U.S. Department of the Interior

U.S. Geological Survey

Open-File Report 2014–1254

Evaluating and Ranking Threats to the Long-Term 

Persistence of Polar Bears
Cover: A family group of polar bears composed of an adult female and two yearlings, along the 

coast of northern Alaska, autumn 2009. Photograph taken by Mike Lockhart, volunteer with the 

U.S. Geological Survey’s Polar Bear Program. 
Evaluating and Ranking Threats to the Long-Term 

Persistence of Polar Bears

By Todd C. Atwood, Bruce G. Marcot, David C. Douglas, Steven C. Amstrup, Karyn D. Rode, George M. Durner, 

and Jeffrey F. Bromaghin

Open-File Report 2014-1254

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

SALLY JEWELL, Secretary

U.S. Geological Survey

Suzette M. Kimball, Acting Director

U.S. Geological Survey, Reston, Virginia: 2015

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Suggested citation:

Atwood, T.C., Marcot, B.G., Douglas, D.C., Amstrup, S.C., Rode, K.D., Durner, G.M., and Bromaghin, J.F., 2015, 

Evaluating and ranking threats to the long-term persistence of polar bears: U.S. Geological Survey Open-File 

Report 2014-1254, 114 p., http://dx.doi.org/10.3133/ofr20141254. 

ISSN 2331-1258 (online)
iii

Contents

Abstract ...................................................................................................................................................................... 1

Introduction................................................................................................................................................................. 2

Materials and Methods ............................................................................................................................................... 5

Revision of the First Generation Model................................................................................................................... 5

Model Structure and Parameterization.................................................................................................................... 6

Model Structure for Conducting Scenario Projections............................................................................................. 6

Model Composition ................................................................................................................................................. 7

Sensitivity Analysis and Influence Runs................................................................................................................ 14

Comparison of Polar Bear Ecoregion Outcomes under Stabilized and Unabated GHG Emissions Pathways...... 15

Results...................................................................................................................................................................... 15

Sensitivity Analysis................................................................................................................................................ 15

Basis for the Normative Model Results ................................................................................................................. 15

Normative Model Results ...................................................................................................................................... 16

Influence Runs ...................................................................................................................................................... 16

Discussion ................................................................................................................................................................ 19

Normative Model Runs.......................................................................................................................................... 20

Effect of Stabilizing GHG Emissions ..................................................................................................................... 22

Stressor Evaluation............................................................................................................................................... 22

Manageability of Influential Stressors.................................................................................................................... 24

Conclusion................................................................................................................................................................ 25

Acknowledgments..................................................................................................................................................... 26

References Cited...................................................................................................................................................... 26

Appendix A. Name, Job Title, Area of Expertise, and Professional Affiliation for Each Subject Matter Expert ......... 50

Appendix B. Additional Information on the Process Used to Develop and Parameterize the Model......................... 51

Appendix C. Description (Including Key Assumptions) of All Nodes in the Second Generation Model..................... 53

Appendix D. Normative Scenarios (Using Expected Values of Input Conditions) Run on the 

Second Generation Model ........................................................................................................................................ 71

Appendix E. Conditional Probability Tables for Each Summary (i.e., Intermediate) Node in the 

Bayesian Network Model .......................................................................................................................................... 77

Appendix F. Influence Run Results for RCP 4.5 and 8.5 by Ecoregion .................................................................... 99
iv

Figures

Figure 1. Map of the four polar bear ecoregions as defined by Amstrup and others (2008)................................... 36

Figure 2. General structure of the second generation model showing the relationships 

among the 10 submodels ........................................................................................................................................ 37

Figure 3. The second generation model used to evaluate the relative influence of stressors on future 

polar bear population outcomes............................................................................................................................... 38

Figure 4. Changes in sea ice extent as simulated by CMIP5 models over the second half of the 

20th century and the entire 21st century under RCP2.6, RCP4.5, RCP6.0, and RCP8.5 for the 

Northern Hemisphere, September (number of models available for each RCP is given in the explanation)............ 39

Figure 5. Sensitivity of the final outcome node “relative influence on population trend” to all 

24 input (parentless) model nodes........................................................................................................................... 40

Figure 6. Polar bear outcomes from the second generation model......................................................................... 41

Figure 7. Influence of “overall sea ice conditions” (a, b) and “marine prey base” (c, d) on the probability 

of a “greatly decreased” outcome for polar bears from the Polar Basin Divergent Ecoregion at four future time 

periods using RCP 4.5 and 8.5................................................................................................................................ 42

Figure 8. Influence of “overall sea ice conditions” (a, b) and “marine prey base” (c, d) on the probability 

of a “greatly decreased” outcome for polar bears from the Polar Basin Convergent Ecoregion at four future 

time periods using RCP 4.5 and 8.5. ....................................................................................................................... 44

Figure 9. Influence of “overall sea ice conditions” (a, b) and “marine prey base” (c, d) on the probability 

of a “greatly decreased” outcome for polar bears from the Seasonal Ice Ecoregion at four future time 

periods using RCP 4.5 and 8.5................................................................................................................................ 46

Figure 10. Influence of “overall sea ice conditions” (a, b) and “marine prey base” (c, d) on the probability of a 

“greatly decreased” outcome for polar bears from the Archipelago Ecoregion at four future time periods 

using RCP 4.5 and 8.5............................................................................................................................................. 48

Table

Table 1. The 13 general circulation models (GCM) used in this study, and the models’ country 

and institute of origin................................................................................................................................................ 35
v 

Conversion Factors

Multiply By To obtain

Length

kilometer (km) 0.6214 mile (mi)

square kilometer 0.3861 square mile (mi2)

meter (m) 3.281 foot (ft)

meter (m) 1.094 yard (yd)

Abbreviations, Acronyms, and Symbols

Abbreviations, Acronyms, and 

Symbols

Meaning

AE Archipelago Ecoregion

BN Bayesian network model

CMIP5 Coupled model intercomparison project phase 5

CO2 Carbon dioxide

CPT Conditional probability table

DLP Defense of Life and Property

ESA Endangered Species Act

GCM General circulation model

GHG Greenhouse gas

NSIDC National Snow and Ice Data Center

PBCE Polar Basin Convergent Ecoregion

PBDE Polar Basin Divergent Ecoregion

RCP Representative concentration pathway

RSF Resource selection function

SIE Seasonal Ice Ecoregion

SRES Special Report on Emissions Scenarios
vi 

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1 

Evaluating and Ranking Threats to the Long-Term 

Persistence of Polar Bears

By Todd C. Atwood1, Bruce G. Marcot2, David C. Douglas1, Steven C. Amstrup3, Karyn D. Rode1, George M. 

Durner1, and Jeffrey F. Bromaghin1

Abstract

The polar bear (Ursus maritimus) was listed as a globally threatened species under the U.S. 

Endangered Species Act (ESA) in 2008, mostly due to the significant threat to their future population 

viability from rapidly declining Arctic sea ice. A core mandate of the ESA is the development of a 

recovery plan that identifies steps to maintain viable populations of a listed species. A substantive 

evaluation of the relative influence of putative threats to population persistence is helpful to recovery 

planning. Because management actions must often be taken in the face of substantial information gaps, 

a formalized evaluation hypothesizing potential stressors and their relationships with population 

persistence can improve identification of relevant conservation actions. To this end, we updated a 

Bayesian network model previously used to forecast the future status of polar bears worldwide. We used 

new information on actual and predicted sea ice loss and polar bear responses to evaluate the relative 

influence of plausible threats and their mitigation through management actions on the persistence of 

polar bears in four ecoregions. We found that polar bear outcomes worsened over time through the end 

of the century under both stabilized and unabated greenhouse gas (GHG) emission pathways. Under the 

unabated pathway (i.e., RCP 8.5), the time it took for polar bear populations in two of four ecoregions to 

reach a dominant probability of greatly decreased was hastened by about 25 years. Under the stabilized 

GHG emission pathway (i.e., RCP 4.5), where GHG emissions peak around the year 2040, the polar 

bear population in the Archipelago Ecoregion of High Arctic Canada never reached a dominant 

probability of greatly decreased, reinforcing earlier suggestions of this ecoregion’s potential to serve as 

a long-term refugium. The most influential drivers of adverse polar bear outcomes were declines to 

overall sea ice conditions and to the marine prey base. Improved sea ice conditions substantively 

lowered the probability of a decreased or greatly decreased outcome, while an elevated marine prey base 

was slightly less influential in lowering the probability of a decreased or greatly decreased outcome. 

Stressors associated with in situ human activities exerted considerably less influence on population 

outcomes. Reduced mortality from hunting and defense of life and property interactions resulted in 

1

U.S. Geological Survey. 

2

U.S. Forest Service.

3

Polar Bears International.
2 

modest declines in the probability of a decreased or greatly decreased population outcome. Minimizing 

other stressors such as trans-Arctic shipping, oil and gas exploration, and point-source pollution had 

negligible effects on polar bear outcomes, but that could be attributed to uncertainties in the ecological 

relevance of those specific stressors. Our findings suggest adverse consequences of loss of sea ice 

habitat become more pronounced as the summer ice-free period lengthens beyond 4 months, which 

could occur in portions of the Arctic by the middle of this century under the unabated pathway. The 

long-term persistence of polar bears may be achieved through ameliorating the loss of sea ice habitat, 

which will likely require stabilizing CO2 emissions at or below the ceiling represented by RCP 4.5. 

Management of other stressors may serve to slow the transition of polar bear populations to 

progressively worsened outcomes, and improve the prospects of persistence, pending GHG mitigation. 

Introduction

The aims of the U.S. Endangered Species Act (ESA) include protecting imperiled species from 

extinction and recovering and maintaining viable populations by mitigating threats to survival. As a 

means toward that goal, the ESA requires the development of a recovery plan that contains objective 

and measurable criteria (i.e., recovery criteria) that address the factors that led to the species being listed 

as threatened or endangered (16 U.S.C. §1533 [f][1][B][ii]). Conceptually, the development of recovery 

criteria follows a two-step process. The first step requires the identification of stressors (i.e., any 

environmental or anthropogenic influence that has the potential to exert a positive or negative effect; 

Bedoya and others, 2011) that are likely to influence population viability, such as changing habitat 

conditions, certain forms of anthropogenic activity (e.g., pollution, industrial development), 

overutilization, disease, and inadequacy of existing regulatory mechanisms. The second step is to 

determine which stressors can be mitigated to achieve a favorable conservation outcome. A critical 

aspect of this approach is explicitly linking stressors to demographic responses (Clark and others, 2002). 

This is a challenging task and recovery plans may be aided by a formal evaluation of the relative value 

that alleviating or mitigating specific stressors may have on recovery goals (Lawler and others, 2002; 

Schultz and Gerber, 2002). 

In 2008, the polar bear (Ursus maritimus) was listed as globally threatened under the ESA. The 

decision to list polar bears was informed by the body of research suggesting that observed and projected 

reductions in sea ice habitat as affected by a warming climate, along with other lesser stressors from 

environmental conditions and anthropogenic activities, collectively, presented a challenge to the 

persistence of polar bears over the foreseeable future (U.S. Fish and Wildlife Service, 2008). The Arctic 

marine “ice cap” may have been in place for more than 5 million years (Polyak and others, 2010), and 

provides critical habitat for 11 ice-adapted marine mammals, including polar bears (Kovacs and others, 

2011). Over the last two decades, increased ambient air temperature (Lindsay and Zhang, 2005), 

advection of warm water into the Arctic Ocean (Shimada and others, 2006), and advection of sea ice out 

of the Arctic Ocean through Fram Strait (Serreze and others, 2007) have driven a decline in the extent 

and thickness of first- and multi-year sea ice, particularly in summer (Arrigo and others, 2008; Wang 

and Overland, 2009; Kovacs and others, 2011). From 2002 to 2013, the extent of summer sea ice 

decreased at a rate of 14 percent per decade (Stroeve and others, 2014). According to the most recent 

predictions, the Arctic may be functionally ice-free (i.e., < 1.0 M km2

) in summer within 10–15 years, 

and perhaps sooner (Overland and Wang, 2013). The climate-driven changes to the Arctic marine 

ecosystem generally are believed to be negative for ice-adapted species such as polar bears (e.g., Stirling 

and Derocher, 1993; Stirling and Derocher, 2012; Derocher and others, 2013). 
3 

Primary foraging habitat for polar bears is annual sea ice that occurs over the biologically 

productive continental shelf and other shallow water areas (Durner and others, 2009). Sea ice serves as a 

substrate that provides access to ringed (Phoca hispida) and bearded (Erignathus barbatus) seals, the 

preferred prey of polar bears (Thiemann and others, 2008). Declines in the extent, connectivity, and 

temporal availability of sea ice over the shelf equates to a loss of functional habitat (Sahanatien and 

Derocher, 2012). Indeed, reduced temporal availability of sea ice, which occurs between ice break-up in 

the spring and freeze-up in the autumn, is perhaps the most pressing concern as it directly constrains 

foraging opportunities. Throughout the Arctic, the period of reduced ice availability (hereafter referred 

to as the open water season) has lengthened—there has been a trend toward an earlier mean date of 

break-up and, in some cases, a trend in later freeze-up (Stroeve and others, 2014). The lengthening open 

water season has been linked to declines in polar bear body condition (Stirling and others, 1999; Obbard 

and others, 2006; Rode and others, 2010), reproductive indices (Stirling and Parkinson, 2006), and 

survival (Regehr and others, 2007). According to energy budget models, a 30-day increase in the length 

of the open water season could lead to a significant increase in reproductive failure and starvation for 

some populations (Molnar and others, 2010; Robbins and others, 2012). In portions of the Arctic, the 

open water season has already increased by 21 days over a 30-year period (Gagnon and Gough, 2005). 

Although some polar bear populations have already experienced sea ice loss and measurable 

negative effects on body condition, reproduction, and survival have been documented, there is 

geographic variation in the rate and extent of declines in sea ice habitat and population responses. For 

example, the Davis Strait polar bear population increased during a time period when the duration of 

seasonal ice declined (Peacock and others, 2013). This effect was largely attributed to an increase in 

availability of new prey, harp seals (Pagophilus groenlandicus), and polar bear population size is 

believed to have stabilized in apparent response to density dependent effects after a period of population 

growth. In the northern Beaufort Sea, sea ice also has declined but thus far the population appears to be 

stable (Stirling and others, 2011). Similarly, body condition and reproduction appears stable in the 

Chukchi Sea population after a period of substantial sea ice decline. The latter may be a result of the 

high biological productivity of the region and the continued availability of sea ice over shallow, 

productive, continental shelf waters even during the annual sea ice minimum (Rode and others, 2014). 

These studies emphasize that geographic variation, ecological conditions, and ecosystem productivity in 

addition to stressors other than declining sea ice also can be important in affecting polar bear population 

dynamics. 

Although the loss of sea ice habitat can have a direct effect on polar bear persistence, other 

factors associated with climate change have the potential to adversely affect populations. In most parts 

of their range, polar bears, with the exception of pregnant denning females, have typically spent most of 

their annual life cycle on the sea ice. In recent years, polar bears are increasingly spending time onshore 

where they interact more with humans and infrastructure, including local residents, tourists, and 

industrial activities. Being onshore increases the potential for lethal outcomes for polar bears. Further, 

historically, sea ice has acted as a structural barrier to anthropogenic activities in the Arctic Ocean. 

Continued declines in the extent of summer sea ice and lengthening of the open water season are 

predicted to result in increased exposure of polar bears to industrial and commercial activities such as 

the exploration and extraction of oil and gas and trans-Arctic shipping (Gautier and others, 2009; Rogers 

and others, 2013; Smith and Stephenson, 2013; Stephenson and others, 2013; Miller and Ruiz, 2014). 
4 

Attendant risks of increased anthropogenic activities include exposure to oil spills (Amstrup and others, 

2006b), industrial chemicals and effluents (Amstrup and others, 1989; Derocher and Stirling, 1991; 

Smit and others, 2008), and human-bear conflict. Similarly, extensive sea ice cover may have 

functioned as a physical barrier to disease agents by limiting contact between polar bears and vectors. 

Currently, exposure to disease and parasites is believed to not represent a threat to polar bears 

(Vongraven and others, 2012). Climate change is predicted to alter host-pathogen associations,

transmission dynamics, and pathogen resistance (Burek and others, 2008), which is cause for concern 

given that polar bears have a naïve immune system (Weber and others, 2013) that may make them more 

vulnerable to emerging infectious agents. The relative effects on population persistence of these 

stressors and others, such as harvest and availability of terrestrial habitat, are therefore also important to 

consider. 

Assessing diverse stressors affecting population persistence requires the development of a 

stressor evaluation model that takes as input the magnitudes of the various stressors and produces as 

output the projected population responses. Importantly for polar bears, such a model needs to be able to 

incorporate ecological complexity exhibited across the species’ range. Probability-based models, in 

particular Bayesian network (BN) models, have the capacity to integrate environmental, ecological, and 

anthropogenic processes, along with an explicit representation of uncertainties, into a unified analytical 

risk-assessment framework (Marcot and others, 2001; Jensen and Nielsen, 2007; Barton and others, 

2012). These models also allow for the incorporation of hypothesized relationships where specific data 

defining relationships between environmental or abiotic stressors are lacking. Uncertainty in 

relationships can be explicitly incorporated by spreading the resultant probability across positive, 

negligible, and negative outcomes. Amstrup and others (2008) developed a BN model (hereafter the first 

generation model), representing the relationships of sea ice habitat and a select group of stressors with 

polar bear demography, to project the future worldwide status of polar bears. A central conclusion was 

that aggressively mitigating greenhouse gas (GHG) emissions, along with managing anthropogenic 

activities, could result in the maintenance of polar bear numbers at reduced but sustainable levels 

throughout the 21st century (Amstrup and others, 2010). Here, we have adapted this evaluation 

framework to focus on better understanding the relationships between different stressors with the 

potential to affect polar bear populations. This framework can be used to identify where focused 

management may be most beneficial to polar bears by finding the combinations of mitigation for 

influential stressors that could result in improved population outcomes (Runge and others, 2007). 

Our goal was to identify the most influential environmental, ecological, and anthropogenic 

stressors affecting polar bear population persistence and the degree to which mitigation of those 

influential stressors could affect their circumpolar persistence. Because quantitative relationships 

between most stressors and polar bear population dynamics are lacking yet management decisions are 

needed to promote polar bear conservation, this formalization is an improvement on making decisions 

where there is not an explicit integration of existing knowledge and hypothesized relationships. This 

analysis provides a tool that can be used to inform decisions regarding the short- and long-term 

management of polar bears. Specifically, we (1) updated the structure and content of the first generation 

BN model using new information available after the model was developed in 2007, (2) identified which 

stressors have the greatest influence on future population outcomes, and (3) identified the form and 

management implications of uncertainties in forecasting the response of polar bears to various stressors 

and their potential mitigations. 
5 

Materials and Methods

Our study area encompassed the circumpolar range of polar bears, which is comprised of the 19 

subpopulations recognized by the Polar Bear Specialists Group of the International Union for the 

Conservation of Nature (Aars and others, 2006). Because polar bears move between subpopulation 

boundaries (e.g., Taylor and others, 2001; Amstrup and others, 2004), Amstrup and others (2008) 

grouped them into four ecoregions based on observed and forecasted future patterns of sea ice dynamics 

and polar bear life history (fig. 1). Two of the ecoregions—the Polar Basin Convergent Ice (PBCE; 

three subpopulations) and Archipelago (AE; six subpopulations) ecoregions—have been characterized 

by persistent multi-year ice that has allowed polar bears to remain on the ice year-round (e.g., Ferguson 

and others, 2000). The Polar Basin Divergent Ice Ecoregion (PBDE; five subpopulations) historically 

has been characterized by mixed annual and perennial ice that advects toward the central polar basin 

(and aggregates in the PBCE) during summer (Durner and others, 2009). For the most part, bears in the 

PBDE have stayed on the sea ice year-round, although recently, in the southern Beaufort Sea and 

Chukchi Sea subpopulations, some bears come ashore during summer (Schliebe and others, 2008; 

Ovsyanikov and Menyushina, 2010). Lastly, the Seasonal Ice Ecoregion (SIE; five subpopulations) is 

characterized by annual sea ice that melts almost entirely each summer (Derocher and Stirling, 1990). 

During the functionally ice-free season, which can begin as early as July and continue through October, 

bears in the SIE come ashore and experience an extended period of food deprivation while waiting for 

sea ice to reform in the autumn (Stirling and Archibald, 1977). 

Revision of the First Generation Model

We used the first generation model developed by Amstrup and others (2008) as the foundation 

for the second generation model. The first generation model was used to project the probability of 

persistence of the circumpolar polar bear population through the 21st century relative to the influence of 

climate change, environmental conditions, and anthropogenic stressors. The purpose of the model was 

associated with a need at the time to inform the ESA listing process by projecting polar bear population 

outcomes within the foreseeable future using the best available scientific information. Our focus in 

developing the second generation model was to characterize the potential influence of specific stressors

to explore normative (most-expected based on best estimates of likely input values) outcomes under 

combinations of ecoregions, time periods, and climate change scenarios, and to evaluate potential 

influences of emissions pathways. The current model incorporates new information since 2007 and 

restructured the first generation model to reflect current understanding of the dynamics of polar bear 

population outcomes. 

In creating the second generation model, we followed guidelines for developing BN models 

(Marcot and others, 2006). First, we established a team of subject matter experts (appendix A) to 

evaluate the first generation influence diagram in light of personal research experience and publications 

that had become available since the first generation model was constructed. The influence diagram is a 

graphical model that depicts the causal web of stressors influencing the outcome of interest (Marcot and 

others, 2006). Following the team’s evaluation, we revised the influence diagram accordingly to specify 

additional stressors and to reflect the current understanding of relationships among stressors that 

mediate population outcomes. The revised influence diagram reflected both new understanding from 

recent research and a desire to provide greater detail in the model about specific influences (e.g., various 

types of potential oil or hydrocarbon spills and pollution associated with energy development activities). 
6 

We updated the sea ice variables based on outputs from current climate models that have been improved 

since the first generation model was developed. We provide details on the process used to develop and 

parameterize the model in appendix B. 

Model Structure and Parameterization

The model consisted of input, intermediate, and outcome nodes, with the former two organized 

into environmental and other stressor submodels (fig. 2). Input nodes are considered “parentless” 

because their values are not informed by other nodes, and their states are parameterized by 

unconditional, prior probability values. By contrast, both intermediate and outcome nodes are 

conditioned on the preceding nodes (or “parent nodes”) to which they are linked, which can be both 

input and other intermediate nodes (Jay and others, 2011). The states of intermediate and outcome nodes 

are parameterized by conditional probability values. Our input nodes consisted of calculated 

environmental (e.g., sea ice metrics) and summary anthropogenic (e.g., shipping, hunting, resource 

extraction) stressors. Ecoregion-specific inputs for parentless sea ice metrics were derived from general 

circulation models (GCM) as described below in section, “Model Composition,” while ecoregionspecific inputs for other parentless nodes were derived through synthesis of available literature and 

expert knowledge. Intermediate nodes consisted of summaries of stressors, environmental 

characteristics, and polar bear demographic processes. The outcome node represented the cumulative 

effect of the input and intermediate nodes, expressed as conditional probabilities of relative influence on

polar bear population trend. 

The model structure was informed by several important aspects of BN models. First, summary 

nodes were used to ensure that no more than 3-4 input nodes informed any one intermediate node. 

Because the combined effects of all inputs at an intermediate node must be estimated, having too many 

inputs results in large numbers of input combinations which, in turn, makes assigning probabilities very 

difficult. Secondly, the effect of a stressor is affected by its proximity to the outcome node. Therefore, 

the model structure had to be designed to ensure that nodes of similar influence were within similar 

proximity to the outcome node. 

Probabilities were assigned at each intermediate and summary node to reflect the range of 

possible outcomes from each combination of input stressors. Available data from the literature were 

used to prescribe the likelihood of each outcome. Where data were lacking, expert judgment was used to 

estimate the range of likely outcomes as well as the degree of certainty. Uncertainty was incorporated 

by prescribing a more uniform distribution of probabilities across all possible outcomes. Where 

outcomes had greater certainty as a result of existing data and well-understood relationships, 

probabilities across outcomes were prescribed more narrowly.

Model Structure for Conducting Scenario Projections

We conducted separate model runs for combinations of the four ecoregions detailed above, and 

six time periods, including one historical, one recent, and four future periods extending to the end of the 

21st century. A 1985–1995 period represented historical relationships and a 2007–2012 period 

represented current conditions. Four future periods (2020–2030, 2045–2055, 2070–2080, 2090–2100) 

represented projected conditions through the end of the century. We evaluated decadal time periods, 

rather than single years or individual-year time series, to focus on the long-term trends rather than the 

natural interannual variation in the environmental inputs to the model (Jay and others, 2011). For each 

of the future time-period model runs, we also evaluated two pathways of GHG forcing (RCP 4.5 and 

RCP 8.5; see section, “Sea Ice Submodel” below), including two approaches for pooling multi-model 
7 

projections (ensemble means and frequency distributions), and three subsets of GCMs based on 

published recommendations of the models’ ability to simulate observed sea ice conditions. By taking an 

ecoregion- and time period-specific approach, we were able to represent geographic and temporal 

variation in stressor effects, GHG emission influences, and uncertainties. A detailed description of 

model nodes, including definitions of node states and key assumptions, is available in appendix C and 

section, “Model Composition.” In total, we ran 204 combinations of ecoregion, time period, GHG 

emission pathway, and GCM subset, on expected (hereafter “normative”) conditions (appendix D). 

Additional “influence runs” were conducted by varying individual input values to determine their 

specific influence on outcomes; see section, “Sensitivity Analysis and Influence Runs” for details. 

Conditional probability tables for all intermediate nodes are documented in appendix E. 

Model Composition

The second generation BN model included 10 interlinked submodels (fig. 2). Each submodel is 

described below and detailed in figure 3. 

1. Analysis scenario submodel.—The first submodel pertains to the analysis scenario combinations of 

ecoregion, time period, GHG emission pathway, and GCM subset. Instead of unduly complicating 

the model structure by showing explicit linkages from ecoregion, time period, climate change 

pathway, and GCM set, to every input variable, the linkages were implicit in providing an input 

“case file” database specifying values of all input variables for each such combination. That is, in 

the input database, each record was a unique combination of ecoregion, time period, emission

pathway, and GCM subset. 

2. Sea ice submodel.―Historical and model-projected data were used to parameterize the sea ice input 

nodes. Historical data consisted of satellite observations of monthly sea ice concentration, obtained 

from the National Snow and Ice Data Center (NSIDC) archives for 1985–2012 (Cavalieri and 

others, 1996). These data were distributed in a gridded polar stereographic map projection with 25-

km pixel resolution. For the four future time periods, we used GCM projections of monthly sea ice 

concentration, obtained from the World Climate Research Programme's Coupled Model 

Intercomparison Project phase 5 (CMIP5) archive (Taylor and others, 2012) at http://cmippcmdi.llnl.gov/ (accessed May 2013). We acquired sea ice concentration outputs from 13 GCMs 

(table 1), 2006–2100, that were produced by each of two CMIP5 GHG forcing experiments: 

Representative Concentration Pathway (RCP) 4.5 and RCP 8.5 (van Vuuren and others, 2011). RCP 

8.5 represents a future characterized by an unabated continuation of current rates of increases in 

anthropogenic components contributing to global warming, and RCP 4.5 reflects a future where 

anthropogenic contributions stabilize near the year 2040. We also obtained hindcasts of monthly sea 

ice concentration, 1996–2005, for each of the 13 GCMs from the CMIP5 ‘historical’ forcing 

experiment. The hindcasts were added to each of the two RCP time series to extend their temporal 

domain into the latter part of the 20th century. We acquired a single realization (run-1) for each 

GCM and forcing combination. All GCM outputs were transformed and resampled (nearest 

neighbor) to a 25-km-resolution polar stereographic grid that was congruent to the NSIDC grid of 

sea ice observations.
8 

We chose the 13 GCMs (table 1) from among dozens in the CMIP5 archive based on studies by 

Massonnet and others (2012), Wang and Overland (2013a) and updated in Wang and Overland 

(2013b). Both studies identified subsets of the CMIP5 models based on their ability to simulate the 

observed record of sea ice dynamics. Wang and Overland (2013b) found that 12 models simulated 

means and seasonal cycles in ice extent to within 20 percent of observations, although Massonnet 

and others (2012) identified six models based also on comparisons with means and seasonal cycles, 

as well as metrics about ice volume and September trend. Together, the two studies identified 15 

unique models, but we excluded 2 lower-resolution models (IPSL-CM5A-LR and MPI-ESM-LR) 

because their medium-resolution counterparts (IPSL-CM5A-MR and MPI-ESM-MR) were 

represented. We also excluded five models for analyses in the Archipelago Ecoregion (table 1) 

because their spatial resolutions did not adequately resolve the region’s channels and fjords. 

Three quantitative sea ice habitat metrics (i.e., nodes), identical to those used by Amstrup and others 

(2008), were calculated for each year and ecoregion in both the modeled and observed time series of 

monthly sea ice concentration maps (appendix C). 

• Total ice habitat.—For the two polar basin ecoregions, the PBDE and PBCE, we calculated 

an annual metric of total ice habitat, depicted by the node “foraging sea ice area,” by 

calculating the 12-month sum of monthly optimal habitat area as defined by Durner and 

others (2009). Monthly maps of optimal sea ice habitat for the PBDE and PBCE were 

derived by applying Durner and others (2009) resource selection functions (RSF) to 

covariates extracted from the respective monthly ice concentration maps. Because the RSF 

models were not developed for the AE and SIE, we derived an annual metric of total ice 

habitat for those ecoregions as the annual 12-month sum of monthly sea ice extent, where ice 

extent was defined as the aerial cover (km2

) of all pixels with ≥50 percent ice concentration. 

• Ice absence.—We calculated the number of reduced-ice months per year over shelf waters 

from each GCM time series and from observations, and used the node “foraging sea ice <50 

percent absence” to quantify information about duration of the summer melt period from the 

preferred continental shelf foraging areas (Durner and others, 2009). A reduced-ice month 

was defined in a respective ecoregion as a month when <50 percent of the area over the 

continental shelf (<300 m depth) was covered by sea ice of ≥50 percent concentration. 

Because deep water is uncommon in the AE and SIE, we considered those ecoregions to be 

entirely comprised of shelf waters. 

• Sea ice shelf distance.—Recognizing the potential importance of how far the ice retreats 

away from the preferred continental shelf foraging areas each year, we calculated a shelf-ice 

distance metric during the month of minimum (but non-zero) ice extent as the mean distance 

from every shelf pixel in the respective ecoregion to the nearest pack-ice pixel. The pack ice 

was defined as the largest contiguous region of ice with >50 percent concentration. The 

shelf-to-ice distance metric, depicted using the node “sea ice shelf distance change,” was not 

calculated for the SIE and AE because we considered all sea ice in those ecoregions to be 

over shelf waters.
9 

We used the node “foraging sea ice quality” to express a subjective assessment of the quality of sea 

ice for foraging by polar bears over time. Recent observations of the changes in sea ice 

characteristics in several Arctic seas and regions (e.g., southern Beaufort and Chukchi Seas 

[Mahoney and others, 2012], Hudson Bay [Gagnon and Gough, 2005; Sahanatien and Derocher, 

2012]) suggest that changes in sea ice phenology have resulted in thinner ice that more easily 

deforms and more frequently rafts over itself. In the model, we incorporated the potential for these 

changes to reduce the quality of sea ice as a foraging substrate in the PBDE and SIE, because

extensive ice deformation can limit access to prey (Stirling and others, 2008). Conversely, we 

incorporated the potential for thinning of thick, multi-year ice in the AE and portions of the PBCE to

increase foraging opportunities in the early- and mid-century periods, and reduced foraging 

opportunities in the late century periods. In both of these cases, due to uncertainty about how sea ice 

characteristics might affect foraging habitat quality, we allowed for a range of negative, negligible, 

and positive impacts that varied with sea ice conditions and ecoregion. We used the intermediate 

node “foraging sea ice distribution” to express how the spatiotemporal retreat of sea ice may affect 

availability of continental shelf habitats. Similarly, we used the intermediate node “overall sea ice 

conditions” to characterize the combination of the quantitative and qualitative ways the retreat of sea 

ice may affect use of continental shelf habitats by polar bears. 

As noted above, we focused our GCM analyses on four future decadal periods and our observationbased analyses on two contemporary periods. Observation-based analyses incorporated all available 

data on known relationships and effects, and also used expert knowledge where data were not 

available. Ice metrics from observations and the individual GCMs were respectively averaged within 

each decadal period to smooth the Arctic’s intrinsic interannual sea ice variability, and improve 

detection of the GHG forced trend. Next, the decadal metrics were expressed in units of change from 

a baseline decade (1996–2006) to lessen the influence of inherent biases among individual models 

(Knutti, 2008). With respect to the baseline decade, total ice habitat was expressed as a percent 

change, ice absence as a change in months, and shelf-ice distance as a change in kilometers. In 

preparation for entry into the model, these continuous variables describing change were assigned to 

categories (bins) defined by the structure of the respective input node. Our input nodes for all three 

metrics of sea ice change were structured similar to those of Amstrup and others (2008), with a 

comparable range of values but partitioned into slightly more bins. One exception, however, was 

that we scaled the bins in the “foraging sea ice <50 percent absence” node (change in the number of 

ice-free months) over a broader range of months to accommodate the RCP 8.5 GHG forcing that 

projects a warmer world with longer ice-free summers compared to the Special Report on Emissions 

Scenarios (SRES) A1B forcing scenario evaluated by Amstrup and others (2008). 

As described above, we prepared six different variants of the sea ice metrics to determine whether

different GCM ensembles and data summaries qualitatively affected the final BN outcomes. We 

considered each of three different GCM ensembles: (1) the 5 models selected by Massonnet and 

others (2012); (2) the 11 models selected by Wang and Overland (2013b); and (3) the 13-model 

union of the aforementioned (hereafter called GCM-05, GCM-11, and GCM-13). Second, for each 

ensemble, we considered two methods of treating a metric’s multi-model variance: (1) pool the 

variance into a multi-model average; and (2) represent the multi-model variance as a frequency 

distribution across all bins of the respective input node. In total, our analytical framework described 

above evaluated the 204 unique BN runs as defined by the combinations of five factors: four 

ecoregions (PBDE, PBCE, SIE, and AE); two GCM forcing pathways (RCP4.5 and RCP8.5); four 

future decades (historical, early century, mid-century, late century, and end century); three GCM 

ensembles (GCM05, GCM11, and GCM13); and two treatments of multi-model variance (average 
10 

and frequency distribution); plus 12 observation-based BN runs that combined three contemporary 

decades across four ecoregions. We hereafter refer to these combinations of factors and their 

respective BN runs as the ‘normative scenarios’ (appendix D). 

3. Marine prey and conditions submodel.―We used the “secondary and new prey abundance” node to 

express changes in the abundance of alternative marine mammal prey, and to allow range expansion 

into Arctic waters by novel prey species, as sea ice extent declines and ice phenology and 

characteristics change over time. Changing sea ice conditions may adversely affect the abundance of 

primary prey thus making secondary prey more important. For example, spotted (Phoca largha) and 

ribbon seals (Histriophoca fasciata) are mostly distributed in the sub-Arctic waters of the Bering 

Sea, although part of their range extends into the Chukchi Sea. As pack ice becomes less available in 

the Bering Sea, ribbon seal range will likely expand northward to maintain access to pack ice during 

the birthing period (Lowry and Boveng, 2009). Spotted seals regularly haul out on shore and are less 

reliant on sea ice. As a result, the range of spotted seals may expand northward as summer ice cover 

declines (Kovacs and others, 2012). Last, ribbon, harp (Pagophilus groenlandicus), and hooded 

seals (Cystophora cristata) can spend long periods pelagically in areas without ice (Kovacs and 

others, 2012), likely making them somewhat resilient to loss of sea ice habitat. The prey abundance 

input nodes do not consider availability because availability to prey was accounted for under nodes 

related to sea ice conditions. We acknowledge that seals may occupy areas that make them less 

available to polar bears even if seals are still relatively abundant. 

We used three input nodes to characterize the abundance of primary and secondary prey. The 

“ringed seal abundance” and “bearded seal abundance” nodes were used to express the probability 

that changes in abundance of primary prey are likely to occur as sea ice cover declines and its 

character changes. For example, we incorporated evidence that earlier spring break-up of sea ice in 

western Hudson Bay is related to declining pup survival (Ferguson and others, 2005). Declining sea 

ice may reduce the availability of birthing and haul-out habitat for seals, and earlier break-up may 

also adversely impact pup survival by interrupting the lactation period (Kelly and others, 2010). 

Ringed seals have the longest lactation period of the Phocidae and need stable ice as well as 

sufficient snow for lairs until neonates are weaned (Lydersen and Kovacs, 1999). We incorporated 

the potential that in some areas, declines in sea ice may improve biological productivity of the 

ocean, which may benefit seals in the near-term. However, we also considered evidence that

increased primary productivity will be greatest during summer (after break-up) and benefits may be 

mitigated by reduced biomass in coastal/shelf areas due to increased river run-off and associated 

changes in turbidity and salinity (e.g., Bluhm and Gradinger, 2008). In the model, we assumed that 

only in the northern part of the ice convergent zone of the Polar Basin and in portions of the 

Archipelago are conditions likely to improve for ringed seal availability—particularly if biomass is 

reduced in coastal/shelf waters but increases elsewhere. However, such improvements are likely to 

be transient perhaps through mid-century. 

Ringed and bearded seal abundance nodes were linked to the “marine prey base quality” 

intermediate node to express cumulative primary and secondary prey abundance. As with the ringed 

seal and bearded seal abundance nodes, the conditional probability table (CPT) for “marine prey 

base quality” was based on expert judgment because little information was available to suggest how 

the prey base quality is likely to change in the future. In developing the conditional probabilities for 

this node, we gave primary prey slightly greater weight than secondary prey. We did this because 

the importance of primary prey has been established in the literature, while there is relatively little 

information available on the likelihood/importance of prey switching. However, we did recognize 
11 

evidence (e.g., Peacock and others, 2013) that increased abundance of alternate prey in some areas 

could compensate for losses of ringed or bearded seals and may even result in transient 

improvements of polar bear survival. 

4. Terrestrial food/prey and conditions submodel.―Use of terrestrial habitat is a significant aspect of 

the life-history of polar bears residing in the SIE and parts of the PBDE (e.g., Schliebe and others,

2008). We expected that use of terrestrial habitat would increase over time. Accordingly, we created

three input nodes to describe the use of terrestrial refugia habitat and food resources. “Humanprovisioned food abundance” was used to express the availability of human-harvested marine 

mammals and other prey to polar bears in terrestrial habitats. The availability of human-provisioned 

food is most pertinent during the late summer and early autumn when sea ice reaches its minimum 

extent. This node is mostly relevant to a part of the PBDE where bears come ashore during the late 

summer to exploit bowhead whale (Balaena mysticetus) carcass sites (Herreman and Peacock, 

2013). The “terrestrial and marine prey access” node expressed the availability of terrestrial prey 

and other terrestrial food resources (e.g., Derocher and others, 1993; Smith and others, 2010). We 

used the intermediate node “overall terrestrial prey/food” to characterize the availability of food 

resources relative to requirements of bears during their stay on shore, while noting that the 

availability of human-provisioned food is mostly relevant to portions of the PBDE and SIE. We 

used the node “bears on shore” to express the length of time each year that bears may spend on 

shore exploiting terrestrial habitat as influenced by changes in overall sea ice conditions, terrestrial 

prey, and ecoregion. “Terrestrial habitat quality” was used to characterize stability in habitat 

structure and extent of human and natural disturbance over time. The node “overall terrestrial 

conditions” was used to depict changes in onshore habitat suitability as a function of habitat quality 

and the length of time bears spend on shore. Last, the nodes representing overall marine and 

terrestrial conditions were used to inform “overall habitat suitability,” where greater importance was 

attributed to “overall marine conditions” because polar bears spend the majority of the year on sea 

ice foraging on marine mammals. 

5. Overall habitat suitability submodel.—This single-node submodel combines overall marine 

conditions with overall terrestrial conditions into a summary of overall habitat suitability which, in 

turn, directly affects polar bear demographic conditions. 

6. Event-driven mortality submodel.―We depicted mortality as a function of specific known and 

hypothesized lethal stressors including harvest and take resulting from human-bear conflict (i.e., 

defense of life and property; DLP). Polar bears are legally harvested in the U.S., Canada, and 

Greenland; harvest is illegal in Russia and Norway. Historical harvest levels were well-documented 

(e.g., Obbard and others, 2010), but future levels are entirely speculative. In parameterizing the 

harvest node, we assumed that future harvest would scale to habitat carrying capacity in an attempt 

to maintain sustainability. The harvest input node has three states and we considered “same as 

recent” and “reduced” to represent harvest at or below sustainable levels; “increased” represented 

harvest above a level considered sustainable. In parameterizing the input node representing DLP 

take resulting from human-bear conflict, historical patterns also were well-documented (e.g., U.S. 

Fish and Wildlife Service, unpub. data; Towns and others, 2009). We assumed that future levels of 

DLP take would increase as sea ice extent declines and forces bears to come ashore in greater 

numbers for increasing lengths of time. Harvest and DLP input nodes were summarized by an 

intermediate node, “event-driven mortality.” 
12 

Additional input nodes used to depict mortality risks included oil spills due to small and large 

exploration and extraction operations, and a catch-all node encompassing multiple risks such as 

those resulting from management and research activities and catastrophic storm events. These nodes 

had three states (increased, baseline, and reduced). Historical conditions were documented to the 

extent possible, and we assumed future conditions might be related to sea ice extent and duration of 

the ice-reduced period. For example, we incorporated within the outcome probabilities a possibility

that declines in sea ice extent and a longer open-water season would facilitate increased oil 

extraction activity and elevate the risk of accidental oil spills (Amstrup and others, 2006a). We also 

considered the potential for these stressors to have no effect on future conditions. These additional 

mortality risk input nodes were summarized by an intermediate node termed “other mortality or 

removal events,” and linked to the “event-driven mortality” node. Event-driven mortality was then 

used to inform adult and subadult survival nodes. 

7. Anthropogenic stressors submodel.―We included several input nodes that allowed for potential 

effects of sub-lethal stressors and pollutants associated with different forms of anthropogenic 

activities. Sub-lethal stressors, such as “human-bear sub-lethal interactions,” “oil, gas, and mining 

activity,” “shipping,” and “tourism” might cause bears to be displaced from foraging and refugia 

habitat. Historically, offshore resource extraction, commercial shipping, and recreational activities 

have been limited by sea-ice extent, but will likely increase as sea ice extent declines (Ebinger and 

Zambetakis, 2009; Peters and others, 2011; Smith and Stephenson, 2013). Likewise, increased levels 

of anthropogenic activity might result in increased human-bear interactions. The effect of these 

stressors was summarized using the “sub-lethal human disturbance” intermediate node. 

We used two nodes, “hydrocarbon/oil spill” and “contaminants,” to characterize potential patterns of 

exposure to specific point-source (e.g., associated with drilling operations and shipping) and 

transported (e.g., organic compounds) pollutants. For the latter, a large body of literature details 

historical patterns of polar bear exposure to persistent organic pollutants (POP; e.g., Sonne and 

others, 2012). For some populations, the concentrations of contaminants in polar bear adipose tissue 

have declined in recent years (e.g., McKinney and others, 2010). However, there are many 

populations for which no information is available and documented population-level effects of 

contaminants are limited. There is concern that extensive melting of multi-year sea ice will release 

sequestered POPs (Ma and others, 2011) that will then increase risk of exposure to bears and other 

marine mammals. We used the “pollution (sub-lethal)” intermediate node to summarize the effects 

of exposure to hydrocarbons and contaminants. Probabilities of outcomes were distributed broadly 

to reflect uncertainty in the potential effects of changes in these stressors over time. We used the 

“anthropogenic stressors” node to summarize the potential cumulative effects of sub-lethal human 

disturbance and pollution, and then linked it to “adult female body condition” given the potential for 

cumulative stress effects to adversely affect fitness (Love and others, 2013; Sheriff and Love, 2013).

8. Other biotic stressors submodel.―Historically, exposure to disease and parasites has not 

represented a threat to the persistence of polar bears. However, climate change may alter hostpathogen associations, transmission dynamics, and host and pathogen resistance (Burek and others,

2008), which might make polar bears increasingly vulnerable to new pathogens over time. Similarly, 

intraspecific predation is believed to be rare (Lunn and Stenhouse, 1985; Derocher and Wiig, 1999; 

Amstrup and others, 2006b), but may increase as bears spend more time on shore and competition 

for food resources intensifies. We summarized the probabilities of potential effects of disease and 

parasites and intraspecific predation with an intermediate node (“other biotic stressors”), which was 

linked to subadult survival. We also linked parasites and disease to an intermediate node 
13 

characterizing adult female body condition, based on the documented relationship between the two 

for other species (e.g., Møller and others, 2003; Irvine and others, 2006). Because of a lack of 

documented population-level effects of disease and predation, we incorporated substantial 

uncertainty when defining probabilities of outcomes associated with these nodes. 

9. Demographic submodel.―The demographic submodel was used to represent the cumulative 

influence of stressors on polar bear vital rates and, ultimately, the relative influence on population 

trend. The “adult survival” node represented the sum of effects on survival as influenced by “overall 

habitat suitability,” “disease and parasites,” and “event-driven mortality.” Based on the link between 

sea ice habitat, body condition, and survival (Regehr and others, 2007, 2010; Rode and others, 

2010), we assumed that declines in habitat suitability would adversely affect condition and survival. 

Likewise, we acknowledged the supposition that sublethal stressors (e.g., disease and parasites, 

pollution, human disturbance) are likely to increase as the Arctic continues to warm (Moore and 

Huntington, 2008), and adversely affect female body condition. Because there is no information to 

establish a direct relationship between sublethal effects and disease to polar bear body condition, we 

relied on literature from other species (e.g., Pioz and others, 2008; Sheriff and others, 2009) to 

inform the influence of these effects in the conditional probability table for “adult female body 

condition.” 

We used the “recruitment” node to reflect the effect of stressors on the ability of females to reach 

traditional denning areas and on numbers of cubs produced. There is a well-documented link 

between adult female body condition and recruitment for polar bears (e.g., Derocher and Stirling, 

1995; Rode and others, 2010) and other ursids (Robbins and others, 2012), as well as information 

reflecting the importance of terrestrial denning habitat for polar bears in multiple ecoregions 

(Richardson and others, 2005; Fischbach and others, 2007; Andersen and others, 2012). In some 

areas, reduced sea ice conditions have led to a reduction in access to and use of traditional denning 

areas (Derocher and others, 2011). In developing the probability table for this node, we considered 

that lengthening of the ice-minimum season could have adverse effects on adult female body 

condition and access to terrestrial denning habitat. However, probabilities were spread to also allow 

for a lack of effect of declining sea ice on female body condition as has been reported from some 

studies (Rode and others, 2014). 

10. Polar bear persistence submodel.―The outcome node “relative influence on population trend” 

represented the hypothesized potential for population viability given the strength of individual and 

cumulative threats deemed likely to affect polar bears. The node was informed by adult survival, 

subadult survival, and recruitment, and in creating the conditional probability table, we attributed the 

greatest weight to adult survival because it is the most important factor affecting population trend 

and also the least sensitive to change (Wielgus and others, 2008). This node has four response states, 

which we defined as follows: 

• increased: polar bears occur in numbers that are increased compared to the recent period and 

distribution is at least the same, although stressors may cause local variation in abundance and 

distribution; 

• same as recent: polar bears occur in numbers and have a distribution that is similar to the recent

period. Although stressors may cause local variation in abundance and distribution, they are not 

expected to induce an increasing or decreasing trend. 

• decreased: polar bears occur in numbers and have a distribution that is decreased compared to 

the recent period, and the reduced numbers and distribution are likely to make the population 

susceptible to stressors that may cause further declines in abundance and/or occupancy; and
14 

• greatly decreased: polar bears occur in numbers and have a distribution that makes them 

difficult to detect, and the reduced numbers make the population vulnerable to stressors that may 

lead to further declines in abundance and polar bears being restricted to a fraction of their 

historical range. 

We chose to simplify the model outcome node by not including a state “greatly increased”. Based on 

prior modeling and much if not all of the polar bear literature on global and ecoregional conditions 

and trends, and on the future climate-change and stressor conditions specified in the modeled 

scenarios, we would never expect future conditions to provide for a “greatly increased” relative 

influence on population trend at the ecoregional scale. Accordingly, a “greatly increased” state 

would remain at zero probability under all projected scenarios, thus adding no information to the 

model outcomes. 

We compared the probabilities of the response states among each analysis scenario to identify 

potential differences in the cumulative effect of stressors on population trend. Similar to Jay and 

others (2011), we considered these results to be normative outcomes based on modeled best 

estimates of the response of polar bears to stressor combinations. That is, normative outcomes are 

expected results given our current understanding of the effects of managed and unmanaged stressors 

on polar bear populations. 

Sensitivity Analysis and Influence Runs 

We conducted sensitivity analysis and influence runs with the modeling shell Netica® using 

methods and interpretations described in Marcot (2006; 2012) and Jay and others (2011). Sensitivity 

analyses were used to determine which model nodes were key drivers of outcomes, and to provide 

information on the inherent underlying conditional probability structure of the model (Morgan and 

Henrion, 1990; Darwiche, 2009).We used influence runs (for both RCP 4.5 and 8.5)—also termed joint 

parametric analysis by Morgan and Henrion (1990)—to examine the effect that mitigation of select 

stressors may have on relative population trend, and to rank stressors in order of their importance. 

Influence runs entail setting specified input variables to their extreme states while holding other inputs 

to their normative scenario values. This helps determine best- and worst-case outcomes, and in 

particular is useful for ascertaining the potential benefits (or detriments) of mitigation of those inputs 

that are potentially controllable by management, including much of event-driven mortality and 

anthropogenic stressors. For individual anthropogenic and biotic stressors, such as trans-Arctic 

shipping, tourism, resource extraction activities, and parasites and disease, we assigned select input 

nodes best- and worst-case scenarios (e.g., reduced activity versus increased activity) and compared 

outcomes between the two cases. We took a similar approach for evaluating the influence of stressors 

comprising the event-driven mortality submodel. Additionally, we conducted group-based influence 

runs on the disparate stressors that comprised the event-driven mortality submodel. This allowed us to 

compare the potential influence of reduction in intentional take (through legal harvest and DLP killing) 

versus a reduction in other lethal stressors (e.g., oiling events, catastrophic weather, illegal killing). We 

then qualitatively compared the outcomes of influence runs to the normative model outcomes to 

determine the relative influence that managing the strength of select stressors may have on model 

outcomes. 
15 

Comparison of Polar Bear Ecoregion Outcomes under Stabilized and Unabated GHG Emissions

Pathways

To examine the effect of GHG emission levels on potential population trend, we compared 

outcomes between RCP 4.5 and RCP 8.5, while holding all other input nodes to their original values for 

future periods. RCP 4.5 and 8.5 represent two possible future GHG emission pathways. RCP 4.5 

corresponds to a stable end-of-century net radiative forcing of +4.5 W/m-2 above preindustrial levels, 

while RCP 8.5 corresponds to an increasing (unstabilized) net forcing of +8.5 W/m-2 at the end of the 

century. For RCP 4.5, CO2 emissions slowly increase until mid-century and then decrease to the last 

quarter of the century where they stabilize at approximately 50 percent of 2000 levels; atmospheric CO2

concentration stabilizes at slightly more than 500 ppm. For RCP 8.5, CO2 emissions quickly rise through 

mid-century and continue to rise through the end of the century; atmospheric CO2 concentration also 

increases through the century reaching well over three times the current level (Meinshausen and others, 

2011; van Vuuren and others, 2011). Projected northern hemisphere September sea ice extent for both 

RCPs is similar during the early part of this century, but significantly diverges by mid-century and 

beyond (fig. 4). 

Results

Sensitivity Analysis

The final outcome node (i.e., “relative influence on population trend”) was most sensitive to 

nodes pertaining directly to sea ice, marine prey (ringed seal abundance), and differences among 

ecoregions (fig. 5). The availability and distribution of sea ice is directly influenced by climate change, 

while the link between marine prey conditions and climate may be direct or indirect, depending on the 

species. The AE and SIE are considered to be entirely comprised of shelf waters and foraging sea ice 

area and change is more gradual than in the PBDE and PBCE, where there is a sharply defined shelfbreak. 

Basis for the Normative Model Results

Outcome probabilities among the four final outcome node states (increased, same, decreased, 

and greatly decreased) were qualitatively similar regardless if sea ice input metrics were derived from 

the 5-model ensemble identified by Massonnet and others (2012), or the 11-model ensemble identified 

by Wang and Overland (2013b). When the multimodel variance for each sea ice metric was represented 

as a frequency distribution, the average absolute difference across all outcome states between the 5-

model and 11-model ensembles (for all but the AE) was less than 1 percent (mean difference = 1.05 

percent ± 1.69 sd, max = 9.94 percent, 95th percentile = 3.78 percent, n = 96). The sample size (96) 

equates to the product of 4 outcome states, 4 decades, 2 forcing pathways, and 3 ecoregions. The AE

was excluded because only two of the five models identified by Massonnet and others (2012) had 

adequate spatial resolution within the Archipelago (table 1). Similarly, when the ice metrics were input 

as ensemble means, the average absolute difference among all outcome probabilities between the 5-

model and 11-model ensembles also was small (mean difference = 1.05 percent ± 1.92 sd, max = 9.40 

percent, 95th percentile = 5.61 percent, n = 96). Given the similarity between the outcome probabilities 
16 

by the 5-model and 11-model ensembles, we henceforth present results derived exclusively with ice 

metrics from the 13-model union of the two ensembles (i.e., all models in table 1). We opted to use the 

larger union of models afforded by the two subsets to broaden the representation of climate model 

constructs in our analysis (Knutti and others, 2010). 

Regardless of whether the sea ice variance among the 13 models was represented as a frequency 

distribution or as an ensemble average, we again found little qualitative absolute difference between the 

resulting outcome probabilities (mean difference = 1.06 percent ± 1.27 sd, max = 6.83 percent, 95th

percentile = 3.53 percent, n = 128). Consequently, we chose to report results based solely on the 13-

model ensemble in which the multimodel variance for each ice metric was represented as a frequency 

distribution of prior probabilities. We refer to the Bayesian model runs that used these sea ice metrics as 

‘normative runs,’ applied once using ice inputs from each of the two GHG forcing pathways: RCP 4.5 

and RCP 8.5. 

Normative Model Results 

For the normative model runs, polar bear outcomes from the historical (1985−1995) and recent 

observation periods (2007−2012) were similar for the AE, SIE, and PBCE, with each of the three

ecoregions having dominant probabilities of either “increased” or “same as recent” (fig. 6). In contrast, 

the PBDE transitioned from a dominant outcome of “increased” during the historical period 

(1985−1995) to “greatly decreased” during the recent (2007−2012) time period. There was considerable 

uncertainty in the PBDE outcome for the recent time period, with probabilities closely distributed 

among “same as recent,” “decreased,” and “greatly decreased.” For future outcomes, the other 

ecoregions reached dominant probabilities of “decreased” and “greatly decreased” at different time 

periods (fig. 6). The PBDE maintained a dominant probability of “greatly decreased” in all future time 

periods under both RCP 4.5 and 8.5. The PBCE transitioned to dominant probabilities of “decreased” in 

the early century (2020−2030) and to “greatly decreased” at mid-century (2045−2055) under both 

RCPs. Similarly, the SIE shifted to a dominant probability of “decreased” in the early century and to 

“greatly decreased” at mid-century under both RCPs. The AE reached a dominant probability of 

“decreased” by the late century (2070−2080) under RCP 4.5 and remained in that state through the end 

of the century (2090−2100). Under RCP 8.5, the AE reached a dominant probability of “decreased” by 

the mid-century time period, and “greatly decreased” by the end of the century.

Uncertainty among normative outcome states decreased monotonically over time, as evidenced 

by the decreasing spread of probabilities among the outcome states (fig. 6). In general, probabilities of 

“increased” and “same as recent” states declined, while probabilities of “decreased” and “greatly 

decreased” states rose. As a result, uncertainty among future model outcomes was mostly distributed 

across those latter two states, with the exception of the AE where uncertainty also was distributed 

among “same as recent.” Overall, uncertainty in polar bear outcomes was greatest for the AE, and least 

for the PBDE.

Influence Runs

 “Overall sea ice conditions” and “marine prey base” consistently had the greatest influence on 

polar bear ecoregion outcomes, followed by “event-driven mortality,” “hunting,” and “defense of life 

and property (DLP; lethal)” (appendix F). Other potential stressors, such as “pollution,” “shipping,” 

“tourism,” and “oil and gas extraction activities” had minimal influence on polar bear outcomes given 

the far greater influence from sea ice and marine prey conditions. Below we examine the specifics by 

ecoregion. 
17 

PBDE.—For the PBDE, maximizing (individually) the stressors “overall sea ice conditions” or 

“marine prey base” (by setting states to minimal values) caused “probabilities of decreased or greatly 

decreased” to rise by 5−11 percent for “overall sea ice conditions” and by 3−8 percent for “marine prey 

base” through the end of the century compared to the normative value. By contrast, minimizing those 

stressors (by setting the states to maximal values) lowered the projected “probabilities of decreased or

greatly decreased” by about 57 percent (for “overall sea ice conditions”) or about 15 percent (for 

“marine prey base”) through the end of the century. Minimizing those stressors had a more pronounced 

effect on the “probability of greatly decreased,” which lowered the projected probabilities of “greatly 

decreased” by 40−75 percent (“overall sea ice conditions”) and 10−20 percent (“marine prey base”) (fig.

7a-d). The lowest levels of “harvest” or “DLP” caused “probabilities of decreased or greatly decreased”

to decline by 1−3 percent through the end of the century. Setting the “harvest” state to the maximal 

value resulted in an increase of “probabilities of decreased or greatly decreased” of 2−5 percent. Setting 

the “DLP” state to the maximal value had a negligible effect on outcomes. 

We also considered cumulative stressor effects by assessing the influence on outcomes of bestand worst-case scenarios for “event driven mortality” and “anthropogenic stressors” nodes. For the 

former, which summarizes the combined influence of “harvest,” “DLP,” and “other mortality or 

removal events” (which includes potential mortality from oil spills), setting the node state to the bestcase scenario (minimal value) lowered “probabilities of decreased or greatly decreased” by 2−6 percent 

through the end of the century. By comparison, the best-case scenario for “anthropogenic stressors” 

lowered probabilities of decreased or greatly decreased” by <3 percent through the end of the century. 

The worst-case scenarios for “event-driven mortality” and “anthropogenic stressors” raised 

“probabilities of decreased or greatly decreased” by 5 and 1 percent, respectively, through the end of the 

century. Last, we conducted a “maximum management” influence run by simultaneously minimizing all 

manageable (i.e., shipping, tourism, oil and gas extraction, human-polar bear interactions [sub-lethal], 

exposure to pollutants, and event-driven mortality) stressors on population outcomes, which lowered 

probabilities of “decreased or greatly decreased” by about 5 percent. 

PBCE.—For the PBCE, “overall sea ice conditions” was the most influential stressor followed 

closely by “marine prey base” for all time periods. As with the PBDE, “event-driven mortality,” 

“hunting,” and “DLP” exerted some influence on polar bear outcomes. The “pollution,” “shipping,” 

“tourism,” and “oil and gas extraction activities” nodes had a negligible influence on outcomes. 

Maximizing “overall sea ice conditions” and “marine prey base” caused “probabilities of decreased or

greatly decreased” to increase by 7−35 percent through the end of the century. Minimizing “overall sea 

ice conditions” lowered “probabilities of decreased or greatly decreased” by about 32−56 percent. 

Similarly, minimizing “marine prey base” lowered “probabilities of decreased or greatly decreased” by 

about 13 percent. Alternatively, maximizing “overall sea ice conditions” caused “probabilities of 

decreased or greatly decreased” to rise by 6−35 percent. Minimizing “overall sea ice conditions” and 

“marine prey base” lowered the projected “probability of greatly decreased” by 20−40 percent 

(fig. 8a-d).

Minimizing “harvest” caused “probabilities of decreased or greatly decreased” to decrease by 

2−5 percent through the end of the century, while maximizing “harvest” resulted in an increase of 

“probabilities of decreased or greatly decreased” of 2−8 percent. Setting the “DLP” state to maximal 

and minimal values had a negligible effect on “probabilities of decreased or greatly decreased.” 

Minimizing the level of “event-driven mortality” lowered “probabilities of decreased or greatly 

decreased” by about 3−5 percent, while maximizing the level increased “probabilities of decreased or 
18 

greatly decreased” by 3−9 percent. By contrast, minimizing or maximizing the level of “anthropogenic 

stressors” had little influence (i.e., < 1 percent) on lowering or increasing “probabilities of decreased or 

greatly decreased”. The combined worst-case scenarios for “event-driven mortality” and “anthropogenic 

stressors” raised the projected “probabilities of decreased or greatly decreased” by 4−10 percent. The 

“best management practices” influence run lowered “probabilities of decreased or greatly decreased” by 

5 percent. 

SIE.—For the SIE, “overall sea ice conditions” was the most influential stressor followed 

closely by “marine prey base” for all time periods. As with the PBDE and PBCE, “event-driven 

mortality,” “hunting,” and “DLP” exerted some influence on polar bear outcomes, while “pollution,” 

“shipping,” “tourism,” and “oil and gas extraction activities” nodes had little influence. Maximizing

“overall sea ice conditions” and “marine prey base” caused “probabilities of decreased or greatly 

decreased” to increase by 8−23 percent through the end of the century. Minimizing “overall sea ice 

conditions” lowered “probabilities of decreased or greatly decreased” by about 32−56 percent, while 

minimizing “marine prey base” lowered “probabilities of decreased or greatly decreased” by about 13 

percent. Maximizing “overall sea ice conditions” caused “probabilities of decreased or greatly 

decreased” to rise by 6−35 percent, and minimizing “overall sea ice conditions” and “marine prey base” 

lowered the projected “probability of greatly decreased” by 5−17 percent (fig. 9a-d).

Minimizing “harvest” caused “probabilities of decreased or greatly decreased” to decrease by 

2−5 percent, while maximizing “harvest” resulted in an increase of “probabilities of decreased or greatly 

decreased” of 2−8 percent. Maximal and minimal values of DLP had a negligible effect on 

“probabilities of decreased or greatly decreased.” Minimizing the level of “event-driven mortality” 

lowered “probabilities of decreased or greatly decreased” by about 3−5 percent, while maximizing the 

level increased “probabilities of decreased or greatly decreased” by 3−9 percent. Changing the level of 

“anthropogenic stressors” had little influence (i.e., < 1 percent) on lowering or increasing “probabilities 

of decreased or greatly decreased”. The combined worst-case scenarios for “event-driven mortality” and 

“anthropogenic stressors” raised the projected “probabilities of decreased or greatly decreased” by about 

1 percent. The “best management practices” influence run lowered “probabilities of decreased or greatly 

decreased” by 5 percent. 

AE.—For the AE, “overall sea ice conditions” and “marine prey base” were the most influential 

stressors, followed by “event-driven mortality,” “hunting,” and “DLP.” Other evaluated stressors had 

minimal influence on polar bear outcomes. Minimizing “overall sea ice conditions” lowered 

“probabilities of decreased or greatly decreased” by 17−30 percent through the end of the century, while 

maximizing the value of that node increased “probabilities of decreased or greatly decreased” by 30−51 

percent (fig. 10a-b). Minimizing “marine prey base” lowered “probabilities of decreased or greatly 

decreased” by about 18 percent, while maximizing the value caused “probabilities of decreased or 

greatly decreased” to increase by 17−32 percent. Minimizing “overall sea ice conditions” and “marine 

prey base” lowered the projected “probabilities of greatly decreased” by 20−40 percent and 5−17

percent, respectively (fig. 10a-d). The relative influence of “harvest” and “DLP” was similar to other 

ecoregions. Minimizing the level of “harvest” lowered “probabilities of decreased or greatly decreased”

by 3−5 percent, while maximizing the level of “harvest” increased “probabilities of decreased or greatly 

decreased” by 5−7 percent. Minimizing the level of “DLP” lowered “probabilities of decreased or 

greatly decreased” by 1−2 percent, while maximizing the level of “harvest” resulted in an increase by a 

similar amount.
19 

Minimizing the level of “event driven mortality” lowered “probabilities of decreased or greatly 

decreased” for the AE by about 6 percent, while maximizing the level increased “probabilities of 

decreased or greatly decreased” by 8−11 percent. Minimizing or maximizing the level of 

“anthropogenic stressors” had little influence (i.e., < 1 percent) on lowering or increasing “probabilities 

of decreased or greatly decreased.” The combined worst-case scenarios for “event-driven mortality” and 

“anthropogenic stressors” raised “probabilities of decreased or greatly decreased” by 10−12 percent, 

while combined best-case scenarios lowered “probabilities of decreased” or greatly decreased” by 6−9

percent. Implementing “best management practices” lowered “probabilities of decreased or greatly 

decreased” by about 7 percent. 

Discussion

We created a second generation model to identify which stressors were likely to have the 

greatest influence on polar bear persistence in four ecoregions, and to characterize the implications of

uncertainties in the future strength of stressors relative to those outcomes. We found that polar bear 

ecoregion outcomes were likely to worsen over time through the end of the century, and be 

accompanied by decreased uncertainty among the “increased” and “same as recent” states. Management 

interventions to mitigate stressors had the greatest positive impact on population outcomes when made 

in the early and mid-century periods; the potential benefits of such interventions declined over time. 

However, it is important to consider model projections within the context of uncertainties we attempted 

to characterize. Uncertainty in our outcomes can arise from a limited understanding of the ecological 

and physical system, how we represent that system in the model, and uncertainty in predictions of 

abundance and distribution (Amstrup and others, 2008). 

Our model comprised elements representing climate-mediated sea ice dynamics, suites of biotic 

and abiotic stressors, and key polar bear population vital rates. The complexity of the system required us 

to incorporate a wide breadth of information regarding the response of polar bears to environmental 

change, including empirical data, model-projected data, and expert judgment, which is a key advantage 

of using a BN approach (Marcot and others, 2006; Marcot, 2007; Pollino and others, 2007). Because we 

were able to leverage the architecture of the first generation BN model (by using that model as a starting 

point in developing the second generation model), our current effort represented a further verification, 

and in some cases refinement, of our understanding of the polar bear’s ecosystem and how to structure it 

in a model framework. 

We also, to the extent practicable, represented uncertainty in the estimation of probabilities 

associated with model nodes. For example, we used GCM outputs, which generally have wide margins 

of uncertainty (Murphy and others, 2004), to prescribe sea ice metrics. To represent the spread of 

uncertainty that accompanies those metrics, we used three different GCM ensembles (GCM05 based on 

models selected by Massonnet and others [2012]; GCM11 based on models selected by Wang and 

Overland [2013b]; and the 13-model union of GCM05 and GCM11), and portrayed variance as both a 

multi-model average and a frequency distribution (e.g., Jay and others, 2011). We reflected uncertainty 

(including potential for interactions between stressors) in other model nodes by spreading probabilities 

among outcome states more uniformly in the input and conditional probability tables. The spread of 

probabilities in non-sea ice nodes, which was based on expert judgment and derived iteratively through 

a repeated group consultation process, conveys important information about our current and future 

understanding of the strength of stressors we evaluated. Our modeling did not explicitly consider 

threshold or “tipping point” effects of rapid reductions in polar bear population trend outcome given 

progressively incremental increases in the various stressors (see Drake and Griffen, 2010, and 
20 

Guntenspergen, 2014, for examples in other ecosystems and theoretical discussions). Although such 

potential threshold effects on polar bears have been postulated by others (Derocher and others, 2013; 

Dai and others, 2012; but see Amstrup and others, 2010), we chose not to explicitly include those 

speculative effects because (1) we lack empirical data or understanding of how such effects would be 

structured (and thus parameterized) and (2) the purpose of our modeling was to provide a description of 

the potential for multiple stressors to affect polar bear populations rather than to determine potential 

thresholds or tipping points in stressor effects. 

Normative Model Runs

Polar bear populations in each ecoregion evidenced a progressively greater probability of a 

declining trend through future time periods. These results are qualitatively similar to the findings of 

Amstrup and others (2008), despite fairly significant differences in key aspects of the modeling 

approach. First, the second generation model was developed primarily to assess the relative influence of 

stressors on population outcomes, while the first generation model was developed to project future 

population numerical and distribution responses. Thus for the second generation model, we incorporated 

a broader complement of known and putative stressors of polar bear populations, some of which are 

well documented while others are not. As a result, we introduced greater uncertainty into the second 

generation model, so the outcomes should be viewed as hypotheses about how future stressor conditions 

may influence ecoregion outcomes. Other key differences between the two efforts include the use of 

GCM ensembles from the CMIP5 archive (Taylor and others, 2012), as opposed to CMIP3 (Meehl and 

others, 2007) used for the first generation model, a larger pool of experts with more diverse experience 

and knowledge, and different outcome states. 

Based on normative model runs, polar bear population status in the PBDE, PBCE, and SIE were 

most likely to experience worsening outcomes through the end of the century. Future outcomes were 

most dire for the PBDE, which transitioned to a dominant probability of “greatly decreased” in the early 

century (2020−2030), regardless of the RCP used. By contrast, the PBCE transitioned to dominant 

probabilities of “greatly decreased” by mid-century (2045−2055) regardless of RCP, while the SIE 

reached a dominant probability of “greatly decreased” by early century for RCP 8.5 and by mid-century 

for RCP 4.5 Ecoregion outcomes were highly sensitive to sea ice metrics, most notably “foraging sea ice 

area,” which expressed the proportional change in area of foraging sea ice habitat (as determined in 

Durner and others, 2009) over time (appendix C). The Arctic-wide sea ice melt season (i.e., period 

between the onset of sea ice melting in summer and freeze-up in autumn) has increased at a rate of 

about 5 days per decade since 1979 (Stroeve and others, 2014). The lengthening melt season has been 

driven primarily by later autumn freeze-up dates in the southern Beaufort, Chukchi, Kara, and Laptev 

Seas (Perovich and others, 2011; Stroeve and others, 2014), all of which are included within the PBDE. 

Polar bears are reliant on sea ice as a substrate to access marine mammal prey, and major changes in the 

availability of sea ice foraging habitat have been linked to declines in body condition, recruitment, 

survival, and abundance in the southern Beaufort Sea (Regehr and others, 2006; Rode and others, 2010).

Thus the finding of worsening population outcomes through time is consistent with historical

observations, although we note there is little data available from the Chukchi, Kara, and Laptev Seas 

subpopulations (Obbard and others, 2010). Recent data from the Chukchi Sea suggest that polar bears 

have maintained body condition and reproduction in response to initial levels of sea ice loss. Although 

we considered these data and included potential stability of female body condition in response to sea ice 

loss (30 percent probability of maintained body condition) over the long-term, the model outcomes 

suggested that polar bears in the PBDE will be the most sensitive to continued sea ice loss.
21 

The SIE also has experienced a trend of lengthening melt season (Gagnon and Gough, 2005). 

For example, polar bears in western Hudson Bay have been accustomed to spending upwards of 4 

months on shore fasting each summer/autumn, when annual sea ice melts completely (Derocher and 

Stirling, 1990). However, over the last three decades the length of the melt season has increased by 

approximately 3 weeks and, similar to findings from the southern Beaufort Sea, there is evidence of 

adverse effects on polar bears (e.g., Rode and others, 2011). For example, reduced availability of sea ice 

in western Hudson Bay has been linked to observed declines in polar bear body mass, body condition, 

and survival (Stirling and others, 1999; Regehr and others, 2007). Further, energetic modeling predicted

that 16 percent of adult males and females could die of starvation if the onshore fasting period lasts 

upwards of 5 months (Robbins and others, 2012). However, SIE future outcomes are characterized by 

greater uncertainty than PBDE outcomes. For example, in the Davis Strait subpopulation of the SIE 

there has been a marked increase in the abundance of harp seals concomitant with declines in sea ice 

availability (Department of Fisheries and Oceans, 2010), which has been posited as a factor contributing 

to an increase in the size of the subpopulation (Peacock, 2009). Thus, uncertainty in SIE outcomes 

might be attributed to the ameliorating effects of increased secondary prey availability in some areas as 

melt season lengthens.

Polar bear outcomes for the PBCE were similar to those from the SIE in terms of trend, but 

uncertainty of outcomes was slightly higher. This is most likely due to more modest projected changes 

in sea ice metrics and the lack of data on polar bears from this region. The PBCE region is characterized 

by heavy multi-year ice that accumulates via advection from the PBDE (Holland and others, 2006; 

Serreze and others, 2007). Advected ice tends to aggregate along coastlines (Stirling and others, 2011), 

which then provides functional connectivity between sea ice and terrestrial denning habitat, unlike in the 

PBDE. The maintenance of connectivity between sea ice and terrestrial habitat may contribute to the 

less dire trend in outcomes. The PBCE is comprised of the East Greenland, Queen Elizabeth, and 

northern Beaufort Sea subpopulations; survival and abundance data are available only for a portion of 

the northern Beaufort Sea subpopulation, where abundance through 2006 was considered to be stable 

(Stirling and others, 2011). The paucity of population and stressor data available from this ecoregion is 

reflected in the level of uncertainty in polar bear outcomes.

Normative outcomes for the AE were the most optimistic among the ecoregions, although also 

marked by the greatest amount of uncertainty. The AE, like the PBCE, is characterized by heavy multiyear ice that remains in the region year-round (Amstrup and others, 2008). As a result, projected 

changes to sea ice metrics are the most modest, and it is possible that, in the near term, thinning of the 

dense ice may actually improve the quality of foraging sea ice habitat (Arrigo and others, 2008). 

However, like the PBCE, there is relatively little data available on polar bear vital rates and stressors 

from the AE. Moreover, we only used 8 GCM models to estimate future sea ice for the AE, which 

increased uncertainty in estimates of sea ice metrics. Nevertheless, the AE appeared to be the most 

resilient to changing environmental conditions and resisted transitioning to a dominant probability of 

“greatly decreased” until the end of the century and only under RCP 8.5. Our findings support those of 

Amstrup and others (2008) and Peacock and others (2015) that the AE is likely to serve as a long-term 

refugium for polar bears. 
22 

Effect of Stabilizing GHG Emissions

Under RCP 8.5, the decline in all three sea ice metrics of importance to polar bears (total ice 

habitat, ice absence, and shelf distance) was hastened. Perhaps most importantly, this unabated emission 

pathway lengthened the duration of the melt season beyond the point (i.e., 5−6 months) at which 

extended fasting is predicted to lead to increased reproductive failure and starvation in polar bears 

(Molnar and others, 2011, 2014; Robbins and others, 2012). Not surprisingly, polar bear outcomes were 

more dire under RCP 8.5, with outcomes in three (PBDE, SIE, and PBCE) of the four ecoregions 

reaching a dominant state of “greatly decreased” by the early or mid-century mark. Stabilizing GHG 

emissions, by adhering to RCP 4.5, generally decreased the probability that a given ecoregion 

transitioned into the “decreased” or “greatly decreased” outcome state by 8–11 percent. However, the 

PBDE, SIE, and PBCE still reached dominant probabilities of “greatly decreased” by the early or midcentury time periods under RCP 4.5. 

Polar bear outcomes benefited most from keeping future GHG emissions consistent with those 

reflected in RCP 4.5. Amstrup and others (2010) came to a similar conclusion with the first generation 

model when comparing “aggressively mitigated” and “business as usual” emission scenarios derived 

from the CMIP3 GCM model set. That effort indicated that reduced radiative forcing with GHG 

mitigation resulted in greater retention of sea ice and less change to polar bear habitat, which increased 

the probability that polar bears could persist through the end of the century for all ecoregions. Our 

comparison between RCP 4.5 and 8.5 is conceptually similar―although RCP 4.5 is considered to 

represent a stabilization rather than abatement pathway because CO2 emissions plateau around 2040;

RCP 8.5 represents a worst-case unabated pathway (Moss and others, 2010). While there are important 

differences in the approaches and data used in the first and second generation polar bear BN models (as 

described directly above and elsewhere), our qualitatively similar findings reinforce the importance of 

stabilizing atmospheric concentrations of CO2 to the long-term persistence of polar bears. 

Stressor Evaluation

Ideally, influential stressors would have some degree of independence from climate-mediated 

changes to the environment, so that climate change is not always the ultimate cause of the interaction 

between polar bears and the stressors. Without some degree of decoupling, actions taken to mitigate 

those stressors are likely to prove ineffective because of the policy, social, and economic implications 

inherent to addressing rising GHG emissions. We do recognize that some stressors are likely somehow 

correlated or even causally linked, such as reduction in summer sea ice leading to increased shipping, 

tourism, and other marine-related anthropogenic stressors. Thus, our influence run analyses focused on 

stressors, individually and in reasonable combinations, that are of greatest concern to managers and span 

a spectrum from those that are relatively easy to manage (e.g., DLP kills) to those that are difficult or 

impossible to manage (e.g., overall sea ice conditions). None of the known and putative stressors we 

assessed appear to be independent from environmental change, thus potentially limiting the efficacy of 

mitigating anthropogenic stressors without mitigating GHG emissions. 

Ice and Prey availability—“Overall sea ice conditions” exerted the most influence on polar bear 

population outcomes. Excepting the AE, sea ice projections under RCP 4.5 indicated that for the 

remaining ecoregions ice-free conditions were likely to exist for 2−6 months by mid-century, and 5−9 

months by the end of the century. Under RCP 8.5, mid-century and end of century ice-free conditions 

were likely to last 3.5−7.5 and 7−11 months, respectively. In the model, the increasing duration of the 

ice-free period is most conspicuously manifested as a decline in overall sea ice conditions and habitat 

suitability. However, the most serious consequence of the lengthened ice-free period is the reduced 
23 

availability of marine mammal prey. Changes in the ice-free duration may or may not be accompanied 

by changes in the abundance of marine prey. However, without the sea ice substrate, prey will be largely 

unavailable to bears (Stirling and Derocher, 1993). The energetic consequences of restricted prey access 

are not fully known, but observational and model-based estimates suggest that increased fasting will 

lead to declines in body condition, adult female and male mass, reproductive potential, and survival 

(e.g., Atkinson and Ramsay, 1995; Derocher and Stirling, 1995; Molnar and others, 2011; Robbins and 

others, 2012). These adverse consequences become more dire as the ice-free period lengthens beyond 4 

months which, according to sea ice projections, is likely to occur for the majority of Arctic by midcentury (2045−2055) under RCP 8.5 and by late century (2070–2080) under RCP 4.5. 

A secondary concern of the lengthening ice-free period is the resulting increase in the length of 

time bears spend on shore. Polar bears appear able to forage effectively over sea ice until ice 

concentration values dip below a threshold ranging from 30 to 50 percent (Stirling and others, 1999; 

Sahanatien and Derocher, 2012; Cherry and others, 2013). Below that threshold range, polar bears 

displace to alternative habitats, which can be sea ice, if available, or terrestrial habitat including barrier 

islands and the mainland. The primary concern with increased use of terrestrial habitat revolves around 

the potential for increased or prolonged exposure to anthropogenic stressors, including stressors that 

bears would not be exposed to if on the sea ice. For example, increased time spent on shore may 

increase the potential for human-polar bear interactions, which could then lead to conflict and an

increased likelihood of mortality from “DLP” kills (Stirling and Derocher, 2012). Likewise, time spent 

on shore could serve to mediate exposure to industrial pollutants and infectious agents and parasites that 

bears would not be exposed to in a marine environment (Stirling and Derocher, 2012). Indeed, the 

hypothesized nexus between climate-mediated changes in sea ice characteristics and exposure to a 

greater array of stressors is a key consideration when attempting to identify which management actions

are likely to be most effective. 

Like “overall sea ice conditions,” “marine prey base” was a highly influential stressor. Under a 

best-case scenario (i.e., where prey base conditions were maximized), elevated “marine prey base”

resulted in a substantial reduction (as high as 51 percent) in the probability that a given ecoregion 

transitioned to a future state of decreased or greatly decreased. However, we note that while “marine 

prey base” is decoupled from sea ice metrics in our model, that does not reflect reality. In developing 

the conditional probability table for this node, we recognized that changes in primary and secondary 

prey abundance, as well as availability, were likely to occur as sea ice cover declines and physical 

characteristics change (e.g., Lydersen and Kovacs, 1999; Bluhm and Gradinger, 2008; Iacozza and 

Ferguson, 2014). At this time, available information is insufficient to quantify the linkage between sea 

ice conditions and polar bear prey abundance. 

Other stressors—“Harvest” and “DLP” had influence on polar bear outcomes, although 

considerably less than “overall sea ice conditions” and “marine prey base.” Minimizing the effect of 

harvest resulted in a 2−5 percent reduction in the probability of transitioning to decreased or greatly 

decreased states, while minimizing DLP resulted in a 1−3 percent reduction. There was no indication 

from the model of an additive effect of these two stressors. The influence run result from the best- and 

worst-case scenario for “event-driven mortality,” which is informed by “harvest” and “DLP,” resulted in 

a 3−6 percent reduction in the probabilities of decreased or greatly decreased outcome states. This 

finding, of no clear additive effect of different forms of mortality, could be due to several reasons. First, 

some polar bear harvest is opportunistic and may result from a DLP encounter. For example in the 

United States, polar bears killed as a result of DLP interactions are treated as harvested bears in the 

sense that they are considered a take to be counted against quota allotments (Schliebe, 1986). Second, 

there is very little information on the cause-specific mortality of polar bears. Amstrup and Durner 
24 

(1995) found that 85 percent of the documented deaths of adult females were attributable to hunting, 

while 90 percent of documented losses of dependent young were attributable to sources other than 

hunting. Given the generally low survival of dependent young compared to subadults and adults (e.g., 

Regehr and others, 2006), we may be underestimating the extent to which other forms of mortality 

contribute to “event-driven mortality.” More information on cause-specific mortality, particularly for 

dependent young, will be needed to know if that mortality is mostly driven by natural (e.g., starvation or 

extreme weather events) or anthropogenic stressors, and thus susceptible to management intervention. 

The remaining stressors had little influence on polar bear outcomes at the ecoregional scale of 

our assessment (although they may play more salient roles locally). For example, minimizing the effect 

of pollutants (e.g., hydrocarbons and other persistent organic compounds) resulted in ≤ 2 percent

reduction in the probability of a decreased or greatly decreased outcome. Similarly, stressors that have 

the potential to result in sub-lethal disturbances, such as non-lethal human-polar bear interaction, 

resource extraction and exploration activity, shipping, and tourism had an even lesser influence on polar 

bear outcomes (< 1 percent). In both cases, the finding of minimal influence is likely due to a lack of 

information on population-level effects. Although there is substantial information characterizing 

exposure of polar bears to pollutants (e.g., Sonne and others, 2012), there is little information linking 

exposure to population vital rates. Also, sub-lethal stressor effects can be cumulative (Holmstrup and 

others, 2010) and given that some of these stressors are relatively new to the Arctic (e.g., shipping and 

tourism) they may not have reached a level of intensity that elicits a measurable response from polar 

bears. Conversely, some of those stressors may not actually affect polar bears. Clearly better 

information is needed to determine the influence these putative stressors are likely to have on polar bear 

populations, and the little resulting influence in our model in part is due to the current uncertainty in the 

extent and effects of such stressors. 

Manageability of Influential Stressors

The most substantive positive effect on polar bear population outcomes resulted from 

improvement in the condition of sea ice habitat resulting from stabilized GHG emissions, followed by 

improved availability and accessibility of marine mammal prey. Improved sea ice habitat, alone, could 

reduce by about 50 percent the probability of polar bear population status in a given ecoregion reaching 

a state of decreased or greatly decreased. However, managing for improvements in sea ice habitat and 

marine mammal prey quality is a daunting challenge that will require a global policy solution and likely 

be years in the making. Because CO2 emissions accumulate over time, there will be a lag, likely on the 

order of several decades, between mitigation of emissions and meaningful stabilization of sea ice habitat 

loss (sensu Allen and Stocker, 2013). 

By contrast, other stressors had substantially less influence on polar bear outcomes, but would 

be inherently more tractable to manage. We found no single stressor that can be targeted with the 

expectation of achieving an improvement in population outcomes comparable to improving sea ice 

conditions or marine prey availability. As evidenced by our wholistic “best management practices” 

influence runs, the best outcomes were reached when all non-sea ice stressor values were set to their 

minimum levels. However, implementing “best management practices” only reduced by about 10 

percent the probability of an ecoregion reaching the decreased or greatly decreased state. Nevertheless, 

there is a fair amount of uncertainty in our population outcomes, and a 10 percent reduction in the 

likelihood of a population becoming decreased or greatly decreased may buy time to achieve mitigation 

of GHG emissions and arrest further sea ice habitat losses. 
25 

The most influential stressors to polar bear populations were “overall sea ice conditions,” 

“marine prey base,” “hunting,” and “DLP kills.” Other stressors, such as “shipping,” “tourism,” and “oil 

and gas extraction activities” appeared to have little influence on population outcomes, mainly because 

of the paucity of information linking those factors to adverse effects on polar bears. As a result, we 

depicted considerable uncertainty in the probabilities of those stressor nodes. This highlights an 

important value of the BN approach: explicit representation of uncertainty (Marcot, 2012), which can 

then be used to direct further research efforts. Accordingly, research investigating the potential for lethal 

and sub-lethal effects of increased anthropogenic activity on polar bears would reduce uncertainty. 

Because such activities are fairly new to the Arctic, simulation-based approaches could be used until 

adequate field data exist to develop empirical models.

Conclusion

We developed a second generation model to evaluate which stressors were most likely to 

measurably affect future polar bear population outcomes. As in the first generation model, model 

outcomes, when projecting sea ice conditions using an ensemble of GCMs and holding other stressors 

unchanged, revealed a trend of progressively greater probabilities of polar bear population outcomes 

becoming decreased or greatly decreased by the end of the century. Although outcome uncertainty 

varied among ecoregions, we found that emissions under the unabated RCP 8.5 caused polar bears in 

most ecoregions to reach a dominant probability of decreased or greatly decreased about 25 years earlier 

than they would under the stabilized RCP 4.5. 

Influence runs showed that model outcomes were driven primarily by changes to sea ice habitat 

and, to a lesser degree, by changes in prey abundance. These stressors are inherently difficult to manage 

because ultimately those changes are modulated by GHG emissions. Minimizing harvest and DLP kills 

had a comparatively small, but not trivial, effect. Because these stressors can be managed in situ, they 

are considerably more tractable from a management perspective. Minimizing other stressors offered

little to modest improvement over minimizing just harvest and DLP. However, the effects of other 

stressors on polar bear vital rates are poorly understood, and we had to reflect that with relatively high 

degrees of uncertainty when parameterizing those input nodes. Thus, the low influence of those stressors 

could be attributed, in part, to our uncertainty about when they might be expressed and their impact on 

polar bear populations. Also, stressors with low influence outcomes in our model at the ecoregional 

scale may still have high influence more locally.

The long-term persistence of polar bears will require stabilizing the projected loss of sea ice 

habitat, which can best be achieved by maintaining GHG emissions consistent with or below the RCP 

4.5 trajectory. By most estimates, it will take several decades for the effects of GHG mitigation to be 

realized via the recovery of sea ice habitat. In those intervening decades, the management of key 

stressors may serve to slow (but not halt or reverse) the transition of polar bear ecoregions to 

progressively more dire outcome states. Doing so could improve the prospects of their persistence until 

sea ice habitat is stable. 
26 

Acknowledgments

This analysis is part of the U.S. Geological Survey’s Changing Arctic Ecosystems Initiative, 

supported by the Ecosystems Mission Area, with additional in-kind contributions from the U.S. Forest 

Service. Additional support was provided by the U.S. Fish and Wildlife Service, Region 7, Marine 

Mammals Management Office. We thank K. Laidre for providing expertise on polar bear ecology and 

for valuable insight throughout the course of this study. We thank the members of the U.S. Polar Bear 

Recovery Team, Science and Traditional Ecological Knowledge Working Group that provided feedback 

on model influence diagrams and structure. We are grateful for the helpful comments and suggestions 

from T. DeBruyn, J. Wilder, M. Runge, and D. Pierce-Williams at various stages in the development of 

this work. K. Oakley, S. Ban, and G. Wilhere provided comments on an earlier version of this 

manuscript that improved this final version. 

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35 

Table 1. The 13 general circulation models (GCM) used in this study, and the models’ country and institute of 

origin. 

[The 13 models were selected from among all CMIP5 models based on recommendations by Wang and Overland (2013a, 2013b) and 

Massonnet and others (2012); denoted with “W” or “M”, respectively. Only 8 models had sufficient spatial resolution for analyses in the 

Archipelago Ecoregion (“Y”)]

Model Country Institute Selection Archipelago

ACCESS1.0 Australia 1CISRO-BOM W, M

ACCESS1.3 Australia CISRO-BOM W, M

CCSM4 USA 2NCAR W Y

CESM1-CAM5.1 USA 3NSF-DOE-NCAR W Y

EC-EARTH European Center 4EC-Earth Consortium W Y

GFDL-CM3 USA 5NOAA GFDL M Y

HadGEM2-AO UK 6MOHC W Y

HadGEM2-CC UK MOHC W Y

HadGEM2-ES UK MOHC W Y

IPSL-CM5A-MR France 7IPSL M

MIROC-ESM Japan 8AORI-NIES-JAMSTEC W

MIROC-ESM-CHEM Japan AORI-NIES-JAMSTEC W

MPI-ESM-MR Germany 9MPI W, M Y

1

Commonweath Scientific and Industrial Research Organization-Bureau of Meteorology. 2

National Center for Atmospheric Research. 3

National Science Foundation-Department of Energy-National Center for Atmospheric Research. 4

European Center-Earth Consortium. 5

National Oceanic and Atmospheric Administration-Geophysical Fluid Dynamics Laboratory. 6

Met Office Hadley Center. 7

Institut Pierre Simon LaPlace. 8

Atmosphere and Ocean Research Institute (University of Toyko)-National Institute for Environmental Studies-Japan Agency for MarineEarth Science and Technology. 9

Max Planck Institute. 
36 

Bea

Figure 1. Map of the four polar bear ecoregions as defined by Amstrup and others (2008). The map includes a 300 

m bathymetry contour denoting where the break in the continental shelf occurs, and a depiction of seasonal 

patterns of ice motion and distribution. The map also shows the boundaries for polar bear subpopulations as 

defined by the IUCN Polar Bear Specialists Group. Polar bear subpopulations comprising the Polar Basin Divergent 

Ecoregion (PBDE) include southern Beaufort Sea (SBS), Chukchi Sea (CS), Laptev Sea (LVS), Kara Sea (KS), 

and the Barents Sea (BS). The Polar Basin Convergent Ecoregion (PBCE) includes East Greenland (EG), Queen 

Elizabeth (QE), and Northern Beaufort Sea (NBS) populations. The Seasonal Ice Ecoregion (SIE) is comprised of 

southern Hudson Bay (SHB), western Hudson Bay (WHB), Foxe Basin (FB), Davis Strait (DS), and Baffin Bay (BB)

populations. The Archipelago Ecoregion (AE) includes populations from the Gulf of Boothia (GB), M’Clintock 

Channel (MC), Lancaster Sound (LS), Viscount-Melville Sound (VM), Norwegian Bay (NW), and Kane Basin (KB). 
37 

Figure 2. General structure of the second generation model showing the relationships among the 10 submodels. 

See section “Model Composition” in text for submodel descriptions. 
38 

Figure 3. The second generation model used to evaluate the relative influence of stressors on future polar bear population outcomes. 
39 

Figure 4. Changes in sea ice extent as simulated by CMIP5 models over the second half of the 20th century and 

the entire 21st century under RCP2.6, RCP4.5, RCP6.0, and RCP8.5 for the Northern Hemisphere, September

(number of models available for each RCP is given in the explanation). The colored lines show the multi-model 

means, the shading denotes the 5 to 95-percent range of the ensemble, and the vertical line marks the end of 

CMIP5 historical climate change simulations. Sea ice extent is defined as the total ocean area where sea ice 

concentration exceeds 15 percent and is calculated on the original model grids, and change is expressed relative to 

the reference period of 1986–2005. Satellite data of Comiso and Nishio (2008, updated 2012) over 1979–2012, and 

updates for 2013 and 2014 using data from NSIDC also are plotted (solid green curves). This figure was modified 

from Collins and others (2013). 
40 

Figure 5. Sensitivity of the final outcome node “relative influence on population trend” to all 24 input (parentless) 

model nodes. Sensitivity is measured as entropy reduction (also called mutual information), which denotes the 

degree to which polar bear outcome probabilities are sensitive to (that is, their information content is increased by) 

a given model input node. The units on the horizontal axis measure the amount of expected reduction in mutual 

information of the outcome node from a finding for a given input node.
41 

Figure 6. Polar bear outcomes from the second generation model. Results are from normative model runs for four 

ecoregions at one historical, one recent and four future time periods. Historical and recent sea ice conditions were 

based on observed data. Future sea ice conditions were derived from 13 GCMs using RCP 4.5 and 8.5. 
42 

a. 

b.

Figure 7. Influence of “overall sea ice conditions” (a, b) and “marine prey base” (c, d) on the probability of a “greatly 

decreased” outcome for polar bears from the Polar Basin Divergent Ecoregion at four future time periods using 

RCP 4.5 and 8.5. Time periods are 2020–2030 (early century), 2045–2055 (midcentury), 2070–2080 (late century), 

and 2090–2100 (end of century).
43 

c. 

d.

Figure 7. —Continued
44 

a.

b.

Figure 8. Influence of “overall sea ice conditions” (a, b) and “marine prey base” (c, d) on the probability of a “greatly 

decreased” outcome for polar bears from the Polar Basin Convergent Ecoregion at four future time periods using 

RCP 4.5 and 8.5.
45 

c.

d. 

Figure 8.—Continued
46 

a.

b.

Figure 9. Influence of “overall sea ice conditions” (a, b) and “marine prey base” (c, d) on the probability of a “greatly 

decreased” outcome for polar bears from the Seasonal Ice Ecoregion at four future time periods using RCP 4.5 and 

8.5.
47 

c. 

d.

Figure 9. —Continued
48 

a.

b.

Figure 10. Influence of “overall sea ice conditions” (a, b) and “marine prey base” (c, d) on the probability of a 

“greatly decreased” outcome for polar bears from the Archipelago Ecoregion at four future time periods using RCP 

4.5 and 8.5.
49 

c.

d.

Figure 10.—Continued
50 

Appendix A. Name, Job Title, Area of Expertise, and Professional Affiliation for 

Each Subject Matter Expert 

We brought together a team representing expertise in different areas of polar bear life history and 

representing different geographic regions. Practical limitations also shaped the development of this 

team. First, because much of the model development occurred in face-to-face meetings, we had to have 

a team that could meet regularly for extended periods. Secondly, a tractable group size can promote 

extended discussions of individual estimates and facilitate achieving a consensus (Martin and others, 

2012). The team involved in this study, however, was larger than the team used to generate the first 

generation model and represented a broader range of expertise.

Name Title Area of expertise Professional affiliation

Bruce Marcot Research Wildlife 

Biologist

Bayesian network 

modeling, biodiversity 

assessment

U.S. Forest Service

David Douglas Research Wildlife 

Biologist

Movement ecology, sea 

ice modeling

U.S. Geological Survey

Kristin Laidre Research Scientist 

and Assistant 

Professor

Polar bear ecology, 

Arctic marine mammal 

ecology

University of Washington

Karyn Rode Research Wildlife 

Biologist

Polar bear ecology, 

physiological ecology

U.S. Geological Survey

George Durner Research Zoologist Polar bear ecology; 

movement ecology

U.S. Geological Survey

Jeffrey Bromaghin Research Statistician Population ecology, 

statistics

U.S. Geological Survey

Steven Amstrup Chief Scientist Polar bear ecology, 

Arctic ecology

Polar Bears International

Todd Atwood Research Wildlife 

Biologist

Polar bear ecology; 

disease ecology

U.S. Geological Survey
51 

Appendix B. Additional Information on the Process Used to Develop and 

Parameterize the Model

We followed published guidelines for developing BN models (Marcot and others, 2006). We 

began by establishing a team of subject matter experts (appendix A). We reviewed post-2007 

publications from relevant subject areas and then evaluated the structure of the first generation influence 

diagram. We met as a team to discuss findings, specify the inclusion of new stressors, revise the 

influence diagram accordingly to reflect the current understanding of relationships among stressors that 

may affect how they mediate population outcomes. 

Next, we specified an adequate set of discrete states for each variable (node). In doing so, all 

states within each node were intended to be mutually exclusive and collectively exhaustive. Then, we 

parameterized the influence diagram with conditional probabilities for each node as informed by 

published literature, empirical and projected data, and expert knowledge. We specified the probability 

values as follows. 

1. First, we met as a team and reviewed the entire model structure, making adjustments in 

definitions of some nodes and their states and in the connections between nodes, as we 

collectively viewed the model as a causal web of affector and effector variables. 

2. Second, we developed a spreadsheet displaying the framework for each node’s conditional 

probability table. The spreadsheet was sent to each team member who was instructed to provide 

a first draft of conditional probability values for tables corresponding to variables pertaining to 

their individual areas of research and expertise. The entire spreadsheet, once compiled and 

completed from all team members’ contributions, was then routed throughout the team in a 

“round-robin” fashion, to elicit any suggested edits to any of the conditional probability values. 

Several round-robin team reviews were conducted until the team reached consensus on the 

probability values, which, being based mostly on expert judgment, were specified to a precision 

of no more than 5 percent increments. At the same time, the probability tables were entered into 

the model and the model performance—as a whole or by subsections—was evaluated for 

consistency with literature and personal research experience. We also used sensitivity analysis 

on subsections of the model and the entire model to adjust or confirm probability values for 

realistic behavior (e.g., as conducted by Jay and others, 2011). All sensitivity analyses were 

conducted by setting the input variables to their default, uniform, prior probability distributions. 

Sensitivity analysis with categorical variables such as found in our model is expressed as 

entropy reduction or mutual information, and reflects the degree to which information in an 

outcome node is increased (entropy or uncertainty is reduced) by an input node. 

3. Third, the final set of conditional probability values were entered into the model. Then, after an 

initial set of runs specifying input conditions by ecoregion, time period, and climate projection 

pathways, minor adjustments were made to a few probability values to eliminate any anomalous 

behaviors, such as multi-modal or non-monotonic posterior probability distributions and 

responses, where clearly uni-modal distributions and monotonic responses would be logical 

outcomes. Such anomalous behaviors were few and minor, not affecting final outcomes 

qualitatively. These few instances seemed to have occurred in the model because of differences 

in the number of states among linked, intermediate nodes. 
52 

Following published model-development protocols, we also invited and incorporated, external 

peer reviews (e.g., members of the U.S. Polar Bear Recovery Team, Science and Traditional Ecological 

Knowledge Working Group) at various stages of model development. 

Calibration of the model consisted of specifying the model structure and probability values to 

match the team’s collective knowledge and judgment. Validation consisted of adjusting the model based 

on review comments, and conducting “hindcast” model runs for historical and recent time periods for 

which sea ice and polar bear conditions have been mostly known or suspected. We then compared 

modes of model results to those known, earlier conditions, ensuring that the model performed correctly. 

Because our model is projecting future conditions, “hindcast” validation is the only option available. 
53 

Appendix C. Description (Including Key Assumptions) of All Nodes in the 

Second Generation Model 

Nodes are listed here by categories of input nodes, intermediate nodes, outcome node, and scenario 

nodes. Under each node name, the number of the submodel to which it belongs is shown in square 

brackets. (Submodels are listed in a footnote below this table and described in section, “Model 

Composition” in the text.) 

Node

Name Node Title Node Description States

Input Nodes

RSAbun

[3] 

Ringed Seal 

Abundance 

This node expresses the observed and expected relationship 

between sea ice coverage and phenology and seal 

abundance. For example, evidence suggests that earlier

spring break-up of sea ice in western Hudson Bay was 

related to declining pup survival (Ferguson and others, 

2005). Declining sea ice likely will directly reduce the 

availability of birthing and haul-out habitat for seals. 

Earlier break-up also may adversely impact pup survival by 

interrupting the lactation period. Ringed seals have the 

longest lactation period of the Phocidae and need stable ice 

until neonates are weaned (Lydersen and Kovacs, 1999). 

In some areas, declines in sea ice may improve biological 

productivity, which may benefit seals in the near-term. 

However, increased primary productivity will be greatest 

during summer (after break-up) and benefits may be 

mitigated by reduced biomass in coastal/shelf area due to 

increased river runoff and associated changes in turbidity 

and salinity (e.g., Bluhm and Gradinger, 2008).

Elevated

Same as 

Recent 

Reduced

BSAbun

[3] 

Bearded Seal 

Abundance 

This node expresses changes in bearded seal abundance 

that are likely to occur as sea ice cover declines and its 

character changes. Bearded seals are believed to have a 

patchy distribution throughout their range. Because of this, 

encounter rates with polar bears likely are lower than for 

ringed seals. However, bearded seals are considerably 

larger than ringed seals, so biomass consumed by polar 

bears is likely similar (e.g., Pilfold and others, 2012. 

Bearded seals prefer drifting pack ice over shallow 

water/coastal areas. Because of this, they may be more 

vulnerable to reductions in biomass in coastal areas due to 

increased river runoff and associated changes in turbidity 

and salinity (e.g., Bluhm and Gradinger, 2008). 

Elevated

Same as 

Recent 

Reduced

MrnPryOth

[3] 

Secondary and 

New Prey 

Abundance 

This node expresses changes in the abundance of secondary 

prey that are likely to occur as sea ice cover declines and its 

physical character changes. Secondary prey can include 

beluga whales (Delphinapterus leucas), harp seals, hooded 

seals, spotted seals, ribbon seals, and other marine 

Elevated

Same as 

Recent 

Reduced
54 

Node

Name Node Title Node Description States

mammals (e.g., Pacific walrus). For example, as pack ice 

becomes less available in the Bering Sea, ribbon seal range 

likely will expand northward to maintain access to pack ice 

during the birthing period (Lowry and Boveng, 2009). 

Likewise, spotted seals regularly haul out on shore and are 

less reliant on sea ice, and may expand northward as 

summer ice cover declines (Kovacs and others, 2011). Last, 

ribbon, harp, and hooded seals can spend long periods 

pelagically in areas without ice (Kovacs and others, 2011, 

making them somewhat resilient to loss of sea ice habitat. 

As the ice retreats into the AE, it is reasonable to expect 

that prey species may penetrate deeper into that region and 

provide at least a transient improvement in alternate prey. It 

is unclear, however, that such changes could persist as

bears are forced onto smaller and smaller areas of ice. 

IceArea

[2] 

Foraging Sea 

Ice Area

This node expresses the proportional change in the area of

polar bear habitat over time, and is derived from the 

satellite passive microwave record for the observational 

period, and from GCM outputs of sea ice for the 1996–

2006 baseline (historical forcing) and future periods 

(RCP4.5 and RCP8.5 forcing). Habitat is expressed as the 

number of square kilometer months of optimal habitat 

(Durner and others, 2009) in the two polar basin 

ecoregions, and as square kilometer months of ice over 

continental shelf in the other ecoregions (AE and SIE). 

Because the AE and SIE are almost entirely shallow water 

areas, the ‘optimal’ habitat in those areas equates to 

essentially the ice extent months for each region. We 

further expressed this as the average percent change in 

quantity of these ice habitats, from the 1996–2006 baseline 

period to the projected future period of interest. 

Interpreting the percent difference must take into account 

that a given percent change may not be directly comparable 

between ecoregions. The absolute change in the AE, for 

example may be very small, but because it is measured 

from essentially 0, it may seem like a great percentage. 

>= 0

-5 to 0 

-10 to -5 

-20 to -10 

-40 to -20 

< -40 

(proportional 

change)

IceQual

[2] 

Foraging Sea 

Ice Quality

This node expresses a subjective assessment of the quality

of sea ice for foraging by polar bears. Recent observations 

of the changes in sea ice characteristics in several Arctic 

seas and regions (e.g., southern Beaufort and Chukchi Seas 

[Mahoney and others, 2012], Hudson Bay [Gagnon and 

Gough, 2005; Sahanatien and Derocher, 2012]) suggest that 

the later freeze up, warmer winters, and earlier ice retreat in 

summer have resulted in thinner ice that more easily 

deforms and more frequently rafts over itself. These 

changes have reduced the quality of ice as a denning 

substrate, and may have reduced its quality as a foraging 

Elevated

Same as 

Recent

Reduced
55 

Node

Name Node Title Node Description States

substrate because the extensive ice deformation can result 

in ice covered refugia for ringed seals, which are less likely 

for polar bears to get into. 

However, very thick and dense ice in the AE and the 

northern part of the PBCE may have limited access to prey, 

and foraging ice quality might first improve with global 

warming and then decline. 

IceShelf

[2] 

Sea Ice Shelf 

Distance 

Change 

This metric expresses the distance (in kilometers) that the 

summer ice retreats from traditional autumn/winter 

foraging areas, which are over the continental shelves and 

other shallow water areas within the polar basin. It was 

calculated by extracting the largest contiguous expanse of 

pack ice whose pixels have >50 percent concentration and 

calculating the mean of the linear distances between all 

pixels in the shelf waters to the nearest pack ice during the 

month of minimum non-zero ice extent. It was expressed as 

the difference between this mean distance averaged for the 

period 1996–2006 and the average distance as calculated 

for other time periods of interest. This metric was derived 

from the satellite passive microwave record for the 

observational period, and from GCM outputs of sea ice for 

the 1996–2006 baseline (historical forcing) and future 

periods (RCP4.5 and RCP8.5 forcing). 

The metric was derived only for the polar basin regions 

(PBDE and PBCE) because polar bears in the AE and SIE

typically go to shore when the summer ice retreats, as 

opposed to retreating with the ice as bears in the polar basin 

tend to do. The distance that summer ice retreats away 

from shelf water areas, where polar bears traditionally 

forage in autumn/winter, lends information to the question 

of can or will bears make round-trips between traditional 

denning/foraging areas and remnant summer pack ice. 

States are discretized ranges of the continuous variable, 

measured as kilometers.

< 0

0 to 200 

200 to 500

500 to 800

>= 800 

(kilometers)

IceChng

[2] 

Foraging Sea 

Ice <50% 

Absence

This node expresses the length in months of ice absence 

from the continental shelf regions historically preferred by 

polar bears. The metric was the number of months during 

which the continental shelf was ice free, where ice free was 

defined as fewer than 50 percent of the pixels over the shelf 

having less than 50 percent ice cover. The metric expresses 

the difference in months between the forecasted number of 

ice free months for future time periods and the number of 

ice free months for the baseline, which was defined as the 

GCM model outputs for the period 1996–2006. This 

metric was derived from the satellite passive microwave 

record for the observational period, and from GCM outputs 

of sea ice for the 1996–2006 baseline (historical forcing) 

< 0

0 to 1 

1 to 2 

2 to 3 

3 to 5 

>= 5

(months) 
56 

Node

Name Node Title Node Description States

and future periods (RCP4.5 and RCP8.5 forcing).

Polar bears in some regions already experience ice free 

periods. The impact of the length of the ice free period is 

dependent mainly on the productivity of the environment 

and the availability of refugia habitat (such as terrestrial 

habitat) and alternative food resources, and has a different 

impact in the Beaufort Sea, for example, than it does in the 

seasonal ice region, which is relatively productive. The 

length in months of ice absence expressed in the 

conditional probability table must be interpreted with 

regard to the fact that in large parts of these areas even at a 

mean 1–3 months of increased ice absence, actual absence 

in some parts of these regions would still be 0. An absence 

difference of >3 months means a mean absence of 4 or 5+ 

months in the PBDE, and 7, 8, or 9+ months in the SIE, but 

only 3+ months in portions of the AE or PBCE. 

HumFood

[4] 

Human 

Provisioned

Food 

Abundance 

This node expresses the availability of human-provisioned 

food and can include remains from harvest of whales, other 

marine mammals, and other game. This node also can refer 

to other organic refuse or food sources for polar bears. The 

availability of human-provisioned food is most pertinent 

during autumn months, as that is likely when the biggest 

nutritional contribution is realized (Herreman and Peacock, 

2013). However, there is some evidence that polar bears 

will visit bone piles throughout the winter. That said, it is 

not expected that they get a substantial nutritional input 

from those winter visitations. This node is mostly relevant 

to the PBDE and the SIE. 

Elevated

Same as 

Recent

Reduced

Absent 

PryAcc

[4] 

Terrestrial and 

Marine Prey 

and Food 

Accessibility

This node expresses the availability of terrestrial prey and 

other food resources, including scavenge subsidies (other 

than human-provisioned) on land or in proximity to land 

(e.g., consolidated glacial ice). Food resources could 

include Arctic char (Salvelinus alpinus), bird colonies, 

berries, vegetation, seaweed, caribou, geese, ice seals on 

glacial ice, beach cast of marine prey (e.g., seal carcasses 

that wash ashore). There is evidence in the literature that 

polar bears have historically exploited terrestrial-based 

foods. There also is some evidence that use of terrestrialbased food is increasing in some ecoregions (e.g., Born and 

others, 2011; Iverson and others, 2014). In general, we 

believe that the availability of terrestrial food may facilitate 

use of terrestrial habitat when sea ice habitat is unavailable. 

However, we note that in most cases, terrestrial food is not 

available in sufficient quantity or nutrient content to 

mitigate declines in body condition associated with bears 

foregoing foraging on marine mammal prey. 

Elevated

Same as 

Recent

Reduced

Absent 

TerrRef

[4]

Terrestrial 

Refugia Quality

This node characterizes stability in structure and freedom 

from human and natural disturbance of a place where polar 

Improved

Not Degraded
57 

Node

Name Node Title Node Description States

bears can wait out the ice-minimum period, avoiding 

excessive expenditure of energy. The characterization 

includes quality of sites used for terrestrial non-maternal 

denning, summertime denning, resting, and unobstructed 

movement. Refugia quality can be compromised by 

erosion, rock slides, fire (e.g., peat ridges adjacent to 

Hudson Bay), reduction in consolidated glacial ice or 

retraction of glaciers, and the presence of anthropogenic 

infrastructure that inhibits movement and/or use of an area. 

We note that terrestrial refuge is most important for the 

SIE, where use of terrestrial habitat has long been a life 

history characteristic, and the PBDE, where use of 

terrestrial habitat is becoming more common. Degradation 

of refugia quality by coastal erosion is a threat for the 

PBDE, as is the presence of anthropogenic infrastructure. 

Reduction of consolidated glacial ice and/or glacial 

retraction is a threat in parts of the SIE, and possibly the 

AE. 

Degraded

HumPB

[7] 

Human-Bear 

Sublethal 

Interaction

This includes sublethal disturbance that may increase as a 

result of increased human-bear interactions due to food 

stressed bears more frequently entering Arctic communities

(e.g., Born and others, 2011). Such takes can displace bears 

from their preferred locations and reduce habitat quality. 

This was separate from the similar interactions that may 

occur around oil and gas or other industrial sites, which 

also can displace bears and lower habitat quality. We 

believe that bear-human interactions will increase as long 

as the ice-minimum season increases. 

Elevated

Same as 

Recent

Reduced

OilAcct

[7] 

Oil, Gas, and 

Mining Activity

This node characterizes the potential for oil, gas and 

mining activity to influence the distribution of polar bears. 

Specifically, it refers to activities and infrastructure that

may physically displace bears from habitat that was 

formally available to them. Industry plans and agency 

permitting activities indicate that oil and gas extraction and 

exploration activity will increase in the polar basin region 

through mid-century and then decline because resources 

will have been tapped. We may see some increase in 

exploration and development in the Archipelago, however, 

as it becomes increasingly accessible. Industrial activity is 

expected to be greatest for the near-shore region and along 

coastlines.

Elevated

Same as 

Recent

Reduced

Ship

[7] 

Shipping As sea ice extent declines spatially and temporally it is 

predicted that shipping in Arctic regions will increase. 

Increased shipping could lead to direct disturbances of 

polar bears as well as to increased levels of contamination. 

The best information available to assess the potential for 

increased shipping activity relies on climate model 

projections to determine potential navigability and season 

Elevated

Same as 

Recent

Reduced
58 

Node

Name Node Title Node Description States

duration (e.g., Smith and Stephenson, 2013). It is predicted 

that by mid-century, changing sea ice conditions will 

enable expanded September navigability for common openwater ships crossing the Arctic along the Northern Sea 

Route, robust new routes for ice-strengthened ships, and 

new routes through the Northwest Passage. As a result, 

shipping activity is likely to affect a portion of all 

ecoregions. Here, we address only the physical presence of 

more vessel traffic. Contamination (e.g., bilge, oil, 

wastewater), and biological effects from introduced 

organisms that may compete with residents of the food web 

or cause disease are covered under the nodes for 

contamination and parasites and disease. 

We note that shipping likely will increase in the foreseeable 

future. That said, there may be variability in the distribution 

of shipping activity based on route availability and 

efficiencies made as trans-Arctic shipping becomes more 

common. For example, even if international shipping does 

not increase, local shipping will because barges and vessels 

are more efficient ways to move fuel and freight into 

remote Arctic locations than aircraft. 

Tour

[7] 

Tourism As sea ice extent declines spatially and temporally, access 

and opportunities for Arctic tourism also will increase. We 

define tourism as including activities centered on polar 

bears, such as recreational viewing, as well as general 

recreational travel throughout polar bear habitat. Increased 

tourism could lead to direct disturbances of polar bears as 

well as to increased levels of contamination. Here, we 

address only the physical presence of more tourism and the 

conveyances used by tourists (vessels, land vehicles, 

aircraft). We believe that tourism will increase in all areas 

of the Arctic through the foreseeable future.

Elevated

Same as 

Recent

Reduced

OilSpill

[7] 

Hydrocarbons, 

Oil Spill

This node refers to the release of oil or oil related products 

into polar bear habitat, defined here only as nonlethal and 

displacement effects, including mortality of prey. Hence, it 

has ramifications for both habitat quality and population 

dynamics directly. Hydrocarbon exploration and 

development are expanding and proposed to expand further 

in the Arctic. Greater levels of such activity are most likely 

to increase the probability of oil spills. Increased shipping 

may result in higher levels of hydrocarbons released into 

Arctic waters. 

Elevated

Same as 

Recent

Reduced

Cont

[7] 

Contaminants This node reflects the possible increase or decrease of 

contamination in the Arctic as a result of modified 

pathways. Contaminants can act to make habitat less 

suitable and directly affect survival and reproduction.

Increased precipitation and glacial melt have recently 

resulted in greater influx of contaminants into the Arctic 

Elevated

Same as 

Recent

Reduced
59 

Node

Name Node Title Node Description States

region from the interior of Eurasia through large northward 

flowing rivers. Melting of multi-year ice has released 

previously bound contaminants into the environment (Ma 

and others, 2011). Similarly, differing atmospheric 

circulation patterns have altered potential pathways for 

contaminants from lower latitudes. The record of reduction 

and the persistence of many of these chemicals in the 

environment suggests the greatest likelihood is for elevated 

levels in the short to medium term with some probability of 

stability or even declines far in the future. 

DLP

[7] 

Human-Bear 

DLP Lethal 

Interactions 

This node expresses change in the occurrence of humanbear DLP (defense of life and property) interactions over 

time. These interactions result in death, as when problem 

bears are shot in defense of life and property. Human-bear 

DLP interactions are a concern throughout the polar bear’s 

range and we believe that bear-human interactions will 

increase as summer sea ice extent declines and until such 

time that either areas are devoid of bears or climate cools 

and ice returns. 

Elevated

Same as 

Recent

Reduced

Hunt

[6] 

Hunting

Mortality 

This node represents direct mortality from legal hunting 

alone, and includes subsistence use. These sources of 

mortality are controllable by regulation. We expect hunting 

mortality to occur for the foreseeable future across polar 

bear range where hunting is legal. This is based on 

importance of subsistence harvest to native communities 

and the recognition by many management authorities that it 

is important to maintain subsistence harvest as long as 

possible.

Elevated

Same as 

Recent

Reduced

OilOpn

[6] 

Oil Spills, 

Small 

Operational 

This node refers to the occurrence of oil spills from small 

extraction operations that would result in mortality of bears 

directly affected by oiling. There is some indication that 

activity by smaller operators is increasing in parts of the 

PBDE. Spills from small operations are expected to be 

relatively small in volume (e.g., < 6,000 barrels; Amstrup 

and others, 2006a) and limited in spatial scale and present a 

risk at the scale of a proportion of a subpopulation. Polar 

bears are often distributed over the continental shelf and 

other shallow waters, areas that overlap oil extraction 

activities. Because of this overlap, polar bears are at risk of 

being exposed to oil spills. 

Although this node primarily considers immediate, acute 

lethal effects on polar bears, we also acknowledge that the 

effects of an oil spill could persist for an extended period 

(i.e., multiple years, given the ice formation-thaw cycle). 

We also note that exposure to oil and related products 

could have sublethal effects, which are accounted for in a 

separate node.

Elevated

Same as 

Recent

Reduced

OilExp Oil Spill, Large This node refers to the occurrence of oil spills from large Elevated
60 

Node

Name Node Title Node Description States

[6] Exploratory exploration operations. Spills from these operations have 

the potential to be large in spatial scale and although the 

risk of a spill is diminished from that of small-scale 

operators, if a spill were to occur, it could be catastrophic. 

A catastrophic spill would present a risk at the scale of ≥ 1 

subpopulation. 

As with the “small operation” node, this node specifically 

considers the risk of the release of oil or oil related 

products into polar bear habitat that would result in 

mortality of bears directly affected by oiling. Alhtough this 

node primarily considers immediate, acute lethal effects on 

polar bears, we also acknowledge that the effects of an oil 

spill could persist for an extended period (i.e., multiple 

years, given the ice formation-thaw cycle). We also note 

that exposure to oil and related products could have 

sublethal effects, which are accounted for in a separate 

node. For a large spill, such effects are expected to persist 

for decades, similar to the Exxon Valdez (Peterson and 

others, 2003). We assumed that a catastrophic spill could 

oil a significant proportion of one or more subpopulations 

and have persistent (i.e., multi-year) lethal effects due to oil 

being bound and re-released as part of the freeze-thaw 

cycle of sea ice. 

Same as 

Recent

Reduced

OthMor2

[6] 

Other Events 

(Lethal Effects)

This node refers to the likelihood of mortality from various 

sources, including illegal killing, drowning, den failure, and 

management and research activities. Because there is little 

data on cause-specific mortality of polar bears, the 

probabilities for this node were developed based on expert 

understandings of polar bears and their ecosystem.

Elevated

Same as 

Recent

Reduced

ParDis

[8] 

Parasites and 

Disease

This node reflects possible change in exposure to disease 

and parasites that may result from changing environmental 

conditions. As the climate warms, regions of the Arctic are 

likely to become more hospitable to endemic and emerging 

parasites and disease agents. It is believed that polar bears 

have been free of most disease and parasite agents, but 

comprehensive surveillance has been lacking. There is 

evidence suggesting that polar bears are now exposed to a 

variety of zoonotic agents and parasites including Brucella 

abortus, Toxoplasma gondii, Coxiella burnettii, rabies, and 

trichinella (e.g., Rah and others, 2005; Kirk and others,

2011). It is also known that polar bears have a relatively 

naive immune system (Weber and others, 2013), which 

may make them vulnerable to invading pathogens. Changes 

in other species’ disease vulnerability suggest that similar 

changes could occur in polar bears so that they could move 

from a position where parasites and disease are not 

influential on a population level to where they are 

influential. 

Elevated

Same as 

Recent

Reduced
61 

Node

Name Node Title Node Description States

Pred

[8] 

Predation This node characterizes the likelihood of intra- and 

interspecific predation over time and relative to changing 

environmental conditions. Predation on polar bears by 

other species is very uncommon partly because bears spend 

almost all of their time on the ice. With more time on land, 

polar bears, especially young will be subject to increased 

levels of predation from other polar bears, wolves, and 

perhaps grizzly bears. Increased use of land during 

summer, and a growing reliance on scavenging beach-cast 

and aggregated marine mammals, may put younger bears at 

greater risk of lethal encounters with adults. Local 

ecological knowledge suggests that most cases of predation 

involve adult males killing cubs of the year (e.g., Born and 

others, 2011). 

Recent observations of predation on other bears by large 

males, in regions where it has not been observed before, are 

consistent with the hypothesis that this sort of behavior 

may increase in frequency if polar bears are nutritionally 

stressed. At present, intraspecific predation is not thought 

to be influential at the population level anywhere in the 

polar bear range, though it appears that the frequency of 

such events may be increasing. 

Elevated

Same as 

Recent

Reduced

Intermediate 

Nodes

PrimPrey

[3] 

Primary Prey 

Abundance 

This node is informed by the “ringed seal abundance” and 

“bearded seal abundance” nodes. Ringed and bearded seals are 

considered primary prey for polar bears based on the findings 

of numerous studies of diet composition (e.g., Thiemann and 

others, 2008) and prey selection (Pilfold and others, 2012). 

This node expresses changes in primary prey abundance that 

are likely to occur as sea ice cover declines and its physical 

character changes. This is largely expert opinion because there 

is little information available regarding the potential for future 

changes in the prey base. However, recent work (e.g., Iacozza 

and Ferguson, 2014) suggests that decreased future snow 

depth over ice may have significant consequences to ringed 

seal abundance through reduced pup survival. Such an 

outcome is most likely for the SIE and PBDE. As ice thins in 

the AE, it is reasonable to expect a transient increase in the 

abundance of primary prey, perhaps until the mid-century 

mark, followed by a decline as sea ice cover once again 

becomes limiting. 

Based on the literature, we weighted the relative importance 

of ringed and bearded seals in developing the probabilities for 

this table. Given that ringed seals usually represent a greater 

proportion of polar bear diet (e.g., Born and others, 2011), and 

are likely more available to all age and sex classes than 

bearded seals, it received a greater weight (0.6). 

Elevated

Same as 

Recent

Reduced
62 

Node

Name Node Title Node Description States

MrnPry

[3] 

Marine Prey 

Base Quality

This node expresses changes in primary and secondary 

prey abundance that are likely to occur as sea ice cover 

declines and its physical character changes. As with the 

prey nodes, the probabilities for this node are largely based 

on expert opinion because there is little information 

available to suggest how the prey base is likely to change in 

the future. In developing the probabilities for this node, 

primary prey was given slightly greater weight than 

secondary prey because the importance of primary prey has 

been established in the literature, although there is 

relatively little information available on the 

likelihood/importance of prey switching. However, there is 

evidence (e.g., Peacock and others, 2013) to suggest that 

increased abundance of alternate prey may elevate total 

survival of polar bears. 

Elevated

Same as 

Recent

Reduced

Ice

[2] 

Overall Sea Ice 

Conditions 

This node expresses the combination of the quantitative 

and qualitative ways the retreat of sea ice may affect use of 

continental shelf habitats. Analyses indicate that, in 

addition to reductions of total ice extent (expressed under 

IceArea), we will see seasonal retreats of the sea ice 

(IceFor) away from coastal areas now preferred by polar 

bears, and these retreats (or ice free periods) are projected 

to progressively become longer. These changes will affect 

polar bears by reducing the total availability of ice 

substrate, altering the spatial distribution of foraging 

patches, and making ice unavailable for extended periods in 

many regions year-round. These changes may lead to a 

shift from year-round to seasonal occupancy in affected 

areas. We defined “greatly reduced” overall sea ice 

conditions as when “foraging sea ice area” was <-40 square 

kilometer months and the availability of “foraging sea ice 

distribution” was greatly reduced. 

Note that because the PBCE includes populations that have 

different starting values for overall sea ice conditions, the 

values in the CPT express an average— similar to the 

approach taken for this area when estimating values for 

IceFor. Similarly, in the SIE, there is a difference between 

several populations, so table values are an average. 

Improved

Same as 

Recent

Reduced

Greatly 

Reduced

IceFor

[2] 

Foraging Sea 

Ice Distribution 

This node combines the influence of Sea Ice Shelf Distance 

Change (IceShelf), Foraging Sea Ice <50 percent Absence 

Change (IceChng), and Ecoregion (EcoReg), expressing 

how the spatiotemporal retreat of sea ice may affect use of 

continental shelf habitats. Our analyses indicate we will see 

seasonal retreats of the sea ice farther away from coastal 

areas now preferred by polar bears, and these retreats (or 

ice free periods) are projected to progressively become 

longer. These changes will make ice unavailable for 

extended periods in many regions bears now occur year 

Improved 

Same as 

Recent

Reduced

Greatly 

Reduced
63 

Node

Name Node Title Node Description States

round. This will result in the opportunity for seasonal 

occupancy but not the year-round occupancy bears have 

had in the past. 

In addition, we assumed that if parent node IceShelf is 

>=800 km then IceChng would almost always be greater 

than 1 month and probably much more (regardless of the 

distribution of patches) and perhaps in the neighborhood of 

≥ 3 months. Thus, when IceShelf is >= 800 km, there is a 

good chance that sea ice phenology will be altered over 

shelf regions and the distribution of foraging sea ice will be 

greatly reduced. However, even if IceChng is < 3 months, 

the arrangement/juxtaposition of ice patches would be so 

dynamic that patch availability would likely be greatly 

reduced. Sahanatien and Derocher (2012) provide a good 

assessment of how changing ice phenology and increased 

fragmentation are likely to adversely affect access to 

foraging habitat. 

Mrn

[3] 

Overall Marine 

Conditions 

This node expresses how quantitative changes in sea ice 

and qualitative changes in prey base quality act in 

combination to determine overall conditions in the marine 

ecosystem. In developing the CPT for this node, we gave 

greater weight of importance to “overall sea ice conditions” 

because sea ice coverage influences access to prey. 

Evidence suggests that polar bears prefer sea ice habitats 

over or adjacent to coastal areas (e.g., Ferguson and others, 

2000, Durner and others, 2009). Seasonal retreat of sea ice 

away from coastal areas reduces access to prey. The iceminimum period is projected to become longer through 

time, which will further reduce access to prey present in 

productive shallow waters. Thus, even though prey 

abundance may remain stable, access to prey will decline in 

response to declining seasonal sea ice extent. As access to 

prey declines, so will overall marine conditions.

Improved

Same as 

Recent

Reduced

Len

[4] 

Bears on Shore This node expresses the length of time each year that bears 

may spend on shore exploiting terrestrial habitat. As overall 

sea ice conditions decline, we expect greater use of onshore 

habitat by bears as posited by Schliebe and others (2008). 

Moreover, the likelihood of bears using onshore habitat 

increases when the availability of terrestrial food resources

increases. However, using the SIE as evidence, we believe 

the primary driver of onshore occurrence is the 

absence/reduced availability of summer sea ice. We 

defined “greatly increased” as a condition where the 

majority of bears in a given ecoregion spend the majority 

of the annual period on shore. 

Characterization of the use of onshore habitat is 

particularly important for the SIE, where use of onshore 

Greatly 

Increased

Increased

Same as 

Recent

Less than 

Recent
64 

Node

Name Node Title Node Description States

habitat has long been a part of bear life history. This node 

also may be important for other ecoregions over time. For 

example, in parts of the PBDE, bears are spending more 

time on shore during the ice-minimum season. We expect 

base conditions to vary by ecoregion. 

TerrPry

[4] 

Overall 

Terrestrial 

Prey/Food 

Availability

This node characterizes the availability of food resources 

relative to requirements of bears during their stay on shore. 

The node is informed by “human-provisioned food 

availability” and “terrestrial and marine prey access” and, 

in turn, influences the length of time bears may spend on 

shore during the ice-minimum season. In developing the 

CPT for this node, we relied on expert knowledge and put 

equal weight on both informing nodes. We noted that 

“human provisioned food availability” is mostly relevant to 

the PBDE and SIE. 

Elevated

Same as 

Recent

Reduced

Absent 

Hunt

[6] 

Hunting 

Mortality 

(legal)

This node represents direct mortality from legal hunting 

alone, and includes subsistence use. These sources of 

mortality are controllable by regulation. We expect hunting 

mortality to occur for the foreseeable future across polar 

bear range where hunting is legal. This is based on 

importance of subsistence harvest to native communities 

and the recognition by many management authorities that it 

is important to maintain subsistence harvest as long as 

possible.

Elevated

Same as base

Reduced

EvMort

[6] 

Event-Driven 

Mortality

This node expresses the combination of different forms of 

mortality polar bears may be exposed to through time. 

There was little information available on cause-specific 

mortality of polar bears, so in developing the conditional 

probability table we relied on expert knowledge to base 

table entries on the relative importance of the different 

forms of mortality.

Elevated

Same as 

Recent

Reduced

Hab

[5] 

Overall Habitat 

Suitability

This node expresses overall habitat suitability as a 

combination of the quality of marine and terrestrial habitats 

used by polar bears. In developing the conditional 

probability table for this node, greater importance was 

given to “overall marine conditions” because polar bears 

spend the majority of the year on sea ice foraging on 

marine mammals. However, we note that over time, the use 

of terrestrial habitat is likely to increase. The “greatly 

reduced” state was defined as occurring when both overall 

marine and terrestrial conditions were “degraded.” 

Based on projections of sea ice extent, we believe overall 

sea ice conditions will degrade over time. One near-term 

exception may be the AE, where climate-mediated 

reductions in sea ice volume (through thinning of multiyear ice) may result in a temporary improvement of 

“overall marine conditions.” We believe overall terrestrial 

conditions are likely to change through time and further 

Elevated

Same as 

Recent

Reduced

Greatly 

Reduced
65 

Node

Name Node Title Node Description States

impact overall habitat suitability. There is some evidence to 

suggest that polar bears may have the ability to forage 

adaptively (e.g., Iverson and others, 2014), but it is unclear 

if such foraging behavior can constitute a meaningful 

energetic benefit. In the absence of such data, we 

considered the contribution of “overall terrestrial 

conditions” to be secondary to that of “overall marine 

conditions.”

Terr

[4] 

Overall 

Terrestrial 

Conditions 

This node expresses change in overall terrestrial conditions 

as a function of refugia quality and the length of time bears 

spend on shore. Refugia quality expresses the ability of 

bears to stay on shore and be free of disturbance. As 

disturbance increases, refugia quality declines, which then 

degrades overall terrestrial conditions. The length of time 

spent on shore also influences overall terrestrial 

conditions—the longer bears spend on shore, the more 

likely that terrestrial conditions become degraded (e.g., 

resource scarcity). The conditional probability table for this 

node was populated based on expert knowledge, as there 

are no data reflecting the relationship between refugia 

quality, time spent on shore, and overall terrestrial 

conditions.

Improved

Same as 

Recent

Degraded

OthMor

[6] 

Other Mortality 

or Removal 

Events

This node refers to mortality from small- and large-scale oil 

operations, and from a “catch-all” node of other mortality 

event. The “other events” includes (but is not limited to) 

drowning, illegal killing, den failure, and management and 

research actions.

In developing the CPT, we attributed greater importance to 

large oil spills given the potential for severe and lingering 

lethal effects. 

Elevated

Same as 

Recent

Reduced

AdSur

[9] 

Adult Survival This node represents the sum of trends of adult survival. 

This node is influenced by “overall habitat suitability,” 

“other biotic stressors,” and “event-driven mortality.” The 

causal link between sea ice habitat and subadult survival is 

based on the literature, which documents a relationship 

between sea ice breakup and survival (Regehr and others, 

2007, 2010). In developing the conditional probability table

for this node, we gave “overall habitat suitability” greater 

weight of importance than “event-driven mortality.” We 

justify the weighting scheme based on the preponderance 

of the evidence suggesting that reductions in “overall 

habitat suitability” will have the most profound effect on 

persistence of polar bears. Although “event-driven 

mortality” and “other biotic stressors” also will have 

adverse effects, it is not expected to eclipse those of habitat 

suitability. The “greatly reduced” state was defined as 

occurring when survival was likely to exhibit a sustained 

declining trend.

Elevated

Same as 

Recent

Reduced

Greatly

Reduced
66 

Node

Name Node Title Node Description States

SaSur

[9] 

Subadult 

Survival

This node represents the sum of trends of post-dependent 

young (aged 2–4 years) surviving to be recruited into the 

“adult” age class. This node is influenced by “overall 

habitat suitability,” “other biotic stressors,” and “eventdriven mortality.” As with “adult survival,” the causal link 

between sea ice habitat and subadult survival is based on 

the literature, which documents a relationship between sea 

ice breakup and survival (Regehr and others, 2007, 2010). 

In developing the conditional probability table for this 

node, we gave “overall habitat suitability” greater weight of 

importance than “event-driven mortality.” We justify the 

weighting scheme based on the preponderance of the 

evidence suggesting that reductions in “overall habitat 

suitability” will have the most profound effect on 

persistence of polar bears. Although “event-driven 

mortality” and “other biotic stressors” also will have 

adverse effects, those effects are not expected to eclipse 

that of habitat suitability. The “greatly reduced” state was 

defined as occurring when subadult survival was likely to 

exhibit a sustained declining trend.

Elevated

Same as 

Recent

Reduced

Greatly 

Reduced

AFBod

[9] 

Adult Female 

Body Condition 

This node expresses how habitat conditions, sublethal 

stressors, and some biotic stressors can influence the body 

condition of adult females. In developing the conditional 

probability table for this node, we gave “overall habitat 

suitability” the greatest weight, acknowledging that 

empirical evidence supports the link between declines in 

sea ice habitat with declines in adult female body condition 

(e.g., Rode and others, 2010). There is no information 

available establishing a relationship between sublethal 

effects or parasites and disease to polar bear body 

condition. However, there is information for other species 

(e.g., Pioz and others, 2008; Sheriff and others, 2009), and 

we relied on that for guidance in completing the CPT. We 

defined “greatly decreased” body condition as occurring 

when declines in condition could lead to reproductive 

failure.

We believe that sea ice projections support the notion that 

“overall habitat suitability” is likely to decline through time 

and thus adversely influence adult female body condition. 

Likewise, there is supposition that exposure to parasites 

and disease agents and other sublethal stressors is likely to 

increase as the Arctic warms (e.g., Moore and Huntington, 

2008). Given that, we believe that adult female body 

condition likely will decline in the future unless significant 

mitigation measures to reduce GHGs are taken.

Increased

Same as 

Recent

Decreased

Greatly 

Decreased

DenAcc

[4] 

Terrestrial 

Maternal Den 

Access

This node expresses change in maternal den access as a 

function of changes to foraging sea ice absence. The 

highest quality foraging sea ice is over and adjacent to 

Elevated

Same as 

Recent
67 

Node

Name Node Title Node Description States

productive shallow waters (i.e., continental shelf water). 

These areas are generally close to coastal habitat and 

provide easy access to terrestrial maternity dens. However, 

climate warming has led to an increase in the length of the 

ice-minimum season and an increase in the distance 

between foraging sea ice and most coastal regions— the 

exception being the Archipelago. If the distance between 

foraging ice and the coast increases—as it is projected to—

then access to maternal dens may become restricted as 

bears would have to cross expanses of open water to reach 

onshore denning habitat. 

Based on projections of sea ice extent and duration of the 

ice-minimum season, we believe that access to terrestrial 

denning habitat may become compromised in the future. 

This is a more pressing concern for the PBDE than for 

other areas, given the pattern of ice recession.

Reduced

Disturb

[7] 

Sub-lethal 

Human 

Disturbance

This node expresses the combination of the changes in nonlethal human disturbances to polar bears. Specifically, it 

covers the direct bear-human interactions that can occur in 

association with industrial development, tourism, and 

shipping. We assumed bear-human interactions to be the 

most influential factor. Although shipping and tourism 

could become a significant problem, they also are issues 

that could be subject to stringent regulations and flexible 

management to minimize effects on bears. Similarly, oil 

and gas companies can be subjected to appropriate 

regulatory responses if bear-human interactions become too 

frequent. Encounters in villages and hunting camps, 

however, may not be as easily regulated and it seemed 

more predictable they would lead to negative effects. 

Increasing bear human interactions will result in 

conditioned bears; however, direct bear mortality is not 

included in this node.

We expect that the magnitude of sub-lethal human 

disturbance will vary seasonally and over time, with the 

greatest effects occurring during the ice-minimum season, 

and increasing as that season lengthens.

Elevated

Same as 

Recent

Reduced

Recr

[9] 

Recruitment This node reflects the sum of trends in numbers of cubs 

produced and the effect of retreating sea ice on the ability 

of females to reach traditional denning areas. There is a 

well-documented link between adult female body condition 

and recruitment for many species, including polar bears 

(e.g., Derocher and Stirling, 1995; Rode and others, 2010). 

There also is information documenting the importance of 

terrestrial denning habitat for polar bears in multiple 

ecoregions (Fischbach and others, 2007; Richardson and 

others, 2005; Andersen and others, 2012). “Greatly 

Increased

Same as 

Recent

Decreased

Greatly 

Decreased
68 

Node

Name Node Title Node Description States

decreased” was defined as the likelihood that the trend in 

the number of cubs exhibited a sustained decline. 

In developing the CPT for this node, we put greater weight 

on the contribution of adult female body condition in 

influencing recruitment. While we note that access to 

terrestrial dens has the potential to become reduced over 

time, as the ice-minimum season lengthens, we believe that 

the increased ice-minimum season will continue to have a 

greater adverse effect on adult female body condition. This 

position is based on the relationship between sea ice and 

condition as outlined by Rode and others (2010).

AntStr

[7] 

Anthropogenic 

Stressors (Sublethal)

This node combines lethal and non-lethal anthropogenic 

stressors. It includes factors (other than the changes in sea 

ice quality and quantity), which may affect both habitat 

suitability and population dynamics. Although quantitative 

information is lacking on the effects of these stressors at 

the population level, we know qualitatively of effects on 

immune function, gene expression, and physiological 

stress. Given that, table entries were based on expert 

knowledge. 

Elevated

Same as 

Recent

Reduced

Pol

[7] 

Pollution (Sublethal)This node is the sum of pollution effects from hydrocarbon 

discharges directly into Arctic waters and from other 

pollutants brought to the Arctic from other parts of the 

world. The U.S. Fish and Wildlife Service listing decision 

for polar bears included pollution as one of the "other 

factors" along with direct human bear interactions that may 

displace bears or otherwise make habitats less satisfactory. 

We viewed the main effect of pollution as a potential effect 

on population dynamics, as it is likely to manifest as 

adverse impacts to immune function, reproductive 

performance, and survival. Although there is relatively 

little empirical data on the effects of hydrocarbon exposure 

on polar bears, information is available for other marine 

mammals. There is a large body of literature on the effects 

of contaminants on polar bears. Information from the 

literature was used to inform table entries for this node.

Elevated

Same as 

Recent

Reduced

BioStr

[8] 

Other Biotic 

Stressors

This node expresses the changing vulnerability of polar 

bears to diseases and parasites, and to potential increases of 

intra- and interspecific predation. We recognize that 

predation/cannibalism is currently rare. We also recognize 

that while there is information summarizing exposure to 

disease agents, there is little such information relative to 

parasites―although there is no information that links these 

stressors to effects on vital rates. Given that, the conditional 

probability table for this node is based largely on expert 

knowledge.

Elevated

Same as 

Recent

Reduced

Outcome 

Node
69 

Node

Name Node Title Node Description States

InflPopn

[10] 

Relative 

Influence on 

Population 

Trend

This node depicts the hypothesized relative influence of 

stressors on population trend as referenced by estimated or 

observed changes to abundance and distribution. The node 

is informed by “adult survival,” “subadult survival,” and 

“recruitment,” and we gave the greatest weight to adult 

survival because it is the least sensitive to change. As such, 

it should take a substantial change in conditions to exert a 

meaningful influence on adult survival. The outcome states 

are qualitative because there is insufficient empirical data 

from a majority of the 19 polar bear subpopulations (e.g., 

Obbard and others, 2010) that would allow us to develop 

quantitative thresholds for transitioning from one state to 

another. As a result, we estimated the relative influence on 

population trend similar to Jay and others (2011), Amstrup

and others (2008, 2010), and Johnson and others (2013) by 

using transition thresholds that reflect qualitative changes 

in abundance, distribution (i.e., use and selection of 

ecosystem elements and features), and strength and 

presence of stressors. 

Increased

Same as 

Recent

Decreased

Greatly 

Decreased

Scenario

Nodes

Per

[1] 

Time Period The states for this node correspond to years —1985 –1995

(historical), 2007–2012 (recent), 2020–2030 (early 

century), 2045–2055 (mid-century), 2070–2080 (late 

century), and 2090–2100 (end of century). 

1985–1995

2007–2012 

2020–2030 

2045–2055 

2070–2080 

2090–2100

Scenario

[1] 

Forcing 

Pahtway and 

GCM Ensemble

The states for this node correspond to the combination of 

RCP (RCP 4.5 and 8.5; van Vuuren and others, 2011) and 

GCM ensemble. We used sea ice concentration outputs 

from 13 GCMs that were produced by each of two CMIP5 

RCP forcing experiments. Hindcasts of monthly sea ice 

concentration were obtained for each of the same 13 GCMs 

from the CMIP5 ‘historical’ forcing experiment. Historical 

data consisted of satellite observations of monthly sea ice 

concentration for 1985–2012. GCM outputs were 

transformed and resampled (nearest neighbor) to a 25-kmresolution polar stereographic grid that was congruent to 

the grid of sea ice observations.

Satellite

RCP 4.5 

Frequency 

RCP 8.5 

Frequency 

RCP 4.5 

Mean

RCP 8.5 

Mean

Ecoreg

[2] 

Ecoregion The states for this node correspond to the four ecoregions 

proposed by Amstrup and others (2008). Ecoregions 

represent an intermediate-scale spatial grouping (i.e., 

between individual populations and the circumpolar 

distribution) of polar bears based on observed and 

forecasted patterns of sea ice dynamics and polar bear life 

history.

AE

PBCE

PBDE

SIE

GCMset

[1] 

GCM Set or 

Subset 

The states for this node correspond to the CMIP5 GCM 

model set or subset used to produce sea ice concentration 

outputs. 13 GCM were chosen from the CMIP5 archive 

Satellite

GCM13Total 

GCM11Wang
70 

Node

Name Node Title Node Description States

based on evaluations performed by Massonnet and others 

(2012) and Wang and Overland (2013b).

GCM05Mass

Submodels (numbers denoted after each node name in the above table): 

[1] Analysis scenario

[2] Sea ice submodel

[3] Marine prey and conditions submodel 

[4] Terrestrial food/prey and conditions submodel 

[5] Overall habitat suitability submodel

[6] Event-driven mortality submodel 

[7] Anthropogenic stressors submodel 

[8] Other biotic stressors submodel 

[9] Demographic submodel 

[10] Polar bear persistence submodel 
71 

Appendix D. Normative Scenarios (Using Expected Values of Input Conditions) 

Run on the Second Generation Model

See section “Materials and Methods” in text for descriptions of ecoregions. Satellite, use of empirical 

satellite data for historical and recent time periods for quantifying sea ice cover; RCP, representative 

concentration pathway; freq, use of frequency distributions of global circulation model (GCM) 

outcomes; ens mean, use of ensemble means of GCM outcomes; NA, not applicable; GCM13Total, use 

of all available GCMs; GCM11Wang, use of GCM model subsets as suggested by Wang and Overland 

(2013b); GCM05Mass, use of GCM model subsets as suggested by Massonnet and others (2012). 

Scenario 

No.

Ecoregion RCP Time period GCM set

ScenNum EcoReg Scenario Per GCMset

1 Archipelago Satellite historical (1985–1995) NA

2 Archipelago Satellite baseline (1996–2006) NA

3 Archipelago Satellite recent (2007–2012) NA

4 Polar Basin Convergent Satellite historical (1985–1995) NA

5 Polar Basin Convergent Satellite baseline (1996–2006) NA

6 Polar Basin Convergent Satellite recent (2007–2012) NA

7 Polar Basin Divergent Satellite historical (1985–1995) NA

8 Polar Basin Divergent Satellite baseline (1996–2006) NA

9 Polar Basin Divergent Satellite recent (2007–2012) NA

10 Seasonal Ice Satellite historical (1985–1995) NA

11 Seasonal Ice Satellite baseline (1996–2006) NA

12 Seasonal Ice Satellite recent (2007–2012) NA

13 Archipelago RCP 4.5 freq early century (2020–2030) GCM13Total

14 Archipelago RCP 4.5 freq mid-century (2045–2055) GCM13Total

15 Archipelago RCP 4.5 freq late century (2070–2080) GCM13Total

16 Archipelago RCP 4.5 freq end of century (2090–2100) GCM13Total

17 Archipelago RCP 8.5 freq early century (2020–2030) GCM13Total

18 Archipelago RCP 8.5 freq mid-century (2045–2055) GCM13Total

19 Archipelago RCP 8.5 freq late century (2070–2080) GCM13Total

20 Archipelago RCP 8.5 freq end of century (2090–2100) GCM13Total

21 Archipelago RCP 4.5 ens mean early century (2020–2030) GCM13Total

22 Archipelago RCP 4.5 ens mean mid-century (2045–2055) GCM13Total

23 Archipelago RCP 4.5 ens mean late century (2070–2080) GCM13Total

24 Archipelago RCP 4.5 ens mean end of century (2090–2100) GCM13Total

25 Archipelago RCP 8.5 ens mean early century (2020–2030) GCM13Total

26 Archipelago RCP 8.5 ens mean mid-century (2045–2055) GCM13Total

27 Archipelago RCP 8.5 ens mean late century (2070–2080) GCM13Total

28 Archipelago RCP 8.5 ens mean end of century (2090–2100) GCM13Total

29 Polar Basin Convergent RCP 4.5 freq early century (2020–2030) GCM13Total
72 

Scenario 

No.

Ecoregion RCP Time period GCM set

ScenNum EcoReg Scenario Per GCMset

30 Polar Basin Convergent RCP 4.5 freq mid-century (2045–2055) GCM13Total

31 Polar Basin Convergent RCP 4.5 freq late century (2070–2080) GCM13Total

32 Polar Basin Convergent RCP 4.5 freq end of century (2090–2100) GCM13Total

33 Polar Basin Convergent RCP 8.5 freq early century (2020–2030) GCM13Total

34 Polar Basin Convergent RCP 8.5 freq mid-century (2045–2055) GCM13Total

35 Polar Basin Convergent RCP 8.5 freq late century (2070–2080) GCM13Total

36 Polar Basin Convergent RCP 8.5 freq end of century (2090–2100) GCM13Total

37 Polar Basin Convergent RCP 4.5 ens mean early century (2020–2030) GCM13Total

38 Polar Basin Convergent RCP 4.5 ens mean mid-century (2045–2055) GCM13Total

39 Polar Basin Convergent RCP 4.5 ens mean late century (2070–2080) GCM13Total

40 Polar Basin Convergent RCP 4.5 ens mean end of century (2090–2100) GCM13Total

41 Polar Basin Convergent RCP 8.5 ens mean early century (2020–2030) GCM13Total

42 Polar Basin Convergent RCP 8.5 ens mean mid-century (2045–2055) GCM13Total

43 Polar Basin Convergent RCP 8.5 ens mean late century (2070–2080) GCM13Total

44 Polar Basin Convergent RCP 8.5 ens mean end of century (2090–2100) GCM13Total

45 Polar Basin Divergent RCP 4.5 freq early century (2020–2030) GCM13Total

46 Polar Basin Divergent RCP 4.5 freq mid-century (2045–2055) GCM13Total

47 Polar Basin Divergent RCP 4.5 freq late century (2070–2080) GCM13Total

48 Polar Basin Divergent RCP 4.5 freq end of century (2090–2100) GCM13Total

49 Polar Basin Divergent RCP 8.5 freq early century (2020–2030) GCM13Total

50 Polar Basin Divergent RCP 8.5 freq mid-century (2045–2055) GCM13Total

51 Polar Basin Divergent RCP 8.5 freq late century (2070–2080) GCM13Total

52 Polar Basin Divergent RCP 8.5 freq end of century (2090–2100) GCM13Total

53 Polar Basin Divergent RCP 4.5 ens mean early century (2020–2030) GCM13Total

54 Polar Basin Divergent RCP 4.5 ens mean mid-century (2045–2055) GCM13Total

55 Polar Basin Divergent RCP 4.5 ens mean late century (2070–2080) GCM13Total

56 Polar Basin Divergent RCP 4.5 ens mean end of century (2090–2100) GCM13Total

57 Polar Basin Divergent RCP 8.5 ens mean early century (2020–2030) GCM13Total

58 Polar Basin Divergent RCP 8.5 ens mean mid-century (2045–2055) GCM13Total

59 Polar Basin Divergent RCP 8.5 ens mean late century (2070–2080) GCM13Total

60 Polar Basin Divergent RCP 8.5 ens mean end of century (2090–2100) GCM13Total

61 Seasonal Ice RCP 4.5 freq early century (2020–2030) GCM13Total

62 Seasonal Ice RCP 4.5 freq mid-century (2045–2055) GCM13Total

63 Seasonal Ice RCP 4.5 freq late century (2070–2080) GCM13Total

64 Seasonal Ice RCP 4.5 freq end of century (2090–2100) GCM13Total

65 Seasonal Ice RCP 8.5 freq early century (2020–2030) GCM13Total

66 Seasonal Ice RCP 8.5 freq mid-century (2045–2055) GCM13Total

67 Seasonal Ice RCP 8.5 freq late century (2070–2080) GCM13Total
73 

Scenario 

No.

Ecoregion RCP Time period GCM set

ScenNum EcoReg Scenario Per GCMset

68 Seasonal Ice RCP 8.5 freq end of century (2090–2100) GCM13Total

69 Seasonal Ice RCP 4.5 ens mean early century (2020–2030) GCM13Total

70 Seasonal Ice RCP 4.5 ens mean mid-century (2045–2055) GCM13Total

71 Seasonal Ice RCP 4.5 ens mean late century (2070–2080) GCM13Total

72 Seasonal Ice RCP 4.5 ens mean end of century (2090–2100) GCM13Total

73 Seasonal Ice RCP 8.5 ens mean early century (2020–2030) GCM13Total

74 Seasonal Ice RCP 8.5 ens mean mid-century (2045–2055) GCM13Total

75 Seasonal Ice RCP 8.5 ens mean late century (2070–2080) GCM13Total

76 Seasonal Ice RCP 8.5 ens mean end of century (2090–2100) GCM13Total

77 Archipelago RCP 4.5 freq early century (2020–2030) GCM11Wang

78 Archipelago RCP 4.5 freq mid-century (2045–2055) GCM11Wang

79 Archipelago RCP 4.5 freq late century (2070–2080) GCM11Wang

80 Archipelago RCP 4.5 freq end of century (2090–2100) GCM11Wang

81 Archipelago RCP 8.5 freq early century (2020–2030) GCM11Wang

82 Archipelago RCP 8.5 freq mid-century (2045–2055) GCM11Wang

83 Archipelago RCP 8.5 freq late century (2070–2080) GCM11Wang

84 Archipelago RCP 8.5 freq end of century (2090–2100) GCM11Wang

85 Archipelago RCP 4.5 ens mean early century (2020–2030) GCM11Wang

86 Archipelago RCP 4.5 ens mean mid-century (2045–2055) GCM11Wang

87 Archipelago RCP 4.5 ens mean late century (2070–2080) GCM11Wang

88 Archipelago RCP 4.5 ens mean end of century (2090–2100) GCM11Wang

89 Archipelago RCP 8.5 ens mean early century (2020–2030) GCM11Wang

90 Archipelago RCP 8.5 ens mean mid-century (2045–2055) GCM11Wang

91 Archipelago RCP 8.5 ens mean late century (2070–2080) GCM11Wang

92 Archipelago RCP 8.5 ens mean end of century (2090–2100) GCM11Wang

93 Polar Basin Convergent RCP 4.5 freq early century (2020–2030) GCM11Wang

94 Polar Basin Convergent RCP 4.5 freq mid-century (2045–2055) GCM11Wang

95 Polar Basin Convergent RCP 4.5 freq late century (2070–2080) GCM11Wang

96 Polar Basin Convergent RCP 4.5 freq end of century (2090–2100) GCM11Wang

97 Polar Basin Convergent RCP 8.5 freq early century (2020–2030) GCM11Wang

98 Polar Basin Convergent RCP 8.5 freq mid-century (2045–2055) GCM11Wang

99 Polar Basin Convergent RCP 8.5 freq late century (2070–2080) GCM11Wang

100 Polar Basin Convergent RCP 8.5 freq end of century (2090–2100) GCM11Wang

101 Polar Basin Convergent RCP 4.5 ens mean early century (2020–2030) GCM11Wang

102 Polar Basin Convergent RCP 4.5 ens mean mid-century (2045–2055) GCM11Wang

103 Polar Basin Convergent RCP 4.5 ens mean late century (2070–2080) GCM11Wang

104 Polar Basin Convergent RCP 4.5 ens mean end of century (2090–2100) GCM11Wang

105 Polar Basin Convergent RCP 8.5 ens mean early century (2020–2030) GCM11Wang
74 

Scenario 

No.

Ecoregion RCP Time period GCM set

ScenNum EcoReg Scenario Per GCMset

106 Polar Basin Convergent RCP 8.5 ens mean mid-century (2045–2055) GCM11Wang

107 Polar Basin Convergent RCP 8.5 ens mean late century (2070–2080) GCM11Wang

108 Polar Basin Convergent RCP 8.5 ens mean end of century (2090–2100) GCM11Wang

109 Polar Basin Divergent RCP 4.5 freq early century (2020–2030) GCM11Wang

110 Polar Basin Divergent RCP 4.5 freq mid-century (2045–2055) GCM11Wang

111 Polar Basin Divergent RCP 4.5 freq late century (2070–2080) GCM11Wang

112 Polar Basin Divergent RCP 4.5 freq end of century (2090–2100) GCM11Wang

113 Polar Basin Divergent RCP 8.5 freq early century (2020–2030) GCM11Wang

114 Polar Basin Divergent RCP 8.5 freq mid-century (2045–2055) GCM11Wang

115 Polar Basin Divergent RCP 8.5 freq late century (2070–2080) GCM11Wang

116 Polar Basin Divergent RCP 8.5 freq end of century (2090–2100) GCM11Wang

117 Polar Basin Divergent RCP 4.5 ens mean early century (2020–2030) GCM11Wang

118 Polar Basin Divergent RCP 4.5 ens mean mid-century (2045–2055) GCM11Wang

119 Polar Basin Divergent RCP 4.5 ens mean late century (2070–2080) GCM11Wang

120 Polar Basin Divergent RCP 4.5 ens mean end of century (2090–2100) GCM11Wang

121 Polar Basin Divergent RCP 8.5 ens mean early century (2020–2030) GCM11Wang

122 Polar Basin Divergent RCP 8.5 ens mean mid-century (2045–2055) GCM11Wang

123 Polar Basin Divergent RCP 8.5 ens mean late century (2070–2080) GCM11Wang

124 Polar Basin Divergent RCP 8.5 ens mean end of century (2090–2100) GCM11Wang

125 Seasonal Ice RCP 4.5 freq early century (2020–2030) GCM11Wang

126 Seasonal Ice RCP 4.5 freq mid-century (2045–2055) GCM11Wang

127 Seasonal Ice RCP 4.5 freq late century (2070–2080) GCM11Wang

128 Seasonal Ice RCP 4.5 freq end of century (2090–2100) GCM11Wang

129 Seasonal Ice RCP 8.5 freq early century (2020–2030) GCM11Wang

130 Seasonal Ice RCP 8.5 freq mid-century (2045–2055) GCM11Wang

131 Seasonal Ice RCP 8.5 freq late century (2070–2080) GCM11Wang

132 Seasonal Ice RCP 8.5 freq end of century (2090–2100) GCM11Wang

133 Seasonal Ice RCP 4.5 ens mean early century (2020–2030) GCM11Wang

134 Seasonal Ice RCP 4.5 ens mean mid-century (2045–2055) GCM11Wang

135 Seasonal Ice RCP 4.5 ens mean late century (2070–2080) GCM11Wang

136 Seasonal Ice RCP 4.5 ens mean end of century (2090–2100) GCM11Wang

137 Seasonal Ice RCP 8.5 ens mean early century (2020–2030) GCM11Wang

138 Seasonal Ice RCP 8.5 ens mean mid-century (2045–2055) GCM11Wang

139 Seasonal Ice RCP 8.5 ens mean late century (2070–2080) GCM11Wang

140 Seasonal Ice RCP 8.5 ens mean end of century (2090–2100) GCM11Wang

141 Archipelago RCP 4.5 freq early century (2020–2030) GCM05Mass

142 Archipelago RCP 4.5 freq mid-century (2045–2055) GCM05Mass

143 Archipelago RCP 4.5 freq late century (2070–2080) GCM05Mass
75 

Scenario 

No.

Ecoregion RCP Time period GCM set

ScenNum EcoReg Scenario Per GCMset

144 Archipelago RCP 4.5 freq end of century (2090–2100) GCM05Mass

145 Archipelago RCP 8.5 freq early century (2020–2030) GCM05Mass

146 Archipelago RCP 8.5 freq mid-century (2045–2055) GCM05Mass

147 Archipelago RCP 8.5 freq late century (2070–2080) GCM05Mass

148 Archipelago RCP 8.5 freq end of century (2090–2100) GCM05Mass

149 Archipelago RCP 4.5 ens mean early century (2020–2030) GCM05Mass

150 Archipelago RCP 4.5 ens mean mid-century (2045–2055) GCM05Mass

151 Archipelago RCP 4.5 ens mean late century (2070–2080) GCM05Mass

152 Archipelago RCP 4.5 ens mean end of century (2090–2100) GCM05Mass

153 Archipelago RCP 8.5 ens mean early century (2020–2030) GCM05Mass

154 Archipelago RCP 8.5 ens mean mid-century (2045–2055) GCM05Mass

155 Archipelago RCP 8.5 ens mean late century (2070–2080) GCM05Mass

156 Archipelago RCP 8.5 ens mean end of century (2090–2100) GCM05Mass

157 Polar Basin Convergent RCP 4.5 freq early century (2020–2030) GCM05Mass

158 Polar Basin Convergent RCP 4.5 freq mid-century (2045–2055) GCM05Mass

159 Polar Basin Convergent RCP 4.5 freq late century (2070–2080) GCM05Mass

160 Polar Basin Convergent RCP 4.5 freq end of century (2090–2100) GCM05Mass

161 Polar Basin Convergent RCP 8.5 freq early century (2020–2030) GCM05Mass

162 Polar Basin Convergent RCP 8.5 freq mid-century (2045–2055) GCM05Mass

163 Polar Basin Convergent RCP 8.5 freq late century (2070–2080) GCM05Mass

164 Polar Basin Convergent RCP 8.5 freq end of century (2090–2100) GCM05Mass

165 Polar Basin Convergent RCP 4.5 ens mean early century (2020–2030) GCM05Mass

166 Polar Basin Convergent RCP 4.5 ens mean mid-century (2045–2055) GCM05Mass

167 Polar Basin Convergent RCP 4.5 ens mean late century (2070–2080) GCM05Mass

168 Polar Basin Convergent RCP 4.5 ens mean end of century (2090–2100) GCM05Mass

169 Polar Basin Convergent RCP 8.5 ens mean early century (2020–2030) GCM05Mass

170 Polar Basin Convergent RCP 8.5 ens mean mid-century (2045–2055) GCM05Mass

171 Polar Basin Convergent RCP 8.5 ens mean late century (2070–2080) GCM05Mass

172 Polar Basin Convergent RCP 8.5 ens mean end of century (2090–2100) GCM05Mass

173 Polar Basin Divergent RCP 4.5 freq early century (2020–2030) GCM05Mass

174 Polar Basin Divergent RCP 4.5 freq mid-century (2045–2055) GCM05Mass

175 Polar Basin Divergent RCP 4.5 freq late century (2070–2080) GCM05Mass

176 Polar Basin Divergent RCP 4.5 freq end of century (2090–2100) GCM05Mass

177 Polar Basin Divergent RCP 8.5 freq early century (2020–2030) GCM05Mass

178 Polar Basin Divergent RCP 8.5 freq mid-century (2045–2055) GCM05Mass

179 Polar Basin Divergent RCP 8.5 freq late century (2070–2080) GCM05Mass

180 Polar Basin Divergent RCP 8.5 freq end of century (2090–2100) GCM05Mass

181 Polar Basin Divergent RCP 4.5 ens mean early century (2020–2030) GCM05Mass
76 

Scenario 

No.

Ecoregion RCP Time period GCM set

ScenNum EcoReg Scenario Per GCMset

182 Polar Basin Divergent RCP 4.5 ens mean mid-century (2045–2055) GCM05Mass

183 Polar Basin Divergent RCP 4.5 ens mean late century (2070–2080) GCM05Mass

184 Polar Basin Divergent RCP 4.5 ens mean end of century (2090–2100) GCM05Mass

185 Polar Basin Divergent RCP 8.5 ens mean early century (2020–2030) GCM05Mass

186 Polar Basin Divergent RCP 8.5 ens mean mid-century (2045–2055) GCM05Mass

187 Polar Basin Divergent RCP 8.5 ens mean late century (2070–2080) GCM05Mass

188 Polar Basin Divergent RCP 8.5 ens mean end of century (2090–2100) GCM05Mass

189 Seasonal Ice RCP 4.5 freq early century (2020–2030) GCM05Mass

190 Seasonal Ice RCP 4.5 freq mid-century (2045–2055) GCM05Mass

191 Seasonal Ice RCP 4.5 freq late century (2070–2080) GCM05Mass

192 Seasonal Ice RCP 4.5 freq end of century (2090–2100) GCM05Mass

193 Seasonal Ice RCP 8.5 freq early century (2020–2030) GCM05Mass

194 Seasonal Ice RCP 8.5 freq mid-century (2045–2055) GCM05Mass

195 Seasonal Ice RCP 8.5 freq late century (2070–2080) GCM05Mass

196 Seasonal Ice RCP 8.5 freq end of century (2090–2100) GCM05Mass

197 Seasonal Ice RCP 4.5 ens mean early century (2020–2030) GCM05Mass

198 Seasonal Ice RCP 4.5 ens mean mid-century (2045–2055) GCM05Mass

199 Seasonal Ice RCP 4.5 ens mean late century (2070–2080) GCM05Mass

200 Seasonal Ice RCP 4.5 ens mean end of century (2090–2100) GCM05Mass

201 Seasonal Ice RCP 8.5 ens mean early century (2020–2030) GCM05Mass

202 Seasonal Ice RCP 8.5 ens mean mid-century (2045–2055) GCM05Mass

203 Seasonal Ice RCP 8.5 ens mean late century (2070–2080) GCM05Mass

204 Seasonal Ice RCP 8.5 ens mean end of century (2090–2100) GCM05Mass
77 

Appendix E. Conditional Probability Tables for Each Summary (i.e., 

Intermediate) Node in the Bayesian Network Model 

Tables E1–E20 are the conditional probability tables for each intermediate node in the Bayesian 

network model. 

E1. Primary Prey Abundance. 

Ringed Seal 

Abundance

Bearded Seal 

Abundance Elevated Same as Recent Reduced

elevated elevated 100 0 0

elevated same as recent 70 30 0

elevated reduced 50 50 0

same as recent elevated 40 60 0

same as recent same as recent 0 100 0

same as recent reduced 0 60 40

reduced elevated 0 50 50

reduced same as recent 0 30 70

reduced reduced 0 0 100

E2. Marine Prey Base Quality. 

Primary Prey 

Abundance

Secondary and New 

Prey Abundance Elevated Same as Recent Reduced

elevated elevated 100 0 0

elevated same as recent 90 10 0

elevated reduced 80 20 0

same as recent elevated 25 75 0

same as recent same as recent 0 100 0

same as recent reduced 0 80 20

reduced elevated 10 50 40

reduced same as recent 0 20 80

reduced reduced 0 0 100

E3. Overall Sea Ice Conditions. 

Foraging 

Sea Ice Area 

Foraging Sea Ice 

Distribution

Foraging Sea Ice 

Quality Improved 

Same as 

Base Reduced 

Greatly 

Reduced

>= 0 improved availability elevated 100 0 0 0

>= 0 improved availability same as recent 100 0 0 0

>= 0 improved availability reduced 80 10 10 0

>= 0 same as recent elevated 90 10 0 0

>= 0 same as recent same as recent 60 40 0 0

>= 0 same as recent reduced 20 70 10 0

>= 0 reduced avail elevated 40 40 20 0

>= 0 reduced avail same as recent 20 60 20 0

>= 0 reduced avail reduced 0 20 60 20
78 

Foraging 

Sea Ice Area 

Foraging Sea Ice 

Distribution

Foraging Sea Ice 

Quality Improved 

Same as 

Base Reduced 

Greatly 

Reduced

>= 0 greatly reduced avail elevated 5 45 30 20

>= 0 greatly reduced avail same as recent 0 20 30 50

>= 0 greatly reduced avail reduced 0 0 20 80

-5 to 0 improved availability elevated 90 10 0 0

-5 to 0 improved availability same as recent 80 20 0 0

-5 to 0 improved availability reduced 50 30 20 0

-5 to 0 same as recent elevated 20 75 5 0

-5 to 0 same as recent same as recent 0 90 10 0

-5 to 0 same as recent reduced 0 60 40 0

-5 to 0 reduced avail elevated 10 50 40 0

-5 to 0 reduced avail same as recent 0 40 60 0

-5 to 0 reduced avail reduced 0 20 70 10

-5 to 0 greatly reduced avail elevated 0 10 80 10

-5 to 0 greatly reduced avail same as recent 0 5 65 30

-5 to 0 greatly reduced avail reduced 0 0 40 60

-10 to -5 improved availability elevated 90 10 0 0

-10 to -5 improved availability same as recent 60 40 0 0

-10 to -5 improved availability reduced 50 40 10 0

-10 to -5 same as recent elevated 40 50 10 0

-10 to -5 same as recent same as recent 10 80 10 0

-10 to -5 same as recent reduced 0 20 80 0

-10 to -5 reduced avail elevated 10 40 50 0

-10 to -5 reduced avail same as recent 0 30 60 10

-10 to -5 reduced avail reduced 0 0 70 30

-10 to -5 greatly reduced avail elevated 0 10 60 30

-10 to -5 greatly reduced avail same as recent 0 5 50 45

-10 to -5 greatly reduced avail reduced 0 0 20 80

-20 to -10 improved availability elevated 50 40 10 0

-20 to -10 improved availability same as recent 20 60 20 0

-20 to -10 improved availability reduced 10 40 50 0

-20 to -10 same as recent elevated 20 60 20 0

-20 to -10 same as recent same as recent 0 40 60 0

-20 to -10 same as recent reduced 0 20 60 20

-20 to -10 reduced avail elevated 5 20 50 25

-20 to -10 reduced avail same as recent 0 10 50 40

-20 to -10 reduced avail reduced 0 0 40 60

-20 to -10 greatly reduced avail elevated 0 10 50 40

-20 to -10 greatly reduced avail same as recent 0 0 30 70

-20 to -10 greatly reduced avail reduced 0 0 10 90

-40 to -20 improved availability elevated 20 60 20 0
79 

Foraging 

Sea Ice Area 

Foraging Sea Ice 

Distribution

Foraging Sea Ice 

Quality Improved 

Same as 

Base Reduced 

Greatly 

Reduced

-40 to -20 improved availability same as recent 10 60 30 0

-40 to -20 improved availability reduced 5 30 45 20

-40 to -20 same as recent elevated 10 40 40 10

-40 to -20 same as recent same as recent 0 20 50 30

-40 to -20 same as recent reduced 0 5 40 55

-40 to -20 reduced avail elevated 0 10 60 30

-40 to -20 reduced avail same as recent 0 5 50 45

-40 to -20 reduced avail reduced 0 0 40 60

-40 to -20 greatly reduced avail elevated 0 0 30 70

-40 to -20 greatly reduced avail same as recent 0 0 10 90

-40 to -20 greatly reduced avail reduced 0 0 0 100

< -40 improved availability elevated 20 60 20 0

< -40 improved availability same as recent 0 20 60 20

< -40 improved availability reduced 0 0 50 50

< -40 same as recent elevated 0 10 60 30

< -40 same as recent same as recent 0 0 30 70

< -40 same as recent reduced 0 0 20 80

< -40 reduced avail elevated 0 10 20 70

< -40 reduced avail same as recent 0 0 10 90

< -40 reduced avail reduced 0 0 0 100

< -40 greatly reduced avail elevated 0 0 0 100

< -40 greatly reduced avail same as recent 0 0 0 100

< -40 greatly reduced avail reduced 0 0 0 100

E4.Foraging Sea Ice Distribution. 

Ecoregion

Forag. Sea 

Ice <50% 

Absence 

Chng (mon.)

Sea Ice Shelf 

Distance 

Chng (km) Improved 

Same as 

Base Reduced 

Greatly 

reduced 

Archipelago < 0 < 0 30 70 0 0

Archipelago < 0 0 to 200 30 70 0 0

Archipelago < 0 200 to 500 30 70 0 0

Archipelago < 0 500 to 800 30 70 0 0

Archipelago < 0 >= 800 30 70 0 0

Archipelago 0 to 1 < 0 60 30 10 0

Archipelago 0 to 1 0 to 200 60 30 10 0

Archipelago 0 to 1 200 to 500 60 30 10 0

Archipelago 0 to 1 500 to 800 60 30 10 0

Archipelago 0 to 1 >= 800 60 30 10 0

Archipelago 1 to 2 < 0 70 30 0 0
80 

Ecoregion

Forag. Sea 

Ice <50% 

Absence 

Chng (mon.)

Sea Ice Shelf 

Distance 

Chng (km) Improved 

Same as 

Base Reduced 

Greatly 

reduced 

Archipelago 1 to 2 0 to 200 70 30 0 0

Archipelago 1 to 2 200 to 500 70 30 0 0

Archipelago 1 to 2 500 to 800 70 30 0 0

Archipelago 1 to 2 >= 800 70 30 0 0

Archipelago 2 to 3 < 0 50 30 20 0

Archipelago 2 to 3 0 to 200 50 30 20 0

Archipelago 2 to 3 200 to 500 50 30 20 0

Archipelago 2 to 3 500 to 800 50 30 20 0

Archipelago 2 to 3 >= 800 50 30 20 0

Archipelago 3 to 5 < 0 30 50 20 0

Archipelago 3 to 5 0 to 200 30 50 20 0

Archipelago 3 to 5 200 to 500 30 50 20 0

Archipelago 3 to 5 500 to 800 30 50 20 0

Archipelago 3 to 5 >= 800 30 50 20 0

Archipelago >= 5 < 0 10 50 40 0

Archipelago >= 5 0 to 200 10 50 40 0

Archipelago >= 5 200 to 500 10 50 40 0

Archipelago >= 5 500 to 800 10 50 40 0

Archipelago >= 5 >= 800 10 50 40 0

Polar Basin Convergent < 0 < 0 0 50 50 0

Polar Basin Convergent < 0 0 to 200 0 40 60 0

Polar Basin Convergent < 0 200 to 500 0 60 40 0

Polar Basin Convergent < 0 500 to 800 0 70 30 0

Polar Basin Convergent < 0 >= 800 0 80 20 0

Polar Basin Convergent 0 to 1 < 0 40 40 20 0

Polar Basin Convergent 0 to 1 0 to 200 50 35 15 0

Polar Basin Convergent 0 to 1 200 to 500 35 50 15 0

Polar Basin Convergent 0 to 1 500 to 800 25 40 35 0

Polar Basin Convergent 0 to 1 >= 800 15 40 45 0

Polar Basin Convergent 1 to 2 < 0 40 40 20 0

Polar Basin Convergent 1 to 2 0 to 200 30 50 20 0

Polar Basin Convergent 1 to 2 200 to 500 20 50 30 0

Polar Basin Convergent 1 to 2 500 to 800 10 40 50 0

Polar Basin Convergent 1 to 2 >= 800 0 30 70 0

Polar Basin Convergent 2 to 3 < 0 30 50 20 0

Polar Basin Convergent 2 to 3 0 to 200 20 50 30 0

Polar Basin Convergent 2 to 3 200 to 500 10 40 50 0

Polar Basin Convergent 2 to 3 500 to 800 0 30 70 0
81 

Ecoregion

Forag. Sea 

Ice <50% 

Absence 

Chng (mon.)

Sea Ice Shelf 

Distance 

Chng (km) Improved 

Same as 

Base Reduced 

Greatly 

reduced 

Polar Basin Convergent 2 to 3 >= 800 0 20 80 0

Polar Basin Convergent 3 to 5 < 0 0 50 40 10

Polar Basin Convergent 3 to 5 0 to 200 0 40 40 20

Polar Basin Convergent 3 to 5 200 to 500 0 20 50 30

Polar Basin Convergent 3 to 5 500 to 800 0 10 50 40

Polar Basin Convergent 3 to 5 >= 800 0 0 50 50

Polar Basin Convergent >= 5 < 0 0 20 80 0

Polar Basin Convergent >= 5 0 to 200 0 10 80 10

Polar Basin Convergent >= 5 200 to 500 0 0 75 25

Polar Basin Convergent >= 5 500 to 800 0 0 60 40

Polar Basin Convergent >= 5 >= 800 0 0 40 60

Polar Basin Divergent < 0 < 0 100 0 0 0

Polar Basin Divergent < 0 0 to 200 90 10 0 0

Polar Basin Divergent < 0 200 to 500 70 30 0 0

Polar Basin Divergent < 0 500 to 800 60 40 0 0

Polar Basin Divergent < 0 >= 800 50 50 0 0

Polar Basin Divergent 0 to 1 < 0 50 45 5 0

Polar Basin Divergent 0 to 1 0 to 200 10 70 20 0

Polar Basin Divergent 0 to 1 200 to 500 0 60 40 0

Polar Basin Divergent 0 to 1 500 to 800 0 50 45 5

Polar Basin Divergent 0 to 1 >= 800 0 30 60 10

Polar Basin Divergent 1 to 2 < 0 25 50 25 0

Polar Basin Divergent 1 to 2 0 to 200 0 60 40 0

Polar Basin Divergent 1 to 2 200 to 500 0 40 60 0

Polar Basin Divergent 1 to 2 500 to 800 0 20 80 0

Polar Basin Divergent 1 to 2 >= 800 0 10 85 5

Polar Basin Divergent 2 to 3 < 0 10 50 40 0

Polar Basin Divergent 2 to 3 0 to 200 0 10 80 10

Polar Basin Divergent 2 to 3 200 to 500 0 0 85 15

Polar Basin Divergent 2 to 3 500 to 800 0 0 70 30

Polar Basin Divergent 2 to 3 >= 800 0 0 40 60

Polar Basin Divergent 3 to 5 < 0 0 0 80 20

Polar Basin Divergent 3 to 5 0 to 200 0 0 55 45

Polar Basin Divergent 3 to 5 200 to 500 0 0 35 65

Polar Basin Divergent 3 to 5 500 to 800 0 0 20 80

Polar Basin Divergent 3 to 5 >= 800 0 0 0 100

Polar Basin Divergent >= 5 < 0 0 0 30 70

Polar Basin Divergent >= 5 0 to 200 0 0 20 80
82 

Ecoregion

Forag. Sea 

Ice <50% 

Absence 

Chng (mon.)

Sea Ice Shelf 

Distance 

Chng (km) Improved 

Same as 

Base Reduced 

Greatly 

reduced 

Polar Basin Divergent >= 5 200 to 500 0 0 10 90

Polar Basin Divergent >= 5 500 to 800 0 0 0 100

Polar Basin Divergent >= 5 >= 800 0 0 0 100

Seasonal Ice < 0 < 0 100 0 0 0

Seasonal Ice < 0 0 to 200 100 0 0 0

Seasonal Ice < 0 200 to 500 100 0 0 0

Seasonal Ice < 0 500 to 800 100 0 0 0

Seasonal Ice < 0 >= 800 100 0 0 0

Seasonal Ice 0 to 1 < 0 0 50 50 0

Seasonal Ice 0 to 1 0 to 200 0 50 50 0

Seasonal Ice 0 to 1 200 to 500 0 50 50 0

Seasonal Ice 0 to 1 500 to 800 0 50 50 0

Seasonal Ice 0 to 1 >= 800 0 50 50 0

Seasonal Ice 1 to 2 < 0 0 20 80 0

Seasonal Ice 1 to 2 0 to 200 0 20 80 0

Seasonal Ice 1 to 2 200 to 500 0 20 80 0

Seasonal Ice 1 to 2 500 to 800 0 20 80 0

Seasonal Ice 1 to 2 >= 800 0 20 80 0

Seasonal Ice 2 to 3 < 0 0 0 80 20

Seasonal Ice 2 to 3 0 to 200 0 0 80 20

Seasonal Ice 2 to 3 200 to 500 0 0 80 20

Seasonal Ice 2 to 3 500 to 800 0 0 80 20

Seasonal Ice 2 to 3 >= 800 0 0 80 20

Seasonal Ice 3 to 5 < 0 0 0 10 90

Seasonal Ice 3 to 5 0 to 200 0 0 10 90

Seasonal Ice 3 to 5 200 to 500 0 0 10 90

Seasonal Ice 3 to 5 500 to 800 0 0 10 90

Seasonal Ice 3 to 5 >= 800 0 0 10 90

Seasonal Ice >= 5 < 0 0 0 0 100

Seasonal Ice >= 5 0 to 200 0 0 0 100

Seasonal Ice >= 5 200 to 500 0 0 0 100

Seasonal Ice >= 5 500 to 800 0 0 0 100

Seasonal Ice >= 5 >= 800 0 0 0 100
83 

E5. Overall Terrestrial Prey/Food Abundance. 

Terr. & Marine 

Prey/Food Access

Human Provisioned 

Food Abund. Elevated Same as Recent Reduced Absent

elevated elevated 100 0 0 0

elevated same as recent 95 5 0 0

elevated reduced 85 10 5 0

elevated absent 75 15 10 0

same as recent elevated 5 95 0 0

same as recent same as recent 0 100 0 0

same as recent reduced 0 95 5 0

same as recent absent 0 90 10 0

reduced elevated 0 10 90 0

reduced same as recent 0 5 95 0

reduced reduced 0 0 100 0

reduced absent 0 0 95 5

absent elevated 0 10 15 75

absent same as recent 0 5 20 75

absent reduced 0 0 0 100

absent absent 0 0 0 100
84 

E6. Bears on Shore. 

Overall Sea Ice 

Conditions Ecoregion

Overall Terrestrial 

Prey/Food 

Availability

Greatly 

Increased Increased

Same as 

Recent

Less than 

Recent

improved Archipelago elevated 0 0 20 80

improved Archipelago same as recent 0 0 15 85

improved Archipelago reduced 0 0 5 95

improved Archipelago absent 0 0 0 100

improved Polar Basin Convergent elevated 0 5 25 70

improved Polar Basin Convergent same as recent 0 0 30 70

improved Polar Basin Convergent reduced 0 0 20 80

improved Polar Basin Convergent absent 0 0 10 90

improved Polar Basin Divergent elevated 0 5 25 70

improved Polar Basin Divergent same as recent 0 0 30 70

improved Polar Basin Divergent reduced 0 0 20 80

improved Polar Basin Divergent absent 0 0 10 90

improved Seasonal Ice elevated 0 0 0 100

improved Seasonal Ice same as recent 0 0 0 100

improved Seasonal Ice reduced 0 0 0 100

improved Seasonal Ice absent 0 0 0 100

same as recent Archipelago elevated 0 15 80 5

same as recent Archipelago same as recent 0 10 80 10

same as recent Archipelago reduced 0 10 75 15

same as recent Archipelago absent 0 5 70 25

same as recent Polar Basin Convergent elevated 0 20 80 0

same as recent Polar Basin Convergent same as recent 0 0 100 0

same as recent Polar Basin Convergent reduced 0 0 40 60

same as recent Polar Basin Convergent absent 0 0 20 80

same as recent Polar Basin Divergent elevated 0 20 80 0
85 

Overall Sea Ice 

Conditions Ecoregion

Overall Terrestrial 

Prey/Food 

Availability

Greatly 

Increased Increased

Same as 

Recent

Less than 

Recent

same as recent Polar Basin Divergent same as recent 0 0 100 0

same as recent Polar Basin Divergent reduced 0 0 40 60

same as recent Polar Basin Divergent absent 0 0 20 80

same as recent Seasonal Ice elevated 0 5 90 5

same as recent Seasonal Ice same as recent 0 5 90 5

same as recent Seasonal Ice reduced 0 5 90 5

same as recent Seasonal Ice absent 0 5 90 5

reduced Archipelago elevated 10 40 50 0

reduced Archipelago same as recent 5 45 50 0

reduced Archipelago reduced 5 50 45 0

reduced Archipelago absent 5 55 40 0

reduced Polar Basin Convergent elevated 30 60 10 0

reduced Polar Basin Convergent same as recent 0 80 20 0

reduced Polar Basin Convergent reduced 0 50 30 20

reduced Polar Basin Convergent absent 0 40 30 30

reduced Polar Basin Divergent elevated 30 60 10 0

reduced Polar Basin Divergent same as recent 0 80 20 0

reduced Polar Basin Divergent reduced 0 50 30 20

reduced Polar Basin Divergent absent 0 40 30 30

reduced Seasonal Ice elevated 5 90 5 0

reduced Seasonal Ice same as recent 5 90 5 0

reduced Seasonal Ice reduced 5 90 5 0

reduced Seasonal Ice absent 5 90 5 0

greatly reduced Archipelago elevated 55 45 0 0

greatly reduced Archipelago same as recent 50 45 5 0

greatly reduced Archipelago reduced 45 50 5 0

greatly reduced Archipelago absent 40 55 5 0

greatly reduced Polar Basin Convergent elevated 90 10 0 0
86 

Overall Sea Ice 

Conditions Ecoregion

Overall Terrestrial 

Prey/Food 

Availability

Greatly 

Increased Increased

Same as 

Recent

Less than 

Recent

greatly reduced Polar Basin Convergent same as recent 80 20 0 0

greatly reduced Polar Basin Convergent reduced 70 30 0 0

greatly reduced Polar Basin Convergent absent 60 40 0 0

greatly reduced Polar Basin Divergent elevated 90 10 0 0

greatly reduced Polar Basin Divergent same as recent 80 20 0 0

greatly reduced Polar Basin Divergent reduced 70 30 0 0

greatly reduced Polar Basin Divergent absent 60 40 0 0

greatly reduced Seasonal Ice elevated 95 5 0 0

greatly reduced Seasonal Ice same as recent 95 5 0 0

greatly reduced Seasonal Ice reduced 95 5 0 0

greatly reduced Seasonal Ice absent 95 5 0 0
87 

E7. Overall Marine Conditions.

Overall Sea Ice 

Conditions Marine Prey Base Quality Improved Same as Recent Degraded

improved availability elevated 100 0 0

improved availability same as recent 60 40 0

improved availability reduced 10 30 60

same as recent elevated 70 30 0

same as recent same as recent 0 100 0

same as recent reduced 0 40 60

reduced avail elevated 10 40 50

reduced avail same as recent 0 40 60

reduced avail reduced 0 30 70

greatly reduced avail elevated 5 30 65

greatly reduced avail same as recent 0 10 90

greatly reduced avail reduced 0 0 100

E8. Overall Terrestrial Conditions. 

Bears on Shore

Terrestrial Refugia 

Quality Improved Same as Base Degraded

greatly increased improved 10 50 40

greatly increased not degraded 0 40 60

greatly increased degraded 0 5 95

increased improved 15 70 15

increased not degraded 0 50 50

increased degraded 0 25 75

same as recent improved 40 60 0

same as recent not degraded 0 100 0

same as recent degraded 0 60 40

less than recent improved 100 0 0

less than recent not degraded 75 25 0

less than recent degraded 25 50 25
88 

E9. Overall Habitat Suitability.

Overall Marine 

Conditions

Overall Terrestrial 

Conditions Elevated

Same as 

Base Reduced

Greatly 

Reduced

improved improved 100 0 0 0

improved same as recent 95 5 0 0

improved degraded 90 5 5 0

same as recent improved 20 80 0 0

same as recent same as recent 0 100 0 0

same as recent degraded 0 60 40 0

degraded improved 0 0 100 0

degraded same as recent 0 0 80 20

degraded degraded 0 0 0 100

E10. Event-Driven Mortality.

Human-Bear DLP 

Lethal 

Interactions

Hunting Mortality 

(legal)

Other Mortality or 

Removal Events Elevated

Same as 

Recent Reduced

elevated elevated elevated 100 0 0

elevated elevated same as recent 95 5 0

elevated elevated reduced 90 5 5

elevated same as recent elevated 30 70 0

elevated same as recent same as recent 20 80 0

elevated same as recent reduced 10 80 10

elevated reduced elevated 10 40 50

elevated reduced same as recent 5 30 65

elevated reduced reduced 5 10 85

same as recent elevated elevated 90 10 0

same as recent elevated same as recent 80 20 0

same as recent elevated reduced 60 30 10

same as recent same as recent elevated 5 95 0

same as recent same as recent same as recent 0 100 0

same as recent same as recent reduced 0 90 10

same as recent reduced elevated 5 20 75

same as recent reduced same as recent 0 15 85

same as recent reduced reduced 0 10 90

reduced elevated elevated 70 25 5

reduced elevated same as recent 60 30 10

reduced elevated reduced 50 30 20

reduced same as recent elevated 5 65 30

reduced same as recent same as recent 0 85 15

reduced same as recent reduced 0 75 25
89 

Human-Bear DLP 

Lethal 

Interactions

Hunting Mortality 

(legal)

Other Mortality or 

Removal Events Elevated

Same as 

Recent Reduced

reduced reduced elevated 0 10 90

reduced reduced same as recent 0 5 95

reduced reduced reduced 0 0 100

E11. Other Mortality or Removal Events.

Oil Spills, Small Oil Spills, Large Other Events Elevated

Same as 

Base Reduced

elevated elevated elevated 100 0 0

elevated elevated same as recent 100 0 0

elevated elevated reduced 80 20 0

elevated same as recent elevated 70 30 0

elevated same as recent same as recent 60 40 0

elevated same as recent reduced 40 50 10

elevated reduced elevated 60 30 10

elevated reduced same as recent 50 40 10

elevated reduced reduced 30 30 40

same as recent elevated elevated 70 30 0

same as base elevated same as recent 60 40 0

same as recent elevated reduced 40 50 10

same as recent same as recent elevated 40 60 0

same as recent same as recent same as recent 0 100 0

same as recent same as recent reduced 0 80 20

same as recent reduced elevated 10 80 10

same as recent reduced same as recent 0 70 30

same as recent reduced reduced 0 40 60

reduced elevated elevated 70 30 0

reduced elevated same as recent 50 25 25

reduced elevated reduced 30 20 50

reduced same as recent elevated 10 80 10

reduced same as recent same as recent 0 60 40

reduced same as recent reduced 0 20 80

reduced reduced elevated 0 30 70

reduced reduced same as recent 0 10 90

reduced reduced reduced 0 0 100
90 

E12. Adult Survival.

Event-driven 

Mortality

Overall Habitat 

Suitability

Parasites and

Disease Increased

Same as 

Base Decreased

elevated elevated elevated 75 15 10

elevated elevated same as recent 80 20 0

elevated elevated reduced 90 10 0

elevated same as recent elevated 0 40 60

elevated same as recent same as recent 0 60 40

elevated same as recent reduced 10 60 30

elevated reduced elevated 0 0 100

elevated reduced same as recent 0 10 90

elevated reduced reduced 0 15 85

elevated greatly reduced elevated 0 0 100

elevated greatly reduced same as recent 0 0 100

elevated greatly reduced reduced 0 0 100

same as recent elevated elevated 80 20 0

same as recent elevated same as recent 90 10 0

same as recent elevated reduced 100 0 0

same as recent same as recent elevated 0 60 40

same as recent same as recent same as recent 0 100 0

same as recent same as recent reduced 30 70 0

same as recent reduced elevated 0 10 90

same as recent reduced same as recent 0 20 80

same as recent reduced reduced 0 25 75

same as recent greatly reduced elevated 0 0 100

same as recent greatly reduced same as recent 0 0 100

same as recent greatly reduced reduced 0 0 100

reduced elevated elevated 90 10 0

reduced elevated same as recent 100 0 0

reduced elevated reduced 100 0 0

reduced same as recent elevated 10 60 30

reduced same as recent same as recent 30 70 0

reduced same as recent reduced 40 60 0

reduced reduced elevated 0 15 85

reduced reduced same as recent 0 25 75

reduced reduced reduced 0 35 65

reduced greatly reduced elevated 0 0 100

reduced greatly reduced same as recent 0 0 100

reduced greatly reduced reduced 0 0 100
91 

E13. Subadult Survival.

Event-driven 

Mortality

Overall Habitat 

Suitability

Other Biotic 

Stressors Increased

Same as 

Recent Decreased

elevated elevated elevated 70 20 10

elevated elevated same as recent 80 15 5

elevated elevated reduced 90 10 0

elevated same as recent elevated 0 40 60

elevated same as recent same as recent 0 60 40

elevated same as recent reduced 15 60 25

elevated reduced elevated 0 0 100

elevated reduced same as recent 0 10 90

elevated reduced reduced 0 20 80

elevated greatly reduced elevated 0 0 100

elevated greatly reduced same as recent 0 0 100

elevated greatly reduced reduced 0 0 100

same as recent elevated elevated 80 20 0

same as recent elevated same as recent 90 10 0

same as recent elevated reduced 100 0 0

same as recent same as recent elevated 0 70 30

same as recent same as recent same as recent 0 100 0

same as recent same as recent reduced 30 70 0

same as recent reduced elevated 0 10 90

same as recent reduced same as recent 0 20 80

same as recent reduced reduced 0 25 75

same as recent greatly reduced elevated 0 0 100

same as recent greatly reduced same as recent 0 0 100

same as recent greatly reduced reduced 0 0 100

reduced elevated elevated 90 10 0

reduced elevated same as recent 100 0 0

reduced elevated reduced 100 0 0

reduced same as recent elevated 5 70 25

reduced same as recent same as recent 20 80 0

reduced same as recent reduced 40 60 0

reduced reduced elevated 0 15 85

reduced reduced same as recent 0 25 75

reduced reduced reduced 0 35 65

reduced greatly reduced elevated 0 0 100

reduced greatly reduced same as recent 0 0 100

reduced greatly reduced reduced 0 0 100
92 

E14. Adult Female Body Condition.

Overall Habitat 

Suitability

Parasites and

Disease

Anthropogenic

Stressors Increased

Same as 

Recent Decreased

Greatly 

decreased

elevated elevated elevated 60 20 15 5

elevated elevated same as recent 70 20 10 0

elevated elevated reduced 80 15 5 0

elevated same as recent elevated 70 20 10 0

elevated same as recent same as recent 95 5 0 0

elevated same as recent reduced 100 0 0 0

elevated reduced elevated 80 15 5 0

elevated reduced same as recent 100 0 0 0

elevated reduced reduced 100 0 0 0

same as recent elevated elevated 0 80 20 0

same as recent elevated same as recent 0 90 10 0

same as recent elevated reduced 10 80 10 0

same as recent same as recent elevated 0 90 10 0

same as recent same as recent same as recent 0 100 0 0

same as recent same as recent reduced 20 80 0 0

same as recent reduced elevated 10 80 10 0

same as recent reduced same as recent 20 80 0 0

same as recent reduced reduced 30 70 0 0

reduced elevated elevated 0 0 80 20

reduced elevated same as recent 0 10 80 10

reduced elevated reduced 0 20 80 0

reduced same as recent elevated 0 10 80 10

reduced same as recent same as recent 0 30 70 0

reduced same as recent reduced 0 40 60 0

reduced reduced elevated 0 20 80 0
93 

Overall Habitat 

Suitability

Parasites and

Disease

Anthropogenic

Stressors Increased

Same as 

Recent Decreased

Greatly 

decreased

reduced reduced same as recent 0 40 60 0

reduced reduced reduced 0 50 50 0

greatly reduced elevated elevated 0 0 0 100

greatly reduced elevated same as recent 0 0 0 100

greatly reduced elevated reduced 0 0 5 95

greatly reduced same as recent elevated 0 0 0 100

greatly reduced same as recent same as recent 0 0 40 60

greatly reduced same as recent reduced 0 15 35 50

greatly reduced reduced elevated 0 10 40 50

greatly reduced reduced same as recent 0 15 35 50

greatly reduced reduced reduced 0 20 30 50
94 

E15. Terrestrial Maternal Den Access.

Ecoregion

Forag. Sea Ice 

<50% Absence 

Chng (mon.) Increased

Same as 

Recent Decreased

Archipelago < 0 0 100 0

Archipelago 0 to 1 0 100 0

Archipelago 1 to 2 0 100 0

Archipelago 2 to 3 0 100 0

Archipelago 3 to 5 0 100 0

Archipelago >= 5 0 100 0

Polar Basin Convergent < 0 0 100 0

Polar Basin Convergent 0 to 1 0 100 0

Polar Basin Convergent 1 to 2 0 100 0

Polar Basin Convergent 2 to 3 0 100 0

Polar Basin Convergent 3 to 5 0 100 0

Polar Basin Convergent >= 5 0 100 0

Polar Basin Divergent < 0 70 30 0

Polar Basin Divergent 0 to 1 50 50 0

Polar Basin Divergent 1 to 2 0 60 40

Polar Basin Divergent 2 to 3 0 40 60

Polar Basin Divergent 3 to 5 0 30 70

Polar Basin Divergent >= 5 0 0 100

Seasonal Ice < 0 0 100 0

Seasonal Ice 0 to 1 0 100 0

Seasonal Ice 1 to 2 0 100 0

Seasonal Ice 2 to 3 0 100 0

Seasonal Ice 3 to 5 0 100 0

Seasonal Ice >= 5 0 100 0
95 

E16. Sub-lethal Human Disturbance.

Shipping

Human-Bear 

Sub-Lethal 

Interactions

Oil, Gas, & 

Mining Activity Tourism Reduced

Same as 

Base Elevated

elevated elevated elevated elevated 0 0 100

elevated elevated elevated same as recent 0 25 75

elevated elevated elevated reduced 5 25 70

elevated elevated same as recent elevated 0 25 75

elevated elevated same as recent same as recent 0 50 50

elevated elevated same as recent reduced 25 25 50

elevated elevated reduced elevated 5 25 70

elevated elevated reduced same as recent 25 25 50

elevated elevated reduced reduced 33 33 33

elevated same as recent elevated elevated 0 25 75

elevated same as recent elevated same as recent 0 50 50

elevated same as recent elevated reduced 25 25 50

elevated same as recent same as recent elevated 0 50 50

elevated same as recent same as recent same as recent 0 75 25

elevated same as recent same as recent reduced 25 50 25

elevated same as recent reduced elevated 25 25 50

elevated same as recent reduced same as recent 25 50 25

elevated same as recent reduced reduced 50 25 25

elevated reduced elevated elevated 5 25 70

elevated reduced elevated same as recent 25 25 50

elevated reduced elevated reduced 33 33 33

elevated reduced same as recent elevated 25 25 50

elevated reduced same as recent same as recent 25 50 25

elevated reduced same as recent reduced 50 25 25

elevated reduced reduced elevated 33 33 33

elevated reduced reduced same as recent 50 25 25

elevated reduced reduced reduced 70 25 5

same as recent elevated elevated elevated 0 25 75

same as recent elevated elevated same as recent 0 50 50

same as recent elevated elevated reduced 25 25 50

same as recent elevated same as recent elevated 0 50 50

same as recent elevated same as recent same as recent 0 75 25

same as recent elevated same as recent reduced 25 50 25

same as recent elevated reduced elevated 25 25 50

same as recent elevated reduced same as recent 25 50 25

same as recent elevated reduced reduced 50 25 25

same as recent same as recent elevated elevated 0 50 50
96 

Shipping

Human-Bear 

Sub-Lethal 

Interactions

Oil, Gas, & 

Mining Activity Tourism Reduced

Same as 

Base Elevated

same as recent same as recent elevated same as recent 0 75 25

same as recent same as recent elevated reduced 25 50 25

same as recent same as recent same as recent elevated 0 75 25

same as recent same as recent same as recent same as recent 0 100 0

same as recent same as recent same as recent reduced 25 75 0

same as recent same as recent reduced elevated 25 50 25

same as recent same as recent reduced same as recent 25 75 0

same as recent same as recent reduced reduced 50 50 0

same as recent reduced elevated elevated 25 25 50

same as recent reduced elevated same as recent 25 50 25

same as recent reduced elevated reduced 50 25 25

same as recent reduced same as recent elevated 25 50 25

same as recent reduced same as recent same as recent 25 75 0

same as recent reduced same as recent reduced 50 50 0

same as recent reduced reduced elevated 50 25 25

same as recent reduced reduced same as recent 50 50 0

same as recent reduced reduced reduced 75 25 0

reduced elevated elevated elevated 5 25 70

reduced elevated elevated same as recent 25 25 50

reduced elevated elevated reduced 33 33 33

reduced elevated same as recent elevated 25 25 50

reduced elevated same as recent same as recent 25 50 25

reduced elevated same as recent reduced 50 25 25

reduced elevated reduced elevated 33 33 33

reduced elevated reduced same as recent 50 25 25

reduced elevated reduced reduced 70 25 5

reduced same as recent elevated elevated 25 25 50

reduced same as recent elevated same as recent 25 50 25

reduced same as recent elevated reduced 50 25 25

reduced same as recent same as recent elevated 25 50 25

reduced same as recent same as recent same as recent 25 75 0

reduced same as recent same as recent reduced 50 50 0

reduced same as recent reduced elevated 50 25 25

reduced same as recent reduced same as recent 50 50 0

reduced same as recent reduced reduced 75 25 0

reduced reduced elevated elevated 33 33 33

reduced reduced elevated same as recent 50 25 25

reduced reduced elevated reduced 70 25 5

reduced reduced same as recent elevated 50 25 25
97 

Shipping

Human-Bear 

Sub-Lethal 

Interactions

Oil, Gas, & 

Mining Activity Tourism Reduced

Same as 

Base Elevated

reduced reduced same as recent same as recent 50 50 0

reduced reduced same as recent reduced 75 25 0

reduced reduced reduced elevated 70 25 5

reduced reduced reduced same as recent 75 25 0

reduced reduced reduced reduced 100 0 0

E17. Recruitment.

Body Condition Den Access Increased

Same as 

Recent Decreased

Greatly 

decreased

increased elevated 100 0 0 0

increased same as recent 100 0 0 0

increased reduced 60 40 0 0

same as recent elevated 20 80 0 0

same as recent same as recent 0 100 0 0

same as recent reduced 0 80 20 0

decreased elevated 0 30 70 0

decreased same as recent 0 10 90 0

decreased reduced 0 0 50 50

greatly decreased elevated 0 0 0 100

greatly decreased same as recent 0 0 0 100

greatly decreased reduced 0 0 0 100

E18. Other Biotic Stressors.

Parasites and Disease Predation Elevated

Same as 

Recent Reduced

elevated elevated 100 0 0

elevated same as recent 50 50 0

elevated reduced 33 34 33

same as recent elevated 50 50 0

same as recent same as recent 0 100 0

same as recent reduced 0 50 50

reduced elevated 33 34 33

reduced same as recent 0 50 50

reduced reduced 0 0 100
98 

E19. Pollution.

Hydrocarbons/Oil Spill Contaminants Reduced

Same as 

Recent Elevated

elevated elevated 0 0 100

elevated same as recent 0 50 50

elevated reduced 33 33 34

same as recent elevated 0 50 50

same as recent same as recent 0 100 0

same as recent reduced 50 50 0

reduced elevated 33 33 34

reduced same as recent 50 50 0

reduced reduced 100 0 0

E20. Anthropogenic Stressors (sub-lethal).

Sub-lethal Human 

Disturbance Pollution (sub-lethal) Elevated

Same as 

Recent Reduced

reduced reduced 0 0 100

reduced same as recent 0 50 50

reduced elevated 33 33 34

same as recent reduced 0 50 50

same as recent same as recent 0 100 0

same as recent elevated 50 50 0

elevated reduced 33 33 34

elevated same as recent 50 50 0

elevated elevated 100 0 0
99 

Appendix F. Influence Run Results for RCP 4.5 and 8.5 by Ecoregion 

Influence runs were used to examine the potential for changes in the strength of select stressors to cause measurable changes in 

relative population trend, and to rank stressors in order of importance to population outcomes. 

Figure F.1a. Polar Basin Divergent Ecoregion, RCP 4.5. 
100 

 

Figure F.1a. Polar Basin Divergent Ecoregion, RCP 4.5.—Continued
101 

Figure F.1b. Polar Basin Divergent Ecoregion, RCP 8.5.
102 

Figure F.1b. Polar Basin Divergent Ecoregion, RCP 8.5.—Continued
103 

Figure F.2b. Polar Basin Convergent Ecoregion, RCP 4.5.
104 

Figure F.2b. Polar Basin Convergent Ecoregion, RCP 4.5.—Continued
105 

 

Figure F.2b. Polar Basin Convergent Ecoregion, RCP 8.5.
106 

Figure F.2b. Polar Basin Convergent Ecoregion, RCP 8.5.—Continued
107 

Figure F.3a. Seasonal Ice Ecoregion 4.5.
108 

Figure F.3a. Seasonal Ice Ecoregion 4.5.—Continued
109 

Figure F.3b. Seasonal Ice Ecoregion 8.5.
110 

Figure F.3b. Seasonal Ice Ecoregion 8.5.—Continued
111 

Figure F.4a. Archipelago Ecoregion, RCP 4.5.
112 

Figure F.4a. Archipelago Ecoregion, RCP 4.5.—Continued
113 

Figure F.4b. Archipelago Ecoregion, RCP 8.5.
114 

Figure F.4b. Archipelago Ecoregion, RCP 8.5.—Continued
Publishing support provided by the U.S. Geological Survey

 Publishing Network, Tacoma Publishing Service Center

For more information concerning the research in this report, contact the

Director, Alaska Science Center

U.S. Geological Survey

4210 University Dr

Anchorage, Alaska 99508-4560

http://alaska.usgs.gov
Atlwood and others—Evaluating and Ranking Threats to the Long-Term Persistence of Polar Bears—Open-File Report 2014–1254

ISSN 2331-1258 (online)

http://dx.doi.org/10.3133/ofr20141254