Estimating basal area coverage of subtidal seagrass beds using underwater videography

Although seagrasses have been identified as vital living marine resources, their distribution has not been rigorously quantified at many locations. This fact is often due to the high cost of sampling seagrass habitats, especially those with deep-water plants that cannot be sensed from aerial platfor...

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Bibliographic Details
Published in:Aquatic botany 1997-10, Vol.58 (3), p.269-287
Main Authors: Norris, James G., Wyllie-Echeverria, Sandy, Mumford, Thomas, Bailey, Allison, Turner, Terry
Format: Article
Language:English
Subjects:
Basal area estimation
Eelgrass beds
Line transect
Marine
Seagrass
Underwater videography
Zostera
Zostera marina
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Summary:Although seagrasses have been identified as vital living marine resources, their distribution has not been rigorously quantified at many locations. This fact is often due to the high cost of sampling seagrass habitats, especially those with deep-water plants that cannot be sensed from aerial platforms. We present a cost-effective method of estimating basal area coverage of submersed vegetation that uses differential global positioning system data linked to underwater video images of the bottom. Our sampling design and statistical procedures are identical to estimating proportions using cluster sampling with unequal cluster sizes. This method has several advantages over other techniques: (1) confidence intervals around basal area coverage estimates permit hypothesis testing of changes over time; (2) sampling efficiency is better than simple random sampling with quadrats; (3) deep-water zones out of the range of aerial platforms can be sampled; (4) video images provide positive identification of plants which is not possible with acoustic techniques; and (5) the techniques provide a permanent archive of visual images that can be analyzed for other bottom attributes, such as other vegetation, macro-invertebrates, and gross sediment types. This method has some limitations: (1) it is not possible to sample extremely shallow or turbid waters and under some physical structures; (2) it is impractical to sample very large regions; (3) errors in differential global positioning system data must be accounted for; and (4) seagrass density must be measured subjectively. We illustrate our sampling methods and data analysis with an example from Puget Sound, Washington, USA.
ISSN:0304-3770
1879-1522
DOI:10.1016/S0304-3770(97)00040-5