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Forecasting Wildlife Response to
Rapid Warming in the Alaskan Arctic
CAROLINE VAN HEMERT, PAUL L. FLINT, MARK S. UDEVITZ, JOSHUA C. KOCH, TODD C. ATWOOD,
KAREN L. OAKLEY, AND JOHN M. PEARCE
Arctic wildlife species face a dynamic and increasingly novel environment because of climate warming and the associated increase in human
activity. Both marine and terrestrial environments are undergoing rapid environmental shifts, including loss of sea ice, permafrost degradation,
and altered biogeochemical fluxes. Forecasting wildlife responses to climate change can facilitate proactive decisions that balance stewardship
with resource development. In this article, we discuss the primary and secondary responses to physical climate-related drivers in the Arctic,
associated wildlife responses, and additional sources of complexity in forecasting wildlife population outcomes. Although the effects of warming
on wildlife populations are becoming increasingly well documented in the scientific literature, clear mechanistic links are often difficult to
establish. An integrated science approach and robust modeling tools are necessary to make predictions and determine resiliency to change. We
provide a conceptual framework and introduce examples relevant for developing wildlife forecasts useful to management decisions.
Keywords: Alaska, Arctic, climate change, modeling, wildlife
T
he Arctic is warming at more than twice the global rate and is undergoing an environmental transition
typified by loss of summer sea ice, permafrost thaw, and
altered nutrient and hydrologic cycling (IPCC 2007). This
rapid change to the cryosphere raises many questions
about the future of this region as it becomes more accessible and as both Arctic and non-Arctic nations respond.
Responsible stewardship of existing natural resources,
including wildlife, is a common goal in the face of emerging economic opportunities. Climate warming will affect
wildlife directly—through physical changes in the environment—and indirectly—through anthropogenic pressures
and community-level interactions. Addressing climate and
development pressures and predicting wildlife outcomes
requires an integrated effort by scientists, managers, and
policymakers (Clement et al. 2013).
The unique ecosystem of the Alaskan Arctic is home to
resident species adapted to cold and also supports numerous
populations of migratory species that take advantage of the
highly productive summers. Wildlife in this region currently
face substantial alterations in habitat, phenology, and food
resources that result from climate-induced changes to the
landscape. As year-round pack ice retreats further from land
each year, ice-dependent marine mammals must cope with
unusually large extents of open water, forcing animals to
adopt new habitats and modify their foraging behaviors (e.g.,
Schliebe et al. 2008, Jay et al. 2012, Noren et al. 2012, Pagano
et al. 2012). Warming temperatures and retreating sea ice
also influence the characteristics of Alaskan coastlines, lakes,
and rivers. Wetlands are changing in extent and distribution
as riverine and coastal habitats become subject to increased
erosion, saltwater inundation, and permafrost degradation (Rowland et al. 2010). The resulting shifts in nutrient
flow are expected to affect primary productivity in marine
and freshwater communities (Schindler and Smol 2006,
Tremblay and Gagnon 2009). Onshore, the ­terrestrial environment is experiencing measurable increases in the onset
and length of the growing season, frequency, and scale of
fires, as well as changes in vegetation communities (Tape et
al. 2006, Hinzman et al. 2013). Peak vegetation and insect
productivity is thought to be highly sensitive to climatic
factors, and wildlife populations that depend on these food
resources may encounter mismatches in timing and availability, as well as less seasonal predictability (e.g., Tulp and
Schekkerman 2008).
Although the effects of warming on wildlife populations
are becoming increasingly evident, such effects are likely to
be mediated through habitat changes, and clear mechanistic
links necessary for predictive modeling are often lacking.
Temperature change will vary spatially and temporally, and
not all ecological components or all regions of the Arctic will
undergo change at similar rates and magnitude. Likewise,
wildlife will respond differently to climate-related pressures;
some species will clearly suffer, whereas others may benefit.
BioScience 65: 718–728. Published by Oxford University Press on behalf of the American Institute of Biological Sciences 2014. This work is written by US
Government employees and is in the public domain in the US.
doi:10.1093/biosci/biv069
Advance Access publication 24 June 2015
718 BioScience • July 2015 / Vol. 65 No. 7
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Managers of Arctic wildlife resources
are tasked with identifying vulnerable
species or populations and determining what actions, if any, are possible for
mitigating the adverse effects of warming
and maintaining population objectives.
Achieving this goal requires knowledge
of the current status of wildlife populations, as well as their projected abundance and distribution under future
climate scenarios. Forecasts of wildlife
response to rapid warming and identification of the primary uncertainties in
such forecasts are therefore necessary to
make proactive decisions that promote
stewardship.
Forecasting wildlife response
to climate change
To forecast the distribution and abundance of focal wildlife species, explicit
links need to be established between
the outputs of climate models (e.g., air
temperature, moisture, and greenhouse
gases) and the specific environmental
drivers that are hypothesized to influence demography. However, the actual
changes in temperature and moisture
regimes that will result from climate
change are unknown. Therefore, identifying the key mechanistic links between
physical drivers and wildlife response is a
crucial first step in developing predictions
for wildlife outcomes. Understanding
Figure 1. Cumulative annual thaw degree days (a) and freezing degree
such relationships makes results portable
days (b) based on daily maximum and minimum temperature records from
across landscapes and adaptable to variBarrow, Alaska. Patterns of warming vary seasonally and therefore result in
ous climate-change scenarios.
different impacts on wildlife throughout the year. At Barrow, there is a slight
To help address the need for proactive
positive trend in summer temperatures but a larger relative increase in winter
decisionmaking, we introduce a conceptemperatures. Climate data such as these are used to develop projections that
tual model (figure 2) and discuss key facare incorporated into models linking physical drivers to wildlife response. The
tors that contribute to wildlife response
data are from the National Oceanic and Atmospheric Administration National
in the Alaskan Arctic. This effort is part
Climatic Data Center.
of an integrated science initiative to forecast the future impacts of climate change
For example, the impacts of warming are expressed difon US Department of the Interior (DOI) trust species in
ferently between marine and terrestrial systems. Extensive
Alaska. Physical drivers (e.g., figure 1) that are expected to
decline in sea-ice cover represents a foundational change to
result in quantifiable effects on animal populations provide
the marine ecosystem, with influences on physical, chemical,
the unifying construct for modeling across a suite of ecologiand biological oceanography as yet unknown (Hinzman et
cally diverse species. The incorporation of species-specific
al. 2013). In the terrestrial environment, changes in permaattributes and community-level factors allows for expansion
frost, hydrology, and phenology are expected to affect wildinto more detailed, ecologically relevant models. To illustrate
life populations, but their impacts may be more subtle and
the projected wildlife outcomes of climate-related changes,
complex, because warming is not directly equated to habitat
we have identified groups of DOI trust species on the basis
loss. Patterns of warming also vary seasonally (figure 1); as
of their key positions in the food web or apparent response
a result, the outcomes of changing temperatures will not be
to climate warming expressed by recent changes in populauniform throughout the annual cycle.
tion abundance and/or distribution. These species represent
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Focal species responses
Physical drivers
Human activities
Community-level
interactions
Availability and quality
of primary forage/prey
and habitat
Secondary effects
Primary effects
Community-level
interactions
Changes in animal
abundance and/or
distribution
Human activities
Biogeochemical
fluxes
Sea ice, lake ice,
snow, and permafrost
Temperature
Moisture
Human activities
Greenhouse
gases
Figure 2. Conceptual model linking physical changes predicted by climate models to realized wildlife species’ response
in the Alaskan Arctic. Some relationships may be direct, whereas others will only be invoked through one or more
intermediate responses. In addition to key physical drivers, changes in community-level interactions and human activities
due to climate warming also affect projected wildlife outcomes. The overarching goal of this type of model is to understand
the mechanisms and variation in climate-driven processes that influence animal populations to allow the development of
forecasts useful to resource managers.
a diversity of foraging guilds and life-history characteristics: ice-dependent marine mammals (Pacific walruses,
Odobenus rosmarus divergens, and polar bears, Ursus maritimus), terrestrial herbivores (geese and caribou, Rangifer
tarandus), terrestrial insectivores (passerines and small
mammals), and freshwater associates (fish and waterbirds).
In the following sections, we discuss links between changing
ecological conditions in the Alaskan Arctic and observed or
predicted wildlife responses. We focus first on the effects of
key physical drivers and then consider additional sources of
complexity that accrue in an ecological context.
Primary response to physical drivers
Climate-induced changes in the Arctic environment include
sea ice loss, permafrost thaw, and biogeochemical fluxes,
all of which may influence wildlife populations that rely on
marine, terrestrial, and freshwater resources.
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Sea ice loss. One of the most well-documented physical
changes in response to rising temperatures in the Arctic has
been the dramatic loss of sea ice, including a shift in sea-ice
phenology, with earlier breakup and later freeze-up than historic means (e.g., Markus et al. 2009). As ice melts, open water
is exposed, allowing solar energy to be absorbed into the
ocean and hastening further melting. The positive ice-albedo
feedback process leads to consistently thinner first-year ice
and an overall lower ice volume, which requires less energy
to melt. If air temperatures are unabated or continue to rise,
the system loses sea ice at an increasing rate and becomes
vulnerable to rapid ice-loss events, such as those observed in
September 2007 and 2012 (reviewed in Hinzman et al. 2013).
Wind effects and increased oceanographic mixing may also
contribute to the thinning of the ice pack and hasten seasonal
sea-ice loss (e.g., Woodgate et al. 2010, Steele et al. 2015).
Cumulatively, the current warming trend has resulted in a
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decline of annual and summer mean sea-ice extent by about
3% and 10%, respectively, per decade since 1979 (e.g., Serreze
and Francis 2006). According to the most recent predictions,
the Arctic could experience its first nearly ice-free summer
(i.e., less than 1.0 million square kilometers) as soon as 2016
(Overland and Wang 2013). The ecological consequences
of sea-ice loss are projected to extend beyond those already
observed in the marine environment, with associated impacts
on terrestrial productivity (Post et al. 2013).
Permafrost thaw. Permafrost is currently undergoing rapid
warming and thawing in Alaska and other parts of the
Arctic (Hinzman et al. 2013), which will probably be exacerbated by increased winter snow depth combined with earlier
spring snowmelt and higher air temperatures during all seasons. Permafrost thaw creates thermokarst terrain because of
the subsequent movement of material as landscapes degrade
and restabilize in response to topography, water presence
and flow, soil properties, vegetation, and snow (Jorgenson
et al. 2013). This process also produces liquid water, which
may enhance runoff and infiltration and, in turn, accelerate
further thaw (Jorgenson et al. 2013). Thawing permafrost,
a longer growing season, and changes in soil moisture are
expected to induce changes in vegetation cover, including
the encroachment of shrubs into tundra habitats. The projected increase in ground temperatures will result in greatly
accelerated thermokarst erosion, which could completely
change the character of lake and river systems. In addition,
reductions in the duration and thickness of lake and river
ice, combined with warmer water temperatures, are likely
to affect freshwater habitats (Quayle et al. 2002). In interior
Alaska, where permafrost is thinner and more easily lost,
thaw has led to regional connectivity between surface water
and groundwater, altering lake-water balances and the timing of river discharge (Walvoord et al. 2012).
Biogeochemical fluxes. Climate warming is also leading to
shifts in Arctic biogeochemistry. As the Arctic warms, limiting nutrients such as carbon, nitrogen, and phosphorus are
released from thawing soils (see Frey and McClelland 2009
and the references therein). These nutrients may be transported to wetlands, ponds, rivers, and the ocean, depending on the balance between surface water and groundwater
fluxes. On the coast and lakeshores, increased storm frequency and fetch due to summer ice loss increases thermomechanical erosion (figure 3; Jones et al. 2009), which
provides a significant source of sediment and nutrients to
lakes and the Arctic Ocean. Storm surges can also push
saltwater many kilometers inland, killing terrestrial vegetation, thawing permafrost, and releasing additional nutrients.
Greater nutrient availability coupled with warmer overall
temperatures affects productivity throughout terrestrial,
freshwater, and marine ecosystems. Increased levels of
carbon dioxide (CO2) in the atmosphere have resulted
in increased levels of carbonic acid in the oceans (ocean
acidification), reducing the availability of calcium carbonate
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required for the shells and exoskeletons of marine invertebrates (Guinotte and Fabry 2008).
Anthropogenic and community-level interactions
The influence of specific physical drivers on population abundance and distribution are variable across taxa.
Therefore, accurate predictions of wildlife outcomes require
species-specific models adapted to unique life-history and
environmental conditions. However, other factors that occur
at a community- or ecosystem-wide level must also be considered across all species. Anthropogenic activities (e.g.,
resource development, transportation corridors, and hunting) and inter- and intraspecific interactions (e.g., predation,
competition, and disease) are subject to change under a
future warming scenario. The location, timing, and magnitude of such occurrences are generally more difficult to
predict than broadscale environmental changes, but their
projected effects should be addressed and incorporated into
forecasts of wildlife outcomes (figure 2).
Anthropogenic pressures on wildlife are expected to
increase with changing environmental conditions in the
Alaskan Arctic. Industrial development, both onshore and
offshore, and the potential for increased overland and
marine-transport corridors present growing potential for
disturbance. A recent analysis of climate models suggests
that new trans-Arctic shipping routes will become navigable
in less than five decades, which could introduce additional
sources of mortality or influence the distribution patterns of
marine animals (Smith and Stephenson 2013). Changes in
accessibility to hunting grounds due to warming may also
result in altered patterns of wildlife harvest.
Many species will face new inter- and intraspecific interactions as a result of community-level changes to the Arctic
environment. Shifts in distribution and the abundance of
potential predators could add additional sources of mortality that are not accounted for through primary or secondary
effects. Crowding due to population increase, habitat limitations, or range contractions will lead to increased competition both within and between species. Competition for
resources may also occur in transitional zones, such as along
the coastline of the Arctic Ocean, at the margins of boreal
and tundra habitats, and in areas of increasing anthropogenic use; such patterns have been observed or predicted
in Alaska for closely related species such as brown (Ursus
arctos) and polar bears and red (Vulpes vulpes) and Arctic
(Vulpes lagopus) foxes (reviewed in Stickney et al. 2014,
Rode et al. 2015).
The distribution and prevalence of infectious diseases
are expected to change in response to warming, potentially
exposing naive populations to new pathogens or parasites
(Bradley et al. 2005, Burek et al. 2008, Post et al. 2013, Van
Hemert et al. 2014). Shifts in animal distributions, including
aggregations of species that have been historically separated,
are likely to facilitate the transfer of infectious diseases
across taxa. For instance, loss of sea ice has led to increasing contact between marine and terrestrial ecosystems and
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Figure 3. Permafrost block on the Arctic coast of Alaska. The combination of mechanical and thermal erosion produced by
warm waves contacting frozen sediments may lead to rapid erosion of lake shores and coastlines, subsequently releasing
sediments and nutrients to aquatic environments. The rate of erosion is increasing with higher storm intensity and
frequency, combined with decreased seasonal ice cover. Photograph: US Geological Survey.
across other geographic boundaries, thereby creating opportunities for novel exchange of pathogens (Post et al. 2013).
Although current knowledge of northern host–pathogen
systems is lacking, making it challenging to predict when
and where potential outbreaks will occur, such factors may
play an important role in the future health of Arctic wildlife.
Forecasted outcomes for Arctic wildlife species
Wildlife responses to climate-related changes in the Arctic
are not expected to be uniform. Here we describe forecasted
outcomes for ice-dependent marine mammals, terrestrial
herbivores, and freshwater associates.
Ice-dependent marine mammals. Temperature and atmospheric
CO2 are key physical drivers in the Arctic marine environment and have direct impacts on sea-ice cover and ocean
chemistry (figure 4). Loss of sea ice affects the quality and
availability of food resources and habitat for ice-dependent
marine mammals, including Pacific walruses and polar
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bears, whereas changes to ocean chemistry influence their
prey base. Animals may also be exposed to new risk factors
because of increasing use of onshore habitats and changes in
human activities.
Walruses are apex predators in a benthic food web, feeding primarily on clams and other invertebrates. Changes
in sea-ice dynamics and water temperature will alter the
spatiotemporal distribution of the primary production that
supports their benthic prey (Grebmeier et al. 2006, Hinzman
et al. 2013), which may also be directly affected by changes
in ocean chemistry (Guinotte and Fabry 2008). Shifts in the
distribution of seasonal sea ice will reduce access to offshore
foraging areas by increasing travel times and associated
energy requirements (Noren et al. 2012) or will restrict
foraging to less productive near-shore areas where local benthic communities are more susceptible to depletion (figure
4; Jay et al. 2011). As the extent of summer sea ice in the
Chukchi Sea has declined, walruses have increased their use
of coastal haulouts in Alaska and Russia, where disturbance
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11,12,13,14
Distribution
Focal species responses
Abundance
16,18,19
6
6
16
7
Secondary effects
7
17
Predators
Benthic prey
Human activity
and harvest
17
8,9,10
3
5
Primary effects
Sea ice
4
15
Ocean chemistry
1
Physical drivers
Temperature
2
Atmospheric CO2
Figure 4. Example of a detailed conceptual model linking physical changes
predicted by climate models to responses by the Pacific walrus population (Jay
et al. 2011). Direct processes are depicted with solid lines; indirect processes
are depicted with dashed lies. Changes in atmospheric carbon and temperature
result in reduced availability of sea ice (1) and increased ocean acidity (2). The
cascading effects of these physical changes include nutrient limitations (3) and
decreased availability of calcium carbonate (4), with consequent changes in
the availability of benthic prey and the ability of walruses to meet nutritional
requirements (7). Sea-ice loss may change the abundance and distribution of
predator species such as polar bears and killer whales (5), which could influence
walrus populations (6). Reduced sea-ice availability also increases the size and
concentration of walrus haulouts (8), the frequency of use of coastal haulouts
(9), and the distance between resting and feeding areas (10), all of which
affect exposure to disease (11), susceptibility to predation (12), and energy
requirements (13). Crowding and disturbances at coastal haulouts also cause
direct mortality by trampling (14). Increased human activity due to sea-ice
reductions (15) may increase disturbance to walruses through vessel traffic
and oil and gas development (16), may affect the abundance and distribution
of prey and predator populations (17), and could lead to mortality from oil
spills (18). Sea-ice reductions may also affect access to walruses for harvest by
humans (19). Abbreviation: CO2, carbon dioxide.
events have caused mass mortality (figure 5; Udevitz et
al. 2013) and where there may be greater exposure to disease (­figure 4). Changes in anthropogenic activity, such as
increased vessel traffic and altered hunting patterns, will create additional sources of disturbance to walruses (figure 4).
Like walruses, polar bears are highly dependent on sea-ice
habitats for many life-history requirements. Polar bears prefer sea ice over shallow waters, such as the continental shelf,
where biological productivity is greatest. Declines in sea-ice
extent have caused the leading edge of the pack ice to retreat
far from the continental shelf during summer, leaving only
unconsolidated or low-concentration sea ice in its place. As
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these key hunting platforms disappear,
polar bears have decreasing access to
seals and other prey, which subsequently
affects bears’ spatiotemporal distribution (Schliebe et al. 2008). For example,
to cope with the absence of sea ice over
continental-shelf foraging grounds, bears
in the southern Beaufort population have
been swimming large distances between
retreating pack ice and the Alaskan mainland (figure 6; Pagano et al. 2012) and
increasing their use of onshore habitat
in late summer. An animal’s decision to
exploit terrestrial versus sea-ice resource
patches is likely to have cascading effects
on foraging behavior, exposure to disease
and other novel threats, and, ultimately,
population dynamics. The forecast of
future polar-bear status developed by
Amstrup and colleagues (2008) projected
that two-thirds of the global population
are at risk of extirpation by midcentury
if business-as-usual rates of greenhouse
gas emissions continue.
Terrestrial herbivores. In the terrestrial
environment, changes in temperature
and moisture result in primary effects
on permafrost, snow cover, and biogeochemical fluxes that influence characteristics of wildlife forage and habitat.
Shifts in vegetation cover and structure
have important implications for terrestrial herbivores. However, in contrast to
ice-dependent marine mammals, warming temperatures may benefit some species, at least seasonally, in terms of forage
availability and quality.
Geese are an important herbivore in
the Arctic during the summer and rely
on a short but productive growing season for breeding. In Alaska, warming
permafrost and increased storm-surge
inundation has led to coastal permafrost
subsidence, sedimentation, and conversion to halophytic
plant communities that provide good goose forage (figure 7 Tape et al. 2013). Manipulative studies on halophytic
meadows using small greenhouses have demonstrated that
increased temperature and growing season length result in
increased plant biomass but little to no reduction in percent
protein. Therefore, Arctic-nesting geese currently face an
abundance of high-quality food, and projections suggest that
forage conditions will continue to improve. Associated with
this favorable habitat shift, the abundance and distribution
of multiple species of geese are changing to take advantage
of new food resources (Flint et al. 2008).
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Figure 5. Female Pacific walruses and calves on coast of northwestern Alaska. Walruses have been increasing their use of
coastal haulouts in Russia and Alaska in recent years as late summer sea ice has retreated from the continental shelf in
the Chukchi Sea. Large numbers of walruses have been crushed during disturbance events that caused herds to panic and
stampede at these haulouts. Calves and yearlings are particularly vulnerable to trampling, and this type of mortality may
have population-level consequences (Udevitz et al. 2013). Photograph: Tony Fischbach, US Geological Survey.
The predominant effects of climate warming on populations of northern ungulates such as caribou are thought
to be nutritionally mediated through vegetative changes
during the summer. For caribou, the high seasonal variation in plant abundance and quality exacerbates the need to
replenish body stores for reproduction and winter survival
(Barboza and Hume 2006). Results from ongoing studies in
the central Alaskan Arctic suggest that an extended growing season associated with climate warming is leading to
increases in vegetative productivity, particularly for shrubs
(Martin et al. 2009). These changes offer nutritional benefits for caribou during summer months, similar to what
is projected for Arctic-nesting geese. Other climate-related
factors, such as increases in snow cover and extreme winter weather events, may be more detrimental to caribou,
but the consequences of such changes have not yet been
measured.
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Terrestrial insectivores. Physical changes due to warming have
resulted in important shifts in phenology, vegetation cover,
and food resources for Arctic-nesting passerines and resident small mammals. For passerines that migrate large
distances to breed in the Arctic, the date of nest initiation
is dependent on local conditions upon arrival, including
the extent and depth of snow cover. The timing of insect
emergence is another key parameter for reproductive success, and changes in phenology may lead to an earlier peak
in invertebrate abundance, as has been described in some
parts of the Arctic (Tulp and Schekkerman 2008). Changes
in invertebrate abundance and distribution are important
not only in terms of food availability but can also affect
exposure to vector-borne diseases such as avian malaria
(Bradley et al. 2005, Garamszegi 2011, Van Hemert et al.
2014). Small mammals, which overlap with migratory landbirds in range, diet, and habitat use, are highly responsive
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fully dry some upland wetlands, whereas others may become
nutrient rich through the thawing of the adjacent permafrost
(Koch et al. 2014). Such changes influence habitat quality
for geese and invertebrate food resources for shorebirds and
loons and may in turn affect community-level factors such
as nesting density, competition, and predation.
Figure 6. Satellite track of a female polar bear swimming
from unconsolidated pack ice to the Arctic coast near
Barrow, Alaska, July 2009. Over the course of 5 days, this
bear swam 303 kilometers (km). Because of recent declines
in summer sea-ice cover, bears either follow the pack ice
as it recedes north over the deep water of the Arctic Basin
or remain over the continental shelf on unconsolidated
ice, which is vulnerable to rapid and complete melt. Bears
that choose the latter are forced to swim long distances to
reach either stable pack ice or land. Source: Adapted from
Pagano et al. (2012).
to local environmental conditions throughout the annual
cycle. Winter conditions, such as snow depth and icing
events, are important determinants of survival (Aars and
Ims 2002). Many species of passerines and small mammals
are currently experiencing shifts in distribution because of
their sensitivity to climate-induced changes in vegetation
structure. Results from avian point-count surveys conducted
over a 25-year period in northwestern Alaska suggest an
increase in shrub-associated passerine species, a pattern that
is consistent with recent shrub encroachment in this region
(Tape et al. 2006). Predictions for future distribution of small
mammal species include a pronounced range contraction for
tundra-associated species, whereas boreal-associated species
are expected to increase in both range and abundance (Hope
et al. 2013).
Freshwater associates. Temperature and moisture directly
affect freshwater habitats through their impacts on permafrost, lake ice, snow cover, and biogeochemical processes.
Resulting climate-induced changes in terrestrial hydrology
are expected to have substantial impacts on fish and piscivorous bird species dependent on large lakes and wetlands for
habitat and food resources. Greater hydrologic variability
will alter stream discharge and lake connectivity, which is
linked to fish presence (Laske et al. 2013). For loons, changing water levels alter the quality of breeding habitat along
lake margins (Haynes et al. 2014). Warmer summers may
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Linking forecasts to decisions
Forecasting wildlife outcomes has been identified as a crucial
need for management and conservation of Arctic ecosystems
(Clement et al. 2013). Rapid rates of ecological change will
drive changes in wildlife population sizes and distributions,
but trends may be subtle and subject to stochastic variation.
Given time lags in population response, especially for longlived species with low reproductive rates, detecting shifts in
distribution or abundance may be so delayed that management options are notably constrained (e.g., Bromaghin et al.
2014). However, with limited inventory and monitoring
effort in the Arctic, very large population-level changes may
occur before they can be measured. Management decisions
must therefore be made on the basis of currently available—
but often incomplete—information.
The conceptual model presented in figure 2 provides a
starting point for considering important physical drivers and
community-level interactions that can be used to predict
wildlife outcomes. Specific analytic modeling approaches
must then build on this framework to be useful to managers. Mechanistic models are ideal for developing predictions
because they explicitly quantify the links between climaterelated changes and wildlife response. However, for large,
remote regions such as the Alaskan Arctic, data to fully
inform such models are often lacking, and an alternative
approach is therefore necessary. Effective modeling tools
will recognize and incorporate the highly dynamic nature
of the Arctic environment under current climate trends and
changing anthropogenic pressures. Such models should also
be adaptable so that new relationships can be represented as
they become understood.
Bayesian network models have proved useful in this context (Amstrup et al. 2008, Jay et al. 2011, Atwood et al. 2015).
Bayesian network models represent interrelated factors that
ultimately affect parameters of interest (e.g., abundance,
distribution, or likelihood of persistence of a species) as
nodes linked by probabilities. Using probability distributions
allows for the explicit representation of the degree of uncertainty, such as that introduced by disagreement in climate
projections. Models can be elaborated to represent the extent
to which relationships are understood, and probabilities are
based on data or subjectively determined using expert opinion when necessary. A significant benefit of this approach
is that not all relationships must be fully understood for the
model to be useful. For example, factors such as disease or
anthropogenic disturbance can be included even if only limited data exist. Recent Endangered Species Act (ESA) decisions in the United States demonstrated the applied benefit
of these flexible, transparent models (Amstrup et al. 2008,
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elaboration of the demographic mechanisms affecting each species and thereby
improves the predictions relating to their
interactions.
The ultimate culmination of this
linking process would be a full ecosystem model. However, ecosystem models may be functionally unobtainable
within budgetary and time constraints.
Furthermore, such detail may not be
necessary to inform many management
decisions. Determining the level to which
models need to be expanded should
be based on the specific management
question and the corresponding sensitivity of the model to the linked inputs.
In the case of polar bear–goose links,
projections suggest that these interactions may have a much greater impact
on population outcomes for geese than
for polar bears; therefore, linked models
would not be expected to have a substantial influence on polar-bear management
decisions, but the converse would be true
for snow geese (box 1).
Figure 7. Example of habitat change on the Arctic coast of Alaska. (a) Diagram
One challenge with any model-based
showing the process by which permafrost degradation and saltwater inundation forecasting effort is that the predictions
facilitate sedimentation, subsidence, and habitat conversion (Tape et al. 2013).
of population outcomes under future
(b) High-altitude infrared image of coastal tundra showing the boundary
conditions are dependent on the assumpbetween upland tundra and halophytic sedge meadows in 1955 (red line) and
tion that relationships measured under
in 1979 (yellow line). (c) Aerial view of this same transition zone in 2012.
the current range of variation remain
(d) Contemporary sedge meadows in former transition zone with evidence
valid when extrapolated to the future.
of buried peat layer indicating historic vegetation. (e) Black Brant (Branta
Violations of this assumption are most
bernicla nigricans) benefit from increase of this productive forage.
severe if thresholds or tipping points
occur outside the range of measured
plate 3; Jay et al. 2011) that characterize key relationships
conditions but within the range of future projections.
between biotic and abiotic factors and provide forecasts of
Rapid environmental changes, such as those observed in
wildlife outcomes.
the Arctic, may result in conditions and, consequently,
Models used for forecasting are easiest to conceptualize
wildlife outcomes that have not been previously observed
when they include only a single species, but this approach
or predicted (e.g., box 1). Therefore, no model should be
may be unrealistically simplistic. Our conceptual model
viewed as static, and a key component of effective wildlife
(figure 2) also incorporates community-level interactions,
forecasting is the need to validate and revise models as new
which include interspecific effects. It is plausible that, for
information becomes available. An example of this process is
some species, factors such as predation or competition
a recent update (Atwood et al. 2015) of the polar bear model
could actually have a larger impact on populations than
produced by Amstrup and colleagues (2008). Although the
primary physical drivers do. In such cases, these interacinitial model was useful for an ESA listing decision, it lacked
tions may be effectively represented by linking individual
sufficient detail to determine which specific stressors, aside
models. For example, because of changing environmental
from climate warming, were most influential in driving
conditions, polar bears are arriving on land earlier during
population outcomes. The update incorporated new inforthe summer and have begun to consume the eggs of snow
mation to identify individual factors (rather than composite
geese (Chen caerulescens; box 1). The strength of this relaclasses) most deserving of mitigation efforts and can be used
tionship (i.e., effect size) will vary depending on how polarto direct management and conservation actions.
bear populations respond to all other influences. Therefore,
Failure to use predictions in developing management
the output from the polar-bear model would provide the
plans will have negative impacts on wildlife populations,
input for a community-level interaction in the goose model.
which are more likely to remain healthy or recover at
Linking the individual models allows for more complete
less cost with prompt action. The primary management
726 BioScience • July 2015 / Vol. 65 No. 7
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Box 1. Polar bears and snow geese: An example of a novel community-level interaction.
The conceptual model presented in figure 2 demonstrates the theoretical response of a population to key physical drivers resulting
from climate change. However, single-species models can be unrealistically simplistic, because interspecific interactions also influence
population outcomes. As a given species is affected by physical climate-related drivers, so, too, are other species, potentially creating
community-level feedback. Ideally, our ecological knowledge of these systems allows us to anticipate such effects and model them
accordingly. However, as entire ecosystems begin to respond, novel and unexpected interactions sometimes occur.
Recent evidence of polar bears feeding on nesting geese provides one such example. In Hudson Bay, polar bears traditionally came on
shore in early July and were thought to primarily survive on stored fat reserves. But as the duration of the ice-free season increased over
time, polar bears were forced onto land sooner, reducing their opportunity to forage on seals and potentially leading to the observed
long-term decline in bear body condition, survival, and abundance (Stirling et al. 1999, Regehr et al. 2007). Concomitantly, snow geese
that nest in dense colonies along the shore of Hudson Bay have increased dramatically in recent years. Snow geese initiate nests soon
after snow melt, and their eggs usually hatch in late June (Rockwell et al. 2011), an event that previously occurred before the arrival
of polar bears on shore. However, sea-ice breakup has advanced more quickly than onshore snow melt so that polar bears appear on
land prior to hatch in some years, creating an opportunity for bears to forage on goose eggs. The energetic significance for polar bears
in Hudson Bay and the broader rangewide relevance of this food source has been questioned and is currently under debate (Rockwell
and Gormezano 2009, Rode et al. 2015).
Although the importance of this interspecific interaction remains unclear for polar bears, the consequences are clearly negative for
snow geese. The snow-goose breeding strategy (i.e., colonial nesting) seems maladaptive in the presence of polar bears, and demographic models for snow geese predict a population decline under bear predation (Rockwell et al. 2011). However, such effects are not
likely to be fixed through time. If nesting goose populations decline, the utility of geese as a food source for bears will also diminish.
Conversely, if terrestrial food sources are inconsequential for polar bears and their populations continue to decline with loss of sea ice,
then the predation rate on nests should decrease. This simple two-species example demonstrates how gradual shifts in the timing of
phenology can create unanticipated community-level interactions that may influence the dynamics of one or both populations.
challenge posed by the rapid pace of change in the Arctic is
the need to make decisions proactively—before an adverse
situation has occurred. Because of the unprecedented scale
of climate warming and associated changes in wildlife distributions, it will no longer be sufficient to routinely consider
fixed, single species models in isolation. Our conceptual
model (figure 2) provides a framework for developing
individual species models that can be linked as necessary.
Forecasts should be implemented using a flexible approach
and must be regularly updated as new information becomes
available. Such forecasting efforts will help to inform the
global stewardship of a new Arctic.
Acknowledgments
This work is part of the Changing Arctic Ecosystems Initiative
supported by the US Geological Survey, Ecosystems Mission
Area, Wildlife Program. Anthony R. DeGange offered helpful feedback during manuscript development. David C.
Douglas provided constructive reviews of earlier versions of
this manuscript. Any use of trade names is for descriptive
purposes only and does not imply endorsement by the US
Government.
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Caroline Van Hemert ([email protected]) is a research wildlife biologist
for the US Geological Survey (USGS) Alaska Science Center who studies
wildlife health, infectious disease, and avian ecology. Paul L. Flint is a research
wildlife biologist for the USGS Alaska Science Center, with a specialty in waterfowl ecology. Mark S. Udevitz is a research statistician for the USGS Alaska
Science Center, with a focus on walrus ecology and biometrics. Joshua C. Koch
is a research hydrologist at the USGS Alaska Science Center who studies water
dynamics, biogeochemistry, and nutrient cycling. Todd C. Atwood is a research
wildlife biologist who leads the polar bear program at the USGS Alaska Science
Center. Karen L. Oakley is a supervisory biologist who serves as the chief
of the Marine and Freshwater Ecology branch at the USGS Alaska Science
Center. John M. Pearce is a supervisory biologist who serves as the chief of the
Wetlands and Terrestrial Ecology branch at the USGS Alaska Science Center.
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