Lucinda M. Tear

Assessment of Selenium Food Chain Transfer and Critical Exposure Factors for Avian Wildlife Species: Need for Site-Specific Data

Observations of selenium poisoning in Belews Lake, NC in the mid-1970s and Kesterson Reservoir, CA in the mid-1980s precipitated a large number of selenium studies. Numerous authors have evaluated the potential for selenium to cause ecologically significant effects via food chain transfer in aquatic ecosystems, especially wetlands. Additionally, bioaccumulation models have been proposed for estimating selenium concentrations in food chains and water that should not be exceeded in order to avoid reproductive effects in avian and aquatic species. The current national chronic ambient water quality criterion (WQC) for protection of aquatic life is 5 μg/L. Scientists with the U.S. Fish and Wildlife Service have recommended setting the ambient water quality criterion at 2 μg/L for both aquatic and wildlife protection. Reported site-specific variations in seleniums effects on aquatic life and birds prompted us to re-evaluate the basis for the 2 μg/L recommendation, and in particular one of the wildlife bioaccumulation models used to support this value. We used a probabilistic approach to assess water, food chain, and bird egg residues from 15 sites. Our data evaluation indicates significant differences in selenium accumulation in invertebrates and bird eggs among sites and among species. Both a two-step regression model (water → food chain → bird eggs) and a one-step regression going directly from waterborne selenium (WS) to mean egg selenium (MES) were fitted to all data for 15 sites and four bird species. The one-step model contained less variability than the two-step model and had a coefficient of variation (r2) of 0.67. Uncertainty analysis of the regression models provided a distribution of waterborne selenium concentrations associated with bird egg tissue residues. Using the 10th and 50th percentiles of these distributions, we calculated waterborne selenium concentrations between 6.8 and 46 μg/L that are protective of birds. These values are associated with an effects threshold of 20 mg/kg selenium dry weight in bird eggs, which is the EC 1 0 for mallard duck embryo teratogenis (Skorupa et al. 1996). The 10th percentile of this distribution, 6.8 μg/L, is slightly above the EPA water quality criterion of 5 μg/L. The water concentrations protective of birds range from slightly more than the current EPA WQC (6.8 vs. 5.0 μg/L) to a factor of 10 or greater at some sites. Our results also indicate a reasonably strong correlation between water and mean egg selenium concentrations. However, site-specific factors strongly influence this relationship, and when waterborne selenium approaches or exceeds the WQC, collection of site-specific data would be appropriate to accurately assess the WS to MES relationship. Evaluation of the site-specific relationship between WS and MES can determine whether site-specific differences are important and whether or not the EPA WQC is likely to be over protective of bird populations. In this paper, we question the need for the WQC to be set at 2 μg/L to protect aquatic birds. Overall, bird-egg residues appear to be the best tool for assessing potential for risk to birds from selenium.

Site-specific approach for setting water quality criteria for selenium : differences between lotic and lentic systems

Results of an in-depth review of the literature indicates there are significant differences in the bioaccumulation of selenium by fishes and invertebrates from lotic (flowing) and lentic (standing) water bodies and that selenate is much less bioaccumulative than selenite. Bioaccumulation in fish is a factor of 10 or more higher in lentic systems as compared to lotic systems. These differences are a function of selenium’s site-specific biogeochemical cycling. Further, we observed considerable variation in bird accumulation of selenium from site to site. To account for differences in bioaccumulation potential of selenium we developed a residue-based Bayesian Monte Carlo model to derive site-specific selenium water quality criteria protective of fish and sensitive avian species. The approach uses data from a given site of interest to calibrate a model based on data from several other similar sites. When evaluating a specific site, the range of water and tissue concentrations is typically limited. This makes it difficult to use site-specific data to identify a water concentration sufficiently low that tissue concentrations do not exceed the tissue-effect threshold. Data from several similar sites provide a broader range of water and tissue residue concentrations that allow for an appropriate statistical extrapolation of the data to the site of interest. The Bayesian Monte Carlo model accounts for the significant site-to-site variability that exists in the relationship between water selenium and the mean tissue residue. In practice, data from similar sites are pooled to define a set of possible water and mean tissue residue relationships. This set of possible relationships is then used with data from the site of interest to determine which relationships, from the set of possibilities, best fit the specific site. Once we have determined which set of possible relationships fit the specific site, we extrapolate from the observed water concentration to a water concentration that results in a tissue residue concentration less than or equal to a chronic effect threshold. This value becomes the chronic water quality criterion. Adams, W.J., J.E. Toll, K.V. Brix, L.M. Tear and D.K. DeForest. 2000. Site-specific approach for setting water quality criteria for selenium: differences between lotic and lentic systems. Proceedings Mine Reclamation Symposium: Selenium Session; Sponsored by Ministry of Energy and Mines, Williams Lake, British Columbia, Canada, June 21-22, 2000.

Setting site-specific water-quality standards by using tissue residue thresholds and bioaccumulation data. Part 2. Calculating site-specific selenium water-quality standards for protecting fish and birds

In a companion paper, a method for deriving tissue residue-based site-specific water-quality standards (SSWQSs) was described. In this paper, the methodology is applied to selenium (Se) as an example. Models were developed to describe Se bioaccumulation in aquatic-dependent bird eggs and whole fish. A simple log-linear model best described Se accumulation in bird eggs (r2 = 0.50). For fish, separate hockey stick regressions were developed for lentic (r2 = 0.65) and lotic environments (r2 = 0.37). The low r2 value for the lotic fish model precludes its reliable use at this time. Corresponding tissue residue criteria (i.e., tissue thresholds) for bird eggs and whole fish also were identified and example model predictions were made. The models were able to predict SSWQSs over a wide range of water-tissue combinations that might be encountered in the environment. The models also were shown to be sensitive to variability in measured tissue residues with relatively small changes in variability (as characterized by the standard error) resulting in relatively large differences in SSWQSs.

Modelling physico-chemical factors affecting occurrences of a non-indigenous planktonic copepod in northeast Pacific estuaries

Estuarine ecosystems along the Pacific coast of North America are vulnerable to invasions by non-indigenous planktonic copepods, with documented invasions by at least nine species introduced via ship’s ballast. One of these, the calanoid copepod Pseudodiaptomus inopinus, now occurs in a relatively wide geographical area in coastal estuaries of Washington and Oregon States. Although it appears to be well established in the region, plankton surveys conducted in 1992, 1996, 2000, and 2004 in estuaries from southern Vancouver Island in British Columbia, Canada, to northern California, United States indicate that it has not expanded its range. This static distribution suggests that P. inopinus has reached a distributional limit, and it may thus be a good organism for applying models for predicting planktonic invasions, by characterizing estuaries with and without populations of the copepod. In this study, we applied both parametric, linear (discriminant function analysis, logistic regression) and nonparametric, non-linear (classification trees) techniques to develop models for occurrence of P. inopinus, to identify parameters that may lead to successful invasions and to identify specific estuaries or regions that might be at risk of invasion by this species. Both model types had similar results, identifying relatively simple salinity- and stratification-based models as good predictors of P. inopinus. While different models selected slightly different sets of variables and thresholds, all models identified relatively low salinity and stratification of water column temperature and salinity as important predictors of P. inopinus presence. The models also identified several “false positives” that mainly occurred in more inland waters of Puget Sound—estuaries that did not have P. inopinus, but had the conditions that support it, and which may be at risk for future invasions by this species.

Multiple linear regression models for predicting chronic aluminum toxicity to freshwater aquatic organisms and developing water quality guidelines

The bioavailability of aluminum (Al) to freshwater aquatic organisms varies as a function of several water chemistry parameters, including pH, dissolved organic carbon (DOC), and water hardness. We evaluated the ability of multiple linear regression (MLR) models to predict chronic Al toxicity to a green alga (Pseudokirchneriella subcapitata), a cladoceran (Ceriodaphnia dubia), and a fish (Pimephales promelas) as a function of varying DOC, pH, and hardness conditions. The MLR models predicted toxicity values that were within a factor of 2 of observed values in 100% of the cases for P. subcapitata (10 and 20% effective concentrations [EC10s and EC20s]), 91% of the cases for C. dubia (EC10s and EC20s), and 95% (EC10s) and 91% (EC20s) of the cases for P. promelas. The MLR models were then applied to all species with Al toxicity data to derive species and genus sensitivity distributions that could be adjusted as a function of varying DOC, pH, and hardness conditions (the P. subcapitata model was applied to algae and macrophytes, the C. dubia model was applied to invertebrates, and the P. promelas model was applied to fish). Hazardous concentrations to 5% of the species or genera were then derived in 2 ways: 1) fitting a log-normal distribution to species-mean EC10s for all species (following the European Union methodology), and 2) fitting a triangular distribution to genus-mean EC20s for animals only (following the US Environmental Protection Agency methodology). Overall, MLR-based models provide a viable approach for deriving Al water quality guidelines that vary as a function of DOC, pH, and hardness conditions and are a significant improvement over bioavailability corrections based on single parameters. Environ Toxicol Chem 2018;37:80-90.

Setting site‐specific water‐quality standards by using tissue residue criteria and bioaccumulation data. Part 1. Methodology

We have developed a method for determining site-specific water-quality standards (SSWQSs) for substances regulated based on tissue residues. The method uses a multisite regression model to solve for the conditional prior probability density function (PDF) on water concentration, given that tissue concentration equals a tissue residue threshold. The method then uses site-specific water and tissue concentration data to update the probabilities on a Monte Carlo sample of the prior PDF by using Bayesian Monte Carlo analysis. The resultant posterior PDF identifies the water concentration that, if met at the site, would provide a desired level of confidence of meeting the tissue residue threshold contingent on model assumptions. This allows for derivation of a SSWQS. The method is fully reproducible, statistically rigorous, and easily implemented. A useful property of the method is that the model is sensitive to the amount of site-specific data available, that is, a more conservative or protective number (water concentration) is derived when the data set is small or the variance is large. Likewise, as the site water concentration increases above the water-quality standard, more site-specific information is needed to demonstrate a safe concentration at the site. A companion paper demonstrates the method by using selenium as an example.

Use of Multiple Linear Regression Models for Setting Water Quality Criteria for Copper: A Complementary Approach to the Biotic Ligand Model

Biotic Ligand Models (BLMs) for metals are widely applied in ecological risk assessments and in the development of regulatory water quality guidelines in Europe, and in 2007 the United States Environmental Protection Agency (USEPA) recommended BLM-based water quality criteria (WQC) for Cu in freshwater. However, to-date, few states have adopted BLM-based Cu criteria into their water quality standards on a state-wide basis, which appears to be due to the perception that the BLM is too complicated or requires too many input variables. Using the mechanistic BLM framework to first identify key water chemistry parameters that influence Cu bioavailability, namely dissolved organic carbon (DOC), pH, and hardness, we developed Cu criteria using the same basic methodology used by the USEPA to derive hardness-based criteria but with the addition of DOC and pH. As an initial proof of concept, we developed stepwise multiple linear regression (MLR) models for species that have been tested over wide ranges of DOC, pH, and hardness conditions. These models predicted acute Cu toxicity values that were within a factor of ±2 in 77% to 97% of tests (5 species had adequate data) and chronic Cu toxicity values that were within a factor of ±2 in 92% of tests (1 species had adequate data). This level of accuracy is comparable to the BLM. Following USEPA guidelines for WQC development, the species data were then combined to develop a linear model with pooled slopes for each independent parameter (i.e., DOC, pH, and hardness) and species-specific intercepts using Analysis of Covariance. The pooled MLR and BLM models predicted species-specific toxicity with similar precision; adjusted R2 and R2 values ranged from 0.56 to 0.86 and 0.66-0.85, respectively. Graphical exploration of relationships between predicted and observed toxicity, residuals and observed toxicity, and residuals and concentrations of key input parameters revealed many similarities and a few key distinctions between the performances of the two models. The pooled MLR model was then applied to the species sensitivity distribution to derive acute and chronic criteria equations similar in form to the USEPAs current hardness-based criteria equations but with DOC, pH, and hardness as the independent variables. Overall, the MLR is less responsive to DOC than the BLM across a range of hardness and pH conditions but more responsive to hardness than the BLM. Additionally, at low and intermediate hardness, the MLR model is less responsive than the BLM to pH, but the two models respond comparably at high hardness. The net effect of these different response profiles is that under many typical water quality conditions, MLR- and BLM-based criteria are quite comparable. Indeed, conditions where the two models differ most (high pH/low hardness and low pH/high hardness) are relatively rare in natural aquatic systems. We suggest that this MLR-based approach, which includes the mechanistic foundation of the BLM but is also consistent with widely accepted hardness-dependent WQC in terms of development and form, may facilitate adoption of updated state-wide Cu criteria that more accurately account for the parameters influencing Cu bioavailability than current hardness-based criteria.

Lentic, lotic, and sulfate‐dependent waterborne selenium screening guidelines for freshwater systems

There is consensus that fish are the most sensitive aquatic organisms to selenium (Se) and that Se concentrations in fish tissue are the most reliable indicators of potential toxicity. Differences in Se speciation, biological productivity, Se concentration, and parameters that affect Se bioavailability (e.g., sulfate) may influence the relationship between Se concentrations in water and fish tissue. It is desirable to identify environmentally protective waterborne Se guidelines that, if not exceeded, reduce the need to directly measure Se concentrations in fish tissue. Three factors that should currently be considered in developing waterborne Se screening guidelines are 1) differences between lotic and lentic sites, 2) the influence of exposure concentration on Se partitioning among compartments, and 3) the influence of sulfate on selenate bioavailability. Colocated data sets of Se concentrations in 1) water and particulates, 2) particulates and invertebrates, and 3) invertebrates and fish tissue were compiled; and a quantile regression approach was used to derive waterborne Se screening guidelines. Use of a regression-based approach for describing relationships in Se concentrations between compartments reduces uncertainty associated with selection of partitioning factors that are generally not constant over ranges of exposure concentrations. Waterborne Se screening guidelines of 6.5 and 3.0 μg/L for lotic and lentic water bodies were derived, and a sulfate-based waterborne Se guideline equation for selenate-dominated lotic waters was also developed. Environ Toxicol Chem 2017;36:2503-2513.

From sediment to tissue and tissue to sediment: an evaluation of statistical bioaccumulation models.

Biota-sediment accumulation factors (BSAFs) and biota-sediment accumulation regressions (BSARs) are statistical models that may be used to estimate tissue chemical concentrations from sediment chemical concentrations or vice versa. Biota-sediment accumulation factors and BSARs are used to fill tissue concentration data gaps, set sediment preliminary remediation goals (PRGs), and make projections about the effectiveness of potential sediment cleanup projects in reducing tissue chemical concentrations. We explored field-based, benthic invertebrate biota-sediment chemical concentration relationships using data from the US Environmental Protection Agency (USEPA) Mid-Continent Ecology Division (MED) BSAF database. Approximately two thirds of the 262 relationships investigated were very poor (r(2)  < 0.3 or p-value ≥ 0.05); for some of the biota-sediment relationships that did have a significant nonzero slope (p-value < 0.05), lipid-normalized tissue concentrations tended to decrease as the colocated organic carbon (OC)-normalized sediment concentration increased. Biota-sediment relationships were further evaluated for 3 of the 262 datasets. Biota-sediment accumulation factors, linear regressions, model II regressions, illustrative sediment PRGs, and confidence intervals (CIs) were calculated for each of the three examples. These examples illustrate some basic but important statistical practices that should be followed before selecting a BSAR or BSAF or relying on these simple models of biota-sediment relationships to support consequential management decisions. These practices include the following: one should not assume that the relationship between chemical concentrations in tissue and sediment is necessarily linear, one should not assume the model intercept to be zero, and one should not place too much stock on models that are heavily influenced by one or a few high chemical concentration data points. People will continue to use statistical models of field-based biota-sediment chemical concentration relationships to support sediment investigations and remedial action decisions. However, it should not be assumed that the models will be reliable. In developing and applying BSAFs and BSARs, it is essential that best practices are followed and model limitations and uncertainties are understood, acknowledged, and quantified as much as possible.