Truong Tuyet T A, Hardy Giles E St J, Andrew Margaret E
Environmental and Conservation Sciences, School of Veterinary and Life Sciences, Murdoch University, PerthWA, Australia.
Faculty of Environment, Thai Nguyen University of Agriculture and ForestryThai Nguyen, Vietnam.
Front Plant Sci. 2017 May 15;8:770. doi: 10.3389/fpls.2017.00770. eCollection 2017.
Invasive weeds are a serious problem worldwide, threatening biodiversity and damaging economies. Modeling potential distributions of invasive weeds can prioritize locations for monitoring and control efforts, increasing management efficiency. Forecasts of invasion risk at regional to continental scales are enabled by readily available downscaled climate surfaces together with an increasing number of digitized and georeferenced species occurrence records and species distribution modeling techniques. However, predictions at a finer scale and in landscapes with less topographic variation may require predictors that capture biotic processes and local abiotic conditions. Contemporary remote sensing (RS) data can enhance predictions by providing a range of spatial environmental data products at fine scale beyond climatic variables only. In this study, we used the Global Biodiversity Information Facility (GBIF) and empirical maximum entropy (MaxEnt) models to model the potential distributions of 14 invasive plant species across Southeast Asia (SEA), selected from regional and Vietnam's lists of priority weeds. Spatial environmental variables used to map invasion risk included bioclimatic layers and recent representations of global land cover, vegetation productivity (GPP), and soil properties developed from Earth observation data. Results showed that combining climate and RS data reduced predicted areas of suitable habitat compared with models using climate or RS data only, with no loss in model accuracy. However, contributions of RS variables were relatively limited, in part due to uncertainties in the land cover data. We strongly encourage greater adoption of quantitative remotely sensed estimates of ecosystem structure and function for habitat suitability modeling. Through comprehensive maps of overall predicted area and diversity of invasive species, we found that among lifeforms (herb, shrub, and vine), shrub species have higher potential invasion risk in SEA. Native invasive species, which are often overlooked in weed risk assessment, may be as serious a problem as non-native invasive species. Awareness of invasive weeds and their environmental impacts is still nascent in SEA and information is scarce. Freely available global spatial datasets, not least those provided by Earth observation programs, and the results of studies such as this one provide critical information that enables strategic management of environmental threats such as invasive species.
入侵杂草是一个全球性的严重问题，威胁着生物多样性并损害经济。对入侵杂草的潜在分布进行建模可以确定监测和防控工作的重点区域，提高管理效率。区域到大陆尺度的入侵风险预测可以通过现成的降尺度气候表面数据，以及越来越多的数字化和地理参考物种出现记录与物种分布建模技术来实现。然而，在地形变化较小的更精细尺度和景观中进行预测，可能需要能够捕捉生物过程和当地非生物条件的预测因子。当代遥感（RS）数据可以通过提供一系列超出气候变量的精细尺度空间环境数据产品来增强预测。在本研究中，我们利用全球生物多样性信息设施（GBIF）和经验最大熵（MaxEnt）模型，对东南亚（SEA）地区14种入侵植物物种的潜在分布进行建模，这些物种选自区域和越南的优先杂草清单。用于绘制入侵风险图的空间环境变量包括生物气候层以及全球土地覆盖、植被生产力（GPP）和基于地球观测数据生成的土壤属性的最新数据。结果表明，与仅使用气候或RS数据的模型相比，结合气候和RS数据减少了适宜栖息地的预测面积，且模型精度没有损失。然而，RS变量的贡献相对有限，部分原因是土地覆盖数据存在不确定性。我们强烈鼓励在栖息地适宜性建模中更多地采用对生态系统结构和功能的定量遥感估计。通过总体预测面积和入侵物种多样性的综合地图，我们发现，在生命形式（草本、灌木和藤本）中，灌木物种在东南亚具有更高的潜在入侵风险。本地入侵物种在杂草风险评估中常常被忽视，可能与非本地入侵物种一样严重。东南亚对入侵杂草及其环境影响的认识仍处于初期阶段，相关信息匮乏。免费提供的全球空间数据集，尤其是地球观测项目提供的数据集以及此类研究的结果，提供了关键信息，有助于对入侵物种等环境威胁进行战略管理。
