Open Access Highly Accessed Research

A spatially distributed, deterministic approach to modeling Typha domingensis (cattail) in an Everglades wetland

Gareth Lagerwall1, Gregory Kiker1*, Rafael Muñoz-Carpena1, Matteo Convertino1, Andrew James2 and Naiming Wang3

Author Affiliations

1 Frazier Rogers Hall, University of Florida, PO Box 110570, Gainesville, FL, 32611-0570, USA

2 Soil and Water Engineering Technology, Inc., 3960 Magnolia Leaf L, Suwanee, GA, 30024, USA

3 Hydrologic and Environmental Systems Modeling, South Florida Water Management District, 3301 Gun Club Rd, West Palm Beach, FL, 33406, USA

For all author emails, please log on.

Ecological Processes 2012, 1:10  doi:10.1186/2192-1709-1-10

Published: 1 November 2012

Abstract

Introduction

The emergent wetland species Typha domingensis (cattail) is a native Florida Everglades monocotyledonous macrophyte. It has become invasive due to anthropogenic disturbances and is out-competing other vegetation in the region, especially in areas historically dominated by Cladium jamaicense (sawgrass). There is a need for a quantitative, deterministic model in order to accurately simulate the regional-scale cattail dynamics in the Everglades.

Methods

The Regional Simulation Model (RSM), combined with the Transport and Reaction Simulation Engine (TARSE), was adapted to simulate ecology. This provides a framework for user-defineable equations and relationships and enables multiple theories with different levels of complexity to be tested simultaneously. Five models, or levels, of increasing complexity were used to simulate cattail dynamics across Water Conservation Area 2A (WCA2A), which is located just south of Lake Okeechobee, in Florida, USA. These levels of complexity were formulated to correspond with five hypotheses regarding the growth and spread of cattail. The first level of complexity assumed a logistic growth pattern to test whether cattail growth is density dependent. The second level of complexity built on the first and included a Habitat Suitability Index (HSI) factor influenced by water depth to test whether this might be an important factor for cattail expansion. The third level of complexity built on the second and included an HSI factor influenced by soil phosphorus concentration to test whether this is a contributing factor for cattail expansion. The fourth level of complexity built on the third and included an HSI factor influenced by (a level 1–simulated) sawgrass density to determine whether sawgrass density impacted the rate of cattail expansion. The fifth level of complexity built on the fourth and included a feedback mechanism whereby the cattail densities influenced the sawgrass densities to determine the impact of inter-species interactions on the cattail dynamics.

Results

All the simulation results from the different levels of complexity were compared to observed data for the years 1995 and 2003. Their performance was analyzed using a number of different statistics that each represent a different perspective on the ecological dynamics of the system. These statistics include box-plots, abundance-area curves, Moran’s I, and classified difference. The statistics were summarized using the Nash-Sutcliffe coefficient. The results from all of these comparisons indicate that the more complex level 4 and level 5 models were able to simulate the observed data with a reasonable degree of accuracy.

Conclusions

A user-defineable, quantitative, deterministic modeling framework was introduced and tested against various hypotheses. It was determined that the more complex models (levels 4 and 5) were able to adequately simulate the observed patterns of cattail densities within the WCA2A region. These models require testing for uncertainty and sensitivity of their various parameters in order to better understand them but could eventually be used to provide insight for management decisions concerning the WCA2A region and the Everglades in general.

Keywords:
Typha; Modeling; Ecology; Dynamics; Model complexity; Water conservation area 2A; Transport and reaction simulation engine; Regional simulation model