Naiming Wang
South Florida Water Management District
5 Papers
Naiming Wang is an academic researcher from South Florida Water Management District. The author has contributed to research in topics: Vegetation & Typha domingensis. The author has an hindex of 3, co-authored 5 publications.
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Papers
Accounting for the Impact of Management Scenarios on Typha Domingensis (Cattail) in an Everglades Wetland
TL;DR: This modeling framework with user-definable complexities and management scenarios, can be considered a useful tool in analyzing many more alternatives, which could be used to aid management decisions in the future.
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Impacts of fire and phosphorus on sawgrass and cattails in an altered landscape of the
Florida Everglades,Yegang Wu,Ken Rutchey,Susan Newman,Shili Miao,Naiming Wang,Fred H. Sklar,William H. Orem +7 more
- 01 Jan 2012
TL;DR: In this article, the effect of fire, nutrients, water depth, and invasive cattails (Typha spp.) on vegetation communities is investigated in the Florida Everglades.
Impacts of fire and phosphorus on sawgrass and cattails in an altered landscape of the Florida Everglades
TL;DR: In this paper, the effect of fire, nutrients, water depth, and invasive cattails (Typha spp.) on vegetation communities is investigated in the Florida Everglades.
A spatially distributed, deterministic approach to modeling Typha domingensis (cattail) in an Everglades wetland
Gareth Lagerwall,Gregory A. Kiker,Rafael Muñoz-Carpena,Matteo Convertino,Andrew I. James,Naiming Wang +5 more
TL;DR: In this article, the authors used the Regional Simulation Model (RSM) combined with the Transport and Reaction Simulation Engine (TARSE) to simulate ecology in the Everglades.
Global uncertainty and sensitivity analysis of a spatially distributed ecological model
TL;DR: In this paper, a global uncertainty and sensitivity analysis (GUSA) was conducted on these five levels of algorithm complexity, and it was determined that the Level 4 complexity algorithm was the most relevant, or best suited, to model cattail densities in the region, as it contains the least uncertainty, without increasing sensitivity, and has less risk of overparameterisation as is potentially the case with Level 5.