Dynamic energy budget models in ecological risk assessment: From principles to applications.

In ecological risk assessment of chemicals, hazard identification and hazard characterisation are most often based on ecotoxicological tests and expressed as summary statistics such as No Observed Effect Concentrations or Lethal Concentration values and No Effect Concentrations. Considerable research is currently ongoing to further improve methodologies to take into account toxico kinetic aspects in toxicological assessments, extrapolations of toxic effects observed on individuals to population effects and combined effects of multiple chemicals effects. In this context, the principles of the Dynamic Energy Budget (DEB), namely the conserved allocation of energy to different life-supporting processes in a wide variety of different species, have been applied successfully to the development of a number of DEB models. DEB models allow the incorporation of effects on growth, reproduction and survival within one consistent framework. This review aims to discuss the principles of the DEB theory together with available DEB models, databases available and applications in ecological risk assessment of chemicals for a wide range of species and taxa. Future perspectives are also discussed with particular emphasis on ongoing research efforts to develop DEB models as open source tools to further support the research and regulatory community to integrate quantitative biology in ecotoxicological risk assessment.

[1]  S. Kooijman,et al.  Effects of uranium on the metabolism of zebrafish, Danio rerio. , 2012, Aquatic toxicology.

[2]  J. Dorne,et al.  Comparative toxicity of pesticides and environmental contaminants in bees: Are honey bees a useful proxy for wild bee species? , 2017, The Science of the total environment.

[3]  R. Nisbet,et al.  Relating suborganismal processes to ecotoxicological and population level endpoints using a bioenergetic model. , 2015, Ecological applications : a publication of the Ecological Society of America.

[4]  S. Holbrook,et al.  Sublethal toxicant effects with dynamic energy budget theory: application to mussel outplants , 2009, Ecotoxicology.

[5]  R. Nisbet,et al.  Sublethal toxicant effects with dynamic energy budget theory: model formulation , 2009, Ecotoxicology.

[6]  Patricia A. Holden,et al.  Modeling Physiological Processes That Relate Toxicant Exposure and Bacterial Population Dynamics , 2012, PloS one.

[7]  V. Zitko.  An equation of lethality curves in tests with aquatic fauna , 1979 .

[8]  Daniel L Villeneuve,et al.  Adverse outcome pathways: A conceptual framework to support ecotoxicology research and risk assessment , 2010, Environmental toxicology and chemistry.

[9]  S. Charles,et al.  Population-level modeling to account for multigenerational effects of uranium in Daphnia magna. , 2012, Environmental science & technology.

[10]  S. Kooijman,et al.  Prediction of daphnid survival after in situ exposure to complex mixtures. , 2009, Environmental science & technology.

[11]  Stephen W. Edwards,et al.  Completing the Link between Exposure Science and Toxicology for Improved Environmental Health Decision Making: The Aggregate Exposure Pathway Framework. , 2016, Environmental science & technology.

[12]  T. Backhaus,et al.  Proposal for environmental mixture risk assessment in the context of the biocidal product authorization in the EU , 2013, Environmental Sciences Europe.

[13]  Model-based experimental design for assessing effects of mixtures of chemicals. , 2010, Environmental pollution.

[14]  R. Plackett,et al.  A Unified Theory for Quantal Responses to Mixtures of Drugs: Non-Interactive Action , 1959 .

[15]  T. Jager,et al.  Capturing the life history of the marine copepod Calanus sinicus into a generic bioenergetics framework , 2015 .

[16]  Nina Cedergreen,et al.  Dynamic modeling of sublethal mixture toxicity in the nematode Caenorhabditis elegans. , 2014, Environmental science & technology.

[17]  Hal Caswell,et al.  Integrating dynamic energy budgets into matrix population models , 2006 .

[18]  Sharon Munn,et al.  Adverse outcome pathway (AOP) development I: strategies and principles. , 2014, Toxicological sciences : an official journal of the Society of Toxicology.

[19]  M. Vijver,et al.  Comparison and evaluation of pesticide monitoring programs using a process‐based mixture model , 2016, Environmental toxicology and chemistry.

[20]  André Gergs,et al.  Modelling survival: exposure pattern, species sensitivity and uncertainty , 2016, Scientific Reports.

[21]  Emilio Benfenati,et al.  Developing innovative in silico models with EFSA's OpenFoodTox database , 2017 .

[22]  A. Hendriks,et al.  The power of size. 2. Rate constants and equilibrium ratios for accumulation of inorganic substances related to species weight , 2001, Environmental toxicology and chemistry.

[23]  C. Klok,et al.  Qualitative use of Dynamic Energy Budget theory in ecotoxicology: Case study on oil contamination and Arctic copepods , 2012 .

[24]  Sebastiaan A.L.M. Kooijman,et al.  The “covariation method” for estimating the parameters of the standard Dynamic Energy Budget model I: Philosophy and approach , 2011 .

[25]  S. Kooijman,et al.  From food‐dependent statistics to metabolic parameters, a practical guide to the use of dynamic energy budget theory , 2008, Biological reviews of the Cambridge Philosophical Society.

[26]  D. Spurgeon,et al.  A simple mechanistic model to interpret the effects of narcotics , 2015, SAR and QSAR in environmental research.

[27]  Roman Ashauer,et al.  General unified threshold model of survival--a toxicokinetic-toxicodynamic framework for ecotoxicology. , 2011, Environmental science & technology.

[28]  Sharon Munn,et al.  Adverse outcome pathway development II: best practices. , 2014, Toxicological sciences : an official journal of the Society of Toxicology.

[29]  Claus Svendsen,et al.  Nested interactions in the combined toxicity of uranium and cadmium to the nematode Caenorhabditis elegans. , 2015, Ecotoxicology and environmental safety.

[30]  R. Nisbet,et al.  Extrapolating ecotoxicological effects from individuals to populations: a generic approach based on Dynamic Energy Budget theory and individual-based modeling , 2013, Ecotoxicology.

[31]  Elke I. Zimmer,et al.  Dynamic energy budgets in population ecotoxicology: Applications and outlook , 2014 .

[32]  Alan R. Boobis,et al.  IPCS Framework for Analyzing the Relevance of a Noncancer Mode of Action for Humans , 2008, Critical reviews in toxicology.

[33]  Virginie Ducrot,et al.  Hormesis on life-history traits: is there such thing as a free lunch? , 2013, Ecotoxicology.

[34]  Volker Grimm,et al.  Dynamic Energy Budget theory meets individual‐based modelling: a generic and accessible implementation , 2012 .

[35]  Tjalling Jager,et al.  Understanding toxicity as processes in time. , 2010, The Science of the total environment.

[36]  O. Tsyusko,et al.  Multigenerational exposure to silver ions and silver nanoparticles reveals heightened sensitivity and epigenetic memory in Caenorhabditis elegans , 2016, Proceedings of the Royal Society B: Biological Sciences.

[37]  Alan R. Boobis,et al.  IPCS Framework for Analyzing the Relevance of a Cancer Mode of Action for Humans , 2006 .

[38]  Sandrine Charles,et al.  Integrating the lethal and sublethal effects of toxic compounds into the population dynamics of Daphnia magna: A combination of the DEBtox and matrix population models , 2007 .

[39]  R. Nisbet,et al.  Impact of engineered zinc oxide nanoparticles on the energy budgets of Mytilus galloprovincialis , 2014 .

[40]  Matthew S. Heard,et al.  Comparing bee species responses to chemical mixtures: Common response patterns? , 2017, PloS one.

[41]  C. I. Bliss.  THE TOXICITY OF POISONS APPLIED JOINTLY1 , 1939 .

[42]  A. Hendriks,et al.  The power of size. 1. Rate constants and equilibrium ratios for accumulation of organic substances related to octanol‐water partition ratio and species weight , 2001, Environmental toxicology and chemistry.

[43]  S. Kooijman,et al.  Sensitivity of animals to chemical compounds links to metabolic rate , 2015, Ecotoxicology.

[44]  S E Belanger,et al.  Mode of Action (MOA) Assignment Classifications for Ecotoxicology: An Evaluation of Approaches. , 2017, Environmental science & technology.

[45]  J. Dorne,et al.  Extending standard testing period in honeybees to predict lifespan impacts of pesticides and heavy metals using dynamic energy budget modelling , 2016, Scientific Reports.

[46]  Tjalling Jager,et al.  Simultaneous modeling of multiple end points in life-cycle toxicity tests. , 2004, Environmental science & technology.

[47]  P. Calow,et al.  Is the per capita rate of increase a good measure of population‐level effects in ecotoxicology? , 1999 .

[48]  Chris Klok,et al.  Extrapolating toxic effects on individuals to the population level: the role of dynamic energy budgets , 2010, Philosophical Transactions of the Royal Society B: Biological Sciences.

[49]  Volker Grimm,et al.  Integrating population modeling into ecological risk assessment , 2010, Integrated environmental assessment and management.

[50]  Bas Kooijman,et al.  Dynamic Energy Budget Theory for Metabolic Organisation , 2005 .

[51]  Sebastiaan A.L.M. Kooijman,et al.  Analysis of toxicity tests on Daphnia survival and reproduction , 1996 .

[52]  P. Kille,et al.  Application of physiologically based modelling and transcriptomics to probe the systems toxicology of aldicarb for Caenorhabditis elegans (Maupas 1900) , 2011, Ecotoxicology.

[53]  Yngvar Olsen,et al.  An Individual-based Population Model for Rotifer (Brachionus plicatilis) Cultures , 2006, Hydrobiologia.

[54]  Roman Ashauer,et al.  Physiological modes of action across species and toxicants: the key to predictive ecotoxicology. , 2018, Environmental science. Processes & impacts.

[55]  Elke I. Zimmer,et al.  Juvenile food limitation in standardized tests: a warning to ecotoxicologists , 2012, Ecotoxicology.

[56]  Oliver A.H. Jones,et al.  Systems toxicology approaches for understanding the joint effects of environmental chemical mixtures. , 2010, The Science of the total environment.

[57]  Tjalling Jager,et al.  A model to analyze effects of complex mixtures on survival. , 2009, Ecotoxicology and environmental safety.

[58]  Roman Ashauer,et al.  Death Dilemma and Organism Recovery in Ecotoxicology. , 2015, Environmental science & technology.

[59]  Rémy Beaudouin,et al.  Energy-based modelling to assess effects of chemicals on Caenorhabditis elegans: a case study on uranium. , 2015, Chemosphere.

[60]  Starrlight Augustine,et al.  The bijection from data to parameter space with the standard DEB model quantifies the supply-demand spectrum. , 2014, Journal of theoretical biology.

[61]  Tjalling Jager,et al.  A biology-based approach for mixture toxicity of multiple endpoints over the life cycle , 2009, Ecotoxicology.

[62]  Ettore Capri,et al.  Guidance on tiered risk assessment for plant protection products for aquatic organisms in edge-of-field surface waters , 2013 .

[63]  Tjalling Jager,et al.  A biology-based approach for quantitative structure-activity relationships (QSARs) in ecotoxicity , 2009, Ecotoxicology.

[64]  Sebastiaan A L M Kooijman,et al.  Making Sense of Ecotoxicological Test Results: Towards Application of Process-based Models , 2006, Ecotoxicology.

[65]  L. Johnson,et al.  Dynamic energy budget theory and population ecology: lessons from Daphnia , 2010, Philosophical Transactions of the Royal Society B: Biological Sciences.

[66]  Tjalling Jager,et al.  Physiological modes of action of toxic chemicals in the nematode Acrobeloides nanus , 2006, Environmental toxicology and chemistry.

[67]  Thomas G Preuss,et al.  Limitations of extrapolating toxic effects on reproduction to the population level. , 2014, Ecological applications : a publication of the Ecological Society of America.

[68]  Sandrine Charles,et al.  Ecotoxicology and population dynamics : Using DEBtox models in a Leslie modeling approach , 2005 .

[69]  R Jeffrey Lewis,et al.  Mode of action human relevance (species concordance) framework: Evolution of the Bradford Hill considerations and comparative analysis of weight of evidence , 2014, Journal of applied toxicology : JAT.

[70]  Cédric Bacher,et al.  Use of dynamic energy budget and individual based models to simulate the dynamics of cultivated oyster populations , 2006 .

[71]  B. Kooi,et al.  Bifurcation theory, adaptive dynamics and dynamic energy budget-structured populations of iteroparous species , 2010, Philosophical Transactions of the Royal Society B: Biological Sciences.

[72]  S. Kooijman,et al.  Modeling the effects of binary mixtures on survival in time , 2007, Environmental toxicology and chemistry.

[73]  Frédéric Y Bois,et al.  Toxicokinetic models and related tools in environmental risk assessment of chemicals. , 2017, The Science of the total environment.

[74]  Harmonization Project Document No . 2 CHEMICAL-SPECIFIC ADJUSTMENT FACTORS FOR INTERSPECIES DIFFERENCES AND HUMAN VARIABILITY : GUIDANCE DOCUMENT FOR USE OF DATA IN DOSE / CONCENTRATION – RESPONSE ASSESSMENT , 2005 .

[75]  M. C. Newman,et al.  The individual tolerance concept is not the sole explanation for the probit dose‐effect model , 2000 .

[76]  L. Seidlein,et al.  A quantitative theory of organic growth (Inquitiesom growth laws II) , 1938 .

[77]  James Devillers,et al.  An Individual-Based Model of Zebrafish Population Dynamics Accounting for Energy Dynamics , 2015, PloS one.

[78]  Sebastiaan A.L.M. Kooijman,et al.  On the dynamics of chemically stressed populations: The deduction of population consequences from effects on individuals , 1984 .

[79]  A. Green,et al.  An international database for pesticide risk assessments and management , 2016 .

[80]  Dries Knapen,et al.  The potential of AOP networks for reproductive and developmental toxicity assay development. , 2015, Reproductive toxicology.

[81]  B. Quéguiner,et al.  How far details are important in ecosystem modelling: the case of multi-limiting nutrients in phytoplankton–zooplankton interactions , 2010, Philosophical Transactions of the Royal Society B: Biological Sciences.

[82]  Sebastiaan A.L.M. Kooijman,et al.  Population consequences of a physiological model for individuals , 1989 .

[83]  Tjalling Jager,et al.  DEBkiss or the quest for the simplest generic model of animal life history. , 2013, Journal of theoretical biology.

[84]  Sebastiaan A.L.M. Kooijman,et al.  The “covariation method” for estimating the parameters of the standard Dynamic Energy Budget model II: Properties and preliminary patterns , 2011 .

[85]  Rodolphe Gilbin,et al.  Dynamic energy-based modeling of uranium and cadmium joint toxicity to Caenorhabditis elegans. , 2016, Chemosphere.

[86]  J. V. D. Meer,et al.  An introduction to Dynamic Energy Budget (DEB) models with special emphasis on parameter estimation , 2006 .

[87]  S. Kooijman,et al.  Dynamic energy budget theory restores coherence in biology , 2010, Philosophical Transactions of the Royal Society B: Biological Sciences.

[88]  O. Maury,et al.  A dynamic and mechanistic model of PCB bioaccumulation in the European hake (Merluccius merluccius) , 2009 .