University of Washington, School of Environmental and Forest Sciences, 123 Anderson Hall, 3715 W. Stevens Way NE, Seattle, WA, USA.
INRAE, URZF, 45075 Orléans, France.
Curr Opin Insect Sci. 2022 Dec;54:100985. doi: 10.1016/j.cois.2022.100985. Epub 2022 Oct 7.
Understanding and predicting the spread of invading insects is a critical challenge in management programs that aim to minimize ecological and economic harm to native ecosystems. Although efforts to quantify spread rates have been well studied over the past several decades, opportunities to improve our ability to estimate rates of spread, and identify the factors, such as habitat suitability and climate, that influence spread, remain. We review emerging sources of data that can be used to delineate distributional boundaries through time and thus serve as a basis for quantifying spread rates. We then address advances in modeling methods that facilitate our understanding of factors that drive invasive insect spread. We conclude by highlighting some remaining challenges in understanding and predicting invasive insect spread, such as the role of climate change and biotic similarity between the native and introduced ranges, particularly as it applies to decision-making in management programs.
理解和预测入侵昆虫的传播是管理项目的一个关键挑战，这些管理项目旨在将对本地生态系统的生态和经济危害降到最低。尽管在过去几十年中，人们一直在努力量化传播速度，但仍有机会提高我们估计传播速度的能力，并确定影响传播的因素，如栖息地适宜性和气候。我们回顾了新兴的数据来源，这些数据可以用来随着时间的推移划定分布边界，从而为量化传播速度提供基础。然后，我们讨论了有助于我们理解驱动入侵昆虫传播的因素的建模方法的进展。最后，我们强调了在理解和预测入侵昆虫传播方面仍然存在的一些挑战，例如气候变化的作用和本地和引入范围之间的生物相似性，特别是在管理项目的决策中。
