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The quality of chemical management depends more or less on practical procedures used to assess chemicals. This study quantitatively assessed the efficacy of a derivation procedure for calculating no‐effect concentrations for screening assessment of environmental hazards under the Chemical Substance Control Law in Japan. We first evaluated the derivation procedure by applying a series of test ecotoxicity datasets to the procedure and calculating the resulting misclassification rates of the hazardous class of chemicals. In this study, a chemical was deemed to have been misclassified if its classification differed from its classification based on the full dataset (chronic toxicity data for three trophic levels), which was defined as the correct assignment. We also calculated the effects of additional uncertainty factors to decrease the variance (i.e., to improve the consistency) of the misclassification rates among cases with different data availability in the derivation procedure. The results showed that the derivation procedure resulted in very high rates of misclassification when only particular sets of ecotoxicity data were available (e.g., only chronic toxicity data of algae were available). Our analyses also showed that the use of additional uncertainty factors improved the consistency of the misclassification rates within the derivation procedure. Our study presents a broadly applicable calculation framework for quantifying error rates in assessment procedures and serves as a case study for future development and reforms of chemical assessment processes and policies, while additional analyses using more extensive ecotoxicity data with various modes of actions are needed in the future. [ABSTRACT FROM AUTHOR]

Recent increases in wildlife cause negative impacts on humans through both economic and ecological damage, as well as the spread of pathogens. Understanding the population dynamics of wildlife is crucial to develop effective management strategies. However, it is difficult to estimate accurate and precise population size over large spatial and temporal scales because of the limited data availability. We addressed these issues by first fitting a random encounter and staying time (REST) model based on camera trap data to construct an informative prior distribution for a capture rate parameter in a harvest‐based Bayesian state‐space model. We constructed a Bayesian state‐space model that integrated administration data on the number of captured wild boar with the prior distribution of capture efficiency estimated by camera trap data. The model with informative prior distribution from the REST model successfully estimated population dynamics, whereas the model using only the administration data did not, owing to a lack of parameter convergence. We identified areas where (1) wild boars exhibit a high potential population growth rate and a high carrying capacity, (2) current trapping efforts are effectively suppressing local populations, and (3) trapping reinforcement is required to control populations in the whole region. The model could be used to predict future trends in populations under the assumptions of ongoing trapping pressure. This will help identify spatially explicit trapping efforts to achieve target population levels. [ABSTRACT FROM AUTHOR]

The quality of chemical management depends more or less on practical procedures used to assess chemicals. This study quantitatively assessed the efficacy of a derivation procedure for calculating no-effect concentrations for screening assessment of environmental hazards under the Chemical Substance Control Law in Japan. We first evaluated the derivation procedure by applying a series of test ecotoxicity datasets to the procedure and calculating the resulting misclassification rates of the hazardous class of chemicals. In this study, a chemical was deemed to have been misclassified if its classification differed from its classification based on the full dataset (chronic toxicity data for three trophic levels), which was defined as the correct assignment. We also calculated the effects of additional uncertainty factors to decrease the variance (i.e., to improve the consistency) of the misclassification rates among cases with different data availability in the derivation procedure. The results showed that the derivation procedure resulted in very high rates of misclassification when only particular sets of ecotoxicity data were available (e.g., only chronic toxicity data of algae were available). Our analyses also showed that the use of additional uncertainty factors improved the consistency of the misclassification rates within the derivation procedure. Our study presents a broadly applicable calculation framework for quantifying error rates in assessment procedures and serves as a case study for future development and reforms of chemical assessment processes and policies, while additional analyses using more extensive ecotoxicity data with various modes of actions are needed in the future., (© 2022 Society for Risk Analysis.)