiple and simultaneous anthropogenic pressures requires reliable and precise data on their distribution, as well as information (data, modelling) on their potential effects on sensitive ecosystems. Focusing on Posidonia oceanica beds, a threatened habitat-forming seagrass species endemic to the Mediterranean, we developed a statistical approach to study the complex relationship between human multiple activities and ecosystem status. We used Random Forest modelling to explain the degradation status of P. oceanica (defined herein as the shift from seagrass bed to dead matte) as a function of depth and 10 anthropogenic pressures along the French Mediterranean coast (1700 km of coastline including Corsica). Using a 50 × 50 m grid cells dataset, we obtained a particularly accurate model explaining 71.3% of the variance, with a Pearson correlation of 0.84 between predicted and observed values. Human-made coastline, depth, coastal population, urbanization, and agriculture were the best global predictors of P. oceanica's degradation status. Aquaculture was the least important predictor, although its local individual influence was among the highest. Non-linear relationship between predictors and seagrass beds status was detected with tipping points (i.e. thresholds) for all variables except agriculture and industrial effluents. Using these tipping points, we built a map representing the coastal seagrass beds classified into four categories according to an increasing pressure gradient and its risk of phase shift. Our approach provides important information that can be used to help managers preserve this essential and endangered ecosystem.

中文翻译：

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基于多个沿海人为压力的预测模型解释了海洋生态系统的退化状况：对管理和保护的影响

摘要 在过去的五十年里，人类活动的发展急剧增加，这对海洋沿海生态系统的威胁尤其严重。管理这些多重和同时发生的人为压力需要有关其分布的可靠和精确数据，以及有关其对敏感生态系统的潜在影响的信息（数据、模型）。我们以地中海特有的受威胁的栖息地形成海草物种 Posidonia Oceanica 床为重点，开发了一种统计方法来研究人类多种活动与生态系统状态之间的复杂关系。我们使用随机森林模型来解释 P 的退化状态。大洋洲（此处定义为从海草床向死海草床的转变）作为深度和法国地中海沿岸（包括科西嘉岛在内的 1700 公里海岸线）沿线的 10 种人为压力的函数。使用 50 × 50 m 网格单元数据集，我们获得了一个特别准确的模型，解释了 71.3% 的方差，预测值和观察值之间的 Pearson 相关系数为 0.84。人造海岸线、深度、沿海人口、城市化和农业是 P. Oceanica 退化状况的最佳全球预测指标。水产养殖是最不重要的预测因素，尽管它对当地个人的影响是最高的。除了农业和工业废水外，所有变量的临界点（即阈值）都检测到预测因子和海草床状态之间的非线性关系。使用这些临界点，我们构建了一张地图，表示根据增加的压力梯度及其相移风险分为四类的沿海海草床。我们的方法提供了重要信息，可用于帮助管理人员保护这一重要且濒临灭绝的生态系统。