Computer Modelling as an Aid to
Marine Planning in the Central Coast
Coastal Zone Planning:
LESSONS AND APPLICATIONS FOR BC’S CENTRAL COAST
ICNRC, Alert Bay, April 2003
Jeff Ardron
GIS Manager
J. Atkinson
B. Helin
Science/Ecology
Socio-Economic
First Nations
CC Marine
Analysis
Reserve Objectives
Representative physical, biophysical, & biological features
and processes
Replication of features
Rare & endangered species
Distinctive features
Separation to mitigate
catastrophes
Proximity to give the network
a total value greater than the
sum of its parts
Existing Reserves, parks,
areas of interest, fishery
closures considered
Human Use acknowledged
CC Marine Analysis
Design Principles
•Full Spectrum of Data:
Physical, Biological, Chemical
•Realistically represents the
environment: classification
•Accuracy & scale: matches
data
•Flexibility to accommodate
information at a later date
•Variety of solutions
A. Hancock
Lessons from Terrestrial Conservation…
Rock & Ice: Land nobody wanted
Freaks of nature
Playgrounds in the wilderness
CC Marine Analysis
Courtesy of E. Gonzales, UBC
5 Lessons Learned
from the Central Coast
1. Scale
British Columbia
A. Hancock
45
Cons
3. Physical
Complexity
4. Site Selection
(MARXAN)
5. Data Sharing
CC Marine Analysis
m
Pros &
0K
2. Hierarchies:
1. Each to Its own
Scale
(Bathymetry 1:250,000)
Error within BC’s Marine
Ecological Classification
Pros & Cons of
Spatial Hierarchies
Simple, spatially discrete
Photic Class
Areas Accurately
classed (27.5%)
Areas Deeper than
classed (11.5%)
Photic Areas
Missed (61.0%)
Data generalization error
Process/ feature is placed in
context of those preceding it
Data error is spread to all
levels below feature
Provides easy to read results
Much important detail and
accuracy is lost
Helpful in planning with a
few good high-resolution data
Not robust to variations in
scale, data, or ecosystems
Can be tailored to model
specific sp. & assemblages
Poor for multiple species /
features analysis
e
p
o
Sl
3. Complexity
Relief
Complexity
Typical Fjord
(representative)
Mod.-High Slope
High relief
Mod.-Low
Complexity
Fjord with Reef
(distinctive)
Mod.-High Slope
High relief
High Complexity
Slope is “steepness”
(average rise over run).
Relief is “roughness”
(max. rise over run).
Complexity is “intricacy”
(the number of changes in
rise over run)
Some features, e.g. shelf
breaks, can be captured
using any of the above
measures.
But, other features, e.g.
reefs, or archipelagos, can
be best differentiated using
complexity.
Therefore complexity is
usually more descriptive.
Modelling
Rockfish
Habitat
Hoeller
Modelling
Rockfish
Habitat
Missed Area
Hoeller
4. Site Selection:
MARXAN
Selecting efficient reserve
networks from thousands of
planning units and dozens
of features is beyond human
ability.
Planning Units: Hexagons
250 hectares each (about
1,700 m across). 11,725 have
a marine component.
R. Bateman
Analysis Units: 0.2 hectare
grid (about 45 m square).
11,369,550 have a marine
component.
Features: Physical and
Biological. There are 61 in
these trials. Targets, penalties,
number of occurrences, and
separation requirements can
be set for each one.
Example
Solution
#100
Overall Reserve = ~30%
C. Stengl
One of several
thousand solutions
Summed Solutions:
A measure of
“Conservation Utility”
High
Low
2,400 solutions =
6 different targets
x 4 levels clumping
x 100 runs each.
CC Marine Analysis
Conservation
Hotspots
Yellow: Places almost
always chosen.
Medium Blue: Areas
chosen about _ the time.
Darker blue: Areas can be
considered useful in only
some reserve networks.
Does this map make
sense..?
D. Comfort
5. Data Sharing
(data symbiosis)
The single biggest
stumbling block was
accessing gov’t data.
Once provided, we
often “cleaned” and
otherwise improved
data, returning those to
the providers.
FoI requests have a
history of acrimony.
We need to cooperate...
We need to cooperate...
CIT Study Area
British Columbia
Shelf Slope
Prince Rupert
Outside
Waters
Passages
Inlets
Alert Bay
I. Fry
CIT Marine Analysis
Next Steps
Data collection
remains on-going.
Traditional & local
ecological knowledge.
Human use & relativecosts in the model.
DATA
Hex
Grid
Regions
Score
Classify
Set
Targets
No
Hierarchy
MARXAN
N
Goals
Met?
Socio-economic
analysis
Adaptive
Management
Revise
Y
Y
I.M.
Others?
N
Stake holders
N
Y
Agree?
Gov’t
Collaborate with other
researchers and
agencies.
Integrated
Management process
whereby new levels of
detail can be added,
and new solutions
examined.
More Information…
Art Show
www.LivingOceans.org/art_show.htm
Map Gallery
www.LivingOceans.org/map_gallery.htm
Library
www.LivingOceans.org/library.htm
CC Marine Analysis
P. Galitzine
Acknowledgments
The authors would like to express their sincere thanks to the following individuals,
who gave their time freely to answer our questions and in the case of Hugh
Possingham and Ian Ball, gave us MARXAN without which our analysis of the Central
Coast would have been impossible. This in no way indicates their acceptance of the
results of these analyses.
Ian Ball, Hugh Possingham, Jacqueline Booth, Bruce McCarter, Kim Conway,
Hussein Alidina, Josh Laughren, Bill Austin, Bill Crawford, Bill Henwood, plus many
more… Thank You!
Central Coast Peer Review Team (on-going): Dr. Satie Airame, Dr. Barbara Dugelby,
Dr. Reed Noss, Dr. Ian Perry, Dr. Callum Roberts, Dr. John Roff.
These projects would not have been possible without the support of:
The David and Lucile Packard Foundation
Henry P. Kendall Foundation
Lazar Foundation
Endswell Foundation
Conservation Technology Support Program
Province of BC Coast Information Team
M. Hobson
V. Plewman
Burkosky