SARF042: Assessing the potential to reduce the infaunal species list required to give an indication of stress in sediments

Start Date: 7th February 2008
End Date: 6th June 2008
Main Contractor(s): Plymouth Marine Laboratory
Other Sponsor(s):  


1. Data organisation: visit SEPA Dingwall to discuss data, acquire data and metadata from SEPA, and manipulate it to make it suitable for analysis with non-parametric multivariate analysis with Primer v6 and other approaches. Gather species-specific information to allow calculation of additional measures.

2. Data Analyses: using the data organised under objective 1 undertake a battery of analyses using a range of methods to determine the extent to which analyses based on subsets of the data (e.g. using a reduced species list) capture the information contained in the full dataset, both on a case-by-case basis and also (and more importantly) looking across all surveys within the database. Determine the extent to which the use of subsets offers savings, and compare with other effort-reduction techniques.

3. Report writing: summarise and synthesise results and review relevant literature to produce final report. In addition to a full technical report we will provide a shorter non-technical report for dissemination. Depending on the outcome, and with the agreement of SARF, we would wish to take results forward for peer-reviewed publication following completion of the project


SEPA require operators to submit environmental surveys of the sea bed around the cages. The analysis of the benthic fauna component of the gathered samples is time consuming, expensive and requires highly trained and experienced analysts. Results of a recent fish farm Data Review Project indicate that there may be potential to develop a reduced species list that might be robust enough to give an indication of stress within the sediments. If such a list could be developed then it would open the way for the analysis of benthic faunal samples to be speeded up, be less expensive and the turnaround times would be more realistic. The purpose of this work is to undertake a comprehensive statistical assessment of the SEPA dataset, primarily using methods developed at PML for this purpose, to determine whether a reduced species list can be achieved. We also propose to examine other data-based effort reduction methods for comparative purposes, with a view to making recommendations about the best way forward.