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S. Alex Covert

Publications and source records attributed to S. Alex Covert.

10 recordsLinked to original sources
USGS2024

Analysis of water use associated with hydraulic fracturing and determination of baseline water quality in watersheds within the shale play of eastern Ohio, 2021–23

The U.S. Geological Survey, in cooperation with the Ohio Department of Natural Resources, performed a two-part study to (1) assess water use and temporal trends and changes in streamflow, and to (2) characterize 2021–23 baseline water quality, as they relate to oil and gas extraction activities in selected eastern Ohio watersheds. Between calendar years 2010 and 2019, hydraulic fracturing water withdrawals totaling about 27,168 million gallons were reported at 643 locations in Ohio. In 2021, wells developed with hydraulic fracturing were the source of most of the oil and gas produced in Ohio. Daily streamflow time-series data from seven study gages and two reference gages were used to assess temporal trends and changes in streamflow. The study gages were in basins with reported water withdrawals for hydraulic fracturing. The reference gages, which have long periods of record and were subject to minimal streamflow regulation, were in nearby basins with no hydraulic fracturing water withdrawals. Trend slopes for the period of record annual minimum and median daily streamflows and for annual daily streamflow nonexceedance probabilities less than 0.9 were all uniformly positive at the study and reference gages. This trend indicates a consistently increasing pattern over the periods of record, except for high flows. In addition, analyses of annual streamflow statistics showed no general indication that low flows or extreme low flows at the reference or study gages have lowered, become more frequent, or lengthened in duration since 2010, when records for hydraulic fracturing water withdrawals began in Ohio. In fact, in almost all cases, the opposite was indicated. Nonexceedance percentiles of daily streamflows were compared between the full and pre-2012 periods of record for reference and study gages. The streamflows associated with nonexceedance percentiles in the lower quartile of daily streamflows determined for the full period of record were larger than or equal to those determined for the pre-2012 period of record for all study and reference gages. This indicates that low flows did not decrease during the post-2011 period of record when water was withdrawn for hydraulic fracturing. Water-quality data were collected eight times at each of eight sampling sites (six of which were colocated with the study gages). Sampling was done during a variety of flow conditions to assess baseline water quality. In 2021, the 8 sampling sites had drainage basins that were wholly or partially within 7 of the 10 most active counties in Ohio for oil and gas development. As part of the record of baseline conditions, water-quality data were used to assess (1) water types based on major-ion chemistry; (2) sources of salinity to streams; (3) exceedances of aquatic life use criteria; and (4) the correlations between water chemistry and drainage-basin characteristics, such as density of oil and gas wells, density of wastewater treatment plants, or the percentage of different types of land cover (agriculture, developed, forest). Seven of the water-quality sampling sites were designated as coal-mine impacted based on criteria developed for assessing mine-drainage impacts in Ohio. Mine drainage from historical coal mining in the region likely affected the quality of these streams and complicated the use of some constituents typically used as indicators of oil and gas influence. Based on major-ion chemistry, three main types of water were in the study area―sulfate (three sites), calcium-bicarbonate (one site), and mixed bicarbonate-chloride (four sites) type waters. One site had samples with a higher proportion of sodium and chloride ions than other stream samples, indicating potential contamination with oil-field brine or road salt. Binary mixing curves revealed that 11 samples from 4 of the sampling sites likely contained a component of brine. The results of the baseline assessment of surface-water quality in the study area showed no exceedances of Ohio Environmental Protection Agency aquatic life use criteria. Spearman’s rank correlation coefficients indicated no significant positive correlations with the density of vertical or horizontal oil and gas wells.

Ohio↗
USGS2024

Chlorophyll a in lakes and streams of the United States (2005–2022)

The concentration of chlorophyll a in phytoplankton and periphyton represents the amount of algal biomass. We compiled an 18-year record (2005–2022) of pigment data from water bodies across the United States (US) to support efforts to develop process-based, machine learning, and remote sensing models for prediction of harmful algal blooms (HABs). To our knowledge, this dataset of nearly 84,000 sites and over 1,374,000 pigment measurements is the largest compilation of harmonized discrete, laboratory-extracted chlorophyll data for the US. These data were compiled from the Water Quality Portal (WQP) and previously unpublished U.S. Geological Survey’s National Water Quality Laboratory (NWQL) data. Data were harmonized for reporting units, pigment type, duplicate values, collection depth, site name, negative values, and some extreme values. Across the country, data show great variation by state in sampling frequency, distribution, and methods. Uses for such data include the calibration of models, calibration of field sensors, examination of relationship to nutrients and other drivers, evaluation of temporal trends, and other applications addressing local to national scale concerns.

Nature Scientific Data↗
USGS2023

Comparison of surrogate models to estimate pesticide concentrations at six U.S. Geological Survey National Water Quality Network sites during water years 2013–18

During water years 2013–18, the U.S. Geological Survey National Water-Quality Assessment Project sampled the National Water Quality Network for Rivers and Streams year-round and reported on 221 pesticides at 72 sites across the United States. Pesticides are difficult to measure, their concentrations often represent discrete snapshots in time, and capturing peak concentrations is expensive. Three types of regression models were developed to estimate concentrations for two selected pesticides at each of six National Water Quality Network for Rivers and Streams sites. The regression models used continuously measured streamflow and water-quality properties (differing combinations of pH, specific conductance, turbidity, and water temperature); discrete water-quality samples analyzed for atrazine, azoxystrobin, bentazon, bromacil, imidacloprid, simazine, and triclopyr; and time as an additional explanatory variable for seasonality. The modeling approaches included (1) a standard regression that included surrogates (differing combinations of pH, specific conductance, turbidity, and water temperature) and periodic functions (sine-cosine) of pesticide application use as predictor variables; (2) the seasonal wave with flow adjustment model that included a seasonal component and flow anomalies but excluded surrogates; and (3) the seasonal wave with flow adjustment model that included a seasonal component, flow anomalies, and surrogates. Models were evaluated using three measures of model performance: generalized coefficient of determination (generalized R 2 ), Akaike’s Information Criteria, and scale (the estimated standard deviation of the tobit regression error term). Because of low observation numbers, results from this study can be considered a pilot effort with the possibility that some models are overfit. In all cases, estimated pesticide concentrations modeled with base SEAWAVE-Q were better than the standard surrogate regression models; all 39 generalized R 2 values increased by 3–56 percent (median of 25 percent) when compared to the standard surrogate regression models, and all Akaike’s Information Criteria and scale values decreased. The addition of surrogate variables such as pH, specific conductance, turbidity, and water temperature to the base SEAWAVE-Q model to improve estimates of pesticide concentrations resulted in only modest improvements; generalized R 2 values increased by only 0–10 percent (median of 3 percent). In some instances, combinations of the surrogates produced more appreciative improvements in model results, but in those instances, we hypothesize that the surrogates correlated with some unknown measure that directly relates to pesticide transport.

Indiana, Kansas, Missouri, North Dakota, Oregon↗
USGS2020

Pesticide mixtures show potential toxicity to aquatic life in U.S. streams, water years 2013-2017

During water years (WY) 2013–2017, the U.S. Geological Survey, National Water-Quality Assessment (NAWQA) Project, sampled the National Water Quality Network – Rivers and Streams (NWQN) year-round and reported on 221 pesticides at 72 sites across the United States in agricultural, developed, and mixed land use watersheds. The Pesticide Toxicity Index (PTI) was used to estimate the potential chronic and acute toxicity to three taxonomic groups – fish, cladocerans, and benthic invertebrates. For invertebrates (either cladocerans, benthic invertebrates, or both), the maximum PTI score exceeded the predicted acute toxicity screening level at 18 of the 72 sites (25%) at some point during WY 2013–2017. The predicted toxicity of a single pesticide compound was found to overwhelm the toxicity of other pesticides in the mixtures after concentrations were toxicity weighted. For this study, about 71%, 72%, and 92% of the Fish-, Cladoceran-, and Benthic Invertebrate-PTI scores, respectively, had one pesticide compound primarily contributing to sample potential toxicity (>50%). There were 17 (13 insecticides, 2 herbicides, 1 fungicide, and 1 synergist) of the 221 pesticide compounds analyzed that were the primary drivers of potential toxicity in each water sample in which the PTI and TUmax (toxic unit score for the pesticide that makes the single largest contribution to the PTI) scores were above predicted chronic (>0.1) or acute (>1) toxicity levels for one of the three taxa. For cladocerans and benthic invertebrates, the drivers of predicted chronic (>0.1) and acute (>1) PTIs were mostly insecticides. For cladocerans, the pesticide compounds driving the PTI scores were bifenthrin, carbaryl, chlorpyrifos, diazinon, dichlorvos, dicrotophos, diflubenzuron, flubendiamide, and tebupirimfos. For benthic invertebrates, atrazine (an herbicide), as well as the insecticides – bifenthrin, carbaryl, carbofuran, chlorpyrifos, diazinon, dichlorvos, fipronil, imidacloprid, and methamidophos – were the drivers of predicted toxicity. For fish, there were three pesticide types that contributed the most to predicted chronic (>0.1) PTIs – acetochlor, an herbicide; carbendazim, a fungicide degradate; and piperonylbutoxide, a synergist.

Science of the Total Environment↗
USGS2018

Baseline water quality of an area undergoing shale-gas development in the Muskingum River watershed, Ohio, 2015–16

In 2015–16, the U.S. Geological Survey, in cooperation with the Muskingum Watershed Conservancy District, led a study to assess baseline (2015–16) surface-water quality in six lake drainage basins within the Muskingum River watershed that are in the early years of shale-gas development. In 2015, 9 of the 10 most active counties in Ohio for oil and gas development were wholly or partially within the Muskingum River watershed. In addition to shale gas development, the area has a history of conventional oil and gas development and coal mining. In all, 30 surface-water sites were sampled: 20 in tributaries flowing to the lakes, 4 in lakes themselves, and 6 downstream of the lakes. At each of the 30 sites, 6 samples were collected to characterize surface-water chemistry throughout a range of hydrologic conditions. The sampling generally occurred during low flows (periods of greater groundwater contribution) rather than during runoff events (periods of high stream stage). Trilinear diagrams of major ion chemistry revealed three main types of water in the study area―sulfate-dominated waters, bicarbonate-dominated waters, and waters with mixed bicarbonate and chloride anions. Most sites produced samples of bicarbonate-dominated water, and 11 sites produced samples with sulfate-type waters. Mixed bicarbonate and chloride waters were found in samples from two of the six lake drainage basins studied. The baseline (2015–16) assessment of surface-water quality in the study area indicated that few water-chemistry constituents and properties occurred at concentrations or levels that would adversely affect aquatic organisms. Chemical-specific, aquatic life use criteria were not met in only three instances: two were for total dissolved solids at sites likely impacted by coal mining in their drainage basins (hereafter referred to as “mine-impacted sites”), and one was for dissolved oxygen. Mine drainage from historical coal mining in the region likely affected the quality of about one-third of the streams sampled. To simplify interpretation of water-chemistry results, 11 sites with sulfate-type water were identified as mine-impacted sites based on water-quality criteria established by Ohio Department of Natural Resources, Division of Mineral Resources Management, and separated out for subsequent statistical analysis. Concentrations or levels of bicarbonate, boron, calcium, carbonate, total dissolved solids, fluoride, magnesium, lithium, pH, potassium, sodium, specific conductance, strontium, sulfate, and suspended sediment in water were higher (significance level of 0.05) at mine-impacted stream sites than at non-mine-impacted stream sites. An accidental release of oil- and gas-related brines could increase salinity (sodium and chloride), the concentration of total dissolved solids in shallow groundwater and streams, and specific conductance. For this study, chloride concentrations in the study area ranged from 2.12 to 76.1 milligrams per liter. Sources of chloride in water samples were evaluated using binary mixing curves and ratios of chloride to bromide. These ratios indicated that 13 samples from 3 sites in the drainage basin that contained the highest density of conventional oil and gas wells in the study, as well as 4 samples collected from other drainage basins, likely contained a component of brine. Concentrations or levels of barium, bromide, chloride, iron, lithium, manganese, and sodium were significantly higher (alpha = 0.05) in samples with a component of brine than in samples without a component of brine. Benzene, toluene, ethylbenzene and xylene (BTEX), compounds that occur naturally in crude oil, made up 24 of the 45 detections (53 percent) of volatile organic compounds in the study area. The BTEX detections were not associated with sites containing a component of brine. The only volatile organic compound detected in any of the 17 samples that contained a component of brine was acetone, detected in 3 (18 percent) of these samples and in 11 percent of samples not containing a component of brine. Considering that BTEX are gasoline hydrocarbons and that most of the detections occurred during warmer months in and around the lakes, the BTEX detections likely are associated with increases in outdoor activities such as automobile and boating traffic. Radium-226 and radium-228 were included in the list of analytes for this study because production water from shale-gas drilling can contain these naturally occurring radioactive materials. Concentrations of radium-226 exceeded background levels in only two surface-water samples. Concentrations of radium-228 exceeded background levels in one surface-water sample. A brine signature potentially indicative of oil and gas contamination was detected in samples collected at two sites that contained active or plugged waste injection wells, or both. Results from the study indicated significant differences in the median concentrations of bromide, chloride, lithium, manganese, sodium, and total dissolved nitrogen between sites with and without injection wells in their drainage areas. Median concentrations of bromide, chloride, lithium, and sodium, which are common oil- and gas-related contaminants, were higher at sites with injection wells in their drainage areas compared to sites without injection wells. Historical (1960s, 1970s, and 1980s) chloride concentrations and streamflow data at or near five of the six sampling sites downstream from each lake dam were compared to current (2015–16) values. An analysis of covariance was done to test the effects of streamflow, time (decade), and the combined effects (cross product) of streamflow and time on chloride concentrations. Those analyses indicated that streamflow was not significant in explaining the variation in chloride concentration, likely because streamflow in those locations is controlled by dam operations; therefore, association between runoff-generating events and streamflow is less direct than in unregulated streams. From the 1980s to the study period (2015–16), data for three of the five lakes indicated an increase in chloride concentrations. The comparison of historical and current (2015–16) study data from samples collected at another lake indicated that chloride concentrations increased from the 1960s to the 1970s, but concentrations in the 1970s and 2015–16 were similar even though 13 samples from this lake drainage basin were classified as having a component of brine. Median chloride concentrations for the fifth lake, however, seemed to decrease from the 1980s to 2015–16.

Ohio↗
USGS2016

FishVis, A regional decision support tool for identifying vulnerabilities of riverine habitat and fishes to climate change in the Great Lakes Region

Climate change is expected to alter the distributions and community composition of stream fishes in the Great Lakes region in the 21st century, in part as a result of altered hydrological systems (stream temperature, streamflow, and habitat). Resource managers need information and tools to understand where fish species and stream habitats are expected to change under future conditions. Fish sample collections and environmental variables from multiple sources across the United States Great Lakes Basin were integrated and used to develop empirical models to predict fish species occurrence under present-day climate conditions. Random Forests models were used to predict the probability of occurrence of 13 lotic fish species within each stream reach in the study area. Downscaled climate data from general circulation models were integrated with the fish species occurrence models to project fish species occurrence under future climate conditions. The 13 fish species represented three ecological guilds associated with water temperature (cold, cool, and warm), and the species were distributed in streams across the Great Lakes region. Vulnerability (loss of species) and opportunity (gain of species) scores were calculated for all stream reaches by evaluating changes in fish species occurrence from present-day to future climate conditions. The 13 fish species included 4 cold-water species, 5 cool-water species, and 4 warm-water species. Presently, the 4 cold-water species occupy from 15 percent (55,000 kilometers [km]) to 35 percent (130,000 km) of the total stream length (369,215 km) across the study area; the 5 cool-water species, from 9 percent (33,000 km) to 58 percent (215,000 km); and the 4 warm-water species, from 9 percent (33,000 km) to 38 percent (141,000 km). Fish models linked to projections from 13 downscaled climate models projected that in the mid to late 21st century (2046–65 and 2081–2100, respectively) habitats suitable for all 4 cold-water species and 4 of 5 cool-water species under present-day conditions will decline as much as 86 percent and as little as 33 percent, and habitats suitable for all 4 warm-water species will increase as much as 33 percent and as little as 7 percent. This report documents the approach and data used to predict and project fish species occurrence under present-day and future climate conditions for 13 lotic fish species in the United States Great Lakes Basin. A Web-based decision support mapping application termed “FishVis” was developed to provide a means to integrate, visualize, query, and download the results of these projected climate-driven responses and help inform conservation planning efforts within the region.

Great Lakes Region↗
USGS2007

Ohio Aquatic Gap Analysis-An Assessment of the Biodiversity and Conservation Status of Native Aquatic Animal Species

The goal of the GAP Analysis Program is to keep common species common by identifying those species and habitats that are not yet adequately represented in the existing matrix of conservation lands. The Gap Analysis Program (GAP) is sponsored by the Biological Resources Discipline of the U.S. Geological Survey (USGS). The Ohio Aquatic GAP (OH-GAP) is a pilot project that is applying the GAP concept to aquatic-specifically, riverine-data. The mission of GAP is to provide regional assessments of the conservation status of native animal species and to facilitate the application of this information to land-management activities. OH-GAP accomplished this through * mapping aquatic habitat types, * mapping the predicted distributions of fish, crayfish, and bivalves, * documenting the presence of aquatic species in areas managed for conservation, * providing GAP results to the public, planners, managers, policy makers, and researchers, and * building cooperation with multiple organizations to apply GAP results to state and regional management activities. Gap analysis is a coarse-scale assessment of aquatic biodiversity and conservation; the goal is to identify gaps in the conservation of native aquatic species. It is not a substitute for biological field studies and monitoring programs. Gap analysis was conducted for the continuously flowing streams in Ohio. Lakes, reservoirs, wetlands, and the Lake Erie islands were not included in this analysis. The streams in Ohio are in the Lake Erie and Ohio River watersheds and pass through six of the level III ecoregions defined by Omernik: the Eastern Corn Belt Plains, Southern Michigan/Northern Indiana Drift Plains, Huron/Erie Lake Plain, Erie Drift Plains, Interior Plateau, and the Western Allegheny Plateau. To characterize the aquatic habitats available to Ohio fish, crayfish, and bivalves, a classification system needed to be developed and mapped. The process of classification includes delineation of areas of relative homogeneity and labeling these areas using categories defined by the classification system. The variables were linked to the 1:100,000-scale streams of the National Hydrography Dataset of the USGS. Through discussions with Ohio aquatic experts, OH-GAP identified eight separate enduring physical features which, when combined, form the physical habitat type: * Shreve link (a measure of stream size) * Downstream Shreve link (a measure of stream connectivity and size) * Sinuosity * Gradient * Bedrock * Stream temperature * Character of glacial drift * Glacial-drift thickness Potential distribution models were developed for 130 fish, 70 bivalve, and 17 native crayfish species. These models are based on 5,686 fish, 4,469 crayfish, and 2,899 freshwater bivalve (mussels and clams) sampling locations, the variables describing the physical habitat types, and variables indicating the major drainage basins and Omernik's Level III ecoregion. All potential species distributions are displayed and analyzed at the 14-digit hydrologic unit (14-HUs), or subwatershed, level. Mainland Ohio contains 1,749 14-HUs. All statistics and conclusions, as well as spatial data, are discussed and presented in terms of these units. The Ohio Aquatic Gap Analysis Project compiled a map of public and private conservation lands and OH-GAP classified the lands into four status categories (status 1 through status 4) by the degree of protection offered based on management practices. A status of 1 denotes the highest, most permanent level of maintenance, and status 4 represents the lowest level of biodiversity management, or unknown status. The results of this mapping show that only about 3.7 percent of the state's land (4.3 percent if lakes and reservoirs are also included) is protected for conservation, either publicly or privately. Of this total, state agencies control about 52 percent, and Federal agencies control about 29 percent. Conservation areas that presently protect

Open-File Report↗
USGS2002

Water Quality in the Mahoning River and Selected Tributaries in Youngstown, Ohio

The lower reaches of the Mahoning River in Youngstown, Ohio, have been characterized by the Ohio Environmental Protection Agency (OEPA) as historically having poor water quality. Most wastewater-treatment plants (WWTPs) in the watershed did not provide secondary sewage treatment until the late 1980s. By the late 1990s, the Mahoning River still received sewer-overflow discharges from 101 locations within the city of Youngstown, Ohio. The Mahoning River in Youngstown and Mill Creek, a principal tributary to the Mahoning River in Youngstown, have not met biotic index criteria since the earliest published assessment by OEPA in 1980. Youngstown and the OEPA are working together toward the goal of meeting water-quality standards in the Mahoning River. The U.S. Geological Survey collected information to help both parties assess water quality in the area of Youngstown and to estimate bacteria and inorganic nitrogen contributions from sewer-overflow discharges to the Mahoning River. Two monitoring networks were established in the lower Mahoning River: the first to evaluate hydrology and microbiological and chemical water quality and the second to assess indices of fish and aquatic-macroinvertebrate-community health. Water samples and water-quality data were collected from May through October 1999 and 2000 to evaluate where, when, and for how long water quality was affected by sewer-overflow discharges. Water samples were collected during dry- and wet-weather flow, and biotic indices were assessed during the first year (1999). The second year of sample collection (2000) was directed toward evaluating changes in water quality during wet-weather flow, and specifically toward assessing the effect of sewer-overflow discharges on water quality in the monitoring network. Water-quality standards for Escherichia coli ( E. coli ) concentration and draft criteria for nitrate plus nitrite and total phosphorus were the regulations most commonly exceeded in the Mahoning River and Mill Creek sampling networks. E. coli concentrations increased during wet-weather flow and remained higher than dry-weather concentrations for 48 hours after peak flow. E. coli concentration criteria were more commonly exceeded during wet-weather flow than during dry-weather flow. Exceedances of nutrient-concentration criteria were not substantially more common during wet-weather flow. The fish and aquatic macroinvertebrate network included Mill Creek and its tributaries but did not include the main stem of the Mahoning River. Persistent exceedances of chemical water-quality standards in Mill Creek and the presence of nutrient concentrations in excess of draft criteria may have contributed to biotic index scores that on only one occasion met State criteria throughout the fish and aquatic macroinvertebrate sampling network. Monitored tributary streams did not contribute concentrations of E. coli , nitrate plus nitrite, or total phosphorus to the Mahoning River and Mill Creek that were higher than main-stem concentrations, but monitored WWTP and sewer-overflow discharges did contribute. Twenty-four hour load estimates of sewer-overflow discharge contributions during wet-weather flow indicated that sewer-overflow discharges contributed large loads of bacteria and inorganic nitrogen to the Mahoning River relative to the instream load. The sewer-overflow loads appeared to move as a slug of highly enriched water that passed through Youngstown on the rising limb of the storm hydrograph. The median estimated sewer-overflow load contribution of bacteria was greater than the estimated instream load by a factor of five or more; however, the median estimated sewer-overflow load of inorganic nitrogen was less than half of the estimated instream load. Sewer-overflow discharges contributed loads of E. coli and nutrients to the Mahoning River and Mill Creek at a point where the streams already did not meet State water-quality regulations. Improvement of water quality of the Mahoning River, Mill Creek, and tributaries at Youngstown would be facilitated by reducing loads from sewer-overflow discharges within Youngstown, by identifying and reducing other sources of E. coli and nutrients within Young-stown, and by reducing discharges of E. coli , nitrate plus nitrite, and total phosphorus to the Mahoning River and Mill Creek upstream from Youngstown.

Water-Resources Investigations Report↗
USGS2001

Comparison of U.S. Geological Survey and Ohio Environmental Protection Agency fish-collection methods using the index of biotic integrity and modified index of well-being, 1996–97

The U.S. Geological Survey (USGS) and Ohio Environmental Protection Agency (OEPA) collected data on fish from 10 stream sites in 1996 and 3 stream sites in 1997 as part of a comparative study of fish community assessment methods. The sites sampled represent a wide range of basin sizes (ranging from 132–6,330 square kilometers) and surrounding land-use types (urban, agricultural, and mixed). Each agency used its own fish-sampling protocol. Using the Index of Biotic Integrity and Modified Index of Well-Being, differences between data sets were tested for significance by means of the Wilcoxon signed-ranks test ( α = 0.05). Results showed that the median of Index of Biotic Integrity differences between data sets was not significantly different from zero ( p = 0.2521); however, the same statistical test showed the median differences in the Modified Index of Well-Being scores to be significantly different from zero ( p = 0.0158). The differences observed in the Index of Biotic Integrity scores are likely due to natural variability, increased variability at sites with degraded water quality, differences in sampling methods, and low-end adjustments in the Index of Biotic Integrity calculation when fewer than 50 fish were collected. The Modified Index ofWell-Being scores calculated by OEPA were significantly higher than those calculated by the USGS. This finding was attributed to the comparatively large numbers and biomass of fish collected by the OEPA. By combining the two indices and viewing them in terms of the percentage attainment of Ohio Warmwater Habitat criteria, the two agencies’ data seemed comparable, although the Index of Biotic Integrity scores were more similar than the Modified Index of Well-Being scores.

Michigan, New York, Ohio, Pennsylvania↗