Tucker Andrew J, Annis Gust, Elgin Erick, Chadderton W Lindsay, Hoffman Joel
The Nature Conservancy, 721 Flanner Hall, University of Notre Dame, IN 46556, USA.
The Nature Conservancy, 101 E. Cesar E. Chavez Ave, Lansing, MI 48906, USA.
Manag Biol Invasion. 2022 Feb 4;13(1):45-67. doi: 10.3391/mbi.2022.13.1.03.
At least 65 aquatic plant species have been identified as part of a surveillance list of non-native species that pose a threat to biodiversity and ecosystem services in the Laurentian Great Lakes. Early detection of these potentially invasive aquatic plants (IAP) could minimize impacts of novel incursions and facilitate successful eradication. We developed, implemented, and then adaptively refined a probabilistic boat-based sampling design that aimed to maximize the likelihood of detecting novel IAP incursions in large (400+ hectares) Great Lakes coastal areas. Surveys were conducted from 2017 to 2019 at five Great Lakes locations - St Joseph River (MI), Saginaw River (MI), Milwaukee (WI), Cleveland (OH), and the Detroit River (MI). Aquatic plant communities were characterized across the five sites, with a total of 61 aquatic plant species detected. One-fifth of the species detected in our surveys were non-native to the Great Lakes basin. Sample-based species rarefaction curves, constructed from detection data from all surveys combined at each location, show that the estimated sample effort required for high confidence (> 95%) detection of all aquatic plants at a site, including potentially invasive species, varies (< 100 sample units for Detroit River; > 300 sample units for Milwaukee, roughly equivalent to 6 to 18 days sampling effort, respectively). At least 70% of the estimated species pool was detected at each site during initial 3-day surveys. Leveraging information on detection patterns from initial surveys, including depth and species richness strata, improved survey efficiency and completeness at some sites, with detection of at least 80% of the estimated species pool during subsequent surveys. Based on a forest-based classification and regression method, a combination of just five variables explained 70% or more of the variation in observed richness at all sites (depth, fetch, percent littoral, distance to boat ramps and distance to marinas). We discuss how the model outcomes can be used to inform survey design for other Great Lakes coastal areas. The survey design we describe provides a useful template that could be adaptively improved for early detection of IAP in the Great Lakes.
至少65种水生植物物种已被确定为非本地物种监测清单的一部分，这些物种对劳伦琴五大湖的生物多样性和生态系统服务构成威胁。尽早发现这些潜在的入侵性水生植物（IAP）可将新入侵的影响降至最低，并有助于成功根除。我们开发、实施并随后进行了适应性优化，采用了一种基于船只的概率抽样设计，旨在最大限度地提高在大型（400多公顷）五大湖沿岸地区发现新的IAP入侵的可能性。2017年至2019年在五大湖的五个地点进行了调查——圣约瑟夫河（密歇根州）、萨吉诺河（密歇根州）、密尔沃基（威斯康星州）、克利夫兰（俄亥俄州）和底特律河（密歇根州）。对这五个地点的水生植物群落进行了特征描述，共检测到61种水生植物物种。我们调查中检测到的物种中有五分之一并非五大湖流域原生。根据每个地点所有调查的检测数据构建的基于样本的物种稀疏曲线表明，要以高置信度（>95%）检测一个地点的所有水生植物，包括潜在的入侵物种，所需的估计样本量各不相同（底特律河<100个样本单位；密尔沃基>300个样本单位，分别大致相当于6至18天的采样工作量）。在最初的3天调查中，每个地点至少70%的估计物种库被检测到。利用初始调查中关于检测模式的信息，包括深度和物种丰富度层次，提高了一些地点的调查效率和完整性，在后续调查中检测到了至少80%的估计物种库。基于一种基于森林的分类和回归方法，仅五个变量的组合就解释了所有地点观察到的丰富度变化的70%或更多（深度、风程、沿岸百分比、到船坡道的距离和到码头的距离）。我们讨论了如何将模型结果用于为其他五大湖沿岸地区的调查设计提供信息。我们描述的调查设计提供了一个有用的模板，可针对五大湖IAP的早期检测进行适应性改进。
