Abstract – Effective Tidal Marsh Restoration


Applying and testing a predictive vegetation model to management of the invasive cattail, Typha angustifolia, in an oligohaline tidal marsh reveals priority effects caused by non-stationarity. Wetlands Ecology and Management DOI10.1007/s11273-013‑9294-6
Greg Hood

Effective tidal marsh restoration requires predictive models that can serve as planning and design tools to answer basic questions such as which, if any, plant species will colonize a proposed restoration site. To develop such a tool, a predictive model of oligohaline tidal marsh vegetation was developed from reference marshes in the Skagit River Delta (Washington, USA) and applied to a 1.1-ha restoration treatment site. Probability curves for the elevational distributions of common marsh species were generated from RTK-GPS point samples of reference tidal marshes. The probability curves were applied to a LIDAR-derived digital elevation model to generate maps predicting the occurrence probability of each species within treatment and control sites. The treatment and control sites, located within a recently restored area that had been diked but never completely drained, were covered by a mono-culture of nonnative Typha angustifolia L. (narrow-leaf cattail) growing 40–60 cm lower in elevation than in the reference marsh. The T. angustifolia was mowed repeatedly in the treatment site to allow colonization by predicted native marsh species. Four years after mowing, T. angustifolia was replaced on 60 % of the treatment site by native sedges (Carex lyngbyei, Eleocharis palustris), consistent with the predictive vegetation model; the control site remained covered by T. angustifolia. The mowing experiment confirmed that pre-emptive competition from T. angustifolia was preventing vegetation recovery in the restoration site following dike removal, and implied that some vegetation species may be refractory to environmental change, such as dike removal or sea-level rise, because of differences in recruitment and adult niches.