What do we want to estimate from observational datasets? Choosing appropriate statistical analysis methods based on the chemical management phase

Abstract

The goals of observational dataset analysis vary with the management phase of environments threatened by anthropogenic chemicals. For example, identifying severely compromised sites is necessary to determine candidate sites in which to implement measures during early management phases. Among the most effective approaches is developing regression models with high predictive power for dependent variable values using the Akaike information criterion. However, this analytical approach may be theoretically inappropriate to obtain the necessary information in various chemical management phases, such as the intervention effect size of a chemical required in the late chemical management phase to evaluate the necessity of an effluent standard and its specific value. However, choosing appropriate statistical methods based on the data analysis objective in each chemical management phase has rarely been performed. This study provides an overview of the primary data analysis objectives in the early and late chemical management phases. For each objective, several suitable statistical analysis methods for observational datasets are detailed. In addition, the study presents examples of linear regression analysis procedures using an available dataset derived from field surveys conducted in Japanese rivers. Integr Environ Assess Manag 2022;18:1414–1422. © 2021 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Predicting macroinvertebrate average score per taxon (ASPT) at water quality monitoring sites in Japanese rivers

“…All predictors except catchment area and minimum pH were included in all of the top 10 models (see Table S3). The negative regression coefficients of BOD, %Urban, SS, %Paddy, and %Urban-3km in the best model, along with the positive coefficient of minimum pH, are all consistent with the expected adverse impacts of these factors on macroinvertebrates as reported in previous studies (Takeshita et al 2022). The positive regression coefficient of elevation aligns with the general expectation that upland river sites at higher elevation would have lower water temperature (resulting in higher dissolved oxygen essential for aquatic organisms) and be less impacted by anthropogenic factors and disturbances.…”

Metals and new approaches for biomonitoring

“…Recently, studies published in IEAM have highlighted the importance of choosing the correct statistical tests to evaluate observational data sets. Evaluating a previously collected data set, Takeshita et al (2022) illustrated that employing general linear regression models leads to inaccurate estimations of the impact of metals and TOC on the diversity index of Insecta in Japan. This research highlights the importance of a regression model that prioritizes predictive accuracy for the dependent variable, which, in this context, is the biological indicator.…”