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首页> 外文期刊>GIScience & remote sensing >Modeling the geographic spread and proliferation of invasive alien plants (IAPs) into new ecosystems using multi-source data and multiple predictive models in the Heuningnes catchment, South Africa
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Modeling the geographic spread and proliferation of invasive alien plants (IAPs) into new ecosystems using multi-source data and multiple predictive models in the Heuningnes catchment, South Africa

机译：使用多源数据和南非海宁恩斯集水区的多源数据和多种预测模型将侵入式外星植物（IAP）的地理蔓延和扩散建模为新的生态系统

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The geographic spread and proliferation of Invasive Alien Plants (IAPs) into new ecosystems requires accurate, constant, and frequent monitoring particularly under the changing climate to ensure the integrity and resilience of affected as well as vulnerable ecosystems. This study thus aimed to understand the distribution and shifts of IAPs and the factors influencing such distribution at the catchment scale to minimize their risks and impacts through effective management. Three machine learning Species Distribution Modeling (SDM) techniques, namely, Random Forest (RF), Maximum Entropy (MaxEnt), Boosted Regression Trees (BRT) and their respective ensemble model were used to predict the potential distribution of IAPs within the catchment. The current and future bioclimatic variables, environmental and Sentinel-2 Multispectral Instrument satellite data were used to fit the models to predict areas at risk of IAPs invasions in the Heuningnes catchment, South Africa. The present and two future climatic scenarios from the Community Climate System Model (CCSM4) were considered in modeling the potential distribution of these species. The two future scenarios represented the minimum and maximum atmospheric carbon Representative Concentration Pathways (RCP) 2.6 and 8.5 for 2050 (average for 2041-2060). The results show that IAPs are predicted to expand under the influence of climate change in the catchment. Concurrently, riparian zones, bare areas, and the native vegetation which is rich in biodiversity will greatly be affected. The mean diurnal range (Bio2), warmest quarter maximum temperature (Bio5), and the warmest quarter precipitation (Bio18) were the most important bioclimatic variables in modeling the spatial distribution of IAPs in the catchment. Comparatively, all the models were successful in predicting the potential distribution of IAPs for all the scenarios. The BRT, MaxEnt, and RF predicted the spatial distribution of IAPs with an Area Under Curve (AUC) of 0.89, 0.92, and 0.94, respectively. The study highlighted the importance of multi-source data and multiple predictive models in predicting the current and potential future IAP distribution. The results from this study provide baseline information for effective land management, planning, and continuous monitoring of the further spread of IAPs within the Heuningnes catchment.
机译：侵入性外星植物（IAP）进入新生态系统的地理蔓延和增殖需要准确，常数和频繁的监测，特别是在不断变化的气氛下，以确保受影响的诚信和恢复和脆弱的生态系统。这项研究旨在了解IAPS的分布和转移以及影响集水区规模这些分布的因素，以尽量减少通过有效管理的风险和影响。三种机器学习物种分布建模（SDM）技术，即随机森林（RF），最大熵（MAXENT），提升回归树（BRT）及其各自的集合模型用于预测集水区内IAP的潜在分布。目前和未来的生物恐星变量，环境和Sentinel-2多光谱仪器卫星数据用于拟合模型，以预测南非亨宁集群中IAPS入侵风险的模型。在建模这些物种的潜在分布，考虑了来自社区气候系统模型（CCSM4）的现在和两个未来的气候情景。两个未来的情景代表了2050年的最小和最大大气碳代表浓度途径（RCP）2.6和8.5（平均为2041-2060）。结果表明，IAPS预计在集水区气候变化的影响下扩大。同时，富力士区，裸露的地区和富有生物多样性的本地植被将极大地受到影响。平均昼夜范围（Bio2），最温暖的季度最高温度（BiO5）和最温暖的季度降水（Bio18）是在集水区内模拟IAP的空间分布中最重要的生物恐星变量。相比之下，所有模型都成功地预测了IAP的所有情景的潜在分布。 BRT，MaxEnt和RF预测IAP的空间分布，分别具有0.89,0.92和0.94的曲线（AUC）的区域。该研究强调了多源数据和多种预测模型在预测电流和潜在未来IAP分布中的重要性。本研究的结果为有效的土地管理，规划和持续监测IAP的进一步传播提供了基线信息。

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