Document Type



Virginia Institute of Marine Science

Publication Date



Marine Ecology Progress Series



First Page


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Recognition of the need for a more holistic, ecosystem approach to the assessment and management of living marine resources has renewed interest in quantitative community eco logy and fueled efforts to develop ecosystem metrics to gain insight into system status. This investigation utilized 12 years (2008 to 2019) of fisheries-independent bottom trawl survey data to quantify and synthesize the spatiotemporal patterns of species assemblages inhabiting the nearshore Mid-Atlantic Bight (MAB). Assemblages were delineated by ecomorphotype (EMT), and all species collected by the survey were allocated among 9 EMTs: demersal fishes; pelagic fishes; flatfishes; skates; rays; dogfishes; other sharks; cephalopods; and benthic arthropods. Annual time series and seasonal spatial distributions of relative aggregate biomass were quantified for each EMT using delta-generalized additive models. Dynamic factor analysis (DFA) revealed that the information content of the 9 annual time series was effectively summarized by 3 common trends, and DFA model fits to each EMT time series represented a new suite of ecosystem indicators for this system. Mean sea surface temperature during winter in the MAB was included in the selected DFA model, suggesting that winter environmental conditions influence the structure of this system at an annual scale. Principal component analysis uncovered a north-to-south gradient in the seasonal spatial distributions of these EMTs and identified a distinct area of elevated biomass for several assemblages along the south shore of Long Island, NY. Taken together, these results characterize the community structure of the nearshore MAB and yield requisite information to support ongoing ecosystem-scale assessment and management activities for this region.



Species assemblages, Spatiotemporal patterns, Mid-Atlantic Bight, Ecosystem approaches, Fisheries management, Generalized additive models, Dynamic factor analysis, Principal component analysis

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.