Takehiko I. Hayashi

Adding Value to Ecological Risk Assessment with Population Modeling

ABSTRACT Current measures used to estimate the risks of toxic chemicals are not relevant to the goals of the environmental protection process, and thus ecological risk assessment (ERA) is not used as extensively as it should be as a basis for cost-effective management of environmental resources. Appropriate population models can provide a powerful basis for expressing ecological risks that better inform the environmental management process and thus that are more likely to be used by managers. Here we provide at least five reasons why population modeling should play an important role in bridging the gap between what we measure and what we want to protect. We then describe six actions needed for its implementation into management-relevant ERA.

Integrating Population Modeling into Ecological Risk Assessment

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Population-level ecological effect assessment: estimating the effect of toxic chemicals on density-dependent populations

We examined the relationship between individual-level and population-level effects of toxic chemicals, employing the equilibrium population size as an index of population-level effects. We first analyzed two-stage matrix models considering four life-history types and four density-dependent models, and then we analyzed ecotoxicological and life-history data of the fathead minnow (Pimephales promelas) and brook trout (Salvelinus fontinalis) as real examples. Our elasticity analysis showed that toxic impacts on density-dependent populations depended largely on the differences in density-dependence and in life histories of the organisms. In particular, the importance of adult survivability was considerably increased in iteroparous organisms with density-dependent juvenile survivability or fertility. Our results also suggested that population-level effects, as indicated by the percentage reduction in equilibrium population size, were often greater than the percentage reductions in vital rates of individuals. Our analysis indicates that assessing population-level risk and developing a risk-reduction strategy without considering density-dependence can be risky.

A Bayesian approach to probabilistic ecological risk assessment: risk comparison of nine toxic substances in Tokyo surface waters

Background, aim, and scopeQuantitative risk comparison of toxic substances is necessary to decide which substances should be prioritized to achieve effective risk management. This study compared the ecological risk among nine major toxic substances (ammonia, bisphenol-A, chloroform, copper, hexavalent chromium, lead, manganese, nickel, and zinc) in Tokyo surface waters by adopting an integrated risk analysis procedure using Bayesian statistics.MethodsSpecies sensitivity distributions of these substances were derived by using four Bayesian models. Environmental concentration distributions were derived by a hierarchical Bayesian model that explicitly considered the differences between within-site and between-site variations in environmental concentrations. Medians and confidence intervals of the expected potentially affected fraction (EPAF) of species were then computed by the Monte Carlo method.ResultsThe estimated EPAF values suggested that risk from nickel was highest and risk from zinc and ammonia were also high relative to other substances. The risk from copper was highest if bioavailability was not considered, although toxicity correction by a biotic ligand model greatly reduced the estimated risk. The risk from manganese was highest if a conservative risk index estimate (90% upper EPAF confidence limit) was selected.ConclusionIt is suggested that zinc is not a predominant risk factor in Tokyo surface waters and strategic efforts are required to reduce the total ecological risk from multiple substances. The presented risk analysis procedure using EPAF and Bayesian statistics is expected to advance methodologies and practices in quantitative ecological risk comparison.

A Bayesian Method for Deriving Species-Sensitivity Distributions: Selecting the Best-Fit Tolerance Distributions of Taxonomic Groups

ABSTRACT We present a Bayesian method for deriving species-sensitivity distributions (SSDs). We employed four Bayesian statistical models to consider differences in tolerance to toxic substances among different taxonomic groups. We first used a Malkov chain Monte Carlo simulation based on these models to estimate the SSD parameters. We then computed deviance information criterion values of the models and compared them in order to select the model with the best predictive ability. We applied this approach to seven substances (zinc, lead, hexavalent chromium, cadmium, nickel, short-chain chloride paraffin, and chloroform) as case examples, and then compared the derived SSDs from the selected models and a model that assumed no tolerance differences among taxonomic groups. We discuss the advantages and limitations of our approach on the basis of our results.

Comparison of population-level effects of heavy metals on fathead minnow (Pimephales promelas).

To evaluate the population-level effects of heavy metals (copper, zinc, cadmium, hexavalent chromium, nickel) on fathead minnow, Pimephales promelas, we first estimated the concentration-effect relationships between the metal concentrations and individual traits (juvenile survivability, hatchability, fertility) by using toxicity data collected from the literature. A Leslie matrix model of fathead minnow was used to calculate population growth rates from these relationships. The population threshold concentrations (PTCs) leading to zero net growth of the fish population were as follows: Cu, 27.4; Cd, 33.2; Zn (soft water), 81.5; Zn (hard water), 85.8; Ni, 504.8; Cr, 3251.6 (microg L(-1)). By comparing the PTCs with no observed effect concentrations (NOECs), we found that some PTCs were equivalent to or even lower than the corresponding NOECs. This result suggests that current ecological risk assessments based on the NOECs can be inadequate for protecting aquatic populations and more efforts on the population-level studies are needed.

Applying biotic ligand models and Bayesian techniques: Ecological risk assessment of copper and nickel in Tokyo rivers

Biotic ligand models (BLMs) have been broadly accepted and used in ecological risk assessment of heavy metals for toxicity normalization with respect to water chemistry. However, the importance of assessing bioavailability by using BLMs has not been widely recognized among Japanese stakeholders. Failing to consider bioavailability may result in less effective risk management than would be possible if currently available state-of-the-art methods were used to relate bioavailable concentrations to toxic effects. In this study, an ecological risk assessment was conducted using BLMs for 6 rivers in Tokyo to stimulate discussion about bioavailability of heavy metals and the use of BLMs in ecological risk management in Japan. In the risk analysis, a Bayesian approach was used to take advantage of information from previous analyses and to calculate uncertainties in the estimation of risk. Risks were judged to be a concern if the predicted environmental concentration exceeded the 5th percentile concentration (HC5) of the species sensitivity distribution. Based on this criterion, risks to stream biota from exposure to Cu were judged not to be very severe, but it would be desirable to conduct further monitoring and field surveys to determine whether temporary exposure to concentrations exceeding the HC5 causes any irreversible effects on the river ecosystem. The risk of exposure to Ni was a concern at only 1 of the 6 sites. BLM corrections affected these conclusions in the case of Cu but were moot in the case of Ni. The use of BLMs in risk assessment calculations for Japanese rivers requires water quality information that is, unfortunately, not always available.

Potential effects of life‐history evolution on ecological risk assessment

We investigated theoretically how the sensitivity of organisms to the toxicity of chemicals varies depending on their life-history traits, which are subject to evolution. We used a resource-allocation model in which organisms allocate their resources to reproduction, maintenance of life (reduction of death), and reduction of the toxicities of chemicals. First we investigated the optimal allocation rates in the absence of chemicals. We found that when evolution occurred in low-density populations, the allocation rate for reproduction was larger than that for maintenance of life, and hence an r-strategy evolved. The r-strategists had lower sensitivity (higher resistance) against the toxicity than K-strategists, which was the optimal strategy in high-density populations. Second, we examined the optimal allocation rates in the presence of chemicals. The allocation rate for the reduction of toxicity varied depending on the shape of functions for the reduction of toxicity. When the efficiency for the reduction was low, organisms did not allocate resources to reduce toxicity, and they remained sensitive to chemicals (sensitive type). When the toxicity was efficiently reduced, the organisms allocated resources to reduce the toxicity and became insensitive to the chemicals (resistant type). When the function for the reduction had a sigmoidal shape, evolutionary bistability appeared, and the organisms eventually evolved either to allocate resources for chemical reduction or not to do so depending on the initial conditions of evolution. This result explains the large variation in the sensitivities to chemicals in organisms collected from polluted areas. We also found that the toxicity required to reduce the population growth rate by 10% (EC10) was higher for the resistant type than for the sensitive type in general; however, when the toxicity tests were conducted under a resource-poor condition, EC10 was even smaller in the resistant type than in the sensitive type (i.e., resistant organisms are more sensitive than sensitive organisms). This counterintuitive result occurred because the allocation of resources for toxicity reduction was larger than needed, and was thus an overinvestment under the resource-poor condition. Together with the results, we conclude that lacking an understanding of the evolutionary aspect may lead to insufficient risk assessment and management.

Ecological risk assessment of herbicides in Japan: Integrating spatiotemporal variation in exposure and effects using a multimedia model and algal density dynamics models

Application of herbicides to paddy fields in Japan has strong seasonality, and their environmental concentrations exhibit clear spatiotemporal variation. The authors developed an approach that combines a multimedia environmental exposure model (Grid-Catchment Integrated Modeling System) and density dynamics models for algae. This approach enabled assessment of ecological risk when the exposure concentration shows spatiotemporal variation. First, risk maps of 5 herbicides (pretilachlor, butachlor, simetryn, mefenacet, and esprocarb) were created from the spatial predictions of environmental concentrations and 50% inhibitory concentrations of the herbicides. Simulations of algal density dynamics at high-risk sites were then conducted by incorporating the predicted temporal dynamics of the environmental concentration of each herbicide at the sites. The results suggested that the risk of pretilachlor was clearly the highest of the 5 herbicides, in terms of both the spatial distributions and the temporal durations. The present study highlights the importance of integrating exposure models and effect models to clarify spatial and temporal risk and to develop management plans for chemical exposure that shows high spatiotemporal variation.

Were the sharp declines of dragonfly populations in the 1990s in Japan caused by fipronil and imidacloprid? An analysis of Hill’s causality for the case of Sympetrum frequens

Neonicotinoids and fipronil are the most widely used insecticides in the world. Previous studies showed that these compounds have high toxicity to a wide taxonomic range of non-target invertebrates. In rice cultivation, they are frequently used for nursery-box treatment of rice seedlings. The use of fipronil and neonicotinoid imidacloprid is suspected to be the main cause of population declines of red dragonflies, in particular Sympetrum frequens, because they have high lethal toxicity to dragonfly nymphs and the timing of the insecticides’ introduction in Japan (i.e., the late 1990s) overlapped with the sharp population declines. However, a causal link between application of these insecticides and population declines of the dragonflies remains unclear. Therefore, we estimated the amount of the insecticides applied for nursery-box treatment of rice seedlings and analyzed currently available information to evaluate the causality between fipronil and imidacloprid usage and population decline of S. frequens using Hill’s causality criteria. Based on our scoring of Hill’s nine criteria, the strongest lines of evidence were strength, plausibility, and coherence, whereas the weakest were temporality and biological gradient. We conclude that the use of these insecticides, particularly fipronil, was a major cause of the declines of S. frequens in Japan in the 1990s, with a high degree of certainty. The existing information and our analyses, however, do not allow us to exclude the possibility that some agronomic practices (e.g., midsummer drainage or crop rotation) that can severely limit the survival of aquatic nymphs also played a role in the dragonfly’s decline.