Inference of ecological networks and possibilistic dynamics based on Boolean networks from observations and prior knowledge
Abstract Being able to infer the interactions between a set of species from observations of the system is of paramount importance to obtain explanatory and predictive models in ecology. We tackled this challenge by employing qualitative modelling frameworks and logic methods for the synthesis of mat...
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| Format: | Article |
| Language: | English |
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Wiley
2025-08-01
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| Series: | Methods in Ecology and Evolution |
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| Online Access: | https://doi.org/10.1111/2041-210X.70090 |
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| author | Loïc Paulevé Cédric Gaucherel |
| author_facet | Loïc Paulevé Cédric Gaucherel |
| author_sort | Loïc Paulevé |
| collection | DOAJ |
| description | Abstract Being able to infer the interactions between a set of species from observations of the system is of paramount importance to obtain explanatory and predictive models in ecology. We tackled this challenge by employing qualitative modelling frameworks and logic methods for the synthesis of mathematical models that can integrate both observations and expert knowledge on the system. Boolean networks is a qualitative modelling framework, which enables reasoning exhaustively on possible dynamics of the system. After devising a formal link between ecological networks and the causal structure of Boolean networks, we applied a generic model synthesis engine to infer Boolean models that are able to reproduce the observed dynamics of a protist community and of a planktonic ecosystem. Our inference method supports optimization criteria to derive the most parsimonious and most precise models. It is also able to integrate prior knowledge on the ecological network, adding constraints on impossible interactions, which is necessary to obtain realistic predictions. Such constraints may, however, prove to be too strict, in which case our method is able to conclude on the absence of a model compatible with both the observations and the input hypotheses. We demonstrated our methodology on experimental data of a protist community and of a planktonic ecosystem and showed in each case its ability to recover essential and sufficient ecological interactions to explain the observed dynamics. |
| format | Article |
| id | doaj-art-2e253821d0af4c5f8f422be57a9cfca7 |
| institution | Kabale University |
| issn | 2041-210X |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | Methods in Ecology and Evolution |
| spelling | doaj-art-2e253821d0af4c5f8f422be57a9cfca72025-08-20T04:02:09ZengWileyMethods in Ecology and Evolution2041-210X2025-08-011681851186710.1111/2041-210X.70090Inference of ecological networks and possibilistic dynamics based on Boolean networks from observations and prior knowledgeLoïc Paulevé0Cédric Gaucherel1University of Bordeaux, CNRS, Bordeaux INP, LaBRI, UMR 5800 Talence FranceAMAP—INRAE, CIRAD, CNRS, IRD, Montpellier University Montpellier FranceAbstract Being able to infer the interactions between a set of species from observations of the system is of paramount importance to obtain explanatory and predictive models in ecology. We tackled this challenge by employing qualitative modelling frameworks and logic methods for the synthesis of mathematical models that can integrate both observations and expert knowledge on the system. Boolean networks is a qualitative modelling framework, which enables reasoning exhaustively on possible dynamics of the system. After devising a formal link between ecological networks and the causal structure of Boolean networks, we applied a generic model synthesis engine to infer Boolean models that are able to reproduce the observed dynamics of a protist community and of a planktonic ecosystem. Our inference method supports optimization criteria to derive the most parsimonious and most precise models. It is also able to integrate prior knowledge on the ecological network, adding constraints on impossible interactions, which is necessary to obtain realistic predictions. Such constraints may, however, prove to be too strict, in which case our method is able to conclude on the absence of a model compatible with both the observations and the input hypotheses. We demonstrated our methodology on experimental data of a protist community and of a planktonic ecosystem and showed in each case its ability to recover essential and sufficient ecological interactions to explain the observed dynamics.https://doi.org/10.1111/2041-210X.70090community ecologyfood websmodellingspecies interactions |
| spellingShingle | Loïc Paulevé Cédric Gaucherel Inference of ecological networks and possibilistic dynamics based on Boolean networks from observations and prior knowledge Methods in Ecology and Evolution community ecology food webs modelling species interactions |
| title | Inference of ecological networks and possibilistic dynamics based on Boolean networks from observations and prior knowledge |
| title_full | Inference of ecological networks and possibilistic dynamics based on Boolean networks from observations and prior knowledge |
| title_fullStr | Inference of ecological networks and possibilistic dynamics based on Boolean networks from observations and prior knowledge |
| title_full_unstemmed | Inference of ecological networks and possibilistic dynamics based on Boolean networks from observations and prior knowledge |
| title_short | Inference of ecological networks and possibilistic dynamics based on Boolean networks from observations and prior knowledge |
| title_sort | inference of ecological networks and possibilistic dynamics based on boolean networks from observations and prior knowledge |
| topic | community ecology food webs modelling species interactions |
| url | https://doi.org/10.1111/2041-210X.70090 |
| work_keys_str_mv | AT loicpauleve inferenceofecologicalnetworksandpossibilisticdynamicsbasedonbooleannetworksfromobservationsandpriorknowledge AT cedricgaucherel inferenceofecologicalnetworksandpossibilisticdynamicsbasedonbooleannetworksfromobservationsandpriorknowledge |