Restoration of Salt and Brackish Tidelands in Southern New England

Abstract

Tidal restriction, dredge spoil deposition, and other fill activities have converted about 2000 ha of Connecticut’s tidal salt marshes to non-tidal or microtidal systems vegetated by near monocultures of Phragmites australis or Typha angustifolia. In addition, Phragmites is also expanding in certain undisturbed brackish tidelands, replacing the typical tidal marsh angiosperms. Returning normal tidal hydrology to formerly restricted polyhaline (18 to 30 ‰) and euhaline (30 to 35 ‰) marshes results over time in re-establishment of typical Spartina-dominated marsh vegetation and associated macroinvertebrate populations. Vegetation and invertebrates, along with full use of these systems by estuarine fish and salt marsh dependent birds, are collectively considered high level integrators of multiple, complex, interacting tidal marsh functions. These various attributes return at different rates, and full functional equivalence relative to undisturbed marshes may require decades. Excavating dredged spoil filled sites to low marsh elevations and restoring tidal action allows natural repopulation by marsh angiosperms and invertebrates. Within the first year these open sites support seedling populations of Spartina alterniflora and annuals such as Salicornia europaea. Within five years Spartina alterniflora dominates and annuals are rare. Initial invertebrate colonizers are those with planktonic larvae, such as Melampus bidentatus, Geukensia demissa and Uca spp. Invasion of brackish tidelands at the mouth of the Connecticut River by Phragmites appears to have little effect on macroinvertebrate populations or fish use. The vegetation of such Phragmites-dominated tidal wetlands can be restored, at least temporarily, by a combination of herbicide and mowing treatments.KeywordsSalt MarshTidal MarshTidal FlowSalt Marsh VegetationReference MarshThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.