Tanaka Yoshinari

In this paper, both the empirical and theoretical genetic aspects of human-mediated introgressive hybridization are reviewed in terms of their association with the breakdown of postzygotic isolating mechanisms. I also compare several simulation models with an ecological or genetic focus that are relevant to the prediction and risk assessment of genetic extinction due to hybridization. One barrier to devising comprehensive risk assessment frameworks is a lack of sufficient population genetic studies that associate introgressive hybridization with specific isolating mechanisms. A gametic model based on multilocus underdominant fitness is one of the best genetic models for introgressive hybridization because it explicitly incorporates the postzygotic isolating mechanism known as Dobzhansky-Muller genetic incompatibility.

Achievements of the research project entitled "Establishment of a scientific framework for the management of toxicity of chemicals based on environmental risk-benefit analysis" supported by the JST were introduced and reviewed, focusing on the development of the methodology for estimating risks; human health risks and ecological risks. The usefulness of loss of life expectancy as a metric for evaluating cancer and noncancer risks was demonstrated. To evaluate ecological risks, three metrics, 1/T, logT and T, developed based on the mean extinction time (T) of species were proposed. Then, their implication and feasibility were examined in terms of what ecological system should be conserved and how easily people can understand the implications of metrics. Protocols for estimating human health risks and ecological risks are illustrated. (C) 2003 Elsevier Ltd. All rights reserved.

For the ecological risk assessment, extrapolation from toxicological data obtained at the individual level into effects at the population level is required. We review the analytical methods for translating chronic toxicity data into the effect on propensity of populations (population growth rate or intrinsic rate of population increase). Actual ecotoxicological data have two major problems, i.e., 1) only a very small fraction of chemicals-species combinations has been examined for chronic toxicity, and 2) it is not feasible for many test species to execute the life-cycle test. As analytical methods in order to circumvent these limitations of data, we focus on the extrapolation method and the life history sensitivity analysis, and discuss these methods in the context of ecological risk assessment. The cxtrapolation method is to infer missing chronic toxicity data from regression of known chronic data to acute data, or from regression of chromic data between different species or life stages. From the inferred and the directly estimated chronic toxicity data, the effect of chemicals to population growth rate is estimated. The life history sensitivity analysis estimates the relative importance of life stages in terms of intrinsic rate of natural increase, and reduces the life table evaluation by excluding the unimportant life stages. These analytical methods that apply the ecological theory may be important for future ecotoxicological data analysis embedded in the ecological risk assessment.

Ecotoxicological data using life table evaluation are reviewed and analyzed with the power function model. Life table evaluation and population growth experiments are proposed as experimental procedures that provide demographic parameters (e.g. intrinsic rate of natural increase) relevant for calculating extinction risk due to pollutant exposure. Totally 47 concentration vs. intrinsic rate data sets were collected and analyzed with two indices in the power function model, α and β. The α-value is the concentration at which the intrinsic rate of increase drops off below zero due to exposure and the β-value represents curvature of the response. The α-values, which represent strength of ecological toxicity, are highly correlated with acute LC50s. It is indicated that the α-values are indirectly predictable from acute LC50s. Such statistical extrapolation may be useful for ecological risk assessment based on extinction probability of populations.