Date of Award


Document Type


Degree Name

Doctor of Philosophy (PhD)


Biological Sciences

First Advisor

James B. Grace


The factors that control plant species diversity have been examined by many researchers, but with little consensus as to the causes of diversity patterns. Plant diversity has been correlated with disturbance, competition, soil resources, and other variables. In the research presented here, I investigated the relationship between plant species richness and biomass, competition, nutrient enrichment, flooding, salinity, and herbivory in field studies in Louisiana coastal marshes. Biomass alone was a poor predictor of richness in two Louisiana marshes. When environmental variables (flooding, salinity, and soil fertility) were included with biomass in a multiple regression model, 82% of the variance in species richness was explained. When nutrients were added, a short-term study demonstrated no significant change in richness though biomass increased substantially. When vines were removed in one study, biomass increased but richness did not change. The relationship between flooding and salinity and richness was also investigated. Increasing flooding stress or salinity levels decreased richness. Relative sea level rise is expected to increase salt water encroachment into fresh coastal areas as well as increase water levels. The results here confirm other work that relative sea level rise will have detrimental effects on coastal plant communities. Herbivory in Louisiana coastal marshes is primarily by mammals. Alone, herbivory did not affect species richness. However, in combination with flooding treatments, herbivory caused a greater decrease in richness. When sods were transplanted from a brackish to a fresh marsh, plants were consumed by the herbivores resulting in an unexpected interaction. Herbivores prevented a decrease in richness with fertilization. Although in later data sets biomass was found to correlate better with species richness, the addition of environmental variables to a statistical model increased predictive ability. A structural equation model developed from descriptive data predicted 47% of the variance in species richness. When the predictions were tested, many were met, with the exception of the interactions described above. When experimental treatments that had not been included in the original model sampling were eliminated from the data set, the predicted richness values explained 63% of the variance in observed richness in the experimental manipulations.