S L Happy
email: s-l.happy@inria.fr

Antitza Dantcheva
email: antitza.dantcheva@inria.fr

Abhijit Rd
email: abhijit.das@inria.fr

Das

Francois Bremond
email: francois.bremond@inria.fr

Radia Zeghari
email: radia.zeghari@gmail.com

Philippe Robert

Apathy Classification by Exploiting Task Relatedness

Keywords: Apathy diagnosis, expression recognition, multitask learning, task relatedness

Apathy is characterized by symptoms such as reduced emotional response, lack of motivation, and limited social interaction. Current methods for apathy diagnosis require the patient's presence in a clinic and time consuming clinical interviews, which are costly and inconvenient for both patients and clinical staff, hindering among others large-scale diagnostics. In this work we propose a multi-task learning (MTL) framework for apathy classification based on facial analysis, entailing both emotion and facial movements. In addition, it leverages information from other auxiliary tasks (i.e., clinical scores), which might be closely or distantly related to the main task of apathy classification. Our proposed MTL approach (termed MTL+) improves apathy classification by jointly learning model weights and the relatedness of the auxiliary tasks to the main task in an iterative manner. Our results on 90 video sequences acquired from 45 subjects obtained an apathy classification accuracy of up to 80%, using the concatenated emotion and motion features. Our results further demonstrate the improved performance of MTL+ over MTL.

I. INTRODUCTION

Apathy is defined as the quantitative reduction of goaldirected activity either in behavioral, cognitive, emotional or social dimensions [START_REF] Robert | Is it time to revise the diagnostic criteria for apathy in brain disorders? the 2018 international consensus group[END_REF]. Within the cognitive dimension loss of interest is a central feature. Given that it is the same in depression, it is not surprising that apathy and depression often co-occur in several psychiatric, neurological and neurodegenerative conditions [START_REF] Benoit | Depression and apathy in alzheimer's disease[END_REF]. It is therefore important to improve the detection of the possible differences. This is the case within the emotional dimension characterized in apathy by a limited emotional response to positive and negative events, whereas in depression the emotional response is always present but with emotional expression only sadness.

While experts suggest that the early indication of apathy could improve the intervention effects and decrease the global burden of the disease [START_REF] Hampel | Biomarkers for alzheimer's disease: academic, industry and regulatory perspectives[END_REF], apathy has been highly underdiagnosed. Its diagnosis, till date, is based on interviews with patients and their caregivers through a series of questionnaires. Towards assisting such subjective assessment, an objective and automated analysis carries the promise to enable early apathy diagnostics, leading to improved intervention effects, potentially increasing the performance of apathy detection in a non-invasive and efficient manner.

Motivated by the above, we introduce an automated system for apathy classification based on facial behavior analysis. Specifically, we investigate the effect of apathy on facial movements and expressions. To validate the reduced emotional responses of apathetic subjects, spontaneous expressions were elicited by asking the subjects to briefly narrate past positive and negative experiences. The clinical diagnosis (clinical scores) of the subjects was carried out by the psychiatrists along with the recording of facial videos during positive and negative narration. We explore the video data for apathy classification while leveraging the information in clinical scores during model training.

When a model is trained for one task, there is a possibility of overfitting the task as it does not generalize the noise pattern. Multi-task learning (MTL) [START_REF] Ruder | An overview of multi-task learning in deep neural networks[END_REF] helps in learning the relevant and irrelevant features for different tasks, thereby learning a suitable general representation that ignores the data-dependent noise. However, the central issue in MTL is to learn and explore the relatedness among tasks. We propose an iterative algorithm (termed MTL+) to jointly learn the MTL deep network parameters and the task relationship alternatively. Different to other MTL works, MTL+ focuses on improving the performance of main task (and ignoring the performance of other tasks) by automatically assigning lower weights to unrelated tasks and higher weights to similar tasks. We use apathy classification as main task, whereas prediction of other clinical scores are considered as auxiliary tasks.

In a nutshell, our proposed approach analyzes the facial expressions and face movement patterns to infer the apathy state. The video-level representation is utilized to regress the apathy related clinical scores through MTL. Further, we propose to jointly learn the model weights and the value of regularization parameters in an iterative manner (MTL+), thereby exploring the relationships of auxiliary tasks to the main task. The contributions of the paper are listed below.

• This paper is among the first to investigate automatic facial behavior analysis (facial movement and expression) for classifying apathy following our initial work [START_REF] Happy | Characterizing the state of apathy with facial expression and motion analysis[END_REF]. • We show that using MTL to estimate the clinical scores as auxiliary tasks improves the performance of apathy classification.

• We propose to learn the value of regularization parameters used in the loss function, thereby exploring the relationships of auxiliary tasks to the main task.

II. LITERATURE REVIEW

Apathy classification: Apathy recognition is a relatively new area of research in computer vision. Literature reported the use of the neuroimaging modalities for apathy diagnosis [START_REF] Agüera-Ortiz | Structural correlates of apathy in alzheimer's disease: a multimodal mri study[END_REF], [START_REF] Theleritis | A review of neuroimaging findings of apathy in alzheimer's disease[END_REF]. Structural and functional alteration of frontalsubcortical networks was observed in apathy patients through single-photon emission computed tomography, positron emission tomography, and diffusion tensor imaging [START_REF] Theleritis | A review of neuroimaging findings of apathy in alzheimer's disease[END_REF].

To the best of our knowledge, we are among the first to propose an approach based on facial behavioral analysis. The only other work on video-based apathy diagnosis used visual scanning behavior analysis [START_REF] Chung | Detection of apathy in alzheimer patients by analysing visual scanning behaviour with rnns[END_REF]. In this work, the sequences of eye fixations and saccades were investigated for emotional and non-emotional visual stimuli. The group difference and individual difference of scanning patterns were explored using recurrent neural networks (RNN). Deviating from their work, we here present a framework for apathy diagnosis based on facial motion and expression analysis from videos.

Multi-task Learning (MTL): MTL promotes sharing of model parameters to exploit the shared information across multiple tasks. An overview of MTL methods adopted in deep neural networks (DNN) was discussed by Ruder [START_REF] Ruder | An overview of multi-task learning in deep neural networks[END_REF]. However, sharing parameters with unrelated and dissimilar tasks usually degrades the performance, which is known as negative transfer. Many solutions are proposed to overcome this problem. Kang et al. [START_REF] Kang | Learning with whom to share in multi-task feature learning[END_REF] and Liu and Pan [START_REF] Liu | Adaptive group sparse multi-task learning via trace lasso[END_REF] grouped multiple tasks into several groups so that the shared features can be learned jointly. Lee et al. [START_REF] Lee | Asymmetric multi-task learning based on task relatedness and loss[END_REF] proposed a asymmetric MTL that jointly learn a regularization graph along with the task predictors to avoid performance degradation due to negative transfer. Pan et al. [START_REF] Pan | Task sensitive feature exploration and learning for multitask graph classification[END_REF] explored the common feature, task auxiliary feature, and task specific feature to indicate the shared features for each tasks. Similar learning of class relationship was carried out by Wu et al. [START_REF] Wu | Exploring interfeature and inter-class relationships with deep neural networks for video classification[END_REF] to train an unified framework for jointly learning feature relationship and class relatedness by imposing regularizations on the weights of the final output layer of DNNs.

In contrast to the aforementioned works, our method explores the relatedness of the predefined auxiliary tasks to the main task via alternating optimization (iteratively optimized). Our goal is to dynamically learn the relatedness of the auxiliary tasks to the main task so that the neural network model improves the performance of the main task while avoiding the negative transfer. Thus, our method allows weights of taskrelated loss to dynamically evolve in an adaptive manner for improving the performance of the main task.

III. PROPOSED METHOD

A. Feature Extraction

Long short-term memory (LSTM) or RNN are usually utilized to encode temporal dynamics [START_REF] Jiang | Modeling multimodal clues in a hybrid deep learning framework for video classification[END_REF] for activity classification or scene understanding. However, the problem at hand (apathy classification from facial videos) is more challenging, as opposed to the categories discussed above. We here note that even psychology experts are challenged in predicting the apathy state, by analyzing merely the face of a subject. Given that there is no particular temporal pattern associated with the collected video data, we approach the problem with a bag of visual words model, in which features extracted from each frame are further pooled for a codebook based representation of the whole video. Though this model lacks temporal relation, it is advantageous for us as the present data possesses no particular temporal pattern.

1) Emotion Features: As facial expressions are related to internal emotions, facial expression recognition has been widely researched for emotion analysis [START_REF] Corneanu | Survey on rgb, 3d, thermal, and multimodal approaches for facial expression recognition: History, trends, and affect-related applications[END_REF]. While expression recognition is predominantly based on the six-expression model [START_REF] Corneanu | Survey on rgb, 3d, thermal, and multimodal approaches for facial expression recognition: History, trends, and affect-related applications[END_REF], as agreed with involved clinicians, we here use three categories of expressions, namely: positive, negative, and neutral. This choice stems from the highly limited expressions expressed by the participants in the relative short video sequences under clinical conditions. Thus, we trained a convolutional neural network (CNN) model for expression classification with these three categories.

The use of pretrained VGG-Face [START_REF] Parkhi | Deep face recognition[END_REF] is a prominent architecture choice among recent works on face analysis. Since VGG-Face is trained with 2.6 million faces, it has been reported as a robust facial feature extractor, achieving promising results in facial expression recognition [START_REF] Ding | Facenet2expnet: Regularizing a deep face recognition net for expression recognition[END_REF], [START_REF] Liu | Conditional convolution neural network enhanced random forest for facial expression recognition[END_REF]. In such works, the last few layers of the VGG-Face are typically fine-tuned for respective applications. Publicly available expression datasets generally contain the universal classes ('anger', 'disgust', 'fear', 'happy', 'sad', and 'surprise') and we here directly use 'happy' and 'neutral' samples from the dataset during training, while grouping the 'sad', 'anger' and 'disgust' samples into the negative class. In our experiments, the first five convolutional blocks use the pretrained weights of VGG-Face, while the latter layer weights are trained with the publicly available datasets.

Symptoms of apathy include reduced emotional responses. Hence, we hypothesize that the expression intensity distribution and the duration of each expression throughout the video can be utilized to infer the apathy state. Therefore, we concatenate these two types of features for both positive and negative narration videos, and call them "emotional features". Expression intensity representation: We assume that the log probabilities of the softmax layer represent the emotion intensities corresponding to each category and pool the framewise expression intensities into a histogram vector. Thus, we obtain a histogram vector (b bins in each histogram) for each expression, which are further combined together (3 × b dimensional feature vector for 3 classes) as a representation of expression intensities for the whole video. Similar to the bag of words analysis, here the histogram features represent the probable occurrence of an expression with certain intensity. As per our hypothesis, apathetic subjects will show less expressions with subtle intensities [START_REF] Dantcheva | Expression recognition for severely demented patients in music reminiscence-therapy[END_REF], [START_REF] Wang | Comparing methods for assessment of facial dynamics in patients with major neurocognitive disorders[END_REF], thereby having higher bin counts in the first few bins. Expression duration: The duration of dominant expressions in a video is an important cue for accessing the overall emotional display. If e-th expression is dominant for n e number of frames out of total N number of video frames, then we use t e = ne N as the expression duration of e-th expression. The expression durations (t pos , t neg , t neut ) are appended to the expression representation, resulting in a 3×(b+1) dimensional feature vector.

2) Motion Features: From the psychological point of view, facial motion can also be a prominent indicator of apathy. Since apathy is characterized by limited verbal or nonverbal interaction along with lack of interest in surrounding environment, we investigate head and facial movements for apathy detection. Inspired by Hammal et al. [START_REF] Hammal | Automatic measurement of head and facial movement for analysis and detection of infants positive and negative affect[END_REF], the dynamics of head and facial landmarks are extracted. We estimate the non-rigid head movements by tracking the facial points. The non-rigid facial landmark movements are associated with lips, eyes, eyebrows, and chin, while having a conversation or showing an expression. Specifically, the average movement of facial landmarks around these regions in successive frames are computed as the motion feature.

Video sequences in our dataset are not time-limited and hence entail different video length. We obtain the statistical features (such as, minimum, maximum, mean, median, standard deviation, skewness, and kurtosis) of the motion information as the motion representation of a video. In addition, we include the b-bin histograms of motion values to preserve the motion intensity distribution information in motion representation, thereby making a vector of b + 7 dimensions.

B. Proposed MTL for learning task relatedness

We here propose an MTL method to leverage the information that can be learned from other related tasks and learn a general representation. In this work, apathy classification is considered as the main task, while the prediction of clinical scores are used as auxiliary tasks. Usually, MTL approaches consider the contribution of different tasks to be equal or based on a prior. However, no prior is available during our model training. Moreover, the relatedness of the tasks are not clear from facial analysis point of view. This motivated us to learn the relatedness of tasks while training MTL model to avoid negative transfer. We employ MTL to improve the performance of the main task (apathy classification) by leveraging the information from the auxiliary tasks (prediction of clinical scores). Usually, MTL approaches consider the contribution of different tasks to be equal or based on a prior. However, in our case, the relatedness of the tasks is not clear from facial analysis point of view. This motivated us to learn the relatedness scores to avoid negative transfer during training of MTL model.

MTL+: Suppose that there are T number of auxiliary supervised tasks for which we would like to find the relatedness to the main task ({λ a } T a=1 as shown in Figure 1). Thus, for each data sample x i , we have T + 1 number of label information (y m i : main task label and {y a i } T a=1 : auxiliary task labels). Let W 0 be the weight of the network which learns the shared parameters, whereas W m and {W a } T a=1 be the network weights related to the main task and the auxiliary tasks respectively. Thus, all the tasks help to learn the same feature space f ∈ F through W 0 followed by learning the weights for individual tasks. For a sample x i , let L(y m i , f (x i ; W 0 , W m )) and L(y a i , f (x i ; W 0 , W a )) be the loss functions associated with the main and auxiliary tasks respectively. MTL optimizes

arg min W0,Wm,{Wt} T a=1 N i=1 L(y m i , f (x i ; W 0 , W m ))+ T a=1 N i=1 λ a L(y a i , f (x i ; W 0 , W a )). (1)
For simplicity, we will use

W : {W 0 , W m , {W a } T a=1 }, L a : N i=1 L(y a i , f (x i ; W 0 , W a )).
In MTL, λ a is set based on prior knowledge. However, we jointly learn W and {λ a } T a=1 in a alternating manner. Note that we seek to improve the performance of main task, irrespective of its performance in auxiliary tasks. Thus, the optimization performed in our framework is

arg min W,{λ a } T a=1 L m + T a=1 λ a L a .
(

) 2
Since there are two parameters to be learned, it is not easy to optimize the loss against both at the same time. Doing so will result in a trivial solution of λ a = 0, ∀a, thus nullifying the loss incurred due to the auxiliary tasks. Therefore, we optimize W, while updating λ a after some epochs iteratively. The weight update is given by

W ←-W -η 1 ∂L ∂W . (3)
We propose to update λ a in a similar manner using the gradient of loss function. However, ∂L ∂λ a = L a . Therefore, λ a can be updated as

λ a ←-λ a -η 2 L a . ( 4
)
λ a can be interpreted as the importance coefficient of the loss contributed by a-th task to the total loss, thus signifies the relatedness of tasks. This learning process might get stuck in a local minima during the initial stages of training. To avoid that, we initialize λ a = 1, ∀a and train W to have fair performance. Then, λ a is updated intermittently, while further optimizing W to improve the performance of the main task. This method is termed as MTL+ in this work. Note that η 1 and η 2 are learning rates.

Early stopping criterion: Since λ a is penalized intermittently according to the loss value incurred by task a, it is possible that this value will reduce close to zero after sufficient number of updates. To avoid that, we propose an effective early stopping criterion to stop the further update of λ a before they begin to over-fit the main task. For a given constant > 0, we stop updating λ a for the task that satisfy

k.med t j=t-k E a val (j) t j=t-k E a val (j) -k.med t j=t-k E a val (j) > , (5)
where E a val (j) is the loss values of the a-th task at j-th iteration for validation data; and med stands for median.

IV. EXPERIMENTS AND RESULTS

A. Dataset Description

The dataset was recorded at the Nice Memory Research Center located at the Institute Claude Pompidou in the Nice University Hospital. The patients suffering from subjective memory complaint to severe cognitive impairment were included in the study. We use the data from 45 subjects in our study, out of which 18 are with apathy condition. Among the apathy and control subjects, the number of female patients were 38% and 62% respectively.

The patient-clinician interview involves (i) the collection of demographic details, (ii) standardized neuropsychological assessment tests, and (iii) a short positive and negative experience narration. The neuropsychological assessment was carried out by one-on-one interview with a battery of cognitive tests to access the anxiety, affect, interest, etc. In our experiment, we use nine clinical scores: mini mental state examination (MMSE) [START_REF] Folstein | mini-mental state: a practical method for grading the cognitive state of patients for the clinician[END_REF], and neuropsychiatric apathy inventory (NPI-apathy, NPI-anxiety, NPI-depression, NPI-total) [START_REF] Cummings | The neuropsychiatric inventory: comprehensive assessment of psychopathology in dementia[END_REF], clinical dementia score (CDR), and apathy inventory (IAaffect, IA-initiative, IA-interest) [START_REF] Benoit | Brain perfusion correlates of the apathy inventory dimensions of alzheimer's disease[END_REF]. To elicit spontaneous facial expressions, in (iii) the participants were asked to narrate some positive and negative events or experiences from their past ("tell me a positive/negative event of your life in one minute"). The video data was recorded with a tablet controlled by the psychologist. Though most of the videos have nearfrontal face, a number of pose variations and facial occlusions are present in the dataset. Moreover, the expressions is very subtle and the average video length is around one minute.

B. Implementation details

Prior to the main framework, we detect faces from the dataset-videos using MTCNN [START_REF] Zhang | Joint face detection and alignment using multitask cascaded convolutional networks[END_REF], followed by face alignment by positioning both eyes at a fixed distance parallel to the horizontal axis. The aligned faces are re-sized to 224×224 resolution, constituting the input for the CNN model. The CNN model is trained to classify the face into three expression classes, namely: positive, negative and neutral. We use Affect-Net [START_REF] Mollahosseini | Affectnet: A database for facial expression, valence, and arousal computing in the wild[END_REF] dataset to train the CNN model. The Adam optimizer with β 1 = 0.9, β 2 = 0.999 and a learning rate of 0.0001 is used for training the deep model. The facial landmarks are detected using DLIB [START_REF] Kazemi | One millisecond face alignment with an ensemble of regression trees[END_REF] in order to compute the motion features. In all our experiments, we consider the histograms with 10 bins (b = 10) for both motion and emotion feature extraction. The extracted features are further normalized to zero mean and unit variance before feeding into the MTL framework, which consists of two dense layers with 128 and 32 units respectively with dropout ratio of 0.5 followed by the output layer with 10 units for classification/regression tasks. In our case, apathy classification is the main task, while predicting the values of the clinical scores through regression is considered as the auxiliary tasks.

All the results reported here are obtained by performing 10fold cross validation. Note that the validation set used in our experiments is different from the test set. 10% of the samples from the train data are selected randomly to constitute the validation set.

C. Experimental Results

Combining features of positive and negative narration:

The performance of apathy classification for various features is provided in Table I. Note that our dataset contains two videos (positive and negative narration) per subject. Here 'after fusion' refers to the experiments where the features from these two videos are concatenated and used for model training. MTL is not performed to obtain the results of Table I.

As per Table I, the performance achieved 'without fusion' is very poor given the data imbalance and the binary classification problem. After fusion, the accuracy of the framework improves from 52% to 64.44% using the emotion features. The best performance (accuracy = 77.77% and F1-score = 0.757) is achieved when emotion features are combined with motion features. This proves the complementary information present in motion and emotion features.

From psychological point of view, apathetic persons are indifferent toward any emotions and hence expressions. However, the healthy subjects exhibit limited expressions in a clinic environment (such as ours) as well, which render classification challenging. Hence, the presence of both positive and negative narrations per subject is pertinent, and combining the features extracted from both videos-sequences provides a broader spectrum of facial expressions instrumental for apathy classification. Performance of proposed MTL: Table II reports the performance of MTL with and without learning the task relatedness. Note that all results reported in Table II are obtained by combining the features from both positive and negative narrations. Comparing the accuracy in Table I and II, we observe performance improvement with MTL when individual features are used (57% to 62% for Motion; and 64% to 66% for Emotion). However, the performance of Motion + Emotion features is degraded from 77% to 71% by using MTL, which is further boosted to 80% by MTL+. We believe that the reduced performance is due to the negative transfer, which was avoided in MTL+ to obtain better performance. As can be seen from Table II, the performance of MTL improves significantly by learning the task relatedness (MTL+). For instance, the accuracy of motion features improved from 62% to 71%, and the accuracy of emotion features improved from 66% to 77%. Similarly, we observe significant improvement of the F1-score for MTL+ as well. The best performance (accuracy = 80% and F1-score = 0.78) is achieved by MTL+ using the concatenated the motion and emotion features. The confusion matrices of different feature extraction methods are reported in Figure 2. Task relatedness: The task relatedness learned by the proposed method is shown in Figure 3. The variation of the task relatedness is visualized here by using median and lower-upper quartile values of {λ t } T t=1 obtained during training over the 10-folds. As can be seen, IA-affect and NPI-depression are found to be highly related to the apathy classification task. More importantly, this illustrates the importance of emotion and motion features for estimating the other clinical scores. The tasks that are assigned low relatedness score contributes less toward the loss function, thus avoiding the negative transfer during model training.

V. CONCLUSIONS

We present an automatic apathy detection method, which analyzes facial emotion and motion while sharing the knowledge contained in the training signals of other tasks. Due to the uncertainty in the relatedness of auxiliary tasks, we propose a framework (MTL+) to jointly learn the model weights along with the task relatedness, thereby avoiding negative transfer by the distantly related tasks. Our framework benefits from the concatenation of features from both positive and negative narration videos. Experimental results show that the performance may improve or degrade by MTL, while the proposed MTL+ consistently achieves better results to other methods. We obtain the best results (accuracy = 80%, F1-score=0.78) by using MTL+ on the combination of emotion and motion features.

Fig. 1 :

 1 Fig. 1: The proposed MTL+ framework for jointly learning the task relatedness and model parameters.

Fig. 2 :

 2 Fig. 2: The confusion matrices obtained with proposed MTL for (a) motion features, (b) emotion features, and (c) combination of motion and emotion features.

Fig. 3 :

 3 Fig. 3: The relatedness of different tasks to apathy classification for the concatenated motion and emotion features.

TABLE I :

 I Performance improvement by fusion of features from positive and negative narration. (without using MTL) Without fusion of positive and negative narration After fusion of positive and negative narration

	Features used	Accuracy	F1-score	Accuracy	F1-score
	Motion Features	58.88	0.505	57.77	0.555
	Emotion features	52.68	0.532	64.44	0.622
	Emotion features + Motion Features	53.86	0.526	77.77	0.757

.

TABLE II :

 II Performance comparison when features from positive and negative narrations are concatenated. (MTL: mutltitask learning considering equal contribution of each task; MTL+: proposed method that exploits task relatedness.) The proposed MTL+ improves the classification accuracy.

	Features used	Accuracy F1-score
	MTL with Motion Features	62.22	0.582
	MTL with Emotion features	66.66	0.638
	MTL with Emotion + Motion Features	71.11	0.716
	MTL+ with Motion Features	71.11	0.679
	MTL+ with Emotion features	77.77	0.776
	MTL+ with Emotion + Motion Features	80.00	0.786


Marina Renedo
email: marina.renedo@ird.fr

David Amouroux

Céline Albert

Sylvain Bérail

Vegard S Bråthen

Maria Gavrilo

David Grémillet

Hálfdán H Helgason

Dariusz Jakubas

Anders Mosbech

Hallvard Strøm

Emmanuel Tessier

Katarzyna Wojczulanis- Jakubas

Paco Bustamante

Jérôme Fort
email: jerome.fort@univ-lr.fr

Contrasting spatial and seasonal trends of methylmercury exposure pathways of Arctic seabirds: combination of large-scale tracking and stable isotopic approaches

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

TOCArt-graphical abstract 1 Introduction

Mercury (Hg) induces major risks for wildlife and human health, especially under its methylated form (methylmercury, MeHg), a potent bioaccumulative neurotoxin 1 , which is mainly assimilated via fish and seafood consumption. In the ocean, MeHg production mainly occurs by biotic in situ methylation of inorganic Hg 2,3 . Once formed, MeHg incorporates into the food webs and biomagnifies its concentrations leading to lifeimpacting levels in top predators and humans. Despite little direct anthropogenic pressure in the Arctic region, Arctic ecosystems are subject to contamination by Hg transported from lower latitudes. Indeed, total Hg concentrations measured in the Arctic surface seawater are up to 2-fold higher compared to other oceanic regions 4,5 . Sea-ice melting, direct atmospheric deposition and continental inputs originating from soil erosion and riverine circulation are considered major drivers of the high Hg levels in the Arctic [6][7][8][9][10] .

However, the MeHg production pathways and zones in the Arctic Ocean are still not completely identified. Several studies demonstrated that Hg in Arctic marine environments may be methylated in the water column or sediments 2,11 . Potential Hg methylating bacteria were also identified in Antarctic sea ice [START_REF] Gionfriddo | Microbial Mercury Methylation in Antarctic Sea Ice[END_REF] . Recent findings and modelling studies evidenced that the largest net MeHg production in Arctic water columns may occur in oxic waters at the subsurface layer (20-200 m) 6,[START_REF] Heimbürger | Shallow Methylmercury Production in the Marginal Sea Ice Zone of the Central Arctic Ocean[END_REF] . A new study also reported the high abundance of Hg methylating gens in the oxic subsurface waters of the global ocean [START_REF] Villar | Widespread Microbial Mercury Methylation Genes in the Global Ocean[END_REF] , where the highest MeHg concentrations are typically observed 4 .

All these findings suggest that Hg methylation in oxic waters could be a significant source of MeHg towards Arctic marine food webs. Although policy implementations for the reduction of anthropogenic Hg emissions were achieved over the last 30 years in some parts of the world, Hg levels continue to increase in biota from several regions of the Arctic [START_REF]Arctic Monitoring and Assessment Program[END_REF] . Medium to high predators such as seabirds are exposed to significant environmental MeHg concentrations through their diet [START_REF]Arctic Monitoring and Assessment Program[END_REF][START_REF]AMAP Assessment 2018: Biological Effects of Contaminants on Arctic Wildlife and Fish[END_REF] and have been extensively studied as bioindicators of Hg exposure in marine food webs (e.g [START_REF] Thompson | Historical Changes in Mercury Concentrations in the Marine Ecosystem of the North and North-East Atlantic Ocean as Indicated by Seabird Feathers[END_REF][START_REF] Carravieri | Penguins as Bioindicators of Mercury Contamination in the Southern Indian Ocean: Geographical and Temporal Trends[END_REF] ), including the Arctic [START_REF] Rigét | Temporal Trends of Hg in Arctic Biota, an Update[END_REF][START_REF] Dietz | What Are the Toxicological Effects of Mercury in Arctic Biota?[END_REF][START_REF] Albert | Using Blood and Feathers to Investigate Large-Scale Hg Contamination in Arctic Seabirds: A Review[END_REF] . Specific foraging habitats and migratory movements of Arctic seabirds strongly determine their exposure to distinct environmental MeHg sources in marine ecosystems [START_REF] Fort | Spatial Ecotoxicology: Migratory Arctic Seabirds Are Exposed to Mercury Contamination While Overwintering in the Northwest Atlantic[END_REF][START_REF] Fleishman | Wintering in the Western Subarctic Pacific Increases Mercury Contamination of Red-Legged Kittiwakes[END_REF] . However, studies on Hg exposure in Arctic seabirds have commonly put the focus towards the breeding season (summer) when seabirds are more accessible for researchers. Consequently, the investigation of Hg exposure during the non-breeding season is still scarce due to sampling logistical difficulties.

The combination of carbon and nitrogen stable isotopes with Hg stable isotopes has demonstrated its suitability for the identification of Hg sources and the associated geochemical processes in the different marine compartments [START_REF] Renedo | Identification of Sources and Bioaccumulation Pathways of MeHg in Subantarctic Penguins : A Stable Isotopic Investigation[END_REF][START_REF] Point | Methylmercury Photodegradation Influenced by Sea-Ice Cover in Arctic Marine Ecosystems[END_REF][START_REF] Day | Mercury Stable Isotopes in Seabird Eggs Reflect a Gradient from Terrestrial Geogenic to Oceanic Mercury Reservoirs[END_REF] . Therefore, its use can help understanding Hg exposure pathways of seabirds according to their migratory behaviour. Hg has seven stable isotopes (196 to 204) and fractionates dependently and independently of the isotopic masses. The combined use of Hg isotopic mass-dependent (MDF, e.g. δ 202 Hg) and mass-independent (MIF, e.g. Δ 199 Hg) fractionation enables the quantification of processes and the identification of sources and pathways of Hg in the environment [START_REF] Blum | Mercury Isotopes in Earth and Environmental Sciences[END_REF] , including marine ecosystems [START_REF] Point | Methylmercury Photodegradation Influenced by Sea-Ice Cover in Arctic Marine Ecosystems[END_REF][START_REF] Day | Mercury Stable Isotopes in Seabird Eggs Reflect a Gradient from Terrestrial Geogenic to Oceanic Mercury Reservoirs[END_REF][START_REF] Li | Assessing Sources of Human Methylmercury Exposure Using Stable Mercury Isotopes[END_REF][START_REF] Cransveld | Mercury Stable Isotopes Discriminate Different Populations of European Seabass and Trace Potential Hg Sources around Europe[END_REF] . MDF of Hg isotopes occurs during many physical, chemical or biological processes [START_REF] Kritee | Mass Dependent Stable Isotope Fractionation of Mercury during Mer Mediated Microbial Degradation of Monomethylmercury[END_REF][START_REF] Kritee | Mercury Stable Isotope Fractionation during Reduction of Hg(II) by Different Microbial Pathways[END_REF][START_REF] Zheng | Mercury Isotope Fractionation during Volatilization of Hg(0) from Solution into the Gas Phase[END_REF][START_REF] Kwon | Application of Mercury Isotopes for Tracing Trophic Transfer and Internal Distribution of Mercury in Marine Fish Feeding Experiments[END_REF] . However, large Hg MIF of odd isotopes (Δ 199 Hg and Δ 201 Hg) is observed during light-induced reactions, such as inorganic Hg photoreduction and MeHg photodemethylation. Hg MIF signature is not affected by biological or trophic processes, so it is preserved up to the food webs [START_REF] Kwon | Absence of Fractionation of Mercury Isotopes during Trophic Transfer of Methylmercury to Freshwater Fish in Captivity[END_REF] , then presenting a significant advantage to trace Hg marine sources. For instance, Arctic marine top predators reported much higher Hg odd MIF values (more photochemically impacted Hg) in non-ice covered regions, relating the importance of the accelerated melting of sea ice on the Hg polar cycle [START_REF] Point | Methylmercury Photodegradation Influenced by Sea-Ice Cover in Arctic Marine Ecosystems[END_REF][START_REF] Masbou | Stable Isotope Time Trend in Ringed Seals Registers Decreasing Sea Ice Cover in the Alaskan Arctic[END_REF] . Also, consistent decrease of Hg odd MIF (and MDF) in pelagic fish according to their foraging depth in the North Pacific Ocean demonstrated the dilution of surface MeHg by in situ methylated Hg at depth 3 . More recently discovered, Hg MIF of even Hg isotopes (reported as Δ 200 Hg) seems to occur during complex atmospheric mechanisms such as photo-oxidation in the tropopause [START_REF] Chen | Unusual Fractionation of Both Odd and Even Mercury Isotopes in Precipitation from Peterborough, ON, Canada[END_REF] . Even-MIF is not induced during any biogeochemical nor photochemical processes in the lower troposphere or the photic zone [START_REF] Chen | Unusual Fractionation of Both Odd and Even Mercury Isotopes in Precipitation from Peterborough, ON, Canada[END_REF][START_REF] Gratz | Isotopic Composition and Fractionation of Mercury in Great Lakes Precipitation and Ambient Air[END_REF][START_REF] Sherman | Frost Flowers Growing in the Arctic Ocean-Atmosphere-Sea Ice-Snow Interface: 2. Mercury Exchange between the Atmosphere, Snow, and Frost Flowers[END_REF] , therefore the signature is preserved and useful to identify major potential Hg sources of atmospheric origin 10,[START_REF] Demers | Mercury Isotopes in a Forested Ecosystem: Implications for Air-Surface Exchange Dynamics and the Global Mercury Cycle[END_REF][START_REF] Enrico | Atmospheric Mercury Transfer to Peat Bogs Dominated by Gaseous Elemental Mercury Dry Deposition[END_REF] . Due to the different combinations of the processes involving Hg transformations in the environment, Hg isotopes fractionate differently and with different degrees of magnitude in every specific environmental compartment. Thus, the analysis of Hg stable isotopes of mobile predators such as Arctic seabirds can give access to interesting information about MeHg trophic sources at large scales of the Arctic Ocean and neighbouring water bodies.

Here we propose an original approach consisting in the combination of isotopic analyses (Hg, C and N) and wildlife tracking to provide new information about MeHg exposure pathways of seabirds at both temporal and spatial scales. For this purpose, we focused on the little auk (or dovekie, Alle alle), the most numerous seabird species breeding in the High Arctic (between 37 to 40 million breeding pairs estimated [START_REF] Keslinka | Determinants of the Little Auk (Alle Alle) Breeding Colony Location and Size in W and NW Coast of Spitsbergen[END_REF][START_REF] Kovacs | Birds and Mammals of Svalbard[END_REF] ). Little auks have several ecological advantages for their use as a bioindicator models. 1) They are zooplanktivorous and mainly feed on copepods belonging to two Calanus species (i.e., C. glacialis and C. hyperboreus) during the breeding period [START_REF] Harding | Can Stable Isotope (Δ13C and Δ15N) Measurements of Little Auk (Alle Alle) Adults and Chicks Be Used to Track Changes in High-Arctic Marine Foodwebs?[END_REF] . Therefore, they reflect MeHg accumulation in a short food chain that is strongly dependent on sea ice abundance and seawater temperature [START_REF] Grémillet | Little Auks Buffer the Impact of Current Arctic Climate Change[END_REF] . 2) They exhibit colony specific wintering areas [START_REF] Fort | Multicolony Tracking Reveals Potential Threats to Little Auks Wintering in the North Atlantic from Marine Pollution and Shrinking Sea Ice Cover[END_REF] , then reflecting wide-ranging spatial variability of Hg exposure [START_REF] Fort | Does Temporal Variation of Mercury Levels in Arctic Seabirds Reflect Changes in Global Environmental Contamination, or a Modification of Arctic Marine Food Web Functioning ?[END_REF] . 3) Little auks moult their feathers twice during their annual cycle: a partial moult (head, neck and throat feathers, hereafter 'head feathers') during the pre-breeding period (in ca. April) and a complete post-breeding moult in September [START_REF] Heimbürger | Shallow Methylmercury Production in the Marginal Sea Ice Zone of the Central Arctic Ocean[END_REF] . During moult, seabirds excrete the Hg accumulated in their body tissues into feathers [START_REF] Furness | Using Bird Feathers to Measure Mercury in the Environment: Relationships between Mercury Content and Moult[END_REF] . Thus, feather Hg reflects blood Hg levels at the time of feather growth which occurred at the last moulting sequence [START_REF] Honda | Seasonal Changes in Mercury Accumulation in the Black-Eared Kite, Milvus Migrans Lineatus[END_REF] , then integrating Hg from current diet and/or the remobilization of Hg from tissues during moult. Thereby, the different Hg integration times between the types of feathers allow studying Hg exposure during both the non-breeding (head feathers) and the breeding (body feathers) periods in a same individual [START_REF] Fort | Does Temporal Variation of Mercury Levels in Arctic Seabirds Reflect Changes in Global Environmental Contamination, or a Modification of Arctic Marine Food Web Functioning ?[END_REF] . Besides, C and N isotopic ratios of blood sampled during the breedingchick rearingperiod can provide information about summer diet and then be compared to Hg levels and isotopic composition in body feathers of little auks [START_REF] Hobson | Assessing Avian Diets Using Stable Isotopes I: Turnover of 13C in Tissues[END_REF][START_REF] Bearhop | Factors That Influence Assimilation Rates and Fractionation of Nitrogen and Carbon Stable Isotopes in Avian Blood and Feathers[END_REF] . We hypothesized that variations of tissue-specific Hg isotopic signatures (body vs head feathers) will allow reflecting the seasonal variability (summer vs winter) on the Hg cycling. Besides, the exploration of both spatial grounds and isotopic information (Hg, C and N) would help tracking distinct sources of Hg contamination along with seabird migratory circulation.

Material and methods

Sampling sites and description of sample collection

This study was conducted during the seabird breeding seasons of 2015 and 2016 at five colonies of the Arctic Ocean: Franz Josef Land (FJL) (Hooker Island; ). Blood and feathers were sampled from ten individuals per colony (n=50 for the 5 colonies). Individuals from all sites, but Thule, were equipped with a miniature geolocator data-logger (GLS, Biotrack MK4083 or Migrate Technology C65) to track their non-breeding movements and distribution, as described in previous works [START_REF] Fort | Multicolony Tracking Reveals Potential Threats to Little Auks Wintering in the North Atlantic from Marine Pollution and Shrinking Sea Ice Cover[END_REF][START_REF] Fort | Remotely-Sensed Oceanography and the Continuous Plankton Recorder Reveal the Environmental Determinants of a Seabird Wintering Hotspot[END_REF][START_REF] Grissot | Breeding Status Influences Timing but Not Duration of Moult in the Northern Fulmar Fulmarus Glacialis[END_REF] . We treated GLS tracking data from 1 st December to 30 th January (period when all little auks were at their winter grounds) and calculated the median individual winter latitude and longitude for each individual separately.

Description of analytical methods

Sample preparation, analyses of total Hg and Hg species concentrations

Body and head feathers were cleaned in a 2:1 chloroform:methanol solution for 5 min in an ultrasonic bath, followed by two methanol rinses to remove surface impurities, and then oven dried at 50 °C during 48 h and homogenized to powder [START_REF] Fort | Does Temporal Variation of Mercury Levels in Arctic Seabirds Reflect Changes in Global Environmental Contamination, or a Modification of Arctic Marine Food Web Functioning ?[END_REF] . Since fluctuations of Hg concentrations have been observed among and within individual feathers from the same bird [START_REF] Carravieri | Moulting Patterns Drive Within-Individual Variations of Stable Isotopes and Mercury in Seabird Body Feathers: Implications for Monitoring of the Marine Environment[END_REF][START_REF] Peterson | Mercury Concentrations Vary Within and Among Individual Bird Feathers: A Critical Evaluation and Guidelines for Feather Use in Mercury Monitoring Programs[END_REF] , we pooled a representative number of feathers of each individual (5-8 feathers) to limit the variability and provide results as accurately as possible. Blood samples were dried, lyophilized and ground to powder as described in a previous work [START_REF] Fort | Does Temporal Variation of Mercury Levels in Arctic Seabirds Reflect Changes in Global Environmental Contamination, or a Modification of Arctic Marine Food Web Functioning ?[END_REF] .

Feather and blood total Hg concentration (hereafter expressed as μg•g -1 , dry weight) was quantified by using an advanced Hg analyser (AMA-254, Altec).

Prior to Hg speciation analyses, blood and feathers (0.01-0.05 g) were digested following a previously developed method by microwave-assisted extraction (using a Discover SP-D microwave, CEM Corporation) [START_REF] Renedo | Assessment of Mercury Speciation in Feathers Using Species-Specific Isotope Dilution Analysis[END_REF][START_REF] Renedo | Seabird Tissues As Efficient Biomonitoring Tools for Hg Isotopic Investigations: Implications of Using Blood and Feathers from Chicks and Adults[END_REF] . We used 5 mL of tetramethylammonium hydroxide (25% TMAH in H2O, Sigma Aldrich) for blood samples and 5 mL nitric acid (HNO3•6N, INSTRA quality) for feather Hg extraction. The extraction was carried out in CEM Pyrex vessels by 1 min of warming up to 75 °C and 3 min at 75 °C with magnetic agitation to homogenise the samples. Quantification of Hg species was carried out by isotope dilution analysis (details in [START_REF] Renedo | Assessment of Mercury Speciation in Feathers Using Species-Specific Isotope Dilution Analysis[END_REF] ), using a GC-ICP-MS Trace Ultra GC equipped with a Triplus RSH autosampler coupled to an ICP-MS XSeries II (Thermo Scientific, USA). We performed Hg speciation analyses of certified reference materials (CRM) for QA/QC purposes (Table S1). Human hair reference material (NIES-13) and feather internal reference material (F-KP, king penguin feathers) were used for validation of feather analyses (keratin-based matrixes). Blood analyses were validated with dogfish liver reference material (Dolt-5) and with internal reference material (RBC-KP, king penguins red blood cells). The reported results total Hg concentrations obtained by Hg speciation analyses (i.e., the sum of inorganic and organic Hg) were compared to total Hg concentrations obtained by AMA-254 to verify the recovery of the extraction. Recoveries of Hg and MeHg concentrations with respect to the reference values for each material varied between 92 and 108% (Table S1).

Total Hg isotope analyses

Feather (and blood) samples (0.05-0.10 g) were digested with 3 or 5 mL of HNO3 acid (65%, INSTRA quality) after a predigestion step overnight at room temperature. Hg extraction was carried out by Hotblock heating at 75 °C during 8 h (6 h in HNO3 and 2 h more after addition of 1/3 of the total volume of H2O2 30%, ULTREX quality). The digest mixtures were finally diluted in an acidic matrix (10% HNO3 and 2% HCl) with final Hg concentrations ranging from 0.5 to 1 ng•mL -1 . Hg isotopic composition was determined using cold-vapor generator (CVG)-MC-ICPMS (Nu Instruments), detailed in previous work [START_REF] Renedo | Seabird Tissues As Efficient Biomonitoring Tools for Hg Isotopic Investigations: Implications of Using Blood and Feathers from Chicks and Adults[END_REF] . Hg isotopic values were reported as delta notation, calculated relative to the bracketing standard NIST SRM-3133 reference material to allow interlaboratory comparisons, as described in the SI. NIST SRM-997 thallium standard solution was used for the instrumental mass-bias correction using the exponential law. Secondary standard NIST RM-8160 (previously UM-Almadén standard) was used for validation of the analytical session (Table S2).

Recoveries of extraction were verified for all samples by checking the signal intensity obtained on the MC-ICPMS for diluted extracts relative to NIST 3133 standard (with an approximate uncertainty of ±15%). Total Hg concentrations in the extract solution were compared to the concentrations found by AMA-254 analyses to assess method recovery.

Total Hg concentrations in the extract solution were compared to the concentrations found by AMA-254 analyses to assess method recovery. Average recoveries obtained were 98 ± 14% for feathers (n = 104) and 100 ± 2% for blood samples (n = 102). Accuracy of Hg isotopic analyses for keratin matrixes was evaluated with validated human hair material NIES-13 isotopic composition [START_REF] Yamakawa | Determination of Hg Isotopic Compositions in Certified Reference Material NIES No. 13 Human Hair by Cold Vapor Generation Multi-Collector Inductively Coupled Plasma Mass Spectrometry[END_REF] . Hg isotopic results for blood samples were validated with reference values of Lake Michigan fish tissue NIST SRM 1947. Internal reference samples of feathers (F-KP) and avian blood (RBC-KP) were also measured. Uncertainty for delta values was calculated using 2SD typical errors for each internal reference material (Table S2).

Carbon and nitrogen stable isotope analyses

Homogenized feather and blood subsamples (aliquots mass: ~0.3 mg) were weighed with a microbalance and packed in tin containers. Carbon (δ 13 C) and nitrogen (δ 15 N) stable isotope ratios were determined with a continuous flow mass spectrometer (Thermo Scientific Delta V Advantage) coupled to an elemental analyser (Thermo Scientific Flash EA 1112). Results are in delta notation relative to Vienna PeeDee Belemnite and atmospheric N2 for δ [START_REF] Heimbürger | Shallow Methylmercury Production in the Marginal Sea Ice Zone of the Central Arctic Ocean[END_REF] C and δ 15 N, respectively. Replicate measurements of internal laboratory standards (acetanilide) indicated measurement errors < 0.15‰ for both δ [START_REF] Heimbürger | Shallow Methylmercury Production in the Marginal Sea Ice Zone of the Central Arctic Ocean[END_REF] C and δ 15 N values. USGS-61 and USGS-62 reference materials were also analysed for calibration.

Statistical analyses

Statistical analyses were performed using the software R 3.3.2 (R Core Team, 2018) [START_REF]A Language and Environment for Statistical Computing[END_REF] .

Before statistical analyses, the data were checked for normality of distribution and homogeneity of variances using Shapiro-Wilk and Breusch-Pagan tests, respectively. Since data did not meet specificities of normality and homoscedasticity, non-parametrical tests (Kruskal-Wallis with Conover-Iman post-hoc test) were performed. Statistically significant results were set at α = 0.05. Statistical significance of Hg concentration and isotopic differences between head and body feathers were assessed using a randomization procedure. A 99% confidence interval was calculated by means of bootstrap estimation method (n=1000 iterations).

We examined the correlations between Hg concentrations, δ [START_REF] Heimbürger | Shallow Methylmercury Production in the Marginal Sea Ice Zone of the Central Arctic Ocean[END_REF] C, Hg MDF (δ 202 Hg) and MIF (∆ 199 Hg and ∆ 200 Hg), latitude and longitude using linear regressions and Spearman correlation rank tests. The influence of the latitude and longitude of their individual breeding and non-breeding distribution on feather Hg isotopic signatures were tested using linear mixed models (LMMs) with colonies as random effect on the whole data set, using the R package "lme4" [START_REF] Bates | Fitting Linear Mixed-Effects Models Using Lme4[END_REF] . Summer latitude, summer longitude and both summer latitude + longitude together were used as predictors for Hg isotopic signatures of body feathers. Similarly, median winter latitude, median winter longitude and both median together were used as predictors of Hg isotopic signatures in head feathers. Variance inflation factors were always < 3 [START_REF] Salmerón | Variance Inflation Factor and Condition Number in Multiple Linear Regression[END_REF] , ensuring that there was not collinearity between latitude and longitude in summer (breeding colonies) and median latitude and longitude in winter (wintering areas) [START_REF] Zuur | Mixed Effects Models and Extensions in Ecology with R[END_REF] . The different models were ranked based on Akaïke's Information Criteria adjusted for small sample sizes (AICc) and compared using ΔAICc and Akaike weights (w) using the R package "wiqid" [START_REF] Meredith | Package ' Wiqid[END_REF] . To assess the explanative power of these models, marginal R 2 was obtained using the R package "r2glmm" [START_REF] Jaeger | Computes R Squared for Mixed (Multilevel) Model. Packag. 'r2glmm[END_REF] .

Results and discussion

3.1 Seasonal and geographical variations of feather MeHg concentrations related to changing foraging habits (ẟ 15 N)

We observed that the dominant fraction of Hg was in the form of MeHg both in body feathers and blood (94 ± 2%, n=20 and 90 ± 3%, n=10; respectively) for all the studied populations of little auks (Table S3). This result is in good agreement with previous studies [START_REF] Renedo | Assessment of Mercury Speciation in Feathers Using Species-Specific Isotope Dilution Analysis[END_REF][START_REF] Renedo | Seabird Tissues As Efficient Biomonitoring Tools for Hg Isotopic Investigations: Implications of Using Blood and Feathers from Chicks and Adults[END_REF][START_REF] Davis | An Accurate and Sensitive Method for the Determination of Methylmercury in Biological Specimens Using GC-ICP-MS with Solid Phase Microextraction[END_REF][START_REF] Bond | Total and Methyl Mercury Concentrations in Seabird Feathers and Eggs[END_REF] and supports that both tissues of little auks principally present Hg as MeHg. Body and head feathers are known to be grown at different times and therefore the Hg excreted in head and body feathers reflects respectively the exposure during their wintering (October to April) and breeding (May to September) periods. Since birds are known to excrete between 70 and 90% of their Hg body burden by feather moult [START_REF] Honda | Seasonal Changes in Mercury Accumulation in the Black-Eared Kite, Milvus Migrans Lineatus[END_REF] , we cannot exclude that some residual Hg accumulated during the non-breeding period could also be excreted during body feather moult, and vice versa, but this fraction would be minor.

Overall, individuals presented higher Hg (MeHg) concentrations in head compared to body feathers, exhibiting up to 2-fold higher concentrations in head feathers in the case of East Greenland and Bear Island populations (Table S4). Higher Hg concentrations of head feathers are coherent with previous observations [START_REF] Fort | Spatial Ecotoxicology: Migratory Arctic Seabirds Are Exposed to Mercury Contamination While Overwintering in the Northwest Atlantic[END_REF] and suggest a higher exposure to MeHg during the non-breeding period outside the High Arctic. For instance, little auks breeding in areas of Spitsbergen and East Greenland are known to mainly forage on copepods (Calanus spp.) during the breeding season [START_REF] Harding | Can Stable Isotope (Δ13C and Δ15N) Measurements of Little Auk (Alle Alle) Adults and Chicks Be Used to Track Changes in High-Arctic Marine Foodwebs?[END_REF] . However, the seasonal vertical migration of their main prey Calanus spp. to inaccessible depths produces a seasonal shift in their diet towards krill Meganyctiphanes norvegica, hyperiid amphipods Themisto spp., and fish larvae [START_REF] Rosing-Asvid | Winter Diet of the Little Auk (Alle Alle) in the Northwest Atlantic[END_REF] . The consumption of higher trophic level prey during winter could explain the higher Hg levels excreted during the spring moult (head feathers), whereas they are probably less exposed to Hg in summer.

We also observed high variations of Hg concentrations in head feathers among individuals of the same colony, especially in Bear Island (from 0.81 to 4.35 µg g -1 ) and Spitsbergen populations (from 1.67 to 3.79 µg g -1 ) (Fig. S1). This could be due to the wide-spread individual foraging specialisation during their non-breeding period and the consumption of a wider range of prey [START_REF] Fort | Geographic and Seasonal Variability in the Isotopic Niche of Little Auks[END_REF] . Conversely, little auks occupy more restricted foraging areas during the breeding season due to the need to frequently feed their chicks and therefore feed on local prey captured near their respective colonies [START_REF] Welcker | Flexibility in the Bimodal Foraging Strategy of a High Arctic Alcid, the Little Auk Alle Alle[END_REF] , leading to less intra-population variability of Hg concentrations in their body feathers.

We observed a consistent longitudinal trend of body feather Hg concentrations (R 2 =0.58, p<0.0001) with increasing Hg levels from eastern (Bear Island and FJL, 0.71 and 0.75 µg•g -1 , respectively) to western colonies (NW Greenland, 2.07 µg•g -1 ) (Figure 1). When applying mixed models, summer longitude was the most supported predictor of body feather Hg concentrations (Table S6). Head feather Hg concentrations were positively correlated both with winter latitude and longitude for the four spatially tracked populations (R 2 =0.54 and R 2 =0.60, respectively; both p<0.0001) (Figure 1). Both variables together were considered as predictors of Hg head feather concentrations by linear mixed models (Table S7). Head feather concentrations were higher in populations wintering in western zones (3.48 µg•g -1 , East Greenland population) and decreased gradually and significantly (H=20.13, p=0.001) in those wintering in northeast areas (1.74 µg•g -1 , FJL population). The consistent longitudinal patterns both in summer and winter reflect a higher accumulation of MeHg in little auks from western regions, whereas colonies breeding in Arctic northern regions seem to be exposed to lower concentrations.

Regression lines presented only for significant relationship between the two variables.

Seabird blood ẟ [START_REF]Arctic Monitoring and Assessment Program[END_REF] N values provide short-to medium-term information (about 1-5 weeks) while feathers ẟ 15 N values reflect the diet at the time they were grown [START_REF] Bearhop | Factors That Influence Assimilation Rates and Fractionation of Nitrogen and Carbon Stable Isotopes in Avian Blood and Feathers[END_REF][START_REF] Hobson | Assessing Avian Diets Using Stable Isotopes II : Factors Influencing Diet-Tissue Fractionation[END_REF] . The distribution of little auk populations in winter was limited to the North Atlantic and the Arctic areas, where large-scale δ [START_REF]Arctic Monitoring and Assessment Program[END_REF] N values are known to be relatively homogeneous at the base of the food web [START_REF] Graham | Using Isoscapes to Trace the Movements and Foraging Behavior of Top Predators in Oceanic Ecosystems. In Isoscapes: Understanding Movement, Pattern, and Process on Earth Through Isotope Mapping[END_REF][START_REF] Mcmahon | A Review of Ecogeochemistry Approaches to Estimating Movements of Marine Animals[END_REF] , then allowing the inter-population comparison. The lower body feather Hg concentrations and blood ẟ [START_REF]Arctic Monitoring and Assessment Program[END_REF] N values observed in little auks (Table S5)

suggest that all birds from the different populations mostly feed at low trophic levels and on Calanus copepods in summer. Contrarily, the interpopulation differences of δ [START_REF]Arctic Monitoring and Assessment Program[END_REF] N values in winter (head feathers) were much more pronounced (Table S4). For instance, little auk populations breeding in FJL and Bear Island exhibited a ~3 ‰ higher δ [START_REF]Arctic Monitoring and Assessment Program[END_REF] N values in head feathers than in blood. This difference highlights the spatial variability of δ [START_REF]Arctic Monitoring and Assessment Program[END_REF] N values in relation to the different winter distribution of little auks in winter. Previous studies have reported significant seasonal variations in copepod δ [START_REF]Arctic Monitoring and Assessment Program[END_REF] N values (up to 6‰) between late winter and spring (highly productive periods) relative to the summer and autumn periods [START_REF] Mcmahon | A Review of Ecogeochemistry Approaches to Estimating Movements of Marine Animals[END_REF][START_REF] Kürten | Tracking Seasonal Changes in North Sea Zooplankton Trophic Dynamics Using Stable Isotopes[END_REF] . The seasonal variability of zooplankton δ [START_REF]Arctic Monitoring and Assessment Program[END_REF] N values is common on the eastern and western parts of the North Atlantic Ocean and needs to be considered here due to the wide spatial distribution of little auks in winter. Therefore, the higher feather Hg concentrations little auk colonies from western parts of the Arctic Ocean could be influenced by their seasonal dietary shifts and different spatial distribution but also by the complex Hg oceanic dynamics or distinct environmental sources that control the level of exposure to MeHg at the different regions. S4). It is known that little auks from FJL are morphologically bigger than those of the populations from Svalbard due to more severe climate conditions in this area [START_REF] Stempniewicz | The Little Auk Alle Alle Polaris of Franz Josef Land: A Comparison with Svalbard Alle a. Alle Populations[END_REF] . Thus, potentially different morphological characteristics associated to their bigger size could contribute to higher feather δ 202 Hg values in this colony. Hg concentrations and δ 202 Hg values of head feathers were highly correlated for the overall populations (R 2 =0.52, p<0.0001), while ∆ 199 Hg signatures were not related to Hg concentration in any type of feather (Figure S2). This observation shows the completely decoupled behavior between S8) suggest a seasonal variability in odd-MIF values. This could be primarily associated to the vertical migration of little auk main prey (copepods) and the consequent seasonal shift on their diet to krill/amphipods during the winter season. Their seasonal diet shift could enhance the accumulation of pelagic MeHg that is less connected to the photic zone during winter, then leading to lower Δ 199 Hg values of the MeHg excreted into head feathers. A previous study on subantarctic penguins documented significant differences of Δ 199 Hg values as a function of their specific foraging depths [START_REF] Renedo | Identification of Sources and Bioaccumulation Pathways of MeHg in Subantarctic Penguins : A Stable Isotopic Investigation[END_REF] , increasing around 0.4 ‰ from benthic to epipelagic populations. Although the little auk populations studied here are known to mainly feed on the same prey items and forage at similar depths, we should consider that changes on the availability of their prey among sites could also contribute to different feather Δ 199 Hg values among populations. Further, due to the diurnal migration of zooplankton from deep water to the surface, the mixed pool of Hg accumulated in these organisms originate from different depths of the water column and therefore, their Δ 199 Hg values represent a mixture from deep (low photodemethylated) Hg and surface Hg uptake [START_REF] Motta | Mercury Cycling in the North Pacific Subtropical Gyre as Revealed by Mercury Stable Isotope Ratios[END_REF] . Together with the trophic and ecological factors, we could expect that the seasonal variability of feather ∆ 199 Hg values (body vs head feathers) of little auks could be also influenced by a higher extent of Hg marine photochemistry occurring during summer. In summertime/ spring, little auks are known to return to their breeding sites, located at northern latitudes, where they are exposed to longer daily photoperiod at this moment of the year (polar day). Nevertheless, the weak differences of ∆ 199 Hg values between body and head feathers of a same population (from 0.26 to 0.50 ‰, Table S8) seem to indicate low variations of MeHg photodemethylation extents between their summer and winter sites. Therefore, the differences on daily photoperiod and/or light penetration between their summer and wintering foraging grounds would have a minor role on Hg isotopic variations.

Concerning the spatial variability of ∆ 199 Hg values among colonies, we observed positive linear relationships between body feather Δ 199 Hg values and summer longitude (R 2 =0.20, p<0.0001) and between head feather Δ 199 Hg values and winter longitude (R 2 =0.22, p<0.0001) (Figure 2). Summer and winter longitudes were respectively the most supported explanatory factors of body and head feather Δ 199 Hg values (Tables S6 andS7). No significant relationships were observed with latitude in summer (R 2 =0.01, p=0.20) nor winter (R 2 =0.07, p=0.06) (Figure 2). FJL population, the northern colony of this study, showed slightly higher body feather ∆ 199 Hg values (1.64 ± 0.15 ‰, n=10, 80ºN) comparing to other studied colonies (1.31 ± 0.20 ‰, n=37 70-77ºN) (H=11.96, p=0.018). FJL individuals also presented higher Δ 199 Hg values of their head feathers (1.13 ± 0.06 ‰) compared to the other colonies (1.00 ± 0.12 ‰) (H=18.55, p=0.001). Previous studies on Alaskan seabirds reported around 2-fold higher mean Δ 199 Hg signatures in lowice-covered oceanic areas (1.13 ± 0.16 ‰; 56-58ºN) than highly ice-covered regions (0.53 ± 0.15 ‰; 68ºN) and revealed that the presence of sea ice inhibits light penetration and therefore, Hg marine photochemistry [START_REF] Point | Methylmercury Photodegradation Influenced by Sea-Ice Cover in Arctic Marine Ecosystems[END_REF] . Compared to the latitudinal trend observed in Alaska, the spatial variations of Δ 199 Hg values between northern and southern populations of little auks are relatively weak and, interestingly, presented an inversed tendency between highly ice-covered (FJL) and non-ice-covered areas (North Atlantic regions).

Therefore, we cannot presume that the presence of sea ice is a driving factor controlling MeHg photochemistry and the related odd-MIF signatures registered in feathers of little auks. The existence of an opposed north-to-south trend of Δ 199 Hg values between the Eastern and Western Arctic Ocean regions reveals different Hg dynamic systems, especially for Hg accumulation pathways in food webs.

According to previous findings, the largest MeHg production in the Arctic water column seems to occur in oxic surface waters just below the productive surface layer 6,[START_REF] Heimbürger | Shallow Methylmercury Production in the Marginal Sea Ice Zone of the Central Arctic Ocean[END_REF] . In the Arctic, additional sources of Hg and carbon are provided by sea ice algae during spring blooms [START_REF] Burt | Mercury Uptake within an Ice Algal Community during the Spring Bloom in First-Year Arctic Sea Ice[END_REF] . The presence of terrestrial organic matter and sea ice layers that concentrates phytoplankton near the MeHg production zone may favour the Hg microbial methylation at shallow depths of the Arctic water column [START_REF] Gionfriddo | Microbial Mercury Methylation in Antarctic Sea Ice[END_REF][START_REF] Schartup | Freshwater Discharges Drive High Levels of Methylmercury in Arctic Marine Biota[END_REF] . Shallower methylation occurring in Arctic waters may result in higher photochemical impact on MeHg before its assimilation in Arctic biota compared to North Atlantic marine food webs. This phenomenon could contribute to the higher feather Δ 199 Hg values of FJL little auks compared to populations breeding at lower latitudes. The slight differences of ∆ 199 Hg values between northern and southern colonies of little auks are similar to the ranges recently observed in seabirds covering a wider latitudinal gradient (37 to 66°S) in the Southern Ocean, for which ∆ 199 Hg values increased from Antarctic (1.31 to 1.73 ‰) to subtropical (1.69 to 2.04 ‰) populations [START_REF] Renedo | Seabird-Eye" on Mercury Stable Isotopes and Cycling in the Southern Ocean[END_REF] . The slight variations of ∆ 199 Hg values also found between these distant sites of the Southern Ocean were translated into low differences of MeHg photochemical demethylation extents among sites and a dominance of MeHg with a mesopelagic origin in these remote environments. As previously discussed, the vertical daily migration of copepods from deep water to the surface leads to the integration of Hg from relatively deep zones of the water column therefore, contributing to the incorporation of low photochemically impacted Hg in consumers [START_REF] Motta | Mercury Cycling in the North Pacific Subtropical Gyre as Revealed by Mercury Stable Isotope Ratios[END_REF] . Therefore, although the planktivourous little auk feed at surface waters on the photic zone, their relatively low feather ∆ 199 Hg values suggest that the Hg accumulated in their main prey could originate from Hg pools from deeper zones of the water column.

Spatial correlation of Hg MIF signatures and carbon stable isotopes (δ 13 C)

The deposition of atmospheric Hg from mid-latitude anthropogenic emissions into the Arctic Ocean could contribute to the accumulation of MeHg from distinct origin in Arctic-North Atlantic food webs 7 . Although body feathers of little auk presented a relative high range of Δ 200 Hg signatures (from -0.23 to 0.17 ‰), the inter-population differences were not significant (H=3.685, p=0.45) (Figure S5). No substantial interpopulation variations [START_REF] Heimbürger | Shallow Methylmercury Production in the Marginal Sea Ice Zone of the Central Arctic Ocean[END_REF] C values of the dissolved inorganic carbon are commonly observed in surface waters as an influence of the physical and biological processes [START_REF] Mcmahon | A Review of Ecogeochemistry Approaches to Estimating Movements of Marine Animals[END_REF] . For instance, it is known that CO2 solubility is favoured in cold oceanic waters and consequently, surface waters at high latitudes have relatively low δ [START_REF] Heimbürger | Shallow Methylmercury Production in the Marginal Sea Ice Zone of the Central Arctic Ocean[END_REF] C values due to the introduction of isotopically light atmospheric CO2.

By contrast, surface waters of outgassing upwelling equatorial areas become enriched on δ [START_REF] Heimbürger | Shallow Methylmercury Production in the Marginal Sea Ice Zone of the Central Arctic Ocean[END_REF] C values [START_REF] Lynch-Stieglitz | The Influence of Airsea Exchange on the Isotopic Composition of Oceanic Carbon: Observations and Modeling[END_REF][START_REF] Gruber | Spatiotemporal Patterns of Carbon-13 in the Global Surface Oceans and the Oceanic Suess Effect[END_REF] . Parallelly, the δ 13 C values of primary producers are strongly influenced by the δ [START_REF] Heimbürger | Shallow Methylmercury Production in the Marginal Sea Ice Zone of the Central Arctic Ocean[END_REF] C values of dissolved inorganic carbon and therefore, by the temperature gradients and CO2 solubility [START_REF] Mcmahon | A Review of Ecogeochemistry Approaches to Estimating Movements of Marine Animals[END_REF] . Spatial gradients of sea surface temperature and CO2 concentrations could thus explain the more depleted δ [START_REF] Heimbürger | Shallow Methylmercury Production in the Marginal Sea Ice Zone of the Central Arctic Ocean[END_REF] C baseline in cold high Arctic marine food webs and the enrichment in δ [START_REF] Heimbürger | Shallow Methylmercury Production in the Marginal Sea Ice Zone of the Central Arctic Ocean[END_REF] C values when going southward to North Atlantic oceanic areas. Furthermore, the dominance of distinct marine currents between the different wintering seabird sites could strongly determine the δ [START_REF] Heimbürger | Shallow Methylmercury Production in the Marginal Sea Ice Zone of the Central Arctic Ocean[END_REF] C at the base of the food webs. The FJL archipelago and surrounding high Arctic areas are strongly impacted by the Makarov and Arctic cold currents flowing southward from the north, and potentially contributing to transport isotopically depleted carbon from high latitude areas.

In contrast, little auk wintering regions near the Newfoundland Island (East and West Greenland populations) are affected by the Gulf Stream and North Atlantic Current [START_REF] Fort | Multicolony Tracking Reveals Potential Threats to Little Auks Wintering in the North Atlantic from Marine Pollution and Shrinking Sea Ice Cover[END_REF] which could supply carbon organic matter from warmer water masses [START_REF] Fontela | Dissolved Organic Carbon in the North Atlantic Meridional Overturning Circulation[END_REF] .

Significant negative linear relationships were obtained between Δ 199 Hg and δ [START_REF] Heimbürger | Shallow Methylmercury Production in the Marginal Sea Ice Zone of the Central Arctic Ocean[END_REF] C values both in summer (R 2 =0.17, p=0.003) and winter (R 2 =0.31, p<0.0001) (Figure 3).

Interestingly, the negative relationship between Δ 199 Hg and δ [START_REF] Heimbürger | Shallow Methylmercury Production in the Marginal Sea Ice Zone of the Central Arctic Ocean[END_REF] C values of little auks contrasts with those previously reported on eggs from guillemot species (or murres, Uria aalge and U. lomvia) breeding in the Alaskan Arctic [START_REF] Day | Mercury Stable Isotopes in Seabird Eggs Reflect a Gradient from Terrestrial Geogenic to Oceanic Mercury Reservoirs[END_REF] . These authors reported a coenrichment of egg δ 13 C and Δ 199 Hg values linked to the transition from terrestrial to marine Hg sources and the subsequent reduction of Hg photochemistry in coastal reservoirs due to higher turbidity [START_REF] Day | Mercury Stable Isotopes in Seabird Eggs Reflect a Gradient from Terrestrial Geogenic to Oceanic Mercury Reservoirs[END_REF] . However, the wintering areas of little auks mainly correspond to more opened oceanic areas as the study in the Bering Sea and probably do not present such a remarkable coastal-oceanic gradient. The significant correlation obtained here between Δ 199 Hg and δ [START_REF] Heimbürger | Shallow Methylmercury Production in the Marginal Sea Ice Zone of the Central Arctic Ocean[END_REF] C signatures both in body and head feathers of little auks reflect common spatial trends summer and winter foraging grounds. This interesting relationship seems be associated to both the spatial gradient of physical parameters controlling C isotopic baselines (temperature and CO2 exchange in surface waters) and to the extent of Hg photochemical processes. Probably, a higher stratification and impact of sea ice cover in high Arctic oceanic zones favours the methylation of Hg in surface waters [START_REF] Heimbürger | Shallow Methylmercury Production in the Marginal Sea Ice Zone of the Central Arctic Ocean[END_REF] , and the extent of photochemical reactions leading to slightly positive Δ 199 Hg values and more negative δ [START_REF] Heimbürger | Shallow Methylmercury Production in the Marginal Sea Ice Zone of the Central Arctic Ocean[END_REF] C values of biota. The dominance of northern marine currents in this area would also contribute to depleted δ [START_REF] Heimbürger | Shallow Methylmercury Production in the Marginal Sea Ice Zone of the Central Arctic Ocean[END_REF] C values. Although we could consider the existence of distinct carbon inputs transported by the marine currents on these ecosystems (i.e. external carbon supply, planktonic production), the complex interaction of oceanographic and physical parameters governing these areas does not allow to provide conclusive evidence from our data.

Figure 1 .

 1 Figure 1. Hg concentration (µg•g -1 ) of little auk body feathers (summer) as a function of latitude (A) and longitude of their breeding sites (B) and head feathers (winter) as a function of the median latitude (C) and longitude (D) of their winter grounds. Regression lines are A) Slope: 0.052±0.013, intercept: -3.420±0.997, R 2 =0.26, p=0.005; B) Slope: -0.012±0.001, intercept: 1.209±0.059, R 2 =0.58, p<0.0001; C) Slope: -0.059±0.011, intercept: 6.399±0.669, R 2 =0.54, p<0.0001; D) Slope: -0.022±0.003, intercept: 2.375±0.120, R 2 =0.60, p<0.0001.

3. 2

 2 Spatio-temporal trends of Hg MDF (δ 202 Hg) in feathers related to ecological aspects Specific Hg integration times of seabird tissues may influence the seasonal incorporation of MeHg from different spatial origin[START_REF] Renedo | Mercury Isotopes of Key Tissues Document Mercury Metabolic Processes in Seabirds[END_REF] . However, the geographical variations in δ 202 Hg values are generally difficult to distinguish since metabolic processes also induce Hg MDF. Head and body feathers showed large ranges of δ 202 Hg values, varying from -0.24 to 1.43 ‰ and from -0.11 to 1.28 ‰, respectively. Although we focused on the study of multiple colonies of the same seabird species to minimize the metabolic or trophic-related effects, we cannot exclude that the variability of δ 202 Hg signatures among colonies is led only by the specific isotopic baseline of their respective foraging habitats. For instance, FJL population exhibited significantly heavier δ 202 Hg values relative to the other four populations, both in head (H=29.42, p<0.0001) and body feathers (H=27.69, p<0.0001) (Table

  photochemistry

Figure 2 . 1 .

 21 Figure 2. Hg odd-MIF (Δ 199 Hg) of little auk body feathers (summer) as a function of latitude (A) and longitude of their breeding sites (B) and head feathers (winter) as a function of the median latitude (C) and longitude (D) of their wintering grounds. Regression lines are A) Slope: 0.014±0.011, intercept: 0.319±0.822; R 2 =0.01, p=0.20; B) Slope: 0.002±0.001, intercept: 1.384±0.031, R 2 =0.20, p<0.0001; C) Slope: 0.004±0.002, intercept: 0.784±0.123, R 2 =0.07, p=0.06; D) Slope: 0.001±0.001, intercept: 1.069±0.022, R 2 =0.22, p=0.002. Regression lines presented only for significant relationship between the two variables.

Figure 3 . 5

 35 Figure 3. Carbon (δ 13 C) vs MIF Hg signatures for A) summer (body feathers) and B) winter (head feathers) periods. Regression lines are A) Slope: -0.122±0.026, intercept: -1.045±0.558, R 2 =0.31, p<0.0001; B) Slope: -0.088±0.028, intercept: -0.746±0.566, R 2 =0.17, p=0.003.

Figure 4 .Figure S2 .Figure S3 .Figure S4 .Figure S5 .Figure S6 .Figure S7 .

 4S2S3S4S5S6S7 Figure 4. Compilation of Hg odd-MIF values (Δ 199 Hg) of marine biota from spatially distant Arctic Regions. The map comprises both little auk breeding sites (orange), individual little auk median winter positions (yellow) and previous published data (green) including seabirds 25,26 , beluga whale, seals and polar bears 79 from Alaskan Regions; pilot whales from Faroe Islands 28 , and fish from the Labrador Sea 83 .

Table S1 .

 S1 Mean values of total Hg and MeHg concentrations and percentage recoveries obtained for

	reference materials with validated reference concentration values: NIES-13 (human hair) and Dolt-5
	(dogfish liver); and previously published intercomparison values for internal reference materials: F-KP
	(penguin feathers) and RBC-KP (penguin red blood cells). Values are Mean±SD. N corresponds to the
	number of analyses (triplicate analyses for each extract, n=3 extractions).
	Reference material		n	MeHg	MeHg recovery	THg	THg recovery
				(µg g -1 )	(%)	(µg g -1 )	(%)
	Human hair NIES-13	This study	9 3.61±0.20	95±5	4.18±0.24	95±4
		Certified values		3.80±0.40		4.42±0.20
	Dogfish liver Dolt-5	This study	9 0.110±0.023	92±11	0.45±0.02	102±8
		Certified values		0.119±0.058		0.44±0.18
	Feathers F-KP	This study	9 2.51±0.11	97±5	3.88±0.90	97±2
		Average previous values 2		2.54±0.4		3.89±0.6
	Blood RBC-KP	This study	9 1.93±0.41	102±1	1.76±0.46	108±3
		Average previous values 3		1.96±0.38		1.98±0.76
							S50

Table S2 .

 S2 Mean values of Hg isotopic composition obtained for reference materials with validated reference isotopic values: NIST RM 8610 (former UM-Almadén) and NIES-13 (human hair); previously published intercomparison values for internal reference materials: F-KP (penguin feathers) and RBC-KP (penguin red blood cells). Values are Mean±2SD. N means number of analyses.

	Reference Material	δ 204 Hg

δ 202 Hg δ 201 Hg δ 200 Hg δ 199 Hg ∆ 204 Hg ∆ 201 Hg ∆ 200 Hg ∆ 199 Hg n NIST RM 8610 (UM Almadén)

	This study
	Average (‰)	-0.80	-0.52	-0.43	-0.25	-0.16	-0.03	-0.04	0.01	-0.03 20
	2SD (‰)	0.23	0.13	0.12	0.13	0.16	0.16	0.11	0.10	0.15
	Reference values
	Average (‰)	-0.82	-0.56	-0.46	-0.27	-0.17	-	-0.04	0.00	-0.03
	2SD (‰)	0.07	0.03	0.02	0.01	0.01	-	0.01	0.01	0.02
	NIES-13
	This study
	Average (‰)	2.87	2.08	3.01	1.13	2.30	-0.08	1.42	0.04	1.75	3
	2SD (‰)	0.17	0.09	0.14	0.10	0.10	0.15	0.05	0.02	0.05
	Yamakawa et al. 2016 4
	Average (‰)	2.76	1.89	2.77	0.98	2.13	-0.04	1.36	0.04	1.65	11
	2SD (‰)	0.16	0.1	0.1	0.08	0.07	0.11	0.07	0.04	0.06
	King penguin feathers (F-KP)
	This study
	Average (‰)	2.17	1.40	2.54	0.71	2.03	0.07	1.48	0.00	1.68	7
	2SD (‰)	0.19	0.15	0.27	0.19	0.23	0.33	0.19	0.13	0.19
	Renedo et al. 2018 3
	Average (‰)	2.40	1.58	2.67	0.80	2.11	0.04	1.48	0.01	1.71	17
	2SD (‰)	0.25	0.22	0.21	0.16	0.18	0.24	0.13	0.09	0.15
	King penguin blood (RBC-KP)
	This study
	Average (‰)	2.00	1.32	2.28	0.64	1.79	0.04	1.29	-0.02	1.46	5
	2SD (‰)	0.18	0.12	0.14	0.14	0.18	0.10	0.09	0.09	0.16
	Renedo et al., 2018 3
	Average (‰)	1.98	1.30	2.26	0.64	1.78	0.04	1.28	-0.01	1.46	7
	2SD (‰)	0.18	0.13	0.14	0.13	0.17	0.1	0.09	0.09	0.14
											S51

Table S3 .

 S3 Total Hg (THg) and MeHg concentrations (µg g -1 , dw) and MeHg proportion (%) in body feathers and blood of little auks. Values are Mean±SD.

	Site	Latitude Longitude n	Tissue	MeHg (µg g -1 ) THg (µg g -1 ) MeHg (%)
	FJL	77º47'N -69º22W 5 body feathers	1.94±1.20	2.06±1.24	94±1
	NW Greenland 70º71'N -21º55'W 5 body feathers	2.74±0.54	2.89±0.57	95±1
	Spitsbergen	76º97'N	15º78'E 5 body feathers	4.28±0.80	4.57±0.91	94±1
	East Greenland 74º45'N	19º04'E 5 body feathers	4.17±1.68	4.53±2.02	93±3
	FJL	77º47'N -69º22W 5	Blood	0.36±0.13	0.40±0.14	90±4
	Bear Island	80º23'N	53º01'E 5	Blood	0.45±0.24	0.50±0.27	91±1
							S52

Table S4 .

 S4 Total Hg concentrations (Mean ± SD, µg g -1 , dw) and Hg stable isotopes (Mean ± 2SD, ‰) of head and body feathers from little auks of the five studied populations. n1, number of individuals for total Hg concentration and C and N isotopic analyses; n2, number of individuals for Hg isotopic analyses). Values not sharing the same superscript letter within each group are statistically different.

	DA50234	2016	Spitsbergen	0.69	NA	NA	NA		NA		NA	NA	NA		NA	NA	-22.31	14.42
	DA50235	2016	Spitsbergen	0.79	0.44	0.41	1.63		0.22		1.61	-0.18	1.32		0.01	1.51	-21.18	14.95
	DA50239	2016	Spitsbergen	1.35	0.77	0.49	1.27		0.25		1.24	0.04	0.90		0.01	1.12	-20.77	13.99
	LIAK15EG01	2015	East Greenland	1.42	0.26	0.19	1.26		0.07		1.35	-0.02	1.11		-0.03	1.30	-21.29	13.49
	LIAK15EG02	2015	East Greenland	1.06	0.53	0.38	1.64		0.14		1.61	-0.03	1.36		-0.05	1.51	-21.26	13.47
	LIAK15EG03	2015	East Greenland	1.07	0.42	0.34	1.82	-0.06		1.89	-0.08	1.57		-0.23	1.80	-20.3	13.09
	LIAK15EG04	2015	East Greenland	1.20	0.74	0.52	1.64		0.41		1.64	-0.02	1.25		0.15	1.51	-20.24	12.78
	LIAK15EG05	2015	East Greenland	1.03	0.69	0.36	1.55		0.16		1.56	0.16	1.28		-0.02	1.47	-20.47	13.54
	LAIK15EG10	Population Feather type 2015 East Greenland 2.32	n1 NA	NA	THg NA	n2	NA	δ 202 Hg NA	∆ 199 Hg NA	∆ 200 Hg NA	NA	∆ 199 Hg/∆ 201 Hg NA -20.71	11.57
	LIAK15EG36 LIAK15EG37	2015 2015	East Greenland East Greenland	2.75 1.39	0.69 0.71	0.43 0.42	(µg g -1 ) 1.36 1.50		0.12 0.25	(‰)	1.24 1.59	(‰) 0.04 1.54	1.04 1.18	(‰)	-0.10 0.04	1.13 ratio 1.49	-19.89 -21	13.45 13.34
	LIAK16EG02 LIAK16EG06	2016 2016	FJL East Greenland head feathers 1.22 East Greenland 1.24	10 0.04 0.21	1.74±0.54 A 0.13 1.25 0.23 1.01	9 0.94±0.30 D 1.13±0.06 B -0.03±0.05 A,B 0.05 1.47 -0.14 1.16 -0.01 0.28 1.13 -0.13 0.84 0.17	1.44 1.18±0.12 A 1.07	-20.6 -20.94	12.77 13.46
	51676 51679 51681	Bear Island 2016 NW Greenland head feathers 1.69 E Greenland head feathers 2016 NW Greenland 2.40 2016 NW Greenland 1.32	10 0.77 10 0.41 0.68	2.65±1.15 A,B 10 0.53±0.25 B,C 1.05±0.12 A,B 0.01±0.05 A,B 0.53 1.35 0.20 1.22 -0.01 0.95 -0.07 3.48±0.58 B 10 0.10±0.20 A 1.00±0.15 A,B 0.02±0.06 B 0.27 1.29 0.15 1.36 0.01 1.08 0.01 0.44 1.42 0.27 1.47 0.03 1.10 0.05	1.09 1.21±0.11 A 1.30 1.25±0.14 A 1.36	-20.44 -19.79 -20.48	15.05 16.08 14.05
	51683	Spitsbergen head feathers 2016 NW Greenland 1.81	10 1.34	2.69±0.72 A,B 9 0.40±0.33 A,B 0.96±0.13 A -0.05±0.04 A 0.94 1.74 0.57 1.49 -0.06 1.04 0.10	1.24±0.09 A 1.26	-19.38	14.15
	51684	NW Greenland head feathers 2016 NW Greenland 1.82	10 1.08	2.15±0.40 A 10 0.80±0.21 C,D 0.97±0.09 A -0.02±0.01 A,B 0.66 1.83 0.38 1.68 0.09 1.33 0.04	1.23±0.10 A 1.51	-19.37	12.98
	51685	2016	NW Greenland	2.04	1.10	0.78	1.67		0.39		1.57	-0.07	1.08		0.00	1.37	-20.2	13.64
	51686	2016	FJL NW Greenland body feathers 2.07	10 0.69	0.75±0.12 A 10 0.96±0.21 D 1.64±0.15 B -0.02±0.05 A 0.44 1.34 0.21 1.24 0.04 1.01 -0.01	1.16±0.05 A 1.13	-19.48	14.5
	51689	2016	NW Greenland	3.28	1.06	0.64	1.35		0.33		1.14	0.11	0.87		0.00	0.98	-20.19	15.97
	51692	Bear Island 2016 NW Greenland body feathers 2.16	10 0.59	0.71±0.18 A 0.40 1.07	8 0.57±0.28 B,C 1.34±0.24 A 0.00±0.04 A 0.21 1.22 0.00 0.77 0.01	1.23±0.20 A 1.12	-19.77	15.21
	51693	E Greenland body feathers 2016 NW Greenland 2.16	10 1.09	1.47±0.58 A,B 9 0.33±0.13 A,B 1.41±0.22 A,B -0.01±0.12 A 0.59 1.35 0.35 1.25 0.21 0.91 0.05	1.18±0.06 A 1.11	-19.64	13.11
		Spitsbergen body feathers	10	1.06±0.41 A,B 9 0.22±0.22 A 1.27±0.18 A -0.03±0.01 A	1.13±0.09 A
		NW Greenland body feathers	10	2.07±0.52 C 10 0.57±0.20 C 1.23±0.16 A 0.01±0.04 A	1.21±0.10 A

Table S10 .

 S10 Winter distribution (median latitude and longitude coordinates) of little auks, total Hg concentrations (µg•g -1 , dw), Hg isotopic composition and carbon and nitrogen isotopic values of head feathers.

	DA29201 DA50239	2015 2016	Bear Island Spitsbergen	68.91 73.24	-10.83 -3.52	2.35 2.34	0.74 0.46	0.57 0.44	1.32 1.13	0.30 0.13	1.27 1.18	-0.10 -0.19	0.90 0.80	0.02 -0.09	1.13 -20.87 13.17 1.07 -20.47 13.84
	DA42341 LIAK15EG01 2015 East Greenland 2015 Bear Island	61.52 46.62	-42.78 -45.35	3.56 3.59	0.62 -0.05	0.36 -0.05	1.10 0.92	0.19 0.09	1.04 1.24	0.08 0.02	0.83 0.96	0.00 0.12	0.94 -20.60 14.11 1.25 -19.40 9.96
	DA47406 LIAK15EG02 2015 East Greenland 2015 Bear Island	64.18 45.72	-38.17 -46.50	4.35 2.63	0.39 0.55	0.39 0.37	1.22 0.92	0.23 0.18	1.20 1.04	-0.19 0.00	0.93 0.64	0.03 0.00	1.10 -20.76 13.96 0.94 -19.34 11.86
	DA47408 LIAK15EG03 2015 East Greenland 2015 Bear Island	68.47 45.08	-11.97 -47.84	2.65 3.63	0.60 0.15	0.34 0.23	1.34 0.95	0.22 0.16	1.22 1.09	0.09 -0.19	1.08 0.78	0.05 0.04	1.14 -20.74 14.43 1.03 -19.50 11.16
	DA47409 LIAK15EG04 2015 East Greenland 2015 Bear Island	51.42 45.67	-47.19 -43.43	2.71 3.69	0.79 0.07	0.52 0.02	1.18 0.83	0.33 -0.01	1.08 1.00	0.01 0.03	0.79 0.82	0.07 -0.02	0.94 -19.97 11.28 0.99 -19.34 10.26
	DA47414 LIAK15EG05 2015 East Greenland 2015 Bear Island	68.17 47.49	-4.49 -40.60	0.81 4.32	1.23 -0.27	0.78 -0.16	1.69 0.67	0.31 -0.01	1.46 1.10	0.06 -0.03	1.10 0.79	-0.08 0.07	1.26 -20.36 13.79 1.14 -19.71 10.10
	DA47415 LAIK15EG10 2015 East Greenland 2015 Bear Island	69.55 46.49	-6.33 -42.32	1.04 4.33	1.57 -0.07	1.14 0.16	1.78 1.17	0.55 0.01	1.44 1.17	-0.14 -0.31	0.92 1.05	-0.02 -0.07	1.15 -19.97 13.85 1.13 -20.14 10.09
	DA47419 LIAK15EG36 2015 East Greenland 2015 Bear Island	59.23 51.16	-43.61 -50.06	3.89 2.83	0.70 0.30	0.38 0.32	1.01 1.05	0.19 0.23	0.98 1.11	0.14 -0.18	0.73 0.81	0.00 0.07	0.89 -19.80 12.24 1.02 -19.39 11.75
	DA47371 LIAK15EG37 2015 East Greenland 2016 Bear Island	NA 46.64	NA -45.25	2.09 3.15	0.68 -0.33	0.43 -0.24	1.16 0.44	0.19 -0.12	1.10 0.66	0.03 0.03	0.83 0.62	-0.03 0.00	0.99 -20.09 13.90 0.72 -18.84 11.94
	DA47373 LIAK16EG02 2016 East Greenland 2016 Bear Island	68.67 NA	-10.95 NA	3.06 3.01	0.53 0.42	0.38 0.21	0.94 0.97	0.26 0.08	1.04 0.97	-0.05 0.10	0.65 0.81	0.07 -0.02	0.94 -20.39 13.74 0.92 -20.16 12.07
	HS012833 LIAK16EG06 2016 East Greenland 2015 FJL	70.82 40.16	53.40 -46.47	1.19 3.60	1.26 0.10	0.63 0.17	1.45 0.92	0.29 0.11	1.21 0.91	0.31 -0.16	0.97 0.79	-0.03 0.02	1.06 -20.61 14.20 0.87 -20.30 13.46
	HS012817 51676	2015 2016 NW Greenland FJL	75.67 NA	51.94 NA	1.19 2.36	1.75 1.38	1.07 0.88	1.92 1.47	0.54 0.41	1.42 1.17	0.15 0.07	1.12 0.81	0.00 -0.03	1.15 -20.15 14.46 0.95 -18.98 13.57
	HS012818 51679	2015 2016 NW Greenland FJL	76.56 NA	48.49 NA	2.28 2.59	1.57 1.52	1.07 0.94	1.82 1.52	0.51 0.45	1.40 1.42	-0.03 0.12	1.01 0.82	-0.02 -0.02	1.13 -20.07 15.58 1.19 -19.98 12.05
	HS012822 51681	2015 2016 NW Greenland FJL	75.44 NA	-1.05 NA	1.91 2.58	0.60 1.04	0.47 0.74	1.19 1.40	0.10 0.44	1.16 1.14	-0.11 -0.07	0.83 0.84	-0.14 0.07	1.05 -21.16 13.99 0.95 -20.19 10.98
	HS012834 51683	2015 2016 NW Greenland FJL	76.07 NA	47.66 NA	1.82 1.49	1.60 1.28	1.08 0.94	1.83 1.45	0.52 0.38	1.52 1.14	-0.01 -0.13	1.02 0.74	-0.02 -0.09	1.24 -20.66 14.34 0.90 -18.78 12.67
	HS012839 51684	2015 2016 NW Greenland FJL	76.05 NA	54.77 NA	1.89 2.47	1.39 0.42	1.03 0.28	1.55 0.88	0.48 0.16	1.36 0.98	-0.16 0.01	0.78 0.67	-0.04 0.02	1.10 -21.00 14.28 0.91 -19.94 10.71
	HS012828 51685	2015 2016 NW Greenland FJL	73.80 NA	12.28 NA	1.50 1.70	NA 1.15	NA 0.86	NA 1.44	NA 0.40	NA 1.15	NA -0.14	NA 0.79	NA -0.03	NA 0.93 -19.53 12.19 -21.01 13.92
	HS012829 51686	2015 2016 NW Greenland FJL	77.14 NA	41.12 NA	1.04 2.44	2.15 1.47	1.43 0.93	1.96 1.51	0.66 0.42	1.47 1.20	0.02 0.08	0.89 0.81	-0.06 -0.05	1.11 -20.08 14.96 0.97 -19.26 12.91
	HS012604 51689	2016 2016 NW Greenland FJL	75.84 NA	31.01 NA	1.31 2.20	1.65 1.80	1.04 0.97	1.87 1.55	0.54 0.51	1.42 1.26	0.10 0.36	1.09 0.82	0.02 0.02	1.16 -20.40 14.18 1.01 -19.66 13.25
	HS012606 51692	2016 2016 NW Greenland FJL	74.14 NA	2.09 NA	2.73 1.81	1.08 1.08	0.66 0.72	1.50 1.25	0.34 0.26	1.36 1.04	0.10 0.01	1.01 0.71	0.01 -0.10	1.20 -20.71 14.17 0.86 -18.41 13.22
	DA50219 51693	2016 2016 NW Greenland Spitsbergen	67.93 NA	-10.49 NA	1.67 1.87	0.97 1.00	0.65 0.72	1.28 1.42	0.29 0.37	1.13 1.16	-0.01 -0.08	0.79 0.88	-0.04 0.00	0.96 -19.84 13.09 0.98 -19.33 12.53
	DA50220	2016	Spitsbergen	67.10	-5.89	2.25	1.13	0.65	1.41	0.31	1.37	0.16	0.92	-0.02	1.20 -20.60 12.83
	DA50221	2016	Spitsbergen	61.95	-53.02	3.79	0.51	0.33	0.99	0.19	1.02	0.01	0.74	0.02	0.93 -20.00 12.50
	DA50224	2016	Spitsbergen	67.89	-11.22	2.10	0.48	0.25	0.87	0.11	0.87	0.11	0.68	-0.01	0.80 -20.34 13.30
	DA50227	2016	Spitsbergen	71.06	4.38	2.58	NA	NA	NA	NA	NA	NA	NA	NA	NA	-20.21 14.48
	DA50229	2016	Spitsbergen	67.35	-8.87	2.74	0.29	0.01	0.77	-0.06	0.84	0.28	0.76	-0.06	0.84 -20.27 12.46
	DA50230	2016	Spitsbergen	61.29	-41.35	3.63	-0.34	-0.15	0.52	-0.18	0.85	-0.12	0.64	-0.10	0.89 -19.94 12.60
	Individual Year DA50234 2016 DA50235 2016	Site Spitsbergen Spitsbergen	Median latitude winter 70.03 68.93	Median longitude winter -9.06 -8.89	Hg (µg•g -1 ) 3.52 2.32	δ 204 Hg (‰) 1.39 0.76	δ 202 Hg (‰) 0.93 0.47	δ 201 Hg (‰) 1.63 1.06	δ 200 Hg (‰) 0.40 0.18	δ 199 Hg (‰) 1.30 0.97	∆ 204 Hg (‰) 0.00 0.06	∆ 201 Hg (‰) 0.93 0.70	∆ 200 Hg (‰) -0.07 -0.05	∆ 199 Hg (‰) 1.06 -20.54 13.77 δ 13 C (‰) δ 15 N (‰) 0.85 -20.32 13.52
																S60 S61

Acknowledgements

The authors wish to thank the fieldworkers who collected the samples. Field procedures were authorized by the Ethics Committee of Institut Polaire Français Paul Emile Victor (IPEV) and by the Comité de l'Environnement Polaire. This study received financial and logistical support from the French Agency for National Research (ANR MAMBA project ANR-16-TERC-0004, ILETOP project ANR-16-CE34-0005), the French Arctic

Initiative -CNRS (PARCS project), the Mission pour l'Interdisciplinarité -CNRS (Changements en Sibérie project) and the French Polar Institute (IPEV -Pgr 388 ADACLIM). The IUF (Institut Universitaire de France) is also acknowledged for its support to PB as a senior member.

Supporting Information

Contrasting spatial and seasonal trends of methylmercury exposure pathways of Arctic seabirds: combination of large-scale tracking and stable isotopic approaches Marina Renedo 1,2,* , David Amouroux 2 , Céline Albert 1 , Sylvain Bérail 2 , Vegard S.

Bråthen 3 , Maria Gavrilo 4 , David Grémillet 5,6 , Hálfdán H. Helgason 7 , Dariusz Jakubas 8 , Anders Mosbech 9 , Hallvard Strøm 7 , Emmanuel Tessier 2 , Katarzyna Wojczulanis-Jakubas 8 , Paco Bustamante 1,10 , Jérôme Fort 1* 1 Littoral Environnement et Sociétés (LIENSs), UMR 7266 CNRS-La Rochelle Université, 2 rue Olympe de Gouges, 17000 La Rochelle, France 2 Universite de Pau et des Pays de l'Adour, E2S UPPA, CNRS, IPREM, Institut des Sciences Analytiques et de Physicochimie pour l'Environnement et les matériaux, Pau, France 3 Norwegian Institute for Nature Research, Trondheim, Norway 4 Arctic and Antarctic Research Institute, 38 Bering Street, 199397, Saint-Petersburg, Russia 5 Centre d'Etudes Biologiques de Chizé, UMR 7372 CNRS -La Rochelle Université, 405 Route de Prissé la Charrière 79360 Villiers-en-Bois, France 6 Percy FitzPatrick Institute, DST/NRF Centre of Excellence, University of Cape Town, Rondebosch, South Africa 7 Norwegian Polar Institute, Tromsø, Norway 8 Gdańsk University, Faculty of Biology, Gdańsk, Poland 9 Aarhus University, Department of Bioscience, Roskilde, Denmark 10 Institut Universitaire de France (IUF), 1 rue Descartes, 75005 Paris, France *Corresponding authors: marina.renedo@ird.fr, jerome.fort@univ-lr.fr

Table of contents

Experimental: Hg isotopic composition analyses

Hg isotopic compositions of samples are reported using delta notation according to the following equations:

MIF signatures are expressed using capital delta notation, as suggested elsewhere 1 , according to the following equations: The low Δ 200 Hg values observed in boreal soils (-0.02 ± 0.03‰ 9 ) and Arctic Ocean sediments (0.00 ± 0.03‰ 11 ) match the close-to-zero mean Δ 200 Hg values registered in feathers of little auks of northern regions. This could suggest a predominance of continental and riverine inputs over the Arctic and North Atlantic Oceans as suggested by recent observations 8,10 . However, the high degree of intra-population uncertainty does not allow us to make conclusive deductions about the contribution of atmospheric, sea ice melting or continental Hg inputs across the wintering sites of little auks.