Academic Journal
An evaluation of vulnerability settings in Ecopath with Ecosim on ecosystem hindcast and forecast skills
| Title: | An evaluation of vulnerability settings in Ecopath with Ecosim on ecosystem hindcast and forecast skills |
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| Authors: | Qingqiang Ren, Yuying Zhang, Jie Yin, Dongyan Han, Min Liu, Yong Chen |
| Source: | Ecological Informatics, Vol 86, Iss , Pp 103040- (2025) |
| Publisher Information: | Elsevier, 2025. |
| Publication Year: | 2025 |
| Collection: | LCC:Information technology LCC:Ecology |
| Subject Terms: | Ecopath with Ecosim, Gulf of Maine, Model fitting, Time series, Vulnerability, Information technology, T58.5-58.64, Ecology, QH540-549.5 |
| More Details: | Ecological model fitting is a critical step in ensuring that models can reflect historical ecosystem dynamics, allowing for an improved understanding of ecological processes and potentially enhancing the reliability of future projections, despite inherent uncertainties. Vulnerability parameters (v), reflecting the predator-prey relationship, play a crucial role in the Ecopath with Ecosim (EwE) model fitting. However, many EwE applications have bypassed tuning the vulnerability parameters due to a lack of historical data, limiting the impacts of vulnerability-unfitted (v-unfitted) models on evaluating management strategies. In this study, we used model skill metrics, including bias, error, and reliability, to evaluate the hindcast and forecast skills of the v-unfitted models with multiple vulnerability settings. The prediction from vulnerability-fitted (v-fitted) model was found to have the best fitness and most accurately replicate historical ecosystem dynamics when compared to observed data. In addition, the v-unfitted model with trophic-level-related vulnerability setting (vTL) exhibited relatively better hindcast ability among the alternative v settings compared with v-fitted model. In terms of forecast skill under both reduced and increased fishing effort scenarios, only the depletion-related vulnerability setting (vB) was found to be robust for v-unfitted models comparing to v-fitted model predictions. We highlight the importance of examining various vulnerability settings, and providing a reference for the application of unfitted models in informing ecosystem-based fisheries management. Our results also reaffirm the critical role of time-series data in applying EwE models. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 1574-9541 |
| Relation: | http://www.sciencedirect.com/science/article/pii/S1574954125000494; https://doaj.org/toc/1574-9541 |
| DOI: | 10.1016/j.ecoinf.2025.103040 |
| Access URL: | https://doaj.org/article/1c65ff77f53c45c9b9f57773c724d855 |
| Accession Number: | edsdoj.1c65ff77f53c45c9b9f57773c724d855 |
| Database: | Directory of Open Access Journals |
| ISSN: | 15749541 |
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| DOI: | 10.1016/j.ecoinf.2025.103040 |
| Published in: | Ecological Informatics |
| Language: | English |