Predicting the Distribution of Acid Volatile Sulfide in Marine Sediment From Colour Analysis of Sediment Profile Images
Measuring the sediment content of acid volatile sulfides (AVS), one indicator of coastal ecosystem functioning and the remineralisation of organic matter, is laborious and therefore rarely considered in routine coastal monitoring. In this thesis, I further develop an approach presented by Bull and Williamson (2001) to estimate the in situ distribution of AVS in subtidal soft sediment from sediment-profile images. I then determine whether this approach is valid at multiple locations in the Hauraki Gulf, New Zealand and investigate sediment chemical properties that may affect the approach. Finally, I apply this approach to assess soft coastal sediment that had been organically enriched by a long-line mussel farm.
I first established a strong correlation (R² = 0.95) between sediment AVS concentration (extracted by cold 1 M HCl) and the colour intensity of sediment collected at 12 m water depth off the eastern coast of Waiheke Island, New Zealand. I then used this AVS/colour correlation to estimate the distribution of AVS in the upper 20 cm of this sediment from sediment profile images. These images were obtained in situ with a lightweight imaging device consisting of a modified flatbed scanner housed inside a watertight acrylic tube (SPI-Scan™, Benthic Science Ltd.). I made two types of estimates from the acquired images: First, I obtained a vertical AVS concentration profile by averaging the colour intensities of horizontally aligned pixels. Second, I created a two-dimensional distribution plot of AVS concentration by assigning individual pixel colour intensities.
I determined whether this approach was valid at other locations by establishing an AVS/colour correlation at each of three locations in the Hauraki Gulf, New Zealand. The slopes of the fits that best described the data at each location were similar, however, the positions of the fits along the grey scale axis were offset. That is, the AVS/colour correlation was site specific and, consequently, combining the data from three locations did not produce an AVS/colour correlation that could accurately predict the sediment AVS concentration at all three locations. I suggested that the observed grey value offsets were caused by differences in the background colour of the sediment, that is, caused by sediment components other than AVS.
I investigated the sediment sulfur chemistry at these three locations to determine the cause of differences in the AVS/colour correlation between locations. Using a sequential extraction technique, I measured three pools of sedimentary sulfides: dissolved porewater sulfide, AVS, and sequentially extracted chromium reducible sulfide (CRSs). Dissolved porewater sulfides contribute to the total AVS concentration but not to the sediment colour. The constituents of CRSs, however, contribute to the sediment colour but not to the total AVS concentration.
The analysis revealed that the concentration of dissolved porewater sulfides was negligible. It also revealed that the relative proportions of AVS and CRSs were functions of sediment age. Sediment at greater depth in the sediment column contained greater proportions of CRSs than surficial sediment because it was older. Black mineral pyrite was the main constituent of CRSs. The transformation of pyrite from its precursors can take up to several years. Contrastingly, the minerals that primarily comprise AVS (mackinawite and greigite) form from their precursors over hours or days. I suggested that the reason for the non-linear AVS/colour correlation was because of a change in the relative proportions of AVS and CRSs with sediment depth.
The slope of the fit describing data from a site with a high AVS concentration and low CRSs concentration will be steep because the majority of the change in sediment colour arises from a change in the concentration of AVS minerals. Contrastingly, the slope of the fit describing data from a site with a low AVS concentration and high CRSs concentration will be less steep because a change in sediment colour intensity will be largely from a change in the concentration of CRSs minerals, rather than AVS minerals.
Finally, I used this approach investigate temporal changes in the extent of the seafloor area underneath a New Zealand long-line mussel farm of elevated sediment AVS content. Such assessment requires accurate detection of the AVS footprint boundary. I demonstrated how to detect this boundary with sediment profile imagery.
I analysed 182 sediment profile images taken along three transects leading from approximately 50 m inside to 200 m outside the long-line mussel farm and found that the mean sediment colour intensity inside the farm boundary was almost 1.5 times lower than that of the sediment away from the farm. Segmented regression analysis of the combined colour 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 AVS footprint is a function of water column depth; organic particles disperse further in a deeper seawater column.
I conclude that for soft coastal sediment, the described sampling and data analysis techniques may provide a rapid and reliable supplement to existing benthic surveys that assess environmental effects of marine farms or other organic enrichment sources.