Impact of spatial heterogeneity in soil contamination on collembolans populations

Mattia Meli, PhD project, Roskilde University, Denmark
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Contamination of soil with toxic heavy metals poses a major threat to the environment and human health. Anthropogenic sources include smelting of ores, municipal wastes, fertilizers, and pesticides. In assessing soil quality and the environmental and ecological risk of contamination with heavy metals, often homogeneous contamination of the soil is assumed. However, soils are very heterogeneous environments. Consequently, both contamination and the response of soil organisms can be assumed to be heterogeneous. This might have consequences for the exposure of soil organisms and for the extrapolation of risk from the individual to the population level.

To obtain a more comprehensive understanding of how behavioural responses such as avoidance affect population dynamics, population structure, and distribution of individuals in soils with heterogeneous contamination, population models can help to overcome the logistical constraints of short-term laboratory experiments.

Within this projects I developed two population models of the collembolan Folsomia candida, and used them to investigate the effects of heterogeneous soil contamination on its population dynamics, thus showing a possible application of ecological models within the perspective of chemicals risk assessment.

A spatially-explicit individual-based population model (IBM), was developed (Figure 1) and tested according to the pattern-oriented modelling theory. Individuals in the model can sense and avoid contaminated habitat with a certain probability, which depends on contamination level. Model rules and parameters are based on previous knowledge of the biology and ecology of the species; for implementation of toxicity, data from standard laboratory tests (survival, reproduction and avoidance) are used. The structural realism of the model, i.e. its ability to make valid independent predictions, was tested, and we found that the model correctly predicted several patterns that were not used for model design and calibration. Results of simulations with homogeneous and heterogeneous scenarios showed that the ability of the individuals to detect and avoid the toxicant, combined with the presence of clean habitat patches which act as “refuges”, allowed the reduction in equilibrium population size due to toxic effects to be much less than proportional to the increase in concentration. Additionally, we also found that the level of heterogeneity among patches of soil (i.e. the difference in concentration) seemed to be very important: at the same average concentration, a homogeneously contaminated scenario was the least favourable habitat, while at increasing levels of heterogeneity among patches corresponded higher population growth rate and equilibrium size.

To explore whether all the complexity included in the IBM is necessary to predict risk for a species with a relatively simple life-cycle such as F. candida, the IBM was contrasted with a simpler, more standardized model, based on the generic metapopulation matrix model RAMAS. I then explored consequences of model aggregation in terms of assessing population-level effects for different spatial distributions of a toxic chemical. Overall, the RAMAS model was less sensitive than the IBM in detecting population-level effects of different spatial patterns of exposure. Therefore, choosing the right model type for risk assessment of chemicals depends on whether or not population-level effects of small-scale heterogeneity in exposure need to be detected. If in doubt, it is recommendable that both model types should be used and compared.

As the results of this project suggest, disregarding spatial heterogeneity in exposure, as it is the case in current ERA procedures for terrestrial ecosystems, may lead to an overestimation of risk if homogeneous contamination is assumed when it is not the case. More generally, these results suggest that a more realistic exposure assessment can significantly influence estimates of risk for soil organisms. Furthermore, there is evidence that the sensitivity of F. candida to sensing and avoiding toxicants varies with the tested compound. Therefore, since this species is used in standard ecotoxicological tests where homogeneous contamination is assumed, it is important to know whether the compound under investigation can be sensed by the organisms, and whether the concentrations in the test soils are really homogeneous, because assuming that they are when in fact patches with lower concentrations are present might lead to an overestimation of the toxicity.

Publications presenting results of this project:

  • Meli, M., Auclerc, A., Palmqvist, A., Forbes, V.E. and Grimm, V., 2013. Population-level consequences of spatially heterogeneous exposure to heavy metals in soil: an individual-based model of springtails. Ecological Modelling 250, 338-351
  • Meli, M., Palmqvist, A., Forbes, V.E., Groeneveld, J. and Grimm, V., 2014. Two pairs of eyes are better than one: combining individual-based and matrix models for ecological risk assessment of chemicals. Ecological Modelling xxx, Available online 29 August 2013
  • Meli, M., Palmqvist, A., Forbes, V.E. and Grimm, V., in press. Comparing microscale patterns of habitat fragmentation and disturbance events: a modelling study of the effects on Folsomia candida (Collembola) populations.
  • Meli, M.,  2013. Effects of spatial heterogeneity in soil contamination : an ecological modelling approach. Ph.D. thesis, Roskilde University, Denmark. Available for download at:

Other publications:

  • Hunka, A.D., Meli, M., Thit, A., Palmqvist, A., Thorbek, P. and Forbes, V.E., 2013. Stakeholders’ perspectives on ecological modelling in environmental risk assessment of pesticides – challenges and opportunities. Risk Analysis 33, 68-79.
  • Seiler, T.-B., Hunka, A.D., Meli, M. and Calow, P. Bridging the gap between risk perception and ecotoxicology research – How can we improve our outreach? 20 Jun 2013 14, 6, SETAC Globe
  • Grimm, V., Focks, A., Frank, B., Gabsi, F., Kulakowska, K., Johnston, A., Liu, C., Martin, B.T., Meli, M., Radchuk, V., Schmolke, A., Thorbek, P. and DeAngelis, D.L., in press. Towards better modelling and decision support: documenting model development, testing, and analysis using TRACE. 

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Supervisor: Valery Forbes (RUC); Annemette Palmqvist (RUC)

Co-supervisor: Ryszard Laskowski (UJAG)
Associated partners: