PI: Roman Ashauer
Funded by: Unilever
Despite the increasing use and call for use of population or ecosystem modelling, the lack of ecological realism is widely recognised as a limitation in prospective environmental risk assessment, as is the failure to account for variability and uncertainty in an explicit and transparent manner. Indeed, it is acknowledged that organisms do not live in a single-stress pristine environment. Rather, they are constantly exposed to a series of stressors, both chemical and ecological. A step toward capturing more ecology in environmental risk assessment if the use of environmental scenarios that represent qualitatively and conceptually the environment in a fit for purpose manner for environmental risk assessment. According to Rico et al (2015), unified environmental scenarios consist of a combination of both biotic and abiotic parameters required to characterise direct and indirect exposure, effects, and recovery of species, and therefore integrating both ecological and exposure scenarios. The aim of the post-doctoral project I am currently carrying out with the environment department of the University of York and the Safety and Environmental Assurance Centre of Unilever is to develop a modelling framework for the environmental risk assessment integrating both ecological factors impacting the life cycle of organisms and chemical stress
Funded by: Ineris
The aim of the study was to develop a population dynamics model for the zebrafish in order to enables the extrapolation of data measured at the individual level to a more relevant organisation level. To achieve this goal, a bioenergetics model for Danio rerio based on the Dynamic Energy Budget (DEB) theory was coupled to an individual based model (IBM). An IBM model usually implies a high level of complexity and of precision that require a fine knowledge and data on the physiological processes of the organism. DEB related models can bring such information. In this study an existing DEB model for Danio rerio was modified in order to propose an alternative hypothesis to the sigmoid growth curve this species can encounter under some conditions (Augustine et al 2011). The authors of the original model explained this growth curve thanks to a metabolic acceleration of the individual between birth and the juvenile stage. We implemented an alternative hypothesis that assumed a limitation of the energetic inputs from food during the early stages of life. Such limitation can be linked to a reduced ability of the individuals to absorb food particles or to access to good quality food particles just after birth. The DEB model parameters were calibrated using a Bayesian inference and experimental and literature data. Priors were based on additional literature data. The IBM model for Danio rerio was developed in order to extrapolate individual data measured in laboratory conditions to biologically pertinent endpoints in order to support decision making in environmental risk assessment of chemicals. The IBM accounted for a population inhabiting a spatialized local environment submitted to abiotic factors such as temperature or photoperiod representing the natural habitat of Danio rerio. The IBM was built using the NetLogo software and was calibrated using the BehaviorSearch software (Stonedahl & Wilensky, 2010) using literature data. The overall results indicate a good consistency between DEB model outputs and experimental data from both laboratory and literature. Similarly, the simulations of Danio rerio population dynamics proved to be close to experimental data from literature.
Period: 2010 – 2013
Funded by: Envirhom-Eco research program supported by the French Institute for Radioprotection and Nuclear Safety (IRSN) and the 190 DRC-08-02 program supported by the French Ministry of Ecology
The assessment of toxic effects at a relevant scale is an important challenge for the ecosystem protection. Indeed, pollutants may impact populations over long-term and represent a new evolutionary force which can be adding itself to the natural selection forces. Thereby, it is necessary to acquire knowledge on the phenotypics and genetics changes that may appear in populations submitted to stress over several generations. Usually statistical analyses are performed to analyse such multigenerational studies. The use of a mechanistic mathematical model may provide a way to fully understand the impact of pollutants on the populations’ dynamics. Such kind of model allows the integration of biological and toxic processes into the analysis of ecotoxicological data and the assessment of interactions between these processes.
The aim of this Ph.D. project was to assess the contributions of the mechanistical modelling to the analysis of evolutionary experiment assessing long-term exposure. To do so, a three step strategy has been developed. Foremost, a multi-generational study was performed to assess the evolution of two populations of the ubiquitous nematode Caenorhabditis elegans in control conditions or exposed to 1.1 mM of uranium. Several generations were selected to assess growth, reproduction, and dose-responses relationships, through exposure to a range of concentrations (from 0 to 1.2 mM U) with all endpoints measured daily. A first statistical analysis was then performed. In a second step, a bioenergetic model adapted to the assessment of ecotoxicological data (DEBtox) was developed on C. elegans. Its numerical behaviour was analysed. Finally, this model was applied to all the selected generations in order to infer parameters values for the two populations and to assess their evolutions.
Results highlighted an impact of the uranium starting from 0.4 mM U on both C. elegans’ growth and reproduction. Results from the mechanistical analysis indicate this effect is due to an impact on the assimilation of energy from food. Both the mechanistic and the classic approaches highlighted individuals’ adaptation to environmental conditions. Despite this, differential evolutions of the individuals from the uranium-selected population were also highlighted. All these results were more in-depth described by the mechanistical analysis. Overall, this work contributes to our knowledge on the effects of pollutants on population dynamics, and demonstrates the contributions of mechanistical modelling which can be applied in other contexts to achieve in fine a better assessment of environmental risks of pollutants