From seafloor geomorphology to predictive habitat mapping: progress in applications of biophysical data to ocean management.

Transcription

1 From seafloor geomorphology to predictive habitat mapping: progress in applications of biophysical data to ocean management. Peter T. Harris Geoscience Australia, Canberra ACT, Australia Currently seconded to: UNEP/GRID Arendal, Norway

2 Acknowledgements Thanks to Thaiënne van Dijk and conference organisers for invitation to attend Hydro12 Geoscience Australia travel support Research products of Geoscience Australia and many other scientists/institutions involved

3 Uses of hydrographic data: Safe Navigation (nautical charts) Offshore oil and gas exploration and development Fisheries management Offshore minerals and aggregates Determining national marine jurisdiction (ECS) Port development and maintenance Computer models of tides, waves, ocean currents Communication (Google Oceans) Naval operations Marine zone management

4 Outline of talk: Introduction - Spatial Marine Management Approaches to seafloor characterisation and applications to decision-making Geomorphic features Unsupervised seascapes classification Supervised predictive habitat models Conclusions

5 Spatial marine management Flaws of the sector-based approach Fishing, oil and gas, shipping etc. managed separately Inconsistent with broader goals of biodiversity and ecosystem conservation Ecosystem based management (EBM) Addresses multiple pressures Acknowledges ecosystem services How to apply EBM? Need a spatial frame of reference (bioregions, ecosystem components, inventory of assets).

6 Mapping approaches that help to define bioregions and habitats: Seafloor geomorphic features Multivariate seascapes Predictive habitat modelling

7 Approaches to seafloor characterisation: Approach How generated? Advantages Disadvantages Geomorphic features Biophysical interpolations unsupervised classifications (eg. seascapes) Predictive habitat maps supervised classifications (maximum entropy, decision-trees, etc.) interpreted from bathymetric data apply classification scheme multivariate analysis to spatially combine several biophysical data layers include direct observations of marine life with biophysical data to predict the potential distribution of species and benthic communities. + simple to communicate, technically easy to generate - limited predictive power + simple to generate with spatial data - limited predictive power, difficult to communicate + good predictive power, performance indicators - Difficult to generate (data hungry), relate to single species or group

8 Applications to decision-making (Australia case study) 1. Geomorphic features used to define Australian marine bioregions

9 Geomorphic features (IHO classes) mapped based on 250 m bathymetric grid of Australia Heap and Harris (2008) Australian Journal of Earth Sciences, 55:

10 Heap and Harris (2008) Geomorphic features map of Australia

11 The correlation between geomorphic features and benthic habitats is the focus of recent GeoHab book:

12 Marine management based on IMCRA provincial bioregions Many boundaries based on geomorphology IMCRA = Integrated Marine and Coastal Regionalisation of Australia

13 Heap and Harris (2008) Biophysical model - Geomorphology

14 Example of application of geomorphic features to assessment of industrial use Petroleum titles cover an area of about 620,000 km 2 or about 8.7% of Australia s EEZ (excluding offshore territories)

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16 Harris et al. (2007) APPEA Journal, 48:

17 Applications to decision-making (Australia case study) 2. Seascapes used to identify biodiversity hotspots and priorities for Marine Protected Areas

18 Integration of ecologically-significant biophysical variables to create a single map (Seascapes) Not scale dependant Integrated product Input physical data (Seascapes) = (e.g., tidal currents) + (e.g., bathymetry) + (e.g., slope) + (e.g., % sand)

19 Seven variables derived from interpolation of bathymetry, samples & modelled data Water Depth Slope %Gravel %Mud Effective Disturbance Seafloor Temperature Primary Productivity Completed using ERMapper ISOClass facility (Iterative Self Organising Classification)

20 Depth Slope %Gravel Grid resolution %Mud 0.01 o, ~5 km Seafloor Effective Primary Grid resolution Temperature 0.01 Productivity Disturbance o, ~5 km Grid resolution 0.01 o, ~5 km Grid resolution 0.01 o, ~5 km Grid Grid resolution o, ~5 o ~5 km km Grid resolution 0.01 o, ~5 km

21 Australia Shelf Seascapes 13 Ecologically unique Seascapes (Unsupervised Classification) 1. Moderate depth, flat, slightly gravelly, cold, low disturbance, moderate primary productivity

22 How relevant to Biodiversity? Seascape heterogeneity based on Focal Variety Analysis Used to identify hotspots of seascape heterogeneity (surrogate for biodiversity) 20 x 20 cell analysis area

23 Australian Shelf Seascapes - Heterogeneity Harris et al. (2008) Ocean Coastal Management, 51:

24 SEWPaC Proposal June 2012, 60 reserves covering 3.1 million square kilometres, largest system of marine reserves in the world. Some MPAs suggested by seascape analysis, others by geomorphology

25 Applications to decision-making (Australia case study) 3. Predictive habitat model of coral habitat distribution in the Great Barrier Reef to assess marine park management scheme

26 Physical measurements: - Depth - Slope - Temperature - Sediment size - Current speed - etc. + = Biological observations Predicted habitat for species or community

27 Predictive Habitat Modeling Techniques (Huang et al., Ecological Informatics, 2011) BIOCLIMatic (BIOCLIM) (Nix, 1986) DOMAIN (Carpenter et al., 1993) Logistic Regression (LoR) (Peeters and Gardeniers, 1998; Ozesmi and Ozesmi, 1999; Felicisimo et al., 2002) Decision Trees (DT) (Zacharias et al., 1999; Pitcher et al., 2007) Genetic Algorithm for Rule-set Production (GARP) (Stockwell and Peters, 1999) Ecological Niche Factor Analysis (ENFA) (Hirzel et al., 2002) Generalised Additive Model (GAM) (Zaniewski et al., 2002) Artificial Neural Networks (ANN) (Joy and Death, 2004) Generalised Linear Model (GLM) (Brotons et al., 2004; Hirzel et al., 2006) Multivariate Adaptive Regression Spline (MARS) (Leathwick et al., 2005) Maximum Entropy (MAXENT) (Phillips et al., 2006) Support Vector Machine (SVM) (Drake et al., 2006; Guo et al., 2005,) Generalised Dissimilar Model (GDM) (Ferrier et al., 2007) Limiting Variable and Environmental Suitability (LIVES) (Li and Hilbert, 2008)

28 Maps of reef distribution based on satellite images and air photographs

29 Reef geology Most Holocene reefs in the GBR have Pleistocene reef limestone foundations. = BANK Most reefs are multi-generation limestone geomorphic banks (but not all banks support reefs) Some reefs were unable to keep pace with post-glacial sea level rise (submerged reefs)

30 Used new 100 m bathymetry grid (Beaman, 2010) - data contoured at 5 m

31 Digitising geomorphic banks: Banks exceed 15 m in elevation and have at least one steep side Digitised by hand NSS coral reef Example from 11 o S

32 Statistics of geomorphic banks in the GBR: Overall mean depth of banks = 27.3 m Total area of all bank types (less NSS reefs) = 25,599 km 2

33 How much of the 25,599 km 2 of bank area actually supports living coral communities? Use small, high-resolution data set to predict area of potential coral habitat Hydrographer s Passage

34 Occurrence records derived from optical images taken by autonomous underwater vehicle, together with: Area of deep coral habitat estimated using Maximum Entropy (MaxEnt) on a data set from Hydrographer s Passage - depth - slope - aspect - rugosity - acoustic backscatter -geomorphic zone (slope, crest, flat or depression) 5 m resolution 70% data used as training set Coral coverage on nine banks = 55 +/- 23%.

35 Interpretation: /- 23% of all banks = 14,000 +/ km 2 deep water coral communities. 2. NSS coral reefs area = 20,679 km 2 3. Therefore the area of coral habitat in the GBR is at least 50% larger and perhaps double the size previously believed to exist. 1 2

36 Protection of banks (deep reefs): Not protected from trawling ZONE TYPE Area (km 2 ) of banks included Number of banks included* Percent of banks by area Preservation Zone Marine National Park Zone 7, Conservation Park Zone Habitat Protection Zone 12, General Use Zone 3, Banks beyond GBR Marine Park 1, *Parts of banks may occur in more than one zone. Harris et al. (in press) ICES Journal of Marine Science

37 Concluding remarks

38 Concluding remarks Bathymetry underpins all seafloor characterisation maps Geomorphic features and seascapes useful for government decision-making and management Predictive habitat modelling the future GeoHab 2013 will be held in Rome, Italy (6-10 May)

39 Thank You!