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Title: Modeling 25 years of food web changes in Narragansett Bay (USA) as a tool for ecosystem-based management
Narragansett Bay (Rhode Island, USA) is an estuary undergoing changes from a combination of rising water temperatures, nutrient fluxes, and human uses. In this study, we created an ecosystem food web model and evaluated its ability to predict functional group biomasses. Specifically, we used Ecopath to construct 2 mass-balanced models covering different time periods in Narragansett Bay: a historical model using data from 1994-1998 and a present-day model that represents 2014-2018. With the historical model as a starting point, we used Ecosim fit to time series data and projected forward to present-day values, forcing the model with both phytoplankton biomass and fishing mortality. The biomass of most mid- and upper trophic level groups increased by 2018, with the exception of carnivorous benthos, which experienced a large decline. There were changes in the composition of fisheries, with a large increase in recreational benthivorous fish landings and a decrease in commercial landings of planktivorous fish and suspension feeding benthos. The inclusion of fishing mortality and phytoplankton biomass as forcing functions, as well as adjusting the vulnerability levels of prey, greatly improved our model fits for all functional groups with the exception of gelatinous zooplankton. Our ecosystem model was able to correctly predict the direction of change for all fish and fished invertebrate groups with a relatively high degree of precision, particularly for the upper trophic levels. Thus, this ecosystem model is broadly applicable and suitable to project trends in the Narragansett Bay food web associated with localized and adaptive ecosystem-based management.  more » « less
Award ID(s):
1655221
NSF-PAR ID:
10248605
Author(s) / Creator(s):
; ; ; ; ;
Date Published:
Journal Name:
Marine Ecology Progress Series
Volume:
654
ISSN:
0171-8630
Page Range / eLocation ID:
17 to 33
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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