IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v14y2025i5p935-d1642361.html
   My bibliography  Save this article

Comparable Riparian Tree Cover in Historical Grasslands and Current Croplands of the Eastern Great Plains, with Model Expansion to the Entire Great Plains, U.S.A

Author

Listed:
  • Brice B. Hanberry

    (USDA Forest Service, Rocky Mountain Research Station, Rapid City, SD 57702, USA)

Abstract

One question about historical grassland ecosystems in the Great Plains region of central North America is the percentage of tree cover overall and near major rivers, compared to current tree cover. Here, I assessed tree cover in reconstructions of historical grasslands in the eastern Great Plains, isolating tree cover adjacent to major rivers, and then compared historical land cover to current (year 2019) land cover. As an extension to supply information for the entire Great Plains region, I modeled historical cover. For the 28 million ha extent of the eastern Great Plains, historical land cover was 86% grasslands and 14% trees, but 57% grasslands and 43% trees within 100 m of rivers. Tree cover near rivers ranged from 5.4% to 90% for 15 large river watersheds, indicating that any amount of tree cover could occur near rivers at landscape scales. Currently, the overall extent was 3.6% herbaceous vegetation and 6.6% forested, with 82% crops and pasture and 8% development. Within 100 m of rivers, crop and pasture decreased to 44% of cover, resulting in 14% herbaceous cover and 38% forested cover. Current tree cover ranged from 6.2% to 66% near rivers in 15 watersheds, which was relatively comparable to historical tree cover (ratios of 0.6 to 1.5). Results generally were similar for combined tree and shrub cover modeled for the entire Great Plains. Variability, even at landscape scales of large watersheds, was the normal condition for tree cover in grasslands and riparian ecosystems of the Great Plains. In answer to the question about tree cover in historical grassland ecosystems in the eastern Great Plains, tree cover typically was about three-fold greater near rivers than tree cover throughout grasslands. Combined tree and shrub cover near rivers was more than two-fold greater than tree and shrub cover throughout the Great Plains. Riparian forest restoration, as a management practice to reduce streambank erosion, overall has been effective, as indicated by current tree cover (38% near rivers in the eastern Great Plains) comparable to historical tree cover (43% near rivers in the eastern Great Plains), albeit as measured at coarse landscape scales with dynamics in vegetation and river locations. As a next step, restoration of grassland vegetation and non-riparian wetlands likely will help reestablish infiltrative watersheds, augmenting riparian forest restoration.

Suggested Citation

  • Brice B. Hanberry, 2025. "Comparable Riparian Tree Cover in Historical Grasslands and Current Croplands of the Eastern Great Plains, with Model Expansion to the Entire Great Plains, U.S.A," Land, MDPI, vol. 14(5), pages 1-17, April.
    • Handle: RePEc:gam:jlands:v:14:y:2025:i:5:p:935-:d:1642361
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/14/5/935/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2073-445X/14/5/935/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kuhn, Max, 2008. "Building Predictive Models in R Using the caret Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i05).
    2. Brice B. Hanberry, 2021. "Timing of Tree Density Increases, Influence of Climate Change, and a Land Use Proxy for Tree Density Increases in the Eastern United States," Land, MDPI, vol. 10(11), pages 1-17, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Brice B. Hanberry, 2022. "Climate Envelopes Do Not Reflect Tree Dynamics after Euro-American Settlement in Eastern North America," Land, MDPI, vol. 11(9), pages 1-12, September.
    2. Prabal Das & D. A. Sachindra & Kironmala Chanda, 2022. "Machine Learning-Based Rainfall Forecasting with Multiple Non-Linear Feature Selection Algorithms," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(15), pages 6043-6071, December.
    3. Paulo Infante & Gonçalo Jacinto & Anabela Afonso & Leonor Rego & Pedro Nogueira & Marcelo Silva & Vitor Nogueira & José Saias & Paulo Quaresma & Daniel Santos & Patrícia Góis & Paulo Rebelo Manuel, 2023. "Factors That Influence the Type of Road Traffic Accidents: A Case Study in a District of Portugal," Sustainability, MDPI, vol. 15(3), pages 1-16, January.
    4. Ephrem Habyarimana & Faheem S Baloch, 2021. "Machine learning models based on remote and proximal sensing as potential methods for in-season biomass yields prediction in commercial sorghum fields," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-23, March.
    5. Crespo, Cristian, 2020. "Two become one: improving the targeting of conditional cash transfers with a predictive model of school dropout," LSE Research Online Documents on Economics 123139, London School of Economics and Political Science, LSE Library.
    6. Alexander Wettstein & Gabriel Jenni & Ida Schneider & Fabienne Kühne & Martin grosse Holtforth & Roberto La Marca, 2023. "Predictors of Psychological Strain and Allostatic Load in Teachers: Examining the Long-Term Effects of Biopsychosocial Risk and Protective Factors Using a LASSO Regression Approach," IJERPH, MDPI, vol. 20(10), pages 1-20, May.
    7. Tang, Kayu & Parsons, David J. & Jude, Simon, 2019. "Comparison of automatic and guided learning for Bayesian networks to analyse pipe failures in the water distribution system," Reliability Engineering and System Safety, Elsevier, vol. 186(C), pages 24-36.
    8. Daifeng Xiang & Gangsheng Wang & Jing Tian & Wanyu Li, 2023. "Global patterns and edaphic-climatic controls of soil carbon decomposition kinetics predicted from incubation experiments," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    9. Joel Podgorski & Oliver Kracht & Luis Araguas-Araguas & Stefan Terzer-Wassmuth & Jodie Miller & Ralf Straub & Rolf Kipfer & Michael Berg, 2024. "Groundwater vulnerability to pollution in Africa’s Sahel region," Nature Sustainability, Nature, vol. 7(5), pages 558-567, May.
    10. Tranos, Emmanouil & Incera, Andre Carrascal & Willis, George, 2022. "Using the web to predict regional trade flows: data extraction, modelling, and validation," OSF Preprints 9bu5z, Center for Open Science.
    11. Štefan Lyócsa & Petra Vašaničová & Branka Hadji Misheva & Marko Dávid Vateha, 2022. "Default or profit scoring credit systems? Evidence from European and US peer-to-peer lending markets," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-21, December.
    12. Marcos Rodrigues & Fermín Alcasena & Pere Gelabert & Cristina Vega‐García, 2020. "Geospatial Modeling of Containment Probability for Escaped Wildfires in a Mediterranean Region," Risk Analysis, John Wiley & Sons, vol. 40(9), pages 1762-1779, September.
    13. Siyu Han & Shixiang Yu & Mengya Shi & Makoto Harada & Jianhong Ge & Jiesheng Lin & Cornelia Prehn & Agnese Petrera & Ying Li & Flora Sam & Giuseppe Matullo & Jerzy Adamski & Karsten Suhre & Christian , 2025. "LEOPARD: missing view completion for multi-timepoint omics data via representation disentanglement and temporal knowledge transfer," Nature Communications, Nature, vol. 16(1), pages 1-20, December.
    14. Natalia Pardo-Lorente & Anestis Gkanogiannis & Luca Cozzuto & Antoni Gañez Zapater & Lorena Espinar & Ritobrata Ghose & Jacqueline Severino & Laura García-López & Rabia Gül Aydin & Laura Martin & Mari, 2024. "Nuclear localization of MTHFD2 is required for correct mitosis progression," Nature Communications, Nature, vol. 15(1), pages 1-23, December.
    15. Andrea Lazzari & Simone Giovinazzo & Giovanni Cabassi & Massimo Brambilla & Carlo Bisaglia & Elio Romano, 2025. "Evaluating Urban Sewage Sludge Distribution on Agricultural Land Using Interpolation and Machine Learning Techniques," Agriculture, MDPI, vol. 15(2), pages 1-13, January.
    16. Giovanny Pillajo-Quijia & Blanca Arenas-Ramírez & Camino González-Fernández & Francisco Aparicio-Izquierdo, 2020. "Influential Factors on Injury Severity for Drivers of Light Trucks and Vans with Machine Learning Methods," Sustainability, MDPI, vol. 12(4), pages 1-28, February.
    17. Zander S. Venter & Adam Sadilek & Charlotte Stanton & David N. Barton & Kristin Aunan & Sourangsu Chowdhury & Aaron Schneider & Stefano Maria Iacus, 2021. "Mobility in Blue-Green Spaces Does Not Predict COVID-19 Transmission: A Global Analysis," IJERPH, MDPI, vol. 18(23), pages 1-12, November.
    18. G. Brooke Anderson & Keith W. Oleson & Bryan Jones & Roger D. Peng, 2018. "Classifying heatwaves: developing health-based models to predict high-mortality versus moderate United States heatwaves," Climatic Change, Springer, vol. 146(3), pages 439-453, February.
    19. Van Belle, Jente & Guns, Tias & Verbeke, Wouter, 2021. "Using shared sell-through data to forecast wholesaler demand in multi-echelon supply chains," European Journal of Operational Research, Elsevier, vol. 288(2), pages 466-479.
    20. Jun Wang & Jinyong Huang & Yunlong Hu & Qianwen Guo & Shasha Zhang & Jinglin Tian & Yanqin Niu & Ling Ji & Yuzhong Xu & Peijun Tang & Yaqin He & Yuna Wang & Shuya Zhang & Hao Yang & Kang Kang & Xinchu, 2024. "Terminal modifications independent cell-free RNA sequencing enables sensitive early cancer detection and classification," Nature Communications, Nature, vol. 15(1), pages 1-13, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jlands:v:14:y:2025:i:5:p:935-:d:1642361. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.