He Keqi, Li Wenhong, Zhang Yu, Sun Ge, McNulty Steve G, Flanagan Neal E, Richardson Curtis J
Earth and Climate Sciences, Nicholas School of the Environment, Duke University, Durham, NC 27708, USA.
Earth and Climate Sciences, Nicholas School of the Environment, Duke University, Durham, NC 27708, USA.
Sci Total Environ. 2023 Oct 10;894:164995. doi: 10.1016/j.scitotenv.2023.164995. Epub 2023 Jun 19.
Coastal wetlands provide critical ecosystem services but are experiencing disruptions caused by inundation and saltwater intrusion under intensified climate change, sea-level rise, and anthropogenic activities. Recent studies have shown that these disturbances downgraded coastal wetlands mainly through affecting their hydrological processes. However, research on what is the most critical driver for wetland downgrading and how it affects coastal wetlands is still in its infancy. This study examined drivers of three types of wetland downgrading, including woody wetland loss, emergent herbaceous wetland loss, and woody wetlands converting to emergent herbaceous wetlands. By using random forest classification models for the wetland ecosystems in the Alligator River National Wildlife Refuge, North Carolina, USA, during 1995-2019, we determined the relative importance of different hydrogeomorphic processes and the dominant variables in driving the wetland downgrading. Results showed that random forest classification models were accurate (> 97 % overall accuracy) in classifying wetland downgrading. Multiple hydrogeomorphic variables collectively contributed to the coastal wetland downgrading. However, the dominant control factors varied across different types of wetland downgrading. Woody wetlands were most susceptible to saltwater intrusion and were likely to downgrade if the saltwater table was shallower than 0.2 m below the land surface. In contrast, emergent herbaceous wetlands were most vulnerable to inundation and drought. The favorable groundwater table for emergent herbaceous wetlands was between 0.34 m above the land surface and 0.32 m below the land surface, beyond which the emergent herbaceous wetland tended to disappear. For downgraded woody wetlands, their distance to canals/ditches played a crucial role in determining their fates after downgrading. The machine learning approach employed in this study provided critical knowledge about the thresholds of hydrogeomorphic variables for the downgrading of different types of coastal wetlands. Such information can help guide effective and targeted coastal wetland conservation, management, and restoration measures.
沿海湿地提供关键的生态系统服务,但在气候变化加剧、海平面上升和人为活动的影响下,正遭受洪水和海水入侵导致的破坏。最近的研究表明,这些干扰主要通过影响其水文过程来降低沿海湿地的等级。然而,关于湿地退化的最关键驱动因素是什么以及它如何影响沿海湿地的研究仍处于起步阶段。本研究调查了三种湿地退化类型的驱动因素,包括木本湿地丧失、挺水草本湿地丧失以及木本湿地转变为挺水草本湿地。通过对美国北卡罗来纳州短吻鳄河国家野生动物保护区1995 - 2019年期间的湿地生态系统使用随机森林分类模型,我们确定了不同水文地貌过程的相对重要性以及驱动湿地退化的主导变量。结果表明,随机森林分类模型在湿地退化分类方面准确率较高(总体准确率>97%)。多个水文地貌变量共同导致了沿海湿地的退化。然而,不同类型的湿地退化主导控制因素各不相同。木本湿地最易受海水入侵影响,如果海水水位比地表以下0.2米浅,则可能退化。相比之下,挺水草本湿地最易受洪水和干旱影响。挺水草本湿地适宜的地下水位在地表以上0.34米至地表以下0.32米之间,超出此范围,挺水草本湿地往往会消失。对于退化的木本湿地,其与运河/沟渠的距离在决定退化后的命运方面起着关键作用。本研究采用的机器学习方法提供了关于不同类型沿海湿地退化的水文地貌变量阈值的关键知识。这些信息有助于指导有效且有针对性的沿海湿地保护、管理和恢复措施。