Understanding the consequences of the combined effects of multiple stressors—including stress from man-made chemicals—is important for conservation management, the ecological risk assessment of chemicals, and many other ecological applications. Our current ability to predict and analyse the joint effects of multiple stressors is insufficient to make the prospective risk assessment of chemicals more ecologically relevant because we lack a full understanding of how organisms respond to stress factors alone and in combination. Here, we describe a Dynamic Energy Budget (DEB) based bioenergetics model that predicts the potential effects of single or multiple natural and chemical stressors on life history traits. We demonstrate the plausibility of the model using a meta-analysis of 128 existing studies on freshwater invertebrates. We then validate our model by comparing its predictions for a combination of three stressors (i.e. chemical, temperature, and food availability) with new, independent experimental data on life history traits in the daphnid Ceriodaphnia dubia. We found that the model predictions are in agreement with observed growth curves and reproductive traits. To the best of our knowledge, this is the first time that the combined effects of three stress factors on life history traits observed in laboratory studies have been predicted successfully in invertebrates. We suggest that a re-analysis of existing studies on multiple stressors within the modelling framework outlined here will provide a robust null model for identifying stressor interactions, and expect that a better understanding of the underlying mechanisms will arise from these new analyses. Bioenergetics modelling could be applied more broadly to support environmental management decision making. (open access article available here)

Bioenergetic models, and specifically dynamic energy budget (DEB) theory, are gathering a great deal of interest as a tool to predict the effects of realistically variable exposure to toxicants over time on an individual animal. Here we use aquatic ecological risk assessment (ERA) as the context for a review of the different model variants within DEB and the closely related DEBkiss theory (incl. reserves, ageing, size & maturity, starvation). We propose a coherent and unifying naming scheme for all current major DEB variants, explore the implications of each model's underlying assumptions in terms of its capability and complexity and analyse differences between the models (endpoints, mathematical differences, physiological modes of action). The results imply a hierarchy of model complexity which could be used to guide the implementation of simplified model variants. We provide a decision tree to support matching the simplest suitable model to a given research or regulatory question. We detail which new insights can be gained by using DEB in toxicokinetic-toxicodynamic modelling, both generally and for the specific example of ERA, and highlight open questions. Specifically, we outline a moving time window approach to assess time-variable exposure concentrations and discuss how to account for cross-generational exposure. Where possible, we suggest valuable topics for experimental and theoretical research. (Link to open access paper)

OpenGUTS is a user-friendly software to perform analyses with GUTS: the General Unified Threshold model for Survival. GUTS is the leading toxicokinetic-toxicodynamic framework for the endpoint survival (and other non-reversible all-or-nothing effects). This site hosts the software itself, as a standalone Windows executable, with manuals and background documentation.

Since openGUTS is (as the name implies) open source, also the source files can be obtained here.   We also provide (links to) further information on openGUTS and GUTS. The main features of the software are:

1. Open and free: the software is open source and freely downloadable. The fully-functional Matlab version that served as the prototype is also available.
2. User-friendly: fit models and derive confidence intervals without requiring user interaction (e.g., no need for starting values).
3. Robust: always find the global optimum and relevant intervals, even for awkward data sets.
4. Flexible: allow time-varying exposure, missing data, simultaneous fitting on multiple data sets, etc.
5. Efficient: rapid screening of exposure profiles (e.g., FOCUS output) by batch processing.
6. Supportive: the software follows the workflow as laid down in the 2018 EFSA opinion on TKTD models.

Survival is a relevant endpoint for many questions related to the effects of chemicals in the environment. Making sense of mortality, as a process over time, requires mechanism-based models, known as toxicokinetic-toxicodynamic (TKTD) models. All published TKTD models for survival can now be viewed as members of an over-arching framework: GUTS.

Click on the book cover or here to view the e-book.

Preface

1 Introduction

2 Description of GUTS

3 Mathematical treatment

4 Case study: dieldrin in guppies

5 Case study: propiconazole in amphipods

6 Use cases

7 Ring test

8 Model evaluation

9 Outlook

Bibliography

Glossary

Appendices

Ecotoxicologists need tools to identify those combinations of man-made chemicals and organisms most likely to cause problems. In other words: which of the millions of species are at risk from pollution? And which of the tens of thousands of chemicals contribute most to the risk? We identified our poor knowledge on physiological modes of action (how a chemical affects the energy allocation in an organism), and how they vary across species and toxicants, as a major knowledge gap. We also find that the key to predictive ecotoxicology is the systematic, rigorous characterization of physiological modes of action because that will enable more powerful in vitro to in vivo toxicity extrapolation and in silico ecotoxicology. In the near future, we expect a step change in our ability to study physiological modes of action by improved, and partially automated, experimental methods. Once we have populated the matrix of species and toxicants with sufficient physiological mode of action data we can look for patterns, and from those patterns infer general rules, theory and models. (open access, link to paper at ESPI)