Biophysical drivers for predicting the distribution and abundance of invasive yellow sweetclover in the Northern Great Plains
Biophysical drivers for predicting the distribution and abundance of invasive yellow sweetclover in the Northern Great Plains
复制标题
DOI：
10.1007/s10980-023-01613-1
复制
发表时间：
2023-03
影响因子：
5.2
通讯作者：
S. Saraf;R. John;Reza Goljani Amirkhiz;V. Kolluru;K. Jain;M. Rigge;Vincenzo Giannico;S. Boyte;Jiquan Chen;G. Henebry;M. Jarchow;R. Lafortezza
中科院分区：
文献类型：
--
作者：
S. Saraf;R. John;Reza Goljani Amirkhiz;V. Kolluru;K. Jain;M. Rigge;Vincenzo Giannico;S. Boyte;Jiquan Chen;G. Henebry;M. Jarchow;R. Lafortezza
黄草木樨（Melilotus officinalis; YSC）是一种入侵的两年生豆科植物，2018-2019年在北方大平原上开花，以应对高于平均水平的降水。YSC可以增加氮（N）水平，并可能导致本地植物物种群落组成的重大变化。有一点知识的时空变化和条件，造成大量广泛的盛开的YSC整个西部南达科他州（SD）.ObjectivesWe的目的是开发一个广义的预测模型，以预测的相对丰度YSC在整个牧场西部南达科他州的2019年在合适的栖息地。我们的研究问题是：（1）YSC在南达科他州西部的空间范围是什么？(2)哪一个模型可以准确地预测YSC的栖息地和覆盖率？以及（3）哪些重要的生物物理驱动因素影响了它在南达科他州西部的存在？MethodsWe trained machine learning models within situdata（2016-2021），Sentinel 2A衍生的地表反射率和指数（10 m，20 m）以及气候、地形和土壤因子的特定站点变量，以优化模型性能。ResultsWe将湿度代理（短波红外反射率和流苏帽湿度的变化）确定为解释YSC存在的重要预测因子。地表水指数和夏季温度的变化是解释YSC丰度的最佳预测因子。我们展示了机器学习算法如何帮助生成有关这种入侵植物空间分布的有价值的信息。我们在南达科他州的巴特、彭宁顿和科森县划定了YSC的主要热点。主要河流，包括白色和坏河，荒地国家公园周围地区的洪泛区也表现出较高的发生概率和覆盖percentage.ConclusionsThese预测图可以帮助土地管理人员在制定管理策略的地区，容易发生YSC爆发。管理工作流程也可以作为一个原型，映射其他入侵植物物种在类似地区。
ContextYellow sweetclover (Melilotus officinalis; YSC) is an invasive biennial legume that bloomed across the Northern Great Plains in 2018–2019 in response to above-average precipitation. YSC can increase nitrogen (N) levels and potentially cause substantial changes in the composition of native plant species communities. There is little knowledge of the spatiotemporal variability and conditions causing substantial widespread blooms of YSC across western South Dakota (SD).ObjectivesWe aimed to develop a generalized prediction model to predict the relative abundance of YSC in suitable habitats across rangelands of western South Dakota for 2019. Our research questions are: (1) What is the spatial extent of YSC across western South Dakota? (2) Which model can accurately predict the habitat and percent cover of YSC? and (3) What significant biophysical drivers affect its presence across western South Dakota?MethodsWe trained machine learning models within situdata (2016–2021), Sentinel 2A-derived surface reflectance and indices (10 m, 20 m) and site-specific variables of climate, topography, and edaphic factors to optimize model performance.ResultsWe identified moisture proxies (Shortwave Infrared reflectance and variability in Tasseled Cap Wetness) as the important predictors to explain the YSC presence. Land Surface Water Index and variability in summer temperature were the top predictors in explaining the YSC abundance. We demonstrated how machine learning algorithms could help generate valuable information on the spatial distribution of this invasive plant. We delineated major YSC hotspots in Butte, Pennington, and Corson Counties of South Dakota. The floodplains of major rivers, including White and Bad Rivers, and areas around Badlands National Park also showed a higher occurrence probability and cover percentage.ConclusionsThese prediction maps could aid land managers in devising management strategies for the regions that are prone to YSC outbreaks. The management workflow can also serve as a prototype for mapping other invasive plant species in similar regions.