Download Demographic models and IPCC climate projections predict the

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.).
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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.
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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
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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
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Fig. S2 (continued).
Jenouvrier et al. www.pnas.org/cgi/content/short/0806638106
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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
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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
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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
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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
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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
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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