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Ensemble approach for potential habitat mapping of invasive Prosopis spp . in Turkana, Kenya

机译:集成法对入侵拟虾的潜在生境定位。在肯尼亚图尔卡纳

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Aim Prosopis spp. are an invasive alien plant species native to the Americas and well adapted to thrive in arid environments. In Kenya, several remote‐sensing studies conclude that the genus is well established throughout the country and is rapidly invading new areas. This research aims to model the potential habitat of Prosopis spp . by using an ensemble model consisting of four species distribution models. Furthermore, environmental and expert knowledge‐based variables are assessed. Location Turkana County, Kenya. Methods We collected and assessed a large number of environmental and expert knowledge‐based variables through variable correlation, collinearity, and bias tests. The variables were used for an ensemble model consisting of four species distribution models: (a) logistic regression, (b) maximum entropy, (c) random forest, and (d) Bayesian networks. The models were evaluated through a block cross‐validation providing statistical measures. Results The best predictors for Prosopis spp . habitat are distance from water and built‐up areas, soil type, elevation, lithology, and temperature seasonality. All species distribution models achieved high accuracies while the ensemble model achieved the highest scores. Highly and moderately suitable Prosopis spp . habitat covers 6% and 9% of the study area, respectively. Main conclusions Both ensemble and individual models predict a high risk of continued invasion, confirming local observations and conceptions. Findings are valuable to stakeholders for managing invaded area, protecting areas at risk, and to raise awareness.
机译:瞄准Prosopis spp。是一种外来入侵植物,起源于美洲,非常适合在干旱环境中生长。在肯尼亚,一些遥感研究得出的结论是,该属在全国范围内已经建立并且正在迅速入侵新地区。这项研究的目的是模拟潜藻的潜在栖息地。通过使用由四个物种分布模型组成的集成模型。此外,还评估了基于环境和专家知识的变量。地点肯尼亚图尔卡纳县。方法我们通过变量相关性,共线性和偏差测试收集并评估了大量基于环境和专家知识的变量。这些变量用于包含四个物种分布模型的集成模型:(a)Logistic回归,(b)最大熵,(c)随机森林和(d)贝叶斯网络。通过提供统计措施的块交叉验证对模型进行了评估。结果最佳的Prosopis spp预测因子。生境是指距水和建筑面积的距离,土壤类型,海拔,岩性和温度季节性。所有物种分布模型均获得了较高的准确度,而集成模型则获得了最高分。高度和中等适宜的Prosopis spp。生境分别占研究区域的6%和9%。主要结论总体模型和个体模型都预测继续入侵的高风险,证实了当地的观察和概念。调查结果对于利益相关者管理入侵区域,保护处于危险中的区域并提高意识非常有价值。

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