Prioritizing the prevention and control of non-native invasive species requires understanding where introductions are likely to occur and cause harm. We developed predictive models for Eurasian watermilfoil (EWM) (Myriophyllum spicatum L.) occurrence and abundance to produce a smart prioritization tool for EWM management. We used generalized linear models (GLMs) to predict species occurrence and extended beta regression models to predict abundance from data collected on 657 Wisconsin lakes. Species occurrence was positively related to the nearby density of vehicle roads, maximum air temperature, lake surface area, and maximum lake depth. Species occurrence was negatively related to near-surface lithological calcium oxide content, annual air temperature range, and average distance to all known source populations. EWM abundance was positively associated with conductivity, maximum air temperature, mean distance to source, and soil erodibility, and negatively related to % surface rock calcium oxide content and annual temperature range. We extended the models to generate occurrence and predictions for all lakes in Wisconsin greater than 1 ha (N = 9825), then prioritized prevention and management, placing highest priority on lakes likely to experience EWM introductions and support abundant populations. This modelling effort revealed that, although EWM has been present for several decades, many lakes are still vulnerable to introduction.

中文翻译：

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使用物种的发生和丰度预测优先管理非本地的欧亚水母

优先预防和控制非本地入侵物种需要了解可能在何处引入并造成危害的地方。我们为欧亚水乳（EWM）（Myriophyllum spicatum）开发了预测模型L.）发生和数量丰富，以生成用于EWM管理的智能优先级排序工具。我们使用广义线性模型（GLM）来预测物种的发生，并使用扩展的beta回归模型来预测从657个威斯康星州湖泊收集的数据中的丰度。物种的发生与附近的机动车道密度，最高气温，湖泊表面积和最大湖泊深度呈正相关。物种的发生与近地表岩性氧化钙含量，年度气温范围以及与所有已知源种群的平均距离负相关。EWM的丰度与电导率，最高气温，距源的平均距离和土壤易蚀性呈正相关，与表面岩石氧化钙含量百分比和年温度范围呈负相关。我们扩展了模型，以生成威斯康星州大于1公顷（N = 9825）的所有湖泊的发生率和预测值，然后优先进行预防和管理，将可能引入EWM并支持大量人口的湖泊放在首位。建模工作表明，尽管EWM已经存在了几十年，但许多湖泊仍然容易引入。