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  Services » Publications » Technical reports - by year » tr201003

Method Development: Assessing the Benthic Impacts of Aquaculture

Report: TR 2010/03
Author: Hilke Giles, NIWA

Abstract

In 2006 NIWA began testing the utility of sediment profile imagery (SPI) for resource monitoring of the seafloor near mussel farms in Wilson Bay, Firth of Thames. Sediment Profile Imagery is an underwater technique for photographing the interface between the seabed and the overlying water. The technique is used to measure or estimate biological, chemical, and physical processes occurring on and in the first few centimetres of the sediment. Projects commissioned by Environment Waikato and the Wilson Bay Group A Consortium as well as NIWA funded research have demonstrated the usefulness of SPI. As a consequence, the benthic monitoring component of the Wilson Bay Group A monitoring programme has been modified by substituting the previous video surveys with SPI surveys.

To aid the interpretation of SPI data sets, NIWA proposed to collect additional sediment profile images in the Firth of Thames in reference regions that are not affected by aquaculture and in regions that are affected by different intensities of mussel farming activities. Environment Waikato commissioned NIWA to conduct such a SPI baseline survey within a method development project funded through the Ministry for the Environment’s Aquaculture Planning Fund (APF) and Environment Waikato.

This report describes the outcomes of the method development project. Specifically, it presents results of two SPI surveys, demonstrates the potential of SPI to underpin the assessment of benthic impacts and provides suggestions on how to develop a SPI-based benthic habitat quality index for the Firth of Thames, which could inform the development of benthic limits of acceptable change (LACs).
In 2007 and 2009 we collected a total of 174 sediment profile images. We identified a range of attributes in the images, including layers defined from colour parameters that are known to relate to the microbial decomposition of organic matter, and attributes that can be directly identified from the images, such as fauna, mussel faecal pellets or burrows.

The variability of attributes among sites suggests that they provide useful information for the assessment of seafloor functioning and thus the benthic effects of aquaculture. We identified a selection of attributes that we consider useful candidates for a Firth of Thames benthic habitat quality index similar to indices used in the assessment of anthropogenic input overseas. These attributes include the depth of layers identified from colour parameters, scanner penetration depth, annelid worms, Echinocardium sp. individuals, epifauna, black/dark patches, shell hash in/on the sediment, mussel faecal pellets and burrows.

A review of advantages and disadvantages of SPI and video surveys, the previously employed method for the assessment of benthic effects of mussel farming in the Wilson Bay Marine Farming Zone, clearly favoured SPI. The key advantages of SPI are the better quality and meaningfulness of data and higher efficiency in data analysis.

Some technical problems experienced during this study were related to the difficulty of scanner penetration under the mussel farms. NIWA has purchased a new SPI device and we are confident that the new device will resolve this problem.

The main conclusions of this method assessment projects were:

  1. Sediment profile imagery is a useful tool for the assessment of benthic aquaculture impacts in the Firth of Thames. It is superior to the previously employed video surveys, primarily due to the better quality and meaningfulness of data and higher efficiency in data analysis.
  2. We believe that the proposed combination of attributes identified from sediment profile images has the potential to form the basis of a SPI-based benthic habitat quality index, which can become a cost-effective and scientifically sound tool for the assessment of benthic habitat quality in the Firth of Thames.
  3. Such a benthic habitat quality index would be independent of the source of impact and could inform the development of benthic limits of acceptable change (LACs).
  4. Future work is required to develop a SPI-based benthic habitat quality index for the Firth of Thames and we suggest the following development process:
    a. The various sources and locations of anthropogenic organic input into the Firth of Thames are identified (e.g., mussel farms, rivers).
    b. The areas affected by these inputs are identified.
    c. In each of these areas transects are generated ranging from maximum organic input to reference areas in which anthropogenic organic input is considered negligible.
    d. Surveys similar to the one conducted in this study are conducted along these transects.
    e. Images are analysed as described in this study and all attributes recommended in this study as being useful for a benthic habitat quality index collated.
    f. Various potential benthic habitat quality indices are calculated from these attributes and examined for their merit in classifying benthic habitats in the Firth of Thames.
    g. The final selection of a benthic habitat quality index is made by Environment Waikato.

Method Development: Assessing the Benthic Impacts of Aquaculture
(776 kb, 110 seconds to download, 56k modem)

Table of contents

  Executive summary iv
1 Introduction 1
2 Methods 3
2.1 Study design and sites 3
2.2 In situ imaging and image analysis 5
2.3 Data analysis 6
3 Results 7
3.1 SPI performance and attributes identified in images 7
3.2 Layers defined from colour parameter 8
3.3 Lower redox transition (T3) 12
3.4 Scanner penetration depth 13
3.5 Attributes directly identified from images 14
3.6 Burrows and total infauna 17
4 Implications for the future assessments of benthic impact in the Firth of Thames 18
4.1 Benthic habitat quality indices 20
4.2 Potential for development of a benthic habitat quality index for the Firth of Thames 20
5 Conclusions 26
6 Future work 27
7 References 28
8 Appendix I: Sample log 30
9 Appendix II: Sample sediment profile images 36
10 Appendix III: Examples of attributes directly identified in images 43
11 Appendix IV: Instructions for use of sediment image database 46
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