From seafloor geomorphology to

predictive habitat mapping:

progress in applications of biophysical

data to ocean management.

Peter Harris

Geoscience Australia, Canberra ACT, Australia

Currently seconded to: UNEP/GRID Arendal, Norway

Habitat mapping workshop, Trondheim, Oct 2012

Outline of talk:

• Purpose of habitat mapping (sectors and clients)

• Review of progress in habitat mapping (science)

• Review of progress in applications to decision-

making (Australia case study)

• Communication

• Best practices for habitat mapping

GeoHab Atlas of

seafloor

geomorphic

features and

benthic habitats –

www.geohab.org

57 Case Studies; 220 authors; 16 countries

What is a habitat map?

Benthic Habitat = Physically distinct areas of seabed

associated with suites of species (communities or

assemblages) that consistently occur together.

Habitat maps are:

1. Communication devices

2. Syntheses of multiple spatial data layers

3. Integration of biological and physical

attributes

What was the main purpose of your habitat

mapping project?

Note most responses relevant mainly

to government management and

planning (rather than to industry).

Who are the main clients for your project?

Note: grouping all industry clients together shows this is the largest single

client group.

Fishing is the greatest threat. Note relative immediate threat of climate

change is not rated as high as other anthropogenic threats.

What are the most immediate anthropogenic threats to habitats?

Coast and shelf shaded

Who funds habitat mapping?

• Government or government funded agencies/institutions

(n=49)

• Private industry (n=7)

• Non-government organisations (n=4)

Progress in habitat mapping

Seabed mapping technology Acoustics

Video systems

AUV

Data reduction technology Data analysis (algorithms for acoustics, video

classification, etc.)

Statistical methods

Predictive Habitat Modelling 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)

Type of habitat map How generated? Advantages

Disadvantages

Direct interpretation (eg.

geomorphology, benthic

community)

interpreted from simple

observations (eg

bathymetric data) – apply

classification scheme

+ simple to communicate,

technically easy to

generate

- limited predictive power

Biophysical interpolations

(eg. seascapes)

multivariate analysis to

spatially combine several

biophysical data layers

+ simple to generate with

spatial data

- limited predictive power,

difficult to communicate

Predictive habitat maps

(maximum entropy,

decision-trees, etc.)

include direct

observations of marine life

with biophysical data to

predict the potential

distribution of species and

benthic communities.

+ good predictive power,

performance indicators

- Difficult to generate

(data hungry), relate to

single species or group

Different approaches to habitat mapping

Add data layers

in GIS

Roff and Taylor

(2000)

Multivariate

seascapes

analysis

Include models

of ecological

processes

Kostylev and

Hannah (2007)

Include

biological data

Classified versus

raster grid

Fuzzy boundaries

(Lucieer and

Lucieer, 2009)

Scale

dependency

(Huang et al,

2010)

Physical

disturbance

regime index

(Harris and

Hughes, 2012)

Predictive Habitat Map

Which surrogates

are best to use?

Which physical surrogates are the most useful?

Determined using ARC GIS (22 out of 39 studies) plus multivariate analysis

methods (15 studies). PRIMER most commonly used to find relationships

between physical and biological data.

How do the surrogates that were measured in each study compare

with those found to be most useful?

Note “success rate”: substrate type (100% success rate) ; wave-current speed

(81% success rate)

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Related issues:

Direct -vs- Indirect variables

Temporal variation

Biological Processes

Physical Processes

Easy to measure -vs-

ecological relevance

Adriatic Sea

Gibralter Bristol Channel

Norwegian Shelf

Review of progress in

applications to decision-making

(Australia case study)

Heap and Harris (2008)

Biophysical model - Geomorphology

Marine management based on IMCRA 2006

41 provincial bioregions

Many boundaries based on

geomorphology

IMCRA = Integrated Marine

and Coastal Regionalisation of

Australia

Petroleum titles cover an area of about 620,000 km2 or about 8.7%

of Australia’s EEZ (excluding offshore territories)

Example of application of geomorphic features to assessment of industrial use

Harris et al. (2007)

APPEA Journal,

48:327-343

How to deal with many

useful surrogates

simultaneously:

Multivariate analysis

Integration of ecologically-significant biophysical variables to create a single map (Seascapes)

Not scale dependant

(e.g., slope)

(e.g., bathymetry)

(e.g., tidal currents)

(Seascapes)

(e.g., %Sand)

Input physical

data

Integrated

product

+

+

+

=

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)

Depth

Grid resolution 0.01o, ~5 km

Slope

Grid resolution 0.01o, ~5 km

%Gravel

Grid resolution 0.01o, ~5 km

%Mud

Grid resolution 0.01o, ~5 km

Effective Disturbance

Grid resolution 0.01o, ~5 km

Seafloor Temperature

Grid resolution 0.01o, ~5 km

Primary Productivity

Grid resolution 0.01o, ~5 km

Australia Shelf Seascapes

13 Seascapes

1. Moderate depth, flat, slightly gravelly, cold,

low disturbance, moderate primary productivity

Seascape heterogeneity based on Focal Variety

Analysis

• Used to identify ‘hotspots’ of seascape

heterogeneity (surrogate for biodiversity)

• 20 x 20 cell analysis area

Australian Shelf Seascapes - Heterogeneity

Harris et al. (2008) Ocean Coastal

Management, 51:701-711.

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

Lessons for habitat mappers:

Science input at start of process (2006/07) – no new

data introduced mid-way through

Geomorphology and seascapes influenced location

of proposed MPAs

Geomorphic features easily understood and

accepted by decision-makers

Seascapes not as clear, not easily accepted

Communication:

Who are YOUR mapping products for?

Senior government bureaucrats?

Politicians?

Marine Industry Reps?

KISS (Keep it simple…)

Easily recognisable terms

Nobody (especially politicians) appreciates complicated explanations

Clear graphics

Maps with obvious colours and labels

Before and after imagery

Underwater pictures and movies

3D bathymetry fly-thrus

Computer animations (current transport paths)

Best practice for habitat mapping surveys

Concluding remarks

• Most habitat mapping is to support government

decision-making

• Uptake for government decision-making lags behind

developments in science and technology

• Disconnect between rate of progress in habitat

mapping science -vs- uptake by decision-makers

• Predictive habitat modelling – the future

• Communication (clear and simple)

• GeoHab 2013 will be held in Rome, Italy (early May)

Thank You!