Ng Wai-Tim, Cândido de Oliveira Silva Alexsandro, Rima Purity, Atzberger Clement, Immitzer Markus
Institute for Surveying, Remote Sensing and Land Information (IVFL) University of Natural Resources and Life Sciences (BOKU) Vienna Austria.
Image Processing Division (DPI) National Institute for Space Research (INPE), Avenida dos Astronautas São José dos Campos Brazil.
Ecol Evol. 2018 Nov 21;8(23):11921-11931. doi: 10.1002/ece3.4649. eCollection 2018 Dec.
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 spp by using an ensemble model consisting of four species distribution models. Furthermore, environmental and expert knowledge-based variables are assessed.
Turkana County, Kenya.
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.
The best predictors for 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 spp habitat covers 6% and 9% of the study area, respectively.
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.
[物种名称]是一种原产于美洲的外来入侵植物物种,非常适应在干旱环境中生长。在肯尼亚,多项遥感研究得出结论,该属在全国范围内已广泛分布,并正在迅速入侵新的地区。本研究旨在通过使用由四种物种分布模型组成的集成模型来模拟[物种名称]的潜在栖息地。此外,还评估了基于环境和专家知识的变量。
肯尼亚图尔卡纳县。
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。这些变量被用于由四种物种分布模型组成的集成模型:(a)逻辑回归,(b)最大熵,(c)随机森林,以及(d)贝叶斯网络。通过提供统计量的块交叉验证对模型进行评估。
[物种名称]栖息地的最佳预测因子是与水体和建成区的距离、土壤类型、海拔、岩性和温度季节性。所有物种分布模型都取得了较高的准确率,而集成模型得分最高。高度适宜和中度适宜的[物种名称]栖息地分别占研究区域的6%和9%。
集成模型和单个模型都预测了持续入侵的高风险,证实了当地的观察结果和认知。研究结果对利益相关者管理入侵区域、保护风险区域以及提高认识具有重要价值。