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!