Yang Xiaolong, Zhang Xiumei, Zhang Peidong, Bidegain Gorka, Dong Jianyu, Hu Chengye, Li Min, Zhang Zhixin, Guo Hao
Fishery College, Zhejiang Ocean University, Zhoushan, 316022, China; State Environmental Protection Key Laboratory of Coastal Ecosystem, National Marine Environmental Monitoring Center, Dalian, 116023, China.
Fishery College, Zhejiang Ocean University, Zhoushan, 316022, China.
J Environ Manage. 2023 Mar 15;330:117108. doi: 10.1016/j.jenvman.2022.117108. Epub 2022 Dec 28.
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.
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适宜栖息地。通常，此类评估是使用单一算法的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。在此，我们通过开发一个集成栖息地模型（EHM）来解决山东省（中国黄海）鳗草（大叶藻）草甸大规模衰退（>80%）的问题，以便为天鹅湖（SL）的鳗草保护和恢复策略提供信息。为此，我们应用了一个从十个单一算法模型（包括剖面、回归、分类和机器学习方法）导出的加权EHM，以生成高分辨率的栖息地适宜性地图。EHM是基于每个模型的预测性能构建的，结合了近年来一系列鳗草存在-缺失数据集以及海洋学和沉积物数据。该模型用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地图。我们的EHM方案在栖息地适宜性方面优于所有单一模型，在真实统计技能（TSS）和曲线下面积（AUC）性能标准方面的得分均约为0.95。机器学习方法优于剖面、回归和分类方法。关于模型解释变量，总体而言，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%。利用我们EHM的结果，一个关于栖息地适宜性与大叶藻生物量和密度关系的局部加权回归（LOESS）模型比经典的普通最小二乘回归模型表现更好。随着数据可用性的提高，EHM是支持温带泻湖鳗草保护和恢复区域的一个有前景的工具。
