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Species distribution models throughout the invasion history of Palmer amaranth predict regions at risk of future invasion and reveal challenges with modeling rapidly shifting geographic ranges

机译:整个Palmer mar菜的入侵历史中的物种分布模型可预测面临未来入侵风险的区域并通过快速变化的地理范围建模揭示了挑战

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摘要

Palmer amaranth (Amaranthus palmeri) is an annual plant native to the desert Southwest of the United States and Mexico and has become invasive and caused large economic losses across much of the United States. In order to examine the temporal and spatial dynamics of past invasion, and to predict future invasion, we developed a broad array of species distribution models (SDMs). In particular, we constructed sequential SDMs throughout the invasion history and asked how well those predicted future invasion (1970 to present). We showed that invasion occurred from a restricted set of environments in the native range to a diverse set in the invaded range. Spatial autocorrelation analyses indicated that rapid range expansion was facilitated by stochastic, long-distance dispersal events. Regardless of SDM approach, all SDMs built using datasets from early in the invasion (1970–2010) performed poorly and failed to predict most of the current invaded range. Together, these results suggest that climate is unlikely to have influenced early stages of range expansion. SDMs that incorporated data from the most recent sampling (2011–2017) performed considerably better, predicted high suitability in regions that have recently become invaded, and identified mean annual temperature as a key factor limiting northward range expansion. Under future climates, models predicted both further northward range expansion and significantly increased suitability across large portions of the U.S. Overall, our results indicate significant challenges for SDMs of invasive species far from climate equilibrium. However, our models based on recent data make more robust predictions for northward range expansion of A. palmeri with climate change.
机译:er菜(Amaranthus palmeri)是一年生植物,原产于美国西南沙漠和墨西哥,已经成为入侵植物,在美国大部分地区造成了巨大的经济损失。为了检查过去入侵的时空动态并预测未来入侵,我们开发了种类繁多的物种分布模型(SDM)。特别是,我们在整个入侵历史中构建了顺序SDM,并询问了这些SDM对未来入侵(1970年至今)的预测情况如何。我们表明,入侵发生的范围是本机范围内的有限环境到入侵范围内的各种环境。空间自相关分析表明,随机,远距离扩散事件促进了快速范围扩展。无论采用哪种SDM方法,所有使用入侵初期(1970-2010年)的数据集构建的SDM都表现不佳,无法预测当前的大部分入侵范围。总之,这些结果表明气候不太可能影响范围扩展的早期阶段。结合了最近一次抽样(2011-2017年)数据的SDM表现要好得多,预测最近被入侵的地区的适应性很高,并将年平均气温确定为限制向北扩展的关键因素。在未来的气候下,模型预测了美国大部分地区会进一步向北扩展,并显着提高其适用性。总体而言,我们的结果表明,对于远离气候平衡的入侵物种的SDM而言,存在巨大挑战。然而,我们基于最新数据的模型对随着气候变化棕榈棕榈向北扩展的预测更为可靠。

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