海草系统正在衰退,主要是由于人为压力和持续的气候变化。实施海草保护和恢复措施需要准确评估合适的栖息地。通常,这种评估是使用单算法栖息地适宜性模型进行的,几乎总是基于低环境分辨率信息和短期物种数据系列。在这里,我们解决了山东省(黄海,中国)通过开发合奏栖息地模型(EHM)来为天鹅湖(SL)的鳗草保护和恢复策略提供信息。为此,我们应用了从10个单算法模型中导出的加权EHM,包括配置文件,回归,分类,和机器学习方法来生成高分辨率的栖息地适宜性图。EHM是根据每个模型的预测性能构建的,通过结合近年来的一系列现有的eelgrass数据集以及海洋学和沉积物数据。该模型与独立的历史数据集进行了交叉验证,并生成了用于保护和恢复的最终栖息地适宜性图。我们的EHM方案在栖息地适用性方面优于所有单一模型,真实统计技能(TSS)和曲线下面积(AUC)表现标准评分为0.95。机器学习方法优于配置文件,回归和分类方法。关于模型解释变量,总的来说,深度(DEP)和海底坡度(SSL)等地形特征是决定鳗草分布的最重要因素。EHM预测,重叠区域几乎占当前鳗草栖息地的90%。使用我们EHM的结果,关于生境适宜性与Z的生物量和密度之间关系的LOESS回归模型。码头的表现优于经典的普通最小二乘回归模型。随着数据可用性的提高,EHM是支持温带泻湖中鳗草保护和恢复区域的有前途的工具。
Seagrass systems are in decline, mainly due to anthropogenic pressures and ongoing climate change. Implementing seagrass protection and restoration measures requires accurate assessment of suitable habitats. Commonly, such assessments have been performed using single-algorithm habitat suitability models, nearly always based on low environmental resolution information and short-term species data series. Here we address
eelgrass (Zoostera marina) meadows\' large-scale decline (>80%) in Shandong province (Yellow Sea, China) by developing an ensemble habitat model (EHM) to inform
eelgrass conservation and restoration strategies in the Swan Lake (SL). For this, we applied a weighted EHM derived from ten single-algorithm models including profile, regression, classification, and machine learning methods to generate a high-resolution habitat suitability map. The EHM was constructed based on the predictive performances of each model, by combining a series of present-absent
eelgrass datasets from recent years coupled with oceanographic and sediment data. The model was cross-validated with independent historical datasets, and a final habitat suitability map for conservation and restoration was generated. Our EHM scheme outperformed all single models in terms of habitat suitability, scoring ∼0.95 for both true statistic skill (TSS) and area under the curve (AUC) performance criteria. Machine learning methods outperformed profile, regression and classification methods. Regarding model explanatory variables, overall, topographic characteristics such as depth (DEP) and seafloor slope (SSL) are the most significant factors determining the distribution of eelgrass. The EHM predicted that the overlapping area was almost 90% of the current
eelgrass habitat. Using results from our EHM, a LOESS regression model for the relationship of the habitat suitability to both the biomass and density of Z. marina outperformed better than the classic Ordinary Least Squares regression model. The EHM is a promising tool for supporting
eelgrass protection and restoration areas in temperate lagoons as data availability improves.