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Ecology and Evolution. 2021;11:10627–10643.
|
10627www.ecolevol.org
Received: 29 September 2020
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Revised: 28 May 2021
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Accepted: 17 June 2021
DOI: 10.1002/ece3.7873
ORIGINAL RESEARCH
Demographic responses to climate change in a threatened
Arctic species
Kylee D. Dunham1 | Anna M. Tucker1 | David N. Koons2 | Asheber Abebe3 |
F. Stephen Dobson4 | James B. Grand5
This is an open access article under the terms of the Creat ive Commo ns Attri bution License, which permits use, distribution and reproduc tion in any medium,
provided the original work is properly cited.
© 2021 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.
1Alabama Cooperative Fish and Wildlife
Research Unit, School of Forestry and
Wildlife Sciences, Auburn University,
Auburn, AL, USA
2Department of Fish, Wildlife, and
Conser vation Biology & Graduate Degree
Program in Ecolog y, Colorado State
University, Fort Collins, CO, USA
3Department of Mathematics and Statistics,
Auburn University, Auburn, AL, USA
4Department of Biological Sciences, Auburn
University, Auburn, AL, USA
5U.S. Geologic al Sur vey, Alabama
Cooperative Fish and Wildlife Research Unit,
Auburn, AL, USA
Correspondence
Kylee D. Dunham, Alabama Cooperative
Fish and Wildlife Research Unit, School
of Forestry and Wildlife Sciences, Auburn
University, Auburn, AL, USA.
Email: kylee583@gmail.com
Present address
Kylee D. Dunham, Department of Biological
Sciences, University of Alberta, Edmonton,
AB, Canada
Anna M. Tucker, U.S. Geological Survey,
Patuxent Wildlife Research Center, Laurel,
MD, USA
Funding information
Bureau of L and Management; Auburn
University School of Forestr y and Wildlife
Sciences; Ducks Unlimited
Abstract
The Arctic is undergoing rapid and accelerating change in response to global warm-
ing, altering biodiversity patterns, and ecosystem function across the region. For
Arctic endemic species, our understanding of the consequences of such change re-
mains limited. Spectacled eiders (Somateria fischeri), a large Arctic sea duck, use re-
mote regions in the Bering Sea, Arctic Russia, and Alaska throughout the annual cycle
making it difficult to conduct comprehensive surveys or demographic studies. Listed
as Threatened under the U.S. Endangered Species Act, understanding the species
response to climate change is critical for effective conservation policy and planning.
Here, we developed an integrated population model to describe spectacled eider
population dynamics using capture– mark– recapture, breeding population survey,
nest survey, and environmental data collected between 1992 and 2014. Our intent
was to estimate abundance, population growth, and demographic rates, and quan-
tify how changes in the environment influenced population dynamics. Abundance of
spectacled eiders breeding in western Alaska has increased since listing in 1993 and
responded more strongly to annual variation in first- year survival than adult survival
or productivity. We found both adult survival and nest success were highest in years
following intermediate sea ice conditions during the wintering period, and both de-
mographic rates declined when sea ice conditions were above or below average. In
recent years, sea ice extent has reached new record lows and has remained below
average throughout the winter for multiple years in a row. Sea ice persistence is ex-
pected to further decline in the Bering Sea. Our results indicate spectacled eiders
may be vulnerable to climate change and the increasingly variable sea ice conditions
throughout their wintering range with potentially deleterious effects on population
dynamics. Importantly, we identified that different demographic rates responded
similarly to changes in sea ice conditions, emphasizing the need for integrated analy-
ses to understand population dynamics.
10628
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DUNHAM et Al.
1 | INTRODUCTION
The Arctic is undergoing rapid and accelerating change in response
to global warming. Changes in the abiotic environment have consid-
erably altered biodiversity patterns and ecosystem function across
the region (Eamer et al., 2013). Drastic and unprecedented changes
in biotic and abiotic processes have had strong and complex impacts
on Arctic species including changes in distribution, abundance, ex-
tinction risk, and trophic interactions (Macias- Fauria & Post, 2018).
Yet, population responses to climate change remain a critical knowl-
edge gap for many Arctic species (Laidre et al., 2015; Macias- Fauria
& Post, 2018).
While environmental conditions in the Arctic are changing and
reshaping ecosystems, the degree to which these changes affect the
population dynamics of individual Arctic species is dependent upon
their life- history strategies. Adaptation to rapid climate change is
likely to be difficult for long- lived an d highl y spe cialized speci es mak-
ing them particularly vulnerable (Berteaux et al., 2004). Frequently,
life- histories of long- lived organisms have evolved to buffer some
demographic rates from environmental variation (Koons et al., 2014;
Pfister, 1998; Saether & Bakke, 2000). For instance, in long- lived
species, adult survival is often resilient to stochastic annual varia-
tions in environmental conditions. However, interannual variation
and directional changes in environmental conditions that exceed
historical bounds may influence these demographic rates with im-
portant consequences for population dynamics and extinction risk
(Schmutz, 2009).
A vast majority of Arctic avifauna are migratory and use the re-
gion seasonally to breed, taking advantage of the short but strong
burst of resources available in the summer months. Demographic
rates respond to spatially and temporally variable environmental
conditions, thus requiring detailed information on a species’ spatial
distribution throughout the annual cycle to understand those rela-
tionships. Processes acting upon individuals may cause immediate
or lagged responses in demographic rates across seasons, creat-
ing carry- over or cross- seasonal effects (Norris & Taylor, 2006;
Sedinger & Alisauskas, 2014). Responses may also vary by age or
sex or may differ across demographic rates (Rushing et al., 2017).
Understanding changes in environmental conditions throughout the
annual cycle and their effec ts on dem ographic rates is therefore crit-
ical for predicting species’ responses to climate change. However,
a large portion of what we understand to be climate effects on
Arctic avifauna is related to conditions just prior to and during the
breeding season (e.g., snow melt dates, phenological mismatches
with weather conditions or food resources), or with conditions ex-
perienced in temperate or tropical latitudes during the nonbreed-
ing season (Deinet et al., 2015; Fox & Leafloor, 2018; Ganter &
Gaston, 2013; Smith et al., 2020). Currently, our understanding of
these effects on avian species endemic to the Arctic is extremely
limited (Ganter & Gaston, 2013). This limitation is often due to a lack
of demographic and spatially relevant environmental data (but see
e.g., Sedinger et al., 2011).
Waterfowl are among the most abundant avifauna on Arctic
coastal and tundra habitats during the breeding period (CAFF, 2013).
Though most species leave the Arctic following the breeding sea-
son, most sea ducks (Tribe: Mergini), and more specifically eiders
(Somateria and Polysticta genera), remain in the Arctic through-
out the annual cycle (Ganter & Gaston, 2013; Savard et al., 2015).
Spectacled eiders (Somateria fischeri) are an endemic Arctic species
and can be found in the Pacific Arctic (Bering and Chukchi Seas),
Alaska, and Russia, a region experiencing drastic ecosystem change
in response to climate change (Huntington et al., 2020). A large
sea duck, spectacled eiders spend most of the annual cycle in high
latitude coastal and open- ocean marine habitats and are often as-
sociated with sea ice (Flint et al., 2016; Savard et al., 2015; Sexson
et al., 2016). Their nonbreeding range remained largely unknown
until 1995 when surveys identified large flocks in openings in the
sea ice in the mid- Bering sea (Petersen et al., 1999). The global pop-
ulation of spec tacled eiders winters near St . Lawrence Island and mi-
grates to breeding grounds in the coastal tundra wetlands of Alaska
and Russia (Sexson et al., 2014). Following marked declines in the
western Alaska breeding population the species was listed as threat-
ened under the Endangered Species Act (ESA, as amended 1973) in
1993 (U.S. Fish & Wildlife Service, 1993).
Spectacled eiders use remote areas throughout the annual cycle
making it particularly challenging to conduct comprehensive surveys
or studies of demography. Several studies have focused on indepen-
dent analyses of data sets to model population dynamics and de-
mography of spectacled eiders (e.g., Christie et al., 2018; Dunham
et al., 2021; Flint et al., 2016); however, estimating abundance and
demographic rates has remained challenging. Evidence suggests that
all four eider species demonstrate demographic responses, changes
in abundance, and distribution shifts related to climate change and
environmental conditions encountered throughout the annual cycle
(e.g., Barry, 1968; Christie et al., 2018; Fournier & Hines, 1994; Frost
et al., 2013; Sexson et al., 2016; Žydelis et al., 2006). Variation in
adult survival, breeding propensity, spring arrival dates, clutch size,
annual nest success, and duckling survival of closely related com-
mon eiders (S. mollissima) has been linked to environmental condi-
tions experienced during both breeding and nonbreeding seasons
(Coulson, 1984; Savard et al., 2015; Waltho & Coulson, 2015).
Recent studies suggest shifts in the Bering sea ecosystem have af-
fected adult survival rates and the molting distribution of spectacled
eiders (Christie et al., 2018; Flint et al., 2016; Sexson et al., 2016);
however, relatively little is known about the effects of changes in
environmental conditions on recruitment or population dynamics.
KEY WORDS
Alaska, Arctic Russia, Bering sea, full annual cycle, integrated population models, Somateria
fischeri, spectacled eiders
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10629
DUNHAM e t Al.
Sea ice is a critical component of spectacled eider wintering
habitat (Eamer et al., 2013; Macias- Fauria & Post, 2018). The Bering
sea is dominated by seasonal pack ice, densely packed ice that may
drift in response to currents, wind, and storms. Pack ice in the mid-
Bering sea, where spectacled eiders winter, is dynamic and highly
variable within and across years (Wang & Overland, 2015). Polynya
and open leads in sea ice benefit spectacled eiders offering access to
benthic prey and providing roosting habitat (Bump & Lovvorn, 2004;
Christie et al., 2018). Concentrated sea ice can dampen the effects
of waves from frequent major storms which results in good roosting
habitat (Cooper et al., 2013). While extensive ice cover may restrict
spectacled eider access to benthic prey and optimal foraging areas
(Lovvorn et al., 2003), by contrast, declining sea ice cover has been
linked to declines in abundance and shifting distributions of benthic
communities, including the preferred prey species of spectacled ei-
ders (Grebmeier, 2012; Grebmeier et al., 2018; Lovvorn et al., 2009,
2015). Sea ice conditions in the Bering Sea are expected to become
increasingly variable and sparse (Huntington et al., 2020; Wang &
Overland, 2015) with deleterious effects on the habitat for spec-
tacled eiders and other species that are dependent on this marine
ecosystem (Smith et al., 2019).
To better understand the effects of climatic change on specta-
cled eider demography and population dynamics, we developed an
integrated population model of the full annual cycle that combined
multiple sources of data collected from the western Alaskan breeding
population. Here, we use the integrated population modeling frame-
work to (1) estimate demographic parameters (age- specific survival
rates, breeding probability, and productivity), (2) evaluate which de-
mographic rates contribute most to annual population growth, and (3)
investigate the effect of environmental conditions on demographic
rates throughout the annual cycle. Recently, Christie et al. (2018)
documented a nonlinear relationship between sea ice conditions and
adult female survival. We were interested in repeating and extending
this analysis within an integrated framework. Thus, we evaluate the
hypothesis that adult survival will vary in response to extreme sea
ice conditions experienced during the wintering period. Similar to
Christie et al. (2018), we predicted that adult female survival would
be highest in years with intermediate sea ice conditions and decline
in years with extremely high or low sea ice conditions, because of
restricted access to food versus reduced roosting habitat, respec-
tively. Because the spatial distribution of first- year birds is generally
unknown, we examined the effect of the Arctic oscillation (AO), an
important indicator of regional conditions, on first- year survival to
account for general environmental conditions experienced through-
out the full annual cycle. Predation pressure and weather conditions
(e.g., precipitation) can have strong impacts on annual nest success
(DeG regorio et al ., 2016; Fli nt et al., 2016; Mallor y, 2015). There fore,
we predicted that annual nest success would decline in years with
poor weather conditions (e.g., high precipitation) and in years with
high predation rates (e.g., increased fox presence). Furthermore,
Lovvorn et al. (2014) docu mente d reduced bo dy conditi on of spe cta-
cled eiders during winter in years with extensive sea ice cover thus,
we predicted that annual nest success would be highest in years with
average sea ice conditions and decline in years following extreme
sea ice conditions in a pattern similar to adult survival. The underly-
ing mechanism for this could involve incubation constancy, whereby
females with higher body condition take fewer and shorter recesses
from incubation (Blums et al., 1997; Gloutney & Clark, 1991), in turn
providing fewer cues to predators and visual exposure of their eggs,
respectively. The results presented here provide useful insight for
conservation and policy planning regarding the species’ current and
future conditions related to climate change.
2 | MATERIAL AND METHODS
2.1 | Study area and species
There are two subpopulations of Alaskan breeding spectacled ei-
ders, one population on the Yukon- Kuskokwim Delta (YKD) and
the other on the Arctic Coastal Plain (ACP). Another much larger
subpopulation breeds in Arctic Russia (Flint et al., 2016; U.S. Fish &
Wildlife Service, 1993) (see map; Figure 1). Both Alaskan subpopula-
tions have been monitored annually since the 1980s using aerial sur-
veys and/or nest monitoring and capture– mark– recapture methods.
We focused on the Yukon- Kuskokwim Delta breeding population,
given the existence of long- term demographic data sets.
The coastal plain of the YKD is one of the largest and most pro-
duc tive waterfowl breeding areas in North Americ a. The YKD is pre-
dominately flat tundra and wetlands interspersed with small ponds,
lakes, rivers, and tidal sloughs. Spectacled eiders typically arrive on
the breeding grounds in late May; males depart 1– 2 weeks after in-
cubation begins, and females and their young leave for the wintering
grounds in late August. Studies have identified strong breeding and
molting site fidelity, important geographical locations, and broad
spatiotemporal patterns of spectacled eider site use throughout
the annual cycle (Lovvorn et al., 2014; Petersen et al., 1999; Sexson
et al., 2014, 2016). The global population of spectacled eiders win-
ters in one distinct region in the Bering Sea south of St. Lawrence
Island (Petersen et al., 1999; Sexson et al., 2014). Females marked
on the YKD used Norton Sound as their primary staging area during
fall. Following the wintering period, individuals previously marked
on the YKD either staged along the coast of the Chukotka Peninsula,
in Norton Sound, or on the YKD prior to the breeding period (Sexson
et al., 2014).
2.2 | Data collection
Aerial surveys of spectacled eiders have been conducted on
12,832 km2 of YKD tundra wetland habitat annually since 1988
(Fischer et al., 2017; Lewis et al., 2019; Platte & Stehn, 2015).
Ground- based surveys have been conducted annually on the YKD
since 1985 to estimate the numbers of nests for geese and eiders.
These ground- based surveys sample randomly selected plots within
the core nesting area of spectacled eiders in the central coast zone,
10630
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DUNHAM et Al.
encompassing 716 km2 (Fischer et al., 2017). Eider density varies
widely across the YK D wi th low densi ties throughout mos t of the re-
gion. Lewis et al. (2019) identified three density- specific strata: low-
density (0– 1.60 nest/km), medium- density (1.60– 3.50 nests/km),
and high- density (>3.50 nests/km). Estimates of nests and aerial
observations among low, medium, and high- density strata on the
YKD were used to calculate density- specific aerial visibility correc-
tion factors (VCF) to account for incomplete detection on the aerial
surveys. The average density- specific visibility correction factors
were used to convert indices of eider abundance to annual estimates
FIGURE 1 Range map of spectacled eiders Somateria fischeri illustrating the three primary breeding areas (Yukon- Kuskokwim Delta,
Arctic Coastal Plain, and Arctic Russia), molting, and wintering areas. (Figure 1 in Flint et al., 2016)
|
10631
DUNHAM e t Al.
of breeding spectacled eiders and variance (Lewis et al., 2019). Both
the estimates of average number of breeding pairs and the annual
variance are included as data in the count sub- model (described
below).
On the YKD, survival and productivity studies were carried out
on Kigigak Island (60°50’N, 165°50’W) between 1992 and 2015 fol-
lowing protocols established by Grand and Flint (1997; see also Flint
et al., 2016). At Kigigak Island, nest searches began in late May and
continued through mid- June. Adult females were captured on nests
and given stainless steel leg bands, numbered plastic leg bands, and
nasal disks. In so me yea rs, brood hens we re marke d with radio tr ans-
mitters and monitored to estimate duckling survival (0 – 30 days).
At approximately 30 days posthatch, ducklings were captured and
marked with stainless steel and plastic bands. Individuals may be
marked as 30- day- old ducklings or as breeding adults on the breed-
ing grounds. Most marked birds were adults and thus classified as
breeding age. Immature and nonbreeding 2- year- olds do not come to
the breeding grounds and are thus unobservable. Only birds marked
as ducklings were of known age upon recapture.
2.3 | Integrated population model
We developed an integrated population model (IPM) to describe
spectacled eider breeding population dynamics for the YKD using
annual estimates of abundance from aerial surveys of the entire YKD
breeding population and demographic data collected from Kigigak
Island from 1992 to 2014. The IPM unified the analysis of aerial
survey data on breeding abundance (includes males and females),
capture– mark– recapture data (CMR; females only), and productivity
data including clutch size at hatch, nest success (1 or more ducklings
hatched), and a cons tant du ckling survi val rate. Aeria l sur vey data in-
clude all relevant information to describe change in abundance over
time. CMR data were used to inform age- specif ic survival an d breed-
ing propensity of 2- year- old females. Productivity data were used
to inform recruitment of female spectacled eiders into the breeding
population.
We constructed the following matrix projection model based on
a prebreeding survey with four stages (Figure 2). To link abundance
to demographic rates, we created a projection matrix (A) comprised
of demographic rates and a vector of stage- specific abundance nt:
where n1 is the number of 1- year- old females, n2 is the number of non-
breeding 2- year- old females, n3 is the number of breeding 2- year- old
females, and n4 is the number of 3+- year- old females, ϕt,j represents
annual survival probabilities for first- year birds, and ϕt,a represents
annual survival for birds 1- year and older (hereafter, adults), and ft is
the number of fledglings produced per breeding female, divided by
2 because the projection matrix only considers females and the sex
ratio is expected to be equal at this life stage (Flint et al., 2016). t refers
to time, in our case a particular year. We assumed birds 1 year and
older had the same survival probability regardless of breeding status
because nonbreeding birds (1- year- olds and nonbreeding 2- year- olds)
are unobservable and lack data to inform independent estimates of
survival. Female spectacled eiders may begin breeding at 2 years of
age, but evidence suggests they are less likely to breed than birds age
3 and older (Flint et al., 2016). Here, we assume all females aged 3 and
older breed each year, that is, breeding propensity of adult females is
assumed to be 100% (Flint et al., 2016). Direct estimates of 2- year- old
breeding propensity (α) were unavailable for this species, and thus, we
wanted to estimate this demographic rate. This matrix model allows
1- year- old individuals to transition to either nonbreeding 2- year- old
birds with probability (ϕt,a * 1 − α) or breeding 2- year- old birds with
probability (ϕt,a * α) with corresponding fecundity estimates. We used
an annual random effects framework to model survival probabilities,
nest success, and recapture probabilities to allow the values to vary
over time. However, due to limited data we chose to model breeding
propensity of 2- year- old females as constant.
2.4 | Count model likelihood
We used a state- space model formulation to model the population
count data using equations that describe how abundance changes
over time. An observation model links the observed population count
(index of breeding abundance, including males and females) with the
estimated abundance from the state process model. Because our
demographic data were specific to female spectacled eiders, our
matrix projection model estimated total female abundance, calcu-
lated as Ntott = (n1,t + n2,t + n3,t + n4,t). However, our count data
⎡
⎢
⎢
⎢
⎢
⎢
⎢
⎣
n1
n2
n3
n4
⎤
⎥
⎥
⎥
⎥
⎥
⎥
⎦t+1
=
⎡
⎢
⎢
⎢
⎢
⎢
⎢
⎣
00ft∕2∗𝜙t,jft∕2∗𝜙t,j
𝜙t,a∗1−𝛼00 0
𝜙t,a∗𝛼00 0
0𝜙t,a𝜙t,a𝜙t,a
⎤
⎥
⎥
⎥
⎥
⎥
⎥
⎦
∗
⎡
⎢
⎢
⎢
⎢
⎢
⎢
⎣
n1
n2
n3
n4
⎤
⎥
⎥
⎥
⎥
⎥
⎥
⎦t
FIGURE 2 Life cycle diagram of spectacled eiders
corresponding to a prebreeding survey female- only model.
Circles represent states, n1 refers to 1- year- olds, n2nB refers to
nonbreeding 2- year- olds, n2B refers to breeding 2- year- olds, and
n3+B refers to breeding adult birds 3 years and older. Shaded circles
represent the observable portion of the population. Solid lines
represent survival and transition probabilities, and dashed lines
refer to the recruitment process
10632
|
DUNHAM et Al.
were an estimate of breeding abundance which included paired
males and females. The number of breeding birds observed during
aerial surveys is calculated as a function of the number of breeding
pairs and lone males which are assumed to represent a breeding pair
(Fischer et al., 2018; Lewis et al., 2019). To link our count data to the
overall population model, we calculated the breeding abundance as
Nbpopt = (n3,t + n4,t) * 2, assuming an equal sex ratio.
The state process model describes the dynamics of the total pop-
ulat ion, but ou r cou nts only inclu ded the br eedi ng ma les and fe mal es.
Thus, the observation model linked the observed number of breed-
ing birds (denoted by y) to Nbpopt through the following equation.
Yt ∼ Normal (Nbpopt, σy,t ).
where σy is the estimated annual observation error from the aerial sur-
veys and was provided as data. In 2011, aerial surveys were not flown
on the YKD, and therefore, there were no observations for that year.
We calculated annual changes in abundance (λt = Nt +1/Nt) and geo-
metric average population growth as derived parameters for the time
series. We used weakly informative priors to inform the initial popula-
tion state with a discrete uniform distribution (Table 1). The complete
likelihood for the population count data was Lss (y, σ2
y | ϕa, α, N, f).
2.5 | Capture- recapture likelihood
We estimated survival and breeding propensity using capture-
mark- recapture data from female spectacled eiders banded as
ducklings or breeders (2 years or older). During the breeding sea-
son between 1992 and 2014, 591 female ducklings (73 recaptures
total, 19 recaptured as 2- year- old breeders) and 661 adult females
(1,335 recaptures) were banded. We used the multistate formula-
tion of the Cormack- Jolly- Seber model with unobservable states
(Arnason, 1972; Kendall & Nichols, 2002; Lebreton et al., 1992). To
decrease computation time and increase efficiency, we converted
capture histories into an m- array and used the multinomial likelihood
formulation of the model (Kéry & Schaub, 2012).
We defined a hierarchical model to estimate survival rate in the
capture– mark– recapture model (ϕ) with a mean and temporal ran-
dom effect following the general structure:
where ϕt is the annual estimate of age- specific survival probability, μϕ
is the age- specific mean logit survival, and εϕ,t is the temporal random
ef fec t for eac h age class . Infor mation on the pr ior dis tribut ion s for each
parameter is available in Table 1.
Recapture probability was conditional on breeding, thus, only
breeding birds could be recaptured. We assumed that the probabil-
ity of detection would be equal for 2- year- old and adult breeding
females. We modeled recapture probability with a mean and annual
temporal random effect:
where pt is the annual recapture probability, μp overall mean logit re-
capture, and εp,t the annual random effect on the logit scale. We as-
sumed the random effect term followed a Normal distribution: εp,t ~
Normal(0,
𝜎2
p
).
The likelihood of the multistate model was denoted as
LCR (
m
|
𝜙
j
,𝜙
a
,𝛼,p
)
where m represents the capture– recapture data
which contains information about age- specific survival (ϕj, ϕa),
breeding propensity (α), and recapture probabilities (p).
2.6 | Productivity likelihood
We modeled productivity (f) as the product of clutch size at hatch
(cs), the probability of nest success (ns), and duckling survival (ds;
0– 30 days posthatch). Nests were monitored near the expected
hatch date and the number of eggs hatched was recorded to account
for egg mortality. Annual clutch size was modeled using a Poisson
distribution where the shape parameter was the average clutch size
over the time series. Probability of nest success was modeled as
proportion of nests with at least one egg hatched out of the total
nests recorded. Nest success was modeled using a binomial regres-
sion with annual random effects. Duckling survival was supplied
as a constant value based on the mean of duckling survival from
Kigigak Island (Flint et al., 2016). The likelihood for productivity was
LPR(ns,cs,ds |f)
.
2.7 | Joint likelihood for the integrated model
The joint likelihood of the integrated population model was the
product of the three likelihoods described above and is written as:
LIPM (m,ns,cs,ds,y,𝝈2
y|𝜙j,𝜙a,p,𝛼,N,f)=LSS (y,𝝈2
y|𝜙j,𝜙a,𝛼,N,f)∗
LPR (ns,cs,ds
|
f)∗LCR(m
|
𝜙j,𝜙a,𝛼,p
)
.
2.8 | Environmental covariates
The descriptions of the model likelihoods and parameters above de-
scribe a model that accounted for temporal variation in demographic
rates but did not include covariates. Here, we describe the environ-
mental factors across the full annual cycle that we expected to be
important influences on demographic change and how they were in-
cluded in the final model structure. We were particularly interested
in understanding how deviations from average sea ice conditions
during the “wintering period” (defined as November 1 – April 30)
would affect demographic rates. We predicted that nest success and
survival of adult females would be affected by sea ice conditions
during the wintering period, with lower survival and nest success in
years with extreme high or low sea ice cover for reasons described
in our introduction. Though breeding propensity of 2- year- olds
might also be influenced by winter sea ice conditions, we did not
log
it
(
𝜙
t)
=𝜇
𝜙
+𝜀
𝜙,t
log
it
(
p
t)
=𝜇
p
+𝜀
p,t
L
IPM
(
m,ns,cs,ds,y,𝝈2
y
|
𝜙j,𝜙a,p,𝛼,N,f
)
=LSS
(
y,𝝈2
y
|
𝜙j,𝜙a,𝛼,N,f
)∗LPR (ns,cs,ds|f)∗LCR(m|𝜙j,𝜙a,𝛼,p)
|
10633
DUNHAM e t Al.
have sufficient data to allow breeding propensity to vary over time.
The Arctic Oscillation is an index used to describe the pattern of
variation in winter sea- level atmospheric pressure that has been cor-
related with changes in Arctic climate (Aanes et al., 2002; Thompson
& Wallace, 1998). The Arctic Oscillation has also been related to pat-
terns in sea ice thickness and persistence as well as regional weather
(Rigor et al., 2002). Thus, it may be a good indicator of habitat con-
ditions spectacled eiders experience throughout the annual cycle.
We calculated the number of days with >95% ice cover (extreme
ice days) within the core wintering area during the wintering period
as an index of sea ice severity. The core wintering area was identi-
fied based on utilization distributions of satellite- tagged individuals
from 1993– 1997 and 2008– 2012 and confirmed by aerial surveys
of the wintering area (Petersen et al., 1999; Sexson et al., 2014).
Observed sea ice concentrations were extracted from the core
area that spans four grid cells (25- km resolution) derived from pas-
sive microwave satellite imagery using the Bootstrap Algorithm and
provided by the National Snow and Ice Data Center. For comparison,
we calculated the number of days with <15% ice cover as a metric
of extreme low sea ice conditions (Christie et al., 2018). However,
we found that extreme ice days and extreme low sea ice conditions
are highly correlated (r = −0.84, Pearson's correlation coefficient).
Given this strong relationship, we chose to include only the stan-
dardized number of days with >95% ice cover (hereafter; ice days)
and interpret negative deviations from the mean to be represen-
tative of low sea ice cover. We included a quadratic effect of “ice
days” for adult survival and nest success and modeled the effects
using linear models on the logit link scale. Though hatch- year birds
are expected to use the same wintering areas and are thus subject
to similar conditions, we expected that due to inexperience, survival
would be sensitive to environmental conditions throughout the an-
nual cycle. Thus, we included a linear term for the effect of the Arctic
Oscillation on first- year survival using a linear model on the logit link
scale (NOAA, 2019).
Parameter Definition Prior
Capture– recapture and productivity model parameters
ɸaSurvival of females 1 year and above Beta (5.5, 1.833)
mu =0.75, sd =0.15
ɸjSurvival of first- year birds (30 days to 1 year) Beta (2.5, 5.833)
mu =0.30, sd=0.1 5
αBreeding probability of 2- year- old females Beta (2.5, 5.833)
mu =0.30, sd=0.1 5
pRecapture probability of breeding females Beta (5.056, 5.056)
mu =0.5, sd =0 .15
ns Nest success (probability of 1 egg hatching) Beta (5.922, 3.189)
mu =0.65, sd=0.15
cs Average clutch size at hatch Gamma (0.1, 0.1)
ds Survival of ducklings (0 to 30 days) 0.67
fFecundity - number of ducklings per female
fect
=
nst
∗
cst
∗
ds
- -
σɸ,α,ns Standard deviation of temporal variability Uniform (0.001, 5)
εAnnual random deviation from the average value
𝜀t
∼Normal
(0,
𝜎
𝜃)
- -
βRegression coefficients Normal (0, 10)
Count model parameters
n1Number of immature (1- year- old) females
n
1,t+1=
fect
2∗𝜙0,t
∗
n3,t+n4,t
Discrete Uniform
(300, 900)
n2Number of 2- year- old nonbreeding females
n2,t
+
1
=𝜙
1,t
∗
(1
−𝛼
t)
∗n
1,t
Discrete Uniform
(10, 200)
n3Number of 2- year- old breeding females
n3,t+1
=
𝜙1,t
∗
𝛼t
∗
n1,t
Discrete Uniform
(10, 100)
n4Number of 3+- y e a r - o l d f e m a l e s
n4,t
+
1
=𝜙
2,t
∗
(
n
2,t
+n
3,t
+n
4,t)
Discrete Uniform
(500, 2000)
Ntot Total female abundance
Ntott
=
(n1,t
+
n2,t
+
n3,t
+
n4,t)
∗
2
- -
Nbpop Breeding abundance (males and females)
Nbpopt
=
(
n
3,t
+n
4,t)
∗
2
- -
yAnnual index of breeding abundance - -
σyAnnual estimated obser vation error of y- -
TABLE 1 Parameters, their definitions,
and prior distributions used in the
spectacled eider integrated population
model. Prior distributions were generated
based on empirical data for spectacled
eiders or other sea duck species
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DUNHAM et Al.
Finally, to determine effects of breeding site conditions on nest
success we included standardized precipitation during the breeding
period (June- August) as well as an index of fox presence (Fischer
et al., 2017). The YKD can experience intense storms along the coast
that may cause flooding and total nest failure. Precipitation data
were recorded at Bethel Airport in Bethel, Alaska at the National
Weather Service Cooperative Network station (Western Regional
Climate Center, 2019). Bethel, AK is 108 miles east of Kigigak Island;
however, it is the closest weather station. Both arctic foxes (Vulpes
lagopus) and red foxes (Vulpes vulpes) can be found on the YKD and
are known nest predators of waterfowl in the region. During nest
plot surveys on the YKD, the proportion of nests plots with recent
fox sign (e.g., observed fox, scat, fur, tracks, and/or active dens) is
recorded annually and we used this as an index of fox presence on
the breeding grounds (refer to Table 5 in Fischer et al., 2017). In ad-
dition to the quadratic term for extreme ice days, we also included
linear terms for precipitation and fox presence in the model for nest
success and used a linear model on the logit scale. Prior distributions
for all parameters including the regression coefficients are described
in Table 2. All covariates were z- standardized with mean = 0 and
standard deviation = 1.
2.9 | Model implementation
We fit both the temporal variation model and the model includ-
ing environmental covariates using Markov Chain Monte Carlo
(MCMC) simulations in a hierarchical Bayesian framework using
JAGS (Plummer, 2003) software (package “jagsUI”, Kellner, 2016) in
R (Versions 4.0.1, R Core Team, 2020). We used 3 chains, each with
900,000 iterations, including an 800,000- burn in. We thinned by 25,
yielding 12,000 posterior samples for each parameter. We assessed
convergence of each model based on the Gelman and Rubin statistic
(R- hat between 1 and 1.05) for al l parameters. Add ition ally, trace plots
were used to visually confirm adequate convergence of the 3 chains.
2.10 | Postmodel analysis
We derived annual population growth rate for both the total female
abundance (Ntot) and the breeding population (Nbpop) by dividing the
abundance in t + 1 by the abundance in t:
We re port both po pul ati on gr owth rate s bec ause th ey ca n be us ed
for different purposes. Spectacled eiders are monitored as breeding
populations, which includes paired males and females of breeding age,
and population growth rate of the breeding population is relevant for
conservation and policy planning. We were also interested in assess-
ing the relative contribution of each demographic rate to the real-
ized variation in total female population growth rate. Due to delayed
breeding, neither nest success nor first- year survival directly contrib-
ute to the change in breeding abundance within that year. Thus, we
calculated the correlation coefficient between each demographic rate
𝜆
t=
N
t+1
)
N
t
TABLE 2 Parameter estimates from an integrated population model including environmental covariates for the Yukon- Kuskokwim
Delta breeding population of spectacled eiders. Model was fit to data collected from 1992 to 2014. Demographic parameter estimates are
reported as the mean and 95% Bayesian credible intervals (CRI) on the probability scale. Regression coefficients are reported on the logit
scale and correspond to the submodel in the integrated population model (IPM). The covariates included within the submodels include
“ice days” which is the number of days where sea ice cover is ≥95% in the core wintering area in the Bering Sea, “arctic oscillation” which
is annual index of the Arctic Oscillation pattern, “fox” which is proportion of nest plots with signs of fox, and “precipitation” which is the
average rain or snowfall measured at Bethel, Alaska between June and the end of August
Parameter Mean 95% CRI
Demographic parameters
Adult survival 0.878 (0.827, 0.921)
Juvenile survival 0.290 (0.148, 0.439)
Breeding propensity of 2- year- olds 0.359 (0.223, 0.531)
Nest success 0.778 (0.703, 0.845)
Clutch size 4.297 (3.063, 5.819)
Geometric average λ breeding population growth 1.075 (1.064, 1.086)
Regression coefficients
β adult sur vival linear: ice days −0.132 (−0.549, 0.266)
β adult sur vival quadratic: ice days −0 .251 (−0.486, −0.015)
β juvenile survival linear: Arctic oscillation 0.201 (−0.913, 1.326)
β nest success linear: ice days −0.026 (−0.446, 0.401)
β nest success quadratic: ice days −0.455 (−0.787, −0.147)
β nest success linear: fox −0.169 (−0.604, 0.262)
β nest success linear: precipitation −0.032 (−0.417, 0.357 )
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DUNHAM e t Al.
and total fem ale popu latio n growth . We use d the full pos teri or sa mpl e
and calculated the probability that the correlations were positive p
(r > 0) (Saunders et al., 2018, 2019; Schaub et al., 2015).
We were interested in understanding how much variation in the
demographic rates could be explained by the climatic variables that
we considered. We fit a temporal variation model and an environ-
mental conditions model (described above) and included terms for
residual variance for each time- varying demographic parameter. This
allowed us to compare the total temporal variance to the residual
variance once covariates were included. The amount of temporal
variance explained by the covariates was calculated as V = (σ2
total –
σ2) / σ2
total where V is the proportion of temporal variance explained
by including the climate variables, σ2
total is the total residual variance
for each demographic parameter estimated by the temporal vari-
ation model, and σ2 is the residual variance for each demographic
parameter estimated by the environmental conditions model (Kéry
& Schaub, 2012).
For both the temporal variation model and the environmental
effects model, we assessed the goodness of fit (GoF) of the nest
success and count models. We calculated the Freeman– Tukey statis-
tic for the nest success model and calculated a Bayesian p- value. To
assess goodness of fit of the count model, we calculated a Bayesian
p- value based on the chi- square statistic.
3 | RESULTS
3.1 | Abundance, productivity, and survival
We fit the data using two models, the first included random effects
with no covariates to calculate total temporal variation in demo-
graphic rates. The second included environmental covariates and
random effects and is the model we used for inference. The results
reported here refer to the second model including the environmen-
tal covariates.
Estimates of abundance and trend indicate that the YKD breed-
ing population has increased over the 23- year study period (1992–
2014; Table 2, Figure 3). Annual population growth rates were
variable but mean geometric population growth for the breeding
population was 1.075 (95% CRI 1.064, 1.086) indicating an overall
positive trend.
Mean adult survival rate (age 1+) was high (0.878; 95% CRI 0.827,
0.921), and though adult sur vival rate was generally stable, annual
po int es t imate s we re mo re va r iable over the pa st 10 year s of th e st udy
(Table 2, Figure 4a). Mean apparent first- year survival rate was 0.290
but annual estimates were variable, and precision was low (Figure 5).
Mean breeding propensity of 2- year- old females was 0.359, which is
consistent with estimates from other eider species (Table 2). Average
probabilit y of nest success was 0.778 but highly variable across years
with significant declines in 2001 and 2013 (Figure 6a). Average clutch
size at hatch was 4.297 with 95% CRI between 3.06 and 5.82 (Table 2).
Because fecundity (f) was derived from the product of nest success,
clutch size, and duckling survival (a constant), the values varied over
time primarily in response to changes in nest success (Figure 7)
and fecundity and nest success probability were highly correlated
(r = 0.93, Pearson's correlation coefficient).
3.2 | Environmental effects on demographic rates
The number of extreme sea ice days (days with ≥95% sea ice cover)
on the core wintering area fluctuated between 16 and 101 days over
the study period (1992– 2014). Only one year (winter 2001) within
the study period had a particularly low number of extreme sea ice
days (16 days ≥95% ice cover; November 2000 – April 2001) which
coincided with the lowest annual estimates of adult survival rate and
nest success probability.
Here, we define “support” for an effect of a covariate on a de-
mographic rate if the 95% credible intervals of the posterior on the
slope parameters do not cross zero. Based on the posterior distri-
butions of the slope parameters, we found support for a quadratic
relationship between the number of extreme sea ice days and adult
survival and a similar relationship for annual nest success (Table 2).
Adult survival was highest in years when the number of sea ice days
was between 50 and 90 (Figure 4b). Nest success was highest when
the average number of sea ice days was between 60 and 80 and
annual success declined with an increase or decrease in the number
of extreme sea ice days (Figure 6b). Based on posterior estimates,
we found no support for a relationship between nest success and
fox presence or precipitation (Table 2). In addition, we found no ev-
idence of a relationship between first- year survival and the annual
Arctic Oscillation index.
The inclusion of covariates explained 16% of the temporal varia-
tion in adult survival (total temporal variance on the logit scale =0.71
(95% CRI: 0.28,1.41), residual temporal variance = 0.59 (95% CRI:
0.20, 1.28), and 44% of the temporal variation in nest success (total
temporal variance on the logit scale = 1.13 (95% CRI: 0.55, 2.02),
residual temporal variance = 0.64 (95% CRI: 0.28, 1.34). The inclu-
sion of the Arctic Oscillation covariate for first- year survival did not
explain temporal variation. Furthermore, the Bayesian p- values for
the nest success model with temporal variation only was 0.298 but
was 0.490 when the environmental covariates were included. These
values indicated that the addition of covariates improved model fit.
Alternatively, the Bayesian p- values for the count model were 0.50,
regardless of the inclusion of covariates; thus, both models fit the
count data well.
3.3 | Demographic contributions to
population growth
First- year survival was highly correlated with variation in female
population growth rates (r = 0.85) and the 95% CRI excluded zero
(Figure 8). Adult survival rate was positively correlated with total
female population growth r = 0.46 (95% CRI 0.27, 0.63) (Figure 8).
Both the probability of nest success and fecundity were positively
10636
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DUNHAM et Al.
correlated with population growth r = 0.32 (95% CRI 0.19, 0.44)
though less so than adult or first- year survival rates.
4 | DISCUSSION
We investigated the full annual cycle population dynamics of specta-
cled eiders using an integrated population model and found that the
climatic conditions experienced during the wintering period affect
both adult survival and nest success (Figures 4b and 6b). Previous
research identified a similar relationship between adult survival
and winter sea ice (Christie et al., 2018); however, conditions out-
side of the breeding season were never considered in analyses of
reproductive success (Flint et al., 2016). Integrating data on multiple
demographic rates provided us with additional information on im-
portant demographic processes that influence population dynamics.
FIGURE 3 Breeding population size estimates for spectacled
eiders breeding on the Yukon- Kuskokwim Delta in western Alaska.
Open gray circles are the point estimates from aerial surveys, black
circles, and vertical dashed lines are the annual means and 95%
Bayesian credible interval estimates from the integrated population
model. Breeding population size includes breeding age males and
females
FIGURE 4 Estimates of annual adult survival of female spectacled eiders (age 1+) breeding on the Yukon- Kuskokwim Delta in western
Alaska (a) and response curves showing the effect of extreme sea ice days (number of days with sea ice concentrations >95%) over the core
wintering area on survival of adult female spectacled eiders (b). (a) Black circles and dashed vertical lines are the annual means and 95%
Bayesian credible interval estimates from the integrated population model. Annual estimates were generally high and became more variable
in the last decade. (b) Black line and gray band are the mean and 95% Bayesian credible interval of the response curve and black circles are
the posterior mean estimates of adult survival from the integrated population model. Adult survival is highest in years with intermediate sea
ice conditions (50– 90 days with extreme sea ice concentrations) and declines with more extreme ice conditions
FIGURE 5 Estimates first- year survival of spectacled eiders
in the Yukon- Kuskokwim Delta breeding population in western
Alaska. Black circles dashed vertical lines are the annual means
and 95% Bayesian credible interval estimates from the integrated
population model including environmental covariates
|
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DUNHAM e t Al.
Specifically, we were able to estimate mean breeding propensity for
2- year- old females and annual survival of first- year birds, quanti-
ties that were not estimable using any of the data streams indepen-
dently. Among the parameters that were allowed to vary over time,
we determined that annual variation in female population growth
was largely influenced by first- year survival, demonstrating addi-
tional benefits of using an integrated modeling approach.
Extreme winter sea ice conditions contributed to annual varia-
tion in both adult survival and nest success. Severe environmental
conditions during the nonbreeding period can influence eider body
condition via effects on resource availability, access to resources,
and energetics (Christie et al., 2018; Cooper et al., 2013; Lovvorn
et al., 2009, 2015). The Bering Sea is a highly productive benthic
ecosystem; however, warming temperatures and declining sea ice
have caused distributional shifts and overall reductions in benthic
prey available to spectacled eiders (Grebmeier et al., 2018; Lovvorn
et al., 2009). In years with heavy ice cover across the core winter-
ing area, individual body condition was documented to be poor in
response to restricted openings in the ice and subsequent lack of
access to suitable prey (Cooper et al., 2013; Lovvorn et al., 2014).
Alternatively, sea ice may dampen the impact of waves and provide
roosting areas for individuals during the nonforaging period, thus
significantly reducing thermoregulation costs (Lovvorn et al., 2009).
A species’ tolerance to environmental conditions is limited due
to physiological and ecological constraints, which accounts for
nonlinear relationships between demographic rates and climate
(Jenouvrier, 2013). Several studies have identified “bell- shaped”
or otherwise nonlinear relationships between climate covariates,
demographic rates, and body condition in sea birds (e.g., Ballerini
et al., 2009; Barbraud et al., 2011; Gremillet et al., 2015). These re-
lationships are similar to the results documented between sea ice
conditions, adult survival, and nest success (Christie et al., 2018, this
study).
FIGURE 6 Estimates of annual nest success of female spectacled eiders (age 1+) breeding on the Yukon- Kuskokwim Delta in western
Alaska (a) and response curves showing the effect of extreme sea ice days (number of days with sea ice concentrations >95%) over the
core wintering area on nest success of female spectacled eiders (b). (a) Black circles and dashed vertical lines are the annual means and 95%
Bayesian credible interval estimates from the integrated population model. Annual estimates were highly variables across time (b) black line
and gray band are the mean and 95% Bayesian credible interval of the response curve and black circles are the posterior mean estimates of
nest success from the integrated population model. Nest success is highest in years with intermediate sea ice conditions (60– 80 days with
extreme sea ice concentrations) and declines with more extreme ice conditions
FIGURE 7 Estimates of fecundity of adult female spectacled
eiders in the Yukon- Kuskokwim Delta breeding population in
western Alaska. Fecundity was estimated as the product of nest
success, clutch size at hatch, and duckling survival (included as a
constant) and estimates the expected number of female ducklings
that survive to 30 days posthatch. Most of the annual variation
in fecundity is a function of the variation in nest success, thus,
the two parameters are highly correlated. Black circles dashed
vertical lines are the annual means and 95% Bayesian credible
interval estimates from the integrated population model including
environmental covariates
10638
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DUNHAM et Al.
Severe winter sea ice conditions can reduce body condition
(Cooper et al., 2013; Lovvorn et al., 2014) affecting survival during
winter and migration to spring staging or breeding grounds. We
found that adult survival was highest when the number of ice days
was between approximately 50 and 90 days, over the 180- day win-
tering period, and subsequently declined beyond those limits. Our
results provide further evidence for a nonlinear effect of winter
sea ice conditions and survival for ice- dependent avifauna (Ballerini
et al., 2009; Barbraud et al., 2011; Christie et al., 2018; Gremillet
et al., 2015; Jenouvrier et al., 2012). Previous analyses of spectacled
eider survival between 1992 and 2004 found support for a linear
effect of winter sea ice conditions (Flint et al., 2016), with the addi-
tional years of data we were able to uncover a more complex pattern
between survival and climatic variation. However, the addition of
the quadratic terms for ice days accounted for only 16% of the tem-
poral variation in adult female survival, suggesting other factors are
also important. For example, female survival may also be affected by
reproductive costs, though these may be mediated to some degree.
Predation by mammalian predators and ingestion of spent lead shot
on the breeding grounds may also contribute to adult female mor-
tality (Flint & Grand, 1997, Grand et al., 1998, Flint et al., 2016) with
the latter suspected to be a factor propelling the initial population
decline (USFWS 1996). Though we did not explore these sources
of mortality, research quantifying the relative effects of different
stressors on adult survival is certainly warranted.
Eiders are capital breeders and thus rely on energy stores ac-
quired prior to breeding for sustenance during the incubation period.
Females can lose 26% of their body mass during incubation (Flint
& Grand, 1999). Arriving at the breeding grounds in poor condition
may force females to increase the frequency and duration of incu-
bation recesses to feed, potentially exposing nests to harsh environ-
mental conditions or increased predation risk and resulting in total
or partial nest failure (Criscuolo et al., 2002; D'Alba et al., 2010; Iles
et al., 2013; Lehikoinen et al., 2006). Furthermore, females in poor
body condition may abandon their nests at higher rates, in favor of
survival and reproduction in the following year. Nest success is es-
timated to be highest between 60 and 80 ice days, indicating that
nest success may be more sensitive to winter sea ice conditions than
adult survival. This is consistent with a life- history trade- off between
survival and reproductive success for long- lived iteroparous species
such as eiders (Orzack & Tuljapurkar, 2001; Saether & Bakke, 2000).
We linked nest success to environmental conditions during the
nonbreeding and breeding seasons. However, we found no evidence
for an effect of fox presence or precipitation during the breeding
season on annual nest success. It is possible that both our index of
fox abundance and precipitation were not accurate metrics of how
FIGURE 8 Annual posterior means of
population growth rate plotted against
annual posterior mean estimates of adult
survival (a), first- year survival (b), annual
nest success (c), and fecundity (d). First-
year survival was the demographic rate
most strongly correlated with variation
in annual population growth. Black
dots indicate mean estimates of the
demographic rates with corresponding
95% Bayesian credible intervals
(gray lines), r is the posterior mode
of the correlation coefficients with
corresponding 95% CRI, and p(r > 0)
indicates whether the correlation was
positive
0.0 0.2 0.4 0.6 0.8 1.0
0.5
1.0
1.5
2.0
Adult survival
Population growth rate
r = 0.46 (0.27, 0.63)
P(r>0) = 1
0.00.2 0.40.6 0.
81
.0
0.5
1.0
1.5
2.0
First year survival
Population growth rate
r = 0.85 (0.65, 0.90)
P(r>0) = 1
0.0 0.2 0.4 0.6 0.8 1.0
0.5
1.0
1.5
2.0
Nest success
Population growth rate
r = 0.32 (0.19, 0.44)
P(r>0) = 1
01234
0.5
1.0
1.5
2.0
Fecundity
Population growth rate
r = 0.32 (0.19, 0.44)
P(r>0) = 1
|
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DUNHAM e t Al.
predation and weather influence spectacled eider nest success.
Mammalian predation is a major cause of partial or total nest failure
for ground- nesting species (DeGregorio et al., 2016; Mallory, 2015;
Quinlan & Lehnhausen, 1982). Foxes are a major nest predator for
waterfowl, however, the data on fox abundance on the Yukon-
Kuskokwim Delta are limited and measured based on the propor-
tion of plots with signs of fox; thus, this index may not adequately
capture predator- prey dynamics. Unfavorable weather conditions
during the nesting period were found to have negative effects on
nest success and recruitment of common eiders (Iles et al., 2013;
Jónsson et al., 2013). However, the closest weather station to
Kigigak Island is nearly 180 km east in Bethel, Alaska and storms and
precipitation in coastal regions are often more extreme than those
experienced further inland. In the Arctic, severe storm frequency is
expected to continue to increase with complex regional impacts on
precipitation (Terenzi et al., 2014) and potentially deleterious effects
on nest success, duckling survival, and nesting habitat (Jorgenson
et al., 2018). Furthermore, phenology of waterfowl nesting on the
YKD is related to the timing of snowmelt and nest success (Lindberg
et al., 1997; Sedinger & Raveling, 1986; but see Babcock et al., 2002).
Depending on the timing, coastal storms and subsequent flooding
can cause localized nest failure or duckling mor tality through a num-
ber of mechanisms including drowning, exposure to cold water tem-
peratures, and stunted growth when exposed to salt water prior to
developing salt glands (Devink et al., 2005; Grand & Flint, 1997; Iles
et al., 2013). Both predation and habitat conditions have been iden-
tified as major influences on survival of spectacled eider ducklings
(Flint et al., 2006). Efforts to gather data on duckling survival in re-
sponse to weather conditions, habitat change, and predation during
the breeding season may help us understand the relative impacts of
changes in recruitment at the population level and identify potential
conservation actions.
Estimating survival of first- year spectacled eiders is a consider-
able challenge because of delayed breeding and lack of information
on geographic distribution of 1- year- olds. Inference can only be
made based on small sample sizes of birds banded as ducklings and
recaptured as breeders (2 years or older). On average, first- year ap-
parent survival was 0.29, which is slightly lower than rates estimated
for other eider species (Koneff et al., 2017) but broadly similar to
those of other sea duck species in Alaska (e.g., common goldeneyes;
Lawson et al., 2017). This value, however, was generally consistent
with estimates for spectacled eiders from other studies (Christie
et al., 2018; Flint et al., 2016). Previous analyses did not estimate
annual survival of first- year birds and were thus unable to test for
an effect of environmental conditions. Prior attempts to monitor
spectacled eiders produced no data on the space use of first- year
birds (Sexson et al., 2014). Because we know so little about the dis-
tribution of first- year birds, we chose to use the Arctic Oscillation as
an index for climate conditions that first- year birds may experience
but were unable to detect any relationship. Our results indicate that
change in first- year apparent survival had the strongest correlation
with variation in female population growth rates. These results are
consistent with those of Saether and Bakke (2000) who found that
highly variable demographic rates may contribute more to variation
in population growth than the demographic rates that contribute
most strongly to asymptotic population growth. Further efforts to
gather such information may reduce uncertainty in annual estimates
of survival and identify appropriate environmental conditions that
may affect survival and ultimately variation in population growth of
spectacled eiders.
Estimates of breeding propensity of 2- year- old birds are broadly
similar to estimates obtained for sea ducks (Koneff et al., 2017). Much
like first- year survival, estimating breeding propensity is challenging
because of low samples sizes for females banded as ducklings and
returning in their second year to breed. Furthermore, it is not pos-
sible to determine the age of birds first marked as breeders, though
we would expect that some unknown portion of individuals are ini-
tially marked as 2- year- olds. It is also likely that breeding propen-
sity of 2- year- olds is related to environmental conditions during the
nonbreeding season. However, we do not have the data required to
make such inference and had to constrain this parameter to be con-
stant over time. In this study, we assumed that breeding propensity
of adults (3+ years) was 100% each year. Many females in our data
set were recaptured multiple years in a row, which provides support
for this assumption. However, intermittent breeding has been iden-
tified in other eider species and may be linked to environmental con-
ditions faced throughout the annual cycle (Coulson, 2010; Hanssen
et al., 2013; Jónsson et al., 2013; Mehlum, 2012). If spectacled eiders
were exhibiting nonbreeding rates similar to common eiders (e.g.,
up to 70% nonbreeding), we may expect the annual estimates of
abundance to vary rather substantially in response (Coulson, 2010).
However, this was not the case in our analysis or in analyses of the
abundance data alone (Dunham et al. in review, Fischer et al., 2018).
Further research is warranted to determine whether spectacled ei-
ders demonstrate a trade- off between survival and reproduction
(i.e., bet- hedging strategy; Saether & Bakke, 2000) through inter-
mittent breeding following severe winter or spring conditions, which
ma y acco unt for some va r iati on in breedin g abunda nce s acros s yea r s.
Breeding abundance increased substantially over our study
period (Figure 3), and our abundance estimates and trend were
largely consistent with recent analyses of the abundance data alone
(Dunham et al., 2021; Fischer et al., 2018). Eiders exhibit a slow
“pace of life” strategy, which often includes high variance (“boom
and bust”) in annual reproductive success and first- year survival
(Orzack & Tuljapurkar, 2001). In structured populations with delayed
breeding, we would expect temporal variation in recruitment to af-
fect population structure and subsequently abundance over the long
term (Gaillard et al., 2008; Pfister, 1998). It is likely that intermit-
tent years of high recruitment could have strongly contributed to
the overall positive trend in population growth, despite additional
variation in adult survival in recent years. For example, in species
with delayed recruitment such as spectacled eiders, variation in
first- year survival can contribute greatly to both recruitment and
population dynamics, as we found. But because we had to assume
constant duckling survival and 2- year- old breeding propensity, and
100% breeding propensity thereafter, we did not pursue a more
10640
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DUNHAM et Al.
formal retrospective decomposition of female population growth
rates (e.g., Koons et al., 2016). Our inference about the relative con-
tribution of demographic parameters to past variation in population
growth rates is limited to the parameters that were allowed to vary
over time in our integrated population model. Furthermore, sea ice
conditions through our study period were often “intermediate” (i.e.,
within one standard deviation from the mean) and extreme years
happened less than half of the time. Therefore, while annual varia-
tion in sea ice conditions may affect demographic rates, it is unlikely
that the negative effects were severe or frequent enough to cause
the population to decline.
Our results add to the evidence that variable sea ice conditions
over the wintering period affect spectacled eider demography
(Christie et al., 2018; Flint et al., 2016). However, during the study pe-
riod there was only a single year with sea ice conditions well below av-
erage. In 2001, the Bering Sea experienced record low sea ice extent
and this year coincided with the lowest estimates of nest success and
adult survival. We acknowledge that the extreme value in 2001 has
considerable influence on our inference, and the relationship between
below- average sea ice conditions and demographic parameters is un-
certain (Christie et al., 2018 and see our Supplementary Material).
This uncertainty is partially reflected in the credible inter vals of the
posterior estimates of the regression coefficients (Table 2), in the
predicted response curves showing the effect of extreme ice days on
survival and nest success (Figures 4 and 6). To further address the po-
tential infl uence of the ex trem e va lue of “ice days” in 2001 , we inc lude
a modified model fit without the 2001 covariate values (Table S1).
Nevertheless, we note that the low sea ice ex tent during the winter of
2001 is not a singular anomaly nor the result of a measurement error.
Sea ice extent in the Bering Sea has remained well below average for
three years in a row (2017– 2019), with a new record low set in 2018
(Huntington et al., 2020). Though efforts resumed in 2019, contin-
uous capture– recapture data were only available through 2015 and
we were unable to model the effects of an extended number of years
with minimal sea ice on spectacled eider demography. The recent ob-
servations of declining sea ice conditions combined with evidence of
negative effects of such conditions on demography should further
motivate detailed demographic studies of spectacled eiders. Studies
on individually marked birds in addition to intensive nest monitoring
would offer the greatest opportunity to test our findings.
Changes in sea ice persistence may have important effects on
spectacled eider diets and range dynamics that compound the del-
eterious effects predicted by our results. Declines in sea ice con-
centrations in the Bering Sea are expected to affect the benthic
faunal composition and biomass that supports the marine ecosystem
and provides food for Arctic marine predators (Grebmeier, 2012;
Grebmeier et al., 2006, 2018). Furthermore, projected changes in
sea ice will increase the duration and extent of open water periods,
likely altering the spatial distribution of suitable refugia and affect-
ing the spatial structure of benthic communities (Zhang et al., 2012).
Since the 1990s spectacled eiders have shifted their molting distri-
bution, likely in response to changing ecosystem conditions (Sexson
et al., 2016). How spectacled eiders adapt (e.g., prey switching, range
shifts) in response to sea ice loss throughout their wintering habitat
remains to be seen. Further research on adaptations to changing sea
ice conditions will be critical for understanding spectacled eider's
short and long- term responses to climate change.
Understanding Arctic species demographic responses to envi-
ronmental conditions has become increasingly important in a chang-
ing climate. Using the integrated population modeling approach, we
were able to identify limiting factors affecting population growth via
different demographic rates throughout the annual cycle. We be-
lieve this study provides further evidence of the importance of long-
term demographic studies to identify demographic responses to
climate change and identify opportunities for conser vation action.
ACKNOWLEDGMENTS
Funding was provided by Bureau of Land Management, Auburn
University School of Forestry and Wildlife Sciences, and Ducks
Unlimited. We would like to thank Conor McGowan, Christopher
Lepczyk, Abigail Lawson, and two anonymous reviewers for provid-
ing reviews that strengthened this manuscript. We also thank the
many field technicians and USFWS biologists that collected these
data. We thank David Douglas for compiling and sharing the sea
ice data. Finally, we thank the USFWS for assembling the demo-
graphic data and allowing us to use it in this analysis. The Alabama
Cooperative Fish and Wildlife Research Unit is sponsored jointly by
the U.S. Geological Survey, Alabama Department of Conservation
and Natural Resources, Auburn University, the Wildlife Management
Institute, and the U.S. Fish and Wildlife Service. Any use of trade,
firm, or product names is for descriptive purposes only and does not
imply endorsement by the U.S. Government.
CONFLICT OF INTEREST
The authors indicate there are no competing interests.
AUTHOR CONTRIBUTION
Kylee Dunham: Conceptualization (lead); Data curation (lead);
Formal analysis (lead); Methodology (lead); Project administra-
tion (supporting); Visualization (lead); Writing- original draft (lead);
Writing- review & editing (equal). Anna M Tucker: Formal analysis
(supporting); Methodology (supporting); Visualization (support-
ing); Writing- review & editing (equal). David Koons: Formal analysis
(supporting); Methodology (supporting); Writing- review & editing
(equal). Ash Abebe: Conceptualization (supporting); Formal analy-
sis (supporting); Methodology (supporting); Writing- review & ed-
iting (equal). F. Stephen Dobson: Conceptualization (supporting);
Methodology (supporting); Writing- review & editing (equal). James
Barry Grand: Conceptualization (supporting); Formal analysis (sup-
porting); Funding acquisition (lead); Methodology (supporting);
Project administration (lead); Resources (lead); Supervision (lead);
Writing- review & editing (equal).
DATA AVA ILAB ILITY STATE MEN T
All data and code used in these analyses are accessible through
Dryad. https://doi.org/10.5061/dryad.4qrfj 6q88
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DUNHAM e t Al.
ORCID
Kylee D. Dunham https://orcid.org/0000-0002-9249-0590
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SUPPORTING INFORMATION
Additional supporting information may be found online in the
Supporting Information section.
How to cite this article: Dunham, K. D., Tucker, A. M., Koons,
D. N., Abebe, A., Dobson, F. S., & Grand, J. B. (2021).
Demographic responses to climate change in a threatened
Arctic species. Ecology and Evolution, 11, 10627– 10643.
https://doi.org/10.1002/ece3.7873
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