Home > Explore Data & Reports > Land Use and Salinity Drive Changes in SAV Abundance and Community Composition


Patrick, C.J., D.E. Weller, R.J. Orth, D.J. Wilcox, and M.P. Hannam. 2017. Land Use and Salinity Drive Changes in SAV Abundance and Community Composition. Estuaries and Coasts. https://doi.org/10.1007/s12237-017-0250-1

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Conserving and restoring submerged aquatic vegetation (SAV) are key management goals for estuaries worldwide because SAV integrates many aspects of water quality and provides a wide range of ecosystem services. Management strategies are typically focused on aggregated abundance of several SAV species, because species cannot be easily distinguished in remotely sensed data. Human land use and shoreline alteration have been shown to negatively impact SAV abundance, but the effects have varied with study, spatial scale, and location. The differences in reported effects may be partly due to the focus on abundance, which overlooks within-community and among-community dynamics that generate total SAV abundance. We analyzed long-term SAV aerial survey data (1984–2009) and ground observations of community composition (1984–2012) in subestuaries of Chesapeake Bay to integrate variations in abundance with differences in community composition. We identified five communities (mixed freshwater, milfoil-Zannichellia, mixed mesohaline, Zannichellia, and Ruppia-Zostera). Temporal variations in SAV abundance were more strongly related to community identity than to terrestrial stressors, and responses to stressors differed among communities and among species. In one fifth of the subestuaries, the community identity changed during the study, and the probability of such a change was positively related to the prevalence of riprapped shoreline in the subestuary. Mixed freshwater communities had the highest rates of recovery, and this may have been driven by Hydrilla verticillata, which was the single best predictor of SAV recovery rate. Additional species-specific and community-specific research will likely yield better understanding of the factors affecting community identity and SAV abundance, more accurate predictive models, and more effective management strategies.

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