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Climate Change Vulnerability Assessment of Arctic Mammals: Technical Summary

Authors:
NO REFUGE
FROM WARMING
SUPPLEMENTARY MATERIALS
by Katie Theoharides
Contents
Methods: Using the Climate Change Vulnerability Index ............................................................... 2
Direct Exposure: Climate Change in the Arctic National Wildlife Refuge .......................................... 3
Sensitivity to Climate Change ...................................................................................................................... 7
Overall Scoring ............................................................................................................................................ 15
General Circulation Models and Downscaling ................................................................................. 16
Data Processing ........................................................................................................................................... 21
Climate Change Vulnerability Index: Caveats Regarding Exposure, Sensitivity, and
Certainty .......................................................................................................................................................... 24
Bibliography ................................................................................................................................................... 26
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Methods: Using the Climate Change Vulnerability Index
The Climate Change Vulnerability Index (Natureserve, undated) requires inputs that measure Direct
Exposure to climate change and Sensitivity to climate change, which includes both Indirect
Exposure and Species Sensitivity. The Index combines data on exposure to climate change (in this
case changes in moisture and temperature) with information about species sensitivity to climate
change resulting from extrinsic factors caused by indirect exposure to changes related to climate
change (e.g. sea level rise) and species specific factors such as flexibility of habitat and dietary
requirements (Figure 1). The index also allows users to include limited information on a species
documented response to recent or ongoing climate change as well as the results of modeling studies.
The output of the Index is a score ranging from extremely vulnerable to not vulnerable/ presumed
stable/expansion likely. The index identifies the critical factors or the elements that make the
species assessed vulnerable. The scores and identification of critical factors can be used to develop
targeted conservation efforts and further research projects to help manage the species in a climate
change future.
Figure 1: Framework for the NatureServe Climate Change Vulnerability Index. Figure from Glick et
al. 2011
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The Index divides vulnerability into two components, the exposure to climate change across the
range of the species within the assessment area, and the sensitivity of the species to climate change
(Figure 1). These two components are mathematically combined to produce the final vulnerability
score. In this way exposure is treated as a modifier of sensitivity. A species with traits that make it
highly sensitive to climate change will not have a high vulnerability score if the climate across the
region it occurs in remains stable (CCVI Guidelines 2010), while a species with broad tolerances and
low sensitivity is unlikely to be vulnerable even if the climate changes drastically across its region.
Adaptive capacity of the species is not explicitly addressed in the index, though several sensitivity
factors and indirect climate change factors overlap with factors that might contribute to or detract
from the adaptive capacity of the species. For example, one factor assesses whether or not the
species has been able to respond to ongoing climate change by changing any aspect of its phenology
in a beneficial way. This trait could arguably be considered part of adaptive capacity rather than
species sensitivity. Similarly dispersal ability, genetic variation, and distribution as related to natural
barriers could all be considered as contributing to the adaptive capacity of the species.
Direct Exposure: Climate Change in the Arctic National Wildlife Refuge
The first factor addressed in the Index is exposure to climate change. Exposure information
captured in the index includes the magnitude of projected changes in average annual temperature
and moisture across the species range in the assessment area. To incorporate exposure information
the Index guidance suggests using ClimateWizard for developing future climate projections.
ClimateWizard, a project of the Nature Conservancy, University of Washington and the University
of Southern Mississippi provides a source of downscaled temperature and precipitation predictions
from 17 Global Circulation Models (GCMS) that can be downloaded and incorporated into GIS for
analysis (Girvetz et al. 2009). See below for a more detailed discussion of the General Circulation
Models used and the downscaling process.
Change in Temperature
Across the Arctic National Wildlife Refuge temperatures are projected to increase over the next 50
years. These changes range from an increase of 4 degrees F in the most southern portion of the
refuge to greater than 6 degrees F in the north of the refuge (Figure 2). Temperature changes will
lead to a variety of impacts including changes in snowfall and snowcover, changes in vegetation,
alteration of the fire regime, and changes in species phenology and species interactions. These more
specific changes are not part of the outputs from the ClimateWizard tool and therefore cannot be
modeled specifically for our assessment.
Table 1 shows the percent of the assessment area in each of the temperature ranges defined in the
index. The rankings in the severity of change column of the table are assigned scores from
NatureServe based on the relative range of expected changes in temperature by Mid-Century. Each
individual species profile describes the changes projected for that species range within the Refuge.
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Figure 2: Departure in average annual temperature across the Alaska by Mid-Century.
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Table 1: Percent of each category of temperature change in the Arctic Refuge based on
ClimateWizard projections. Scope must sum to 100 percent.
Severity of Change Temperature Range Scope (percent of range)
High
>5.5° F (3.1° C) warmer
7.79%
Medium High
5.1-5.5° F (2.8-3.1° C) warmer
57.14%
Medium Low
4.5-5.0° F (2.5-2.7° C) warmer
27.27%
Low
3.9-4.4° F (2.2-2.4° C) warmer
7.8%
Insignificant
< 3.9° F (2.2° C) warmer
0%
Total:
100%
Change in Moisture
In the lower 48 states the Index version 2.0 includes a Hamon AET:PET moisture metric, rather
than changes in precipitation. The Index made this change from the use of precipitation data in the
original Index version 1.0 to a more biologically relevant climate variable as species are impacted by
available moisture and not precipitation levels directly. The Hamon AET:PET moisture metric used
in the Index integrates temperature and precipitation through a ratio of actual evapotranspiration
(AET) to potential evapotranspiration (PET), with consideration of total daylight hours and
saturated vapor pressure. However, the Hamon AET-PET index employed in the CCVI for the
lower 48 states is not available in Alaska so we instead used the percent departure in the historical
ratio of Actual Evapotranspiration (AET) to Potential Evapotranspiration to the mid-century
projected ratio to indicate how moisture is changing in Alaska. This ratio is available through the
ClimateWizard Custom Analysis Tool. Potential Evapotranspiration is defined as the amount of
evaporation that would occur if a sufficient water source were available. The actual
evapotranspiration (AET) is considered the net result of atmospheric demand for moisture from a
surface and the ability of the surface to supply moisture, and PET is a measure of the demand side
for moisture. Surface and air temperatures, insolation, and wind all affect this ratio. A loss of
moisture over time is indicated by a negative percent departure in the ratio, while a moisture gain is
indicated by a positive change (See Table 2). Across the Arctic Refuge moisture change will not be
significant as indicated by the AET:PET ratio and may in fact be slightly positive (Figure 3).
Changes in the ratio ranged from an increase of .08827 to an increase of .02040. For some caveats
about the projected moisture change in the Arctic National Wildlife Refuge, see below.
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Table 2: Difference in the ratio of annual AET:PET by mid-century.
Severity Moisture range Scope (percent of range)
Very High < -0.119
0%
High -0.097 - -0.119
0%
Medium High -0.074 - -0.096
0%
Medium Low -0.051 - -0.073
0%
Low -0.028 - -0.050
0%
Insignificant >-0.028
100%
Total:
100%
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Figure 3: Change in the ratio of AET:PET by mid-century. Change across the refuge was slightly
positive, but considered insignificant based on the NatureServe scoring.
Sensitivity to Climate Change
The Index assesses sensitivity by scoring species against 20 factors divided into two categories:
indirect exposure to climate change (extrinsic sensitivity) and species-specific sensitivity
(intrinsic sensitivity). Extrinsic sensitivity is sometimes considered adaptive capacity, but in this case
the Index treats it as a component of sensitivity.
Species receive a score for each factor ranging from greatly increasing to having no effect on, to
decreasing the species vulnerability. If information is not available the factor can be skipped; the
Index can calculate an overall score with as few as 13 of 20 factors. The creators of the Index
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recommend estimating scores for as many factors as possible and capturing uncertainty and a lack of
data by selecting multiple scores for each factor. For detailed descriptions of each factor, please
reference the NatureServe Climate Change Vulnerability Index guidance document. Explanations of
how each sensitivity factor was treated in our analysis, including any assumptions made, are provided
below. We also include details on the background materials used to score each species.
Indirect Exposure to Climate Change
Many species will be affected not only by direct changes in temperature and precipitation, but also
by more indirect effects of climate change, such as exposure to sea level rise, and barriers to
dispersal and movement. Below are a list of the factors considered in the Indirect Exposure to
Climate Change category and a brief description of how I treated these.
Sea Level Rise
NatureServe suggests using the scenario of 0.5 to 1m of sea level rise for the assessment. Sea level
rise is only an issue for species with ranges that are all or partially within a region that may be subject
to the effects of 0.5 to 1m sea level rise and the influences of storm surges in the next 50 years. For
example, species whose range within the assessment area occurs 90% of the time in areas subject to
sea level rise (e.g. low-lying islands or the coastal zone) will have greatly increased vulnerability due
to sea level rise. For our analysis we used imagery available from the Center for Remote Sensing of
Ice Sheets (www.cresis.ku.edu/data/sea-level-rise-maps), which provides imagery of the impacts of
sea level rise in Alaska and other regions of the world based on different sea level rise scenarios
(Figure 4). Most species in our assessment range were not affected by sea level rise because their
ranges were not coastal. However, a few species, including the polar bear and the arctic fox, do
range in coastal areas and thus they were scored accordingly. Of note: the index does not access
whether or not sea level rise will pose a problem for the species, it simply addresses whether the
species current range will be impacted by sea level rise. A species like the polar bear that may be
able to move further inland to den and then hunt on top of ice may not in fact be impacted by sea
level rise, so scoring here is questionable.
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Figure 4: Inundated area of land under a scenario of 1 meter of inundation from sea level rise.
Natural Barriers
The index considers natural barriers to be topographic, geographic or ecological barriers that limit a
species ability to move in response to climate change. The index defines barriers as features or
areas that completely or almost completely prevent movement or dispersal of species (Young et al.
2010). The inherent assumption is that species will be more vulnerable if they are prevented from
moving in response to climate change. Species in the Arctic National Wildlife Refuge are keenly
impacted by barriers to northward movement in the form of the Beaufort Sea and arctic sea ice.
Most of the species assessed are at the northern edge of their range in our assessment area due to
the simple fact that they run out of land and suitable habitat to the north. While some species may
be able to move east into Canada in order to go further north and respond to shifting tundra habitat
and warming temperatures, the ocean coupled with the mountainous terrain presents many natural
barriers to the species assessed. Species that make their home in the tundra may be particularly
vulnerable because of projected shrub and boreal vegetation encroachment to the south, coupled
with meeting a hard barrier of ice and ocean as well as rising sea levels to the north. For species not
expected to see significant habitat shift in next 50 years (e.g. species who live in boreal habitat), or
species whose range does not extend to the northern edge of the refuge the impact of barrier was
usually scored as neutral.
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Anthropogenic Barriers
Anthropogenic barriers are treated the same as natural barriers except that they result from human
land use such as areas of intensive urban or agricultural development, waters subject to chemical
pollution, or dams that block fish movement. NatureServe suggests assessing the intensity of land
use in the assessment area and in the direction of expected species movements using the Wildland-
Urban Interface of the Silvis Lab (University of Wisconsin-Madison and the USDA Forest Service).
This dataset is not available in Alaska, so we used the National Land Cover Dataset (NLCD) for
2001 from the Multi-Resolution Land Characteristics Consortium (http://www.mrlc.gov/). NLCD
2001 data maps standardized land cover components in the following categories:
Open water
Perennial snow/ice
Developed, open space
Developed, low intensity
Developed, medium intensity
Developed, high intensity
Barren land
Deciduous forest
Evergreen forest
Mixed forest
Dwarf scrub
Shrub/scrub
Grassland/Herbaceous
Sedge/Herbaceous
Moss
Pasture Hay
Cultivated crops
Woody wetlands
Emergent herbaceous wetlands
We downloaded the NLCD data and brought it into a GIS environment to analyze landcover across
the assessment area and in a 60-mile buffer on the east and west of the refuge, which represents the
expected direction of species movement. Significant developed and agricultural lands were not
located within the refuge or in the buffer around it so this factor was scored as NEUTRAL for all
species. If significant oil and gas development were to be allowed in the refuge or to take place in
the buffer area in the future, anthropogenic barriers could become a problem for some species.
Land Use Changes Designed to Mitigate Climate Change Impacts
The index also addresses the effects of actions that are taken by human communities to mitigate or
adapt to climate change on species in the assessment area. For example, a high future wind or solar
power development in an assessment area may negatively impact certain species like bats or desert
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tortoises. The Index suggests that areas with a high likelihood of wind or solar power development
based on maps of resource potential or other knowledge should be scored to reflect this risk to
species that could be impacted The National Renewable Energy Laboratory (NREL.gov) provides
maps of energy potential for different types of renewable energy including wind and solar. Similarly,
actions taken to adapt to rising seas by building fortifications such as sea walls and dykes may be
detrimental to species that use wetlands and beaches. This factor is not intended to capture habitat
loss from on-going human activities, such as oil and gas development, deforestation or high intensity
agriculture. Because we are assessing a National Wildlife Refuge we made the assumption that
activities related to mitigation or adaptation are unlikely to occur on a large enough scale within the
Refuge to impact the species we assessed. Shoreline fortifications in response to sea level rise may
occur in the area of Kaktovik in the 92,000 acres of land owned by the Kaktovik Inupiat
Corporation which falls within refuge boundaries. However, the species assessed are not likely to be
adversely impacted by shoreline fortifications and it is unlikely that these fortifications would occur
across a large enough area to have a significant impact. Another threat in some areas is aforestation
as a mitigation strategy. While aforestation may take place in some southern refuges, we made the
assumption that a large-scale tree planting program in the High Arctic would not be a high priority,
especially given concerns over the loss of tundra habitat.
Species-Specific Sensitivity
To assess species intrinsic sensitivity to climate change the Index asks the user to enter information
about the species dispersal and movement ability, its temperature and moisture regime, dependence
on disturbance events, relationship with ice or snow-cover habitats, physical specificity to geological
features, interactions with other species, and phonological responses to changes in climate. In order
to characterize species sensitivity to climate change based on life history data and species ecology we
completed a literature review for each species. This review involved extensive searching of scientific
databases for peer-reviewed studies as well as the use of species databases such as the NatureServe
Explorer which provides access to summarized species information based on already compiled data
and literature review. Because many of these factors may be unknown for certain species the index
allows the user to only enter data on 13 of the 20 sensitivity factors. The more information
provided, the better the accuracy of the score.
The factors below are described in further detail in the Index guidelines provided by NatureServe.
C1. Dispersal and Movements: This factor assesses the species ability to disperse and move across the
landscape, based on the assumption that species that have high dispersal capacity may be less
vulnerable because they have the capacity to move in response to habitat shifts caused by climate
change. Species were scored here according to the Index guidelines. No assumptions were made
beyond the directed scoring procedure described in the index guidelines (see p. 21 of guidelines
document). Information on dispersal distances was collected from literature review and use of online
databases.
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C2: Predicted Sensitivity to Temperature and Moisture Changes: This factor scores each species based on the
conditions of temperature and moisture that the species can exist under successfully. Species with
more narrow abiotic tolerances or requirements, such as species who live in vernal pools or cold
alpine environments may be more vulnerable to habitat loss from climate change than species with
more widespread distributions (Young et al. 2010).
a. Temperature: This factor has two components, historical thermal niche and physiological thermal
niche. Historical thermal niche (exposure to past variations in temperature): The index quantifies
large-scale variation in temperature that a species has experienced in the last 50 years as
approximated by mean seasonal temperature variation (difference between highest mean
monthly maximum temperature and lowest mean monthly minimum temperature) for
occupied cells within the assessment area. It is a proxy for species' temperature tolerance at a
broad scale (Young et al. 2010). To assess this factor we used past climate data from the
ClimateWizard (available at the 4km2 scale) to make a map in GIS of the difference between
the highest mean monthly temperature (July) and the lowest mean monthly temperature
(January). We extracted this map of differences using the boundaries of the Arctic Refuge
and completed a calculation using raster calculator that provided the difference in
temperature across every 4km2 grid cell in the park between the average annual high and low.
We compared this range to the range of temperature variation given in the NatureServe
guidelines to score the factor.
It should be noted that scoring for the factor is based on comparisons in temperature
variation to the lower 48 states and may not be relevant in Alaska. Also of concern is the fact
that this variable is only considered across the range of the species within the assessment
area, rather than across the species entire distribution. Because the assessment area in this
study was small and is an area of relatively stable seasonal temperature variability, historical
thermal niche was scored as a factor increasing vulnerability for every species considered in
this analysis. For species like the coyote or shrew that have a large range extending into the
southern U.S. looking only at temperature variation within the assessment area would seem
to falsely amplify the importance of this factor in determining the species vulnerability.
However, we believe that inclusion of physiological thermal niche (see below) in the analysis
helps to mitigate this potential problem by allowing separate consideration of the species
thermal tolerances across the breadth of its range.
Physiological thermal niche: The physiological thermal niche factor is scored based on how
restricted a species is to relatively cool or cold habitats within the assessment area that are likely
to be vulnerable to loss in extent as a result of climate change. This could include species
that occur in the assessment areas northernmost areas, highest elevation zones, or coldest
waters (Young et al. 2010). The Index is not asking about the species distribution relative to
other species anywhere in the world, but rather to other species within the assessment area. So it
is really a question of the relative thermal habitat requirements of the species. If it is
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distributed widely across the assessment area and does not appear to require a certain cool,
or colder than average habitat type within the assessment area than it may be less vulnerable
than a species who is limited to alpine pockets with very cold temperatures. For our
assessment species that were limited to arctic tundra, alpine areas, or the northern-most
portions of the refuge were considered the most sensitive to changes in temperature (that is
this factor would Greatly Increase their vulnerability to climate change). Species with wide
ranges throughout Canada and the lower 48 states and species that make their primary
habitat in boreal forests or other forest types were considered less vulnerable or not at all
vulnerable under this factor (Neutral). Species that rely on snow and ice are scored later in
the assessment. The Index guidance notes that temperature and hydrologic regime are often
difficult to separate and suggest that if temperature is the overriding factor it should be
scored here. This is the assumption we worked with.
b. Precipitation: As with temperature, this factor has two components, historical hydrological niche
and physiological hydrological niche.
Historical hydrological niche: The index quantifies large-scale variation in temperature that a
species has experienced in the last 50 years using mean annual variation in precipitation the
species has experienced across the assessment area. The guidance instructs the user to
overlay the species range on the Climate Wizard mean annual precipitation map and subtract
the lowest pixel value from the highest pixel value to assess this factor, using the extremes
within the assessment area. Again, it should be noted that scoring for the factor is based on
comparisons in temperature variation to the lower 48 states and may not be as relevant in
Alaska. Also of concern is the fact that this variable is only considered across the range of
the species within the assessment area, rather than across the species entire distribution. For
species like the coyote with large ranges covering a variety of moisture regimes, examining
variation within the assessment area seems to falsely amplify the importance of this factor in
determining the species vulnerability.
Physiological hydrological niche: Scores for this factor are based on species requirements for
a very specific precipitation or hydrologic regime, such as strongly seasonal patterns of
precipitation or specific wetland or aquatic habitats such as seeps or vernal pools that may be
highly vulnerable to loss across the assessment area. The dependence on these habitats can
be permanent or seasonal (Young et al. 2010). In order for this factor to greatly increase or
increase a species sensitivity to climate change the species must be dependent on a very
narrowly defined regime. Species that live near wetlands, riparian areas or other moist
areas were not considered to be strongly tied to a specific hydrologic regime. Examples of
species that may be quite sensitive to this factor are species dependent on ephemeral pools.
This factor also asks the assessor to consider the direction of expected climate change in
their ranking. Since the Arctic Refuge assessment area is not expected to see significant
changes in moisture based on our ClimateWizard projections this factor was often less
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important. One item of note: Species that are dependent on snow falling as dry snow rather
than heavy wet snow or ice were given a score of increase under this factor. These include
species like muskoxen that depend on snow that is light and dry to allow them access for
grazing in the winter. This appears to be the best place to score a change in the
characteristics of precipitation.
c. Dependence on a specific disturbance regime: This factor was scored using the following guidance
(for specific scoring see guidance doc). This factor pertains to a species' response to specific
disturbance regimes such as fires, floods, severe winds, pathogen outbreaks, or similar events. It
includes disturbances that impact species directly as well as those that impact species via abiotic
aspects of habitat quality. For example, changes in flood and fire frequency/intensity may cause
changes in water turbidity, silt levels, and chemistry, thus impacting aquatic species sensitive to these
aspects of water quality. The potential impacts of altered disturbance regimes on species that require
specific river features created by peak flows should also be considered here; for example, some fish
require floodplain wetlands for larval/juvenile development or high peak flows to renew suitable
spawning habitat. Use care when estimating the most likely effects of increased fires; in many
ecosystems, while a small increase in fire frequency might be beneficial, a greatly increased fire
frequency could result in complete habitat destruction. Finally, be sure to also consider species that
benefit from a lack of disturbance and may suffer due to disturbance increases when scoring this
factor” (Young et al. 2010).
Fires were one of the main disturbances we considered under this category as studies suggest fire
activity will increase in Alaska often leading to changes in age structure and species dominance in
boreal forest (Rupp 2008). Other disturbances affecting species in our assessment included increased
parasite and pest outbreaks and increased flooding. Some changes in disturbance regime may
actually benefit species and the index is constructed to reflect this.
d. Dependence on ice, ice-edge, or snow cover habitats: This factor assesses a species dependence
on habitats associated with ice or snow across its range in the assessment area. A score of “greatly
increase is for species that are highly dependent (more than 80% of occurrences in range) on snow
or ice habitat, such as the polar bear. Many of our species use the snow for burrowing, hiding from
predators or hunting. These species were scored as increase or somewhat increase, depending
on how strongly they were tied to snow use for these activities. Similarly, species that molt in the
winter and take on a white coat were considered to fit into the “increase category as lack of snow
would make them highly visible to predators. Changes in snow condition (i.e. icing over, wetter
snow, etc) were considered under the physiological hydrological niche category.
C3: Restriction to uncommon geological features or derivatives: This factor was scored exactly as according to
the guidance document for the index. Information on restriction to uncommon geologic features
was collected from literature review and use of online databases.
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C4: Reliance on interspecific interactions
a. Dependence on other species to generate habitat: Scored as described in guidance document.
b. Dietary versatility: Scored as described in guidance. If species that make up the diet of the
species being assessed were considered vulnerable to climate change we used this information
as well (e.g. lemmings are an important prey item for arctic fox and are considered extremely
vulnerable to climate change).
c. Pollinator versatility: plants only, not considered in our assessment.
d. Dependence on other species for propagule dispersal: mainly for plants, insects and species
with immobile progeny; not a factor in our assessment
e. Forms part of an interspecific interaction not covered by 4a-d: Scored as described in
guidance. Not a major factor for most of our species. It is important to note that competitive
relationships (or other negative interactions) are not considered under this heading. All species
interactions described are positive and changes in competitive interactions are not considered
anywhere in the index.
C5: Genetic factors
a. Measured genetic variation: Scored as described in guidance document.
b. Occurrence of bottlenecks in recent evolutionary history: Scored as described in guidance
document.
C6: Phenological response to changing seasonal temperature and precipitation dynamics: Scored as described in
guidance document. This factor assesses the degree to which a species has been able to respond to
ongoing climate change through phenological changes (such as the timing of breeding or end of
hibernation). This factor was of limited use for our assessment because much of the available data
on phenology was not from studies in the assessment area as required by the index. It also does not
make sense that this factor was considered in this section rather than section D on observed or
modeled responses to climate change. It might be more useful if the index included a sensitivity trait
to account for species with life histories that make them particularly susceptible from a phenology
standpoint (i.e. species that hibernate, species that time their breeding cycles with emergence of
other species, species that molt).
Overall Scoring
The following excerpt from the creators of the index describes how the scoring for the tool works.
Excerpt from:
Young, B. E., K. R. Hall, E. Byers, K. Gravuer, G. Hammerson, A. Redder, and K. Szabo. 2010. A
natural history approach to rapid assessment of plant and animal vulnerability to climate
change. In Conserving Wildlife Populations in a Changing Climate, edited by J. Brodie, E. Post, and
D. Doak. University of Chicago Press, Chicago, IL.
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To calculate an overall score, the index first combines information on exposure and sensitivity to
produce a numerical sum, calculated by adding subscores for each of the extrinsic and intrinsic
species sensitivity factors. Factors scored to somewhat increase, “increase, and “greatly increase
sensitivity to climate change receive a values of 1.0, 2.0, and 3.0, respectively. Those scored to
somewhat decrease and decrease sensitivity receive values of -1.0 and -2.0, respectively. Factors
for which there are no data or that are scored as neutral” to vulnerability receive a value of zero. If
a factor is scored in multiple levels (e.g., both somewhat increase and “increase), the index uses
an average of the values for these levels.
The value for each factor is weighted by exposure to calculate a subscore for the factor. Climate
influences vulnerability factors in different ways. For most factors, the exposure weighting is a
climate stress value that combines data on projected change in both temperature and precipitation.
In these cases, the weighting factor is the product of weightings for temperature (0.5, 1.0, 1.5, or 2.0
depending on whether the temperature across the range of the species is predicted to increase by
less than zero, one, two, or greater than two standard deviations of the average temperature increase
for the conterminous United States) and precipitation (0.5, 1.0, 1.5, or 2.0 depending on whether the
precipitation across the range of the species is predicted to increase or decrease by less than zero,
one, two, or greater than two standard deviations of the average precipitation change for the
conterminous United States). Other weightings are either fixed at 1.0 in the case of sea level rise
(which occurs independent of local climate), tied solely to temperature for historical and
physiological thermal niche (thus ranging from 0.5-2.0 as described above), or the average of four
times the precipitation and one time the temperature weighting (roughly accounting for how
temperature interacts with precipitation) for historical and physiological hydrological niche.
General Circulation Models and Downscaling
To build a downscaled climate model the ClimateWizard requires the user to select a General
Circulation Model or ensemble models (Table 3) and a future emissions scenario. General
Circulation Models (GCMs) simulate the complex interactions of the atmosphere, oceans, land
surface and ice. The models work by balancing (or nearly balancing) incoming energy in the form of
short wave electromagnetic radiation with outgoing energy in the form of long wave electromagnetic
radiation; any imbalance will result in a change in the average temperature of the earth
(www.climatewizard.org).
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Table 3: Global Circulation Models available for downscaling through ClimateWizard. Table from
www.climatewizard.org
BCCR-BCM2.0 Norway Bjerknes Centre for Climate Research
CGCM3.1(T47) Canada Canadian Centre for Climate Modelling & Analysis
CNRM-CM3 France Météo-France / Centre National de Recherches
Météorologiques
CSIRO-Mk3.0 Australia CSIRO Atmospheric Research
GFDL-CM2.0 USA US Dept. of Commerce / NOAA / Geophysical Fluid
Dynamics Laboratory
GFDL-CM2.1 USA US Dept. of Commerce / NOAA / Geophysical Fluid
Dynamics Laboratory
GISS-ER USA NASA / Goddard Institute for Space Studies
INM-CM3.0 Russia Institute for Numerical Mathematics
IPSL-CM4 France Institut Pierre Simon Laplace
MIROC3.2(medres)
Japan
Center for Climate System Research (The University of
Tokyo), National Institute for Environmental Studies, and
Frontier Research Center for Global Change (JAMSTEC)
ECHO-G Germany /
Korea
Meteorological Institute of the University of Bonn,
Meteorological Research Institute of KMA, and Model and
Data group.
ECHAM5/MPI-
OM Germany Max Planck Institute for Meteorology
MRI-CGCM2.3.2
Japan
Meteorological Research Institute
CCSM3 USA National Center for Atmospheric Research
PCM USA National Center for Atmospheric Research
UKMO-HadCM3 UK Hadley Centre for Climate Prediction and Research / Met
Office
GCMs are driven by emission scenarios or assumptions about how population, energy use and
technology are likely to change and develop in the future and the resulting emissions of
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greenhouse gases. Emission scenarios are essentially storylines that describe what the future
might look like taking different social, economic, cultural, technological, and other human-based
factors into account. Emission scenarios are used as inputs into these models to simulate
changes in temperature, precipitation and other climate variables.
In order to make meaningful predictions about how temperature and moisture will change
across a particular region, these global models need to be downscaled. ClimateWizard allows
the user to downscale any or all of its GCMs using the method described below:
The following was taken from Maurer, E. P., L. Brekke, T. Pruitt, and P. B. Duffy (2007),
Fine-resolution climate projections enhance regional climate change impact studies, Eos Trans. AGU,
88(47), 504 and describes the data presented in the ClimateWizard:
A statistical technique was used to generate gridded fields of precipitation and surface air
temperature over the conterminous United States and portions of Canada and Mexico.
The method involves (1) a quantile mapping approach that corrects for GCM biases,
based on observations of 19501999; and (2) interpolation of monthly bias-corrected
GCM anomalies onto a fine-scale grid of historical climate data, producing a monthly
time series at each 1/8-degree grid cell. The method has been used extensively for
hydrologic impact studies (including many with ensembles of GCMs) and in a variety of
climate change impact studies on systems as diverse as wine grape cultivation, habitat
migration, and air quality.
The downscaled data are freely available for download at the Green Data Oasis, a large
data store at LLNL for sharing scientific data (http://gdo-
dcp.ucllnl.org/downscaled_cmip3_projections/).
Users can specify particular models, emissions scenarios, time periods, geographical areas,
and raw data or summary statistics. All data are archived in a standard netCDF format, a
self-describing machine-independent format for sharing gridded scientific data. The full
text of this article can be found in the electronic supplement to this EOS issue
(http://www.agu.org/eos_elec/).
DEVELOPING A FUTURE CLIMATE CHANGE SCENARIO USING
CLIMATEWIZARD
The user interface on ClimateWizard is shown in Figure 5 below. In order to build a scenario of
future climate change the user must select key inputs into the climate model and then download the
data in a GIS compatible format. The user is asked to select an analysis area or spatial extent of the
data, the time period (mid-century, end of century or past 50 years), type of map, measurement
19
(precipitation or temperature) and the key inputs into the future climate model (emission scenario
and general circulation model).
Figure 5: ClimateWizard user interface. The tool asks the user to select the analysis area, the time
period, the type of map, measurement and the future climate model inputs (www.climatewizard.org).
For our analysis in Alaska we used a global climate model that combined an average ensemble model
of all 17 available GCMs and a High A2 emissions scenario to produce both temperature and
moisture data (Table 4). Because we used moisture data and not just standard precipitation data we
needed to use the ClimateWizard Custom Analysis Tool (www.climatewizard.org/custom) which
provides access to more types of data analysis and projections. All projections were made for the
middle of the century as directed by the NatureServe CCVI guidance document.
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Table 4: Data inputs used for climate projections in the Arctic Refuge
Temperature Moisture
General Circulation
Model Ensemble Average Ensemble Average
Emission Scenario High A2 High A2
Time period Mid-Century Mid-Century
Data produced Average annual change in
temperature as ASCII file for
input in ArcGIS
environment
Percent departure from
historical ratio of AET:
PET downloaded as
ASCII map for input into
ArcGIS
Spatial resolution 50km2 50km2
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Data Processing
All data was processed in an ESRI ArcGIS 10.0 environment and a full list of steps is provided in
Table 6 below along with a brief narrative. This information will not be particularly relevant to non-
GIS users.
In order to use the climate exposure data produced with the ClimateWizard tool, we downloaded
both temperature and moisture data for the state of Alaska based on the Climate Model described
above. The data is downloaded in ASCII (American Standard Code for Information Exchange)
format. ASCII is a character encoding scheme based on an ordering of the English alphabet. ASCII
files can be imported into a GIS environment and converted into grids or raster data. We brought
both the temperature and moisture ASCII files into a GIS environment by using the ArcGIS
toolbox to convert the ASCII files to grid files. Grid files display the data as pixels containing
different values. We also imported a shapefile of the Alaska National Wildlife Refuge boundaries
into the GIS and standardized the projections of all files to NAD_1983_NSRS2007_Alaska_Albers.
Once we created grids of temperature and moisture change I had to change these grids from grids
with floating point pixels to integer pixels so that their attribute information could be viewed. In
order to preserve the accuracy of the data (integer grids cannot store decimals) we first multiplied
the temperature and moisture data by 100 and then converted each grid to an integer file using the
raster calculator. We used the Extract by Mask tool with the boundaries of the Arctic Refuge set as
the mask to produce maps of change across our assessment area, the Alaska National Wildlife
Refuge. This process extracts only data from areas inside the assessment area so that calculations can
be made only in the area in question.
The Index requires that the user enter the portion of the species range over the assessment area that
falls into the following temperature exposure categories: <3.9 degrees F, 3.9 4.4 degrees F, 4.5
5.0 degrees F, 5.1 5.5. degrees F and > 5.5 degrees F. To calculate the portion of each species
range that falls into the above temperature exposure categories, we needed to assess the change of
temperature across the species range in the Arctic Refuge. This required an additional extraction of
temperature and moisture data using species range data as an additional mask. Species ranges were
downloaded in GIS format (as vector files) from the NatureServe Explorers Digital Distribution
Maps of Mammals of the Western Hemisphere
(http://www.natureserve.org/getData/animalData.jsp). Once downloaded, we standardized the
projections of these files to NAD_1983_NSRS2007_Alaska_Albers. These maps are used as a mask
to extract the temperature and moisture data in order to obtain information about the degree of
climate change a species will be exposed to in the assessment area.
We extracted temperature and moisture data for each species and exported the attribute tables as dbf
files. We then opened the exported dbf files in Excel and calculated the percentage of each species
range that fell into the exposure categories for temperature and moisture, described above. The
calculation is done by using the Counts field in t