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As most ecosystems are being challenged by multiple, co‐occurring stressors, an important challenge is to understand and predict how stressors interact to affect biological responses. A popular approach is to design factorial experiments that measure biological responses to pairs of stressors and compare the observed response to a null model expectation. Unfortunately, we believe experiment sample sizes are inadequate to detect most non‐null stressor interaction responses, greatly hindering progress. Using both real and simulated data, we show sample sizes typical of many experiments (<6) can (i) only detect very large deviations from the additive null model, implying many important non‐null stressor‐pair interactions are being missed, and (ii) potentially lead to mostly statistical outliers being reported. Computer code that simulates data under either additive or multiplicative null models is provided to estimate statistical power for user‐defined responses and sample sizes, and we recommend this is used to aid experimental design and interpretation of results. We suspect that most experiments may require 20 or more replicates per treatment to have adequate power to detect nonadditive. However, estimates of power need to be made while considering the smallest interaction of interest, i.e., the lower limit for a biologically important interaction, which is likely to be system‐specific, meaning a general guide is unavailable. We discuss ways in which the smallest interaction of interest can be chosen, and how sample sizes can be increased. Our main analyses relate to the additive null model, but we show similar problems occur for the multiplicative null model, and we encourage similar investigations into the statistical power of other null models and inference methods. Without knowledge of the detection abilities of the statistical tools at hand or the definition of the smallest meaningful interaction, we will undoubtedly continue to miss important ecosystem stressor interactions. [ABSTRACT FROM AUTHOR]

The benchmark dose (BMD) concept is increasingly utilized to analyze quantitative dose-response relationships in genetic toxicology. This methodology requires the user (i.e. the toxicologist) to a priori define a small increase over controls that is 'acceptable' to be induced by a genotoxic test substance. The increase is called benchmark response (BMR) or critical effect size (CES), depending on the software used. To render the metrics calculated from the data of animals treated with the test substance applicable for risk assessment, the BMR or CES must represent biologically relevant changes of parameters measured in in vivo genotoxicity assays such as the Micronucleus, Comet, Transgenic rodent or Pig-a assay. Current recommendations for CES in genotoxicology are arbitrary (10% increase over mean vehicle controls) or based on limited, usually 5-6, data points (i.e. the standard deviation of the concurrent vehicle control group). We have, therefore, analyzed historical vehicle control data of standard in vivo genotoxicity test systems with statistical methods. Based on this evaluation, we illustrate limitations of the currently recommended CES values and propose a pragmatic approach that may contribute to better defining endpoint-specific CES values for BMD software like PROAST. [ABSTRACT FROM AUTHOR]

A general theory on effect size for continuous data predicts a relationship between maximum response and within-group variation of biological parameters, which is empirically confirmed by results from dose–response analyses of 27 different biological parameters. The theory shows how effect sizes observed in distinct biological parameters can be compared and provides a basis for a generic definition of small, intermediate and large effects. While the theory is useful for experimental science in general, it has specific consequences for risk assessment: it solves the current debate on the appropriate metric for the Benchmark response in continuous data. The theory shows that scaling the BMR expressed as a percent change in means to the maximum response (in the way specified) automatically takes “natural variability” into account. Thus, the theory supports the underlying rationale of the BMR 1 SD. For various reasons, it is, however, recommended to use a BMR in terms of a percent change that is scaled to maximum response and/or within group variation (averaged over studies), as a single harmonized approach. [ABSTRACT FROM AUTHOR]

Normal ranges are some fraction of a reference distribution deemed to represent an expected condition, typically 95%. They are frequently used as the basis for generic criteria for monitoring programs designed to test whether a sample is outside of "normal," as in reference-condition approach studies. Normal ranges are also the basis for criteria for more classic environmental effects monitoring programs designed to detect differences in mean responses between reference and exposure areas. Limits on normal ranges are estimated with error that varies depending largely on sample size. Direct comparison of a sample or a mean to estimated limits of a normal range will, with some frequency, lead to incorrect conclusions about whether a sample or a mean is inside or outside the normal range when the sample or the mean is near the limit. Those errors can have significant costs and risk implications. This article describes tests based on noncentral distributions that are appropriate for quantifying the likelihood that samples or means are outside a normal range. These noncentral tests reverse the burden of evidence (assuming that the sample or mean is at or outside normal), and thereby encourage proponents to collect more robust sample sizes that will demonstrate that the sample or mean is not at the limits or beyond the normal range. These noncentral equivalence and interval tests can be applied to uni- and multivariate responses, and to simple (e.g., upstream vs downstream) or more complex (e.g., before vs after, or upstream vs downstream) study designs. Statistical procedures for the various tests are illustrated with benthic invertebrate community data collected as part of the Regional Aquatics Monitoring Program (RAMP) in the vicinity of oil sands operations in northern Alberta, Canada. An Excel workbook with functions and calculations to carry out the various tests is provided in the online Supplemental Data. [ABSTRACT FROM AUTHOR]

Potential adverse effects of chemical substances on thyroid function are usually examined by measuring serum levels of thyroid-related hormones. Instead, recent risk assessments for thyroid-active chemicals have focussed on iodine uptake inhibition, an upstream event that by itself is not necessarily adverse. Establishing the extent of uptake inhibition that can be considered de minimis, the chosen benchmark response (BMR), is therefore critical. The BMR values selected by two international advisory bodies were 5% and 50%, a difference that had correspondingly large impacts on the estimated risks and health-based guidance values that were established. Potential treatment-related inhibition of thyroidal iodine uptake is usually determined by comparing thyroidal uptake of radioactive iodine (RAIU) during treatment with a single pre-treatment RAIU value. In the present study it is demonstrated that the physiological intra-individual variation in iodine uptake is much larger than 5%. Consequently, in-treatment RAIU values, expressed as a percentage of the pre-treatment value, have an inherent variation, that needs to be considered when conducting dose-response analyses. Based on statistical and biological considerations, a BMR of 20% is proposed for benchmark dose analysis of human thyroidal iodine uptake data, to take the inherent variation in relative RAIU data into account. Implications for the tolerated daily intakes for perchlorate and chlorate, recently established by the European Food Safety Authority (EFSA), are discussed. [ABSTRACT FROM AUTHOR]

Community-scale impacts of glyphosate-based herbicides on wetland plant communities and the magnitude of those impacts that should be considered biologically relevant are poorly understood. We contrast three different thresholds for setting biologically meaningful critical effect sizes for complex ANOVA study designs. We use each of the of the critical effect sizes to determine optimal α levels for assessment of how different concentrations of glyphosate-based herbicides affect wetland plant communities over two years of herbicide application (alone and in combination with agricultural fertilizers) and two subsequent years without herbicide (or fertilizer) application. The application of glyphosate-based herbicides was found to result in a decrease in macrophyte species richness, an increase in macrophyte species evenness, a decrease in macrophyte cover and a reduction in community similarity. There was little evidence that nutrient additions directly or indirectly affected plant community endpoints. The glyphosate effects were evident in the first year of herbicide application in 2009, and became more pronounced in the second year of herbicide application in 2010. However, when herbicides were not applied in 2011, recovery was observed in most endpoints, with the exception being species evenness, for which partial recovery was not observed until 2012. Optimal α levels differed among the three critical effect sizes for each ANOVA term and endpoint combination, however regardless of differences in α levels, conclusions were generally consistent across all critical effect sizes. Image 1 • Glyphosate-based herbicides reduced macrophyte cover and species richness. • Herbicide application increased species evenness and reduced community similarity. • Cover and diversity recovered in 2011, the year after herbicide applications. • Our conclusions were consistent across different target effect sizes. [ABSTRACT FROM AUTHOR]