Projections for the Future of Chondrus crispus (Irish moss)

download report

Transcript Projections for the Future of Chondrus crispus (Irish moss)

Behind the Map: Predicting Marine Species’ Habitat Change Using Global Climate Models
Elizabeth Flanary and Sarah Vereault Department of Geography, McGill University
Faculty of Science
Research Conference
1st Prize
Earth System Sciences
Supervisor: Dr. Gail Chmura Department of Geography, McGill University
Data and Methodology
In this study we have applied sea surfaces temperatures (SSTs)
derived from remote sensing and Global Climate Models (GCMs)
projections to examine future potential distribution of Chondrus
crispus, Irish Moss. The geographic range of organisms is
related to climate, particularly on continental scales.
Temperature can be a useful first indicator to habitat suitability
(Pearson and Dawson, 2003).
SSTs were downloaded from the
Intergovernmental Panel on Climate Change
( for four GCMs (Table 1),
encompassing the years 1961-1999 , and
2079-2099. The years 1961-1999 were used
as the baseline, and the years 2079-2099
were selected as the future period because
that is when 4°C atmospheric warming is
supposed to occur. Only the A2 scenario,
which predicts the highest increase in
emissions, was included to assess maximum
potential change (Nikicenovic, 2000).
Remote sensing uses satellites to obtain data that may
otherwise be unobtainable on the surface of the earth. This is a
benefit in oceanographic studies, as the vast extent of oceans
are difficult to sample. The National Oceanic and Atmospheric
Administration Advanced Very High-Resolution Radiometer
(NOAA AVHRR) uses the thermal infrared channels ( = 3.0 –
1000 m) to remotely sense SST.
To address yearly variability, for example the North Atlantic
Oscillation (NAO), we took the average monthly temperatures of
the NOAA AVHRR temperatures over the past 12 yr. (The NAO is
a cycle of warming and cooling caused by reversal of pressure
that affects waters and weather of Europe and the east coast of
North America (Ahrens, 2003)).
GCM simulations differ on regional trends, and on climate
variables, but all predict that the global average surface air
temperature will increase by 4°C in the next 100 yr (Cubasch et
al., 2001). The extent of climate change predicted is dependent
on the emissions of atmospheric greenhouse gases and
aerosols, largely influenced by carbon dioxide (Environment
Canada, 2004). In addition, treatment of surface conditions,
such as ocean currents and circulation, and wind patterns vary
amongst models.
GCMs are often produced on a coarse resolution grid, and need
to be “downscaled” in order to be evaluated at the finer
resolution needed for impact studies. Direct interpolation is one
method that has been implemented in studies that predict
species habitat change (e.g., Bartlein et al., 1997).
We generated change fields (increase or
decrease in SSTs) by subtracting the baseline
(average of 1961 -1999) from the average of
the future time period.
z(u 0 ) 
Figure 1. GFDL grid in vector format.
Spatial Resolution
Canadian Centre
for Climate
Modeling and
Analysis; Canada
°lat x °long
3.75 x 3.75
416 x 281
Centre for
Climate System
Research; Japan
5.6 x 5.6
Scientific and
3.2 x 5.6
355 x 420
2.25 x 3.75
250 x 281
GFDL R30 C Geophysical Fluid
United States
622 x 420
Figure 2. CCSR grid in vector format.
The data was imported into ERSI
ArcGIS 9.0, and a vector layer was
generated which showed temperature
change at spatially referenced points
on a global scale. They were assigned
the Geographic Coordinate System
WGS 1984, to correspond with the
NOAA AVHRR data. The data was
trimmed to include only points that
occurred in the NW Atlantic, between
25 and 65°N, and 30 and 80°W. The
visual presentation of the data
reveals the breadth of spatial
resolutions (Figs 1 & 2).
Application of GCM SST data
Chondrus crispus, or Irish moss, is a red algae and a source of carrageenan,
commonly used as a thickener and stabilizer for processed foods such as ice
cream and luncheon meats. It is also used in cosmetics to soften skin and as
an herbal supplement. Historically there has been a large Irish moss industry
on the South Shore of Massachusetts and along the Maine coast (http://www.
Figure 11. Future distribution of
C. crispus according to CCCMA.
 ij
Equation 1. Formula for
Inverse Distance
Weighted Interpolation.
Table 1. Models used in analysis.
 z(u )d
Figure 13. Change in the distribution
of C. crispus according to CCCMA.
Figure 3. GFDL changefield for February.
z(u0) = estimated SST at
unknown points
z(ui) = known data
dij = the distance
between each data point
and the unknown point.
p = power
source: Lo, and Yeung,
Figure 5. February AVHRR SST.
Figure 4. CCSR changefield for August.
Interpolation is the process by which
known data values and mathematical
equations are used to accurately
estimate values at unknown locations.
Inverse distance weighted (IDW)
interpolation supposes that the value
of an unknown point is inversely
proportional to a power of its distance
to known points (Equation 1). IDW
was used to interpolate the change
fields, using the distance squared,
and a radius of 8 nearest neighbours.
This follows Tobler’s Law that things
that are closer together are more
closely related. This process produces
a smooth surface.
Figure 7. GFDL prediction for Feb SST,
Figure 6. August AVHRR SST.
downloaded from the Physical
Oceanography Distributed Active
Archive Center (http://podaac-www. in pentad format. It
had a spatial resolution of 9 km at
the equator, and was global in extent.
It was imported into Idrisi 32 v.2,
and clipped to the NW Atlantic study
area, then imported into ArcGIS.
Figure 8. CCSR prediction for Aug SST,
The GCM change fields were added to
the NOAA AVHRR data to create the
final output showing predicted future
sea surface temperatures.
When examining and applying the data and results it is important to
keep in mind the original scale (Table 1). Since the data was obtained
at a very coarse resolution the temperature predictions are likely not
viable at a local scale. A large portion of the data was generated
through interpolation, and so is not exactly known. However, at a
regional level predictions from GCMs can be used to evaluate general
Despite variations in magnitude and some local
cooling effects, all of the GCMs examined predict
that over time the average sea surface
temperature will rise. Changes in biogeographic
ranges due to rising SSTs could result in the
depletion of other species that are economically
valuable in certain areas. As well, the disruption in
habitat and food webs could have far-reaching
ecological consequences.
Other methods of downscaling data to a finer resolution include
statistical and dynamical downscaling. However, in a study using GCMs
the results obtained from a dynamical coupling of a GCM and finer
resolution Regional Climate Model showed no significant difference from
direct interpolation of the GCM (Murphy, 2000).
The implications from this could have noticeable
effects on marine flora and fauna, local
communities connected to the sea, and the global
community as well.
In regard to the distribution of marine species, temperature is not the
only factor that determines suitable habitat. Salinity, substrate, nutrient
availability, and interaction with other species were not included in this
study, and may be equally useful in predicting distribution change.
Figure 9. Chondrus crispus current
known distribution.
Chondrus crispus occurs on the NW
Atlantic coast, to 20 m water depth
and between 40 and 60°N, from
New Jersey to Labrador (Lee,
1977). The maximum and minimum
temperature extremes that occur in
C. crispus’ range are in August and
February, respectfully. From the
NOAA AVHRR layer, data was
selected within the depth and
latitude parameters, and the
corresponding temperature
extremes were recorded. The
temperatures bounding C. crispus’
range are –2.1 & 21.3°C.
Figure 10. Distribution of waters
-2.1 to 21.3°C annually.
A current thermal distribution layer
was created, which selected areas
from the NOAA AVHRR data that
was bounded by the depth and
temperature restrictions. This layer
corresponded with the known
distribution, and thus the
temperature bounds are likely a
good predictor of habitable areas
for C. crispus. If the two
distributions did not correspond,
and areas of acceptable
temperatures were not in the
known distribution, it is likely that
other variables have a stronger
influence on species distribution,
such as substrate, salinity, food
sources, or predators.
Figure 12. Future distribution of
C. crispus according to CCSR.
To predict where C. crispus will be
able to live in the future, the
bounding temperatures of –2.1 and
21.3°C were found in layers of future
SSTs generated by the GCMs, in
areas where the depth was also
within limits. Only areas that fell into
this range in both February and
August were selected as suitable.
Funding for this project was provided by the Climate Change Action Fund (CCAF), the World Wildlife Fund (WWF), and the
Natural Sciences and Engineering Research Council (NSERC). Results were generated using the Walter Hitschfeld Geographic
Information Centre undergraduate lab’s GIS software and computers. Thank you to Dr. Gerhard Pohle and Mr. Lou Van Guelpen
of the Atlantic Reference Centre (ARC), Dr. Gail Chmura, Dr. Jonathan Seaquist, Mr. Graham MacDonald, and Mr. Tim Horton.
Figure 14. Change in the distribution
of C. crispus according to CCSR.
A change image was produced by
subtracting the current thermal
distribution layer from the future
thermal distribution layer using raster
calculations. Notably, all four models
predict retraction of the range of C.
crispus in southern New England,
where Irish moss is currently
harvested. The degree to which the
models predict loss is variable, and
some also predict loss in northern
Labrador, around Prince Edward
Island and northern Nova Scotia.
Works Cited
Ahrens, C.D. 2003. Meteorology Today, 7th Ed. Brooks/Cole-Thompson Learning: Pacific Grove, USA.
Bartlein, P. J., Whitlock, C., and S. L. Shafer. 1997. Future Climate in the Yellowstone National Park and Its Potential Impact on Vegetation. Conservation Biology
11: 780-792.
Cubasch U, Meehl GA, Boer GJ, Stouffer M, Dix M, Noda A, Senior CA, Raper S, Yap KS (2001) Projections of future climate change. Pp. 525-582 In Houghton JT,
Ding Y, Griggs DJ, Noguer M, VAN DER Linden PJ, Dai X, Maskell K, Johnson CA (eds.) Climate change 2001: the scientific basis, contribution of working group 1
to the third assessment report of the intergovernmental panel on climate change. Cambridge University Press.Environment Canada. The first generation
coupled Global Climate Model (7/6/2004). Retrieved 9/23/2005, from
Irish Moss (N.D). Retrieved 9/25/2005, from
Irish Moss (N.D.). Retrieved 9/25/2005, from
Lee, T. 1977. The seaweed handbook: an illustrated guide to seaweeds from North Carolina to the Arctic, Mariners Press, Boston.
Lo, C. P., and A. K. W. Yeung. 2002. Concepts and Techniques of Geographic Information Systems. Prentice Hall, Upper Saddle River, NJ.
Murphy, James. 2000. Predictions of climate change over Europe using statistical and dynamical downscaling techniques. International Journal of Climatology 20:
489 – 501.
Nakicenovic N, Swart R (Eds.) (2001) Emissions Scenarios 2000, Special Report of the Intergovernmental Panel on Climate Change. Cambridge University Press,
UK. 570 pp.
Pearson, R. G., and T. P. Dawson. 2003. Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful? Global
Ecology & Biology 12: 361-371.