A big data approach to macrofaunal baseline assessment, monitoring and sustainable exploitation of the seabed

Transcription

1 Oil & Gas UK Environmental Seminar Aberdeen, 12 Dec 2017 A big data approach to macrofaunal baseline assessment, monitoring and sustainable exploitation of the seabed Keith Cooper & Jon Barry Centre for Environment, Fisheries and Aquaculture Science, Pakefield Road, Lowestoft, Suffolk, NR33 0HT, UK

2 Presentation Structure 1. The Aggregates Industry 2. Impacts 3. Recovery 4. Restoration 5. Monitoring 6. Big data approach (RSMP) 7. Relevance to Oil & Gas industry

3 1. The Industry Produces sand and gravel Licensed extraction areas Purpose built vessels Uses: construction, fill and coastal defense (Source:

4 2. Impacts Direct: - Seabed topography - Loss of fauna Indirect: - sediment plumes - sediment composition - smothering of fauna Impacts are variable Implications for recovery

5 3. Recovery Wide variation in reported recovery times

6 3. Recovery Changes in sediment composition can prolong faunal recovery period Waye-Barker, G., Cooper, K.M., McIlwaine, P., Lozac, S. The effects of sand and gravel extraction on the sediment particle size and macrofaunal community of a commercial dredging site (15 years postdredging).

7 4. Restoration Restoration? Actions to promote recovery Passive vs Active Trials: - Shell cultch (Collins & Mallinson, 2007) - Gravel seeding Can be done Better termed enhancement

8 4. Restoration Challenging Expensive Failure of monitoring / management

9 5. Monitoring What do we want from monitoring? Focus on what happens to site after dredging New big data approach: Regional Seabed Monitoring Plan (RSMP) Relationship between fauna and sediments Focus on monitoring sediments

10 Study Area Samples Objectives: (i) Faunal baseline (ii) Understand relationship between fauna and sediments (iii) Acceptable change methodology for sediments

11 Summary The dataset

12 (i) Faunal baseline Univariate summary measures

13 (i) Faunal baseline Faunal Assemblages Macrofaunal assemblages identified using k means clustering

14 (i) Faunal baseline By Year By Season Temporal Assessment

15 (i) Faunal baseline Faunal Assemblages Explanatory variables Explaining faunal distribution patterns Table 1. Explanatory variables used in the study. Fig. 6. Distance-based redundancy analysis (dbrda) ordination showing sampling sites (coloured by faunal assemblage group) and vectors for the main environmental predictor variables.

16 (i) Faunal baseline Faunal Assemblages Explanatory variables Explaining faunal distribution patterns Table 1. Explanatory variables used in the study. Fig. 6. Distance-based redundancy analysis (dbrda) ordination showing sampling sites (coloured by faunal assemblage group) and vectors for the main environmental predictor variables.

17 (i) Faunal baseline Regions of aggregate dredging interest Assemblages a b Diversity Fig. 7. Faunal cluster group and diversity (taxon Richness) for samples by sub region (see Figure 1b for submap extents). Areas of aggregate dredging interest (licensed and application areas) shown as solid black lines, whilst areas of potential secondary effect are shown as dashed black lines.

18 (i) Faunal baseline b Assemblages Diversity Regions of aggregate dredging interest c Fig. 7. Cont d.

19 (ii) Faunal-Sediment relationships Faunal Assemblages Sediment composition Sediment composition by faunal cluster group

20 (iii) Assessing sediment change Use Mahalanobis distance (MD) to assess departure from an underlying distribution Baseline (2005) Station 102 (Faunal cluster B1a) Monitoring (2010) Test sample data (Station 102) Mean and covariance matrix for B1a (n=948) Statistical vs Ecological significance Adaptive management to address problems M-test result: Fail Reasons for failure

21 Humber RSMP Design includes monitoring stations for sediment only and sediment & fauna Acoustic surveys undertaken in vicinity of extraction sites Similar designs for Anglian, Thames, East Channel, South Coast, Bristol Channel and North-West dredging regions)

22 Automate analysis & reporting of monitoring data (R shiny web based tool) Future developments

23 7. Relevance to Oil & Gas Sector Assessing significance of sediment change Monitoring health of seabed Oil spill response - Baseline - Network of existing monitoring stations Integrating monitoring efforts with other sectors Development of shiny app to meet your needs Answering other big data questions?

24 CONCLUSION Thanks for listening A big data approach offers valuable insights into the natural variability inherent within ecosystems. This understanding makes it possible to differentiate between human induced impacts which are, and are not likely to have longterm ecological significance. This leads to more effective management, innovative and cheaper monitoring solutions, and, ultimately, better environmental sustainability. For more information see: Data providers Funders: ACKNOWLEDGEMENTS More useful info on marine mining (environmental effects, legislation, best practice) from the ICES Working Group on the Effects of Extraction of Marine Sediments on the Marine Ecosystem (WGEXT)