EEMtoolbox: A user‐friendly R package for flexible ensemble ecosystem modelling
Abstract Forecasting ecosystem changes due to disturbances or conservation interventions is essential to improve ecosystem management and anticipate unintended consequences of conservation decisions. Mathematical models allow practitioners to understand the potential effects and unintended consequen...
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| Format: | Article |
| Language: | English |
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Wiley
2025-05-01
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| Series: | Methods in Ecology and Evolution |
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| Online Access: | https://doi.org/10.1111/2041-210X.70032 |
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| author | Luz Valerie Pascal Sarah A. Vollert Malyon D. Bimler Christopher M. Baker Maude Vernet Stefano Canessa Christopher Drovandi Matthew P. Adams |
| author_facet | Luz Valerie Pascal Sarah A. Vollert Malyon D. Bimler Christopher M. Baker Maude Vernet Stefano Canessa Christopher Drovandi Matthew P. Adams |
| author_sort | Luz Valerie Pascal |
| collection | DOAJ |
| description | Abstract Forecasting ecosystem changes due to disturbances or conservation interventions is essential to improve ecosystem management and anticipate unintended consequences of conservation decisions. Mathematical models allow practitioners to understand the potential effects and unintended consequences via simulation. However, calibrating these models is often challenging due to a paucity of appropriate ecological data. Ensemble ecosystem modelling (EEM) is a quantitative method used to parameterize models from theoretical ecosystem features rather than data. Two approaches have been considered to find parameter values satisfying those features: a standard accept–reject algorithm, appropriate for small ecosystem networks, and a sequential Monte Carlo (SMC) algorithm that is more computationally efficient for larger ecosystem networks. In practice, using SMC for EEM generation requires advanced statistical and mathematical knowledge, as well as strong programming skills, which might limit its uptake. In addition, current EEM approaches have been developed for only one model structure (generalised Lotka–Volterra). To facilitate the usage of EEM methods, we introduce EEMtoolbox, an R package for calibrating quantitative ecosystem models. Our package allows the generation of parameter sets satisfying ecosystem features by using either the standard accept–reject algorithm or the novel SMC procedure. Our package extends the existing EEM methodology, originally developed for the generalised Lotka–Volterra model, to two additional model structures (the multispecies Gompertz and the Bimler–Baker model) and additionally allows users to define their own model structures. We demonstrate the usage of EEMtoolbox by simulating changes in species abundance immediately after the release of the sihek (Todiramphus cinnamominus, extinct‐in‐the‐wild species) on Palmyra Atoll in the Pacific Ocean. With its simple interface, our package facilitates straightforward generation of EEM parameter sets, thus unlocking advanced statistical methods supporting conservation decisions using ecosystem network models. |
| format | Article |
| id | doaj-art-1e35068d56cd433ca6557213bee50f0a |
| institution | Kabale University |
| issn | 2041-210X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | Methods in Ecology and Evolution |
| spelling | doaj-art-1e35068d56cd433ca6557213bee50f0a2025-08-20T03:52:41ZengWileyMethods in Ecology and Evolution2041-210X2025-05-0116592192910.1111/2041-210X.70032EEMtoolbox: A user‐friendly R package for flexible ensemble ecosystem modellingLuz Valerie Pascal0Sarah A. Vollert1Malyon D. Bimler2Christopher M. Baker3Maude Vernet4Stefano Canessa5Christopher Drovandi6Matthew P. Adams7School of Mathematical Sciences Queensland University of Technology Brisbane AustraliaSchool of Mathematical Sciences Queensland University of Technology Brisbane AustraliaSchool of BioSciences The University of Melbourne Parkville Victoria AustraliaSchool of Mathematics and Statistics The University of Melbourne Parkville Victoria AustraliaDivision of Conservation Biology Institute of Ecology and Evolution, University of Bern Bern SwitzerlandDivision of Conservation Biology Institute of Ecology and Evolution, University of Bern Bern SwitzerlandSchool of Mathematical Sciences Queensland University of Technology Brisbane AustraliaSchool of Mathematical Sciences Queensland University of Technology Brisbane AustraliaAbstract Forecasting ecosystem changes due to disturbances or conservation interventions is essential to improve ecosystem management and anticipate unintended consequences of conservation decisions. Mathematical models allow practitioners to understand the potential effects and unintended consequences via simulation. However, calibrating these models is often challenging due to a paucity of appropriate ecological data. Ensemble ecosystem modelling (EEM) is a quantitative method used to parameterize models from theoretical ecosystem features rather than data. Two approaches have been considered to find parameter values satisfying those features: a standard accept–reject algorithm, appropriate for small ecosystem networks, and a sequential Monte Carlo (SMC) algorithm that is more computationally efficient for larger ecosystem networks. In practice, using SMC for EEM generation requires advanced statistical and mathematical knowledge, as well as strong programming skills, which might limit its uptake. In addition, current EEM approaches have been developed for only one model structure (generalised Lotka–Volterra). To facilitate the usage of EEM methods, we introduce EEMtoolbox, an R package for calibrating quantitative ecosystem models. Our package allows the generation of parameter sets satisfying ecosystem features by using either the standard accept–reject algorithm or the novel SMC procedure. Our package extends the existing EEM methodology, originally developed for the generalised Lotka–Volterra model, to two additional model structures (the multispecies Gompertz and the Bimler–Baker model) and additionally allows users to define their own model structures. We demonstrate the usage of EEMtoolbox by simulating changes in species abundance immediately after the release of the sihek (Todiramphus cinnamominus, extinct‐in‐the‐wild species) on Palmyra Atoll in the Pacific Ocean. With its simple interface, our package facilitates straightforward generation of EEM parameter sets, thus unlocking advanced statistical methods supporting conservation decisions using ecosystem network models.https://doi.org/10.1111/2041-210X.70032approximate Bayesian computationensemble ecosystem modellingpopulation dynamicsR packagesequential Monte Carlo |
| spellingShingle | Luz Valerie Pascal Sarah A. Vollert Malyon D. Bimler Christopher M. Baker Maude Vernet Stefano Canessa Christopher Drovandi Matthew P. Adams EEMtoolbox: A user‐friendly R package for flexible ensemble ecosystem modelling Methods in Ecology and Evolution approximate Bayesian computation ensemble ecosystem modelling population dynamics R package sequential Monte Carlo |
| title | EEMtoolbox: A user‐friendly R package for flexible ensemble ecosystem modelling |
| title_full | EEMtoolbox: A user‐friendly R package for flexible ensemble ecosystem modelling |
| title_fullStr | EEMtoolbox: A user‐friendly R package for flexible ensemble ecosystem modelling |
| title_full_unstemmed | EEMtoolbox: A user‐friendly R package for flexible ensemble ecosystem modelling |
| title_short | EEMtoolbox: A user‐friendly R package for flexible ensemble ecosystem modelling |
| title_sort | eemtoolbox a user friendly r package for flexible ensemble ecosystem modelling |
| topic | approximate Bayesian computation ensemble ecosystem modelling population dynamics R package sequential Monte Carlo |
| url | https://doi.org/10.1111/2041-210X.70032 |
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