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Biophysical drivers for predicting the distribution and abundance of invasive yellow sweetclover in the Northern Great Plains

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Abstract

Context

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).

Objectives

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?

Methods

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.

Results

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.

Conclusions

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.

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Acknowledgements

We thank all the institutes, organizations, and developers of the various datasets for making their products freely available. Thanks are extended to Dr. Marissa Ahlering and Alison Long (The Nature Conservancy), Dr. Amy Symstad, Dr. Aaron Johnston, and Todd Preston (U.S. Geological Survey), Jacob Dyer (South Dakota Game Fish, and Parks), Milton Haar (Badlands National Park and National Park Service Inventory & Monitoring Network), Carmen Drieling (Bureau of Land Management), and Braden Burkholder (Montana Natural Heritage Program), Bruce Wylie for their support and for freely sharing their data. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

Funding

This study was supported by the South Dakota Board of Regents(SDBOR) Competitive Research Grant and Research Infrastructure Development (RDI), NSF RII Track-4:NSF2229746, NASA LCLUC Program-80NSSC20K0410, and NSF RII Track-2 FEC1632810.

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Authors and Affiliations

  1. Department of Biology, University of South Dakota, Vermillion, SD, 57069, USA

    Sakshi Saraf, Ranjeet John & Reza Goljani Amirkhiz

  2. Department of Sustainability and Environment, University of South Dakota, Vermillion, SD, 57069, USA

    Ranjeet John, Venkatesh Kolluru, Khushboo Jain & Meghann Jarchow

  3. Center for Earth Resources Observation & Science, U. S. Geological Survey, Sioux Falls, SD, 57198, USA

    Matthew Rigge & Stephen Boyte

  4. Department of Soil, Plant and Food Sciences (DISSPA), University of Bari “Aldo Moro”, Via Amendola 165/A, 70126, Bari, Italy

    Vincenzo Giannico & Raffaele Lafortezza

  5. Department of Geography, Environment, and Spatial Sciences, and Center for Global Change and Earth Observations, Michigan State University, East Lansing, MI, 48823, USA

    Jiquan Chen & Geoffrey Henebry

  6. Center for Global Change and Earth Observations, Michigan State University, East Lansing, MI, 48823, USA

    Jiquan Chen

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  1. Sakshi Saraf
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  8. Stephen Boyte
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Contributions

RJ and SS contributed to the study’s conception and design. SS, VK and KJ performed data collection; SS performed the material preparation, analysis and prepared the first draft of the manuscript. All authors read and approved the manuscript.

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Correspondence to Sakshi Saraf.

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Saraf, S., John, R., Goljani Amirkhiz, R. et al. Biophysical drivers for predicting the distribution and abundance of invasive yellow sweetclover in the Northern Great Plains. Landsc Ecol 38, 1463–1479 (2023). https://doi.org/10.1007/s10980-023-01613-1

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