Incorporating temperature-dependent fish bioenergetics into a Narragansett Bay food web model
Date of Original Version
Food web models capture shifting species interactions, making them useful tools for exploring community responses to disturbances. The inclusion of environmental drivers, such as temperature, can improve model predictions, as energy demands of an organism can be temperature specific. While mass-balance models such as Ecopath with Ecosim (EwE) and the R implementation, Rpath, have included some thermal responses in past work, models have yet to include temperature-dependent energetic demands and metabolic costs. Our work demonstrates the inclusion of temperature-dependent bioenergetics into an Rpath food web model using the case study of a warming estuary: Narragansett Bay (Rhode Island, U.S.A). Thermal response parameters from literature were used to construct Kitchell curves describing temperature-dependent consumption and modified Arrhenius curves describing temperature-dependent respiration. Surface water temperature time series from 1994 to 2054 for high and low warming scenarios were created using observed temperatures and projections from the Coupled Model Intercomparison Project (CMIP6) multi-model ensemble. The integration of temperature-dependent fish bioenergetics resulted in lower projected biomasses as energetic demands increased. The degree to which biomass was impacted varied by functional group, though piscivorous fishes were particularly affected as both that group and their prey groups had forced bioenergetic changes. The differences in the model-predicted biomasses highlight the importance of accounting for thermal effects on marine species in ecosystem models, which will become increasingly important as ocean temperatures continue to rise in Narragansett Bay and elsewhere.
Publication Title, e.g., Journal
Heinichen, Margaret, M. C. McManus, Sean M. Lucey, Kerim Aydin, Austin Humphries, Anne Innes-Gold, and Jeremy Collie. "Incorporating temperature-dependent fish bioenergetics into a Narragansett Bay food web model." Ecological Modelling 466, (2022). doi: 10.1016/j.ecolmodel.2022.109911.