Assessing the Sulfide Footprint of Mussel Farms With Sediment Profile Imagery: A New Zealand Trial

Date
2015
Authors
Wilson, PS
Vopel, K
Supervisor
Item type
Journal Article
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Publisher
PLOS
Abstract

Growing numbers and increased stocking of marine mussel farms make reliable techniques for environmental effect assessment a priority. Previously, we showed how the color intensity of soft sediment could be used to estimate its acid volatile sulfide (AVS) content, a product of the anaerobic microbial degradation of organic matter deposits. We then proposed to include assessments of the AVS farm footprint in marine farm monitoring, in particular, to investigate temporal changes in the extent of the seafloor area of elevated sediment AVS content. Such assessment requires accurate detection of the AVS footprint boundary. Here, we demonstrate how to detect this boundary with analyses of sediment color intensity. We analyzed 182 sediment profile images taken along three transects leading from approximately 50 m inside to 200 m outside a long-line mussel farm in New Zealand and found that the mean sediment color intensity inside the farm boundary was almost one third lower than that of the sediment distant from the farm. Segmented regression analysis of the combined color intensity data revealed a breakpoint in the trend of increasing grey values with increasing distance from the farm at 56 ± 13 m (± 95% confidence interval of the breakpoint) outside the mussel farm. Mapping of grey value data with ArcMap (ESRI, ArcGIS) indicated that the extent of the color intensity footprint is a function of water column depth; organic particles disperse further in a deeper seawater column. We conclude that for soft coastal sediments, our sampling and data analysis techniques may provide a rapid and reliable supplement to existing benthic surveys that assess environmental effects of mussel farms.

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Source
PloS one, 10(6), e0129894.
Rights statement
© 2015 Wilson, Vopel. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.