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Demographic models and IPCC climate projections predict the decline of an emperor penguin population Stéphanie Jenouvriera,b,1, Hal Caswella,1, Christophe Barbraudb, Marika Hollandc, Julienne Strœved, and Henri Weimerskirchb aDepartment of Biology, MS-34, Woods Hole Oceanographic Institution, Woods Hole, MA 02543; bCentre d’Etudes Biologiques de Chizé, Centre National de la Recherche Scientifique, F-79360 Villiers en Bois, France; cOceanography Section, National Center for Atmospheric Research, Boulder, CO 80305; and dNational Snow and Ice Data Center, Boulder, CO 80309 Edited by Joel E. Cohen, The Rockefeller University, New York, NY, and approved December 2, 2008 (received for review July 10, 2008) Studies have reported important effects of recent climate change on Antarctic species, but there has been to our knowledge no attempt to explicitly link those results to forecasted population responses to climate change. Antarctic sea ice extent (SIE) is projected to shrink as concentrations of atmospheric greenhouse gases (GHGs) increase, and emperor penguins (Aptenodytes forsteri) are extremely sensitive to these changes because they use sea ice as a breeding, foraging and molting habitat. We project emperor penguin population responses to future sea ice changes, using a stochastic population model that combines a unique long-term demographic dataset (1962–2005) from a colony in Terre Adélie, Antarctica and projections of SIE from General Circulation Models (GCM) of Earth’s climate included in the most recent Intergovernmental Panel on Climate Change (IPCC) assessment report. We show that the increased frequency of warm events associated with projected decreases in SIE will reduce the population viability. The probability of quasi-extinction (a decline of 95% or more) is at least 36% by 2100. The median population size is projected to decline from !6,000 to !400 breeding pairs over this period. To avoid extinction, emperor penguins will have to adapt, migrate or change the timing of their growth stages. However, given the future projected increases in GHGs and its effect on Antarctic climate, evolution or migration seem unlikely for such long lived species at the remote southern end of the Earth. bird populations ! climate change ! quasi-extinction ! sea ice ! stochastic matrix population models A major challenge in ecology and conservation is to project the ecological responses of future climate changes (1, 2), using the reported effects of recent climate change on ecological processes. Recently, Thomas et al. (3) predicted that future climate change may cause the extinction of between 15% and 37% of species by 2050, based on species-specific climate envelopes. However, they project 0% species extinction risk for the ice biome, although there is clear evidence of dramatic changes in polar ecosystems related to anthropogenic warming, which may lead to extinctions [e.g., penguins in the Antarctic Peninsula (4), polar bears in the Arctic (5)]. To project a population’s response to future climate change one must (i) quantify the effects of climate on vital rates, (ii) project future climate conditions (as we do here with GCM climate models), and (iii) integrate these effects into population models. Most of the literature on population-climate studies has focused on one part of the life cycle [e.g., changing timing of life history in relation to climate conditions (6, 7)], because of the difficulty of measuring climate influences on life history traits over the entire life cycle (8). Few studies have adressed the population response to observed climate change (but see refs. 9–11); even fewer have predicted the population response to future changes (but see ref. 5). Based on knowledge of the effects of sea ice on the vital rates and population of emperor penguins (12, 13), we develop a stochastic population model to estimate population growth rates 1844 –1847 ! PNAS ! February 10, 2009 ! vol. 106 ! no. 6 and probabilities of quasi-extinction under projections of future ice conditions from climate models used in the latest IPCC Fourth Assessment Report (14). Emperor penguins reproduce during the harsh Antarctic winter in dense colonies distributed around Antarctica. Sea ice is a key breeding and feeding habitat for emperor penguins. Colonies are formed on sea ice many kilometers from the open sea, and breeding emperors make foraging trips between the colony and areas of open water during the entire incubation and chick rearing periods (15). In years with dense and extensive sea ice cover, foraging trips are longer, energetic costs for adults are higher, and offspring provisioning is lower (16), resulting in lower hatching success (12). Alternatively, absence of, or early break-up of, the winter sea ice holding up the colony may cause low breeding success (17). Sea ice extent during winter also affects the abundance of prey for emperor penguins. Winters with extensive sea ice enhance krill abundance (18), and emperor penguins mainly feed on fish species that depend on krill and other crustaceans (19, 20). In accordance with this, years with reduced sea ice extent coincide with reduced adult survival rates (12). The effects of sea ice on vital rates of the emperor penguin are thus complex (4, 21) and affect the dynamics of its populations. A dramatic example of such effects, which provides a key element in our analysis, occurred in Terre Adélie between 1972 and 1981. A sudden decrease in winter sea ice extent during several consecutive years, by ! 11% on average, coincided with an abrupt decline of the population, by !50% (13), and these abrupt changes were attributed to a regime shift (22) (see Fig. S1). Analyses of ice core data (23) suggest a change in meridional atmospheric circulation during the 1970s, bringing more moisture from warm subtropical sources to the Antarctic coast, possibly causing the highest winter air temperature and the lowest winter SIE observed in Terre Adélie during the regime shift (22). Results and Discussion We developed a stochastic version of a stage-classified population model (13) (see Materials and Methods) to determine the effects of a fluctuating sea ice environment on the stochastic population growth rate. Following previous studies (see review in ref. 24) we classified the environment into 2 distinct states: outside (1962–1971; 1982–2006) and inside (1972–1981) the Author contributions: S.J., H.C., C.B., and H.W. designed research; S.J., H.C., C.B., M.H., J.S., and H.W. performed research; S.J. and H.C. contributed new reagents/analytic tools; S.J., H.C., C.B., M.H., J.S., and H.W. analyzed data; and S.J. and H.C. wrote the paper. The authors declare no conflict of interest. This article is a PNAS Direct Submission. See Commentary on page 1691. 1To whom correspondence may be addressed. E-mail: [email protected] or [email protected]. This article contains supporting information online at www.pnas.org/cgi/content/full/ 0806638106/DCSupplemental. © 2009 by The National Academy of Sciences of the USA www.pnas.org"cgi"doi"10.1073"pnas.0806638106 regime shift period (see Materials and Methods). We will refer to these as ‘‘normal’’ and ‘‘warm’’ conditions, respectively. The environment follows a 2-state Markov chain that determines the frequency and duration of warm conditions (see Materials and Methods). Fig. 1 shows the resulting stochastic growth rate as a function of the frequency and duration of warm events. It decreases dramatically as the frequency increases. The duration (reflecting the autocorrelation of the environment) has little effect, as expected for such long-lived species. A frequency greater than w ! 0.03 produces a negative long term growth rate. Thus, if climate change increases the frequency of warm events, it will reduce the population viability of the emperor penguin. Fig. 2. Sea ice anomolies and warm events. (A) Proportional change in winter sea ice extent anomalies (SIEa) in Terre Adélie (sector 120°E-160°E), measured relative to the mean over the period 1982–2006, produced by 10 coupled IPCC climate models from 1900 to 2100 (for line colors, see legend). (B) Frequency of warm events calculated from the backward projections of SIEa for 10 IPCC climate models from 1900 to 2006 (color lines) and calculated from SIEa satellite observations from 1979 to 2006 (black line) as a function of the threshold defining a warm event. The frequency of warm events experienced by emperor penguin between 1952 and 2006 is 0.18, and is represented by the dotted line. (C) Frequency of warm events from 1900 to 2100 calculated from the SIEa produced by 10 IPCC climate models with the most conservative warm event threshold of $0.15. The dotted black line represent wt calculated from the sequence of warm and normal events define by the observed regime shift from 1952 to 2006. Jenouvrier et al. PNAS ! February 10, 2009 ! vol. 106 ! no. 6 ! 1845 SEE COMMENTARY ECOLOGY Fig. 1. Stochastic growth rate (log!s) as a function of the frequency w and mean duration d of warm events (in years). It was calculated from a stochastic model with 2 states: normal and warm environmental conditions. The contour w . The denotes log!s " 0. The frequency of warm events must satisfy d # (w $ 1) dark area indicates impossible combinations of w and d. Although climate models differ on the sign of Antarctic sea ice trends at the end of the 20th century (25) (see SI Text and Table S1), suggesting a strong influence of natural variability, nearly all models project that sea ice will shrink in future global warming scenarios (26). To project the consequences of this decline, we used a 3-step approach (as in ref. 5), which we will describe first in outline and later in more detail. First, we obtained forecasts of sea ice from a set of IPCC climate models from the CMIP3 archive (see Materials and Methods and SI Text). Second, we classified each year as warm or normal, by comparing the proportional decline in ice in that year to a specified SIE threshold value. The result is a binary sequence of warm and normal years, produced by each climate model, for the rest of the century. These sequences were translated into forecasts of the frequency of warm events, using nonparametric smoothing (5, 27). Finally, the frequencies were used to produce stochastic population projections. Therefore, the analysis depends on the set of climate models, on the sea ice forecasts, and on the SIE threshold defining a warm event. The sea ice forecasts were extracted from the output of a set of 16 IPCC models over the emperor penguin foraging sector (16) in Terre Adélie, i.e., sector 120°E–160°E (see SI Text and Fig. S2). The climate models were forced with the ‘‘business as usual’’ forcing scenario A1B, which describes a future world of very rapid economic growth that depends on fossil and nonfossil energy sources in balanced proportions. Under this scenario, CO2 levels double from the preindustrial level of 360 parts per million (ppm) to 720 ppm by 2100. We selected 10 of these models, in which the statistical properties of SIE match with those of the satellite observations (28, 25) (see Fig. S3), for analysis. We transformed the SIE from each model to proportional anomalies (SIEa), measured relative to the mean from 1982–2006, as shown in Fig. 2A for each of the 10 models. We defined a warm event to occur whenever SIEa drops below a specified SIE threshold. Fig. 2B shows the frequency of warm events, as a function of the SIE threshold, for model projections and for observations. To identify an appropriate SIE threshold, we note that the 56-year penguin observation period includes 10 warm events, for a frequency of w! o " 10/56 " 0.18 (dotted line of Fig. 2B). Thus, for each climate model, we calculated the SIE threshold that produced a frequency of w! o " 0.18 over the observation period; these thresholds range from $0.32 for model IPSL-CM4 to $0.02 for model UKMO-HadGEM1, with 50% of the threshold values falling in the range [$0.14,$0.07]. The same calculation applied to SIE from satellite data yields a threshold of $0.15. Thus, we used a range of SIE threshold values from $0.07 (the most generous) to $0.15 (the most conservative). Each model, and each SIE threshold, generates a binary time series of warm and normal years for the rest of this century. We used nonparametric binary regression (27) to transform the binary time series into probabilities wt of a warm event (see Fig. 2C for the conservative threshold of $0.15). All 10 climate models predict increased frequencies of warm events by the end of this century. Finally, using the probabilities wt, we generated 1000 stochastic population projections for each of the 10 climate models (thus 10000 population trajectories), and calculated the probability of quasi-extinction (defined as a decline by 95%) occurring by 2100. Fig. 3A shows the results. There is a high probability of quasi-extinction by the end of the century (0.36 and 0.84 for thresholds of $0.15 and $0.07, respectively). The median of the 10,000 population projections is nearly constant between 2000 and 2006, which is in good agreement with observed data (Fig. 3B). After 2006 it decreases slowly, and then more rapidly after 2018, to !400 breeding pairs by 2100 for the most conservative threshold. This is a decline of 93%. Each stochastic population trajectory shows a different pattern with periods of relative stability, gradual decline, and abrupt decrease. Some decline late [e.g., trajectories from climate model MIROC3.2(medres)] whereas others decline early (e.g., trajectories from climate model CNRM-CM3). By the end of this century, all of the population realizations that had not become quasi-extinct by 2100, were at that point declining strongly. These projections are based on a model that includes the entire life cycle and its response to climate (8), integrated with projection of future climate variability. Analyses that include only part of the life cycle (for example, see ref. 29) may reveal some aspects of population response, but cannot predict future trends and variability in a stochastic environment. Conclusions We conclude that if winter sea ice extent declines at the rates projected by IPCC models, and continues to influence emperor penguin vital rates as it did in the second half of the 20th century, the emperor penguin population in Terre Adélie will decline dramatically by 2100. Our conclusions are robust for the following reasons. First, our projections of sea ice are based on 10 diverse IPCC climate models. Second, our 2-state environment model assumes that future warm events have no greater impact on penguins than did the warm events during the 1970s. The IPCC models, however, predict that warm years will become warmer; a modified model taking that into account would only increase the probability of quasi-extinction (see SI Text and Fig. S4). Finally, our demographic model is well supported by the data (13). Including other demographic processes, such as density dependence (30), or sex ratio effects, would increase the probability of quasi-extinction. To avoid extinction, the emperor penguin must adapt by microevolutionary changes or phenotypic plasticity [e.g., by changing timing of their growth stages (2, 31)]. So far, the dates of arrival to the breeding colony and of egg laying have not changed in emperor penguins as they have in other Antarctic seabirds in Terre Adélie (32), suggesting a slow rate of adaptation. Emperor penguins may respond slowly to new selective 1846 ! www.pnas.org"cgi"doi"10.1073"pnas.0806638106 Fig. 3. Quasi-extinction and population projection. (A) Probability of quasiextinction (a decline of 95% or more) of the emperor penguin population in Terre Adélie by 2100, as a function of the warm event thresholds. The distribution of warm event thresholds calculated from Fig. 2B for the 10 IPCC models is represented by the gray bars, and warm event threshold calculated from Fig. 2B for the satellite observation by the vertical dotted line. The probability of quasi-extinction varied from 0.36 to 0.84 over the range of likely thresholds [$0.15; 0.07]. (B) Observations and projections of the emperor penguin population. The thick dark line is the observed number of breeding pairs. The thick orange line from 1962 to 2000 is the projected number of breeding pairs based on the observed sequence of warm events. The thick red line from 2005 to 2100 is the median of 10,000 stochastic projections based on the forecasts of SIEa produced by the 10 IPCC models. To give a sense of the variability in these projections, 30 projections from 2005 to 2100 (three for each climate model) are shown; line colors as in Fig. 2. pressure due to their long generation time (2, 33). The geographical range of Antarctic penguins may shrink following climate warming because the continent limits their movement south. In the Antarctic Peninsula, where warming has been the most pronounced during the second half of the 20th century (34), the northernmost emperor penguin population decreased from 150 breeding pairs in 1950s to only a few breeding pairs today (35). The southernmost emperor penguin populations (!25% of the world population), in the Ross sea, are currently stable (36) as ice conditions there have slightly increased in recent years (37). As the Antarctic warms, the Ross sea may be the last sanctuary for emperor penguin populations. However, this region too will eventually experience reduced sea ice extent as concentrations of atmospheric GHGs increase further (26). Materials and Methods Demographic and climatic time series. Monitoring of the emperor penguin breeding population and counts of fledged chicks started in 1952 and have Jenouvrier et al. Stochastic stage-classified model. The stage-classified model distinguishes breeding adults, nonbreeding adults, and 5 age classes of prebreeders. The demography is described by: n(t % 1) " Atn(t), where the matrix At projects the population vector n from t to t % 1. Estimation of the vital rates and construction of matrices is described in ref. 13. The stochastic population growth rate was calculated by Monte Carlo simulation as: To take into account the effect of warm events such as occurred during the regime shift, we constructed a stochastic model with 2 states: normal and warm environmental conditions. We constructed projection matrices, AN and AW, for normal and warm conditions, as averages of annual matrices outside and inside the regime shift, respectively (see Table S2). At each time step, a matrix is selected according to a Markov chain with the transition matrix: normal warm $ normal 1$p p warm q 1$q % [2] where w is the transition probability from normal to warm conditions, and w the transition probability from warm to normal conditions. The long-term frequency and the average duration of warm events are respectively: w " p/(p % q) and d " 1/q. using T " 50,000. If log !s # 0, the population will become extinct. The initial population had the stable age distribution and the number of breeding pairs observed in 1962. ACKNOWLEDGMENTS. We thank the wintering fieldworkers involved in the long-term monitoring programs in Terre Adélie since 1963 for their efforts; Dominique Besson for her constant support and help in the management of the database; Christine Hunter for contributing to the modeling approach used here; and 3 reviewers for their comments that improved the manuscript. We thank the National Snow and Ice Data Center, the modeling groups, the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and the WCRP’s Working Group on Coupled Modeling (WGCM) for their roles in making available the WCRP CMIP3 multimodel dataset. This work was supported Expéditions Polaires Francaises (for a long-term study from which we derived the penguin data), Institut Paul Emile Victor Program IPEV 109 and Terres Australes et Antarctiques Francaises; The Office of Science, U.S. Department of Energy; a Marie-Curie fellowship (to S.J.); the UNESCO/L’OREAL Women in Science program; and the National Science Foundation (H.C.). 1. Berteaux D, et al. (2006) Constraints to projecting the effects of climate change on mammals. Climate Research 32:151–158. 2. Visser ME (2008) Keeping up with a warming world; assessing the rate of adaptation to climate change. Proc R Soc London Ser B 275:649 – 661. 3. Thomas CD, et al. (2004) Extinction risk from climate change. Nature 427:145–148. 4. Croxall JP, Trathan PN, Murphy EJ (2002) Environmental change and antarctic seabirds populations. Science 297:1510 –1514. 5. Hunter C, et al. 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Climate Dynamics 21:153–166. 24. Caswell H (2001) Matrix population models. (Sinauer Associates, Sunderland, MA). 25. Lefebvre W, Goosse H (2008) Analysis of the projected regional sea-ice changes in the Southern Ocean during the twenty-first century. Climate Dynamics 30:59 –76. 26. Meehl GA, et al. (2007) in Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, eds Solomon S, et al. (Cambridge Univ Press, Cambridge, UK), pp 747– 846. 27. Copas JB (1983) Plotting p against x. Appl Stat 1:25–31. 28. Holland MM, Raphael MN (2006) Twentieth century simulation of the southern hemisphere climate in coupled models. Part II: Sea ice conditions and variability. Climate Dynamics 26:229 –245. 29. Le Bohec C, et al. (2008) King penguin population threatened by Southern Ocean warming. Proc Natl Acad Sci USA 105:2493–2497. 30. Saether BE, Bakke O (2000) Avian life history variation and contribution of demographic trait to the population growth rate. Ecology 81:642– 653. 31. Charmantier A, et al. (2008) Adaptive Phenotypic Plasticity in Response to Climate Change in a Wild Bird Population. Science 320:800 – 803. 32. Barbraud C, Weimerskirch H (2006) Antarctic birds breed later in response to climate change. Proc Natl Acad Sci USA 103:6248 – 6251. 33. Berteaux D, Reale D, McAdam AG, Boutin S (2004) Keeping Pace with Fast Climate Change: Can Arctic Life Count on Evolution? Integrative Comparative Biol 44:140 –151. 34. Vaughan DG, Marshall GJ, Connolley WM, King JC, Mulvaney R (2001) Devil in the detail. Science 293:1777–1779. 35. Scientific Committee on Antarctic Research (2003) Management Plan for Antarctic Specially Protected Area No. 107 Emperor Island, Dion Islands, Marguerite Bay, Antarctic Peninsula (SCAR, Cambridge). 36. Barber-Meyer SM, Kooyman GL, Ponganis PJ (2008) Trends in Western Ross Sea emperor penguin chick abundances and their relastionships to climate. Antarctic Sci 20:3–11. 37. Zwally HJ, Comiso JC, Parkinson CL, Cavalieri DJ, Gloersen P (2002) Variability of Antarctic sea ice 1979 –1998. J Geophys Res 107:2–21. 38. Rodionov SN (2004) A sequential algorithm for testing climate regime shifts. Geophys Res Lett 31:L09204 –L09208. log ! s " lim 1 log #AT$1 . . . A 0 n'0(# T [1] ECOLOGY T3& SEE COMMENTARY been carried out every year since 1962 (12) (see Fig. S1). These data permit calculation of breeding success each year for the entire period. Markrecapture data collected between 1971 and 1995 permit estimation of other vital rates including adult survival and the proportion of breeders (see ref (13). for more details on the methodology). The regime shift is defined as a rapid change from one relatively stable state to another. We applied sequential t tests and F tests (38) to the times series of the number of breeding pairs and chicks (see Fig. S1) to detect the regime shift in mean and variance of the signal. Fig. S1 shows the regime shift between 1972–1981. Data on future sea ice extent were obtained from 16 climate models participating in the IPCC assessment report (26) and averaged over the austral winter (July to September). The data are part of the World Climate Research Program’s (WCRP’s) Coupled Model Intercomparison Project phase 3 (CMIP3) multimodel dataset, which is available at www.pcmdi.llnl.gov/ipcc/about_ ipcc.php. Table S1 summarizes information about these 16 IPCC models. Jenouvrier et al. PNAS ! February 10, 2009 ! vol. 106 ! no. 6 ! 1847 Supporting Information Jenouvrier et al. 10.1073/pnas.0806638106 SI Text Selection of IPCC Climate Models. We used sea ice extent averaged over a sector between the longitude 120°E and 160°E. This sector is centered on Dumont d’Urville (66⬘40°S, 140⬘01°E) where the emperor penguin colony is located. This sector includes the foraging area of emperor penguin in Terre Adélie (16), and a surrounding area within which large scale sea ice effects on the food web are likely. We explored other choices of spatial resolution; Fig. S2 shows that the statistical properties of sea ice extent from small (135°E–145°E) to large spatial scales (120°E and 160°E) are similar. From the original 16 GCMs, we selected 10 models whose output agrees with the observed Antarctic sea ice data. We removed models CGCM3.1(T63), GISS-AOM, and CCSM3 because they produced a mean SIE at least 50% higher than the observed mean during winter (July to September) between 1978 to 2007. We removed models GISS-ER and ECHO-G because they produced a mean at least 50% lower than the observed mean (Fig. S3). Finally, we removed model INMCM3.0 because it does not include dynamic processes for modeling sea ice (see more information on the models at www. pcmdi.llnl.gov/ipcc/model_documentation). As shown by Table S1, the observed sea-ice trend in our study area is at present slightly positive, and 4/10 of our selected models show such a trend for the 1979–2006 time period. The 10 selected models predict a range of trends over the 21st century in winter SIE in Terre Adélie ranging from very little change (0.1% per decade for model IPSL-CM4) to a strong negative trend (⫺3.7% per decade for model MRI-CGCM2.3.2) (see Table S1). By using 10 GCMs we account for some of the uncertainties related to any single climate projection. Consequences of a Binary Environment Model Stochastic growth depends on the relation between the environment and the vital rates. Our 2-state environment model simplifies that relationship. It does so in a biologically reasonable way, approximating the expected sigmoid response of the vital rates with a Heaviside function. A reviewer questioned our results might be an artifact of the simplification from a continuous to a discrete environment. To investigate this, we con- Jenouvrier et al. www.pnas.org/cgi/content/short/0806638106 structed what may be the simplest continuous-environment model compatible with our observations. It includes a linear dependence of the entries of the population projection matrix A on the proportional SIE anomaly x, obtained by interpolating between the observed matrices for normal and warm conditions. This linear function can produce mathematically impossible results, and so must be constrained at the extremes. We imposed the following constraints on A[x(t)]. First, all matrix entries must be non-negative. If any entries became negative, they were set to 0. Second, emperor penguins cannot produce more than a single offspring. Thus, if fertility ever exceeded 0.5, it was set to 0.5. Third, no new individuals should be produced by the transitions and survival of already existing individuals. This required that 冘 7 aij关x共t兲兴 ⭐ 1 for i ⫽ 1,䡠 䡠 䡠,7. j⫽2 If this sum ever exceeded 1, we rescaled the column of the matrix to sum to 1. Each of the 10 IPCC climate model produces a single time series x(t) for t ⫽ 2000, 2001, . . . , 2100. The demography is described by: n(t ⫹ 1) ⫽ A[x(t)]n(t), where the matrix A[x(t)] projects the population vector n from t to t ⫹ 1, according to the proportional SIE anomalies x at time t. For each climate model, we created an initial population in the year 2000 with the stable stage distribution and average number of breeding pairs over the period 1982–2005. This approach necessarily treats the IPCC model output as a deterministic, because stochastic climate projections for each IPCC model are not available. Fig. S4 shows the results. All 10 IPCC models predict a decline in the penguin population by the end of the century. The median of the 10 projections declined from 3,000 to 3 breeding pairs. These results are qualitatively the same as our binaryenvironment model, but quantitatively more extreme than those produced by the binary environment model. Because the continuous model requires extrapolation of the SIE dependence of the vital rates beyond the observed range of variation, its results are unreliable, but it shows that the model based on the 2-state environment produces conservative results. 1 of 8 Fig. S1. (Upper) The number of breeding pairs (black line) and chicks (gray line) of the emperor penguin in Terre Adélie, Antarctica, between 1962 and 2005. The thick light gray line gives the number of breeding pairs calculated by a deterministic matrix model with 2-states, with warm and regular environmental conditions during and outside the regime shift periods respectively. (Lower) The rate of increase of the population calculated as the log of the deterministic growth rate. The rectangle identifies the period of the regime shift. Jenouvrier et al. www.pnas.org/cgi/content/short/0806638106 2 of 8 Fig. S2. Sea ice extent (m2) during winter over different spatial sectors centered on Dumont d’Urville (66⬘40°S, 140⬘01°E), Antarctica, between 1979 and 2006 for the satellite observation, and between 1900 and 2099 for each of the IPCC climate models. Jenouvrier et al. www.pnas.org/cgi/content/short/0806638106 3 of 8 Fig. S2 (continued). Jenouvrier et al. www.pnas.org/cgi/content/short/0806638106 4 of 8 Fig. S3. Mean sea ice extent (m2) during winter over the sector between 120°E and 160°E in Terre Adélie, Antarctica, during 1979 –2006, for the observed satellite data (dark green) and from 16 IPCC climate model (see legends). The error bars indicate the interannual variability in sea ice extent. The dotted lines represent values 50% higher or lower than the observed satellite data mean. The models that were outside these two dotted lines were eliminated from our analysis. Jenouvrier et al. www.pnas.org/cgi/content/short/0806638106 5 of 8 Fig. S4. Observations and projections of the emperor penguin population. Population projections come from a deterministic population model in which the vital rates are a continuous linear function of SIE, obtained by interpolating between the projection matrices in warm and normal conditions. The projections are based on the SIEa forecasts produced by the 10 IPCC models; thin line colors as in Fig. 2. The thick dark line is the observed number of breeding pairs. The thick orange line from 1962 to 2000 is the projected number of breeding pairs based on the observed sequence of warm events. The thick red line from 2005 to 2100 is the median of 10 projections based on the IPCC projections of SIE. Jenouvrier et al. www.pnas.org/cgi/content/short/0806638106 6 of 8 Table S1. Information about the climate models from the Intergovernmental Panel on Climate Change (IPCC) assessment report that we used in our analysis Sources Modeling groups Country Observation National Snow and Ice Data Center USA Climate models used in our analysis CGCM3.1(T47) CNRM-CM3 Canadian Centre for Climate Modelling and Analysis Canada Météo-France-Centre National de Recherches France Météorologiques CSIRO Atmospheric Research Australia Institut Pierre Simon Laplace France Japan Center for Climate System Research (The University of Tokyo), National Institute for Environmental Studies, and Frontier Research Center for Global Change (JAMSTEC) Japan Center for Climate System Research (The University of Tokyo), National Institute for Environmental Studies, and Frontier Research Center for Global Change (JAMSTEC) Max Planck Institute for Meteorology Germany Meteorological Research Institute Japan Hadley Centre for Climate Prediction and UK Research/Met Office Hadley Centre for Climate Prediction and UK Research/Met Office Climate models excluded from our analysis CSIRO-Mk3.0 IPSL-CM4 MIROC3.2(hires) MIROC3.2(medres) ECHAM5/MPI-OM MRI-CGCM2.3.2 UKMO-HadCM3 UKMO-HadGEM1 CGCM3.1(T63) GISS-AOM GISS-ER INM-CM3.0 CCSM3 ECHO-G Canadian Centre for Climate Modelling and Analysis NASA Goddard Institute for Space Studies NASA Goddard Institute for Space Studies Institute for Numerical Mathematics National Center for Atmospheric Research Meteorological Institute of the University of Bonn and Meteorological Research Institute of KMA Canada USA USA Russia USA Germany and Korea Present SIE trend Future SIE trend 2.93 ⫺0.96 ⫺8.34 ⫺2.45 ⫺2.79 4.64 5.75 ⫺4.89 ⫺0.18 0.14 ⫺1.37 4.14 ⫺1.71 ⫺1.66 ⫺5.73 3.72 ⫺2.74 ⫺3.71 ⫺1.63 ⫺3.97 ⫺1.91 ⫺3.53 ⫺4.13 17.08 ⫺23.31 ⫺3.43 39.70 ⫺3.98 1.31 ⫺11.74 ⫺13.37 ⫺1.52 5.45 The present winter SIE trend during the satellite period of 1979 –2006 and the future trend for the period of 2006 –2098 are calculated for a sector between 120E and 160 E in Terre Adélie, Antarctica. Trends are measured in % per decade. More information on the models is available from http://www-pcmdi.llnl.gov/ ipcc/model_documentation. The 6 climate models excluded from our analysis are at the bottom of the table. Jenouvrier et al. www.pnas.org/cgi/content/short/0806638106 7 of 8 Table S2. Population projection matrices, AN and AW, for normal and warm conditions, as averages of annual matrices outside and inside the regime shift, respectively Normal conditions PB-1 PB-2 PB-3 PB-4 PB-5 NB B Warm conditions PB-1 PB-2 PB-3 PB-4 PB-5 NB B PB-1 PB-2 PB-3 PB-4 PB-5 NB B 0 0.9064 0 0 0 0 0 PB-1 0 0.8689 0 0 0 0 0 0 0 0.852 0 0 0 0.0544 PB-2 0 0 0.8168 0 0 0 0.0521 0 0 0 0.7432 0 0 0.1632 PB-3 0 0 0 0.7125 0 0 0.1564 0 0 0 0 0.6889 0 0.2175 PB-4 0 0 0 0 0.6604 0 0.2085 0 0 0 0 0.7971 0 0.1093 PB-5 0 0 0 0 0.7724 0 0.0965 0 0 0 0 0 0.179 0.7274 NB 0 0 0 0 0 0.1954 0.6735 0.2291 0 0 0 0 0.179 0.7274 B 0.1487 0 0 0 0 0.1954 0.6735 The 7 stages of the population are specified, with the 5 age classes of pre-breeders (PB-i for age i), non-breeding (NB) and breeding adults (B). Jenouvrier et al. www.pnas.org/cgi/content/short/0806638106 8 of 8 COMMENTARY Icy insights from emperor penguins Colleen Cassady St. Clair1 and Mark S. Boyce Department of Biological Sciences, University of Alberta, Edmonton, Canada T6G 2E9 A lmost 100 years ago, 3 stalwart members of Scott’s Polar Expedition marched into the darkness of the Antarctic winter on what would become known as The Worst Journey in the World (1). Their goal was to recover the eggs of emperor penguins whose embryos might reveal important insights about the nowdefunct theory that ontogeny recapitulates phylogeny. The men walked 108 km in temperatures as cold as ⫺60 °C to reach the eggs, just as emperor penguins have done for millennia. Although 3 preserved eggs survived the journey, British embryologists dismissed their evolutionary insights. In this issue of PNAS, Jenouvrier et al. (2) reveal another kind of insight from emperor penguins: the population consequences of changes in sea ice that might forecast the fates of dozens of other species in the coming century. Emperor penguins journey from the Antarctic oceans where they forage to breeding colonies on the landfast ice. There, they court, mate, and produce a single egg, which male penguins incubate on their feet for most of the 2-month gestation. Females journey back to the ocean to forage, returning to regurgitate seafood just as the hungry chicks hatch. In the ensuing weeks, both parents make multiple trips to the ocean to feed their growing chicks which, ideally, fledge as the melting ice edge approaches the breeding colony and food abundance peaks in the austral summer (3). The success of the emperors appears to be critically dependent on the extent of the sea ice. When there is too much ice, hatching success declines (4), perhaps because females take too long to journey to and from the ocean and males abandon their eggs to save themselves from starvation. When there is little ice, adult mortality increases (4), perhaps because declining sea ice reduces the production of krill (5), which is the keystone of the Antarctic food web for emperors and many other species (6). The reason why krill depend on sea ice is not fully understood (5). One possibility is that melting glaciers reduce the salinity of seawater to favor smaller cryptophytes over larger diatoms in the phytoplankton community, causing a shift from krill to less nutritious salps in the zooplankton (7). The relationship emperors exhibit between population success and sea ice (4) is also exhibited by other seabirds (8) www.pnas.org兾cgi兾doi兾10.1073兾pnas.0812940106 Fig. 1. Life cycle for emperor penguins at Terre Adélie, Antarctica. Survival probability is Si for stage i. Minimum age at first breeding was 3 years; once recruited, birds reproduce annually with probability Sa Pb, where Pb is the proportion of breeders. BS is overall breeding success. Translation of this life cycle into a population projection matrix was described by Jenouvrier et al. (13). In the current article, Jenouvier et al. (2) link past demographic responses to warm years to IPCC models of projected climate change to predict a persistent decline in the emperor penguin population at Terre Adélie and a 36% likelihood of extinction by 2100. Photo of emperor penguins reproduced with permission from ref. 13 (Copyright 2005, Ecological Society of America). Population model from Jenouvrier et al. (13). and several marine mammals (9, 10). Although the sign and magnitude of these relationships varies among species (8), changes in sea ice extent are typically linked to prevailing sea surface temperatures, which are determined by patterns of global circulation. In the Antarctic, the Southern Oscillation produced an unusually warm year each 5 or 6 years through much of the last century (11), but climate change appears to be increasing the frequency, duration, and magnitude of warm phases in this and other oscillations (12). The ubiquity of climate-mediated changes to marine food webs and the extraordinary time series of breeding data available for emperor penguins (4) give this new model by Jenouvrier the potential for far-reaching insights. The authors combined what is known about past responses by emperor penguins to variation in sea ice extent with projections about global climate to predict the future of the colony at Terre Adélie (2) (Fig. 1). They obtained climate forecasts from 10 Intergovernmental Panel on Climate Change (IPCC) models, categorized each into a series of warm and normal years based on various sea ice thresholds, and then modeled stochastic population projections based on the frequency of warm year events. The results indicate precipitous declines in the population by 2100 with a 36% likelihood of extinction. The analyses (2) hinge on extrapolating the consequences for emperor penguins of a regime shift that occurred between 1972 and 1981, a period with unusually low sea ice extent that corresponded to a 50% decline in the emperor penguin population at Terre Adélie (2, 4). Jenouvrier et al. use this period to categorize the time series of emperor penguin reproduction into normal and warm years and then identify the thresholds at which deviations from normal could generate the same frequency of warm years (10 of 56 years or 18%) in the IPCC models. The standardized deviations needed to produce this frequency of warm years ranged from tiny (2%) to substantial (32%), but all of the models predicted increasing frequency of warm years and concomitant decreases in sea ice extent. Comparable to their previous work (13), Jenouvrier et al. (2) used two projection matrices for their population model, each with 5 prereproductive age classes as well as breeding and nonbreeding adults. The model employs field data from a near-annual record that began in 1952, balancing details in Author contributions: C.C.S. and M.S.B. wrote the paper. The authors declare no conflict of interest. See companion article on page 1844. 1Author to whom correspondence should be addressed. E-mail: [email protected]. © 2009 by The National Academy of Sciences of the USA PNAS 兩 February 10, 2009 兩 vol. 106 兩 no. 6 兩 1691–1692 vital rates against the sampling error inherent in such data. Without this remarkable long-term dataset the authors would not have been able to estimate all of the vital rates necessary for their demographic projections—previous attempts at population viability analysis have been criticized for failing to consider sampling error (14, 15). Although the model assumes that cohorts diminish in size only by mortality, and that any individual in the population could theoretically live ‘‘forever,’’ senescence is functionally contained in the adult survival term of the model with no real consequences for population dynamics. These population projections for penguins are realistic. The 10 IPCC models, which span a 200-year period between 1900 and 2100, were selected to match the actual surface ice information measured by satellites (2) in the actual foraging area used by the penguins (16). There is little doubt that the world will warm for emperor penguins and sea ice will diminish. Moreover, the models are based on the demographic responses of emperors to the warming that occurred in the 1970s; future warming events are 1. Cherry-Garrard A (1922/1989) The Worst Journey in the World (Carroll and Graf Publishers, New York). 2. Jenouvrier S, et al. (2009) Demographic models and IPCC climate projections predict the decline of an emperor penguin population. Proc Natl Acad Sci USA 106:1844 –1847. 3. Williams TD (1995) The Penguins (Oxford Univ Press, New York). 4. Barbraud C, Weimerskirch H (2001) Emperor penguins and climate change. Nature 411:183–186. 5. Loeb VJ, et al. (1997) Effects of sea-ice extend and krill or salp dominance on the Antarctic food web. Nature 387:897–900. 6. Murphy EJ, et al. (2007) Climatically driven fluctuations in Southern Ocean ecosystems. Proc R Soc London Ser B 274:3057–3067. relies on the prediction of climate for 100 years. Ecologists and meteorologists are notoriously poor at predicting the future. An accumulation of estimation errors makes projections increasingly tenuous in the distant future, highlighting the importance of ongoing monitoring of population responses to current climate conditions (17). A second limitation is the binary depiction used to categorize the variability of temperature and sea ice extent into warm and normal years. Although this is a helpful simplification for population modeling, both sea surface temperatures and sea ice extent vary continuously and a categorical response to them may not be realistic. In sum, the authors use an extraordinary time series to provide what may be the best example to date of a prediction of population trends based on climate projections. Their model paints a chilling picture for the future of emperor penguins. The next step will be to elucidate the mechanisms that link sea ice extent and population declines. Such understanding is needed if ecologists are to predict the diverse and paradoxical effects of climate change (17). In the southern oceans, those mechanisms likely hinge on krill productivity (5, 7) cascading through entire food chains. Emperors, near the top of the food chain, are likely foreshadowing the fates of dozens of other Antarctic species (8). Although these primitive birds did not reveal much about phylogeny 100 years ago, breeding records over half of the ensuing century have made them excellent harbingers of the ecological effects of climate change to come. 7. Moline MA, et al. (2004) Alteration of the food web along the Antarctic Peninsula in response to a regional warming trend. Global Change Biol 10:1973–1980. 8. Croxall JP, et al. (2002) Environmental change and antarctic seabird populations. Science 297:1510 – 1514. 9. Moore SE, Huntington HP (2008) Arctic marine mammals and climate change: Impacts and resilience. Ecol Appl 18:S157–S165. 10. Hunter C, et al. (2007) Polar Bears in the Southern Beaufort Sea II: Demography and Population Growth in Relation to Sea Ice Conditions (U.S. Geological Survey, Reston, VA), U.S. Geological Survey Administrative Report. 11. Zhang Y, et al. (1997) ENSO-like interdecadal variability: 1900 –93. J Climate 10:1004 –1020. 12. Thompson DWJ, Solomon S (2002) Interpretation of recent Southern Hemisphere climate change. Science 296:895– 899. 13. Jenouvrier S, et al. (2005) Long-term contrasted responses to climate of two Antarctic seabird species. Ecology 86:2889 –2903. 14. Ludwig D (1999) Is it meaningful to estimate a probability of extinction? Ecology 80:298 –310. 15. Barbraud C, et al. (2008) Are king penguin populations threatened by Southern Ocean warming? Proc Natl Acad Sci USA 105:E38 (lett). 16. Zimmer I, et al. (2008) Foraging movements of emperor penguins at Pointe Geologie, Antarctica Polar Biol 31:229 –243. 17. Berteaux D, et al. (2006) Constraints to projecting the effects of climate change on mammals. Climate Res 32:151–158. predicted to be more extreme with, presumably, even more pronounced effects on Antarctic food webs. Notwithstanding the considerable strengths of this work, there are a couple of limitations. First, the projection There is little doubt that the world will warm for emperor penguins. 1692 兩 www.pnas.org兾cgi兾doi兾10.1073兾pnas.0812940106 St. Clair and Boyce