Appendix S4
Details on the shipping layer and implementation of the CM2.1 model
A global map of commercial shipping traffic was obtained from Halpern et al. (2008). This
layer was derived from 12 months (Oct 2004 – Sep 2005) of ship location data, which
amounted to 1,189,127 data points from 3,374 commercial and research vessels. This data
represents roughly 11% of the 30,851 merchant ships >1000 gross tonnage at sea in 2005.
These points were then connected to create ship tracks, removing any tracks that crossed
land. The remaining 799,853 line segments were buffered 1 km wide to account for the width
of shipping lanes and converted to a raster of 1 km2 grid cells representing the density of
shipping traffic. Halpern et al. (2008) note that an incomplete dataset was used to derive this
layer, as not all ships reported locations. Therefore, it is possible that shipping traffic may be
underestimated in some locations. This layer of shipping traffic was overlaid on prediction
maps of southern right whale habitat suitability to identify potential areas of risk. We assume
there is no seasonality to shipping traffic and a yearly composite is relevant to all seasonal
maps of whale distribution. Overlap between shipping traffic density and predicted habitat
suitability for southern right whales is variable (Fig. 1). Shipping traffic density in the
Australasian region is generally lower than regions with large population centres such as
Europe, the US east coast, and the coasts of China and Japan.
Predicted ocean temperature data were derived from the CM2.1 coupled climate model,
generated by the NOAA Geophysical Fluid Dynamics Laboratory (Delworth et al., 2006;
Gnanadesikan et al., 2006; http://data1.gfdl.noaa.gov/CM2.X/). These CM2.1 model
simulations contributed to the Intergovernmental Panel on Climate Change (IPCC) fourth
assessment report (AR4). The CM2.1 model was determined to be among the two best
models that contributed to AR4 (Russell et al., 2006). We used the 'business as usual' IPCC
emissions scenario (A2) that generated outputs with 1° grid cell resolution. To describe
seasonal variability, the mean temperature from each month for the 2090 -2100 decade was
derived. Monthly vertical averages of projected temperatures for t0 (surface) and t0_200 were
obtained because these variables contributed significantly to BRT models of southern right
whale distribution. All averages were weighted by the volume of the vertical grid boxes in the
CM2.1 model. These data were converted to in situ temperature (C°) from the potential
temperature, and then used to calculate seasonal averages. Grids of these temperature layers
were converted to 0.5° resolution to match the other environmental layers implemented in the
boosted regression tree models. Predictions of southern right whale habitat suitability
produced using these predicted ocean temperature layers describe how whale distribution
patterns may shift in 90 years due to climate change.
Ocean temperature change is not consistent across the study region, but rather is dynamic
spatially and temporally. Seasonal plots of the predicted change in surface temperature (t0)
between today and the 2090-2100 decade (future – present) illustrate that within our spatial
extent some areas increase in temperature while others decrease, and these areas shift
seasonally (Fig. 2).
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Figure 1. Spatial overlap of shipping traffic on current predictions of southern right whale
distribution. Colour scale of habitat suitability and ship traffic density are standardized across
all prediction maps. (a) Eastern region, spring; (b) Eastern region, summer; (c) Eastern
region, autumn; (d) Western region, spring; (e) Western region, summer. Maps in Mercator
projection, datum wgs1984.
a
c
b
d
Figure 2. Seasonal change in surface temperature (t0) between today and the 2090-2100
decade (future - present). Colour ramp indicates positive or negative changes in degrees
Celsius. (a) spring, (b) summer, (c) autumn, (d) winter.