Fasting season length sets temporal limits for global polar bear persistence

Abstract

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.

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Fig. 1: Polar bear ecoregions and subpopulations.
Fig. 2: Method for estimating fasting impact thresholds beyond which cub recruitment and adult survival begin to decline rapidly.
Fig. 3: Estimated annual fasting period lengths of polar bears in the SIE, DIE and CIE from 1979–2016, in relation to estimated cub recruitment and adult male survival impact thresholds.
Fig. 4: Modelled timelines of risk, as quantified by the years when projected annual fasting period lengths exceed cub recruitment and adult survival impact thresholds in different subpopulation regions.

Data availability

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.

Code availability

All analyses were conducted in MATLAB version R2016a. The computer scripts are available from the corresponding authors upon reasonable request.

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Acknowledgements

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.

Author information

Affiliations

Authors

Contributions

S.C.A. conceived the study. P.K.M. and S.C.A. conceptualized all polar bear analyses. P.K.M. wrote the polar bear fasting model, and P.K.M. and S.R.P. performed the simulations. C.M.B. analysed the sea-ice observations. C.M.B. and M.M.H. analysed the climate model output. J.E.K. organized the climate model integrations. P.K.M., S.C.A. and C.M.B. co-wrote the paper.

Corresponding authors

Correspondence to Péter K. Molnár or Cecilia M. Bitz.

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The authors declare no competing interests.

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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.

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Extended data

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.

Extended Data Fig. 10 Uncertainty of the projected demographic impacts due to climate variability.

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.

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Supplementary Tables 1 and 2, Figs. 1–5 and references.

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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

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