Document Type



Virginia Institute of Marine Science

Publication Date



Marine Ecology Progress Series



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Measures of condition in fishes are often used to assess the general well-being of fish populations since condition reflects the biotic and abiotic factors experienced by individuals over moderate time scales. Fish condition can also be used as an indicator of ecosystem suitability in the context of ecosystem-based management. From an ecosystem perspective, evaluation of fish condition is best described over multiple spatiotemporal scales and in a multi-species context. This study analyzed 14 yr (2002-2015) of fisheries-independent trawl survey data to evaluate trends in condition for 16 demersal fishes inhabiting Chesapeake Bay, the largest estuary in the USA. Seasonal and spatial variability in condition were inferred from linear mixed-effects models, while dynamic factor analysis (DFA) was used to reveal coherence among and drivers of annual trends in condition across all species and for 3 subgroups representing trophic guilds. Patterns of intra-annual condition varied among species, likely reflecting life history strategies and physiological responses to seasonal environmental conditions, while spatial patterns showed improved condition for both coastal and oligohaline species with increasing distance from their source. Annual trends in condition showed remarkable coherence for all fishes and for species within each trophic guild, suggesting that factors influencing condition-based indicators of ecosystem suitability operate at the community level. Spring mean surface chl a concentration was included in the selected DFA model for nearly all groups (exception: benthivores) and was statistically significant for several species, indicating the importance of bottom-up processes on bay-wide annual fish condition.


Fish condition · Bottom-up controls · Chesapeake Bay · Ecosystem-based management · Linear mixed effects models · LME · Dynamic factor analysis · DFA

Creative Commons License

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