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Zebra mussel invasion of Texas lakes: estimating dispersal potential via boats
Biological Invasions ( IF 2.9 ) Pub Date : 2020-08-26 , DOI: 10.1007/s10530-020-02333-2
Josi J. Robertson , Todd M. Swannack , Monica McGarrity , Astrid N. Schwalb

Invasive zebra mussels (Dreissena polymorpha) pose both a significant economic and environmental threat to aquatic systems yet there are currently no effective methods for achieving large-scale eradication. As a result, predicting and preventing their spread play a critical role in management efforts. Zebra mussels were first found in Texas in 2009 and, as of November 2019, have invaded 39 lakes across five river basins. Prior state-specific risk-assessments have been solely based on habitat suitability and have not considered dispersal potential. We developed a water body specific, constrained gravity model incorporating habitat suitability and dispersal potential to predict potential future invasion patterns. We examined the relative importance of habitat suitability, lake attractiveness, the relative risk of different boater types, and the impact of boater compliance with recommended prevention measures. Differences in lake attractiveness resulted in different boater dispersal patterns but the impact on projected lake invasions were reduced by variation in habitat suitability. The model projected zebra mussels to be mainly limited by habitat conditions in east Texas and by dispersal in west Texas. Most lakes in central Texas were projected to become invaded in the near future unless boater compliance with preventive management was high.



中文翻译:

斑马贻贝入侵得克萨斯州的湖泊:通过船只估算扩散潜力

入侵斑马贻贝(Dreissena polymorpha)对水生系统造成重大的经济和环境威胁,但目前尚无有效的方法来实现大规模根除。结果,预测和防止它们的传播在管理工作中起着至关重要的作用。斑马贻贝于2009年在德克萨斯州首次发现,截至2019年11月,已侵入五个流域的39个湖泊。先前针对州的特定风险评估仅基于栖息地的适宜性,并未考虑扩散潜力。我们开发了一种特定于水体的受约束重力模型,该模型结合了栖息地的适宜性和扩散潜力来预测未来的潜在入侵模式。我们研究了栖息地适宜性,湖泊吸引力,不同划船者相对风险的相对重要性,以及划船者遵守建议的预防措施的影响。湖泊吸引力的差异导致了不同的划船者分布模式,但栖息地适宜性的变化降低了对预计的湖泊入侵的影响。该模型预测斑马贻贝主要受得克萨斯州东部的栖息地条件和得克萨斯州西部的扩散的限制。除非船民对预防性管理的遵守程度很高,否则预计德克萨斯州中部的大多数湖泊将在不久的将来被入侵。

更新日期:2020-08-26
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