Section: Ecotoxicology & Environmental Chemistry
Topic: Environmental sciences, Chemistry
Chemical effects on ecological interactions within a model-experiment loop
10.24072/pcjournal.209 - Peer Community Journal, Volume 3 (2023), article no. e3.
Get full text PDF Peer reviewed and recommended by PCI
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 μg.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.
Type: Research article
CC-BY 4.0
@article{10_24072_pcjournal_209,
author = {Lamonica, Dominique and Charles, Sandrine and Cl\'ement, Bernard and Lopes, Christelle},
title = {Chemical effects on ecological interactions within a model-experiment loop},
journal = {Peer Community Journal},
eid = {e3},
publisher = {Peer Community In},
volume = {3},
year = {2023},
doi = {10.24072/pcjournal.209},
url = {https://peercommunityjournal.org/articles/10.24072/pcjournal.209/}
}
TY - JOUR AU - Lamonica, Dominique AU - Charles, Sandrine AU - Clément, Bernard AU - Lopes, Christelle TI - Chemical effects on ecological interactions within a model-experiment loop JO - Peer Community Journal PY - 2023 VL - 3 PB - Peer Community In UR - https://peercommunityjournal.org/articles/10.24072/pcjournal.209/ DO - 10.24072/pcjournal.209 ID - 10_24072_pcjournal_209 ER -
%0 Journal Article %A Lamonica, Dominique %A Charles, Sandrine %A Clément, Bernard %A Lopes, Christelle %T Chemical effects on ecological interactions within a model-experiment loop %J Peer Community Journal %D 2023 %V 3 %I Peer Community In %U https://peercommunityjournal.org/articles/10.24072/pcjournal.209/ %R 10.24072/pcjournal.209 %F 10_24072_pcjournal_209
Lamonica, Dominique; Charles, Sandrine; Clément, Bernard; Lopes, Christelle. Chemical effects on ecological interactions within a model-experiment loop. Peer Community Journal, Volume 3 (2023), article no. e3. doi : 10.24072/pcjournal.209. https://peercommunityjournal.org/articles/10.24072/pcjournal.209/
Peer reviewed and recommended by PCI : 10.24072/pci.ecotoxenvchem.100002
Conflict of interest of the recommender and peer reviewers:
The recommender in charge of the evaluation of the article and the reviewers declared that they have no conflict of interest (as defined in the code of conduct of PCI) with the authors or with the content of the article.
[1] Bayesian experimental design for a toxicokinetic–toxicodynamic model, Journal of Statistical Planning and Inference, Volume 142 (2012) no. 1, pp. 263-275 | DOI
[2] The Influence of Design Characteristics on Statistical Inference in Nonlinear Estimation: A Simulation Study Based on Survival Data and Hazard Modeling, Journal of Agricultural, Biological, and Environmental Statistics, Volume 5 (2000) no. 3 | DOI
[3] Modelling survival: exposure pattern, species sensitivity and uncertainty, Scientific Reports, Volume 6 (2016) no. 1 | DOI
[4] Effects of contaminants and trophic cascade regulation on food chain stability: Application to cadmium soil pollution on small mammals – Raptor systems, Ecological Modelling, Volume 382 (2018), pp. 33-42 | DOI
[5] Bayesian modelling of daphnid responses to time-varying cadmium exposure in laboratory aquatic microcosms, Ecotoxicology and Environmental Safety, Volume 74 (2011) no. 4, pp. 693-702 | DOI
[6] Comparison of bioassays with different exposure time patterns: The added value of dynamic modelling in predictive ecotoxicology, Ecotoxicology and Environmental Safety, Volume 75 (2012), pp. 80-86 | DOI
[7] Statistical cautions when estimating DEBtox parameters, Journal of Theoretical Biology, Volume 254 (2008) no. 1, pp. 55-64 | DOI
[8] Population-Level Modeling to Account for Multigenerational Effects of Uranium in <i>Daphnia magna</i>, Environmental Science & Technology, Volume 46 (2012) no. 2, pp. 1136-1143 | DOI
[9] Are single species toxicity tests alone adequate for estimating environmental hazard?, Environmental Monitoring and Assessment, Volume 4 (1984) no. 3, pp. 259-273 | DOI
[10] Freshwater Multi-Species Test Systems, Handbook of Ecotoxicology, Blackwell Publishing Ltd., Oxford, UK, 1997, pp. 101-116 | DOI
[11] Handbook of ecotoxicology, Blackwell Scientific Publications, London, Boston, 1993
[12] Optimizing the design of a reproduction toxicity test with the pond snail Lymnaea stagnalis, Regulatory Toxicology and Pharmacology, Volume 81 (2016), pp. 47-56 | DOI
[13] Cost-effective experimental design to support modeling of concentration–response functions, Chemosphere, Volume 72 (2008) no. 5, pp. 803-810 | DOI
[14] Comparison of laboratory batch and flow-through microcosm bioassays, Ecotoxicology and Environmental Safety, Volume 108 (2014), pp. 217-223 | DOI
[15] The design of mesocosm experiments, Environmental toxicology and chemistry, Volume 11 (1992) | DOI
[16] Do we have to incorporate ecological interactions in the sensitivity assessment of ecosystems? An examination of a theoretical assumption underlying species sensitivity distribution models, Environment International, Volume 34 (2008) no. 3, pp. 390-396 | DOI
[17] Développement d’un bioessai de toxicité chronique en microcosme aquatique de laboratoire et évaluation de l’outil au travers de l’étude des effets du cadmium. PhD thesis, INSA Lyon (2011)
[18] Feeding selectivities and relative ingestion rates of <i>Daphnia</i> and <i>Bosmina</i>, Limnology and Oceanography, Volume 27 (1982) no. 3, pp. 518-527 | DOI
[19] Size relations of the filtering area of two daphnia species, Limnology and Oceanography, Volume 16 (1971) no. 6, pp. 900-905 | DOI
[20] Ecotoxicologie: theorie et applications / Ecotoxicology in theory and practice, 1997
[21] Survival data analyses in ecotoxicology: critical effect concentrations, methods and models. What should we use?, Ecotoxicology, Volume 21 (2012) no. 4, pp. 1072-1083 | DOI
[22] Inference from Iterative Simulation Using Multiple Sequences, Statistical Science, Volume 7 (1992) no. 4 | DOI
[23] One and multi-compartments toxico-kinetic modeling to understand metals’ organotropism and fate in Gammarus fossarum, Environment International, Volume 156 (2021) | DOI
[24] The ODD protocol: A review and first update, Ecological Modelling, Volume 221 (2010) no. 23, pp. 2760-2768 | DOI
[25] Interactive Metal Mixture Toxicity to Daphnia magna Populations as an Emergent Property in a Dynamic Energy Budget Individual‐Based Model, Environmental Toxicology and Chemistry, Volume 40 (2021) no. 11, pp. 3034-3048 | DOI
[26] Optimal experimental designs for dose–response studies with continuous endpoints, Archives of Toxicology, Volume 89 (2015) no. 11, pp. 2059-2068 | DOI
[27] Optimal test design for binary response data: the example of the fish embryo toxicity test, Environmental Sciences Europe, Volume 27 (2015) no. 1 | DOI
[28] Optimal Design for Estimating Parameters of the 4-Parameter Hill Model, Nonlinearity in Biology, Toxicology, Medicine, Volume 1 (2003) no. 3 | DOI
[29] Survey of the Quality of Experimental Design, Statistical Analysis and Reporting of Research Using Animals, PLoS ONE, Volume 4 (2009) no. 11 | DOI
[30] Limitations of Laboratory Bioassays: The Need for Ecosystem-Level Testing, BioScience, Volume 35 (1985) no. 3, pp. 165-171 | DOI
[31] Alterations of physiological energetics, growth and reproduction of Daphnia magna under toxicant stress, Aquatic Toxicology, Volume 53 (2001) no. 2, pp. 79-90 | DOI
[32] Data, scripts and supplementary information for analysis of the functioning of a three-species microcosm under cadmium pressure, Zenodo, 2022 | DOI
[33] Modelling algae–duckweed interaction under chemical pressure within a laboratory microcosm, Ecotoxicology and Environmental Safety, Volume 128 (2016), pp. 252-265 | DOI
[34] Mechanistic modelling of daphnid-algae dynamics within a laboratory microcosm, Ecological Modelling, Volume 320 (2016), pp. 213-230 | DOI
[35] Assessing population impacts of toxicant-induced disruption of breeding behaviours using an individual-based model for the three-spined stickleback, Ecological Modelling, Volume 387 (2018), pp. 107-117 | DOI
[36] Survival of <i>daphnia magna</i> and <i>hyalella azteca</i> in cadmium-spiked water and sediment, Environmental Toxicology and Chemistry, Volume 5 (1986) no. 10, pp. 933-938 | DOI
[37] rjags: Bayesian graphical models using mcmc. Rpackage version 1.0.3-12., 2009
[38] Indirect Effects in Aquatic Ecotoxicology: Implications for Ecological Risk Assessment, Environmental Management, Volume 29 (2002) no. 3, pp. 311-323 | DOI
[39] Dictionnaire encyclopédique des sciences de la nature et de la biodiversité, Dunod, 2002 (ISBN: 9782100492824)
[40] Accumulation-depuration data collection in support of toxicokinetic modelling, Scientific Data, Volume 9 (2022) no. 1 | DOI
[41] La numération des algues en sol submergé : Loi de distribution des organismes et densité d’échantillonnage, Rev. Ecol. Biol. Sol., Volume 15 (1978)
[42] Periphyton as alternative food source for the filter-feeding cladoceran <i>Daphnia magna</i>, Freshwater Biology, Volume 54 (2009) no. 1, pp. 15-23 | DOI
[43] Optimal designs for binary response experiments with two design variables, Statistica Sinica, Volume 5 (1995)
[44] R: A Language and Environment for Statistical Computing, 2013
[45] New approaches to the ecological risk assessment of multiple stressors, Marine and Freshwater Research, Volume 67 (2016) no. 4 | DOI
[46] A quantitative theory of organic growth, Hum. Biol., Volume 10 (1938)
[47] D-optimal designs for Poisson regression models, Journal of Statistical Planning and Inference, Volume 136 (2006) no. 8, pp. 2831-2845 | DOI
[48] Optimal Experimental Design for a Nonlinear Response in Environmental Toxicology, Biometrics, Volume 62 (2006) no. 3, pp. 886-892 | DOI
Cited by Sources: