Unlocking ensemble ecosystem modelling for large and complex networks.
The potential effects of conservation actions on threatened species can be predicted using ensemble ecosystem models by forecasting populations with and without intervention. These model ensembles commonly assume stable coexistence of species in the absence of available data. However, existing ensem...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2024-03-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011976&type=printable |
| _version_ | 1797234235373780992 |
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| author | Sarah A Vollert Christopher Drovandi Matthew P Adams |
| author_facet | Sarah A Vollert Christopher Drovandi Matthew P Adams |
| author_sort | Sarah A Vollert |
| collection | DOAJ |
| description | The potential effects of conservation actions on threatened species can be predicted using ensemble ecosystem models by forecasting populations with and without intervention. These model ensembles commonly assume stable coexistence of species in the absence of available data. However, existing ensemble-generation methods become computationally inefficient as the size of the ecosystem network increases, preventing larger networks from being studied. We present a novel sequential Monte Carlo sampling approach for ensemble generation that is orders of magnitude faster than existing approaches. We demonstrate that the methods produce equivalent parameter inferences, model predictions, and tightly constrained parameter combinations using a novel sensitivity analysis method. For one case study, we demonstrate a speed-up from 108 days to 6 hours, while maintaining equivalent ensembles. Additionally, we demonstrate how to identify the parameter combinations that strongly drive feasibility and stability, drawing ecological insight from the ensembles. Now, for the first time, larger and more realistic networks can be practically simulated and analysed. |
| first_indexed | 2024-04-24T16:28:50Z |
| format | Article |
| id | doaj.art-1136c54445ae4b4894b6885ba228ebfa |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-04-24T16:28:50Z |
| publishDate | 2024-03-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-1136c54445ae4b4894b6885ba228ebfa2024-03-30T05:31:54ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582024-03-01203e101197610.1371/journal.pcbi.1011976Unlocking ensemble ecosystem modelling for large and complex networks.Sarah A VollertChristopher DrovandiMatthew P AdamsThe potential effects of conservation actions on threatened species can be predicted using ensemble ecosystem models by forecasting populations with and without intervention. These model ensembles commonly assume stable coexistence of species in the absence of available data. However, existing ensemble-generation methods become computationally inefficient as the size of the ecosystem network increases, preventing larger networks from being studied. We present a novel sequential Monte Carlo sampling approach for ensemble generation that is orders of magnitude faster than existing approaches. We demonstrate that the methods produce equivalent parameter inferences, model predictions, and tightly constrained parameter combinations using a novel sensitivity analysis method. For one case study, we demonstrate a speed-up from 108 days to 6 hours, while maintaining equivalent ensembles. Additionally, we demonstrate how to identify the parameter combinations that strongly drive feasibility and stability, drawing ecological insight from the ensembles. Now, for the first time, larger and more realistic networks can be practically simulated and analysed.https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011976&type=printable |
| spellingShingle | Sarah A Vollert Christopher Drovandi Matthew P Adams Unlocking ensemble ecosystem modelling for large and complex networks. PLoS Computational Biology |
| title | Unlocking ensemble ecosystem modelling for large and complex networks. |
| title_full | Unlocking ensemble ecosystem modelling for large and complex networks. |
| title_fullStr | Unlocking ensemble ecosystem modelling for large and complex networks. |
| title_full_unstemmed | Unlocking ensemble ecosystem modelling for large and complex networks. |
| title_short | Unlocking ensemble ecosystem modelling for large and complex networks. |
| title_sort | unlocking ensemble ecosystem modelling for large and complex networks |
| url | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011976&type=printable |
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