Consistent declines in aquatic biodiversity across diverse domains of life in rivers impacted by surface coal mining.

The rivers of Appalachia (United States) are among the most biologically diverse freshwater ecosystems in the temperate zone and are home to numerous endemic aquatic organisms. Throughout the Central Appalachian ecoregion, extensive surface coal mines generate alkaline mine drainage that raises the pH, salinity, and trace element concentrations in downstream waters. Previous regional assessments have found significant declines in stream macroinvertebrate and fish communities after draining these mined areas. Here, we expand these assessments with a more comprehensive evaluation across a broad range of organisms (bacteria, algae, macroinvertebrates, all eukaryotes, and fish) using high-throughput amplicon sequencing of environmental DNA (eDNA). We collected water samples from 93 streams in Central Appalachia (West Virginia, United States) spanning a gradient of mountaintop coal mining intensity and legacy to assess how this land use alters downstream water chemistry and affects aquatic biodiversity. For each group of organisms, we identified the sensitive and tolerant taxa along the gradient and calculated stream specific conductivity thresholds in which large synchronous declines in diversity were observed. Streams below mining operations had steep declines in diversity (-18 to -41%) and substantial shifts in community composition that were consistent across multiple taxonomic groups. Overall, large synchronous declines in bacterial, algal, and macroinvertebrate communities occurred even at low levels of mining impact at stream specific conductivity thresholds of 150-200 µS/cm that are substantially below the current U.S. Environmental Protection Agency aquatic life benchmark of 300 µS/cm for Central Appalachian streams. We show that extensive coal surface mining activities led to the extirpation of 40% of biodiversity from impacted rivers throughout the region and that current water quality criteria are likely not protective for many groups of aquatic organisms.

Shifting Patterns of Summer Lake Color Phenology in Over 26,000 US Lakes.

Lakes are often defined by seasonal cycles. The seasonal timing, or phenology, of many lake processes are changing in response to human activities. However, long-term records exist for few lakes, and extrapolating patterns observed in these lakes to entire landscapes is exceedingly difficult using the limited number of available in situ observations. Limited landscape-level observations mean we do not know how common shifts in lake phenology are at macroscales. Here, we use a new remote sensing data set, LimnoSat-US, to analyze U.S. summer lake color phenology between 1984 and 2020 across more than 26,000 lakes. Our results show that summer lake color seasonality can be generalized into five distinct phenology groups that follow well-known patterns of phytoplankton succession. The frequency with which lakes transition from one phenology group to another is tied to lake and landscape level characteristics. Lakes with high inflows and low variation in their seasonal surface area are generally more stable, while lakes in areas with high interannual variations in climate and catchment population density show less stability. Our results reveal previously unexamined spatiotemporal patterns in lake seasonality and demonstrate the utility of LimnoSat-US, which, with over 22 million remote sensing observations of lakes, creates novel opportunities to examine changing lake ecosystems at a national scale.

Creating a More Perennial Problem? Mountaintop Removal Coal Mining Enhances and Sustains Saline Baseflows of Appalachian Watersheds.

Mountaintop removal coal mining (MTM) is a form of surface mining where ridges and mountain tops are removed with explosives to access underlying coal seams. The crushed rock material is subsequently deposited in headwater valley fills (VF). We examined how this added water storage potential affects streamflow using a paired watershed approach consisting of two sets of mined and unmined watersheds in West Virginia. The mined watersheds exported 7-11% more water than the reference watersheds, primarily due to higher and more sustained baseflows. The mined watersheds exported only ~1/3 of their streamflow during storms, while the reference watersheds exported ~2/3 of their annual water yield during runoff events. Mined watersheds with valley fills appear to store precipitation for considerable periods of time and steadily export this alkaline and saline water even during the dry periods of the year. As a result, MTMVFs in a mixed mined/unmined watershed contributed disproportionately to streamflow during baseflow periods (up to >90% of flow). Because MTMVFs have both elevated summer baseflows and continuously high concentrations of total dissolved solids, their regional impact on water quantity and quality will be most extreme and most widespread during low flow periods.

Deep Impact: Effects of Mountaintop Mining on Surface Topography, Bedrock Structure, and Downstream Waters.

Land use impacts are commonly quantified and compared using 2D maps, limiting the scale of their reported impacts to surface area estimates. Yet, nearly all land use involves disturbances below the land surface. Incorporating this third dimension into our estimates of land use impact is especially important when examining the impacts of mining. Mountaintop mining is the most common form of coal mining in the Central Appalachian ecoregion. Previous estimates suggest that active, reclaimed, or abandoned mountaintop mines cover ∼7% of Central Appalachia. While this is double the areal extent of development in the ecoregion (estimated to occupy <3% of the land area), the impacts are far more extensive than areal estimates alone can convey as the impacts of mines extend 10s to 100s of meters below the current land surface. Here, we provide the first estimates for the total volumetric and topographic disturbance associated with mining in an 11 500 km(2) region of southern West Virginia. We find that the cutting of ridges and filling of valleys has lowered the median slope of mined landscapes in the region by nearly 10 degrees while increasing their average elevation by 3 m as a result of expansive valley filling. We estimate that in southern West Virginia, more than 6.4km(3) of bedrock has been broken apart and deposited into 1544 headwater valley fills. We used NPDES monitoring datatsets available for 91 of these valley fills to explore whether fill characteristics could explain variation in the pH or selenium concentrations reported for streams draining these fills. We found that the volume of overburden in individual valley fills correlates with stream pH and selenium concentration, and suggest that a three-dimensional assessment of mountaintop mining impacts is necessary to predict both the severity and the longevity of the resulting environmental impacts.

National-scale remotely sensed lake trophic state from 1984 through 2020.

Lake trophic state is a key ecosystem property that integrates a lake's physical, chemical, and biological processes. Despite the importance of trophic state as a gauge of lake water quality, standardized and machine-readable observations are uncommon. Remote sensing presents an opportunity to detect and analyze lake trophic state with reproducible, robust methods across time and space. We used Landsat surface reflectance data to create the first compendium of annual lake trophic state for 55,662 lakes of at least 10 ha in area throughout the contiguous United States from 1984 through 2020. The dataset was constructed with FAIR data principles (Findable, Accessible, Interoperable, and Reproducible) in mind, where data are publicly available, relational keys from parent datasets are retained, and all data wrangling and modeling routines are scripted for future reuse. Together, this resource offers critical data to address basic and applied research questions about lake water quality at a suite of spatial and temporal scales.

User-focused evaluation of National Ecological Observatory Network streamflow estimates.

Accurately estimating stream discharge is crucial for many ecological, biogeochemical, and hydrologic analyses. As of September 2022, The National Ecological Observatory Network (NEON) provided up to 5 years of continuous discharge estimates at 28 streams across the United States. NEON created rating curves at each site in a Bayesian framework, parameterized using hydraulic controls and manual measurements of discharge. Here we evaluate the reliability of these discharge estimates with three approaches. We (1) compared predicted to observed discharge, (2) compared predicted to observed stage, and (3) calculated the proportion of discharge estimates extrapolated beyond field measurements. We considered 1,523 site-months of continuous streamflow predictions published by NEON. Of these, 39% met our highest quality criteria, 11% fell into an intermediate classification, and 50% of site-months were classified as unreliable. We provided diagnostic metrics and categorical evaluations of continuous discharge and stage estimates by month for each site, enabling users to rapidly query for suitable NEON data.

A functional microbiome catalog crowdsourced from North American rivers.

Predicting elemental cycles and maintaining water quality under increasing anthropogenic influence requires understanding the spatial drivers of river microbiomes. However, the unifying microbial processes governing river biogeochemistry are hindered by a lack of genome-resolved functional insights and sampling across multiple rivers. Here we employed a community science effort to accelerate the sampling, sequencing, and genome-resolved analyses of river microbiomes to create the Genome Resolved Open Watersheds database (GROWdb). This resource profiled the identity, distribution, function, and expression of thousands of microbial genomes across rivers covering 90% of United States watersheds. Specifically, GROWdb encompasses 1,469 microbial species from 27 phyla, including novel lineages from 10 families and 128 genera, and defines the core river microbiome for the first time at genome level. GROWdb analyses coupled to extensive geospatial information revealed local and regional drivers of microbial community structuring, while also presenting a myriad of foundational hypotheses about ecosystem function. Building upon the previously conceived River Continuum Concept 1 , we layer on microbial functional trait expression, which suggests the structure and function of river microbiomes is predictable. We make GROWdb available through various collaborative cyberinfrastructures 2, 3 so that it can be widely accessed across disciplines for watershed predictive modeling and microbiome-based management practices.

Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google Earth Engine.

Surface mining for coal has taken place in the Central Appalachian region of the United States for well over a century, with a notable increase since the 1970s. Researchers have quantified the ecosystem and health impacts stemming from mining, relying in part on a geospatial dataset defining surface mining's extent at a decadal interval. This dataset, however, does not deliver the temporal resolution necessary to support research that could establish causal links between mining activity and environmental or public health and safety outcomes, nor has it been updated since 2005. Here we use Google Earth Engine and Landsat imagery to map the yearly extent of surface coal mining in Central Appalachia from 1985 through 2015, making our processing models and output data publicly available. We find that 2,900 km2 of land has been newly mined over this 31-year period. Adding this more-recent mining to surface mines constructed prior to 1985, we calculate a cumulative mining footprint of 5,900 km2. Over the study period, correlating active mine area with historical surface mine coal production shows that each metric ton of coal is associated with 12 m2 of actively mined land. Our automated, open-source model can be regularly updated as new surface mining occurs in the region and can be refined to capture mining reclamation activity into the future. We freely and openly offer the data for use in a range of environmental, health, and economic studies; moreover, we demonstrate the capability of using tools like Earth Engine to analyze years of remotely sensed imagery over spatially large areas to quantify land use change.