Xuân-Nga Cao

Cyrille Dakhlia
email: dakhliacyrille@gmail.com

Patricia Del Carmen
email: patdelcarmen@hotmail.com

Mohamed-Amine Jaouani
email: jaouani.mohamed.amine@gmail.com

Malik Ould-Arbi
email: m.ouldarbi@gmail.com

Emmanuel Dupoux
email: emmanuel.dupoux@gmail.com

BabyCloud, a Technological Platform for Parents and Researchers

Keywords: language acquisition, cognitive development, linguistic development, day-long recordings, child speech corpus, child activity corpus

In this paper, we present BabyCloud, a platform for capturing, storing and analyzing day-long audio recordings and photographs of children's linguistic environments, for the purpose of studying infant's cognitive and linguistic development and interactions with the environment. The proposed platform connects two communities of users: families and academics, with strong innovation potential for each type of users. For families, the platform offers a novel functionality: the ability for parents to follow the development of their child on a daily basis through language and cognitive metrics (growth curves in number of words, verbal complexity, social skills, etc). For academic research, the platform provides a novel means for studying language and cognitive development at an unprecedented scale and level of detail. They will submit algorithms to the secure server which will only output anonymized aggregate statistics. Ultimately, BabyCloud aims at creating an ecosystem of third parties (public and private research labs...) gravitating around developmental data, entirely controlled by the party whose data originate from, i.e. families.

Introduction

During their first years, infants develop both physically and cognitively at an amazing speed: many parents are eager to know more about this general process and about how well their child is doing. Recent progress in infant speech database collection [START_REF] Xu | The lena language environment analysis system: the interpretive time segments (its) file[END_REF][START_REF] Roy | New horizons in the study of child language acquisition[END_REF][START_REF] Vandam | Homebank, an online repository of daylong child-centered audio recordings[END_REF][START_REF] Casillas | A new workflow for semi-automatized annotations: Tests with long-form naturalistic recordings of childrens language environments[END_REF][START_REF] Warlaumont | Homebank: A repository for long-form real-world audio recordings of children[END_REF] and computational modeling of developmental processes [START_REF] Martin | Mothers speak less clearly to infants: A comprehensive test of the hyperarticulation hypothesis[END_REF][START_REF] Ludusan | Learnability of prosodic boundaries: Is infant-directed speech easier[END_REF]Carbajal et al., 2016a;[START_REF] Carbajal | Modeling language discrimination in infants using i-vector representations[END_REF][START_REF] Ludusan | The role of prosody and speech reg-ister in word segmentation: A computational modelling perspective[END_REF] open up possibilities to measure and model the progress of children using data collected in their natural environment. In other words, it becomes technically possible to offer parents scientifically grounded analytics and tools to explore and document their child's progress.

In this paper, we introduce BabyCloud, a platform for capturing, storing and analyzing day-long audio and photo recordings of children's linguistic environments. The platform is structured around 4 components: (a) Baby Logger which collects high definition pictures and high-quality recordings to be later decoded by automatic speech recognizers, (b) Baby Dock which transfers the data to a secure cloud server, recharges the recorder's batteries, (c) Baby SmartBox, a secure cloud database which stores and indexes raw data and extracts metadata and developmental analytics (child's linguistic landmarks, linguistic and social interactions clips and analyses) using machine learning algorithms and (d) Baby Explorer, which consists itself in two sub-components: a mobile application which allows families to manage and search their data, and an API accessible to scientists, will let them query summary statistics of the data and remotely run algorithms (no access to raw data).

Related Work

One of the more comprehensive resource for child language acquisition research is the CHILDES database [START_REF] Macwhinney | The CHILDES Project: Tools for analyzing talk[END_REF], which, though a tremendous source of data for language studies, presents some shortcomings. The corpora were primarily constructed to document the child's productions, and only secondarily their input; as a result, many recordings are done in the laboratory setting, and are not entirely representative of his or her activities and input in their natural environment. In addition, the majority of the corpora contain only orthographic transcriptions, many of the audio recording were done for human transcription and the quality is not ideal for automatic speech processing.

An ambitious project for gathering day-long home audio recordings was initiated at MIT with the Human Speechome Project [START_REF] Vosoughi | An automatic childdirected speech detector for the study of child language development[END_REF]. It consisted in a continuous recording of all of the child's environment (audio and video), some of which has been transcribed. However, due to the private nature of the data, it is not accessible to outside researchers. HOMEBANK (VanDam et al., 2016) created by the DARCLE 1 group, is another repository for day-long family audio recordings and addresses some of the limitations encountered with the CHILDES database or the Human Speechome Project. It gathers large datasets of audio (and sometimes video) recordings of the infant in his or her natural environment. It addresses the privacy problem by a legally binding protocol of nondisclosure agreement signed by the researchers, their institutions and the repository. This only partially addresses the problem however, given once the agreement is signed, the data can be copied onto the researcher's computer, with no possibility to control what becomes of the data. This is a problem as data protection laws differ across countries (for example, the General Data Protection Regulation law will be enforced in May 2018, aiming at protecting and securing all European residents' personal data, while the US Data Privacy regulation ensures only online consumer's data security and privacy is under FTC authority since 2000).

Another technical issue for the automatic processing of large quantities of speech data is the audio quality of the initial recordings. One recorder developed with this objective in mind is the LENA 2 [START_REF] Xu | The lena language environment analysis system: the interpretive time segments (its) file[END_REF]Oller, D.K, 2011). The device has been miniaturized and engineered to maximize the quality of the recording, while worn by children of various ages. The LENA system also provides an analysis package using machine learning technology to automatically segment the recordings in audio chunks labelled with broad classes (target child, adult male near, background noise, etc). One main limitation of this tool is that the data and processing tools are proprietary and only optimized for English households, making it difficult to adapt to other languages or projects. In addition, it offers no search tools for the parents.

The BabyCloud Platform

We developed BabyCloud, an innovative open-source platform that strives to connect two communities of users: families and academic researchers. It stands out from existing models with its commitment to protect, first and foremost, the collected data and to give the full control and ownership to the party whose data originate from: the families.

Many parents who contribute to science by donating their infant's data and their time also care about their child's development and his/her prospects at school. To address this, the platform will enable the development of novel services such as the ability to follow the development of their child on a regular basis through language and cognitive analytics (growth curves in number of words, verbal complexity, social skills, etc.) even before he/she attends school. In addition, they will have complete control of their data usage, with the option to open up null/partial/full data access to researchers and/or third parties.

For academic research, the platform provides a novel instrument for studying language and cognitive development, potentially, at an unprecedented scale and detail level. Unlike existing data repositories, our platform ensures simultaneously total privacy protection and openness: the data are encrypted and never leave the secure server; however, researchers are able to submit algorithms to the server to analyze the data and only anonymized aggregate statistics will be allowed to get out of the server (see figure 1 for the BabyCloud workflow).

2 https://www.lena.org/

The Baby Logger Component

The Baby Logger is a light, ergonomic and wearable recorder. It has been designed to combine efficiency and convenience for daily life usage. The device records sounds thanks to an array of eight high-quality microphones, captures high resolution pictures and data from a three-axis accelerometer. It is protected by an open source hardware licence, and can be reconfigured for particular projects (see figure 2 for more details).

Operating Modes

The operating modes of the device optimize ease of use and respect of privacy. Two versions of the device have been designed, one for the parents and the other for the child. They have the same features, but the one worn by the parents stops recording and goes into hibernation mode when they go far away from the child. This proximity detection is possible thanks to a radio-frequency identification (RFID) chip affixed on the parents' recorder whereas the child's one only possesses a tag. This setup gives more privacy to the parents and protects the child from a permanent wave exposure. In addition to this automatic setting, a manual privacy mode can be selected, with the parents temporarily stopping the recording on both devices. This situation may occur when the conversation contain sensitive data or other guests are present and don't want to be recorded. The camera will also be equipped with a flap to hide the lens if the family doesn't want the Baby Logger to take pictures. The decision to add the camera is to have pictures from the child's perspective, which can shed light on his/her home environment and development.

The user interface is very simple with only two buttons, one for turning on/off the device and the other, easily accessible for selecting the privacy mode. Two LEDs indicate each one of the following status: privacy vs recording mode, and a warning for docking station operation (low battery or full memory). The Baby Logger is equipped with a large internal memory to allow a maximum of 24 hours of continuous sound recordings and a stream of images every 10 seconds. Less dense acquisition settings are available, notably, random or periodic 2 to 10 minutes sample. The battery has been designed to last for at least 24 hours under the maximum recording density.

Choice of Technology

The choice of the electronic components has been made to optimize data quality and power usage. Our recorder is built around a high performance microcontroller, equipped with a Cortex-M7 ARM microprocessor providing enhanced performance and optimized power consumption, representing the most relevant solution for IoT and multimedia systems. Audio acquisition is performed with an array of 8 MEMS (Micro ElectroMechanical Systems) microphones, a new generation of digital microphones directly built on semiconductors and integrating digital circuits (ASIC). MEMS are an attractive solution because of their small size and weight, allowing systems to be miniaturized. The camera is a 5 Mega Pixels CMOS digital camera. It has a Digital Signal Processor (DSP) and

The Baby Dock Component

While the device is recording, no data is being transferred to save on battery life and to protect the child from wave exposure. The wireless data synchronization is performed only when the battery is being recharged onto the Baby Dock (see figure 3 for more details).

The Baby Dock represents the interface between the wearable recorder and the secure cloud. A Raspberry Pi 3 (our preferred solution but any computer with a Wifi module working as a host for a local network can be used) is connected to the Internet thanks to an ethernet wire which ensures the link with the home network. A three-phase cyclical software will run on the Baby Dock:

• Recorder synchronization Phase: During this stage, the connection to the Internet is disabled to ensure the security of the raw data being transferred from the recorders to the Baby Dock. The transfer is done instead via the local Wifi network setup between the Life Logger and the Dock. Simultaneously the Life Logger's batteries are recharged through inductive charging. At the end of synchronization, the data is removed from the Baby Logger's memory.

• Data Filtering Plugin: Signal processing algorithms are applied offline to the data in order to separate speech from background noise (source separation, speech enhancement) and segment the recordings according to two classes (speech, non-speech). This significantly reduces the amount of data transferred to the cloud. We will use open-source sytems [START_REF] Schultz | Speaker segmentation and clustering in meetings[END_REF][START_REF] Rudzicz | Adjusting dysarthric speech signals to be more intelligible[END_REF] which will be tuned to the baby logger data and customized to fit into the baby dock.

• Cloud synchronization Phase: The last phase of the cycle ensures the transfer of the encrypted data to the secure cloud server via an ethernet connection and their removal from the Dock.

The Baby Smartbox Component

The Baby Smartbox stores the collected raw data, runs advanced signal processing and machine learning algorithms to automatically add meta-data (speaker diarization, activity detection, estimation of the child's vocal maturity, etc), and provides a database server. The server is protected to provide access only to authorized users via a login authentification process. It interfaces with the Baby Explorer via a RESTful API developed within the Python Flask framework (see figure 4 for more details).

This component will have a core secure database management system selectively granting access to data to different classes of users (parents, researchers). In addition, it will have a set of machine learning algorithms (plugins) which will automatically data annotations at various linguistic levels. We are currently focusing on replicating some of the annotation layers of the LENA system, using state-of-theart, open source and retrainable software. These include:

• Segmentation broad class speaker ID. This requires algorithms that perform Speech or Voice Activity Detection: they segments speech from background noise [START_REF] Uhle | Voice activity detection[END_REF]. Broad class speaker ID classifies the resulting segments into a small number of categories, like target child, other child, males, females.

• Speaker diarization. This task is more difficult than the above, because it requires to classify utterances according to an unknown number of speakers. The task is made easier if the speakers can be 'enrolled' in advance, i.e. that a few minutes are manually annotated. The current performance of speaker diarization systems based on I-vectors is dependant on utterance length. Typically, performance is not very good with short utterances [START_REF] Kanagasundaram | Dnn based speaker recognition on short utterances[END_REF].

Other plugins will be added as they become available in open source format. In particular, we are working in collaboration with the ACLEW3 consortium. This is funded by a transatlantic research initiative (Digging Into Data project4 ) aiming at creating a common annotation scheme for diverse (culturally and linguistically) infant datasets, and at building tools to semi-automatically analyze those datasets. The plug-ins currently under development are:

• Syllabic segmentation. Syllables are acoustically saillant events in speech and can be efficiently detected [START_REF] Räsänen | Prelinguistic segmentation of speech into syllable-like units[END_REF]. In turn these can be used as proxy for utterance length.

• Vocal maturity. Infant produce several stages of vocalization at different developmental stages (vegetative vocalizations, crying, laughter, canonical babbling, varigated babbling, etc.). Given their rather stable shape across languages, it seems feasible, using standard machine learning tools (markov models, SVM or random forest models) to construct a retunable classifier that will enable to produce developmental statistics based on these categories.

• Child-directed vs. adult-directed speech. Parents in many culture address their infants in specific ways [START_REF] Fernald | Acoustic determinants of infant preference for motherese speech[END_REF], and child directed input seems to be a better predicted for language acquisition than total input [START_REF] Weisleder | Talking to Children Matters: Early Language Experience Strengthens Processing and Builds Vocabulary[END_REF].

Being able to automatically annotate this difference in register could help predictive models of language development.

As these plugins are developed and open sourced, they will be incorporated into the platform. We construct all of these modules to be trainable. Which means that the automatic annotation can be retuned to specific recordings, provided the parents gave authorization for a subset of the data to be annotated manually. In that case, the annotated data is split into a training set and a test set to evaluate the reliability of the machine annotation. Similarly, manual annotations are required to adapt any new plugin to the particular data collected by the baby logger. All of the automatic annotations are considered derived data and added to the raw data. They are used to derived analytics, such as daily vocal activity, linguistic complexity (mean utterance length), and social responsiveness indexes (turn taking), which will be accessible to the parent for their child and to the scientist as aggregate statistics (see section 3.4.2).

The Baby Explorer Component

The Baby Explorer is designed to cater to 2 types of users: parents and researchers.

The Baby Explorer: parents' interface

It is a hybrid mobile/web application developed within the Ionic framework using the following technologies: Typescript (Javascript) and Angular 4 for data processing and actions linked to screen/hardware events, HTML5 and CSS3 for graphic user interfaces, Cordova for hardware access to use mobile native functionalities, and PostgreSQL for databases. The objective is to actively engage families with their dataset by offering them an attractive tool with several functionalities: (a) browse their child's recordings using time and/or activity filters, (b) track their child's language and cognitive development with the provided analytics and statistics, (c) manage the data access for researchers (see figure 5 for more details).

To ensure that parents fully understand the extent of their rights with regards to data protection, an online consent form must be signed when parents log in for the first time onto the Baby Explorer. Unlike traditional terms of agreement clauses, this one is aimed at protecting the user's rights and privacy and making sure that the user understands the terms of contract he/she is agreeing on. This last step is verified by an online quiz while the user is going through the consent form. Since parents own the data, they have the option to update, erase, remove them or ask for their portability from the secured cloud, using the interface.

In the same way, the child, once he/she turns 13 years old (based on Children's Online Privacy Protection Rule aka "COPPA"), will be required to express his/her free consent to continue or stop his/her participation to the project.

• Data View: The data view interface helps families browse through their infant's data via a customized and ergonomic time-line, allowing to simultaneously access audio recordings and their associated pictures. On this timeline parents can control the audio and image channels (play, stop, rewind, re-scale, etc), and can filter the data by activity. They can bookmark their favorite recordings or pictures, share them with relatives or friends and set privacy settings on their data with regards to their use for research. The pictures taken from the child's device will inform parents and researchers on his/her perception of his local environment (people he/she is looking at, location, activities such as playing, eating, napping...).

• Dashboard: The dashboard displays an overview of the metrics about the child's linguistic development.

It allows parents to follow his/her cognitive development on a daily basis through analytics and statistics (growth curves in number of words, verbal complexity, social skills, etc.).

• Access Authorization: This interface allows families to see all the requests made by researchers to access their data. Parents are able to read information about the enquirers such as their name, contact information, status, summaries of their projects and collaborations, publications, etc. Acceptance from the parents is a pre-requisite for scientists to work on the data. Parents have the option to define presets to automatically accept/reject/block certain types of requests (educational research, medical research, etc.). They can also grant/revoke access on a project by project basis.

The Baby Explorer: researchers' interface

This API (Application Programming Interface) offers a bundle of functionalities and tools dedicated to researchers.

They will have at their disposal new and large dataset with fine layers of annotations, ready for analysis and obeying the regulations on data protection. Beforehand, all researchers will be required to pre-register their studies and get approved by an ethics committee. Once their algorithms have been tested with public data on the virtual machine sandbox at their disposal, scientists can send access requests to parents to run these algorithms on their meta-data. When the access is granted, they have to use the validated virtual machine, designed to make sure that the raw data cannot be retrieved. The only output retrieved by the researchers is anonymized statistics of the data. In a later part of the project, we will add the possibility for scientists to request algorithmic access to the raw data in order to add new layers of meta-data (see figure 6 for more details).

Completed Work

At the time of writing, we have completed the following steps: we have a working prototype of the Baby Logger, of the Baby Dock, of the Secure Cloud Database and of the Baby Explorer parent's interface. The algorithms for speech analysis are being developed in parallel by the ACLEW team (see section 3.3 above). We have submitted the project description to our local ethics committee. It has been reviewed favorably and will be approved after revisions. The proposal has also been submitted for a formal authorization to the National Committee for data protection. It is currently under review. The miniaturization and baby friendly design of the Life Logger is underway, and the scientist's interface under study.

We also conducted a short pilot with six families to apprehend the parents' needs and expectations. These families were selected from our babylab's database which comprises more than 1000 volunteer families. The infant wore a T-shirt with a pocket hiding the audio recorder (such as the LENA or a USB key recorder). The recording happened at home for one/two day(s). We asked for the parents' feedback afterwards about the experiment. All were in favor of pursuing the study but they expressed reservations on the recording duration (once or twice a week instead of continuous recordings), were interested in a more practical and fit-all recorder. There were mixed reactions on the camera function (some parents wanted to disable it completely). We annotated (with the parents' consent) a small sample of the collected data in order to integrate the analytics into the application. A second interview with the families will be scheduled to show the results on the application and have their feedback on the user experience to improve on the functionalities of the platform.

Once the protocols are validated by the data protection and ethics committees, we plan to scale up the project in terms of number of families (20 families) and duration (one year or more). We aim to achieve a functional platform with agile software/hardware development through rapid iteration cycles. During these cycles of improvement, we will pay particular attention to user experience and data protection.

Expected Impact and Conclusion

In this paper, we presented an innovative platform aimed at reinforcing collaboration between parents and researchers.

The benefits of such a platform for the community users and for society are diverse.

The benefits for families are twofold. The first one is health related: the language and cognitive analytics could help parents spot potential developmental delays and trigger early medical intervention. The platform does not propose medically validated diagnostic tools but parents may use the provided data and consult speech and language therapists to get further assistance and medical advice.

A secondary benefit is related to the quality of life. Our platform will provide cloud storage space and indexing services related to the child: automatically extracted pictures and audio snippets. In addition, parents will be able to include other types of information (potentially, school and medical records), which security level will be guaranteed. The whole portfolio will constitute a digital asset for the child, which can be permanently accessible as a memory of early childhood.

For academia, large recordings of infant development will allow researchers to establish new quantitative and predictive models at a level that was not possible before.

These models will feed back into better analytics and in a long term, detect early signs of potential cognitive delays.

An additional benefit will be the involvement of parents into science (citizen science), who will help collect data and perhaps, volunteer annotations.

In a later phase, we plan to include a third community to interact with the families and researchers: private companies. For private IT companies, personal data is a valuable source for big data analytics and building business strategies based on the customer's preferences. The platform will offer access to large and rich amount of data but which will be protective of families' privacy. Under this strict privacy preserving protocol, companies would be able to offer families new tools and analytics.

Vice-versa, families may open up, through smart contracts, partial aspects of their data to companies for specific purposes. These smart contracts will remain under the lab's supervision to ensure that the family's privacy and data protection are safeguarded.

Finally, society may benefit from the platform in the area of developmental pathologies. Developmental disorders are typically diagnosed too late (i.e., at a time when the child is already lagging behind in school), resulting in mental strains and heavy financial costs for families and also for society. Tools that will raise awareness about these disorders and their impact may help alleviate those costs.

Figure 1 :

 1 Figure 1: BabyCloud Workflow.

Figure 2 :

 2 Figure 2: Baby Logger Architecture.

Figure 3 :

 3 Figure 3: Baby Dock Architecture.

Figure 4 :

 4 Figure 4: Baby Smartbox Architecture with example machine learning plugins.

Figure 5 :

 5 Figure 5: Baby Explorer Architecture: Parent's Application.

Figure 6 :

 6 Figure 6: Baby Explorer Architecture: Researcher's API.

Day-long Audio Recording of Children's Linguistic Environments

Analyzing Child Language Experience around the World: https://sites.google.com/view/aclewdid/home

https://diggingintodata.org/


Griet Neukermans
email: griet.neukermans@obs-vlfr.fr

Laurent Oziel

Marcel Babin

Increased intrusion of warming Atlantic water leads to rapid expansion of temperate phytoplankton in the Arctic

Keywords: atlantification, biogeography, climate change, coccolithophore, Emiliania huxleyi, phytoplankton, range expansion, remote sensing

The Arctic Ocean and its surrounding shelf seas are warming much faster than the global average, which potentially opens up new distribution areas for temperate-origin marine phytoplankton. Using over three decades of continuous satellite observations, we show that increased inflow and temperature of Atlantic waters in the Barents Sea resulted in a striking poleward shift in the distribution of blooms of Emiliania huxleyi, a marine calcifying phytoplankton species. This species' blooms are typically associated with temperate waters and have expanded north to 76°N, five degrees further north of its first bloom occurrence in 1989. E. huxleyi's blooms keep pace with the changing climate of the Barents Sea, namely ocean warming and shifts in the position of the Polar Front, resulting in an exceptionally rapid range shift compared to what is generally detected in the marine realm. We propose that as the Eurasian Basin of the Arctic Ocean further atlantifies and ocean temperatures continue to rise, E. huxleyi and other temperate-origin phytoplankton could well become resident bloom formers in the Arctic Ocean.

| INTRODUCTION

The most rapid and substantial climate driven changes in marine ecosystems are expected at high latitudes in regions within or bordering the Arctic Ocean where rates of warming are more than double the global average [START_REF] Blunden | State of the climate in 2015[END_REF].

One such region is the Barents Sea (Figure 1), a highly productive Arctic shelf sea on the main northward path of Atlantic water into the Arctic Ocean [START_REF] Smedsrud | The role of the Barents Sea in the Arctic climate system[END_REF]. Since 1980, the inflow of Atlantic waters into the Barents Sea has doubled (Oziel, Sirven, & Gascard, 2016) and warmed by 1.4°C (Figure 1), at a rate exceeding four times the global average [START_REF] Ipcc | Climate change 2013: The physical science basis[END_REF]. In the last decade, temperatures hit highs not seen in a 110 years of recorded history [START_REF] Boitsov | Atlantic water temperature and climate in the Barents Sea, 2000-2009[END_REF]. The increased inflow of Atlantic waters is referred to as atlantification (sensu Arthun, Eldevik, Smedsrud, Skagseth, & Ingvaldsen, 2012;Oziel et al., 2016), which is thought to be the main cause for the dramatic loss of half of the winter sea ice cover in the area [START_REF] R E F E R E N C E S Arthun | Quantifying the influence of atlantic heat on barents sea ice variability and retreat[END_REF] and a northeastward shift in the position of the temperature front (Oziel et al., 2016) where advected warm and saline Atlantic water meets southward flowing fresh and cold Arctic water (Figure 1). As a consequence of climate change, Atlantic waters thus carry nutrients and plankton further and further northeast in a continuous advective domain, creating trails of life or death for warmer-affinity species depending on whether organisms are able to survive and thrive in their new environments [START_REF] Wassmann | The contiguous domains of Arctic Ocean advection: Trails of life and death[END_REF].

Phytoplankton, which form the foundation of the marine food web, are particularly responsive to climate change due to their short generation times, on the order of days, and being numerous and free-floating, so they rapidly track changing environmental conditions [START_REF] Boyd | Environmental control of open-ocean phytoplankton groups: Now and in the future[END_REF]. Here, we investigate the response of the bloom-forming species Emiliania huxleyi, a cosmopolitan coccolithophore, omnipresent in all except the polar oceans [START_REF] Winter | Biogeography of living coccolithophores in ocean waters[END_REF]. More specifically, we examine the recent suggestion that the species may be expanding its range into the polar oceans [START_REF] Winter | Poleward expansion of the coccolithophore Emiliania huxleyi[END_REF] and its potential causes. E. huxleyi plays a crucial role in ocean biogeochemistry and various climate change feedback mechanisms. For example, E. huxleyi is known to alter the Earth's radiation budget [START_REF] Tyrrell | Optical impacts of oceanic coccolithophore blooms[END_REF], decrease oceanic CO 2 uptake through the reduction of surface layer alkalinity [START_REF] Frankignoulle | Marine calcification as a source of carbon dioxide: Positive feedback of increasing atmospheric CO 2[END_REF] and may enhance carbon flux to depth by providing calcite ballast to sink organic particles [START_REF] Riebesell | Competitive fitness of a predominant pelagic calcifier impaired by ocean acidification[END_REF].

Emiliania huxleyi is known to form extensive blooms in summer, especially in the temperate and subpolar Atlantic Ocean covering areas up to 250,000 km 2 (e.g. [START_REF] Holligan | A biogeochemical study of the coccolithophore, Emiliania huxleyi, in the North Atlantic[END_REF]. Like all coccolithophores, E. huxleyi cells are covered by calcite plates or coccoliths, but E. huxleyi is unique in the sense that it overproduces coccoliths, which are released into the water at the final bloom stage [START_REF] Paasche | A review of the coccolithophorid Emiliania huxleyi (Prymnesiophyceae), with particular reference to growth, coccolith formation, and calcification-photosynthesis interactions[END_REF]. The resulting accumulated E. huxleyi cells (typical densities >10 9 cells/m 3 ) and detached coccoliths in surface waters scatter so much light they turn seawater bright milky-turquoise, making these blooms easily detectable from space both by modern ocean colour satellites (1998-present) and by older and less sensitive optical satellite sensors (1982-present), such as the advanced very high-resolution radiometer, AVHRR (Smyth, Tyrell, & Tarrant, 2004).

To examine the poleward range expansion of E. huxleyi and the environmental conditions driving the range shift, we analyse a continuous, long-term (1982-present) dataset, which combines optical satellite observations of E. huxleyi blooms with satellite data of seasurface temperature (SST), and other environmental parameters in the Barents Sea. We first investigate the intensity and distribution area of E. huxleyi summer blooms from remotely sensed particulate inorganic carbon concentration, PIC, derived from ocean colour satellites (1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016). In conjunction, we use remotely sensed SST data to locate the temperature front that separates Atlantic from Arctic water masses. We further extend the time series of E. huxleyi bloom areal distribution and SST back to the first bloom occurrence in 1989 using observations from the AVHRR optical sensor (Smyth et al., 2004) to quantify long-term shifts in the leading edge of E. huxleyi's bloom distribution.

| MATERIALS AND METHODS

| Remote sensing data

NASA's daily level 3 products from the SeaWiFS (1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)9.2 km spatial resolution) and MODIS (2003MODIS ( -2016, 4, 4.6 km spatial resolution) ocean colour sensors were downloaded from NASA's ocean colour website (http://oceandata.sci.gsfc.nasa.gov) and included Chlorophyll-a concentration (Chl a), PIC, Photosynthetically available radiation (PAR) and euphotic zone depth (Z eu ). The ocean colour satellite PIC product is derived from NASA's standard PIC algorithm [START_REF] Balch | Calcium carbonate measurements in the surface global ocean based on moderate-resolution imaging spectroradiometer data[END_REF][START_REF] Gordon | Retrieval of coccolithophore calcite concentration from SeaWiFS Imagery[END_REF], which is based on a robust relationship between the light backscattering coefficient of particles suspended in seawater and the concentration of coccospheres and coccoliths, calcite plates forming the coccosphere of coccolithophores [START_REF] Balch | Biological and optical properties of mesoscale coccolithophore blooms in the Gulf of Maine[END_REF][START_REF] Paasche | A review of the coccolithophorid Emiliania huxleyi (Prymnesiophyceae), with particular reference to growth, coccolith formation, and calcification-photosynthesis interactions[END_REF]. Some coccolithophore species, including E. huxleyi, shed excess coccoliths into the water during the later stages of a bloom [START_REF] Paasche | A review of the coccolithophorid Emiliania huxleyi (Prymnesiophyceae), with particular reference to growth, coccolith formation, and calcification-photosynthesis interactions[END_REF], creating large patches of highly reflective waters, which can be easily observed from space [START_REF] Holligan | A biogeochemical study of the coccolithophore, Emiliania huxleyi, in the North Atlantic[END_REF]. Satellite-derived PIC concentration provides a F I G U R E 1 Conceptual scheme of increased inflow and warming of Atlantic water into the Barents Sea. Conditions of diminishing sea ice and Arctic water masses, depicted on a bathymetric background. Warming rates for surface waters in summer were computed over the period 1982-2014 (Table S1). Arrows indicate major currents (redrawn from the Norwegian Environment Agency). NAC, Norwegian Atlantic Current; WSC, West Spitzbergen Current; NCC, North Cape Current; NZC, Novaya Zemlya Current; PC, Persey Current; ESC, East Spitzbergen Current (after [START_REF] Harris | Water mass distribution and polar front structure in the western Barents Sea[END_REF]]) good proxy for E. huxleyi calcite concentration in the Barents Sea because the coccolithophore population there is of low diversity and overwhelmingly dominated by E. huxleyi [START_REF] Giraudeau | A survey of the summer coccolithophore community in the western Barents Sea[END_REF]. Furthermore, satellite PIC products have been successfully validated during E. huxleyi blooms in the Barents Sea [START_REF] Burenkov | Satellite observations of the coccolithophorid bloom in the Barents Sea[END_REF][START_REF] Giraudeau | A survey of the summer coccolithophore community in the western Barents Sea[END_REF][START_REF] Hovland | Dynamics regulating major trends in Barents Sea temperatures and subsequent effect on remotely sensed particulate inorganic carbon[END_REF] and in other bloom areas [START_REF] Balch | Calcium carbonate measurements in the surface global ocean based on moderate-resolution imaging spectroradiometer data[END_REF][START_REF] Holligan | A biogeochemical study of the coccolithophore, Emiliania huxleyi, in the North Atlantic[END_REF]. The exclusion of PIC data in shallow coastal waters with bottom depth below 100 m helped minimize spurious high PIC concentrations caused by strong light backscattering from resuspended particles [START_REF] Broerse | The cause of bright waters in the Bering Sea in winter[END_REF] and excluded coastal fjords in which the coccolithophore population is more diverse than in offshore waters [START_REF] Volent | Improved monitoring of phytoplankton bloom dynamics in a Norwegian fjord by integrating satellite data, pigment analysis, and Ferrybox data with a coastal observation network[END_REF].

| Mixed-layer depth

Daily density-based estimates of mixed-layer depth at 0.25°spatial resolution were obtained from global ocean reanalysis products, GLORYS2v3, of the European Union Copernicus Marine Environment Monitoring Service (http://marine.copernicus.eu/). The reanalysis are built to be as close as possible to the observations (of altimetric sea level anomaly, temperature and salinity in situ profiles, sea-surface temperature observations, and sea ice concentration) and in agreement with the model physics.

| Water mass classification and validation

Classification of water masses from 1982 till 2014 was based on (i) remotely sensed daily sea-surface temperature at 4 km spatial resolution from the AVHRR sensor obtained through the National Oceanic and Atmospheric Administration (NOAA) (Baker-Yeboah et al., 2016) and (ii) sea ice concentration at 25 km spatial resolution from the Special Sensor Microwave Imager (SSMI) (1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007) and SSMI/Sounder (2008[START_REF] Winter | Poleward expansion of the coccolithophore Emiliania huxleyi[END_REF] sensors obtained through the National Snow and Ice Data Center (NSIDC) (Cavalieri, Parkinson, Gloersen, & Zwally, 1996). To determine the position of the Polar Front, which separates warm Atlantic waters from cold Arctic waters, we performed a local variance filter with a window size of 7 9 7 pixels on the March-April mean SST data. This time window optimizes remotely sensed SST data availability at a time the water column is vertically well mixed and well before Atlantic waters thermally stratify.

The position of the Polar Front is seasonally stable (Figure S1); therefore, the position of the SST front in March-April is a good indicator for the annual extent of Atlantic waters. A histogram of pixels where the standard deviation exceeded the absolute value of the mean SST within the moving window was built and the Polar Front waters, PFW, were defined as those pixels that had SST between the 16 th and 84 th percentiles of the corresponding histogram of SST values (Oziel et al., 2016). Over the 1982-2014 period, the lower and upper SST bounds of Polar Front waters were rather stable with respective mean values (AEstandard deviation) of À0.47°C (AE0.07°C) and 0.50°C (AE0.09°C) (Table S2). Six water masses were derived as follows: (i) North Atlantic waters, NAW, with March-April mean SST above 3°C (Loeng, 1991), (ii) Modified Atlantic Waters, MAW, between NAW and PFW, (iii) PFW with temperature ranges shown in Table S2, (iv) the seasonal ice zone, SIZ, with maximum winter sea ice extent above 80% and summer minimum extent below 20%, (v) the perennial ice covered waters, PIW, with summer minimum ice cover above 20% and (vi) Arctic Waters, ArW, between the SIZ and the PFW. If no March-April mean SST data were available in ice-free waters due to persistent cloudiness, pixels were considered unclassified, which was the case for less than 5% of the Barents Sea coverage (Figure S2). The remote sensing-based classification of water masses was validated using a collection of hydrographic in situ data with over 130,000 profiles of temperature and salinity collected between 1980 and 2012 (Figure S3).

| RESULTS

Our analyses of the intensity and distribution area of E. huxleyi summer blooms (1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016) in conjunction with water mass distribution show that E. huxleyi blooms are confined to Atlantic waters and closely track the position of the temperature front (Figure 2). The front has strongly shifted northeastward commensurate with a doubling in areal coverage of Atlantic water over the study period from 10% in 1998 to 23% in 2014 and alongside a strong reduction in the areal coverage of Arctic waters from 60% to 30% (Figure S2). In the Eastern Barents Sea, the leading edge of E. huxleyi's bloom distribution has shifted northward by 3°latitude, equivalent to 20.5 km per year over the period 1998-2016 (Figure 3).

Extending the time series of E. huxleyi bloom areal distribution back to the first bloom occurrence in 1989 (Smyth et al., 2004) gives a northward shift of the leading edge of 501 km in nearly 30 years (Figure 3; Table S3). The rate of range shift has clearly accelerated in the present decade (56 km per year in 2010-2016), in pace with the accelerated northward shift of the Polar Front. Moreover, three out of four of the most expansive blooms on record occurred in the present decade (2011-2012, and 2016, Figure 2; Figure S4).

| DISCUSSION

Poleward expansions at the leading edges of marine species have been observed across all ocean regions with global average rate of 72 AE 13 km per decade [START_REF] Poloczanska | Global imprint of climate change on marine life[END_REF][START_REF] Poloczanska | Responses of marine organisms to climate change across oceans[END_REF], as expected for biological and ecological responses to ocean warming.

Fastest leading-edge expansions were seen for highly mobile or dispersive pelagic organisms such as bony fish (278 AE 77 km per decade), invertebrate zooplankton (142 AE 28 km per decade) and phytoplankton (358 km AE 68 per decade). Here, we quantify for the first time the rate of poleward range shift of E. huxleyi, the most abundant and productive coccolithophore species. In contrast to the two phytoplankton range shifts reported in a recent meta-analysis (310 km per decade for Ceratium trichoceros; 406 km per decade for Noctiluca scintillans) [START_REF] Poloczanska | Global imprint of climate change on marine life[END_REF][START_REF] Poloczanska | Responses of marine organisms to climate change across oceans[END_REF] We propose that the recent rapid expansion of E. huxleyi blooms in the Barents Sea is triggered by concurrent increases in the areal extent and temperature of Atlantic waters. On the one hand, plankton and nutrients propagate further northeast, while on the other hand rising temperature of Atlantic water in the Barents Sea has created an environment in which E. huxleyi is able to form extensive blooms since the late 1980s, with an interruption in the mid-1990s due to colder water temperature (Smyth et al., 2004) (Figure S5).

Changing water masses and fronts has also facilitated plankton shifts in other ocean regions when accompanied by increasing ocean temperature [START_REF] Poloczanska | Responses of marine organisms to climate change across oceans[END_REF], which allows marine organisms to survive and thrive in new environments.

Our observations highlight two distinct barriers to E. huxleyi dispersal. First, blooms were exclusively found in Atlantic waters, suggesting that the Polar Front represents a physical barrier to dispersal (Figure 2). Second, no blooms were detected in Atlantic waters with summer mean sea-surface temperature below 6°C, suggesting a thermal barrier to bloom occurrence. This is exemplified in Figure 4, which contrasts observations of 1998 and 2012 corresponding to minimal and maximal Atlantic water extent, respectively (Figure S2).

In 1998, no E. huxleyi bloom was observed in Arctic waters even if F I G U R E 2 Time series of Emiliania huxleyi bloom calcite concentration and areal extent. Summer maximum calcite concentration in the Barents Sea was obtained from SeaWiFS (1998)(1999)(2000)(2001)(2002) and MODIS (2003MODIS ( -2016) ) ocean colour satellite sensors. Red line delineates the extent of Atlantic waters (1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014). Areas shallower than 150 m were masked in white and no data in grey the summer mean sea-surface temperature was >6°C, whereas in 2012, no blooms were detected in waters with summer mean seasurface temperature <6°C even if they were of Atlantic origin. A minimum summer SST of 6°C in Atlantic water appears to be conducive to E. huxleyi blooms (Figure 5a; Figure S6). This is consistent with experimental data on the temperature dependency of E. huxleyi maximum growth rates, which exhibits a similarly stark rise at a temperature of 6°C. Indeed, maximum growth rates in the 2À5°C temperature range are on the order of 0.1 per day and then jump to 0.5 per day at 6°C, beyond which growth rates increases as a power function of temperature [START_REF] Fielding | Emiliania huxleyi specific growth rate dependence on temperature[END_REF]. Polar diatoms and other cold-water adapted phytoplankton have typical growth rates above 0.5 per day in the À1 to 5°C range, thus outcompeting E. huxleyi in cold waters [START_REF] Lacour | Growth, Chl a content, photosynthesis, and elemental composition in polar and temperate microalgae[END_REF]. Compared to bottomup processes, top-down control of E. huxleyi blooms is thought to be of limited importance as it has been shown that calcification is an effective strategy to reduce grazer growth by creating indigestion or prolonging digestion time, resulting in net growth of E. huxleyi [START_REF] Harvey | Consequences of strain variability and calcification in Emiliania huxleyi on microzooplankton grazing[END_REF][START_REF] Kolb | An inducible antipredatory defense in haploid cells of the marine microalga Emiliania huxleyi (Prymnesiophyceae)[END_REF][START_REF] Monteiro | Why marine phytoplankton calcify[END_REF].

The northern limit of E. huxleyi blooms in a warming Barents Sea is thus set by the position of the Polar Front, which is associated with a strong gradient in water temperature, and across which there is little exchange between Atlantic and Arctic water masses. However, as Arctic surface waters continue to warm at an average rate of 0.6°C per decade and Atlantic waters at a slightly slower rate of 0.4°C per decade (Figure S7; Table S1), we expect that the temperature front will weaken throughout the century. By the end of the century, E. huxleyi may start forming blooms in Arctic water masses, which will then be pushed northward of 82°N [START_REF] Wassmann | The contiguous domains of Arctic Ocean advection: Trails of life and death[END_REF]. Furthermore, it has been hypothesized that the Barents Sea will further atlantify through a self-amplifying positive feedback loop in which increased Atlantic water inflow enhances sea ice melt and anomalous cyclonic winds, which lead to an amplification of Atlantic water inflow [START_REF] Smedsrud | The role of the Barents Sea in the Arctic climate system[END_REF]. We can therefore expect E. huxleyi blooms to expand further north along with Atlantic water masses. This is also supported by geological studies that revealed the presence of E. huxleyi in the Arctic Ocean during conditions of diminished sea ice and increased Atlantic inflow of past interglacial times [START_REF] Backman | Biochronology and paleoceanography of late Pleistocene and Holocene calcareous nannofossil abundances across the Arctic Basin[END_REF] although we acknowledge that presence does not imply blooms.

Besides Atlantic water extent and water temperature, northward shifts of E. huxleyi will be constrained by other environmental conditions such as the strong seasonality of day length in the high Arctic and associated short annual window for phytoplankton growth [START_REF] Kaartvedt | Photoperiod may constrain the effect of global warming in arctic marine systems[END_REF], water column stability and nutrient availability [START_REF] Tyrrell | Emiliania huxleyi: Bloom observations and the conditions that induce them[END_REF]. The extreme light conditions in the high North, however, do not appear to be a limitation for E. huxleyi as viable populations have been found in Atlantic water north of Svalbard (>80°N) [START_REF] Hegseth | Intrusion and blooming of Atlantic phytoplankton species in the high Arctic[END_REF] and laboratory experiments have shown the species' tolerance to low as well as high light, suggesting its capacity for survival in the high Arctic. Shallow mixed layers between 10 and 20 m are known to favour E. huxleyi blooms [START_REF] Tyrrell | Emiliania huxleyi: Bloom observations and the conditions that induce them[END_REF], consistent with our observations of significant negative correlation between E. huxleyi bloom magnitude and summer mixed-layer depth which ranged between 10.5 and 19 m F I G U R E 3 Shifting position of the leading edge of Emiliania huxleyi bloom distribution from 35 years of remote sensing data for three longitude bands in the Barents Sea. (a) 20°E-30°E, (b) 30°E-40°E, (c) 40°E-50°E. Bloom areal coverage was obtained from a continuous record of optical satellite data, including the advanced very high-resolution radiometer (AVHRR) sensor (Smyth et al., 2004) (green), from which the first conspicuous bloom was detected in 1989, and the SeaWiFS (blue) and MODIS (red) ocean colour sensors (r = À0.51 AE 0.16, p = .00003; Figure 5b; Table S4). Such mixedlayer depths are typical for the Arctic Ocean in summer (Peralta-Ferriz & Woodgate, 2015), thereby favouring northward expansion of E. huxleyi blooms. Due to its high nutrient affinity, E. huxleyi also performs well in nutrient-depleted environments and therefore blooms often occur after the demise of the diatom spring bloom [START_REF] Iglesias-Rodr Iguez | Representing key phytoplankton functional groups in ocean carbon cycle models: Coccolithophorids[END_REF], such as observed in the Barents Sea in summer [START_REF] Oziel | Role for Atlantic inflows and sea ice loss on shifting phytoplankton blooms in the Barents Sea[END_REF]. Since the early 1990s, nutrient stocks in the Atlantic water of the Barents Sea have gradually declined, particularly silicates which are experiencing a much larger decrease than nitrates [START_REF] Rey | Declining silicate concentrations in the Norwegian and Barents Seas[END_REF]. Such conditions are favourable for E. huxleyi at the expense of diatoms and modelling studies suggest that nutrient concentrations will continue to decline in the Arctic Ocean throughout this century, due to shoaling of the mixed layer and decreasing nutrients in the advected North Atlantic and Pacific waters (Slagstad, Wassmann, & Ellingsen, 2015;[START_REF] Vancoppenolle | Future Arctic Ocean primary productivity from CMIP5 simulations: Uncertain outcome, but consistent mechanisms[END_REF]. Warm-water species such as E. huxleyi tend to be smaller and less energy-rich than polar species. Indeed, an atlantification of marine organisms at higher trophic levels has also been reported in the Barents Sea, along with a poleward retraction of cold-water species (e.g. [START_REF] Fossheim | Recent warming leads to a rapid borealization of fish communities in the Arctic[END_REF]. This suggests that E. huxleyi blooms can serve as early warnings of impending effects on the marine food web. Furthermore, it is expected that other temperate-origin phytoplankton species, including harmful ones, may also progress northward as temperatures within polar and subpolar regions become more permissive for species from lower latitudes [START_REF] Gobler | Ocean warming since 1982 has expanded the niche of toxic algal blooms in the North Atlantic and North Pacific oceans[END_REF]. Indeed, in the Bering Sea, E. huxleyi bloom occurrences have been considered harbingers of blooms of the toxic temperate-origin dinoflagellate Alexandrium tamarense [START_REF] Walsh | Trophic cascades and future harmful algal blooms within ice-free Arctic Seas north of Bering Strait: A simulation analysis[END_REF]. This dinoflagellate has also become increasingly prevalent in waters off northern Norway since the late 1990s, whereas it was rare before [START_REF] Walsh | Trophic cascades and future harmful algal blooms within ice-free Arctic Seas north of Bering Strait: A simulation analysis[END_REF].

We note that there is a second route along which Atlantic-origin species may expand poleward into the Arctic Ocean as Atlantic waters flow northward through the wide and deep Fram Strait on the West Spitzbergen Current (WSC, Figure 1), which subducts and, north of Svalbard, turns eastwards and continues along the continental slope. In recent years, the influence of Atlantic waters along the WSC has also increased [START_REF] Walczowski | Changes in Atlantic water properties: An important factor in the European Arctic marine climate[END_REF], even extending into the eastern Eurasian Basin (atlantification sensu [START_REF] Polyakov | Greater role for Atlantic inflows on sea-ice loss in the Eurasian Basin of the Arctic Ocean[END_REF]. Many studies have reported increased importance of Atlantic species and atlantification of the marine food web west and north of Svalbard [START_REF] Berge | Ocean temperature oscillations enable reappearance of blue mussels Mytilus edulis in Svalbard after a 1000 year absence[END_REF][START_REF] Hegseth | Intrusion and blooming of Atlantic phytoplankton species in the high Arctic[END_REF][START_REF] Orma Nczyk | Zooplankton structure in high latitude fjords with contrasting oceanography (Hornsund and Kongsfjorden, Spitsbergen)[END_REF][START_REF] Wassmann | The contiguous domains of Arctic Ocean advection: Trails of life and death[END_REF][START_REF] We Z Sławski | Ecosystem maturation follows the warming of the Arctic fjords[END_REF]. One in situ study in the summer of 2003 reported that E. huxleyi bloomed and dominated the phytoplankton assemblage in Atlantic waters that surfaced north of Svalbard (80-81°N, 30°E) with cell numbers up to 10 8 /m 3 [START_REF] Hegseth | Intrusion and blooming of Atlantic phytoplankton species in the high Arctic[END_REF]. Such occurrences of E. huxleyi blooms were not included in the present study because of prevailing low sun elevation at those latitudes and lower concentrations of E. huxleyi there. However, our analyses indicate that E. huxleyi does form blooms in the surface waters of the WSC southwest of Svalbard (Figures 2 and5a), but they are weaker in intensity possibly due to a deeper summer mixed layer in the waters off the shelf (Figure 5b). This suggests that the dominance and blooming of E. huxleyi associated with atlantification may extend beyond the Barents Sea shelf. Furthermore, we expect that shoaling of Atlantic waters and decreased vertical stratification increasingly observed throughout the Eurasian Basin (atlantification sensu [START_REF] Polyakov | Greater role for Atlantic inflows on sea-ice loss in the Eurasian Basin of the Arctic Ocean[END_REF] will favour the expansion of Atlantic-origin phytoplankton by decreasing their time spent in darkness and promoting their transport to surface waters.

In conclusion, using over three decades of continuous satellite observations, we provide evidence that E. huxleyi blooms have expanded poleward by 501 km since first occurrence in the Barents Sea in 1989 (Smyth et al., 2004). This northward range shift, which reached speeds up to 56 km per year in the present decade, represents one of the most rapid poleward expansions of marine organisms reported so far [START_REF] Poloczanska | Global imprint of climate change on marine life[END_REF][START_REF] Poloczanska | Responses of marine organisms to climate change across oceans[END_REF]. We show that E. huxleyi's blooms keep pace with the speed of local climate velocities (sensu [START_REF] Pinsky | Marine taxa track local climate velocities[END_REF]; closely tracking increasing temperatures and the shifting position of the Polar Front that separates Atlantic from Arctic waters. E. huxleyi blooms, which can be easily observed from space, thus testify of the encroaching atlantification of the Barents Sea [START_REF] R E F E R E N C E S Arthun | Quantifying the influence of atlantic heat on barents sea ice variability and retreat[END_REF]Oziel et al., 2016) and may be considered as harbingers of cascading effects on the entire food web [START_REF] Dalpadado | Climate effects on Barents Sea ecosystem dynamics[END_REF][START_REF] Fossheim | Recent warming leads to a rapid borealization of fish communities in the Arctic[END_REF] and of blooms of toxic temperate-origin phytoplankton [START_REF] Walsh | Trophic cascades and future harmful algal blooms within ice-free Arctic Seas north of Bering Strait: A simulation analysis[END_REF]. We propose that as the Eurasian Basin of the Arctic Ocean further atlantifies [START_REF] Polyakov | Greater role for Atlantic inflows on sea-ice loss in the Eurasian Basin of the Arctic Ocean[END_REF] and Atlantic Ocean temperatures continue to rise, E. huxleyi and other temperate-origin phytoplankton (including harmful species), could well become resident bloom formers in the Arctic Ocean.

to unrealistic summer heat and volume transport and/or warming by the atmosphere. The model also shows interannual variability of the position of the front with a more northward position during warm years (2000-2002, 2005-2008, 2012) compared to colder years (1998-1999, 2003-2004, 2009-2011), which is in agreement with interannual variability of its position obtained from remote sensing data. Even though the SINMOD model accurately represents seasonal variations, it is unable to reproduce climatic trends (Ellingsen et al., 2009;Slagstad et al., 2015), which is why we used the position of the front obtained from remote sensing.

Calculation of mean PAR in the mixed-layer

The mean photosynthetically available radiation, PAR, in the mixed-layer was obtained using the following formula:

!"# !" = 1 !"# !"# 0 -exp (-! !"# !) !"# ! !"
where MLD is the depth of the mixed layer (see Methods), !"# 0is the photosynthetically available radiation just below the ocean surface and ! !"# is its diffuse attenuation coefficient obtained from the depth of the euphotic zone, ! !" as:

! !"# = -log (0.01) ! !"
where !"# 0and ! !" are obtained from NASA's standard daily level 3 products from the SeaWiFS (1998-2007, 9.2 km spatial resolution) and MODIS (2003MODIS ( -2016, 4.6 , 4.6 km spatial resolution) ocean colour sensors, downloaded from NASA's ocean colour website (http://oceandata.sci.gsfc.nasa.gov).

Correlation analyses between maximum summer PIC and !"# !" in summer is shown in Supplementary Table S4.

  , our analyses are based on continuous long-term synoptic observations of areal distribution and bloom magnitude at spatiotemporal scales unattainable by traditional ship-based sampling.

  The expansion and warming of Atlantic water masses in the Barents Sea and the associated transport and thriving of Atlantic-originF I G U R E 4Physical and thermal barriers to dispersal of Emiliania huxleyi blooms in the Barents Sea. Exemplified in two contrasting years, 1998 (a, c) and 2012 (b, d). Remotely sensed E. huxleyi bloom magnitude in summer (a, b) indicate that E. huxleyi blooms in Atlantic waters delineated by the red line, with summer mean temperatures >6°C (c, d) F I G U R E 5 Environmental controls of Emiliania huxleyi bloom magnitude in the Barents Sea. Ocean colour satellite-derived E. huxleyi bloom magnitude in different water masses (see Methods) vs. (a) median summer sea-surface temperature (SST) and (b) median depth of the mixed layer in summer from SeaWiFS (circles) and MODIS (diamonds) ocean colour sensors phytoplankton is expected to affect the entire marine food web.

ACKNOWLEDG EMENTS

We thank NASA Ocean Biology Processing Group and the NSIDC for providing ocean colour and sea ice concentration satellite data, respectively. We thank E. Devred for downloading and reprojecting AVHRR SST data provided by the GHRSST and the US National Oceanographic Data Center, T. Jessin for designing Figure 1, and D. Slagstad for providing the SINMOD model. We further thank E. Rehm and M. Benoit-Gagn e for programming support and T. Smyth for providing a high-resolution version of Figure 2 in Smyth et al. (2004). G.N. was supported by a Banting Postdoctoral Fellowship from the Government of Canada (2015-2017) and by a European Union's Horizon 2020 Marie Sklodowska-Curie grant (no. 749949). Finally, we thank Ulf Riebesell, Maurice Levasseur, Geir Johnsen, Luc Beaufort and Herv e Claustre for feedback on our manuscript.

Funding information

Horizon 2020 Framework Programme, Grant/Award Number: Marie Curie grant 749949; Natural Sciences and Engineering Research Council of Canada, Grant/Award Number: Banting Postdoctoral Fellowship, Canada Excellence Research Chair in Remote Sensing

Supplementary information for

Increased intrusion of warming Atlantic water leads to rapid expansion of temperate phytoplankton in the Arctic

Griet Neukermans 1,2,* , Laurent Oziel 1 , and Marcel Babin

Supplementary methods and results

Supplementary figures

Supplementary tables

Author contributions

Supplementary references

Supplementary methods and results

Validation of water mass classification

To validate the remote sensing-based water mass classification we used one of the most complete hydrographic in situ collections of the Barents Sea with over 130 000 profiles of temperature and salinity collected between 1980 and 2012 (Oziel et al., 2016).

We intersected the remotely sensed water mass classification with the in situ surface data in winter over the 1998-2012 time period. The resulting potential temperature (θ)-salinity (S) diagram in Supplementary Fig. S3 shows good correspondence between the remote and in situ water mass definitions. Most of the in situ data identified as ArW fall within theoretical θ-S boundaries for Arctic waters, which are typically defined as waters with temperatures below 0 °C and S between 34.0 and 34.7 PSU, depending on the study (Oziel et al., 2016), (Loeng, 1991). About half of the in situ data identified as AW fall within theoretical θ-S boundaries for Atlantic waters, whereas the other half corresponds to Norwegian coastal water which have lower salinities than Atlantic-origin waters and cannot be differentiated from each other by remote sensing of SST alone. The PFW fall in between as they represent a zone where Atlantic and Arctic water masses meet and mix.

Next, we examined the seasonal stability of the temperature front for which we used the SINMOD (SINtef Ocean MODel) model [START_REF] Slagstad | Modelling the carbon export and air-sea flux of CO2 in the Greenland Sea[END_REF]. This is a coupled 3-D hydrodynamic and bio-geochemical model that has been under continuous development, assessment, and validation in the Barents Sea over the past 30 years [START_REF] Slagstad | Modelling the carbon export and air-sea flux of CO2 in the Greenland Sea[END_REF](Slagstad et al., , 2015;;[START_REF] Slagstad | Modeling the ecosystem dynamics of the Barents sea including the marginal ice zone: I. Physical and chemical oceanography[END_REF]Ellingsen et al., 2009;Oziel et al., 2016). Here, we analyzed the high spatial resolution output at 4 km resolution and applied the same local variance filter and Polar Front detection methods as for the remote sensing data. This analysis shows that overall the seasonal variability of the temperature front is weak (supplementary Fig. S1). In some years differences were found which may be attributed S2.

Supplementary Figures

Supplementary Tables

Supplementary Table S1. Coefficient estimates and statistics of linear regression analyses of annual mean and summer mean surface temperature over the time period