Modelling algae–duckweed interaction under chemical pressure within a laboratory microcosm

Lamonica, Dominique; Clément, Bernard; Charles, Sandrine; Lopes, Christelle

doi:10.1016/j.ecoenv.2016.02.008

Cited by 8 publications

(29 citation statements)

References 55 publications

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“…All assumptions are based on the experimental design described in "Experiments and observed data" section. As mentioned in [Lamonica et al, 2016a],…”

Section: Processes and Assumptionsmentioning

confidence: 92%

Section: Processes and Assumptionsmentioning

confidence: 99%

“…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%

“…Two processes are related to intrinsic algal dynamics: settling of suspended algae, described by an exponential decay of algal cells in the water column, and logistic growth of both sus-pended and settled algae (Lamonica, B Clément, et al, 2016). Duckweed growth is related to the intrinsic duckweed dynamics through a logistic function (Lamonica, Herbach, et al, 2016). The algae-duckweed interaction is an interspecific competition, described with a Lotka-Volterra type I model (Lamonica, Herbach, et al, 2016).…”

Section: Dynamic Modellingmentioning

confidence: 99%

“…*Prior distribution: N stands for the normal law, U stands for the uniform law. **Sources: [1][Lamonica et al, 2016a], [2][Lamonica et al, 2016b], [3] [Billoir et al, 2008.…”

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confidence: 99%

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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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“…All assumptions are based on the experimental design described in "Experiments and observed data" section. As mentioned in [Lamonica et al, 2016a],…”

Section: Processes and Assumptionsmentioning

confidence: 92%

Section: Processes and Assumptionsmentioning

confidence: 99%

Section: Experiments and Observed Datamentioning

confidence: 99%

Section: Dynamic Modellingmentioning

confidence: 99%

“…*Prior distribution: N stands for the normal law, U stands for the uniform law. **Sources: [1][Lamonica et al, 2016a], [2][Lamonica et al, 2016b], [3] [Billoir et al, 2008.…”

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confidence: 99%

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Chemical effects on ecological interactions within a model-experiment loop

Lamonica

Charles

Clément

et al. 2022

Preprint

Self Cite

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A Bayesian estimation approach for the mortality in a stage-structured demographic model

et al. 2017

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Control interventions in sustainable pest management schemes are set according to the phenology and the population abundance of the pests. This information can be obtained using suitable mathematical models that describe the population dynamics based on individual life history responses to environmental conditions and resource availability. These responses are described by development, fecundity and survival rate functions, which can be estimated from laboratory experiments. If experimental data are not available, data on field population dynamics can be used for their estimation. This is the case of the extrinsic mortality term that appears in the mortality rate function due to biotic factors. We propose a Bayesian approach to estimate the probability density functions of the parameters in the extrinsic mortality rate function, starting from data on population abundance. The method investigates the time variability in the mortality parameters by comparing simulated and observed trajectories. The grape berry moth, a pest of great importance in European vineyards, has been considered as a case study. Simulated data have been considered to evaluate the convergence of the algorithm, while field data have been used to obtain estimates of the mortality for the grape berry moth.

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Fate, toxicity and bioconcentration of cadmium on Pseudokirchneriella subcapitata and Lemna minor in mid-term single tests

Clément

Lamonica

2017

Ecotoxicology

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In the frame of a project which consists in modeling a laboratory microcosm under cadmium pressure, we initiated this study on the fate and effects of cadmium in the presence of either the microalga Pseudokirchneriella subcapitata or the duckweed Lemna minor, two organisms of the microcosm. For each organism, growth inhibition tests on a duration of 2-3 weeks were carried out with the objective of linking effects with total dissolved, ionic and internalized forms of cadmium. Numbers of organisms (algal cells or duckweed fronds) in 2-L beakers filled with synthetic nutritive medium containing EDTA were counted during the course of assays, while cadmium concentrations in the water and in the organisms were measured. Free cadmium fraction was calculated using PHREEQC, a computer program for chemical speciation. Results showed that cadmium toxicity to microalgae could be correlated to the free divalent fraction of this metal, limited by the presence of EDTA, and to its concentration in the organisms. Bioconcentration factors for our medium were suggested for P. subcapitata (111,000 on the basis of free cadmium concentration) and L. minor (17,812 on the basis of total dissolved concentration). No effect concentrations were roughly estimated around 400 µg/g for Pseudokirchneriella subcapitata and 200-300 µg/g for Lemna minor. This study is a first step towards a fate model based on chemical speciation for a better understanding of microcosm results.

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Modelling algae–duckweed interaction under chemical pressure within a laboratory microcosm

Cited by 8 publications

References 55 publications

Chemical effects on ecological interactions within a model-experiment loop

Chemical effects on ecological interactions within a model-experiment loop

A Bayesian estimation approach for the mortality in a stage-structured demographic model

Fate, toxicity and bioconcentration of cadmium on Pseudokirchneriella subcapitata and Lemna minor in mid-term single tests

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