ArticleScientific reports2016
Modelling survival: exposure pattern, species sensitivity and uncertainty.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it, 73 citations in OpenAlex.
- The relative sensitivity of freshwater species to antimony(III): Implications for water quality guidelines and ecological risk assessments.Environmental science and pollution research international · 2017Guideline
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- MIXCOP-Implementation of a model to assess the time-dependent effects of mixed pesticides on a collembolan population.MethodsX · 2025Article
- Comparison of visual assessment and quantitative goodness-of-fit metrics on GUTS model fits.Environmental toxicology and chemistry · 2025Article
- Integrating Time-ResolvedEnvironmental science & technology · 2024Article
- Correspondence on "Mortality Pattern ofEnvironmental science & technology · 2024Article
- Mortality Pattern ofEnvironmental science & technology · 2024Article
- Modeling Time-Dependent Aquatic Toxicity of Hydrocarbons: Role of Organism Weight, Temperature, and Substance Hydrophobicity.Environmental toxicology and chemistry · 2022Article
- BeeGUTS-A Toxicokinetic-Toxicodynamic Model for the Interpretation and Integration of Acute and Chronic Honey Bee Tests.Environmental toxicology and chemistry · 2022Article
- Fish Species Sensitivity Ranking Depends on Pesticide Exposure Profiles.Environmental toxicology and chemistry · 2022Article
- TWAc-Check: A New Approach to Determine the Appropriate Use of Time-Weighted Average Concentration in Aquatic Risk Assessment.Environmental toxicology and chemistry · 2022Article
- The application and limitations of exposure multiplication factors in sublethal effect modelling.Scientific reports · 2022Article
- Continuous high-frequency pesticide monitoring to observe the unexpected and the overlooked.Water research X · 2021Article
- Application of General Unified Threshold Models of Survival Models for Regulatory Aquatic Pesticide Risk Assessment Illustrated with an Example for the Insecticide Chlorpyrifos.Integrated environmental assessment and management · 2021Article
- Simulating Honey Bee Large-Scale Colony Feeding Studies Using the BEEHAVE Model-Part I: Model Validation.Environmental toxicology and chemistry · 2020Article
- Recommendations to address uncertainties in environmental risk assessment using toxicokinetic-toxicodynamic models.Scientific reports · 2019Article
- Exploring inter-species sensitivity to a model hydrocarbon, 2-Methylnaphtalene, using a process-based model.Environmental science and pollution research international · 2019Article
- Calibration and validation of toxicokinetic-toxicodynamic models for three neonicotinoids and some aquatic macroinvertebrates.Ecotoxicology (London, England) · 2018Article
- Scientific Opinion on the state of the art of Toxicokinetic/Toxicodynamic (TKTD) effect models for regulatory risk assessment of pesticides for aquatic organisms.EFSA journal. European Food Safety Authority · 2018Article
- Pesticide exposure assessment for surface waters in the EU. Part 2: Determination of statistically based run-off and drainage scenarios for Germany.Pest management science · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
21 authors at 13 institutions in 9 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.