Modelling survival: exposure pattern, species sensitivity and uncertainty

The General Unified Threshold model for Survival (GUTS) integrates previously published toxicokinetic-toxicodynamic models and estimates survival with explicitly defined assumptions. Importantly, GUTS accounts for time-variable exposure to the stressor. We performed three studies to test the ability of GUTS to predict survival of aquatic organisms across different pesticide exposure patterns, time scales and species. Firstly, using synthetic data, we identified experimental data requirements which allow for the estimation of all parameters of the GUTS proper model. Secondly, we assessed how well GUTS, calibrated with short-term survival data of Gammarus pulex exposed to four pesticides, can forecast effects of longer-term pulsed exposures. Thirdly, we tested the ability of GUTS to estimate 14-day median effect concentrations of malathion for a range of species and use these estimates to build species sensitivity distributions for different exposure patterns. We find that GUTS adequately predicts survival across exposure patterns that vary over time. When toxicity is assessed for time-variable concentrations species may differ in their responses depending on the exposure profile. This can result in different species sensitivity rankings and safe levels. The interplay of exposure pattern and species sensitivity deserves systematic investigation in order to better understand how organisms respond to stress, including humans.

[1]  J. Beck,et al.  Estimation of Small Failure Probabilities in High Dimensions by Subset Simulation , 2001 .

[2]  M. Newman,et al.  The theory underlying dose‐response models influences predictions for intermittent exposures , 2007, Environmental toxicology and chemistry.

[3]  Sebastiaan A.L.M. Kooijman,et al.  Waste to hurry: Dynamic energy budgets explain the need of wasting to fully exploit blooming resources. , 2013 .

[4]  L. Gavrilov,et al.  The reliability theory of aging and longevity. , 2001, Journal of theoretical biology.

[5]  S. Raimondo,et al.  Influence of taxonomic relatedness and chemical mode of action in acute interspecies estimation models for aquatic species. , 2010, Environmental science & technology.

[6]  L. Madden,et al.  New applications of statistical tools in plant pathology. , 2004, Phytopathology.

[7]  Valery E Forbes,et al.  Next-generation ecological risk assessment: Predicting risk from molecular initiation to ecosystem service delivery. , 2016, Environment international.

[8]  T. Keefe,et al.  Effects of malathion on survival, growth, development, and equilibrium posture of bullfrog tadpoles (Rana catesbeiana) , 2001, Environmental toxicology and chemistry.

[9]  Sovan Lek,et al.  Using phylogenetic information and chemical properties to predict species tolerances to pesticides , 2014, Proceedings of the Royal Society B: Biological Sciences.

[10]  Rémy Beaudouin,et al.  Individual sensitivity distribution evaluation from survival data using a mechanistic model: implications for ecotoxicological risk assessment. , 2012, Chemosphere.

[11]  R. Collins,et al.  The effects of lowering LDL cholesterol with statin therapy in people at low risk of vascular disease: meta-analysis of individual data from 27 randomised trials , 2012, The Lancet.

[12]  Roman Ashauer,et al.  Toxicokinetic and toxicodynamic modeling explains carry-over toxicity from exposure to diazinon by slow organism recovery. , 2010, Environmental science & technology.

[13]  Sandrine Charles,et al.  Constructing Time-Resolved Species Sensitivity Distributions Using a Hierarchical Toxico-Dynamic Model. , 2015, Environmental science & technology.

[14]  Monica D. Poteat,et al.  Phylogeny and size differentially influence dissolved Cd and Zn bioaccumulation parameters among closely related aquatic insects. , 2014, Environmental science & technology.

[15]  William J. Kolarik,et al.  Real-time performance reliability prediction , 2001, IEEE Trans. Reliab..

[16]  Colin Ockleford,et al.  Scientific Opinion on good modelling practice in the context of mechanistic effect models for risk assessment of plant protection products , 2014 .

[17]  J. Hermens,et al.  Qualitative and quantitative modelling of toxic effects of organophosphorous compounds to fish. , 1991, The Science of the total environment.

[18]  Hans R. Künsch,et al.  Bayesian experimental design for a toxicokinetic–toxicodynamic model , 2012 .

[19]  T. Jager,et al.  Linking survival and biomarker responses over time , 2013, Environmental toxicology and chemistry.

[20]  Steve Selvin.  Survival Analysis for Epidemiologic and Medical Research: Contents , 2008 .

[21]  C. Stamm,et al.  Significance of urban and agricultural land use for biocide and pesticide dynamics in surface waters. , 2010, Water research.

[22]  Roman Ashauer,et al.  Toxicology across scales: Cell population growth in vitro predicts reduced fish growth , 2015, Science Advances.

[23]  T. Preuss,et al.  Body size-dependent toxicokinetics and toxicodynamics could explain intra- and interspecies variability in sensitivity. , 2015, Environmental pollution.

[24]  Roman Ashauer,et al.  New ecotoxicological model to simulate survival of aquatic invertebrates after exposure to fluctuating and sequential pulses of pesticides. , 2007, Environmental science & technology.

[25]  R. Collins,et al.  Statins for people at low risk of cardiovascular disease – Authors' reply , 2012, The Lancet.

[26]  Roman Ashauer,et al.  Toxicodynamic assumptions in ecotoxicological hazard models. , 2008, Environmental toxicology and chemistry.

[27]  Roman Ashauer,et al.  Advantages of toxicokinetic and toxicodynamic modelling in aquatic ecotoxicology and risk assessment. , 2010, Journal of environmental monitoring : JEM.

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

[29]  Lorraine Maltby,et al.  Insecticide species sensitivity distributions: Importance of test species selection and relevance to aquatic ecosystems , 2005, Environmental toxicology and chemistry.

[30]  Wout Slob,et al.  A Comparison of Three Methods for Calculating Confidence Intervals for the Benchmark Dose , 2004, Risk analysis : an official publication of the Society for Risk Analysis.

[31]  Roman Ashauer,et al.  Toxicokinetic-toxicodynamic modelling of survival of Gammarus pulex in multiple pulse exposures to propiconazole: model assumptions, calibration data requirements and predictive power , 2012, Ecotoxicology.

[32]  T. Jager,et al.  Stage‐dependent and sex‐dependent sensitivity to water‐soluble fractions of fresh and weathered oil in the marine copepod Calanus finmarchicus , 2016, Environmental toxicology and chemistry.

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

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

[35]  Ryszard Laskowski,et al.  Some good reasons to ban the use of NOEC, LOEC and related concepts in ecotoxicology , 1995 .

[36]  J. Berkson.  Why I Prefer Logits to Probits , 1951 .

[37]  Tjalling Jager.  Reconsidering sufficient and optimal test design in acute toxicity testing , 2013, Ecotoxicology.

[38]  S. Jay Olshansky,et al.  Mortality Partitions and their Relevance to Research on Senescence , 2006, Biogerontology.

[39]  S. Kooijman,et al.  A safety factor for LC50 values allowing for differences in sensitivity among species , 1987 .

[40]  Martin A. Hamilton,et al.  Toxicity Curve Estimation: Fitting a Compartment Model to Median Survival Times , 1985 .

[41]  Tjalling Jager,et al.  Some good reasons to ban ECx and related concepts in ecotoxicology. , 2011, Environmental science & technology.

[42]  Roman Ashauer,et al.  Acute toxicity of organic chemicals to Gammarus pulex correlates with sensitivity of Daphnia magna across most modes of action. , 2011, Aquatic toxicology.

[43]  Roman Ashauer,et al.  The Insecticide Imidacloprid Causes Mortality of the Freshwater Amphipod Gammarus pulex by Interfering with Feeding Behavior , 2013, PloS one.

[44]  Marie Laure Delignette-Muller,et al.  MOSAIC_SSD: A new web tool for species sensitivity distribution to include censored data by maximum likelihood , 2014, Environmental toxicology and chemistry.

[45]  Jens C. Streibig,et al.  Bioassay analysis using R , 2005 .

[46]  Benjamin Daniels,et al.  Life-stage-dependent sensitivity of the cyclopoid copepod Mesocyclops leuckarti to triphenyltin. , 2013, Chemosphere.

[47]  R. Ferrari,et al.  Chromogranin A in heart failure; a novel neurohumoral factor and a predictor for mortality. , 2002, European heart journal.

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

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

[50]  C. I. Bliss,et al.  THE METHOD OF PROBITS. , 1934, Science.

[51]  R. M. Roberts,et al.  Confidence region estimation techniques for nonlinear regression in groundwater flow: Three case studies , 2007 .

[52]  G. R. Smith,et al.  Combined effects of malathion and nitrate on early growth, abnormalities, and mortality of wood frog (Rana sylvatica) tadpoles , 2011, Ecotoxicology.

[53]  Roman Ashauer,et al.  Framework for traits‐based assessment in ecotoxicology , 2011, Integrated environmental assessment and management.

[54]  John P Giesy,et al.  Ecological risk assessment of atrazine in North American surface waters , 2013, Environmental toxicology and chemistry.

[55]  Z. Ahmad.  Toxicity bioassay and effects of sub-lethal exposure of malathion on biochemical composition and haematological parameters of Clarias gariepinus , 2012 .

[56]  Steve Selvin.  Survival Analysis for Epidemiologic and Medical Research: Survival Analysis for Epidemiologic and Medical Research , 2008 .

[57]  Steve Selvin,et al.  Survival Analysis for Epidemiologic and Medical Research , 2008 .

[58]  Richard J. Williams,et al.  Worldwide estimation of river concentrations of any chemical originating from sewage-treatment plants using dilution factors , 2013, Environmental toxicology and chemistry.

[59]  Cheryl A Murphy,et al.  Development and application of the adverse outcome pathway framework for understanding and predicting chronic toxicity: I. Challenges and research needs in ecotoxicology. , 2015, Chemosphere.

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

[61]  David R Fox,et al.  Time‐dependent species sensitivity distributions , 2013, Environmental toxicology and chemistry.

[62]  Luis A. Escobar,et al.  Teaching about Approximate Confidence Regions Based on Maximum Likelihood Estimation , 1995 .

[63]  Thomas G. Preuss,et al.  Coupling different mechanistic effect models for capturing individual- and population-level effects of chemicals: Lessons from a case where standard risk assessment failed , 2014 .

[64]  G. Guo.  Event-history analysis for left-truncated data. , 1993, Sociological methodology.

[65]  Christopher Zorn,et al.  Nonproportional Hazards and Event History Analysis in International Relations , 2003 .

[66]  Roman Ashauer,et al.  A METHOD TO PREDICT AND UNDERSTAND FISH SURVIVAL UNDER DYNAMIC CHEMICAL STRESS USING STANDARD ECOTOXICITY DATA , 2013, Environmental toxicology and chemistry.

[67]  Donald J. Baird,et al.  Species traits as predictors for intrinsic sensitivity of aquatic invertebrates to the insecticide chlorpyrifos , 2012, Ecotoxicology.

[68]  Roman Ashauer,et al.  Simulating toxicity of carbaryl to Gammarus pulex after sequential pulsed exposure. , 2007, Environmental science & technology.

[69]  V. Grimm,et al.  Chemical and natural stressors combined: from cryptic effects to population extinction , 2013, Scientific Reports.

[70]  André Gergs,et al.  Body size-mediated starvation resistance in an insect predator. , 2014, The Journal of animal ecology.

[71]  Theodore Garland,et al.  Aquatic insect ecophysiological traits reveal phylogenetically based differences in dissolved cadmium susceptibility , 2008, Proceedings of the National Academy of Sciences.