Linking mountaintop removal mining to water quality for imperiled species using satellite data.

Environmental laws need sound data to protect species and ecosystems. In 1996, a proliferation of mountaintop removal coal mines in a region home to over 50 federally protected species was approved under the Endangered Species Act. Although this type of mining can degrade terrestrial and aquatic habitats, the available data and tools limited the ability to analyze spatially extensive, aggregate effects of such a program. We used two large, public datasets to quantify the relationship between mountaintop removal coal mining and water quality measures important to the survival of imperiled species at a landscape scale across Kentucky, Tennessee, Virginia, and West Virginia. We combined an annual map of the extent of surface mines in this region from 1985 to 2015 generated from Landsat satellite imagery with public water quality data collected over the same time period from 4,260 monitoring stations within the same area. The water quality data show that chronic and acute thresholds for levels of aluminum, arsenic, cadmium, conductivity, copper, lead, manganese, mercury, pH, selenium, and zinc safe for aquatic life were exceeded thousands of times between 1985 and 2015 in streams that are important to the survival and recovery of species on the Endangered Species List. Linear mixed models showed that levels of manganese, sulfate, sulfur, total dissolved solids, total suspended solids, and zinc increased by 6.73E+01 to 6.87E+05 μg/L and conductivity by 3.30E+06 μS /cm for one percent increase in the mined proportion of the area draining into a monitoring station. The proportion of a drainage area that was mined also increased the likelihood that chronic thresholds for copper, lead, and zinc required to sustain aquatic life were exceeded. Finally, the proportion of a watershed that was mined was positively related to the likelihood that a waterway would be designated as impaired under the Clean Water Act. Together these results demonstrate that the extent of mountaintop removal mining, which can be derived from public satellite data, is predictive of water quality measures important to imperiled species-effects that must be considered under environmental law. These findings and the public data used in our analyses are pertinent to ongoing re-evaluations of the effects of current mine permitting regulations to the recovery and survival of federally protected species.

Introduction

Natural ecosystems continue to degrade despite substantial global conservation efforts, in large part due to synergistic and large-scale impacts of human activities [1, 2]. For environmental laws to effectively protect natural ecosystems, a complete picture of the direct, indirect, and cumulative effects of potentially damaging, regulated actions needs to be analyzed and accounted for during the permitting process. While many direct, acute impacts of environmentally regulated actions are easy to measure, the myriad indirect and aggregate effects of human activities may require additional data or new techniques to uncover. For example, surface coal mining is regulated in many countries and directly impacts forest ecosystems through land clearing [3], which is easy to measure. However, surface mining also degrades surrounding forest by increasing fragmentation contributing to edge effects [4] and facilitating increased levels of non-native vegetation by compacting soil and altering its chemistry [5, 6]. Without consideration of these indirect, aggregate effects, environmental laws and policies will fail in their mission to protect the natural ecosystems upon which biodiversity and human life depend [7]. This degradation may be further underestimated if the disturbance of individual mines is evaluated in isolation without considering the accumulative effects of present, or future mines. Thus, data and approaches that can accommodate potentially complicated dynamics at large scales are needed for decision makers to come to better choices and meet their legal requirements [8, 9].

Mountaintop removal mining with valley fill (MTMVF) can have particularly extensive negative effects on environmental quality at large, landscape scales. MTMVF is a coal extraction practice in which coal seams in rugged terrain are accessed by first clearing overlying forest and then using explosives to remove overlying soil and bedrock. The leftover rock is then deposited into headwater stream valleys [10, 11], affecting aquatic and terrestrial ecosystems well beyond the immediate footprint of a mine. The conversion from forested land to bare earth decreases water quality, and streams linked to mine sites have higher rates of erosion and nutrient pollution and decreased habitat quality for aquatic species [5, 6, 11]. The release of alkaline mine drainage from weathering of rock and site waste at mine sites elevates conductivity and concentrations of metallic ions that negatively impact aquatic biota [12], leading to decreases in aquatic biodiversity [10, 13]. Additionally, pollutants can be transferred across food webs to terrestrial ecosystems [14].

The potential for MTMVF to negatively impact biodiversity through these interrelated direct and indirect effects is exacerbated by the massive spatial scale and extent of the practice. In the United States, MTMVF has been particularly prominent across Central Appalachia–a region encompassing the Appalachian Mountains in Kentucky, Tennessee, Virginia, and West Virginia—such that MTMVF is the primary cause of land cover change in the region [15]. Over 5,900 km2 of Central Appalachian forest has been cleared for mining or mining related activities [16]. This level of landscape modification could pose a substantial threat to biodiversity anywhere, and in Central Appalachia the impacts of MTMVF are of particular concern because the region is a hot spot for endemic imperiled species [5]. Over 50 species on the endangered species list are found in the region. Many of these species are aquatic (e.g., darters, salamanders, and crayfish), making them particularly vulnerable to the negative water quality impacts of MTMVF.

In the United States, two critical environmental laws protecting aquatic habitats and species are the Clean Water Act (CWA) and the Endangered Species Act (ESA). Enacted in 1972, the CWA requires states to develop water quality standards for waterways to meet different usage criteria, including suitability for aquatic life, and restricts the discharge of pollutants into waterways not meeting these criteria [17]. The ESA focuses on the status of species and requires federal agencies to ensure that their actions do not jeopardize the existence of species listed on the endangered species list (hereafter ‘listed species’) and do not adversely modify their designated critical habitat [18]. In terrestrial and freshwater contexts, federal agencies must consult with the US Fish and Wildlife Service (hereafter “the Service”) on the effects of proposed projects and regulations on listed species [19]. In Oct. 2020, the Service completed a re-initiated consultation on the Surface Mining Control and Reclamation Act (SMCRA) [20], which defines regulations and procedures by which state regulatory authorities can issue mining permits. As with the original consultation on SMCRA in 1996, the Service found these procedures sufficient to prevent jeopardization of any listed species potentially affected by surface mining [20, 21] These conclusions were based on the requirement of permit seekers to analyze and present plans to minimize adverse effects to listed species. While spatially extensive analyses were considered impractical in 1996, new data and technology now make landscape-scale assessments possible.

In this paper, we use large public datasets and remote sensing analyses to quantify the potential indirect and aggregate impacts of MTMVF on imperiled species in Central Appalachia. The purpose of this study was to assess the impacts of MTMVF on water quality through the lens of protections to federally protected species required under the ESA. We evaluate whether statistical relationships exist between mountaintop removal mining activities as determined from satellite imagery and downstream water quality as measured at thousands of monitoring stations across the region. If downstream water quality can be predicted from remotely sensed, landscape-scale data of mining impacts, then improved estimates of baseline conditions and predictions of future conditions for imperiled species can be made. In turn, those results would be used to ensure regulatory decisions, such as those made under the ESA, are meeting the purposes of the law. To that end, we evaluated the relationships between mined area and:

observed values of water quality measures relevant to aquatic life;

the frequency that water quality thresholds for aquatic life were exceeded; and

the frequency of waterway impairment under the Clean Water Act.

We demonstrate that such relationships often exist and link them to the specific laws and policies that provide requirements and mechanisms to ameliorate them. We focus on variables that are known or believed to be important for aquatic species persistence to provide insights that can be used to directly improve the conservation prospects for federally protected species.

Methods

Mining data

We obtained spatial data delineating the footprints of all large surface mines across Central Appalachia in each year from 1985 to 2015. These data were generated using Landsat satellite imagery in a previous analysis measuring trends in the extent of mining activities over time [16]. In order to avoid commission errors in mine identification affecting subsequent analyses, we cross-referenced these footprints with each year of available data from the National Land Cover Dataset [22] and eliminated all footprints that overlapped with areas flagged as agriculture or development. We used these mine footprints to define our study area as a contiguous selection of all the US counties containing these mines (Fig 1).

Fig 1

Chronic exposure thresholds for aquatic life were exceeded thousands of times in Central Appalachian waterways between 1985 and 2015.

Map shows the locations of water quality monitoring stations within the study area encompassing parts of Kentucky, Tennessee, Virginia, and West Virginia shown on the inset map. Colors indicate the mean number of different water quality measures for which chronic exposure thresholds were exceeded each time a sample was taken at a given location. The basemap contains USGS/NASA Landsat data from 2020, accessed through Google Earth Engine.

Water quality data

We obtained measurements of water quality from the national water quality data portal [23] using the dataRetrieval package [24] for R [25]. The national water quality data portal aggregates data from monitoring stations nationally, primarily from the U.S. Geological Survey and the Environmental Protection Agency, but also the National Park Service and other state agencies. We selected a set of water quality measures related to mining activity that can also affect the health of aquatic species (Table 1). Additionally, we collected flow rate and temperature data, although these measures were not ultimately used. Water quality data from 1985 through 2015 were collected—corresponding to the same period over which mining footprint data was available—from all monitoring stations within the counties comprising our study area. Water quality data were provided in different units, and we standardized all measures of concentration to ug/L, conductivity to μS /cm, and turbidity to NTU. To account for potentially mis-recorded data, we flagged as outliers any observations above the 99.9th percentile for a given measure. This threshold was determined empirically by plotting the number of observations falling outside successively larger quantiles and selecting the quantile at which observations appeared to plateau.

Table 1

Chronic and acute toxicity thresholds were exceeded many times in waterways important to listed aquatic species.

Measure	Chronic	Acute	No. sites (%)
Aluminum	3,853	871	973 (51.6)
Arsenic	39	39	831 (44.0)
Cadmium	659	629	853 (45.2)
Calciuma	-	-	-
Conductivity	2,246	2,244	226 (12.0)
Copper	1,052	750	927 (49.1)
Irona	-	-	-
Lead	795	271	878 (46.5)
Manganese	3,651	2,276	1,073 (56.9)
Mercury	72	62	722 (38.3)
pH	15,419	15,419	1,187 (62.9)
Selenium	113	79	793 (42.0)
Sulfatea	-	-	-
Turbiditya	-	-	-
Total dissolved solidsa	-	-	-
Total suspended solidsa	-	-	-
Zinc	451	451	997 (52.8)

aNo chronic or acute thresholds provided for measure.

Table shows the number of times that any recorded value (e.g. ‘Dissolved’, ‘Total’, etc.) of each water quality measure exceeded standard thresholds for aquatic life, and the number of different monitoring stations at which these events occurred. These data only consider measures taken from 1,887 monitoring stations whose drainage basin contained a stream that was designated as important to species survival and recovery.

To identify acute and chronic toxicity thresholds for different water quality measures, we used the state water quality standards for aquatic life administered by Virginia under the Clean Water Act [1]. These standards are approved by the Environmental Protection Agency and are used to determine waterway impairment requiring mitigation. The water quality standards thus represent an agreed upon set of thresholds necessary to maintain suitability of waterways for aquatic species. The Virginia standards were identical to those from Kentucky, Tennessee, and West Virginia. For each measure we flagged any observation where recorded levels exceeded either the acute or chronic exposure thresholds. Finally, we also obtained the locations of all waterways declared as ‘impaired’ under section 303(d) of the Clean Water Act occurring within our study area from the EPA’s Environmental Dataset Gateway [26]. Impaired waterway designations were made on biannual cycles beginning in 1991.

Imperiled species data

We compiled a list of ESA-listed aquatic species whose range overlapped the study area (S1 Table) by using the ECOS Data Explorer (https://ecos.fws.gov/ecp/report/adhocDocumentation?catalogId=species&reportId=species). Potentially important water quality measures were determined by examining federal documents pertaining to these species including listing decisions, recovery plans, and five-year reviews (available at https://ecos.fws.gov/ecp). We generated a list of streams that were important to the survival and recovery of listed aquatic species as those streams identified in these same documents as containing extant populations or as being necessary for recovery. For species with designated critical habitat, we included these waterways as well. We refer to this combined list as streams important to imperiled species.

Spatial analyses

To assess the relationship between mining and water quality measures recorded at monitoring stations, we associated mines and monitoring locations based on hydrography. We used the watershed and flow modeling tools from the pyshed package [27] for Python to delineate the geographic areas that drained into each monitoring station. These analyses required a model of surface elevation, and we used a 30m digital elevation model provided by NASA [28] clipped to our study area. Once drainage basins were delineated for each monitoring station, we calculated the proportion of each basin covered by surface mines in each year from 1985 to 2015. We also created a many-to-one spatial join identifying which mines fell within the drainage basin of each monitoring station.

We used the annual Cropland Data Layers [29] and NLCD data to estimate the proportion of drainage basins that were covered by agriculture or impervious surface in each year for which mining footprint data was available (1985–2015). Cropland data were available annually beginning in 2009, and NLCD impervious surface data were available from 1992, 2001, 2004, 2006, 2008, 2011 and 2013. We interpolated between and extrapolated beyond these observed data points to estimate agricultural and impervious surface data for missing years between 1985 and 2015.

In analyses pertaining to 303(d) impaired waterways we consider mined area within watersheds containing the waterway. We used U.S. Geological Survey HUC12 hydrologic units [30] to represent watersheds. We then repeated the above interpolation and extrapolation procedure to obtain the area mined and covered by agriculture or impervious surface within a contiguous selection of watersheds overlapping the mine footprint data set.

Unless otherwise indicated, all spatial analyses were performed using the Google Earth Engine python API [31].

Statistical analyses

We tallied the frequency with which water quality standards were exceeded in streams important to imperiled species by spatially joining the locations of monitoring stations to linear stream features with attributes indicating whether the stream was important to imperiled species.

In all analyses estimating the relationship between mined area and water quality measures, we attempted to account for attenuation in pollutant concentrations with increasing distance between monitoring stations and mines. We adjusted the area of each mine footprint within a given drainage basin in proportion to the square root of the distance from the mine to the corresponding monitoring station. We refer to this measure as adjusted mined area.

Our first objective was to quantify the relationship between values of each water quality measure and the proportion of drainage basins that were mined each year, while controlling for agriculture and impervious surface in the drainage basin. To do so, we created linear mixed effects models with normal error distributions and random intercepts per year nested within monitoring sites. These models were used to estimate the increase or decrease in mean water quality measures as a function of adjusted mined area, percent agriculture, and percent impervious surface within drainage basins.

Our second objective was to determine whether the proportion of a drainage basin that was mined each year affected the probability that pollutant levels would exceed thresholds deemed safe for aquatic life. We specified a generalized linear mixed model using a binomial error distribution and logit link, with random intercepts per year nested within monitoring sites. These models were used to predict the probability that an observed value for a given measure would exceed chronic thresholds as a function of adjusted mined area, percent agriculture, and percent impervious surface within drainage basins.

Our last objective was to estimate the relationship between the proportion of a watershed that was mined and the probability that a waterway therein would be designated as impaired based on the standards for aquatic life under the Clean Water Act. We specified a generalized linear mixed model with a logit link and binomial error distribution with random intercepts per year nested within monitoring stations. These models were used to predict the probability that a watershed would contain an impaired waterway at the end of a given biannual evaluation cycle as a function of adjusted mined area, percent agriculture, and percent impervious. Because impaired waterways are only tallied during biannual cycles, we used the maximum percent agriculture and impervious surface within two-year cycles as predictor variables. To account for lagged and accumulative effects, we used the cumulative sum of percent mined area within drainage basins over time.

In all regression analyses we included only sites with at least 10 observations. We estimated model parameters in a Bayesian framework using the rstanarm [32] package for R. For each model we generated four MCMC chains and tested for convergence using the Rhat statistic. A significant relationship was determined between mined area and response variables if the 95% credible interval around the relevant parameter estimate did not overlap zero. We tested for collinearity among the predictor variables used in linear models (percent mined area, percent agriculture, percent impervious surface) using pairwise correlation coefficients. Code used in all analyses is available through an Open Science Framework repository [33].

Results

We obtained water quality data from 4,260 different water quality monitoring sites across our study area. Distances between stations and mines were exponentially distributed, ranging from < 1 to 343 km (x = 77.7 km, σ2 = 4,071 km). The number of observations (i.e., occasions on which water quality data was recorded) at each site ranged from 1 to 275. There were 569 sites with at least 10 observations and drainage basin areas greater than 400 km2 that were included in modeling analyses. None of the predictor variables exhibited evidence of collinearity (-0.09 < R2 < 0.07).

Linear mixed models indicated significant positive relationships between the proportion of a drainage basin that was mined and measured levels of conductivity, manganese, sulfate, sulfur, total dissolved solids, total suspended solids, and zinc (Fig 2). No measures were significantly negatively associated with adjusted mined area (Table 2).

Fig 2

Increases in the proportion of drainage basins that were mined lead to increases in multiple measures of water quality that are detrimental to aquatic species.

Graphs show the change in water quality measures per change in mined area as estimated by linear mixed models. Dashed lines encompass a 95% credible interval around estimated relationships.

Table 2

Significant positive relationships were estimated between mined area and nine measures of water quality.

Toxin	Measure	Median	2.5%	97.5%	Rhat
Aluminum (μg/L)	Total	3.57E+03	-1.01E+03	8.17E+03	1.01
Aluminum (μg/L)	Dissolved	3.74E+03	-9.82E+02	8.52E+03	1.01
Arsenic (μg/L)	Total	-5.03E+01	-1.38E+02	3.52E+01	1.00
Arsenic (μg/L)	Dissolved	3.39E-01	-5.26E+00	5.70E+00	1.00
Cadmium (μg/L)	Total	-8.21E+02	-2.08E+03	4.52E+02	1.01
Cadmium (μg/L)	Dissolved	-6.92E+00	-4.39E+01	3.11E+01	1.00
Calcium (μg/L)	Total	6.34E+04	-4.87E+02	1.25E+05	1.01
Calcium (μg/L)	Dissolved	8.04E+04	-3.35E+04	1.92E+05	1.00
Conductivity (μS/L)	Total	3.30E+06	1.96E+06	4.67E+06	1.00
Copper (μg/L)	Total	-1.20E+02	-4.52E+02	2.03E+02	1.00
Copper (μg/L)	Dissolved	-1.26E+02	-4.67E+02	2.04E+02	1.00
Iron (μg/L)	Total	-2.04E+03	-2.23E+04	1.87E+04	1.00
Iron (μg/L)	Dissolved	-1.44E+04	-3.48E+04	5.94E+03	1.00
Lead (μg/L)	Total	-5.12E+01	-2.32E+02	1.28E+02	1.00
Lead (μg/L)	Dissolved	-5.32E+01	-2.27E+02	1.16E+02	1.00
Manganese (μg/L)	Total	1.24E+04	3.25E+02	2.40E+04	1.01
Manganese (μg/L)	Dissolved	1.24E+04	3.25E+02	2.40E+04	1.01
Mercury (μg/L)	Total	-5.52E+00	-3.42E+01	2.33E+01	1.00
Mercury (μg/L)	Dissolved	-5.77E-01	-2.82E+00	1.76E+00	1.01
pH	Dissolved	2.68E-01	-3.37E-01	9.02E-01	1.00
Selenium (μg/L)	Total	-1.78E+02	-4.01E+02	4.65E+01	1.00
Selenium (μg/L)	Dissolved	-1.76E+02	-4.10E+02	5.36E+01	1.00
Sulfate (μg/L)	Dissolved	6.08E+05	1.98E+05	1.01E+06	1.00
Sulfur (μg/L)	Total	6.58E+05	3.58E+05	9.45E+05	1.00
Total dissolved solids (μg/L)	Total	6.87E+05	2.25E+05	1.14E+06	1.00
Total dissolved solids (μg/L)	Dissolved	7.74E+05	-3.56E+04	1.55E+06	1.00
Total suspended solids (μg/L)	Total	3.01E+05	1.96E+05	4.02E+05	1.00
Turbidity (NTU)	Total	-7.06E+02	-2.79E+03	1.33E+03	1.00
Zinc (μg/L)	Total	6.73E+01	3.19E+01	1.03E+02	1.01
Zinc (μg/L)	Dissolved	6.85E+01	3.32E+01	1.04E+02	1.01

Table shows the regression coefficients quantifying the relationship between adjusted mined area on measures, as estimated by linear mixed models. The 50th, 2.5th, and 97.5th percentile of the posterior distribution, as well as a measure of MCMC chain convergence (Rhat) are included. Bold text indicates estimates with 95% credible intervals that did not overlap zero.

We also found significant positive relationships between the cumulative proportion of a watershed that was mined over time and the log odds that a stream in that watershed would be designated as impaired for aquatic life under the Clean Water Act (Table 3).

Table 3

Significant positive relationships were estimated between mined area in a watershed and the probability that streams therein were designated as impaired.

Max percentage	Median	2.5%	97.5%	Rhat
Mined	23.04	20.34	25.88	1.00
Impervious	97,232.78	74,617.36	118,089.30	1.02
Cultivated	-36,071.50	-40,454.80	-32,007.00	1.00

Table shows the 50th, 2.5th, and 97.5th percentiles of posterior distributions of regression coefficients quantifying the relationship between and probability of impairment, as estimated by logistic regression models with random effects. Measures of MCMC chain convergence (Rhat) are included.

Measures of conductivity all exceeded chronic exposure thresholds, and we were unable to model the probability of exceeding thresholds as a function of land cover predictors. Models indicated that the probabilities that chronic exposure thresholds for copper, lead, and manganese were exceeded were all positively related to the area mined within drainage basins (Fig 3). The probabilities for no water quality measures exhibited a significant negative relationship.

Fig 3

Increases in the proportion of drainage basins that were mined increased the probability that chronic exposure toxicity thresholds would be exceeded for three water quality measures.

Graphs show the change in probability of exceedance per change in mined area as estimated by linear mixed models. Dashed lines encompass the 95% credible interval around estimated relationships.

As of 2018, 55 ESA-listed aquatic species potentially occurred within the counties comprising our study area. These included 39 mollusk, 12 fish, 3 crustacean, and 1 snail species. Of these listed species, 16 had designated critical habitat (S1 Table). Additionally, for 50 of these species we were able to identify specific streams that were important to the species survival and recovery in either critical habitat designations, listing decisions, five-year reviews, or recovery plans. Of the 4,260 monitoring stations, 2,881 of these drained areas containing important streams. Chronic and acute toxicity thresholds for aquatic life were exceeded thousands of times at these monitoring stations (Table 1). The most frequently exceeded threshold was that for pH, followed by manganese, aluminum, and conductivity (Table 1). 54 streams designated as critical habitat were sampled directly by 209 monitoring stations. Water quality thresholds were exceeded at least once at each of those monitoring stations a total of 5,592 times with a maximum of 272 at a single station.

Discussion

The scale of the human enterprise is large and growing [7]. To minimize the environmental impacts of human activities, analytical approaches that can address the full scale and complexity of their effects are required. Many laws and regulations are theoretically capable of handling such large-scale effects, but too often the data and science needed to fully understand the effects of our actions are limited [8]. Here we combine two classes of large, public datasets to demonstrate that mountaintop mining with valley fill (MTMVF) in Central Appalachia is associated with degraded water quality at landscape scales in ways that affect the survival and recovery of federally protected species. We found consistent evidence linking changes in mined area with increases in concentrations of toxics, conductivity, and dissolved and suspended solids. Far from being innocuous side effects, the measures with the strongest relationships to mining were among those that directly affect the survival of aquatic species [12]. These findings demand regulatory action under federal environmental laws including the Endangered Species Act and Clean Water Act, because these activities are concentrated in an area with high numbers of imperiled species.

The positive relationships between the extent of mined areas and degradation in water quality that we identified were not surprising. A large body of previous ecological and hydrologic research has shown that surface mining can negatively impact water quality and reduce the suitability of streams for aquatic species at local and regional scales [3, 10, 11, 13, 14]. Consistent with this research, we found substantial increases in stream conductivity, and the concentrations of manganese, sulfate, sulfur, zinc, and dissolved and suspended solids associated with increases in the proportion of upstream areas that were mined. Our results build on previous work by illustrating that MTMVF not only degrades water quality immediately proximate to mines, it does so at a landscape scale—establishing a direct relationship between the aggregate area mined on the landscape and degradation in water quality and habitat suitability for aquatic species. In the context of environmental regulations governing the permitting and operation of MTMVF mines, these direct and aggregate effects illustrate the importance of considering a spatially extensive set of conditions when evaluating the environmental baseline and potential impacts to protected ecosystems and species.

Accounting for the aggregate effects of mine permitting at landscape scale is particularly important in the context of conserving species threatened with or on the brink of extinction, such as those on the endangered species list. We found that degradation in water quality progressed to potentially lethal levels for aquatic life—documenting thousands of instances in which chronic and acute water quality thresholds were exceeded between 1985 and 2015. Our analyses demonstrate a positive relationship between the probability that chronic thresholds were exceeded and the amount of upstream area that was mined. Measured levels of dissolved copper, lead, and manganese were more likely to exceed safe thresholds for aquatic life as the proportion of upstream area that was mined increased. Together, these dynamics likely contributed to the emergent outcome we observed that a waterway was more likely to fail to meet water quality standards and be declared impaired under the CWA as the proportion of its watershed that was mined increased.

Mining activities thus directly impacted listed species, based on the observation that water quality thresholds were exceeded thousands of times at monitoring stations on streams designated as critical habitat and stations draining streams identified by the US Fish and Wildlife Service as important to listed species survival and recovery. For instance, monitoring station 211WVOWR-KE-000-004.50 collected water from a section of the Elk River designated as critical habitat for the diamond darter (Crystallaria cincotta). Out of 48 occasions, chronic and acute thresholds for aluminum were exceeded 33 and 8 times, respectively; manganese 14 and 5 times; copper 2 and 2 times; and cadmium once. The repeated exceedance of acute toxicity thresholds for aquatic life within designated critical habitat could constitute an adverse modification of critical habitat–an outcome federal agencies must legally avoid under the ESA. The regular and repeated exceedance of water quality thresholds for aquatic life in both critical habitat, and waterways important to the survival and recovery of listed species would seem to conflict with the purpose of the ESA, protecting and recovering imperiled species.

Evidence that the extent of impacts of MTMVF can include entire watersheds should inform the implementation of federal environmental regulations. Regulations under SMCRA require mine operators to minimize the impact of their actions within ‘adjacent areas,’ which are defined as the areas within which imperiled species ‘…reasonably could be expected to be adversely impacted by proposed mining operations’ [20]. That is, if the areas impacted by MTMVF extend into broader areas where ESA-listed species occur, those impacts must be considered in consultation [19]. Under SMCRA, mine operators must also minimize adverse impacts and enhance natural resources during mine reclamation. Reclamation activities are increasingly relevant for the protection and recovery of imperiled species as the use of coal for energy, and hence its production, has been declining in the United States and is expected to continue to decline [34]. Reclamation efforts have historically been unsuccessful re-establishing native Appalachian forests following MTMVF [15], as they often emphasize restoring vegetation to mined sites without prioritizing native species [35, 36]. Even efforts that successfully re-establish native communities often exclude rare species [37]. However, the Forestry Reclamation Approach provides management practices which allow operators to meet the reclamation requirements of SMCRA, while also restoring native forests [38]. Both the Forestry Reclamation Approach, and the data presented here constitute ‘best available scientific and commercial information,’ which must be considered under the ESA [18]. Whether their exclusion from the Service’s past [20, 21] or future analyses of the effects of implementing SMCRA violates this requirement is an outstanding question beyond the scope of this paper, but may be a significant legal vulnerability.

Of course, water quality degradation and waterway impairment are not solely attributable to surface mining, and more than half of US waterways do not meet CWA water quality standards. In addition to point source pollution, many land use factors can contribute to the degradation of aquatic conditions, including the prevalence of impervious surfaces and agriculture within watersheds [39, 40]. These pathways were reflected in our results, which show positive relationships between impervious surface and agricultural extent within watersheds and the likelihood that an encompassed waterway would be impaired. In recent years, however, energy has been the largest driver of land use change in the United States [41], and policies that minimize the negative environmental impacts of energy exploration, extraction, and production thus have the potential to make a significant improvement to the conservation prospects of biodiversity.

Conclusions

Our results demonstrate that even after accounting for additional sources of water quality degradation, surface mining contributed to further increases in the concentration of toxics that can impair aquatic biota in Central Appalachia. These findings indicate that, in situ, the growth, continued operation, and legacy effects of MTMVF in the region likely directly limit the prospects for survival and recovery of over 50 federally protected species. Federal agencies will need to take at least two steps to meet their obligations under federal statutes given these results. First, existing and new critical habitat designations must be accurately accounted for by federal agencies as they implement programs such as SMCRA and carry out consultation on those programs under the ESA. Absent this, the ESA is at risk of being nothing more than a “paper tiger” in which protection only exists on paper rather than benefiting species [42]. Additionally, the requirements for adverse effect minimization and resource enhancement under SMCRA can use the same publicly available water quality and remote sensing data and analytical methods presented here to account for the effects of mine operation beyond the immediate footprint. While approved practices for assessing and permitting the operation of an individual mine may successfully mitigate impacts to immediately surrounding ecosystems, our results demonstrate that a landscape scale assessment is necessary to fully account for the impacts of surface mining on imperiled species.

55 aquatic species that occur within Central Appalachia are listed on the Endangered Species List.

Table shows the common and scientific names of listed species, dates on which species were added to the Endangered Species List and their current status (Threatened or Endangered), and the date at which any critical habitat was designated. aProposed critical habitat after completion of the study.

(DOCX)

Click here for additional data file.

15 Jan 2021

PONE-D-20-28542

Mountaintop Removal Mining Threatens the Survival and Recovery of Imperiled Species

PLOS ONE

Dear Dr. Evans,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

REVIEWER I

Major comments

The introduction is too long and should be shortened and made more concise

There are also two major flaws with the manuscript

1). Analysis of biota in contaminated and uncontaminated areas is never explicitly compared, paired tests and analysis are needed to establish a causal impact. The manuscript repeatedly states that tolerances are being exceeded, but the impacts on biota are not clearly analysed. Much of the manuscript reads as if the initial aim was to look at mining and pollution, and that biodiversity was added later to increase readership and relevance

Many more explicit details on the impacts are needed.

Secondly, and following on this, better species specific analysis (i.e. by guild) are needed in relation to sensitivity, and contaminant specific analysis also needed. The main headline here is "mining contaminates rivers, this could be problematic" but explicit analysis on how species are impacted is needed. This will require extensive restructuring and further analysis to elaborate the findings of the paper.

REVIEWER II

The study provides important insights into how mining operations conducted upstream can have cascading effects downstream, degrading aquatic ecosystems and causing detrimental effects on biodiversity.

Following are some minor revisions suggested:

1. Introduction: In the first paragraph, the authors have reiterated about “detrimental effects of environmentally damaging activities”. I suggest the authors to provide a few examples of such damaging activities and their potential impacts to introduce the study in a more comprehensible manner.

2. Please rephrase the line – “Additionally, pollutants can be transferred through food webs to downstream terrestrial ecosystems (14)”. I think that the authors meant to convey that pollutants can be transferred downstream by water flow, across food webs, and into terrestrial ecosystems.

3. I suggest the authors to provide the year of enactment of the acts mentioned in the paper to provide non-native readers an idea about the timeline.

4. Were any correlation tests performed to rule out correlated variables from the GLMMs?

5. The authors should provide a table of the variables used in the study.

6. Figure 1: Please provide a map of the USA with the highlighted study area.

Please submit your revised manuscript by 15th February, 2021. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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We look forward to receiving your revised manuscript.

Kind regards,

Shalini Dhyani, Ph.D

Academic Editor

PLOS ONE

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Shalini Dhyani

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2. We noted in your submission details that a portion of your manuscript may have been presented or published elsewhere.

"Yes. The mine footprint data used to estimate was previously published in Pericak A et al. (2018) Mapping the yearly extent of surface coal mining in central appalachia using landsat and Google Earth Engine. PLoS One 13(7). These data are publicly available at: " ext-link-type="uri" xlink:type="simple">https://skytruth.org/mtr-data-files/"

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The following resources for replacing copyrighted map figures may be helpful:

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Natural Earth (public domain): http://www.naturalearthdata.com/

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

4. Please include captions for your Supporting Information files at the end of your manuscript, and update any in-text citations to match accordingly. Please see our Supporting Information guidelines for more information: http://journals.plos.org/plosone/s/supporting-information.

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The study provides important insights into how mining operations conducted upstream can have cascading effects downstream, degrading aquatic ecosystems and causing detrimental effects on biodiversity. The authors can improve the manuscript by going through some minor changes suggested in this review.

Following are some minor revisions suggested:

1. Introduction: In the first paragraph, the authors have reiterated about “detrimental effects of environmentally damaging activities”. I suggest the authors to provide a few examples of such damaging activities and their potential impacts to introduce the study in a more comprehensible manner.

2. Please rephrase the line – “Additionally, pollutants can be transferred through food webs to downstream terrestrial ecosystems (14)”. I think that the authors meant to convey that pollutants can be transferred downstream by water flow, across food webs, and into terrestrial ecosystems.

3. I suggest the authors to provide the year of enactment of the acts mentioned in the paper to provide non-native readers an idea about the timeline.

4. Were any correlation tests performed to rule out correlated variables from the GLMMs?

5. The authors should provide a table of the variables used in the study.

6. Figure 1: Please provide a map of the USA with the highlighted study area.

Reviewer #2: The introduction is too long and should be shortened and made more concise

There are also two major flaws with the manuscript

1). Analysis of biota in contaminated and uncontaminated areas is never explicitly compared, paired tests and analysis are needed to establish a causal impact. The manuscript repeatedly states that tolerances are being exceeded, but the impacts on biota are not clearly analysed. Much of the manuscript reads as if the initial aim was to look at mining and pollution, and that biodiversity was added later to increase readership and relevance

Many more explicit details on the impacts are needed

Secondly, and following on this, better species specific analysis (i.e. by guild) are needed in relation to sensitivity, and contaminant specific analysis also needed. The main headline here is "mining contaminates rivers, this could be problematic" but explicit analysis on how species are impacted is needed. This will require extensive restructuring and further analysis to elaborate the findings of the paper

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Syed Ainul Hussain, Ph.D., D.Sc.

Wildlife Institute of India, Dehradun, India

Reviewer #2: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

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19 Mar 2021

REVIEWER 1

Comment 1: Introduction: In the first paragraph, the authors have reiterated about “detrimental effects of environmentally damaging activities”. I suggest the authors to provide a few examples of such damaging activities and their potential impacts to introduce the study in a more comprehensible manner.

Response: Thank you for the suggestion; we have incorporated examples of the ways in which surface mining degrades surrounding forested ecosystems, previously in the second Introduction paragraph, into this first paragraph to provide a comprehensible example of these dynamics.

Comment 2: Please rephrase the line – “Additionally, pollutants can be transferred through food webs to downstream terrestrial ecosystems (14)”. I think that the authors meant to convey that pollutants can be transferred downstream by water flow, across food webs, and into terrestrial ecosystems.

Response: We have revised the sentence. It now reads "Additionally, pollutants can be transferred across food webs to terrestrial ecosystems."

Comment 3: I suggest the authors to provide the year of enactment of the acts mentioned in the paper to provide non-native readers an idea about the timeline.

Response: We have added the years in which the ESA and CWA were passed.

Comment 4: Were any correlation tests performed to rule out correlated variables from the GLMMs?

Response: Thank you for the question. We now describe the correlation test used to evaluate collinearity among predictor variables used in linear models at the end of the Methods, and report that these variables did not exhibit evidence of collinearity in the Results.

Comment 5: The authors should provide a table of the variables used in the study.

Response: All variables used in this study are currently listed in Tables 1 and 3. Tables 1 and 2 include all water quality measures that served as response variables in the different analyses. Table 3 provides AIC weights for each of the landscape condition variables (e.g. percent impervious, percent ag, etc.) that were used as predictor variables.

Comment 6: Figure 1: Please provide a map of the USA with the highlighted study area.

Response: We have added an inset map of the United States to this figure, which shows the larger study area.

REVIEWER 2

Comment: The introduction is too long and should be shortened and made more concise

Response: We have revisited the introduction and made revisions that have shortened the length substantially.

Comment: 1). Analysis of biota in contaminated and uncontaminated areas is never explicitly compared, paired tests and analysis are needed to establish a causal impact. The manuscript repeatedly states that tolerances are being exceeded, but the impacts on biota are not clearly analysed. Much of the manuscript reads as if the initial aim was to look at mining and pollution, and that biodiversity was added later to increase readership and relevance.

Response: These comments are helpful for understanding how readers may interpret the purposes and scope of this work, which we address in two parts.

First, the goals of this research are to test if there are relationships between remotely sensed data (satellite imagery) of MTR to in-stream water quality data for parameters related to wildlife health and conservation, then link any such relationships to the regulatory implications under, especially, the ESA. We believe it is unnecessary and beyond the scope of this work to have to analyze the impacts of contamination on biota for this contribution to be acceptable for publication. These results have been reported in other literature and are used by scientists and regulatory entities like those that implement the ESA. Taken to its logical end, the reviewer's comment would require that any research published not only cite other work but replicate such work before inferences can be drawn. That written, we have revisited the manuscript throughout to ensure that the scope is clear and that potentially causal relationships are conveyed appropriately.

Second, the biodiversity component of the research was first-and-foremost; two of the authors are from a wildlife-focused nonprofit, and the third from one that has a great interest in biodiversity. We have revisited the manuscript with your observation in mind and made edits to help clarify that focus. In the Introduction we make explicit our interest in assessing the potential impacts of mining on imperiled species through its effects on water quality.

Comment: Many more explicit details on the impacts are needed.

Response: While it is not entirely clear which impacts the reviewer is referencing, following on our response above, we respectfully disagree that this contribution requires many more explicit details of impacts [on biodiversity] to be a contribution that advances science. As noted above, the impacts of the water quality measures we analyze to various taxa have been documented elsewhere, and we reference this literature throughout the manuscript.

Comment: Secondly, and following on this, better species specific analysis (i.e. by guild) are needed in relation to sensitivity, and contaminant specific analysis also needed. The main headline here is "mining contaminates rivers, this could be problematic" but explicit analysis on how species are impacted is needed. This will require extensive restructuring and further analysis to elaborate the findings of the paper.

Response: Related to the comments above, we believe there is a misunderstanding of the goal of the present contribution. Our goal is to test the hypothesis that there is a relationship between MTR changes as detected using satellite imagery and in-stream water quality measurements in the catchments of those MTR footprints for parameters that have known effects on biodiversity, in particular ESA-listed species for which regulations have established thresholds of exposure given the best available scientific information. Our goal is not to do a meta-analysis of the effects of particular contaminants on species or the sensitivity of those species, nor to do in a single undertaking a full causal chain analysis that goes from satellite to organism response. The latter is very interesting and building on this contribution could be a subsequent step in this research program, but for our purposes, the satellite to instream measurements connection, placed in the context of ESA regulations, is sufficient.

JOUNRAL

Comment 1: Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

Response: We have made changes throughout the manuscript to meet the requirements.

Comment 2: We noted in your submission details that a portion of your manuscript may have been presented or published elsewhere. "Yes. The mine footprint data used to estimate was previously published in Pericak A et al. (2018) Mapping the yearly extent of surface coal mining in central appalachia using landsat and Google Earth Engine. PLoS One 13(7). These data are publicly available at: https://skytruth.org/mtr-data-files/" Please clarify whether this publication was peer-reviewed and formally published. If this work was previously peer-reviewed and published, in the cover letter please provide the reason that this work does not constitute dual publication and should be included in the current manuscript.

Response: The MTR footprint data were the focus of a paper published in PLoS One, as noted in the comment. We do not believe this could possibly be considered dual publication because the previous paper did not connect the MTR data to in-stream water quality data, which is the focus of the present contribution; we simply are using the MTR as an input dataset. We have provided an explanation to this effect in the cover letter.

Comment 3: We note that Figure 1 and Supplementary Figures F1, F2 in your submission contain map/satellite images which may be copyrighted. All PLOS content is published under the Creative Commons Attribution License (CC BY 4.0), which means that the manuscript, images, and Supporting Information files will be freely available online, and any third party is permitted to access, download, copy, distribute, and use these materials in any way, even commercially, with proper attribution. For these reasons, we cannot publish previously copyrighted maps or satellite images created using proprietary data, such as Google software (Google Maps, Street View, and Earth). For more information, see our copyright guidelines: http://journals.plos.org/plosone/s/licenses-and-copyright. We require you to either (1) present written permission from the copyright holder to publish these figures specifically under the CC BY 4.0 license, or (2) remove the figures from your submission:

3.1. You may seek permission from the original copyright holder of Figure 1 and Supplementary Figures F1, F2 to publish the content specifically under the CC BY 4.0 license.

We require you to either (1) present written permission from the copyright holder to publish these figures specifically under the CC BY 4.0 license, or (2) remove the figures from your submission:

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We recommend that you contact the original copyright holder with the Content Permission Form (http://journals.plos.org/plosone/s/file?id=7c09/content-permission-form.pdf) and the following text:

“I request permission for the open-access journal PLOS ONE to publish XXX under the Creative Commons Attribution License (CCAL) CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). Please be aware that this license allows unrestricted use and distribution, even commercially, by third parties. Please reply and provide explicit written permission to publish XXX under a CC BY license and complete the attached form.”

Please upload the completed Content Permission Form or other proof of granted permissions as an "Other" file with your submission.

In the figure caption of the copyrighted figure, please include the following text: “Reprinted from [ref] under a CC BY license, with permission from [name of publisher], original copyright [original copyright year].”

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The following resources for replacing copyrighted map figures may be helpful:

USGS National Map Viewer (public domain): http://viewer.nationalmap.gov/viewer/

The Gateway to Astronaut Photography of Earth (public domain): http://eol.jsc.nasa.gov/sseop/clickmap/

Maps at the CIA (public domain): https://www.cia.gov/library/publications/the-world-factbook/index.html and https://www.cia.gov/library/publications/cia-maps-publications/index.html

NASA Earth Observatory (public domain): http://earthobservatory.nasa.gov/

Landsat: http://landsat.visibleearth.nasa.gov/

USGS EROS (Earth Resources Observatory and Science (EROS) Center) (public domain): http://eros.usgs.gov/#

Natural Earth (public domain): http://www.naturalearthdata.com/

Response: We have replaced the basemap imagery in Figure 1 with Landsat data, which are provided publicly by the USGS and NASA. We have added language to the Figure 1 caption providing attribution to this source as recommended by NASA https://landsat.visibleearth.nasa.gov/. We do not intend to publish any supplemental figures. Those referenced, unless we are mistaken, refer to the figures contained in the study previously published in PLoS One whose output data we build upon. This document was included for review purposes to make explicit where data used in this manuscript had previously been published. We have not modified or added new legends for these figures.

Comment 4: Please include captions for your Supporting Information files at the end of your manuscript, and update any in-text citations to match accordingly. Please see our Supporting Information guidelines for more information: http://journals.plos.org/plosone/s/supporting-information.

Response: We have updated the file name for our supplemental material, as well as all in-text references in accordance with PLoS standards.

Submitted filename: Response_to_reviewers.docx

Click here for additional data file.

21 Jul 2021

PONE-D-20-28542R1

Linking mountaintop removal mining to water quality for imperiled species using satellite data

PLOS ONE

Dear Dr. Evans,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Editor's comments.  I think this manuscript is nearly there.  I do suggest that the discussion could be shortened and focussed more closely on the title.  This may then deal with many of the third reviewer's comments

For Lab, Study and Registered Report Protocols: These article types are not expected to include results but may include pilot data.

==============================

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We look forward to receiving your revised manuscript.

Kind regards,

Judi Hewitt

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments (if provided):

Editor's comments. I think this manuscript is nearly there. I do suggest that the discussion could be shortened and focussed more closely on the title. This may then deal with many of the third reviewer's comments

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #3: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #3: Partly

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: N/A

Reviewer #3: No

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #3: No

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #3: No

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: I have checked this paper earlier. The paper is now ready for acceptance. I have no further comment.

Reviewer #3: The water quality data show that chronic and acute thresholds for aquatic life were exceeded thousands of times between 1985 and 2015 in streams that are important to the survival and recovery of species on the Endangered Species List. {which parameters? Name these parameters]

. Linear mixed models showed that levels of conductivity, manganese, sulfate, sulfur, total dissolved solids, total suspended solids, and zinc increased as the proportion of the area draining into a monitoring station that was mined increased. [mention the increase as %]

Introduction:

The release of alkaline mine drainage from weathering of rock and site waste at mine sites elevates conductivity and concentrations of metallic ions that negatively impact aquatic biota [12], leading to decreases in aquatic biodiversity [10,13]

METHODS

This portion is not clear. Be specific, for examples, under “Water Quality data” -- . Additionally, we collected flow rate and temperature data. # where it has been used? And how it has been used?

Table 1. Chronic and acute toxicity thresholds were exceeded many times in waterways important to listed aquatic species [NOT CLEAR?], For examples, Arsenic 39 (chronic) 39 (acute), what are these values? Is that concentration?? Or what? Not Clearly explained in the text.

# Water quality data were provided in different units, and we standardized all measures of concentration to ug/L, temperature to degrees Celsius, conductivity to µS /cm, and turbidity to NTU.

## wright inside the table for better clarity.

For Zinc 451(ug/L) for Acute and 451 (ug/L) for chronic. PLEASE CHECK THE DATA from the sources, where it has been collected? How concentration of Zn (451 ug/L) will be same for chronic and acute toxicity??

RESULTS

From m 0 to 343 km (x = 77.7 km, σ 2 = 4071 km), check data

# The number of observations at each site ranged from 1 to 275. What is the meaning?

Table 2: Total dissolved solids Total

Total dissolved solids Dissolved* (* implies dissolved?)

Total suspended solids Total

Check the solids nomenclature,

CONCLUSIONS

The conclusions must be based on the study undertaken. Entire conclusions discussed impacts of mining on imperiled species through degradation of water quality. It that is so, then in results section provide some tables which are now added as supplementary materials.

Overall comments:

There has been nobility in this approach, and could be useful, but methods results are not clear. It is more of secondary data used in a statistical tools. In such cases, a flow chart should be provided for meaning full interpretation of existing data.

Introduction part is very lengthy, it can be reduced substantially (at least 30-40%)

Methods: section needs through rewriting and Cleary mention commensurate with objectives of study.

I have a strong reservation for recommending this paper for publication, however, this article may be again rewrite and resubmit.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

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Reviewer #1: Yes: Syed Ainul Hussain, Wildlife Institute of India

Reviewer #3: Yes: Prof Subodh Kumar Maiti, IIT(ISM) Dhanbad, India

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14 Sep 2021

Editor's comments.

I think this manuscript is nearly there. I do suggest that the discussion could be shortened and focussed more closely on the title. This may then deal with many of the third reviewer's comments

We appreciate the positive feedback. In response to this and the reviewers’ suggestions we have shortened both the Introduction and Discussion. Specifically, we narrow and limit the scope of our discussion of conservations laws and regulations, except to make direct connections to the ways in which the results of this analysis directly inform their implementation.

Reviewer #1:

I have checked this paper earlier. The paper is now ready for acceptance. I have no further comment.

We appreciate the repeated review and help in shaping this manuscript for publication, and are happy to hear that we were able to address the previous suggestions.

Reviewer #3:

The water quality data show that chronic and acute thresholds for aquatic life were exceeded thousands of times between 1985 and 2015 in streams that are important to the survival and recovery of species on the Endangered Species List. {which parameters? Name these parameters]

As suggested, we have included the names of each of the parameters for which chronic and acute aquatic life thresholds were exceeded: Aluminum; Arsenic; Cadmium; Conductivity; Copper; Lead; Manganese; Mercury; pH; Selenium; and Zinc

Linear mixed models showed that levels of conductivity, manganese, sulfate, sulfur, total dissolved solids, total suspended solids, and zinc increased as the proportion of the area draining into a monitoring station that was mined increased. [mention the increase as %]

We have included the range of beta coefficients for these analyses in this sentence, presenting the average increase in each measure for a one percent increase in mined area. The sentence now reads:

“Linear mixed models showed that levels of conductivity, manganese, sulfate, sulfur, total dissolved solids, total suspended solids, and zinc increased by 6.73E+01 to 3.30E+06 for one percent increase in the mined proportion of the area draining into a monitoring station.”

Introduction:

The release of alkaline mine drainage from weathering of rock and site waste at mine sites elevates conductivity and concentrations of metallic ions that negatively impact aquatic biota [12], leading to decreases in aquatic biodiversity [10,13]

As far as we can tell, this sentence is identical to what is currently in the manuscript. If the reviewer is suggesting any changes it is unclear what they are.

Introduction part is very lengthy, it can be reduced substantially (at least 30-40%)

We have condensed the two Introduction paragraphs describing the legal and regulatory context in this work into a single paragraph. This aligns with a shortening of the Discussion aimed at focusing the text more closely on the results of this study. Additionally, we have made reductions in the length of the Introduction by eliminating and shortening sentences throughout.

Methods: section needs through rewriting and Cleary mention commensurate with objectives of study.

We have edited several sentences that were flagged as confusing or lacking clarity below, as well as re-organized the order in which the Table 1 is presented relative to text describing its contents.

This portion is not clear. Be specific, for examples, under “Water Quality data” -- .

We are not entirely clear as to what this comment pertains, but have re-read the Methods section for clarity, rearranging sections to improve the logical presentation of the data collection and analyses.

Additionally, we collected flow rate and temperature data. # where it has been used? And how it has been used?

We initially collected these variables for potential use in linear mixed models. We have amended this sentence acknowledging that these variables were not used. Additionally, in the following section we omit describing the conversion of temperature to degrees Celsius.

Table 1. Chronic and acute toxicity thresholds were exceeded many times in waterways important to listed aquatic species [NOT CLEAR?], For examples, Arsenic 39 (chronic) 39 (acute), what are these values? Is that concentration?? Or what? Not Clearly explained in the text.

These values are counts representing the number of times a recorded water quality measure exceeded standard thresholds for aquatic life, and thus are unitless. This description is provided in the Table footer:

Thresholds are not species specific, and we describe their origin in the Methods section as follows:

“To identify acute and chronic toxicity thresholds for different water quality measures, we used the state water quality standards for aquatic life administered by Virginia under the Clean Water Act. These standards are approved by the Environmental Protection Agency and are used to determine waterway impairment requiring mitigation. The water quality standards thus represent an agreed upon set of thresholds necessary to maintain suitability of waterways for aquatic species. The Virginia standards were identical to those from Kentucky, Tennessee and West Virginia.”

We have added a citation for the Vriginia Clean Water Act standards. We have moved the location of Table 1 to follow this description so that the context of the data presented in the table is more clear.

# Water quality data were provided in different units, and we standardized all measures of concentration to ug/L, temperature to degrees Celsius, conductivity to µS /cm, and turbidity to NTU.

## wright inside the table for better clarity.

We have added units to Table 2, which presents regression coefficients estimating the scale of increase or decrease in each water quality measure. As noted above, Table 1 reports the frequency with which thresholds for aquatic life established for each measure were exceeded. Thus, we omit units here.

For Zinc 451(ug/L) for Acute and 451 (ug/L) for chronic. PLEASE CHECK THE DATA from the sources, where it has been collected? How concentration of Zn (451 ug/L) will be same for chronic and acute toxicity??

Assuming these comments refer to Table 1, the reviewer has misunderstood the data being presented in the table, which consists of counts and not concentrations. We have moved the location of the table to the end of the Methods section, to provide more complete context for these data.

RESULTS

From m 0 to 343 km (x = 77.7 km, σ 2 = 4071 km), check data

We have double checked our data and confirm this statement is correct. We have modified how we present the results, as 0 km may cause confusion. We now state that:

“Distances between stations and mines were exponentially distributed, ranging from 1 to 343 km (x = 77.7 km, σ2 = 4,071 km).”

# The number of observations at each site ranged from 1 to 275. What is the meaning?

We have added language clarifying that an observation refers to an occasion on which water quality data was recorded at a site. The sentence now reads:

‘The number of observations (i.e., occasions on which water quality data was recorded) at each site ranged from 1 to 275.’

Table 2: Total dissolved solids Total

Total dissolved solids Dissolved* (* implies dissolved?)

Total suspended solids Total

Check the solids nomenclature,

These are the measures provided by the national water quality data portal, as recorded by the U.S. Geological Survey, National Park Service, and other state and federal agencies.

Conclusions

The conclusions must be based on the study undertaken. Entire conclusions discussed impacts of mining on imperiled species through degradation of water quality. It that is so, then in results section provide some tables which are now added as supplementary materials.

We have reduced the length of the Discussion by truncating sections less directly related to the study at hand. Specifically we shorten our discussion of SMCRA and the regulatory obligations of the Fish and Wildlife Service under section 7 of the Endangered Species Act.

We mirror these changes in the Conclusion, although retain a focus on placing the findings of this study in a broader conservation and regulatory context. We have edited language to connect the outcomes of this study more clearly to these broader conclusions.

Overall comments:

There has been nobility in this approach, and could be useful, but methods results are not clear. It is more of secondary data used in a statistical tools. In such cases, a flow chart should be provided for meaning full interpretation of existing data.

Previous reviewers and editors have not had challenges understanding the study design, use or analysis of data. In response to this comment, we have invited a colleague unfamiliar with the project to evaluate the manuscript for clarity and interpretability. They did not identify any major barriers to understanding the methodology and analytical approach, beyond clarifying some of the points regarding units and measures raised above. At this point, we are not inclined to provide a flow chart as an additional figure, and believe the changes made to the Methods and Results are sufficient to provide a clear interpretation of how the data were used and analyzed.

I have a strong reservation for recommending this paper for publication, however, this article may be again rewrite and resubmit.

We appreciate the feedback provided and believe we have been able to address all current and previously raised concerns.

Submitted filename: Response_to_reviewers_2.docx

Click here for additional data file.

14 Oct 2021

Linking mountaintop removal mining to water quality for imperiled species using satellite data

PONE-D-20-28542R2

Dear Dr. Evans,

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Additional Editor Comments (optional):

Reviewers' comments:

26 Oct 2021

PONE-D-20-28542R2

Linking mountaintop removal mining to water quality for imperiled species using satellite data

Dear Dr. Evans:

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