Geology topics

Lindsey J. Boyle

Publications and source records attributed to Lindsey J. Boyle.

6 recordsLinked to original sources

USGS2026

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

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.

Delaware, Maryland, New York, Pennsylvania, Virgin↗

USGS2026

Fish introductions related to strong and diversifying effects on zooplankton assemblages in high-elevation mountain lakes

Freshwater environments are threatened by multiple anthropogenic stressors. High-elevation mountain lakes are particularly vulnerable to introduced nonnative fish and nutrient deposition because they were historically fishless and typically oligotrophic. To understand the potential effects of fish introduction and nutrient levels on high-elevation lake ecosystems, we assessed differences in zooplankton size, biomass, and density in 76 alpine and subalpine lakes in the Wind River Range, Wyoming, USA, and related those differences to the presence of introduced trout and to food quality (seston nutrient content) and quantity (chlorophyll a concentration). Trout presence, and to a lesser extent trout species, were the strongest predictors of zooplankton composition. Fishless lakes were dominated by low densities of copepods and other large-bodied taxa, and lakes with introduced trout were dominated by high densities of cladocerans, rotifers, and other small-bodied taxa. These assemblage differences are likely because trout reduce or eliminate all large zooplankton taxa by size-selective predation, including predation on Hesperodiaptomus shoshone (S. A. Forbes, 1893), a keystone species that effectively controls populations of rotifers and small crustacean zooplankton taxa. In contrast, the quantity and quality of seston was not associated with zooplankton assemblages. Zooplankton composition in lakes with Rocky Mountain Cutthroat Trout Oncorhynchus virginalis (Girard, 1856) or Golden Trout Oncorhynchus aguabonita (Jordan, 1892) was highly variable, but in lakes with primarily Brook Trout Salvelinus fontinalis (Mitchill, 1814), zooplankton composition was consistently distinct from that in fishless lakes, suggesting that Brook Trout introductions altered the zooplankton assemblage to a greater extent than Cutthroat or Golden trout did. These results contribute to global evidence that predatory fish introductions fundamentally restructure alpine lake food webs. The slow or incomplete recovery of native zooplankton assemblages following fish removal suggests long-term ecological legacies of fish introductions and highlights the importance of understanding factors that promote resilience in high-elevation lake ecosystems.

Wyoming↗

USGS2025

Assessing streams in the Chesapeake Bay Watershed to guide conservation and restoration activities

Freshwater streams in the Chesapeake Bay watershed are home to numerous aquatic organisms (like fish, amphibians, mussels, and insects) and provide drinking water and recreational opportunities to people living in or visiting the watershed. Land-use changes, such as urban development and increased activities in certain agricultural sectors, have degraded water quality and altered conditions in these streams, thereby affecting their health and function. The U.S. Geological Survey (USGS) is working with Federal, State, and local partners to develop modeled assessments of stream health in freshwater streams and rivers within the Chesapeake Bay watershed. The USGS compiled large datasets for multiple stream health indicators, including instream stressors (salinity, water temperature, physical habitat, and streambank erosion) and living resources (macroinvertebrates and fish communities; fig. 1). These datasets were used by USGS scientists to develop models to predict stream health conditions across the entire region, including areas with little or no monitoring data. Collectively, these stream health assessments provide critical information to natural resource managers who implement restoration and conservation activities in the region.

Chesapeake Bay Watershed↗

USGS2025

Achieving interpretable machine learning by functional decomposition of black-box models into explainable predictor effects

Machine learning (ML) models are often based on complex black-box architectures that are difficult to interpret. This interpretability problem can hinder the use of ML in fields like medicine, ecology, and insurance, and has boosted research in interpretable machine learning (IML). Here, we propose a novel approach for the functional decomposition of black-box predictions, which is a core concept of IML. This approach replaces the prediction function with a surrogate model consisting of simpler subfunctions, providing insights into the direction and strength of the main feature contributions and their interactions. Our method is based on a concept termed “stacked orthogonality”, which ensures that the main effects capture as much functional behavior as possible. To compute the subfunctions, we combine neural additive modeling with an efficient post-hoc orthogonalization procedure. Our method yielded plausible results in an analysis of stream biological condition in the Chesapeake Bay watershed (United States).

Chesapeake Bay watershed↗

USGS2025

Tracking status and trends in seven key indicators of river and stream condition in the Chesapeake Bay watershed

Freshwater streams and rivers are recognized as vital habitats within the Chesapeake Bay watershed, which has been undergoing extensive restoration efforts for more than 30 years. Resource managers need to understand stream and river condition and how these conditions are changing over time to determine whether regional long-term restoration and conservation goals are being met. The objective of this report was to document the spatial and temporal variability of conditions for seven indicators of river and stream health across the nontidal Chesapeake Bay watershed. The framework for the U.S. Geological Survey’s Nontidal Network (NTN), a network of more than 100 nutrient and suspended sediment monitoring locations, was extended to assess conditions for six additional indicators of stream health: temperature, salinity, toxic contaminants, streamflow, hydromorphology, and biological aquatic communities. For each indicator, the latest available data from multiple sources were compiled and harmonized, and key metrics were identified to describe indicator conditions across space and time. A status condition was defined for each indicator to describe overall spatial variability in recent condition, and trend analyses were used to describe changes in each indicator metric over time. The analysis revealed clear differences in spatial and temporal data coverage across the seven indicators, so individual indicator trend analyses were not constrained to a common time interval. However, a status snapshot was conducted across all indicators for the 2015–17 period to simultaneously explore spatial variability across all indicators. The status snapshot highlighted general degraded conditions across multiple indicators in large metropolitan regions, such as the Baltimore–Washington, D.C., metropolitan area. Regression analysis between indicator status metrics and major land cover for the sites suggest urbanization as a potential driver of degraded conditions for many of the indicator metrics, including total phosphorus, salinity, temperature, high-flow frequency, and metrics of habitat and biological assemblage quality. A final analysis exploring the spatial representation of each indicator network showed that some indicator monitoring networks did not cover certain settings, such as small watersheds. These results provided an initial assessment of stream health status and trends and will continue to be leveraged to describe conditions across the Chesapeake Bay watershed to help inform local and regional management decisions. These results also highlighted the need for improved coordination among monitoring organizations to support long-term multi-indicator monitoring and assessment.

Chesapeake Bay watershed↗

USGS2023

Parallel shifts in trout feeding morphology suggest rapid adaptation to alpine lake environments

Eco-evolutionary interactions following ecosystem change provide critical insight into the ability of organisms to adapt to shifting resource landscapes. Here we explore evidence for the rapid parallel evolution of trout feeding morphology following eco-evolutionary interactions with zooplankton in alpine lakes stocked at different points in time in the Wind River Range (Wyoming, USA). In this system, trout predation has altered the zooplankton species community and driven a decrease in average zooplankton size. In some lakes that were stocked decades ago, we find shifts in gill raker traits consistent with the hypothesis that trout have rapidly adapted to exploit available smaller-bodied zooplankton more effectively. We explore this morphological response in multiple lake populations across two species of trout (cutthroat trout, Oncorhynchus clarkii , and golden trout Oncorhynchus aguabonita ) and examine the impact of resource availability on morphological variation in gill raker number among lakes. Furthermore, we present genetic data to provide evidence that historically stocked cutthroat trout populations likely derive from multiple population sources, and incorporate variation from genomic relatedness in our exploration of environmental predictors of feeding morphology. These findings describe rapid adaptation and eco-evolutionary interactions in trout and document an evolutionary response to novel, contemporary ecosystem change.

Evolution↗