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  • Biophysical drivers for predicting the distribution and abundance of invasive yellow sweet clover in the Northern Great Plains
Title: Biophysical drivers for predicting the distribution and abundance of invasive yellow sweet clover in the Northern Great Plains
Yellow 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). We 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? We trained machine learning models with in situ data (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. We 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. These 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.  more » « less
Award ID(s):
2229746
PAR ID:
10472103
Author(s) / Creator(s):
Editor(s):
Jianguo 
Publisher / Repository:
Springer
Date Published:
Journal Name:
Landscape ecology
Volume:
38
ISSN:
1572-9761
Page Range / eLocation ID:
1463–1479
Subject(s) / Keyword(s):
Abundance Habitat suitability model Ensemble model Northern Great Plains Sentinel 2 Plant invasion
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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