Dynamic energy budget theory (DEB) and the metabolic theory of ecology (MTE) both seek to quantify the processes of resource acquisition and use but differ in their underlying mechanisms and assumptions. Some in-depth comparisons of the theories have been conducted in the literature but require a level of knowledge that is likely to be beyond most newcomers to the topic. We reduce the theories to their simplest forms, their models for growth under optimal conditions, and present a side by side comparison of the model equations and key assumptions. This shows considerable overlap in how both theories characterise growth rate while also highlighting fundamental differences, such as MTE’s use of taxon specific parameters. Comparing DEB and MTE in this way provides an accessible platform to help beginners gain a better understanding from the existing literature. (link to paper)

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.

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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)