Although previous studies suggest that greenhouse gas (GHG) emissions from reservoir sediment exposed to the atmosphere during drought may be substantial, this process has not been rigorously quantified. Here we determined carbon dioxide (CO₂) and methane (CH₄) emissions from sediment cores exposed to a drying and rewetting cycle. We found a strong temporal variation in GHG emissions with peaks when the sediment was drained (C emissions from permanently wet sediment and drained sediments were, respectively, 251 and 1646 mg m⁻² d⁻¹ for CO₂ and 0.8 and 547.4 mg m⁻² d⁻¹ for CH₄) and then again during rewetting (C emissions from permanently wet sediment and rewetted sediments were, respectively, 456 and 1725mg m⁻² d⁻¹ for CO₂ and 1.3 and 3.1 mg m⁻² d⁻¹ for CH₄). To gain insight into the importance of these emissions at a regional scale, we used Landsat satellite imagery to upscale our results to all Brazilian reservoirs. We found that during the extreme drought of 2014–2015, an additional 1299 km² of sediment was exposed, resulting in an estimated emission of 8.5 × 10¹¹ g of CO₂-eq during the first 15 d after the overlying water disappeared and in the first 33 d after rewetting, the same order of magnitude as the year-round GHG emissions of large (∼mean surface water area 454 km²) Brazilian reservoirs, excluding the emissions from the draw-down zone. Our estimate, however, has high uncertainty, with actual emissions likely higher. We therefore argue that the effects of drought on reservoir GHG emissions merits further study, especially because climate models indicate an increase in the frequency of severe droughts in the future. We recommend incorporation of emissions during drying and rewetting into GHG budgets of reservoirs to improve regional GHG emission estimates and to enable comparison between GHG emissions from hydroelectric and other electricity sources. We also emphasize that peak emissions at the onset of drought and the later rewetting should be quantified to obtain reliable emission estimates.

In this study, a trait-based macroinvertebrate sensitivity modeling tool is presented that provides two main outcomes: (1) it constructs a macroinvertebrate sensitivity ranking and, subsequently, a predictive trait model for each one of a diverse set of pre-defined Modes of Action (MOAs) and (2) it reveals data gaps and restrictions, helping with the direction of future research. Besides revealing taxonomic patterns of species sensitivity, we find that there was not one genus, family or class which was most sensitive to all MOAs, and that common test taxa were often not the most sensitive at all. Traits like life cycle duration and feeding mode were identified as important in explaining species sensitivity. For 71% of the species, no or incomplete traits data were available, making the lack of trait data the main obstacle in model construction. Research focus should therefore be on completing trait databases, and enhancing them with finer morphological traits, focusing on the toxicodynamics of the chemical (e.g. target site distribution). Further improved sensitivity models can help with the creation of ecological scenarios by predicting the sensitivity of untested species. Through this development, our approach can help reduce animal testing and contribute towards a new predictive ecotoxicology framework.

Ecosystems are usually populated by many species. Each of these species carries the potential to show a different sensitivity towards all of the numerous chemical compounds that can be present in their environment. Since experimentally testing all possible species-chemical combinations is impossible, the ecological risk assessment of chemicals largely depends on cross-species extrapolation approaches. This review overviews currently existing cross-species extrapolation methodologies, and discusses i) how species sensitivity could be described, ii) which predictors might be useful for explaining differences in species sensitivity, and iii) which statistical considerations are important. We argue that risk assessment can benefit most from modelling approaches when sensitivity is described based on ecologically relevant and robust effects. Additionally, specific attention should be paid to heterogeneity of the training data (e.g. exposure duration, pH, temperature), since this strongly influences the reliability of the resulting models. Regarding which predictors are useful for explaining differences in species sensitivity, we review interspecies-correlation, relatedness-based, traits-based, and genomic-based extrapolation methods, describing the amount of mechanistic information the predictors contain, the amount of input data the models require, and the extent to which the different methods provide protection for ecological entities. We develop a conceptual framework, incorporating the strengths of each of the methods described. Finally, the discussion of statistical considerations reveals that regardless of the method used, statistically significant models can be found, although the usefulness, applicability, and understanding of these models varies considerably. We therefore recommend publication of scientific code along with scientific studies to simultaneously clarify modelling choices and enable elaboration on existing work. In general, this review specifies the data requirements of different cross-species extrapolation methods, aiming to make regulators and publishers more aware that access to raw- and meta-data needs to be improved to make future cross-species extrapolation efforts successful, enabling their integration into the regulatory environment.

Risk assessment for bees is mainly based on data for honey bees; however, risk assessment is intended to protect all bee species. This raises the question of whether data for honey bees are a good proxy for other bee species. This issue is not new and has resulted in several publications in which the sensitivity of bee species is compared based on the values of the 48-h median lethal dose (LD50) from acute test results. When this approach is used, observed differences in sensitivity may result both from differences in kinetics and from inherent differences in species sensitivity. In addition, the physiology of the bee, like its overall size, the size of the honey stomach (for acute oral tests), and the physical appearance (for acute contact tests) also influences the sensitivity of the bee. The recently introduced Toxicokinetic–Toxicodynamic (TKTD) model that was developed for the interpretation of honey bee tests (Bee General Uniform Threshold Model for Survival [BeeGUTS]) could integrate the results of acute oral tests, acute contact tests, and chronic tests within one consistent framework. We show that the BeeGUTS model can be calibrated and validated for other bee species and also that the honey bee is among the more sensitive bee species. In addition, we found that differences in sensitivity between species are smaller than previously published comparisons based on 48-h LD50 values. The time-dependency of the LD50 and the specifics of the bee physiology are the main causes of the wider variation found in the published literature. Environ Toxicol Chem 2024;43:1431–1441. © 2024 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Current chemical risk assessment approaches rely on a standard suite of test species to assess toxicity to environmental species. Assessment factors are used to extrapolate from single species to communities and ecosystem effects. This approach is pragmatic, but lacks resolution in biological and environmental parameters. Novel modelling approaches can help improve the biological resolution of assessments by using mechanistic information to identify priority species and priority regions that are potentially most impacted by chemical stressors. In this study we developed predictive sensitivity models by combining species-specific information on acute chemical sensitivity (LC50 and EC50), traits, and taxonomic relatedness. These models were applied at two spatial scales to reveal spatial differences in the sensitivity of species assemblages towards two chemical modes of action (MOA): narcosis and acetylcholinesterase (AChE) inhibition. We found that on a relative scale, 46% and 33% of European species were ranked as more sensitive towards narcosis and AChE inhibition, respectively. These more sensitive species were distributed with higher occurrences in the south and north-eastern regions, reflecting known continental patterns of endemic macroinvertebrate biodiversity. We found contradicting sensitivity patterns depending on the MOA for UK scenarios, with more species displaying relative sensitivity to narcotic MOA in north and north-western regions, and more species with relative sensitivity to AChE inhibition MOA in south and south-western regions. Overall, we identified hotspots of species sensitive to chemical stressors at two spatial scales, and discuss data gaps and crucial technological advances required for the successful application of the proposed methodology to invertebrate scenarios, which remain underrepresented in global conservation priorities. (C) 2020 The Author(s). Published by Elsevier B.V.