Large-Scale Mapping and Predictive Modeling of Submerged Aquatic Vegetation in a Shallow Eutrophic Lake
A spatially intensive sampling program was developed for mapping the submerged aquatic vegetation (SAV) over an area of approximately 20,000 ha in a large, shallow lake in Florida, U.S. The sampling program integrates Geographic Information System (GIS) technology with traditional field sampling of...
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| Format: | Article |
| Language: | English |
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Wiley
2002-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1100/tsw.2002.194 |
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| author | Karl E. Havens Matthew C. Harwell Mark A. Brady Bruce Sharfstein Therese L. East Andrew J. Rodusky Daniel Anson Ryan P. Maki |
| author_facet | Karl E. Havens Matthew C. Harwell Mark A. Brady Bruce Sharfstein Therese L. East Andrew J. Rodusky Daniel Anson Ryan P. Maki |
| author_sort | Karl E. Havens |
| collection | DOAJ |
| description | A spatially intensive sampling program was developed for mapping the submerged aquatic vegetation (SAV) over an area of approximately 20,000 ha in a large, shallow lake in Florida, U.S. The sampling program integrates Geographic Information System (GIS) technology with traditional field sampling of SAV and has the capability of producing robust vegetation maps under a wide range of conditions, including high turbidity, variable depth (0 to 2 m), and variable sediment types. Based on sampling carried out in AugustœSeptember 2000, we measured 1,050 to 4,300 ha of vascular SAV species and approximately 14,000 ha of the macroalga Chara spp. The results were similar to those reported in the early 1990s, when the last large-scale SAV sampling occurred. Occurrence of Chara was strongly associated with peat sediments, and maximal depths of occurrence varied between sediment types (mud, sand, rock, and peat). A simple model of Chara occurrence, based only on water depth, had an accuracy of 55%. It predicted occurrence of Chara over large areas where the plant actually was not found. A model based on sediment type and depth had an accuracy of 75% and produced a spatial map very similar to that based on observations. While this approach needs to be validated with independent data in order to test its general utility, we believe it may have application elsewhere. The simple modeling approach could serve as a coarse-scale tool for evaluating effects of water level management on Chara populations. |
| format | Article |
| id | doaj-art-b5cc97ba36f5443d8bead891cc18873a |
| institution | Kabale University |
| issn | 1537-744X |
| language | English |
| publishDate | 2002-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-b5cc97ba36f5443d8bead891cc18873a2025-02-03T01:11:20ZengWileyThe Scientific World Journal1537-744X2002-01-01294996510.1100/tsw.2002.194Large-Scale Mapping and Predictive Modeling of Submerged Aquatic Vegetation in a Shallow Eutrophic LakeKarl E. Havens0Matthew C. Harwell1Mark A. Brady2Bruce Sharfstein3Therese L. East4Andrew J. Rodusky5Daniel Anson6Ryan P. Maki7South Florida Water Management District, West Palm Beach, FL 33406, USASouth Florida Water Management District, West Palm Beach, FL 33406, USASouth Florida Water Management District, West Palm Beach, FL 33406, USASouth Florida Water Management District, West Palm Beach, FL 33406, USASouth Florida Water Management District, West Palm Beach, FL 33406, USASouth Florida Water Management District, West Palm Beach, FL 33406, USASouth Florida Water Management District, West Palm Beach, FL 33406, USASouth Florida Water Management District, West Palm Beach, FL 33406, USAA spatially intensive sampling program was developed for mapping the submerged aquatic vegetation (SAV) over an area of approximately 20,000 ha in a large, shallow lake in Florida, U.S. The sampling program integrates Geographic Information System (GIS) technology with traditional field sampling of SAV and has the capability of producing robust vegetation maps under a wide range of conditions, including high turbidity, variable depth (0 to 2 m), and variable sediment types. Based on sampling carried out in AugustœSeptember 2000, we measured 1,050 to 4,300 ha of vascular SAV species and approximately 14,000 ha of the macroalga Chara spp. The results were similar to those reported in the early 1990s, when the last large-scale SAV sampling occurred. Occurrence of Chara was strongly associated with peat sediments, and maximal depths of occurrence varied between sediment types (mud, sand, rock, and peat). A simple model of Chara occurrence, based only on water depth, had an accuracy of 55%. It predicted occurrence of Chara over large areas where the plant actually was not found. A model based on sediment type and depth had an accuracy of 75% and produced a spatial map very similar to that based on observations. While this approach needs to be validated with independent data in order to test its general utility, we believe it may have application elsewhere. The simple modeling approach could serve as a coarse-scale tool for evaluating effects of water level management on Chara populations.http://dx.doi.org/10.1100/tsw.2002.194 |
| spellingShingle | Karl E. Havens Matthew C. Harwell Mark A. Brady Bruce Sharfstein Therese L. East Andrew J. Rodusky Daniel Anson Ryan P. Maki Large-Scale Mapping and Predictive Modeling of Submerged Aquatic Vegetation in a Shallow Eutrophic Lake The Scientific World Journal |
| title | Large-Scale Mapping and Predictive Modeling of Submerged Aquatic Vegetation in a Shallow Eutrophic Lake |
| title_full | Large-Scale Mapping and Predictive Modeling of Submerged Aquatic Vegetation in a Shallow Eutrophic Lake |
| title_fullStr | Large-Scale Mapping and Predictive Modeling of Submerged Aquatic Vegetation in a Shallow Eutrophic Lake |
| title_full_unstemmed | Large-Scale Mapping and Predictive Modeling of Submerged Aquatic Vegetation in a Shallow Eutrophic Lake |
| title_short | Large-Scale Mapping and Predictive Modeling of Submerged Aquatic Vegetation in a Shallow Eutrophic Lake |
| title_sort | large scale mapping and predictive modeling of submerged aquatic vegetation in a shallow eutrophic lake |
| url | http://dx.doi.org/10.1100/tsw.2002.194 |
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