De Schamphelaere, Karel

Koch, Josef

English

Ghent University Academic Bibliography

Estimating realistic biological variability in Dynamic Energy Budget model parameters of a copepodLaboratory of Environmental Toxicology and Aquatic Ecology, Environmental Toxicology Unit (GhEnToxLab), Ghent University, Ghent, BelgiumJosef Koch and Karel De SchamphelaereReferences[1] Oliver, T.H., et al., Biodiversity and Resilience of Ecosystem Functions. Trends in Ecology & Evolution, 2015. 30(11): p. 673-684.[2] Koch, J., et al., Temperature and Food Quantity Effects on the Harpacticoid Copepod Nitocra spinipes: Combining in Vivo Bioassays with Population Modeling. PLoS ONE, 2017. 12(3): p. e0174384.[3] Martin, B.T., et al., Limitations of extrapolating toxic effects on reproduction to the population level. Ecological Applications, 2014. 24(8): p. 1972-1983.[4] Martin, B.T., et al., Dynamic Energy Budget Theory Meets Individual-Based Modelling: A Generic and Accessible Implementation. Methods in Ecology and Evolution, 2012. 3(2): p. 445-449.Copepod DEB-IBMPurpose: Extrapolation of individual-level effects to populationsSpecies: Harpacticoid copepod Nitocra spinipesApplied concepts:● Dynamic Energy Budget theory (DEB)● Individual-Based Modelling (IBM)DEB-IBM visualisation in NetLogoBiological variability● Makes populations more resilient to stress and environmental changes[1].● Is key to evolution (not considered in the model at this point in time).Challenge● Make realistic estimates of variability in DEB parameters from variation in measured data.josef.koch@ugent.bewww.ecotox.ugent.be@GhEnToxLab @ugentGhent University Gamma distribution fitted to simulateddata with not-yet-optimal variability parameterIntroductionMaterial and Methods1. Development time data were extracted from literature[2] and normalized by dividing all values by the mean development time per data set.2. A gamma distribution was found to give a good fit with just one shape parameter α.6. The difference between the measured and the simulated data was assessed by the loss function (αmeasured – αsimulated)2.7. The variability parameter (CV of log-normal distribution) was adjusted iteratively to minimize the loss function.Gamma distribution fitted to measureddata3. Variability was added to one DEB parameter (here somatic maintenance rate  𝑝𝑀 drawn from a log-normal distribution).4. The life histories of 107animals were simulated.5. A gamma distribution was fitted to the simulated data.● Data on further endpoints such as the reproduction rate per female can be included as a next step. It is, however, important that the datasets are big enough to allow for proper analysis of the endpoints’ distributions.Results and Discussions● Scattering the somatic maintenance rate  𝑝𝑀 around a log-normal distribution resulted in a distribution of development times that resembles the distribution in the measured data the closest.Conclusions● By the use of 107 simulations (Monte Carlo method) of individual life histories per iteration step, the variability parameter (CV of log-normal distribution) could be estimated with high accuracy.● This approach allows us to simulate life histories of copepods with a realistic variation in development time by adding variability to just one DEB parameter.● Since biological variability affects the resilience of a population, we expect the model to give better predictions of population dynamics at stress conditions.Comparison of shape parameters:αmeasured vs. αsimulatedIterative optimization of the variability parameter until:αmeasured = αsimulatedDEB parameter CV log conventional CV log best fitKS test p-valuemeas. vs. sim. data 𝑝𝑀 - 0.291 0.17 𝑝𝐴𝑚 0.05[3] – 0.1[4] 0.184 0.012𝜅 - 0.087 1.4 × 10-10

Estimating realistic biological variability in Dynamic Energy Budget model parameters of a copepod

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Estimating realistic biological variability in Dynamic Energy Budget model parameters of a copepod

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