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<p>Stream ecosystems worldwide face ongoing degradation, underscoring the urgent need for conservation and restoration. Regional analyses of stream condition have been limited by sparse spatial and temporal data, particularly at long time scales. To address this gap, we used observed data to predict annual biological condition for 360,893 small, nontidal stream reaches in the Chesapeake Bay watershed from 1985 to 2023 (39&#xa0;years). Predictions were generated using random forest models trained on extensive benthic macroinvertebrate data sets and predictors including natural landscape features, land cover, and climate variables. Four biological metrics were assessed: percent Ephemeroptera, Plecoptera, and Trichoptera excluding Hydropsychidae (EPT-H), percent Ephemeroptera, percent clinger functional group, and the Index of Biological Integrity (IBI). Results revealed degraded biological conditions near Washington, D.C. and Baltimore, Maryland, with declining trends across all metrics in these urbanized areas. Spatial heterogeneity was evident: IBI and clinger percentages increased in many southern streams but declined in northern streams, whereas EPT-H and Ephemeroptera decreased watershed wide except in the Southeastern Plains bioregion. By 2021, watershed wide IBI improvements were predicted for 0.9–1.1% of stream length, falling short of management goals. This study demonstrates the utility of long-term data and machine learning for predicting stream condition, identifying key stressors, and guiding restoration and conservation site selection.</p>

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Long-term predictive modeling of stream condition suggests wide-spread changes within the Chesapeake Bay Watershed, USA

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月28日

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  • Kelly O. Maloney,
  • Benjamin P. Gressler,
  • Lindsey J. Boyle,
  • Alexander H. Kiser

摘要

Stream ecosystems worldwide face ongoing degradation, underscoring the urgent need for conservation and restoration. Regional analyses of stream condition have been limited by sparse spatial and temporal data, particularly at long time scales. To address this gap, we used observed data to predict annual biological condition for 360,893 small, nontidal stream reaches in the Chesapeake Bay watershed from 1985 to 2023 (39 years). Predictions were generated using random forest models trained on extensive benthic macroinvertebrate data sets and predictors including natural landscape features, land cover, and climate variables. Four biological metrics were assessed: percent Ephemeroptera, Plecoptera, and Trichoptera excluding Hydropsychidae (EPT-H), percent Ephemeroptera, percent clinger functional group, and the Index of Biological Integrity (IBI). Results revealed degraded biological conditions near Washington, D.C. and Baltimore, Maryland, with declining trends across all metrics in these urbanized areas. Spatial heterogeneity was evident: IBI and clinger percentages increased in many southern streams but declined in northern streams, whereas EPT-H and Ephemeroptera decreased watershed wide except in the Southeastern Plains bioregion. By 2021, watershed wide IBI improvements were predicted for 0.9–1.1% of stream length, falling short of management goals. This study demonstrates the utility of long-term data and machine learning for predicting stream condition, identifying key stressors, and guiding restoration and conservation site selection.