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An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

Field survey-based ecological risk assessments for trace metals are conducted to examine the necessity and/or effectiveness of management intervention, such as setting of environmental quality standards. Observational datasets often involve confounders that may bias estimation of the effects of intervention (e.g., reduction of trace-metal concentrations through regulation). The field of ecotoxicology lags behind some other research fields in understanding proper analytical procedures for causal inference from observational datasets; there are only a few field survey-based ecotoxicological studies that have explicitly controlled for confounders in their statistical analyses. In the present study, we estimated the effect of intervention in nickel concentrations on Ephemeroptera, Plecoptera, and Trichoptera richness in rivers in Japan. We also provide detailed explanations for the backgrounds of spurious associations derived from confounders and on proper analytical procedures for obtaining an unbiased estimate of the targeted intervention effect by using regression analysis. We constructed a multiple regression model based on a causal diagram for aquatic insects and environmental factors, and on "the backdoor criterion," that enabled us to determine the set of covariates required to obtain an unbiased estimate of the targeted intervention effect from regression coefficients. We found that management intervention in nickel concentrations may be ineffective compared to intervention in organic pollution, and that analysis ignoring the confounders overestimated the effect of intervention in nickel concentrations. Our results highlight the fact that confounders can lead to misjudging the necessity for management of anthropogenic chemical substances. Confounders should be explicitly specified and statistically controlled to achieve a comprehensive assessment of ecological risks for various substances. Image 1 • Proper causal inference procedures are poorly understood in ecotoxicology. • We applied causal inference to an observational dataset for rivers in Japan. • We estimated the causal effect of free Ni-ion concentrations on aquatic insects. • The effect of intervention was not significant even though an association existed. • Intervention in environmental Ni based solely on an association can be ineffective. We illustrated proper analytical procedures for causal inference from field survey-based datasets and its importance by using ecological risk assessment of nickel as an example. [ABSTRACT FROM AUTHOR]

Climate warming is of concern as a key factor in the worldwide decline in insect populations. In Japan, numbers of a common dragonfly in rice paddy fields, Sympetrum frequens, decreased sharply in the 1990s. Because S. frequens migrates to cooler mountains in summer, climate warming has been suggested as one of the main causes of the population decline in addition to agronomic factors. Here, we analysed the relation between summer temperatures and population densities of S. frequens and the related S. infuscatum, which does not migrate to mountains in summer, using published population monitoring data and temperature data from three regions (Toyama, Ishikawa, and Shizuoka) in Japan. Decadal differences in summer temperatures lay within the range of fluctuations among years, suggesting that an increase in summer temperatures cannot explain the past sharp population declines. However, regression analyses using monitoring data from Toyama showed that the population dynamics of both species in autumn are negatively correlated with summer temperatures in the same year. These results suggest that high temperatures in summer directly affect adult mortality to an extent that results in a decrease in population growth.

Ecological niche models (ENMs) are widely used in spatial prioritization for biodiversity conservation (e.g. selecting conservation areas). However, it is unclear whether ENMs are always beneficial for such purposes. We quantified the benefit of using ENMs in conservation prioritization, comparing the numbers of species covered by conservation areas selected on the basis of probabilities estimated by ENMs (ENM approach) and those selected on the basis of raw observation data (raw-data approach), while controlling survey range, survey bias, and target size of conservation area. We evaluated three ENM algorithms (GLM, GAM, and random forests). We used virtual community data generated by simulation for the evaluation. ENM approach was effective when survey bias is strong, survey range is narrow, and target size of conservation area is moderate. The percentage of cases in which the ENM approach outperformed the raw-data approach ranged from 0.0 to 33% (GLM), 31% (GAM), and 75% (random forests) depending on conditions. The number of rare species (< 20 presence records) included in the conservation area based on the ENM approach was less than, or the same as, that of the raw-data approach. The unexpectedly limited cases in which the ENM approach was effective in the present research may depend on the conservation target we used (to cover as many species as possible in conservation area). Our results highlight urgent need for evaluating ENM's effectiveness under other conservation targets for wise use of ENM in conservation prioritization., Competing Interests: The authors have declared that no competing interests exist.

We conducted a broad-scale ecological effect assessment of agricultural chemicals where we combined biomonitoring databases of riverine invertebrate communities with predictions of environmental concentrations of chemicals, based on an exposure evaluation model for Japanese rivers. One of the difficulties of broad-scale assessment arises from the use of biomonitoring databases for which the monitoring sites are often spread across different geographic regions, with varying species compositions and heterogeneous environmental factors. This problem was circumvented using a trait-based approach, which extracts patterns of ecological properties of species response to changes in either chemical concentration or environmental factors. We identified groups of species that had particular trait categories that were negatively correlated with herbicide pollutants (the predicted concentration divided by the acute toxic concentration). Numerical abundances of species groups classified by trait categories had more sensitive responses to herbicide pollutants than total species abundance. However, a finding that trait diversity and species diversity indexes in the communities examined did not change with herbicide pollutants means that the two indexes showed resistance to chemical stresses. We inferred that the reason for the greater resistance in terms of trait and species diversity was that compositional changes of species caused by increasing herbicide pollutions were simply a shift from communities composed of susceptible species to those composed only of tolerant species., (Copyright © 2018 Elsevier Inc. All rights reserved.)