Shay S. Keretz , David T. Zanatta , Todd J. Morris , Ashley K. Elgin , Edward F. Roseman , Daelyn A. Woolnough
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Two types of species distribution models (SDMs), MaxEnt and classification and regression tree analysis (CART), were created using dreissenid and habitat data collected in both the Detroit and St. Clair rivers. The SDMs were then used to predict presence of dreissenids in the St. Clair River. The St. Clair River had more <em>D. r. bugensis</em> (mean density = 486 ± 152 individuals/m<sup>2</sup>) than <em>D. polymorpha</em> (mean density = 3 ± 1 individuals/m<sup>2</sup>). The SDMs created from the Detroit River data reliably predicted presence of dreissenids in the St. Clair River. Depending on the river and species, CART models identified velocity and depth to be important predictor variables, while distance to river inlet/outlet were the most influential variables in the MaxEnt models. 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Two types of species distribution models (SDMs), MaxEnt and classification and regression tree analysis (CART), were created using dreissenid and habitat data collected in both the Detroit and St. Clair rivers. The SDMs were then used to predict presence of dreissenids in the St. Clair River. The St. Clair River had more <em>D. r. bugensis</em> (mean density = 486 ± 152 individuals/m<sup>2</sup>) than <em>D. polymorpha</em> (mean density = 3 ± 1 individuals/m<sup>2</sup>). The SDMs created from the Detroit River data reliably predicted presence of dreissenids in the St. Clair River. Depending on the river and species, CART models identified velocity and depth to be important predictor variables, while distance to river inlet/outlet were the most influential variables in the MaxEnt models. 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引用次数: 0
摘要
劳伦伦五大湖区最受瞩目的入侵物种是 Dreissena polymorpha 和 D. rostriformis bugensis(统称为 Dreissenids)。尽管它们数量庞大,对整个生态系统都有影响,但人们对它们在湖泊之间大型连接水道中的分布却知之甚少。这项研究的目的是估算和记录整个圣克莱尔河中的裸裂叶藻密度及其栖息地特征,比较裸裂叶藻的物种分布情况,并预测五大湖两个连接水域(圣克莱尔河和底特律河)之间的空间分布情况。利用在底特律河和圣克莱尔河收集到的底栖生物和栖息地数据,创建了两种物种分布模型(SDM),即 MaxEnt 模型和分类与回归树分析模型(CART)。然后利用 SDM 预测圣克莱尔河中是否存在沉积物。圣克莱尔河中的 D. r. bugensis(平均密度 = 486 ± 152 个/平方米)多于 D. polymorpha(平均密度 = 3 ± 1 个/平方米)。根据底特律河数据创建的 SDM 可以可靠地预测圣克莱尔河中是否存在 Dreissenids。根据河流和物种的不同,CART 模型确定速度和深度是重要的预测变量,而在 MaxEnt 模型中,与河流入口/出口的距离是影响最大的变量。大多数关于鼠海豚分布建模的研究都集中在确定潜在的扩散区域;然而,这项研究通过对鼠海豚的存在(D. polymorpha 和 D. r. bugensis)进行建模,提出了一个独特的视角,这两种鼠海豚分别存在于和一起存在于它们已经存在了 30 多年的地方。
Species distribution models effectively predict the detection of Dreissena spp. in two connecting waters of the Laurentian Great Lakes
Among the highest profile invasive species in the Laurentian Great Lakes region are Dreissena polymorpha and D. rostriformis bugensis (collectively dreissenids). Despite their abundance and ecosystem-wide effects, little is known about dreissenid distributions in large connecting channels between lakes. The objectives of this study were to estimate and document dreissenid densities and their habitat characteristics throughout the St. Clair River, to compare dreissenid species demographics, and predict spatial distributions between two connecting waters of the Great Lakes: the St. Clair and Detroit rivers. Two types of species distribution models (SDMs), MaxEnt and classification and regression tree analysis (CART), were created using dreissenid and habitat data collected in both the Detroit and St. Clair rivers. The SDMs were then used to predict presence of dreissenids in the St. Clair River. The St. Clair River had more D. r. bugensis (mean density = 486 ± 152 individuals/m2) than D. polymorpha (mean density = 3 ± 1 individuals/m2). The SDMs created from the Detroit River data reliably predicted presence of dreissenids in the St. Clair River. Depending on the river and species, CART models identified velocity and depth to be important predictor variables, while distance to river inlet/outlet were the most influential variables in the MaxEnt models. Most research on dreissenid distribution modeling is focused on determining areas for potential spread; however, this study presents a unique perspective by modeling dreissenid presence, both D. polymorpha and D. r. bugensis separately and together, where they have been established for more than 30 years.
期刊介绍:
Published six times per year, the Journal of Great Lakes Research is multidisciplinary in its coverage, publishing manuscripts on a wide range of theoretical and applied topics in the natural science fields of biology, chemistry, physics, geology, as well as social sciences of the large lakes of the world and their watersheds. Large lakes generally are considered as those lakes which have a mean surface area of >500 km2 (see Herdendorf, C.E. 1982. Large lakes of the world. J. Great Lakes Res. 8:379-412, for examples), although smaller lakes may be considered, especially if they are very deep. We also welcome contributions on saline lakes and research on estuarine waters where the results have application to large lakes.