Plant community distribution in wetlands is dynamic both spatially and temporally, primarily in response to hydrology. In this presentation, we describe a simple model to predict the occurrence of plant communities along a topographic gradient under varying hydrologic conditions. Model results can be incorporated into a spatial analysis to aid in the decision-making process for selecting desired water management scenarios for wetland restoration projects. We included five general plant community associations that spanned the topographic gradient to develop our model:1) open water, 2) slough, 3) emergent wetland, 4) transitional wetland, and 5) upland. We defined relationships between these communities and four hydrologic statistics (inundation frequency, average depth, maximum 1-day depth, and minimum 1-day depth) based on 36 studies located primarily in Florida, USA. We then developed a weighted scoring system to predict plant community occurrence using a given set of hydrologic statistics provided by long-term modeled hydrologic data.
Results/Conclusions
The model has been used in multiple, large wetland restoration projects to assist in selecting a water management plan that maximizes project objectives. We will show an example where the model was used to select a water management scenario that maximized flood control and water quality treatment while still maintaining wetland habitat in a 4,050-ha restoration project. We validated model predictions in completed wetland restoration projects. Given that model predictions are intended to represent long-term equilibrium conditions, we selected our oldest restoration area (10y post-flooding) to compare pre- and post-flooding vegetation community patterns. While mapped plant communities generally agreed with model predictions, we identified some considerations that will be important to include in modeling future conditions. For example, natural, but extreme, events (e.g., extended flooding or drying) and management practices (e.g., water level management, burning, mechanical or chemical treatments) caused deviations from the expected restoration trajectory. Ultimately, this model provides a useful tool for evaluating multiple hydrologic scenarios to choose a restoration plan that maximizes the probability of meeting restoration objectives.