Soil-3

Disturbance interactions: modelling environmental and demographic stochasticity for populations exposed to toxicants

Natnael Tesfaye Hamda, PhD project, Jagiellonian University, Krakow, Poland
Contact:  Enable-Javascript@To-Read-E-mail-Address

Nowadays, one of the big challenge in ecotoxicology is to understand how individually measured effects can be used as predictive indices at the population level. It is also clear that, most important outcomes of a toxic stress on a population level manifests as the probability of extinction during a certain time and the predicted time to extinction. These two measures of a risk may, however be strongly affected by stochastic events that can arise from population structure, density dependence, timing of exposure, and environmental fluctuations; which are not considered in laboratory experiments.

The project aimed on developing of well-tested mechanistic models that can be used to predict and extrapolate the combined effects of toxicants and environmental as well as demographic stochasticity. The research project will focus on collembolans; soil anthropoid used for standard soil pollution test. In the project empirical data sets to be generated in Soil-2 will be used for testing the models, and model outputs will be employed for developing appropriate experimental designs. This “feedback loop” between models and experiments will allow for developing reliable ecotoxicological tests, which can be used in ecological risk assessment of soil pollution.

Integrated modelling approach which combines the Matrix population modelling and Individual based models will be used. In the model both sexually reproducing (either Folsomia fimetaria or Sinella curviseta) and asexually reproducing species (Folsomia Candida) will be considered and different chemical stress effects (i.e. both Chronic and acute) will be analysed in the model.

Based on information to be obtained from literature and primary data on the vital rates of the two species; a matrix model will be developed. In order to consider the temporal variation in vital rates which driven by changes in the alteration of the environment; an environmental stochasticity will be linked with each of the vital rates. Similarly; in order to integrate temporal variations driven by chance variation in the actual fates of different individuals, demographic stochasticity will also be linked to the matrix element.

The integration of the environmental and demographic stochasticity into the matrix will provide us to determine the stochastic growth rate of the population (ls), which is an important and robust parameter to extrapolate effects of the toxic compound observed at the individual level to the population level. Despite of all these advantages; matrix model has some trade-offs, hence it lacks consideration of the effects of correlations among matrix elements. This trade-off may arise due to phenotypic variation. This can be addressed by accounting variation and co-variation among the matrix elements. The variation in, and co-variation among matrix elements will be estimated from the individual based model to be developed along with the stochastic matrix model. Thus, the matrix model will be further tuned by integrating with an Individual Based Model and the matrix elements can be estimated from phenotypic data on individuals. The exact IBM theory I am going to apply will be decided in the near future. However, based on the information I have so far, and considering availability of data; the Dynamic Energy Budget (DEB) Theory will be the most suitable theoretical framework for parameterization my model. DEB theory aids the extraction of mechanistic information from life – cycle history data which in turn supports an educated extrapolation to the population level.

Fig. 1: Conceptual Representation of the modeling process

The structure of the modlling process will follow consistent and logical principles of Good Modelling Practice (GMP) for chemical risk assessment and follows cyclical and iterative procedures which enhances the transparency and rigorousness of the models to be used as decision support tool (Fig.1). The modelling process will be based on ODD’s protocol. Each stage of the modelling process will be also documented based on TRACE’s documentation procedure and the TRACE will be used as a quality assurance document.

Supervisor: Ryszard Laskowski (UJ)
Co-supervisor: Valery Forbes (RUC)
Associated partners:
ANSES; Bayer; BASF; KEMI