Back to Search
Start Over
Impacts of climate change on the Bay of Seine ecosystem: Forcing a spatio‐temporal trophic model with predictions from an ecological niche model
- Source :
- Fisheries Oceanography (1054-6006) (Wiley), 2021-09 , Vol. 30 , N. 5 , P. 471-489
- Publication Year :
- 2021
-
Abstract
- Climate change is already known to cause irreversible impacts on ecosystems that are difficult to accurately predict due to the multiple scales at which it will interact. Predictions at the community level are mainly focused on the future distribution of marine species biomass using ecological niche modelling, which requires extensive efforts concerning the effects that trophic interactions could have on the realized species dynamics. In this study, a set of species distribution models predictions were used to force the spatially‐explicit trophic model Ecospace in order to evaluate the potentials impacts that two 2,100 climate scenarios, RCP2.6 and RCP8.5, could have on a highly exploited ecosystem, the Bay of Seine (France). Simulations demonstrated that both scenarios would influence the community of the Bay of Seine ecosystem: as expected, more intense changes were predicted with the extreme scenario RCP8.5 than with the RCP2.6 scenario. Under both scenarios, a majority of species underwent a decrease of biomass, although some increased. However, in both cases the stability of the majority of species dynamics was lowered, the sustainability of the fishery. Differences between niche modelling predictions and those obtained through the forcing in Ecospace highlighted the paramount importance of considering trophic interactions in climate change simulations. These results illustrate the requirement of multiplying novel approaches for efficiently forecasting potential impacts of climate change.
Details
- Database :
- OAIster
- Journal :
- Fisheries Oceanography (1054-6006) (Wiley), 2021-09 , Vol. 30 , N. 5 , P. 471-489
- Notes :
- application/pdf, English
- Publication Type :
- Electronic Resource
- Accession number :
- edsoai.on1306531646
- Document Type :
- Electronic Resource
- Full Text :
- https://doi.org/10.1111.fog.12531