Predictive occurrence models for coastal wetland plant communities: Delineating hydrologic response surfaces with multinomial logistic regression

Background/Question/Methods

Understanding the mechanisms that govern the distribution patterns of organisms along environmental gradients has long been a major goal of ecology.  In coastal wetland ecosystems throughout the world, sea level rise and coastal restoration measures may alter salinity and inundation regimes, and understanding how plant community zonation varies along estuarine stress gradients is critical for effective conservation and restoration of these landscapes.  We related the presence of wetland plant community types to estuarine hydrology at 173 sites across coastal Louisiana.  Percent relative cover by species was assessed at each site near the end of the growing season in 2008, and hourly water level and salinity were recorded at each site Oct 2007-Sep 2008.  Plant community types were delineated with k-means clustering, and indicator species were identified for each of the community types with indicator species analysis.  Multinomial logistic regression (MLR) and Akaike’s Information Criterion (AIC) were used to predict the probability of occurrence of the vegetation communities as a function of salinity and tidal amplitude.

Results/Conclusions

The nine community types identified by k-means clustering strongly corresponded with those obtained from previous studies of coastal plant communities in Louisiana.  Canonical correspondence analysis (CCA) effectively segregated the sites across ordination space by community type, and indicated that a primary salinity gradient and a nearly orthogonal, secondary gradient in tidal amplitude were the most important drivers of vegetation composition.  The probability surfaces obtained from the MLR model corroborated the CCA results, and the correct classification rate was 61% when leave-one-out cross-validation was applied to the MLR model.  Our results suggest that models based on a few key hydrologic variables can be valuable tools for predicting vegetation community development when restoring and managing coastal wetlands.  Updating existing coastal landscape simulation models by incorporating community occurrence probabilities obtained with approaches similar to those presented here would enhance their ability to predict landscape change to continued hydrologic alterations such as river diversions, levee construction, reduced freshwater inflows, and sea-level rise that are occurring in coastal wetlands throughout the world.