Estimation of Chemical Toxicity to Wildlife Species Using Interspecies Correlation Models

U.S. Environmental Protection Agency, National Health and Environmental Effects Laboratory, Gulf Ecology Division, 1 Sabine Island Drive, Gulf Breeze, Florida 32561, USA.
Environmental Science and Technology (Impact Factor: 5.33). 08/2007; 41(16):5888-94. DOI: 10.1021/es070359o
Source: PubMed


Ecological risks to wildlife are typically assessed using toxicity data for relatively few species and with limited understanding of differences in species sensitivity to contaminants. Empirical interspecies correlation models were derived from LD50 values for 49 wildlife species and 951 chemicals. The standard wildlife test species Japanese quail (Coturnix japonica) and mallard (Anas platyrhynchos) were determined to be good surrogates for many species within the database. Cross-validation of all models predicted toxicity values within 5-fold and 10-fold of the actual values with 85 and 95% certainty, respectively. Model robustness was not consistently improved by developing correlation models within modes of action (MOA); however, improved models for neurotoxicants, carbamates, and direct acting organophosphorous acetylcholenesterase inhibiting compounds indicate that toxicity estimates may improve if MOA-specific models are built with robust datasets. There was a strong relationship between taxonomic distance and cross-validation prediction success (chi2 = 297, df = 12, p < 0.0001), with uncertainty increasing with larger taxonomic distance between the surrogate and predicted species. Interspecies toxicity correlations provide a tool for estimating contaminant sensitivity with known levels of uncertainty for a diversity of wildlife species.

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    • "Liess and Van der Ohe, 2005), particularly for hydrophobic pyrethroids. Because toxicity data is not available for all species in the ecosystem, information on sensitivity-related traits (Rubach et al. 2010; Rico and Van den Brink 2015), taxonomy (Raimondo et al. 2007) or phylogeny (Guénard et al. 2014) can be used to perform a preliminary sensitivity ranking. The study by Rico and Van den Brink (2015) shows an example of sensitivity rankings for several insecticide classes, and demonstrated that the relative sensitivity of invertebrates varies according to the insecticidal toxic mode-of-action. "
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    ABSTRACT: The prospective aquatic Environmental Risk Assessment (ERA) of pesticides is generally based on the comparison of predicted environmental concentrations in edge-of-field surface waters with regulatory acceptable concentrations derived from laboratory and/or semi-field experiments with aquatic organisms. New improvements in mechanistic effect modelling have allowed a better characterization of the ecological risks of pesticides through the incorporation of biological trait information and landscape parameters to assess individual, population and/or community-level effects and recovery. Similarly to exposure models, ecological models require scenarios that describe the environmental context in which they are applied. In this paper we propose a conceptual framework for the development of ecological scenarios that, when merged with exposure scenarios, will constitute environmental scenarios for prospective aquatic ERA. These ‘unified’ environmental scenarios are defined as the combination of the biotic and abiotic parameters that are required to characterize exposure, (direct and indirect) effects and recovery of aquatic non-target species under realistic worst-case conditions. Ideally, environmental scenarios aim to avoid a potential mismatch between the parameter values and the spatial-temporal scales currently used in aquatic exposure and effect modelling. This requires a deeper understanding of the ecological entities we intend to protect, which can be preliminarily addressed by the formulation of ecological scenarios. In this paper we present a methodological approach for the development of ecological scenarios and illustrate this approach by a case-study for Dutch agricultural ditches and the example focal species Sialis lutaria. Finally, we discuss the applicability of ecological scenarios in ERA and propose research needs and recommendations for their development and integration with exposure scenarios. This article is protected by copyright. All rights reserved
    Integrated Environmental Assessment and Management 10/2015; DOI:10.1002/ieam.1718 · 1.38 Impact Factor
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    • "It also assumes that exposure is a continuous condition and occurs at a constant rate and is unaffected by ecological interactions with the environment (e.g., habitat preferences or feeding frequency). Numerous recent publications have discussed the importance of each of these factors: environmental parameters (McLaughlin and Smolders 2001; USEPA 2007a; Zhao et al. 2007); bioavailability adjustments (Anderson et al. 2012; DeForest et al. 2012); dose–response information (Chapman et al. 1996; Crane and Newman 2000; Allard et al. 2010; Landis and Chapman 2011), and cross‐species extrapolation (Newman et al. 2000; Raimondo et al. 2007; Awkerman et al. 2008, 2009). Furthermore, whereas existing Eco‐SSLs for wildlife have focused exclusively on birds and mammals, recent toxicity data for amphibians and reptiles have become available such that these receptors can be examined in greater depth in regards to protective soil levels (James et al. 2004a, 2004b; Johnson et al. 2004; Johnson et al. 2007; Bazar et al. 2008, 2009, 2010; McFarland et al. 2008, 2009, 2011; Sparling et al. 2010). "
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    ABSTRACT: The development of media-specific ecological values for risk assessment includes the derivation of acceptable levels of exposure for terrestrial wildlife (e.g., birds, mammals, reptiles, and amphibians). Though the derivation and subsequent application of these values can be used for screening purposes, there is a need to identify toxicological effects thresholds specifically for making remedial decisions at individual contaminated sites. A workshop was held in the fall of 2012 to evaluate existing methods and recent scientific developments for refining ecological soil screening levels (Eco-SSLs) and improving the derivation of site-specific ecological soil cleanup values (Eco-SCVs). This included a focused session on the development and derivation of toxicity reference values (TRVs) for terrestrial wildlife. Topics that were examined included: methods for toxicological endpoint selection, techniques for dose-response assessment, approaches for cross-species extrapolation, and tools to incorporate environmental factors (e.g., metal bioavailability and chemistry) into a reference value. The workgroup also made recommendations to risk assessors and regulators on how to incorporate site-specific wildlife life history and toxicity information into the derivation of TRVs to be used in the further development of soil cleanup levels. Integr Environ Assess Manag © 2013 SETAC.
    Integrated Environmental Assessment and Management 07/2014; 10(3). DOI:10.1002/ieam.1474 · 1.38 Impact Factor
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    • "Unfortunately, we did not find species-specific data suitable to establish response curves, that is, quantitative data on reproductive success along a gradient of toxicant concentrations in eggs. Because differences in sensitivity between species can be large (Hoffman et al. 1998) and tend to increase with taxonomic distance (Raimondo, Mineau & Barron 2007), we searched for data from species as closely related as possible (same genus). For DDE, we used field data on DDE egg residues and reproductive success reported for a population of merlins Falco columbarius between 1969 and 1973 (Fox 1979). "
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    ABSTRACT: A major challenge in the conservation of threatened and endangered species is to predict population decline and design appropriate recovery measures. However, anthropogenic impacts on wildlife populations are notoriously difficult to predict due to potentially nonlinear responses and interactions with natural ecological processes like density dependence.Here, we incorporated both density dependence and anthropogenic stressors in a stage-based matrix population model and parameterized it for a density-dependent population of peregrine falcons Falco peregrinus exposed to two anthropogenic toxicants [dichlorodiphenyldichloroethylene (DDE) and polybrominated diphenyl ethers (PBDEs)]. Log-logistic exposure–response relationships were used to translate toxicant concentrations in peregrine falcon eggs to effects on fecundity. Density dependence was modelled as the probability of a nonbreeding bird acquiring a breeding territory as a function of the current number of breeders.The equilibrium size of the population, as represented by the number of breeders, responded nonlinearly to increasing toxicant concentrations, showing a gradual decrease followed by a relatively steep decline. Initially, toxicant-induced reductions in population size were mitigated by an alleviation of the density limitation, that is, an increasing probability of territory acquisition. Once population density was no longer limiting, the toxicant impacts were no longer buffered by an increasing proportion of nonbreeders shifting to the breeding stage, resulting in a strong decrease in the equilibrium number of breeders.Median critical exposure concentrations, that is, median toxicant concentrations in eggs corresponding with an equilibrium population size of zero, were 33 and 46 μg g−1 fresh weight for DDE and PBDEs, respectively.Synthesis and applications. Our modelling results showed that particular life stages of a density-limited population may be relatively insensitive to toxicant impacts until a critical threshold is crossed. In our study population, toxicant-induced changes were observed in the equilibrium number of nonbreeding rather than breeding birds, suggesting that monitoring efforts including both life stages are needed to timely detect population declines. Further, by combining quantitative exposure–response relationships with a wildlife demographic model, we provided a method to quantify critical toxicant thresholds for wildlife population persistence.
    Journal of Applied Ecology 12/2013; 50(6). DOI:10.1111/1365-2664.12142 · 4.56 Impact Factor
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