Arctic climate change and pollution impact little auk foraging and fitness across a decade

ABSTRACT Ongoing global changes apply drastic environmental forcing onto Arctic marine ecosystems, particularly through ocean warming, sea-ice shrinkage and enhanced pollution. To test

impacts on arctic marine ecological functioning, we used a 12-year integrative study of little auks (_Alle alle_), the most abundant seabird in the Atlantic Arctic. We monitored the foraging

ecology, reproduction, survival and body condition of breeding birds, and we tested linkages between these biological variables and a set of environmental parameters including sea-ice

concentration (SIC) and mercury contamination. Little auks showed substantial plasticity in response to SIC, with deeper and longer dives but less time spent underwater and more time flying

when SIC decreased. Their diet also contained less lipid-rich ice-associated prey when SIC decreased. Further, in contrast to former studies conducted at the annual scale, little auk fitness

proxies were impacted by environmental changes: Adult body condition and chick growth rate were negatively linked to SIC and mercury contamination. However, no trend was found for adult

survival despite high inter-annual variability. Our results suggest that potential benefits of milder climatic conditions in East Greenland may be offset by increasing pollution in the

Arctic. Overall, our study stresses the importance of long-term studies integrating ecology and ecotoxicology. SIMILAR CONTENT BEING VIEWED BY OTHERS POPULATION CHANGES IN A SOUTHERN OCEAN

KRILL PREDATOR POINT TOWARDS REGIONAL ANTARCTIC SEA ICE DECLINES Article Open access 28 October 2024 KEYSTONE SEABIRD MAY FACE THERMOREGULATORY CHALLENGES IN A WARMING ARCTIC Article Open

access 04 October 2023 LONG-DISTANCE WINTER MIGRATIONS OF CHINSTRAP PENGUINS AND ELEPHANT SEALS TO A PERSISTENT BLOOM AT THE EDGE OF THE ROSS GYRE Article Open access 21 March 2025

INTRODUCTION Arctic biotas are facing rapid environmental modifications. The Arctic is warming twice as fast as any other place on earth, with visible negative impacts on sea-ice

distribution, significantly changing wind regimes and precipitation levels1,2. The concurrent emergence of competitors3, parasites4 or pollutants5,6 poses additional new threats for Arctic

wildlife7. In this context, there is an urgent need for long-term monitoring programs to investigate Arctic species and ecosystem reactions to multiple environmental

modifications8,9,10,11,12. This is especially true in the North as arctic biomes are poorly studied compared to the rest of the world7. In this region, coastal ecosystems deserve particular

attention11; they host endemic seabirds which have been identified as powerful ecological indicators, and are emblematic for Arctic peoples9. Seabirds are subjected to a fair number of

long-term monitoring programs in polar regions8,13,14,15,16,17,18. Yet most of these studies focus on the sub-Antarctic and the Antarctic, and integrative, long-term studies of the impacts

of environmental changes on the foraging ecology and fitness proxies of arctic seabirds are rare (e.g.8,15,17,19). Those are however needed to fully apprehend the incidence of ecosystem

modifications on these vulnerable species. In this study, we focused on little auks (_Alle alle_), the most abundant seabird breeding in the Arctic (estimated 40 to 80 million

individuals20). Little auks are ecologically important in regional food webs21 and could be negatively impacted by ongoing environmental change22,23,24. Notably, little auks are

zooplanktivorous, and the distribution of their main prey, Calanoid copepods, is changing along with the warming of the North Atlantic. As a result, the range of the smallest and less

energy-rich species, _Calanus finmarchicus_, of Atlantic origin, is extending northwards25. It may result in replacement of the two larger and energetically favoured Arctic species, _C_.

_glacialis_ and _C_. _hyperboreus_, that are preferred by little auks26,27. Such a change could prevent birds from covering their energetic needs22,28. Although previous studies highlighted

that little auks from different colonies can demonstrate strong behavioural plasticity to foraging conditions and prey availability, these studies were performed at the scale of one or a few

breeding seasons29,30 and longer term impacts are unknown. Furthermore, the largest little auk populations rely on sea-ice and polynya31, which are likely to disappear soon from their

summer foraging grounds according to IPCC predictions1, and this could further modify bird foraging behaviour and reproduction32. Similarly, changes in wind regimes could directly affect

little auk energetics33 and their capacity to respond to the aforementioned changes. At a broader spatial scale, the North Atlantic Oscillation (NAO) index reflects climatic conditions and

is commonly used to test for the effects of climate on seabirds (e.g.34,35). The NAO index seems to be particularly well suited to studying the population dynamics of migrants that rely on

climatic clues36. As an example, survival of little auks breeding in Spitsbergen was linked to winter NAO with a time lag of 2 years, with negative effects on the birds being possibly

mediated through varying intakes of little auk prey23. In addition to these climatic and resource modifications, little auks could face large changes in the contamination of their

environment. For instance, mercury (Hg) concentrations measured in little auks from East Greenland and reflecting the contamination of their environment have increased by 3.4% per year over

the last decade37. Mercury is a powerful neurotoxin as well as an endocrine disruptor38 which could therefore have significant impacts on the reproduction of this arctic seabird

species39,40. High mercury concentrations could also act as an additional stress factor for adult birds and, in combination with other aforementioned environmental changes, indirectly impact

their body condition or foraging performances41. In this context, we propose to examine the multiannual behavioural plasticity of this species in response to environmental change and to

investigate impacts on bird fitness. More specifically, and based on recent findings for little auks and their prey, we tested the following hypotheses: (1) the proportion of ice-associated

prey in little auk chick diet is decreasing with decreasing sea-ice extent, and the proportion of _Calanus finmarchicus_ is increasing25. (2) Adult little auks modify their foraging

behaviour to cope with a changing environment during summer, to maintain their body condition and the provisioning of their chicks, thereby also maintaining chick growth rates29,42. (3)

Increasing Hg contamination of little auk environments directly impacts their breeding performances (hatching date, chick growth rate)39,40 and acts as an additional stress factor for

adults, affecting their body condition41. (4) Little auk inter-annual survival is impacted by environmental conditions, both during the breeding and the inter-breeding seasons23,43. To test

these hypotheses, we used the longest integrative time-series currently available with respect to little auk ecology and ecotoxicology, which we collected at the breeding colony of

Ukaleqarteq (East Greenland, Supplementary Fig. S1) during 12 consecutive summers (2004–2015). We investigated adult foraging behaviour, adult and chick diet, adult winter survival, chick

growth, hatching date, and tested the incidence of environmental conditions (sea-surface temperature, sea-ice concentration (SIC), wind force, North Atlantic Oscillation and Hg

concentrations inferred from levels measured in bird feathers). RESULTS A summary of sample sizes and biological parameters monitored annually is presented in Table 1. Despite a 12-year long

dataset, we were limited by gaps in some of our biological measures and environmental variables (Table 1). This prevented the use of a global approach with causal inference, such as path

analysis44, as well as the use of principal component analysis to reduce the number of environmental covariables45. For this reason, we decided to apply a hypothesis-based approach to target

specific questions. We therefore built independent regression models for each biological parameter studied, to test the effects of one or two environmental covariates at a time, to avoid

overfitting45. A summary of the results is presented in Table 2. ENVIRONMENTAL VARIABLES Over the period 2004–2015, mean summer SIC varied between 1.8 and 19.8% within the foraging range of

little auks (Fig. 1), allowing us to study the links between ice conditions and little auk ecology. SIC was negatively related to sea-surface temperature (SST, n = 12, R² = 0.74, p < 

0.001, y = −6.1x + 13.6) and to wind speed (n = 8, R² = 0.57, p = 0.03, y = −1.3x + 42.9, Fig. 1). No temporal trend was found for summer SIC during the study period, however, a decrease in

SIC was found in the same area for the period 1979–201426. In addition, the range of SIC encountered in our study period was lower than the range of SIC for the period 1979–2000 (10 to

40%26). Summer Hg contamination of little auk environment (derived from body feathers, BF, see methods for details) did not vary with SIC (p > 0.1). VARIATIONS IN CHICK AND ADULT DIET We

found an inter-annual variability in chick diet (Figs 2 and 3, n = 298). Changes in the proportions of _Calanus finmarchicus_, _C_. _glacialis_ and other prey were best explained by GAMs

(Fig. 3a–c) while proportions of _Apherusa glacialis_ and _C_. _hyperboreus_ were best explained by linear models (Fig. 3d,e). The proportion of _Calanus finmarchicus_ increased at higher

SIC (N = 298, p = 0.001, R² = 0.05, Fig. 3a). The proportion of _Calanus glacialis_ was lower when SIC was >10% (N = 298, p < 0.0001, R² = 0.13, Fig. 3b). The proportion of _Apherusa

glacialis_ increased with SIC (N = 298, p = 0.01, R² = 0.04, y = 0.035x − 2.1, Fig. 3d). The proportion of _C_. _hyperboreus_ did not vary with SIC (N = 298, p = 0.25, R² = 0.005, Fig. 3e).

The proportion of other prey was higher when SIC was <5% (N = 298, p < 0.0001, R² = 0.28, Fig. 3c). Adult diet, as reflected by δ15N isotopic values, did not change along with SIC (n =

 167, p > 0.1). However, adult foraging ecology changed over time (Fig. 4, trophic status δ15N: n = 167, R² = 0.32, p < 0.001, y = −138 + 0.07x, feeding habitat δ13C: n = 167, R² = 

0.36, p < 0.001, y = 96.4 − 0.06x). FORAGING BEHAVIOUR Analysis of dive depths for all dives across the study period showed a bimodal pattern with two distinct peaks: one with dives <7

 m and one with dives between 10 and 40 m (Fig. 5). Further investigations showed a link between diving behaviour and SIC (Fig. 6). Dives were deeper and the proportion of shallow dives

(<7 m) decreased when there was less ice (Fig. 6a,b; Maximum depth: n = 67, R² = 0.26, p < 0.001, y = −0.40x + 21.7, Proportion of shallow dives: n = 67, R² = 0.40, p < 0.001, y = 

1.68x + 5.74). Dives were also longer and birds performed fewer dives per day when SIC decreased (Fig. 6c,d; Dive duration: n = 67, R² = 0.38, p < 0.001, y = −0.91x + 63.0, Number of

dives per 24 h: n = 61, R² = 0.26, p < 0.001, y = 9.6x + 226.8). Birds spent slightly less time underwater and more time flying when SIC was low (Fig. 6e,f; Time underwater: n = 61, R² = 

0.09, p = 0.02, y = 0.31x + 17.0, Time spent flying: n = 53, R² = 0.12, p = 0.01, y = −0.62x + 38.0). There was no link between foraging trip duration and SIC (n = 53, R² = 0.004, p > 

0.1). ADULT BODY CONDITION, CHICK GROWTH AND HATCHING DATE Hatching date was delayed when SIC increased (n = 303, R² = 0.08, p < 0.001, y = 16.7 + 0.15x, Fig. 7a) but was not linked to

female exposure to Hg during winter (n = 225, p > 0.1). Chicks hatched later over time (n = 303, R² = 0.11, p < 0.001, y = −660 + 0.34x). A decrease in chick growth rate (mass gained

per day) was observed when SIC increased (n = 252, R² = 0.03, p = 0.007, y = −0.037x + 6.79, Fig. 7b). Chick growth rate decreased when summer Hg increased (n = 165, R² = 0.03, p = 0.02, y =

 −0.66x + 7.2, Fig. 7c) and chick growth rate tended to decrease over time (n = 252, R² = 0.02, p = 0.052, y = 103–0.05x). Adult body condition worsened with increasing summer Hg and

increasing SIC (n = 1051, R² = 0.04, p(Hg) < 0.001, p(SIC) < 0.001, y = 9.9–0.030x1–4.2x2–0.18x3, with x1 = day of year, x2 = Hg, x3 = SIC, Fig. 8), but there was no difference between

years (n = 1787, p > 0.1). SURVIVAL Results of the goodness-of-fit tests are detailed in the methods and in Table 3. A summary of the selection model process is presented in Table 4. We

constructed a model with capture heterogeneity due to the physical structure of the colony where some burrow entrances were harder to monitor46. Survival probabilities for the best model

(φ(t), p(het + t)) is presented in Fig. 9. No direct relationship was found between survival and environmental parameters (tested one by one: North-Atlantic Oscillation (current year,

previous year and two years before23), SST in their wintering area (current and previous year) and in their breeding area, SIC, wind conditions, summer and winter Hg). Survival probability

was lower for two years: 2006–2007 and 2012–2013 (Fig. 9). DISCUSSION Using a unique dataset of biological parameters from a 12-year long-term monitoring program in East Greenland, we found

that little auks are impacted by current environmental changes occurring in the Arctic. (1) As expected, the proportion of ice-associated species in chick diet was related to SIC, but the

proportion of _C_. _finmarchicus_ (copepod of Atlantic origin) did not decrease with increasing SIC. (2) Despite substantial plasticity in foraging behaviour and diet, adult body condition

and chick growth rates decreased when SIC increased. (3) Adult body condition and chick growth rate were negatively related to summer levels of mercury. (4) Despite these changes, adult

survival was not linked to environmental variables. METHODOLOGICAL CAVEATS Overall, we detected strong environmental impacts on little auks, but our work entails methodological limitations.

Indeed, organism answer to overall forcing is the integration of all environmental parameters, and disentangling the relative importance of each factor as well as their interactions requires

advanced statistical methods that we could not apply due to temporal gaps in our datasets (Table 1). Consequently, as we used only one or two environmental independent variables at a time

to explain one biological parameter with a hypothesis-based approach, the percentage of covariance explained by our significant relationships was low. It is therefore crucial to continue

long-term monitoring programs to increase sample sizes. In addition, we possibly missed important additional environmental factors. For instance, we know that the timing of breeding in

little auks is linked to the timing of snow melt in spring, which determines nest accessibility47. Spring snow melt can be approximated by spring temperature, but we did not have access to

this information at our study site. Also, other pollutants could, in addition to Hg, impact little auk reproduction and survival (e.g.48,49) but were not considered in the present study.

Concerning survival data, we could not take oil spills into account50, although it is known that little auks can be highly impacted during winter, with for instance an estimated 22,000

guillemots and little auks killed by hydrocarbon contamination in 2011–2012 off Newfoundland51. FORAGING PLASTICITY AS A BUFFER TO CLIMATE CHANGE Among all biological parameters

investigated, foraging behaviour was the most variable. While little auks foraged in the same areas at the shelf break with or without sea-ice26, we found that diving behaviour changed with

SIC: Birds performed shallower and shorter dives when SIC was the highest (Fig. 6). Thereby, dives <7 m (Fig. 5, Supplementary Fig. S2) probably reflected foraging directly underneath the

sea ice, to feed on sympagic species such as _Apherusa glacialis_, and indeed the proportion of this species in chick diet increased along with SIC (Fig. 3d). This was also supported by

preliminary results concerning birds for which diving behaviour and diet were collected simultaneously (n = 15, Amélineau _et al_. unpublished). Little auks seem capable of switching from

pelagic to below-ice feeding, and therefore to cope with a wide range of foraging conditions. Their energy expenditure as determined using the doubly-labelled water (DLW) technique thereby

seemed to remain unchanged29, yet additional studies combining 3D acceleration recordings of their actual foraging movements, and DLW are needed to fully test the impact of foraging

plasticity on energy balance52,53,54. Changes in little auk diet reflect their preferences, as well as prey availability in the environment. Recent studies suggest that little auks favour

larger and fattier species26,27 and, therefore, observed changes in prey proportions are likely to reflect the availability of larger prey species in the foraging range of birds. In the

North Atlantic Arctic, it is predicted that smaller _C_. _finmarchicus_ should be present during “warm” conditions (high SST, low SIC), and larger _C_. _glacialis_ and _C_. _hyperboreus_ in

“cold” conditions (low SST, high SIC)25,55,56. However, proportions of little auk main prey, the three species of _Calanus_, did not vary as expected: the proportion of _C_. _finmarchicus_

slightly increased at higher SIC, and the proportion of _C_. _glacialis_ decreased at higher SIC. Underlying mechanisms driving zooplankton abundance at a given place are complex and do not

depend solely on local summer SIC/SST, but might also vary with, for instance salinity and depth57 which were not included in this study. However, little auks have access to distant foraging

areas spread over dozens of kilometers where depth and salinity and thus prey availability might vary substantially27. In addition, it should be noted that although slightly increasing with

SIC, the proportion of _C_. _finmarchicus_ in chick diet remained low in comparison to other _Calanus_ species, whatever the SIC (Fig. 2). Interestingly, despite no clear pattern in chick

diet, adult diet shifted to a higher trophic level (higher δ15N values) and to more offshore feeding habitats (lower δ13C values) during the study period (Fig. 4). Such stable isotope

analyses are particularly integrative and could therefore reflect fine changes occurring in the longer term among the zooplankton community. Moreover, it is still unclear whether adults and

chicks feed on the exact same prey because the compositions of their diets have never been measured concomitantly. While stomach contents of adults are comparable to gular pouch

samples58,59, stable isotope studies suggest that there could be a difference60,61, as do our observations. More generally, little auks seem able to cope with different prey assemblages in

their environment27,30,62,63,64. DO LITTLE AUK LIVING CONDITIONS IMPROVE IN THE ABSENCE OF SEA-ICE? Little auks have a non-obligate affinity to sea-ice which varies according to location or

timing of the year. During the breeding season, they can forage at the marginal ice zone in Spitsbergen32,65 but can also thrive in the absence of sea-ice26,66. After the breeding season,

birds from different colonies migrate towards higher latitudes and the MIZ to moult67,68, before reaching their wintering grounds. At our study site, contrasting SIC from year to year

allowed to study impacts on little auk foraging and fitness proxies. Interestingly, adult body condition and chick growth rates were higher when SIC was low, meaning that less sea-ice and

higher SSTs provided better environmental conditions for breeding little auks in East Greenland. The link between environmental conditions and fitness proxies could be direct, as higher

temperatures reduce energy requirements for thermoregulation33, and energy gained could then be reallocated to body maintenance or chick rearing. Linkages could also be indirect, via trophic

interactions and bottom-up effects: lower SIC during summer reflects an earlier sea-ice breakup and a shorter lag between ice-algae bloom and pelagic phytoplankton bloom69. Our results

suggest that prey quality and/or availability would be better when sea-ice breakup occurs earlier. However, this is not in accordance with previous findings from Spitsbergen, where earlier

sea-ice breakup lead to a mismatch between algal blooms and copepod _C_. _glacialis_ phenology, and ultimately to a lower chick survival in little auks and Brünnich’s guillemots70,71.

Changes in the trophic interactions occurring in the Western Greenland Sea are probably more complex and reflects high variability of local conditions throughout the Arctic12. Lastly, SIC

encountered during the study period (2004–2015, 9.1 ± 6.8%) were already lower than SIC encountered during the period 1979–2003 (20.0 ± 10.4%)26 and may already feature suboptimal conditions

for little auks. Regarding SSTs, previous studies suggested that the most profitable foraging areas for little auks are located in the cold waters encountered in the Sørkapp Current (SW

Spitsbergen) and in the East Greenland Current that contain bigger _Calanus_ species27,72. Our results contrast with those from previous studies performed in Spitsbergen where higher SSTs

were associated with a higher proportion of _C_. _finmarchicus_ in the environment and in chick diet, and led to a lower chick survival or probability to fledge22,72, and to a lower adult

survival23. SSTs are higher in Western Spitsbergen than in East Greenland, and these contrasting findings suggest that little auk fitness could follow a quadratic relationship with SST,

peaking at intermediate SST. An increase in SST could then be beneficial at low SSTs (East Greenland, mean SST of 0.67 °C) but detrimental at high SSTs (Spitsbergen, mean SST of 1.81 °C at

Hornsund and 4.51 °C at Kongsfjorden). However, little auks did not seem impacted by observed changes in SST around Bjørnøya, where they foraged in warm waters (median SST of 6.6 °C) during

the period 2005–201266. Among pagophilic seabird species, reactions to variations in SIC are diverse and depend on species-specific sea-ice affinity. High SIC during the breeding season can

reduce access to prey and lead to lower breeding success for moderately pagophilic seabirds15,16,73. Species that are more dependent on sea-ice, on the contrary, have lower breeding success

and survival when SIC is reduced, and have to travel over longer distances to reach the MIZ74,75. At the larger temporal and spatial scales, changes in SIC likely lead to changes in species

range to the detriment of pagophilic species9,76. POLLUTANTS OFFSET OBSERVED BENEFITS FROM LOWER SIC Hg concentrations during summer increased in adult little auks, likely linked to an

increase in prey Hg concentrations over time37, but also to changes in diet towards prey that are higher in the food chain (biomagnifications)77, as reflected by the increase in δ15N in

adult blood (Fig. 4a). Although measured Hg concentrations were below toxicity thresholds38,78, they were negatively related to adult body condition and chick growth rate. These observed

effects suggest that Hg might act as a cumulative stressor which, in combination with other environmental constraints like the quality of their habitat or resource availability41, could

impact the condition of marine predators beyond its single effects. In addition, one should also bear in mind that little auks are exposed not only to Hg, but to a variety of pollutants

reaching the Arctic from northern mid-latitude industrial regions, some of them emerging but already of high concern in this sensitive region79. We specifically focused on Hg, which is known

to bioaccumulate in polar regions and severely impact marine top predators38,78, but these other pollutants may as well impact little auk metabolism and ultimately their body condition and

growth rate, such as organochlorine pesticides or PFASs80. In addition, Hg disrupts breeding behavior in black-legged kittiwakes and snow petrels40,81 and this mechanism could also explain

reduced chick growth when Hg concentrations are high in our study. However, no link was found between Hg and adult survival in our study, as well as in other seabird species48,82,83,84. Hg

levels in the Arctic are modified by ongoing environmental changes85. In particular, increasing Hg trends are expected with permafrost thawing, the warming of ocean water masses and

increasing human activities in the Arctic85,86,87. According to our results, negative effects of increasing Hg could offset observed positive effects of climate warming in East Greenland.

This stresses again the complexity of biological answers to environmental changes and the need for integrative approaches. CONCLUSION Understanding how animals will cope with environmental

changes is a topical challenge in ecology. Since the Arctic is warming twice as fast as the rest of the world it can be seen as a natural laboratory to anticipate changes occurring at a more

global scale. Unfortunately, logistical constraints, including year round access, limit fieldwork studies in this part of the world. In addition, biological responses are complex and

integrate environmental constraints which may be logistically difficult to evaluate at remote locations. Our results highlight the importance of pursuing long term monitoring programs in the

Arctic to improve dataset length and quality, and gain power to elaborate more complex models88. METHODS GENERAL FIELDWORK CONTEXT All field work in East Greenland was conducted in

accordance with guidelines for the use of animals89. Experiments were approved by the Danish Polar Center and the Government of Greenland, Ministry of Environment and Nature and Department

of Fisheries, Hunting and Agriculture (No. 512–240 (2005), No. 512-258 (2006), No. 07-501 (2007), No. 66.24/23 (2008), No. 66.01.13 (2009 and 2010), No. 2011-047447 (2011), No. 2012-065815

(2012), No. 2013- 083634 (2013), No. 2014-098814 (2014) No. 2015-115290 (2015). Little auks from Ukaleqarteq (Kap Höegh, East Greenland, 70°44′N, 21°35′W, Supplementary Fig. S1a) were

studied during the breeding season (mid-July/mid-August) from 2004 to 2015. Birds breed under rocks in steep boulder fields, where they raise a single offspring. Adult birds fly out to sea

where they forage on zooplankton, which they bring back to their chick in a sublingual pouch. During the inter-breeding period (Sept-May), birds migrate to wintering areas in the North

Atlantic, notably off Newfoundland90. Each summer, a set of biological parameters detailed below were monitored, and sample sizes are presented in Table 1. Adult birds were caught either in

their nest or on the surrounding rocks using a lasso or noose carpets. Breeding status was assessed by the presence of a chick, a full sublingual pouch or a brood patch. Handling time was

<10 min. For all sampling except for the survival study (see below), each year different individuals were studied. Therefore, our investigations were mainly conducted at the population –

and not at the individual – level. CHICK AND ADULT DIET Breeding adults were captured on arrival at the colony and the content of their sublingual pouch (chick diet) was removed and stored

either in 4% borax-buffered formaldehyde solution (2005 to 2007) or in 70% ethanol (2008 and beyond). Samples were identified to the lowest possible taxonomical level under a

stereomicroscope following groups presented in Harding _et al_.28. Adult diet was estimated from stable isotope analyses (δ15N and δ13C) performed on total blood samples61. δ15N isotopic

values reflect the relative trophic position of birds and are considered an indicator of their diet a couple of weeks before the sampling91. δ13C was also considered as an indicator of bird

foraging habitats with higher values representing more coastal habitats91. Blood samples (0.3 ml) were collected from bird brachial vein, stored in 70% ethanol and kept frozen at −20 °C.

Prior to analyses, blood samples were freeze-dried for 48 h and homogenized. Stable isotope analyses were then performed on ~0.5 mg subsamples of homogenized, non-lipid extracted whole blood

loaded into tin cups, and using an elemental analyzer (Flash EA 1112, Thermo Fisher) coupled in continuous flow mode to an isotope ratio mass spectrometer (Delta V Advantage, Thermo Fisher,

Bremen, Germany). Stable isotope abundances were expressed in δ notation as the deviation from standards in parts per thousand (‰) according to the equation: δX = [(Rsample/Rstandard) − 1] 

× 1000 where X is 13C or 15N and R is the corresponding ratio 13C/12C or 15N/14N. Standard values were Vienna Pee Dee Belemnite (VPDB) for C and atmospheric N2 (air) for N. Replicate

measurements of internal laboratory standards (acetanilide) indicated that the measurement error was <0.2% for both δ15N and δ13C values. FORAGING BEHAVIOUR Numbers of equipped birds are

presented in Table 1. Breeding adults were equipped with temperature-depth recorders (TDRs) attached ventrally, recording at 0.2, 0.5 or 1 Hz for 2–5 d during the chick-rearing period.

Details on TDR types and attachment methods are presented in Supplementary Methods and Supplementary Table S1. Data were analyzed with MultiTrace™ to extract maximum dive depth, dive and

pause duration for each dive. Depth was corrected for Star Oddi devices because they showed a slight underestimation of depth according to a calibration made in Amélineau _et al_.26. We also

measured time spent flying and foraging trip duration following Welcker _et al_.92, and calculated the time spent underwater, and the number of dives per day. For each parameter, a mean

value per individual was calculated and used for statistical analyses. HATCHING DATE AND CHICK GROWTH Nests were controlled for hatching date and chicks were weighed every second day. For

each chick, we calculated the chick growth rate (g d−1) as the slope of the linear growth period26 (4–14 days). Due to logistical constraints usually preventing measurements after August 10,

we could not control all nests until fledging to measure fledging success (range of fledging age = 21–31 days93). ADULT BODY CONDITION AND MERCURY CONTAMINATION Each handled adult was

weighed (g), and wing and head-bill lengths were measured (mm). We constructed a body condition index, correcting adult body mass by wing length and head-bill length to take bird size into

account. The body condition index was calculated as the residual body mass from a regression of body mass on wing length and head-bill length43. Total Hg was measured on one complete back

cover feather (abbreviated BF hereafter) or one complete throat feather (abbreviated HF hereafter) using an advanced Hg analyzer spectrophotometer (Altec AMA 254) as described in Bustamante

_et al_.94. Hg in little auk BF reflect the amount of Hg accumulated by birds during the previous breeding season spent in East Greenland (year preceding the sample), while Hg in HF reflect

the amount of Hg accumulated during the previous winter (see Fort _et al_.37). Hence, BF collected from 2007 to 2016 were analyzed for comparison with the biological time-series. Previous

studies showed that >90% of Hg in seabird feathers is methyl-Hg (Me-Hg), the most toxic form of Hg95. Total Hg measured in feathers is thus considered as an indicator of bird exposure to

Me-Hg. Hg concentrations measured in bird feathers are also an indicator of the contamination level of the food chain on which birds feed during both seasons37. Analyses were repeated two or

three times (two or three feathers) for each bird and feather type until the relative standard deviation for two samples was <10%; samples not meeting this criterion were excluded from

the analysis. The mean Hg concentrations for those two or three measurements were then considered for statistical analyses. To ensure the accuracy of measurements, a certified reference

material (CRM) was used [Lobster Hepatopancreas Tort-2; NRC, Canada; Hg concentration of 0.27 ± 0.06 µg.g−1 of dry weight (dw)]. The CRM was measured every 10 samples and the average

measured value was 0.26 ± 0.01 µg.g−1 dw (n = 113). Additionally, blanks were run at the beginning of each sample set. The detection limit of the method was 0.005 µg.g−1 dw. SURVIVAL

ANALYSIS One plot of the colony was dedicated to a capture-mark-recapture experiment. Birds (n = 333) were marked with a unique code composed of 3 colour rings and one metal ring. Each

season, recapture sessions lasted 6 d with 7 h of continuous observation per day. Data were analyzed using a capture-recapture model96 with E-SURGE. We first built a structural model without

any external covariate. To define the structural model, we first did single state goodness-of-fit tests (GOF, Table 3) using U-CARE97. Only the 2.CT test was significant, indicating a

difference in the probability of being recaptured at i + 1 for birds seen and not seen at occasion i. In order to take into account recapture heterogeneity among marked birds, we used a

model with two classes of capture98 and defined three states: individuals with a high recapture probability, individuals with a low recapture probability and dead individuals. Changes of

state between high and low recapture probability were not permitted. Such a structure explained our data better than a model including trap-dependence or allowing changes of recapture

probability through time (if, for example, this was linked to breeding status). Biologically, recapture heterogeneity was due to the structure of the colony, where some birds nest in areas

where it is more difficult to see them enter and leave their burrows. Such a model with recapture heterogeneity was used for least auklets (_Aethia pusilla_) that also breed in burrows like

little auks46. The model selection was conducted with E-SURGE99. The general starting model was (φ(t), p(het.t)), where “het” denotes the heterogeneous effect on capture with two levels

(seen with a high or low probability), t denotes the time effect. Models were selected based on Akaike100 Information Criterion (AIC) corrected for sample sizes and overdispersion (QAICc).

During first step, we selected the best model with only time and state as explanatory variables. In step two, we included environmental variables (one or two at a time45, the absence of

correlation was verified when two environmental variables were included simultaneously) to the model with the best structure in the first step (Table 4). ENVIRONMENTAL DATA For the summer

period, environmental data were dealt with within a 160 × 200 km plot surrounding the colony, which also included little auk at-sea habitats as determined through GPS tracking26

(Supplementary Fig. S1b). Monthly SST came from the Multi-scale Ultra-high resolution (MUR) SST analysis from the NASA (v4.1, global 0.01° resolution, monthly) and were acquired from

http://coastwatch.pfeg.noaa.gov/erddap/index.html. Monthly wind data came from the Metop/ASCAT data set (0.25° resolution, monthly, starting in 2007) from CERSAT and were acquired from

http://cersat.ifremer.fr/. Daily sea-ice concentration (SIC, percentage of sea surface covered by ice in a given area) came from the Eumetsat OSI SAF and were acquired from

http://osisaf.met.no/, using the Global Sea Ice Concentration reprocessing dataset (0.25° resolution, daily). For wind and SST data, we used monthly values for July and we calculated the

mean annual value for the foraging area defined above. For SIC, we calculated a mean annual value from the daily SIC concentration in the area between 15th July and 15th August.

Overwintering locations of birds from Ukaleqarteq were known from birds equipped with geolocators90, and we defined the core wintering area of birds as the 50% kernel area of positions

between 1st November to 28th February obtained for 94 little auks equipped between 2009 and 2015101. Yearly winter environmental conditions (wind speed, SST) were calculated as a mean value

within the core wintering area from November to February from monthly values of the datasets mentioned above. In addition, we calculated the proportion of days with high winds (mean daily

wind speed > 40 km/h) during the same period, using daily wind speeds from the Metop/ASCAT data set (0.25° resolution). Data for the North Atlantic Oscillation (NAO) came from the UCAR

and were acquired from https://climatedataguide.ucar.edu/climate-data. Herein we used the winter NAO index23. Winter environmental parameters were used for the survival analyses only.

STATISTICAL ANALYSES All analyses were performed with the R software (v. 3.4.2; R core team 2017). We adopted an hypothesis-based approach to study the link between environmental variables

and biological parameters, i.e. we tested specific linear models with one or two explanatory variables at a time that are meaningful in a biological context, instead of testing all possible

combinations of factors. This ensured to reduce type 1 errors, and to avoid overfitting, as well as issues regarding autocorrelation among environmental variables45. In particular, summer 

SIC, summer SST and summer wind were highly related (Fig. 1). Among these environmental variables, SIC was selected as the main environmental parameter to test, due to its strong and direct

influence on the foraging behaviour of little auks at our study site. Mean yearly Hg concentrations in head or body feathers were considered as environmental variables reflecting Hg found in

the environment in winter and in the previous summer, respectively37. Hg was either tested independently when a direct influence is expected (chick growth rate and hatching date), or tested

concomitantly with SIC when it is expected to be an additional stress factor (adult body condition). We also investigated temporal variations of biological parameters.”Day of year” was

included in the models with adult body condition as a response variable as body mass is slightly decreasing along the breeding season. For adult diet, foraging behaviour, adult body

condition and chick growth rate, we performed linear regressions to model the relationship between biological variables and environmental variables, when the assumptions were met. We did not

use mixed-effects models because each bird was only sampled one time. Results of the tests were considered significant when p-value was <0.05. For changes in prey proportion in chick

diet in relation to SIC, we first used Generalized Additive Models (GAMs) as no linear curve was expected. We performed a logit transformation of the proportions (p): log((p + a)/(1 − (p + 

a)), and where _a_ is a constant (_a_ = 0.1) in order to get a logit for all proportions including null values. Each GAM was then compared with a linear model and the model with the lower

AIC was retained (all differences in AIC were greater than 2). DATA AVAILABILITY All data are available on request from the CEFE CNRS database accessible here:

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Download references ACKNOWLEDGEMENTS This study and long-term monitoring program on little auks have been funded by the French Polar Institute (grant 388 to DG and JF), the National Science

Foundation (NSF Grant 0612504 to Nina Karnovsky & AMAH), the National Geographic Society (GEFNE 41–12 grant to JF), the Belmont Forum (TAMANI project to DG), the European Commission

(Marie Curie IEF and Marie Curie CIG to JF, Projects 273061 and 631203), the French Arctic Initiative – CNRS (PARCS project) and the Mission pour l’Interdisciplinarité – CNRS (Changements en

Sibérie project). We thank all the field assistants for their great work, Delphine Bonnet and Rumsais Blatrix for their help/technical assistance for the zooplankton identification, Francis

Crenner who kindly provided 4 LULs and Jean-Yves Barnagaud for inspiring discussions on the methods. We also thank the platforms ‘Spectrométrie Isotopique’ and ‘Analyses Elémentaires’ at

LIENSs, as well as G. Guillou and M. Brault-Favrou for their help with stable isotope and Hg analyses. AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * Centre d’Ecologie Fonctionnelle et

Evolutive (CEFE) UMR 5175, CNRS – Université de Montpellier – Université Paul-Valéry Montpellier – EPHE, Montpellier, France Françoise Amélineau, David Grémillet & Rémi Choquet *

Littoral Environnement et Sociétés (LIENSs), UMR 7266 CNRS - Université de La Rochelle, La Rochelle, France Françoise Amélineau & Jérôme Fort * Environmental Science Department, Alaska

Pacific University, Anchorage, AK, USA Ann M. A. Harding * Freshwater Institute, Fisheries and Oceans Canada, 501 University Crescent, Winnipeg, MB, Canada Wojciech Walkusz * Institute of

Oceanology, Polish Academy of Sciences, Sopot, Poland Wojciech Walkusz * Percy FitzPatrick Institute and DST/NRF Excellence Centre at the University of Cape Town, Rondebosch, South Africa

David Grémillet Authors * Françoise Amélineau View author publications You can also search for this author inPubMed Google Scholar * David Grémillet View author publications You can also

search for this author inPubMed Google Scholar * Ann M. A. Harding View author publications You can also search for this author inPubMed Google Scholar * Wojciech Walkusz View author

publications You can also search for this author inPubMed Google Scholar * Rémi Choquet View author publications You can also search for this author inPubMed Google Scholar * Jérôme Fort

View author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS Conceived and designed the experiments: D.G., A.M.A.H., J.F. Performed the experiments:

D.G., A.M.A.H., J.F., F.A. Analyzed the data: F.A., W.W., R.C. Wrote the paper: F.A., J.F., D.G. All authors reviewed the manuscript. CORRESPONDING AUTHOR Correspondence to Françoise

Amélineau. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no competing interests. ADDITIONAL INFORMATION PUBLISHER’S NOTE: Springer Nature remains neutral with regard to

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climate change and pollution impact little auk foraging and fitness across a decade. _Sci Rep_ 9, 1014 (2019). https://doi.org/10.1038/s41598-018-38042-z Download citation * Received: 13

June 2018 * Accepted: 10 December 2018 * Published: 31 January 2019 * DOI: https://doi.org/10.1038/s41598-018-38042-z SHARE THIS ARTICLE Anyone you share the following link with will be able

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