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Predicting the likelihood of Eurasian watermilfoil presence in lakes, a macrophyte monitoring tool

机译：预测欧亚水草在湖泊中存在的可能性，一种大型植物监测工具

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In regions with abundant and diverse freshwater resources, it is difficult and costly to survey all lakes at the level required to detect invasive plants. Effective allocation of monitoring resources requires tools that identify waterbodies where exotic species are most likely to invade. We developed and tested models that predict conditions in which Eurasian watermilfoil, Myriophyllum spicatum, is most likely to survive and successfully colonize. We used logistic regression to model the likelihood of M. spicatum presence or absence using a suite of biological, chemical, and physical lake characteristics which are easily obtainable from public databases. We evaluated model fit by the Aikake criterion and model performance by the percentage of misclassification errors as well as the costs associated with acquiring data for variables modeled. Several models fit our data well, misclassifying only 1.3-11.0% of the lakes where M. spicatum was observed, and used relatively inexpensive landscape variables (percent forest cover in a drainage basin, presence and type of public boat launch, and bedrock type) that typically exist as information layers in geographic information systems (GISs) or recreational atlases. We found that the most important factors affecting the presence or absence of M. spicatum were those that influence water quality factors known to impact M. spicatum growth, rather than factors associated with human activity and dispersal potential. In particular, the amount of forest cover in the lake watershed was consistently important and could control the level of dissolved inorganic carbon in lakes, one of the factors known to affect M. spicatum growth rates. Factors such as the number of game fish species and number and types of boat ramps or proximity to roads were generally less important lake characteristics. Our models can be useful tools for developing management strategies to prevent or slow the spread of M. spicatum and aquatic invaders, such as the zebra mussel, that can attach to it and thus be dispersed. Our models also exemplify a general approach for slowing or stopping the spread of other invading species. [References: 66]
机译：在拥有丰富多样的淡水资源的地区，以检测入侵植物所需的水平来调查所有湖泊是困难且昂贵的。有效分配监控资源需要使用工具来识别最有可能入侵外来物种的水体。我们开发并测试了模型，这些模型可预测欧亚水乳白（Myriophyllum spicatum）最有可能存活并成功定殖的条件。我们使用逻辑回归通过一套易于从公共数据库获得的生物学，化学和物理湖泊特征模型，对角叉孢子菌存在或不存在的可能性进行建模。我们通过Aikake准则评估了模型拟合，并通过错误分类错误的百分比以及与获取建模变量数据相关的成本评估了模型性能。几个模型很好地拟合了我们的数据，仅对观察到角叉菜的湖泊进行了错误分类（仅占1.3-11.0％），并使用了相对便宜的景观变量（流域的森林覆盖率，公共船只下水的存在和类型以及基岩类型）通常作为地理信息系统（GIS）或娱乐地图集中的信息层而存在。我们发现，影响辣根分枝杆菌存在或不存在的最重要因素是那些影响已知影响辣根分枝杆菌生长的水质因素，而不是与人类活动和传播潜能相关的因素。特别是，湖泊流域的森林覆盖量一直很重要，并且可以控制湖泊中溶解的无机碳含量，这是已知的影响角果分枝杆菌生长速率的因素之一。诸如野味鱼类的数量，舷梯的数量和类型或靠近道路等因素通常对湖泊的影响并不那么重要。我们的模型可以用作开发管理策略的有用工具，以防止或减缓钉螺分枝杆菌和诸如斑马贻贝之类的水生入侵者的扩散，这些扩散可以依附并分散。我们的模型还举例说明了减慢或阻止其他入侵物种扩散的一般方法。 [参考：66]

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