Catastrophes, Near-Catastrophes and the Bounds of Expectation: Success Criteria for Macroscale Marsh Restoration

Abstract

Most tidal marsh on Delaware Bay has a history of diking for purposes of salt hay (Spartina patens) production and wildlife management. Extensive ditching for drainage and mosquito control has also altered natural hydrological cycles, and combined with diking or other water control structures has provided suitable conditions for invasion by Phragmites australis. Where diking and other water control measures have been in place for extended periods, in some instances back to colonial times, marsh surfaces have subsided by oxidation and compaction, and high marsh species of plants are maintained artificially at low marsh elevations. These conditions lead to potential catastrophic “blow-outs” when dikes are rapidly breached, principally by storms. Although seawater may enter the breaches and fill the marsh, the absence of a typical fourth order drainage system (long filled by farming practices and sedimentation) prevents efficient return of tidal water to the adjacent bay. Massive circulation patterns and standing water combined with the low marsh plain elevation kill extant plants and prevent recolonization by low marsh species. The result may be destruction of the root mat and “fluidization” of the entire marsh surface—replaced by an open water lagoon environment. It may take many decades for the marsh to begin to reestablish itself, if ever. Without further intervention, the slowly recovering marsh is characterized by “tree-like” drainage configurations that appear to exhibit low drainage density downstream, low overall sinuosity and higher order intertidal streams. It is in this framework that the ecological engineering of macroscale marsh restoration and the criteria that determine its success, the “bound of expectation,”is undertaken.KeywordsSalt MarshDrainage DensityTidal CreekTidal MarshMarsh SurfaceThese 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.