Polar bears (Ursus maritimus) require sea ice for capturing seals and are expected to decline range-wide as global warming and sea-ice loss continue1,2. Estimating when different subpopulations will likely begin to decline has not been possible to date because data linking ice availability to demographic performance are unavailable for most subpopulations2 and unobtainable a priori for the projected but yet-to-be-observed low ice extremes3. Here, we establish the likely nature, timing and order of future demographic impacts by estimating the threshold numbers of days that polar bears can fast before cub recruitment and/or adult survival are impacted and decline rapidly. Intersecting these fasting impact thresholds with projected numbers of ice-free days, estimated from a large ensemble of an Earth system model4, reveals when demographic impacts will likely occur in different subpopulations across the Arctic. Our model captures demographic trends observed during 1979–2016, showing that recruitment and survival impact thresholds may already have been exceeded in some subpopulations. It also suggests that, with high greenhouse gas emissions, steeply declining reproduction and survival will jeopardize the persistence of all but a few high-Arctic subpopulations by 2100. Moderate emissions mitigation prolongs persistence but is unlikely to prevent some subpopulation extirpations within this century.
This is a preview of subscription content
Subscribe to Journal
Get full journal access for 1 year
only $8.25 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
CESM large ensemble output is available from the Climate Data Gateway at the National Center for Atmospheric Research via https://www.earthsystemgrid.org. The PMW satellite data are available from the National Snow and Ice Data Center at https://doi.org/10.5067/8GQ8LZQVL0VL. The polar bear data used in this study are available from the corresponding authors upon reasonable request.
All analyses were conducted in MATLAB version R2016a. The computer scripts are available from the corresponding authors upon reasonable request.
Amstrup, S. C. et al. Greenhouse gas mitigation can reduce sea-ice loss and increase polar bear persistence. Nature 468, 955–958 (2010).
Regehr, E. V. et al. Conservation status of polar bears (Ursus maritimus) in relation to projected sea-ice declines. Biol. Lett. 12, 20160556 (2016).
Molnár, P. K., Derocher, A. E., Thiemann, G. W. & Lewis, M. A. Predicting survival, reproduction and abundance of polar bears under climate change. Biol. Conserv. 143, 1612–1622 (2010); corrigendum 177, 230–231 (2014).
Kay, J. E. et al. The Community Earth System Model (CESM) Large Ensemble Project: a community resource for studying climate change in the presence of internal climate variability. Bull. Am. Meteorol. Soc. 96, 1333–1349 (2015).
Rode, K. D., Robbins, C. T., Nelson, L. & Amstrup, S. C. Can polar bears use terrestrial foods to offset lost ice-based hunting opportunities? Front. Ecol. Environ. 13, 138–145 (2015).
Stern, H. S. & Laidre, K. L. Sea-ice indicators of polar bear habitat. Cryosphere 10, 2027–2041 (2016).
Stirling, I. & Derocher, A. E. Effects of climate warming on polar bears: a review of the evidence. Glob. Change Biol. 18, 2694–2706 (2012).
Regehr, E. V., Lunn, N. J., Amstrup, S. C. & Stirling, I. Effects of earlier sea ice breakup on survival and population size of polar bears in Western Hudson Bay. J. Wildl. Manag. 71, 2673–2683 (2007).
Rode, K. D., Amstrup, S. C. & Regehr, E. V. Reduced body size and cub recruitment in polar bears associated with sea ice decline. Ecol. Appl. 20, 768–782 (2010).
Rode, K. D. et al. A tale of two polar bear populations: ice habitat, harvest, and body condition. Popul. Ecol. 54, 3–18 (2012).
Bromaghin, J. F. et al. Polar bear population dynamics in the Southern Beaufort Sea during a period of sea ice decline. Ecol. Appl. 25, 634–651 (2016).
Lunn, N. J. et al. Demography of an apex predator at the edge of its range: impacts of changing sea ice on polar bears in Hudson Bay. Ecol. Appl. 26, 1302–1320 (2016).
Obbard, M. E. et al. Trends in body condition in polar bears (Ursus maritimus) from the Southern Hudson Bay subpopulation in relation to changes in sea ice. Arctic Sci. 2, 15–32 (2016).
Hunter, C. M. et al. Climate change threatens polar bear populations: a stochastic demographic analysis. Ecology 91, 2883–2897 (2010).
Molnár, P. K., Derocher, A. E., Klanjscek, T. & Lewis, M. A. Predicting climate change impacts on polar bear litter size. Nat. Commun. 2, 186 (2011).
De la Guardia, L. C., Derocher, A. E., Myers, P. G., van Scheltinga, A. D. T. & Lunn, N. J. Future sea ice conditions in Western Hudson Bay and consequences for polar bears in the 21st century. Glob. Change Biol. 19, 2675–2687 (2013).
Hamilton, S. G. et al. Projected polar bear sea ice habitat in the Canadian Arctic Archipelago. PLoS ONE 9, e113746 (2014).
Cavalieri, D., Parkinson, C., Gloersen, P. & Zwally, H. J. Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Passive Microwave Data Version 1 (1979–2016) (NASA DAAC at the National Snow and Ice Data Center, accessed 7 June 2017).
Arnould, J. P. Y. & Ramsay, M. A. Milk production and milk consumption in polar bears during the ice-free period in western Hudson Bay. Can. J. Zool. 72, 1365–1370 (1994).
Dyck, M., Campbell, M., Lee, D. S., Boulanger, J. & Hedman, D. Aerial Survey of the Western Hudson Bay Polar Bear Sub-Population 2016. 2017 Final Report (Wildlife Research Section, Department of Environment, Government of Nunavut, 2017).
Manning, T. H. Geographical Variation in the Polar Bear Ursus Maritimus Phipps. Rep. Ser. No. 13 (Canadian Wildlife Service, 1971).
Derocher, A. E. & Stirling, I. Geographic variation in growth of polar bears (Ursus maritimus). J. Zool. Lond. 245, 65–72 (1998).
Derocher, A. E. & Wiig, Ø. Postnatal growth in body length and mass of polar bears (Ursus maritimus) at Svalbard. J. Zool. Lond. 256, 343–349 (2002).
Obbard, M. E. et al. Re-assessing abundance of Southern Hudson Bay polar bears by aerial survey: effects of climate change at the southern edge of the range. Arctic Sci. 4, 634–655 (2018).
Peacock, E., Taylor, M. K., Laake, J. & Stirling, I. Population ecology of polar bears in Davis Strait, Canada and Greenland. J. Wildl. Manag. 77, 463–476 (2013).
Galicia, M. P., Thiemann, G. W., Dyck, M. G. & Ferguson, S. H. Characterization of polar bear (Ursus maritimus) diets in the Canadian high arctic. Polar Biol. 38, 1983–1992 (2015).
Laidre, K. L. et al. Interrelated ecological impacts of climate change on an apex predator. Ecol. Appl. 30, e02071 (2020).
Stapleton, S., Peacock, E. & Garshelis, D. Aerial surveys suggest long-term stability in the seasonally ice-free Foxe Basin (Nunavut) polar bear population. Mar. Mammal Sci. 32, 181–201 (2016).
Regehr, E. V. et al. Integrated population modeling provides the first empirical estimates of vital rates and abundance for polar bears in the Chukchi Sea. Sci. Rep. 8, 16780 (2018).
Stirling, I., McDonald, T. L., Richardson, E. S., Regehr, E. V. & Amstrup, S. C. Polar bear population status in the Northern Beaufort Sea, Canada, 1971–2006. Ecol. Appl. 21, 859–876 (2011).
Pagano, A. M. et al. High-energy, high-fat lifestyle challenges an Arctic apex predator, the polar bear. Science 359, 568–572 (2018).
Aars, J. et al. The number and distribution of polar bears in the western Barents Sea. Polar Res. 36, 1374125 (2017).
Moss, R. et al. Towards New Scenarios for Analysis of Emissions, Climate Change, Impacts, and Response Strategies (IPCC, 2008).
Molnár, P. K., Derocher, A. E., Lewis, M. A. & Taylor, M. K. Modelling the mating system of polar bears: a mechanistic approach to the Allee effect. Proc. R. Soc. B 275, 217–226 (2008).
Ingolfsson, O. & Wiig, Ø. Late Pleistocene fossil find in Svalbard: the oldest remains of a polar bear (Ursus maritimus Phipps, 1744) ever discovered. Polar Res. 28, 455–462 (2008).
Notz, D. & Stroeve, J. Observed Arctic sea-ice loss directly follows anthropogenic CO2 emission. Science 354, 747–750 (2016).
Durner, G. M. et al. Predicting 21st century polar bear habitat distribution from global climate models. Ecol. Monogr. 79, 25–58 (2009).
Cherry, S. G., Derocher, A. E., Thiemann, G. W. & Lunn, N. J. Migration phenology and seasonal fidelity of an Arctic marine predator in relation to sea ice dynamics. J. Anim. Ecol. 82, 912–921 (2013).
Smith, R. D., Kortas, S. & Meltz, B. J. A. Curvilinear Coordinates for Global Ocean Models. Tech. Note LA-UR-95-1146 (Los Alamos National Laboratory, 1997).
Amstrup, S. C., Marcot, B. G. & Douglas, D. C. in Arctic Sea Ice Decline: Observations, Projections, Mechanisms, and Implications (eds DeWeaver, E. T. et al.) 213–268 (American Geophysical Union, 2008).
Regehr, E. V., Hunter, C. M., Caswell, H., Amstrup, S. C. & Stirling, I. Survival and breeding of polar bears in the Southern Beaufort Sea in relation to sea ice. J. Anim. Ecol. 79, 117–127 (2009).
Whiteman, J. P. et al. Summer declines in activity and body temperature offer polar bears limited energy savings. Science 349, 295–298 (2015).
Whiteman, J. P. et al. Phenotypic plasticity and climate change: can polar bears respond to longer Arctic summers with an adaptive fast? Oecologia 186, 369–381 (2018).
Fetterer, F., Knowles, K., Meier, W. & Savoie, M. Sea Ice Index (National Snow and Ice Data Center, 2002).
Furnell, D. J. & Oolooyuk, D. Polar bear predation on ringed seals in ice-free water. Can. Field-Nat. 94, 88–89 (1980).
Stirling, I., Lunn, N. J. & Iacozza, J. Long-term trends in the population ecology of polar bears in western Hudson Bay in relation to climate change. Arctic 52, 294–306 (1999).
Cavalieri, D. J., Parkinson, C. L., Gloersen, P., Comiso, J. C. & Zwally, H. J. Deriving long-term time series of ice cover from satellite passive-microwave multisensor data sets. J. Geophys. Res. 104, 15803–15814 (1999).
Meier, W. N. & Stewart, J. S. Assessing uncertainties in sea ice extent climate indicators. Environ. Res. Lett. 14, 035005 (2019).
Durner, G. M. et al. Increased Arctic sea ice drift alters adult female polar bear movements and energetics. Glob. Change Biol. 23, 3460–3473 (2017).
Durner, G. M. et al. Consequences of long-distance swimming and travel over deep-water pack ice for a female polar bear during a year of extreme sea ice retreat. Polar Biol. 34, 975–984 (2011).
Pagano, A. M., Durner, G. M., Amstrup, S. C., Simac, K. S. & York, G. S. Long-distance swimming by polar bears (Ursus maritimus) of the Southern Beaufort Sea during years of extensive open water. Can. J. Zool. 90, 663–676 (2012).
Derocher, A. E. & Stirling, I. Aspects of survival in juvenile polar bears. Can. J. Zool. 74, 1246–1252 (1996).
Molnár, P. K., Klanjscek, T., Derocher, A. E., Obbard, M. E. & Lewis, M. A. A body composition model to estimate mammalian energy stores and metabolic rates from body mass and body length, with application to polar bears. J. Exp. Biol. 212, 2313–2323 (2009).
Best, R. C. Thermoregulation in resting and active polar bears. J. Comp. Physiol. 146, 63–73 (1982).
Mathewson, P. D. & Porter, W. P. Simulating polar bear energetics during a seasonal fast using a mechanistic model. PLoS ONE 8, e72863 (2013).
Pagano, A. M. et al. Energetic costs of locomotion in bears: is plantigrade locomotion energetically economical? J. Exp. Biol. 221, jeb175372 (2018).
Derocher, A. E. & Stirling, I. Distribution of polar bears (Ursus maritimus) during the ice-free period in Western Hudson Bay. Can. J. Zool. 68, 1395–1403 (1990).
Lunn, N. J., Stirling, I., Andriashek, D. & Richardson, E. Selection of maternity dens by female polar bears in western Hudson Bay, Canada and the effects of human disturbance. Polar Biol. 27, 350–356 (2004).
Parks, E. K., Derocher, A. E. & Lunn, N. J. Seasonal and annual movement patterns of polar bears on the sea ice of Hudson Bay. Can. J. Zool. 84, 1281–1294 (2006).
Derocher, A. E., Stirling, I. & Andriashek, D. Pregnancy rates and serum progesterone levels of polar bears in Western Hudson Bay. Can. J. Zool. 70, 561–566 (1992).
Lee, P. C., Majluf, P. & Gordon, I. J. Growth, weaning and maternal investment from a comparative perspective. J. Zool. Lond. 225, 99–114 (1991).
Oftedal, O. T. The adaptation of milk secretion to the constraints of fasting in bears, seals, and baleen whales. J. Dairy Sci. 76, 3234–3246 (1993).
Derocher, A. E., Andriashek, D. & Arnould, J. P. Y. Aspects of milk composition and lactation in polar bears. Can. J. Zool. 71, 561–567 (1993).
Stapleton, S., Atkinson, S., Hedman, D. & Garshelis, D. Revisiting Western Hudson Bay: using aerial surveys to update polar bear abundance in a sentinel population. Biol. Conserv. 170, 38–47 (2014).
Calvert, W. & Ramsay, M. A. Evaluation of age determination of polar bears by counts of cementum growth layer groups. Ursus 10, 449–453 (1998).
Regehr, E. V., Wilson, R. R., Rode, K. D. & Runge, M. C. Resilience and Risk—a Demographic Model to Inform Conservation Planning for Polar Bears Open-File Report 2015–1029 (US Geological Survey, 2015).
Molnár, P. K., Lewis, M. A. & Derocher, A. E. Estimating Allee dynamics before they can be observed: polar bears as a case study. PLoS ONE 9, e85410 (2014).
Rode, K. D. et al. Variation in the response of an Arctic top predator experiencing habitat loss: feeding and reproductive ecology of two polar bear populations. Glob. Change Biol. 20, 76–88 (2014).
Laliberté, F., Howell, S. E. L. & Kushner, P. J. Regional variability of a projected sea ice‐free Arctic during the summer months. Geophys. Res. Lett. 43, 256–263 (2016).
Massonnet, F. et al. Constraining projections of summer Arctic sea ice. Cryosphere 6, 1383–1394 (2012).
McNab, B. K. Geographic and temporal correlations of mammalian size reconsidered: a resource rule. Oecologia 164, 13–23 (2010).
McNutt, J. W. & Gusset, M. Declining body size in an endangered large mammal. Biol. J. Linn. Soc. 105, 8–12 (2012).
Amstrup, S. C. & Durner, G. M. Survival rates of radio-collared female polar bears and their dependent young. Can. J. Zool. 73, 1312–1322 (1995).
The polar bear data used in this study were collected by the late M. Ramsay of the University of Saskatchewan, and we thank F. Messier for access to these data. Dates of polar bear on-shore arrival and departure in the Western Hudson Bay subpopulation were collected and provided by A. Derocher. Sea-ice projections were produced by the CESM Large Ensemble Community Project and the CESM Medium Ensemble using high-performance computing support from Yellowstone (ark:/85065/d7wd3xhc). P.K.M. is grateful for support from a Natural Sciences and Engineering Research Council of Canada Discovery Grant (RGPIN-2016-06301), the Canada Foundation for Innovation John R. Evans Leaders Fund (grant number 35341) and the Ministry of Research, Innovation and Science Ontario Research Fund. C.M.B. is grateful for support from NOAA (grant NA18OAR4310274). M.M.H. acknowledges support from NSF. J.E.K. acknowledges start-up funds from the University of Colorado. S.R.P. is grateful for support from Polar Knowledge Canada through their Northern Scientific Training Program. S.C.A. is grateful for 30 years of research opportunity at USGS, to the staff, board and many supporters at PBI, and to the University of Wyoming.
The authors declare no competing interests.
Peer review information Nature Climate Change thanks Nadja Steiner and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended Data Fig. 1 The annual extent of sea ice in the Western Hudson Bay subpopulation region as derived from PMW data.
Grey lines are daily extent in each year for 1979-2016 based on satellite observations. The colored curves are means of the daily extent: 1979-1988 (blue), 1989-1999 (green), 2000-2009 (yellow), and 2010-2016 (red). The thick black line is the 1979-1988 March mean extent, and the thin black line is the critical extent, taken as 30% of the 1979-1988 March mean. The first (last) day-of-the-year when the extent drops below (rises above) the critical extent in each year is marked with a magenta (turquoise) square.
Extended Data Fig. 2 Annual start and end day of the polar bear fasting season in Western Hudson Bay as estimated from PMW and the days-of-year when bears arrive on shore and depart back to the sea ice.
Days-of-year of observed bear on-shore arrival (triangles) and departure to sea ice (circles) are from ref. 38. The estimated start (magenta) and end (turquoise) dates of the fasting season are the days-of-year corresponding to the squares in Extended Data Fig. 1 with 27 and 3 day offsets to match the timing of the on-shore arrival and departure to sea ice of bears in Western Hudson Bay, respectively.
Extended Data Fig. 3 Fasting season lengths, as estimated from PMW observations in 1979-2016 and CESM1 simulations for 1979-2100.
All fasting durations were calculated as 24 days shorter than the summer period with sea ice extent <30%. Red lines are using PMW observations. Black and grey lines use the ensemble means of the CESM1 simulations with the RCP8.5 and RCP4.5 scenarios, respectively, with both having the same historical integrations through year 2005 (the RCP4.5 scenario was only run to 2080 due to computational limitations). The range of the ensemble of simulations is shown by shading plus and minus one standard deviation about the ensemble means. Numbers show the mean per-decade increase in the number of fasting days from 1979-2016, from PMW (red) and from the full range of CESM1 ensemble members (grey: RCP4.5; black: RCP8.5). Ice-free season lengths from CESM1 nearly always agree with or slightly underestimate the ice-free season lengths observed from PMW, again rendering our forecasts optimistic. Green background: SIE subpopulations; red background: DIE subpopulations; blue background: CIE subpopulations.
Extended Data Fig. 4 Estimation of fasting impact thresholds for adult females without dependent offspring and for adult females with dependent yearlings.
Panels follow the same logical outline as in Fig. 2, where impact threshold for adult males and adult females with cubs are estimated. (a-c) Fast-initiating masses and lengths of a, adult females without dependent offspring (green crosses) and of b-c, adult females with dependent yearlings (purple crosses), shown relative to DEB-estimates for the number of days to death by starvation; starvation times for females with yearlings are calculated once assuming full lactation until death (b), and once assuming no lactation when fasting (c). d, e, Cumulative distribution of estimated starvation times, and linear regressions through the 5th-95th percentile of these distributions estimating (d) a survival impact threshold for solitary adult females (255 days) beyond which mortality increases by ~ 0.4% for each additional fasting day (regression slope), and (e) lower (dark purple) and upper (light purple) boundaries for the survival impact thresholds of females with yearlings (185-232 days), of which the lower estimate also doubles as a yearling recruitment impact threshold. f, g, Sensitivity analyses illustrating the dependence of impact thresholds on the fast-initiating masses of bears – obtained by adjusting all WH89-96 masses upwards or downwards by a specified percentage within biologically reasonable bounds (cf. Extended Data Figs. 5 and 7 and Supplementary Fig. 5 for details).
Extended Data Fig. 5 Sensitivity of the survival and recruitment impact thresholds to changes in body mass and/or body length.
The sensitivities of all survival and recruitment impact thresholds are evaluated by simultaneously adjusting the body masses and/or body lengths of all WH89-96 bears upwards or downwards by the same percentage (cf. Supplementary Fig. 5). Contour lines show the estimated fasting impact thresholds (units: days); a circle marks the threshold estimate for each bear group in the WH89-96 reference subpopulation.
Extended Data Fig. 6 Sensitivity of the demographic impact analyses shown in Figs. 3 and 4 to assumptions about polar bear ice use patterns in the DIE and CIE.
The baseline assumption of a ‘fast duration that is 24 days shorter than the summer period with sea ice extent <30%’ (as in SIE bears) is contrasted against a scenario where ‘fasting begins as soon as extent <30%’. a, Demographic impact hindcasts as in Fig. 3; solid black line: baseline scenario, dot-dashed black line: early fast initiation; b, Demographic impact forecasts: as in Fig. 4, but now for the early fast initiation scenario; c, Difference between the baseline and early fast scenarios for the projected crossing of the first (-20%) impact threshold (that is, the difference between panel b & Fig. 4). Magenta: cub recruitment; blue: adult male survival; red: adult female survival. Crosses indicate cases where no impacts are predicted within the modelled timeframe for either scenario; asterisks mark cases where impacts occur with early fasting but not in the baseline, with the bar showing the minimum difference between the two scenarios in these cases. Red background: DIE subpopulations; blue background: CIE subpopulations.
Extended Data Fig. 7 Physiologically feasible bounds of body mass for a viable polar bear population.
Contour lines show the estimated energy density of polar bears (stored energy relative to lean body mass53; units: MJ kg−1) as a function of straight-line body length and total body mass. All bears die at zero energy density (lower thick lines) and females have never been observed to give birth if their energy density is below 20 MJ kg−1 before entering a maternity den15 (middle thick lines, panels c and d). An approximate upper bound to total body mass (upper thick lines) is estimated as M=59.76L3, which is approximately four times a bear’s structural mass53). G(M0,L0)(WH89-96) is shown in each panel as black circles. Top row: all body masses decreased by 20% (orange; resulting in reproductive failure in at least half of all solitary adult females, panel c), and 40% (red; resulting in reproductive failure in all females); bottom row: all body masses increased by 20% (green), and by 40% (blue; resulting in unrealistically high body masses in several bears). Based on this, we conclude that the fast-initiating body masses of a viable polar bear population are likely within the −20% to +40% range of WH89-96 values, with values at the lower (upper) end only possible if bears are simultaneously also shorter (longer), which would somewhat reduce their energetic requirements (increase their maximum possible body mass).
Extended Data Fig. 8 Illustration of how timelines of risk are calculated and interpreted, using the example of recruitment in the East Greenland subpopulation (CIE) under the RCP8.5 scenario.
First row shows projected fast durations till the end of the century, estimated from all thirty ensemble members of CESM1 simulations of ice-free season lengths. Horizontal lines are as in Fig. 3, showing recruitment impact thresholds, assuming masses that are 20% lower (light shade), the same (medium-light shade), 20% higher (medium-dark shade), or 40% higher (dark shade) than in WH89-96. For each ensemble member, a threshold is defined to be crossed as the first occasion when three of the next five years exceed a fasting impact threshold, and we consider the mean across all thirty ensemble members to estimate years of first impact on polar bears. Recruitment declines are expected at a threshold crossing if the subpopulation’s fast-initiating body masses fall below the corresponding value. For example, recruitment declines would be expected in 2032 if the population’s G(M0,L0) is 20% or more below the G(M0,L0)(WH89-96) distribution in that year (vertical arrows and timeline of risk in second row). Third row: minimum convex polygons of the G(M0,L0)-distributions for the -20% (light shade), 0% (medium-light shade), +20% (medium-dark shade), and +40% (dark shade) body mass scenarios, showing for which G(M0,L0)-distributions recruitment declines would be expected (thick boundaries) or not (thin boundaries) at each threshold crossing. Risk increases with darker colors, both because higher body conditions are required to sustain increasingly longer fasts (contrast the four panels in third row), and because high body conditions become increasingly unlikely with longer fasts.
Extended Data Fig. 9 Sensitivity of the demographic impact analyses shown in Figs. 3 and 4 to uncertainty regarding the energetic costs of maintenance.
Projections using our best estimate of m = 0.077 MJ kg−1 d−1 are contrasted against projections using the upper boundary estimate identified in Supplementary Fig. 3, m = 0.090 MJ kg−1 d−1. a, Demographic impact hindcasts: as in Fig. 3, but now for m = 0.090 MJ kg−1 d−1; b, Demographic impact forecasts: as in Fig. 4, but now for m = 0.090 MJ kg−1 d−1; c, Differences between the projected crossings of the first (−20%) impact threshold when using m = 0.090 MJ kg−1 d−1 instead of m = 0.077 MJ kg−1 d−1 (that is, the difference between panel b & Fig. 4). Magenta: cub recruitment; blue: adult male survival; red: adult female survival. Crosses indicate cases where no impacts are predicted within the modelled timeframe using either value of m; asterisks mark cases where impacts occur with m = 0.090 MJ kg−1 d−1 but not with m = 0.077 MJ kg−1 d−1, with the bar showing the minimum difference between the two scenarios in these cases. Green background: SIE subpopulations; red background: DIE subpopulations; blue background: CIE subpopulations.
Stars show the estimated years of first impact on cub recruitment (magenta), adult male survival (blue), and adult female survival (red) from Fig. 4, that is, for our baseline assumption of a 30% critical sea ice extent threshold, and body masses, body lengths, and energy usage as in WH89-96. Error bars around the stars illustrate the uncertainty due to climate variability by indicating the 25-75 percentile range of the earliest impact years from the CESM1 model ensembles. Year of first impact is defined as in Fig. 4.
About this article
Cite this article
Molnár, P.K., Bitz, C.M., Holland, M.M. et al. Fasting season length sets temporal limits for global polar bear persistence. Nat. Clim. Chang. 10, 732–738 (2020). https://doi.org/10.1038/s41558-020-0818-9
Energetic and health effects of protein overconsumption constrain dietary adaptation in an apex predator
Scientific Reports (2021)
Fatty acid profiles of feeding and fasting bears: estimating calibration coefficients, the timeframe of diet estimates, and selective mobilization during hibernation
Journal of Comparative Physiology B (2021)
Yes, they can: polar bears Ursus maritimus successfully hunt Svalbard reindeer Rangifer tarandus platyrhynchus
Polar Biology (2021)