Mechanistic modelling of daphnid-algae dynamics within a laboratory microcosm

Lamonica, Dominique; Herbach, Ulysse; Orias, Frédéric; Clément, Bernard; Charles, Sandrine; Lopes, Christelle

doi:10.1016/j.ecolmodel.2015.09.020

Cited by 13 publications

(43 citation statements)

References 76 publications

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“…When duckweeds were present, 8 fronds of duckweeds were introduced into beakers. The algal density in the water column was measured every two to three days with a particle counter (Lamonica, B Clément, et al, 2016). The algal density at the bottom of the beakers was measured once during the experiment (Lamonica, Herbach, et al, 2016).…”

Section: Experiments and Observed Datamentioning

confidence: 99%

Chemical effects on ecological interactions within a model-experiment loop

Lamonica

Charles

Clément

et al. 2022

Preprint

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We propose in this paper a method to assess the effects of a contaminant on a micro-ecosystem, integrating the population dynamics and the interactions between species. For that, we developed a dynamic model to describe the functioning of a microcosm exposed to a contaminant and to discriminate direct and indirect effects. Then, we get back from modelling to experimentation in order to identify which of the collected data have really been necessary and sufficient to estimate model parameters in order to propose a more efficient experimental design for further investigations. We illustrated our approach using a 2-L laboratory microcosm involving three species (the duckweed Lemna minor, the microalgae Pseudokirchneriella subcapitata and the daphnids Daphnia magna) exposed to cadmium contamination. We modelled the dynamics of the three species and their interactions using a mechanistic model based on coupled ordinary differential equations. The main processes occurring in this three-species microcosm were thus formalized, including growth and settling of algae, growth of duckweeds, interspecific competition between algae and duckweeds, growth, survival and grazing of daphnids, as well as cadmium effects. We estimated model parameters by Bayesian inference, using simultaneously all the data issued from multiple laboratory experiments specifically conducted for this study. Cadmium concentrations ranged between 0 and 50 mug.L-1. For all parameters of our model, we obtained biologically realistic values and reasonable uncertainties. The cascade of cadmium effects, both direct and indirect, was identified. Critical effect concentrations were provided for the life history traits of each species. An example of experimental design adapted to this kind a microcosm was also proposed. This approach appears promising when studying contaminant effects on ecosystem functioning.

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Section: Experiments and Observed Datamentioning

confidence: 99%

Chemical effects on ecological interactions within a model-experiment loop

Lamonica

Charles

Clément

et al. 2022

Preprint

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“…Such experimental systems provide an important middle-ground, allowing replication of experiments in semi-natural scenarios, but with fewer confounding variables. Recent combination of mechanistic modeling with model-guided experiments in a microcosm has proved very helpful in understanding the stressors’ effects on this simplified ecosystem functioning ( Lamonica et al, 2016 ).…”

Section: Contextualizing the Consensus Statementsmentioning

confidence: 99%

Addressing ecological effects of radiation on populations and ecosystems to improve protection of the environment against radiation: Agreed statements from a Consensus Symposium

Bréchignac

Oughton

Mays

et al. 2016

Journal of Environmental Radioactivity

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This paper reports the output of a consensus symposium organized by the International Union of Radioecology in November 2015. The symposium gathered an academically diverse group of 30 scientists to consider the still debated ecological impact of radiation on populations and ecosystems. Stimulated by the Chernobyl and Fukushima disasters’ accidental contamination of the environment, there is increasing interest in developing environmental radiation protection frameworks. Scientific research conducted in a variety of laboratory and field settings has improved our knowledge of the effects of ionizing radiation on the environment. However, the results from such studies sometimes appear contradictory and there is disagreement about the implications for risk assessment. The Symposium discussions therefore focused on issues that might lead to different interpretations of the results, such as laboratory versus field approaches, organism versus population and ecosystemic inference strategies, dose estimation approaches and their significance under chronic exposure conditions. The participating scientists, from across the spectrum of disciplines and research areas, extending also beyond the traditional radioecology community, successfully developed a constructive spirit directed at understanding discrepancies. From the discussions, the group has derived seven consensus statements related to environmental protection against radiation, which are supplemented with some recommendations. Each of these statements is contextualized and discussed in view of contributing to the orientation and integration of future research, the results of which should yield better consensus on the ecological impact of radiation and consolidate suitable approaches for efficient radiological protection of the environment.

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“…IBM parametrization still widely uses a ‘piecemeal’ approach, in which different submodels are parametrized independently (Clark & Gelfand, 2006; Moran & Clark, 2011). Bayesian inference allows the use of multiple data types, collected at different scales, as well as the simultaneous estimation of all parameters (Gopalaswamy et al, 2012; Lamonica et al, 2016). Bayesian approaches to inference typically depend on having explicit functional forms for the likelihood.…”

Section: Introductionmentioning

confidence: 99%

“…Bayesian inference allows the use of multiple data types, collected at different scales, as well as the simultaneous estimation of all parameters (Gopalaswamy et al, 2012;Lamonica et al, 2016).…”

Section: Introductionmentioning

confidence: 99%