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

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.

Shorebird patches as fingerprints of fractal coastline fluctuations due to climate change

Introduction:The Florida coast is one of the most species-rich ecosystems in the world.This paper focuses on the sensitivity of the habitat of threatened and endangered shorebirds to sea level rise induced by climate change,and on the relationship of the habitat with the coastline evolution.We consider the resident Snowy Plover(Charadrius alexandrinus nivosus),and the migrant Piping Plover(Charadriusmelodus)and Red Knot(Calidris canutus)along the Gulf Coast of Mexico in Florida.Methods:We analyze and model the coupled dynamics of habitat patches of these imperiled shorebirds and of the shoreline geomorphology dictated by land cover change with consideration of the coastal wetlands.The land cover is modeled from 2006 to 2100 as a function of the A1B sea level rise scenario rescaled to 2 m.Using a maximum-entropy habitat suitability model and a set of macroecological criteria we delineate breeding and wintering patches for each year simulated.Results:Evidence of coupled ecogeomorphological dynamics was found by considering the fractal dimension of shorebird occurrence patterns and of the coastline.A scaling relationship between the fractal dimensions of the species patches and of the coastline was detected.The predicted power law of the patch size emerged from scale-free habitat patterns and was validated against 9 years of observations.We predict an overall 16%loss of the coastal landforms from inundation.Despite the changes in the coastline that cause habitat loss,fragmentation,and variations of patch connectivity,shorebirds self-organize by preserving a power-law distribution of the patch size in time.Yet,the probability of finding large patches is predicted to be smaller in 2100 than in 2006.The Piping Plover showed the highest fluctuation in the patch fractal dimension;thus,it is the species at greatest risk of decline.Conclusions:We propose a parsimonious modeling framework to capture macroscale ecogeomorphological patterns of coastal ecosystems.Our results suggest the potential use of the fractal dimension of a coastline as a fingerprint of climatic change effects on shoreline-dependent species.Thus,the fractal dimension is a potential metric to aid decision-makers in conservation interventions of species subjected to sea level rise or other anthropic stressors that affect their coastline habitat.