Alexander H. Kiser

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.

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.

Human impacts on aquatic ecosystems have resulted in systemic declines of global freshwater species abundance and richness. Conservation and governmental groups worldwide have designated protected areas to preserve the remaining diversity. The biodiversity hotspot approach, which designates areas based on high levels of species richness, has been useful for identifying areas to protect both terrestrial and aquatic species. However, for freshwater species, additional approaches are warranted to identify specific stream reaches for protection and/or restoration. To address this issue, we present a methodology to create a Gridded River Identification System (GRIS) for river segments based on 30 arc‐second grids (~0.9 km) using the USGS National Hydrography High Resolution Dataset. To demonstrate the utility of this approach, we obtained occurrence data for six imperiled freshwater mussel species in Texas and created ensemble species distribution models (ESDMs) based on climate and topographical variables. Predicted occupancies were overlayed onto the GRIS in Texas. The predicted occupancies were rank ordered from 1 to 5, with 1 being the lowest probability of occupancy and 5 being the greatest. The rank‐ordered segments were then used to identify reaches for conservation and restoration activities. Our approach is novel and widely applicable to other freshwater species so long as distribution information is available. The GRIS can also be easily developed for stream systems outside of the current study area. Future studies could build upon our framework by incorporating additional taxa data and projected changes in climate and land use.

Freshwater mussels are globally imperilled, which is attributed to their sensitivity to changes in streamflow and temperature. The Black River in south‐eastern New Mexico, USA, harbours a stronghold population of the federally endangered unionid Popenaias popeii (Texas hornshell). Decreasing discharge and elevated water temperatures are considered the primary factors responsible for the species’ decline. However, the impact of these factors has not been quantified directly, which may hinder conservation efforts. The upper thermal tolerances (LT₀₅ and LT₅₀) of Black River P. popeii larvae (glochidia) and newly transformed juveniles were evaluated. Individuals were acclimated to 27°C and then immersed at five experimental temperatures (28, 30, 32, 34 and 36°C) for 24 h (glochidia) or 96 h (juveniles). The thermal tolerances of P. popeii and its presumed host fishes were then overlain onto in situ water temperature and discharge data to determine thermal exceedances in the past 15 years. Temperature was hindcast back to 2007 using machine learning (random forest model) and coupled with discharge. For glochidia, LT₀₅ was exceeded frequently (41%) and LT₅₀ was exceeded occasionally (13%) during spring and summer periods, whereas juvenile thresholds were never exceeded. Upper continuous duration above threshold (UCAT) analysis revealed periods of catastrophic high temperature, which were used to determine discharge bottlenecks. This study demonstrates how laboratory‐derived physiological thresholds can be used in conjunction with environmental data to evaluate the hydrological needs of aquatic organisms, which is useful in efforts to maintain flow regimes that protect native ecosystems.

Species distribution models (SDMs) are an increasingly important tool for conservation particularly for difficult‐to‐study locations and with understudied fauna. Our aims were to (1) use SDMs and ensemble SDMs to predict the distribution of freshwater mussels in the Pánuco River Basin in Central México; (2) determine habitat factors shaping freshwater mussel occurrence; and (3) use predicted occupancy across a range of taxa to identify freshwater mussel biodiversity hotspots to guide conservation and management. In the Pánuco River Basin, we modeled the distributions of 11 freshwater mussel species using an ensemble approach, wherein multiple SDM methodologies were combined to create a single ensemble map of predicted occupancy. A total of 621 species‐specific observations at 87 sites were used to create species‐specific ensembles. These predictive species ensembles were then combined to create local diversity hotspot maps. Precipitation during the warmest quarter, elevation, and mean temperature were consistently the most important discriminatory environmental variables among species, whereas land use had limited influence across all taxa. To the best of our knowledge, our study is the first freshwater mussel‐focused research to use an ensemble approach to determine species distribution and predict biodiversity hotspots. Our study can be used to guide not only current conservation efforts but also prioritize areas for future conservation and study.

Mussel–flow relationships are not well defined and studies linking behavioural, physiological and life‐history traits to hydrological conditions remain scant. This lack of information hinders not only understanding of how flow shapes population and community processes but the development of evidence‐based flow recommendations to mitigate mussel declines. Mussel–flow relationships were evaluated for Potamilus amphichaenus (Texas heelsplitter), Pleurobema riddellii (Louisiana pigtoe) and Truncilla macrodon (Texas fawnsfoot) from the Sabine, Trinity, Neches and Cypress River basins of East Texas using Indicators of Hydraulic Alteration and random forest models. These species were chosen because all three are currently being evaluated for listing under the US Endangered Species Act. Analyses were performed on US Geological Survey stream gauge stations and mussel records within 20 km of each other. Only gauges with at least 15 years of continuous data and ≥70% overlap of flow records from 1968 to 2018 were included. For selected 28 gauges, Indicators of Hydraulic Alteration were used to calculate 47 hydrological parameters that describe the magnitude, frequency and duration of high and low flow events. Random forest classification models across species were significant and had low error rates (3.57–21.43%), moderate to high sensitivity (0.81–0.93) and moderate to high specificity (0.71–1.00). Life‐history theory was useful for explaining how mussel species cope with hydrological variation. Potamilus amphichaenus and T. macrodon/Truncilla donaciformis, considered r‐selected or opportunistic strategists, were more likely to occur in basins characterized by significant changes in flow and land use compared with P. riddellii, considered a K‐selected or equilibrium strategist. The analytical framework presented in this study should be helpful to managers and conservationists interested in determining how the flow regime is shaping aquatic communities in their region and developing reference points that could serve for ecological research, conservation and management.