Alice Carravieri
email: alice.carravieri@gmail.com

Sophie Lorioux

Frédéric Angelier

Olivier Chastel

Vegard Céline Albert

Sandøy Bråthen

Émile Brisson-Curadeau

Manon Clairbaux

Karine Delord

Mathieu Giraudeau

Vegard Sandøy Bråthen

Samuel Perret

Timothée Poupart

Cécile Ribout

Amélia Viricel- Pante

David Grémillet

Paco Bustamante

Jérôme Fort

Carryover effects of winter mercury contamination on summer concentrations and reproductive performance in little auks

Keywords: blood, chick growth, feathers, reproduction, migration, seabird, telomeres

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I. Introduction

Many animals migrate, commuting between breeding and non-breeding grounds to adapt to seasonal environmental changes [START_REF] Newton | The migration ecology of birds[END_REF]. In long-lived birds such as seabirds, the non-breeding period is essential to renew the plumage, recover from the past breeding season and prepare for the upcoming one [START_REF] Dunn | A year in the life of a North Atlantic seabird: behavioural and energetic adjustments during the annual cycle[END_REF][START_REF] Reynolds | Among-colony synchrony in the survival of Common Guillemots Uria aalge reflects shared wintering areas[END_REF].

Environmental conditions experienced on non-breeding grounds can differ substantially from those encountered during breeding [START_REF] Lambert | No evidence that seasonal changes in large-scale environmental conditions drive migration in seabirds[END_REF], and can have carryover effects on subsequent reproductive performance and survival [START_REF] Norris | Carry-over effects and habitat quality in migratory populations[END_REF][START_REF] Szostek | Survival and local recruitment are driven by environmental carry-over effects from the wintering area in a migratory seabird[END_REF].

For instance, non-breeding habitat and diet quality can affect body condition, survival probability, breeding propensity and success in seabirds (e.g., [START_REF] Bogdanova | Seasonal interactions in the black-legged kittiwake, Rissa tridactyla: links between breeding performance and winter distribution[END_REF][START_REF] Sorensen | Carry-over effects in a Pacific seabird: stable isotope evidence that pre-breeding diet quality influences reproductive success[END_REF]. Migration to distant non-breeding grounds can also expose seabirds to patterns or/and quantities of harmful chemical contaminants that are different to those encountered during breeding (e.g., [START_REF] Lavoie | Contamination of Mercury during the Wintering Period Influences Concentrations at Breeding Sites in Two Migratory Piscivorous Birds[END_REF][START_REF] Miller | Influence of overwinter distribution on exposure to persistent organic pollutants (POPs) in seabirds, ancient murrelets (Synthliboramphus antiquus), breeding on the Pacific coast of Canada[END_REF]. However, despite well-known toxic effects of contaminants on reproduction and survival [START_REF] Walker | Principles of Ecotoxicology, Fourth Edition[END_REF], the carryover effects of non-breeding contamination on seabird reproductive performance have been sparsely studied, with mixed results (e.g., [START_REF] Bustamante | High feather mercury concentrations in the wandering albatross are related to sex, breeding status and trophic ecology with no demographic consequences[END_REF][START_REF] Mills | Mercury exposure in an endangered seabird: long-term changes and relationships with trophic ecology and breeding success[END_REF]. This gap of knowledge limits our capacity to quantify the effect of multiple environmental pressures along the annual cycle of seabirds.

Seabirds are exposed to a plethora of chemical contaminants (e.g., [START_REF] Walker | Principles of Ecotoxicology, Fourth Edition[END_REF]. Among these, mercury (Hg) is of high environmental concern, especially in its methylated and most toxic form (MeHg), which is easily bioaccumulated by marine biota, and biomagnifies in marine food webs [START_REF] Eagles-Smith | Modulators of mercury risk to wildlife and humans in the context of rapid global change[END_REF]. In the Arctic, growing environmental change is modifying the Hg contamination of seabirds, with recently observed or predicted increases (e.g., [START_REF] Foster | Climate Influence on Legacy Organochlorine Pollutants in Arctic Seabirds[END_REF][START_REF] Tartu | A U-Turn for Mercury Concentrations over 20 Years: How Do Environmental Conditions Affect Exposure in Arctic Seabirds?[END_REF]. Recent findings point to a net MeHg production at relatively shallow depths (20-200 m) in the Arctic Ocean, which is believed to contribute to the high total Hg concentrations measured in Arctic surface seawater, and in Arctic predators [START_REF] Dietz | A risk assessment review of mercury exposure in Arctic marine and terrestrial mammals[END_REF]Heimbürger et al., 2015). In addition, Arctic seabirds can acquire MeHg outside the Arctic during the non-breeding period. Previous work has shown that the oceanic waters off Newfoundland in the western North Atlantic Ocean are a hotspot of MeHg transfer to several Arctic seabird species that spend the winter there [START_REF] Albert | Seasonal variation of mercury contamination in Arctic seabirds: A pan-Arctic assessment[END_REF][START_REF] Fort | Spatial ecotoxicology: migratory arctic seabirds are exposed to mercury contamination while overwintering in the Northwest Atlantic[END_REF][START_REF] Renedo | Contrasting Spatial and Seasonal Trends of Methylmercury Exposure Pathways of Arctic Seabirds: Combination of Large-Scale Tracking and Stable Isotopic Approaches[END_REF]. However, the environmental drivers that determine these hotspots are yet to be identified. Several factors, such as latitude, primary productivity, climatic and weather indices, air and seawater temperature, can play a role in the transfer of MeHg to food webs [START_REF] Foster | Climate Influence on Legacy Organochlorine Pollutants in Arctic Seabirds[END_REF][START_REF] Fitzgerald | Marine biogeochemical cycling of mercury[END_REF]. High MeHg quantities are found in both coastal and shelf sediments, and in subthermocline low-oxygen oceanic waters in the mesopelagic zone, notably in association with active populations of Hg methylating microorganisms [START_REF] Fitzgerald | Marine biogeochemical cycling of mercury[END_REF]. Consumers have higher MeHg burdens in oligotrophic rather than mesotrophic environments due to the bioconcentration effect, whereby MeHg partitions among fewer cells in low productivity systems [START_REF] Chouvelon | Oligotrophy as a major driver of mercury bioaccumulation in marine medium-to high-trophic level consumers: A marine ecosystem-comparative study[END_REF][START_REF] Pickhardt | Algal blooms reduce the uptake of toxic methylmercury in freshwater food webs[END_REF]. Also, top predators in complex food webs can have higher MeHg burdens than those in short food webs, because of enhanced biomagnification [START_REF] Braune | Changes in food web structure alter trends of mercury uptake at two seabird colonies in the Canadian Arctic[END_REF][START_REF] Lavoie | Biomagnification of Mercury in Aquatic Food Webs: A Worldwide Meta-Analysis[END_REF]. Understanding which environmental drivers best explain Arctic seabird MeHg accumulation can help us predict future exposure and risk scenarios.

This study aims to understand the environmental drivers of winter MeHg contamination and its carryover effects on fitness proxies in Arctic-breeding little auks (or dovekies, Alle alle), the most abundant seabird in the North Atlantic (>40 million birds, [START_REF] Egevang | Estimating colony area and population size of little auks Alle alle at Northumberland Island using aerial images[END_REF]. Auks experience two moults per year: all feathers are replaced at the end of the breeding season (complete moult), then head feathers are replaced again at the end of the non-breeding period (partial pre-breeding moult in winter) (Albert et al., 2019 and references therein). In birds, MeHg accumulates in blood and internal tissues after dietary exposure, and deposits into growing feathers during moult (e.g., [START_REF] Furness | Using bird feathers to measure mercury in the environment: relationships between mercury content and moult[END_REF]. Therefore, feather Hg concentrations represent exposure during the inter-moult period, i.e. winter months for head feathers of auks sampled on breeding grounds [START_REF] Albert | Using blood and feathers to investigate large-scale Hg contamination in Arctic seabirds: a review[END_REF][START_REF] Fort | Spatial ecotoxicology: migratory arctic seabirds are exposed to mercury contamination while overwintering in the Northwest Atlantic[END_REF]. Auks are thus excellent models to identify MeHg hotspots over North Atlantic wintering grounds through feather sampling during the following breeding season [START_REF] Albert | Seasonal variation of mercury contamination in Arctic seabirds: A pan-Arctic assessment[END_REF][START_REF] Fort | Spatial ecotoxicology: migratory arctic seabirds are exposed to mercury contamination while overwintering in the Northwest Atlantic[END_REF][START_REF] Renedo | Contrasting Spatial and Seasonal Trends of Methylmercury Exposure Pathways of Arctic Seabirds: Combination of Large-Scale Tracking and Stable Isotopic Approaches[END_REF]. On breeding sites, blood can also be sampled, and is often used as short-term integrator of MeHg (e.g., [START_REF] Evers | The effects of methylmercury on wildlife: a comprehensive review and approach for interpretation[END_REF]). Yet, blood MeHg concentrations represent a balance of recent and past dietary intake redistributed from internal tissues, and the contribution of past (e.g., winter) and more recent (e.g., summer) Hg exposure on blood Hg concentrations is rarely studied (e.g., [START_REF] Lavoie | Contamination of Mercury during the Wintering Period Influences Concentrations at Breeding Sites in Two Migratory Piscivorous Birds[END_REF]. We used biologging (Global Location Sensor, GLS) to identify spatial differences in winter MeHg contamination, feather and blood Hg concentrations as proxy of winter and summer exposure, and multiple indicators of short-term reproductive performance and individual quality to quantify MeHg effects. Our specific aims were to assess whether: 1) wintering median position of GLS-tracked little auks affects their head feather Hg concentrations (proxy of winter MeHg contamination), and if environmental drivers (bathymetry, productivity, sea-surface temperature and mixed layer depth) can explain spatial differences in Hg concentrations; 2) winter MeHg contamination affects MeHg concentrations of breeding adults (blood Hg concentrations) and their offspring; 3) winter MeHg contamination is associated with short-term reproductive performance (egg volume, hatching date and success, chick growth rate and survival) and individual health and quality (body condition, e.g., [START_REF] Peig | New perspectives for estimating body condition from mass/length data: the scaled mass index as an alternative method[END_REF]and adult telomere length, e.g., Angelier et al., 2019).

Based on previous results, we expected winter MeHg contamination to increase westward [START_REF] Renedo | Contrasting Spatial and Seasonal Trends of Methylmercury Exposure Pathways of Arctic Seabirds: Combination of Large-Scale Tracking and Stable Isotopic Approaches[END_REF][START_REF] Fort | Spatial ecotoxicology: migratory arctic seabirds are exposed to mercury contamination while overwintering in the Northwest Atlantic[END_REF], and to increase with decreasing productivity (bioconcentration effect, [START_REF] Chouvelon | Oligotrophy as a major driver of mercury bioaccumulation in marine medium-to high-trophic level consumers: A marine ecosystem-comparative study[END_REF][START_REF] Pickhardt | Algal blooms reduce the uptake of toxic methylmercury in freshwater food webs[END_REF]. MeHg concentrations were expected to be higher in head feathers (winter) than in blood (summer) [START_REF] Albert | Seasonal variation of mercury contamination in Arctic seabirds: A pan-Arctic assessment[END_REF], and to be significantly associated with each other because of retention of MeHg from winter to summer [START_REF] Lavoie | Contamination of Mercury during the Wintering Period Influences Concentrations at Breeding Sites in Two Migratory Piscivorous Birds[END_REF]. Given MeHg toxic effects on bird health [START_REF] Chastel | Mercury contamination and potential health risks to Arctic seabirds and shorebirds[END_REF][START_REF] Whitney | Impacts of sublethal mercury exposure on birds: a detailed review[END_REF], and interannual repeatability of Hg contamination in seabird feathers [START_REF] Carravieri | Penguins as bioindicators of mercury contamination in the Southern Ocean: Birds from the Kerguelen Islands as a case study[END_REF][START_REF] Bertram | Mercury contamination level is repeatable and predicted by wintering area in a long-distance migratory seabird[END_REF], we expected winter Hg contamination to be negatively associated with telomere length measured in summer. As MeHg can be transferred from mothers to eggs in avian species (reviewed in [START_REF] Ackerman | Synthesis of maternal transfer of mercury in birds: implications for altered toxicity risk[END_REF] and have negative effects on hatchability and chick health [START_REF] Whitney | Impacts of sublethal mercury exposure on birds: a detailed review[END_REF][START_REF] Yu | Acute embryotoxic effects but no long-term reproductive effects of in ovo methylmercury exposure in zebra finches (Taeniopygia guttata)[END_REF], MeHg concentrations were expected to be correlated between mothers, their offspring and their health. In addition, egg volume was expected to decrease with increasing maternal head feather MeHg concentrations, as previously found in this population [START_REF] Fort | Spatial ecotoxicology: migratory arctic seabirds are exposed to mercury contamination while overwintering in the Northwest Atlantic[END_REF].

II. Material and methods

II.1. Study site and sample collection

This study was conducted on the little auk population of Ukaleqarteq, Kap Höegh (East Greenland; 70°44'N, 21°35'W) in July-August of 2018 and 2019. The little auk is a long-lived seabird (>20 years) producing a single egg per year. Adults provide bi-parental care until late chick-rearing, when only males feed the chick. Little auks are zooplanktivorous and specialise on large copepods, diving at depths of 20-50m during winter [START_REF] Fort | The feeding ecology of little auks raises questions about winter zooplankton stocks in North Atlantic surface waters[END_REF]. A total of 92 breeding adults (N = 62 in 2018, 32 females, 29 males, one unknown sex; N = 30 in 2019, 10 females, 19 males, one unknown sex, see Section II.4. for molecular sexing) were caught in the nest or in its proximity by hand or with noose traps during incubation or chick-rearing. Handling of birds for morphometric measurements (see Section II.2.), as well as blood and feather sampling, lasted ~10 min. Blood (approx. 0.5 ml) was collected from the brachial vein with heparinised syringes, and centrifuged for 10 minutes at 3500 rpm, within six hours from collection. Red blood cells and plasma were separately stored at -20°C until further analyses.

Red blood cells were used for Hg (see Section II.5.) and telomere length (see Section II.6.) quantification, while plasma will be used in further investigations. Feathers (7-10 per individual) were collected from the throat (hereafter head feathers) and stored at ambient temperature in sealed plastic bags. To track wintering movements, 28 of the 92 sampled adults (N = 11 in 2018, 8 females, 3 males; N = 17 in 2019, 5 females, 12 males) had been equipped with a miniature GLS data-logger (F100 and C65-SUPER, Migrate Technology, mass of 1.5 g, ~1% of adult body mass), mounted on a conventional metal ring, in the previous year. All birds were sampled only once (no repeated observations).

II.2. Nest monitoring, reproductive output and morphological data

In 2018 and 2019, 70 and 39 nests, respectively, were monitored to quantify hatching date, success, chick growth and survival. When accessible, the single egg was measured to calculate its volume as detailed in [START_REF] Fort | Spatial ecotoxicology: migratory arctic seabirds are exposed to mercury contamination while overwintering in the Northwest Atlantic[END_REF]. Nests were checked every second day to determine the hatching date. Eggshell fragments were collected and stored in sealed plastic bags at -20°C until Hg analyses. Once chicks were at least one day old, they were weighed (electronic scale to the nearest gram) every second day to quantify their growth. Chick growth rate was inferred from the slope of the relationship between chick mass and age during the linear phase of growth (4-14 days of age, [START_REF] Amélineau | Where to Forage in the Absence of Sea Ice? Bathymetry As a Key Factor for an Arctic Seabird[END_REF]. Survival of chicks was estimated when they were 18 ± 3 days old (mean ± SD), i.e., shortly before they left the nest.

Chick age at survival estimation was accounted for in statistical analyses concerning chick growth rate (see Section II.7.). On the day of chick survival estimation, four breast feathers (hereafter chick body feathers) were sampled, following the same procedures as in adults (see Section II.1). In 2018, one or both parents of 43 out of the 70 monitored nests were sampled for blood and head feathers to determine Hg contamination (see Section II.1. and II.5.) and its fitness proxies' correlates (see Section II.7.). For logistical reasons, parents of only two out of the 39 monitored nests could be sampled for blood and head feathers in 2019, and were thus not included in statistical analyses looking at fitness proxies' correlates of Hg contamination.

Adults and chicks were weighed (±1 g), measured with a calliper (head-bill, culmen, tarsus, ±1 mm), and a ruler (flattened wing, ±1 mm). Initially we considered calculating a scaled mass index (SMI, [START_REF] Peig | New perspectives for estimating body condition from mass/length data: the scaled mass index as an alternative method[END_REF] in adults as proxy of body condition. However, slope estimates of the correlation between mass and the different length measures available (headbill, culmen, tarsus, wing) were not significant. Therefore, we considered adult body mass alone as body condition index, which is a reliable condition indicator within a population [START_REF] Peig | New perspectives for estimating body condition from mass/length data: the scaled mass index as an alternative method[END_REF].

II.3. Movement and environmental data

GLS loggers measured daylight level intensity each minute and recorded its maximum intensity each 5 min. Following the standardised procedure described in [START_REF] Bråthen | An automated procedure (v2.0) to obtain positions from light-level geolocators in large-scale tracking of seabirds. A method description for the SEATRACK project[END_REF], we used a threshold method to estimate latitudes from the length of day and night and longitudes on the timing of noon and midnight [START_REF] Hill | Theory of geolocation by light levels[END_REF][START_REF] Wilson | Estimation of location: global coverage using light intensity[END_REF], giving a maximum of two locations per day, and subsequently filtered erroneous locations. We treated GLS tracking data from December 1 to January 31 (the "core wintering period", when all birds were at their wintering grounds, [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]. We extracted the following environmental variables on a daily basis over 1° x 1° cells: sea-surface temperature (SST), mixed-layer depth (MLD), seasurface chlorophyll a concentration (chla, as a proxy of productivity) and bathymetry.

II.5. Hg analysis

Quantification of total Hg (hereafter Hg) was carried out in adult head feathers and chick body feathers and adult red blood cells, and in eggshell membranes at the laboratory Littoral, Environment and Societies (LIENSs, CNRS-La Rochelle Université). Total Hg concentrations in these three tissues are a close approximation of MeHg concentrations, as they contain virtually 100% MeHg [START_REF] Bond | Total and methyl mercury concentrations in seabird feathers and eggs[END_REF][START_REF] Renedo | Mercury isotopes of key tissues document mercury metabolic processes in seabirds[END_REF]. In the blood compartment, Hg is preferentially found within red blood cells, and red blood cell Hg concentrations are a very close approximation of whole blood Hg concentrations (Tavares et al., 2013 and authors' unpublished data). Hereafter, red blood cell will be abbreviated to "blood" unless otherwise specified. Whole blood and red blood cell MeHg concentrations represent a balance of recent and past dietary intake redistributed from internal tissues. The biological half-life of Hg is 30-40 days during moult, and longer than 65 days outside moult [START_REF] Monteiro | Kinetics, Dose-Response, and Excretion of Methylmercury in Free-Living Adult Cory's Shearwaters[END_REF]. Feather synthesis is an important MeHg excretion pathway in birds; up to 90% of the body burden can be found in feathers after moult [START_REF] Bond | Total and methyl mercury concentrations in seabird feathers and eggs[END_REF][START_REF] Braune | Mercury levels in Bonaparte's gulls (Larus philadelphia) during autumn molt in the Quoddy region, New Brunswick, Canada[END_REF]. After completion of telomere length analysis on wet red blood cells (see Section II.6.), these were freeze-dried for 48h and homogenised to enable Hg determination on the dry mass, which is necessary when using direct Hg analysers (see below).

To remove external contamination, seven to 10 head feathers, and four chick body feathers, were cleaned in a 2:1 chloroform:methanol solution in an ultrasonic bath for 3 min, then rinsed twice with methanol before being dried in aluminium foil for 24h at 45°C (details in [START_REF] Carravieri | Penguins as bioindicators of mercury contamination in the Southern Ocean: Birds from the Kerguelen Islands as a case study[END_REF]. Three feathers of each type were randomly selected and homogenised by cutting them into small fragments with stainless scissors. Membranes were first separated from eggshells using stainless tweezers, and then placed in a net before cleaning, to avoid the loss of small particles in the cleaning mixture. The cleaning procedure was the same as for feathers.

Head feather, red blood cells, and eggshell membrane Hg concentrations were quantified using an advanced Hg analyser (AMA-254, Altec) and are expressed as µg g -1 dry weight (dw). All analyses were carried out in duplicate (relative standard deviation <10%) and average values used in data analyses. The certified reference material (CRM) TORT-3 (certified Hg concentration: 0.292 ± 0.022 µg g -1 dw) was measured at the beginning and end of each set of analysis, and every 10-15 samples. Measured TORT-3 values were 0.284 ± 0.010 µg g -1 dw (N = 7) for the set of blood samples, 0.289 ± 0.004 µg g -1 dw (N = 9) for adult head feathers, 0.289 ± 0.003 µg g -1 (N = 15) for chick body feathers, and 0.290 ± 0.005 µg g -1 dw (N = 5) for eggshell membranes. CRM mass (and thus Hg quantity introduced in the analyser) was adjusted to mirror the Hg mass present in samples. Blanks were analysed at the beginning of each set of samples and the limit of detection of the Hg analyser was 0.05 ng.

II.6. Telomere length quantification

Telomeres are repetitive, non-coding DNA sequences found at the end of chromosomes, which protect genome integrity [START_REF] Blackburn | Telomeres and telomerase: their mechanisms of action and the effects of altering their functions[END_REF]. These sequences shorten at each cell division, but also as a result of environmental stress (e.g., [START_REF] Angelier | Do glucocorticoids mediate the link between environmental conditions and telomere dynamics in wild vertebrates? A review[END_REF]. Telomere length was determined by quantitative PCR (qPCR; BioRad CFX 96, Bio-Rad USA) according to [START_REF] Cawthon | Telomere measurement by quantitative PCR[END_REF], and adapted for the little auk, at the Centre d'Etudes Biologiques de Chizé (CEBC, CNRS-La Rochelle Université). Briefly, 5 µL of red blood cells were digested with proteinase K and DNA was extracted using the Nucleospin Tissue Kit (Macherey-Nagel),

following the manufacturer's instructions. DNA concentration and purity were assessed with a Nanodrop ND1000 spectrophotometer (Thermo Scientific). The telomere primers were similar to those previously used in other seabird species (e.g., [START_REF] Sebastiano | Exposure to PFAS is Associated with Telomere Length Dynamics and Demographic Responses of an Arctic Top Predator[END_REF]. The control single-copy gene Recombination Activating Gene 1 (RAG1) was selected and amplified using specific primers designed for the little auk. All qPCR runs were performed using 7.5 ng of DNA per reaction and using the BioRad SYBR Green Supermix. The universal telomere primers were used at a concentration of 800 nM, and RAG1-F/RAG1-R at 300 nM. To generate a six-point standard curve (from 20.0 ng to 0.62 ng) for controlling the amplifying efficiency of the reactions, serial dilutions of DNA from a pooled sampled of 10 little auks were included on the plate. All samples were randomly distributed across the PCR plates. The efficiency of the telomere and RAG1 assays ranged from 103.0 to 108.6% and 100.7 to 103.0%, respectively, and all r² were very high (>0.990). Three reference samples ('golden samples') were run in triplicate on all plates to account for inter-plate variation, and each sample was run in duplicate on every plate. The interplate repeatability was 0.959 (intra-class correlation coefficient). The relative telomere length was calculated as the telomere copy number (T) relative to single-copy gene (S), expressed as TS ratio.

II.7. Data analysis

Data exploration, visualisation and statistical analyses were performed in R version 4.0.5 (R Core Team, 2021). To address our aims, we used linear or generalised linear models with different specifications depending on the response variable and error distribution, and different explanatory variables depending on aims and sample size (Table S1, Supplementary Material). Model specification and fit were validated via residual analysis of initial models (e.g., homoscedasticity of residuals). When multiple explanatory variables were continuous, we tested for the absence of collinearity (variance inflation factor, VIF < 3) and standardised them (mean = 0, SD = 1) to facilitate comparison of effect sizes [START_REF] Zuur | Mixed Effects Models and Extensions in Ecology with R. Springer. Table S2. Descriptive statistics of the environmental variables encountered by little auks from Ukaleqarteq, East Greenland, on wintering grounds in 2018 and 2019. Environmental variable Mean ± SD median [min; max] Sea-surface temperature (°C)[END_REF].

II.7.a Aim 1

The effect of the median latitude and longitude of adult position during the core winter, and the associated environmental variables, was tested on head feather (i.e., winter) Hg concentrations. Environmental variables (bathymetry, chla, MLD, SST) were strongly collinear (VIF > 10). Therefore, chla, MLD, and SST data were log-transformed to achieve a normal distribution (raw bathymetry data was normally distributed), and all variables were scaled, to perform a principal component analysis (PCA, R package ade4, [START_REF] Dray | The ade4 package: implementing the duality diagram for ecologists[END_REF]. The first and second axis of the PCA explained 93% of total variation in environmental data over wintering grounds (Fig. S1). Bathymetry was positively related to MLD (i.e., deeper waters were associated with larger MLD) and these two variables were mainly associated with PC1 scores (decreasing PC1 scores indicated deeper waters and MLD). SST and chla were not related and were mainly associated with PC2 (decreasing PC2 scores indicated higher SST and chla values, Fig. S1). Principal component (PC) scores of PC1 and PC2 were extracted to obtain uncorrelated variables, and then used as explanatory variables of head feather Hg concentrations alongside latitude and longitude (no collinearity detected, all VIF < 3).

II.7.c Aim 2

The effect of adult head feather (i.e., winter) Hg contamination, year, sex and their interaction (sex:Hg) was tested on adult blood (i.e., summer) Hg concentrations. The sex:Hg interaction was considered because sexual differences in Hg accumulation and toxic effects have been previously described in seabirds [START_REF] Robinson | Evidence for sex differences in mercury dynamics in double-crested cormorants[END_REF]2012). We also tested whether (i) eggshell membrane Hg concentrations (i.e., offspring contamination) were related to maternal head feather (i.e., winter) Hg concentrations, (ii) maternal and paternal head feather Hg concentrations were related to chick body feathers (Table S1).

II.7.d Aim 3

This aim tested (i) the effect of maternal head feather Hg contamination on egg volume;

(ii) the effect of winter Hg concentrations, sex and their interaction, year and sampling date (as Julian day), on telomere length and body condition, which were measured in both 2018 and 2019; and (iii) the effect of winter Hg contamination, sex and their interaction on four variables of short-term reproductive performance (hatching date and success, chick growth and survival, only measured in 2018, Table S1). Sampling date was considered as explanatory because body condition can vary temporally across the breeding season (e.g., [START_REF] Moe | Changes in body condition in breeding kittiwakes Rissa tridactyla[END_REF]. Also, the proportion of breeders of different ages and/or quality, which can drive telomere length [START_REF] Angelier | Is telomere length a molecular marker of individual quality? Insights from a long-lived bird[END_REF], can vary across the breeding season in seabirds (e.g., [START_REF] De Forest | The Effect of Age on Timing of Breeding and Reproductive Success in the Thick-Billed Murre[END_REF][START_REF] González-Solís | Individual changes underlie agespecific pattern of laying date and egg-size in female common terns Sterna hirundo[END_REF]. Birds were of unknown age, so accounting for sampling date can help controlling for the potential age bias. We only considered head feather Hg concentrations, and not blood Hg concentrations, to test carryover effects of winter Hg contamination on reproductive performance, as concentrations in blood also reflect shorter-term exposure (here the summer breeding season). For binomial models (response variables:

hatching success and chick survival, Table S1), model fit was checked through the overdispersion term value. Chick age (days) at survival estimation was accounted for in chick survival models.

II.7.d Model selection

For all our aims, we used Akaike's information criterion corrected for small sample sizes (AICc) to select the best models (R package MuMIn, Bartón 2020). We compared a list of biologically meaningful initial candidate models. The initial and best models selected for each specific question within each aim, as well as their sample sizes, are presented in Table S1.

The AICc, the difference between AICc of the specific model and the best model (ΔAICc), and the AICc weight (normalized weight of evidence in favour of the specific model), and the explained deviance (indicative of the proportion of variation explained by the model, similar to R 2 for linear models, [START_REF] Zuur | Mixed Effects Models and Extensions in Ecology with R. Springer. Table S2. Descriptive statistics of the environmental variables encountered by little auks from Ukaleqarteq, East Greenland, on wintering grounds in 2018 and 2019. Environmental variable Mean ± SD median [min; max] Sea-surface temperature (°C)[END_REF] were calculated. If the null model ranked as the best model, the effects of all explanatory variables were considered statistically insignificant. When multiple models performed better than the null model and had ΔAICc < 2, we applied model averaging on those models to make an inference. This produced averaged parameter estimates (β) of the predictor variables included in those models, weighted using AICc weights. AICc tables for candidate models with significant and insignificant effects are presented in Table 1 and Table S2, respectively. When model averaging was applied, the averaged β ± standard error (SE), and 95% confidence interval (CI), are also reported for each predictor in Table 1.

III. Results

III.1. Influence of wintering median position and environment on head feather Hg concentrations GLS-tracked little auks spent the core wintering period (1 st December -31 st January) of 2018 and 2019 on average 2598 km (range 1743-3995 km) from their breeding colony, between latitudes 42.6°N and 59.3°N, and longitudes 67.1°W and 30.5°W (Fig. 1). Descriptive statistics of the environmental characteristics at these locations are presented in Table S3. The targeted environmental variables (represented by PC1 and PC2 scores) did not drive variation in head feather Hg concentrations, as models including them were not retained by AICc selection (Table 1). In contrast, the median longitude of winter location had a clear effect on head feather Hg concentrations (β ± SE on scaled values: 0.196 ± 0.058, 95% CI: 0.085-0.309): winter Hg exposure increased eastward (Fig. 1), with no effect of year or sex (Table 1).

III.2. Association of head feather Hg concentrations with blood and offspring Hg concentrations

Hg concentrations in head feathers (range 1.18-7.93 µg g -1 dw, Table S4) were similar between sexes and years (Table 1). The best model explaining Hg concentrations in blood (range 0.4-2.0 µg g -1 dw, Table S4) included head feather Hg concentrations, sex and year as predictors, with no sex:Hg interaction (Table 1). Blood Hg concentrations were significantly higher in males than females (sexM: 0.203 ± 0.046, 95% CI: 0.113-0.293), and in 2018 than 2019 (year2019: -0.130 ± 0.048, CI: -0.222--0.034), and increased with increasing head feather Hg concentrations (0.145 ± 0.022, 95% CI: 0.101-0.189) (Table 1, Fig. 2). Eggshell membrane Hg concentrations were not related to maternal head feather Hg concentrations (Table S2).

Maternal and paternal head feather Hg concentrations were not related to chick body feather Hg concentrations (Table S2).

III.3. Association of head feather Hg concentrations with fitness proxies

Four models had similar support in explaining variation in telomere length (range of TS ratio: 0.65-1.32, Table S4), indicating that Julian day, year, sex and head feather Hg concentrations were significant predictors (Table 1). However, model averaging showed that only Julian day and year had a clear statistical effect: telomere length increased as the sampling season progressed (averaged β ± SE, 0.049 ± 0.014, 95% CI: 0.021-0.078), and telomeres were on average longer in 2019 than 2018 (year2019: 0.078 ± 0.030, 95% CI: 0.017-0.138, Table 1, Fig. S2). Telomere length showed a weak, decreasing trend with increasing head feather Hg concentrations, and tended to be longer in males than females (Table 1). Variation in adult body mass during the breeding period (range 125-182 g, Table S4) was explained by four models with similar support, including Julian day, year and head feather Hg concentrations as predictors (Table 1). However, model averaging showed that Julian day only had a clear statistical effect: body mass decreased with increasing Julian day (averaged β ± SE, -0.74 ± 0.31, 95% CI: -1.35--0.13, Table 1). In addition, all models explained very little of the total variation (maximum explained deviance of 8%, Table 1). Egg volume (range 21.9-31.0 cm 3 , Table S4) was not related to female head feather Hg concentrations (Table S2). Hatching success was 64.3% and 87.2% of monitored nests (70 and 39), and chick survival was 72.5% and 85.3% of monitored chicks (30 and 34) in 2018 and 2019, respectively (Table S4). In 2018, hatching success and chick survival were not related to parental head feather Hg concentrations (Table S2). Chick growth rate (range 2.71-6.70 g day -1 ) decreased with increasing parental head feather Hg concentrations (-0.555 ± 0.215, 95% CI: -0.976--0.123, Fig. 3), irrespective of parent sex (Table 1).

IV. Discussion

By combining biologging, nest monitoring and molecular biomarkers, we showed that adult winter Hg exposure influences blood Hg concentrations during breeding, and is negatively associated with chick growth rate, without apparent effects on hatching success and chick survival. We hypothesise that trans-generational Hg effects may be mediated by a decrease in parental care, which should be confirmed by additional evidence from studies of parental behaviour and physiology during chick-rearing. Further investigations on a larger sample size are also needed to clarify the relationship between Hg contamination and telomere length, as well as telomere dynamics, in this population. Hg contamination was similar to previously published work in the same population for both summer (whole blood Hg concentrations around 0.8 µg g -1 , Fort et al., 2014) and winter (head feather Hg concentrations around 3.8 µg g -1 , Fort et al., 2014), which identifies little auks' Hg contamination as low when compared to other Arctic breeding seabirds [START_REF] Albert | Seasonal variation of mercury contamination in Arctic seabirds: A pan-Arctic assessment[END_REF][START_REF] Chastel | Mercury contamination and potential health risks to Arctic seabirds and shorebirds[END_REF]. This has two important implications: 1) ecological and physiological effects may arise even at low concentrations in this species (see also Grunst et al., under review) and2 North Atlantic waters [START_REF] Zhang | A global model for methylmercury formation and uptake at the base of marine food webs[END_REF]. Alternatively, environmental and/or ecological drivers of winter Hg contamination in little auks differ temporally in the North Atlantic, leading to variation in the spatial pattern of Hg exposure found in different years [START_REF] Fort | Spatial ecotoxicology: migratory arctic seabirds are exposed to mercury contamination while overwintering in the Northwest Atlantic[END_REF]Renedo et al., 2020, present study). Temporal variation in seawater Hg concentrations and/or in the transfer of Hg to food webs is likely, because they depend on several environmental [START_REF] Foster | Climate Influence on Legacy Organochlorine Pollutants in Arctic Seabirds[END_REF][START_REF] Stern | How does climate change influence Arctic mercury?[END_REF] and trophic factors [START_REF] Braune | Changes in food web structure alter trends of mercury uptake at two seabird colonies in the Canadian Arctic[END_REF]. However, none of the environmental variables that were studied here had a clear role in explaining the spatial pattern of little auks' winter Hg contamination. Previously, productivity has been associated

with Hg biodilution in zooplankton [START_REF] Pickhardt | Algal blooms reduce the uptake of toxic methylmercury in freshwater food webs[END_REF] and fish [START_REF] Chouvelon | Oligotrophy as a major driver of mercury bioaccumulation in marine medium-to high-trophic level consumers: A marine ecosystem-comparative study[END_REF], while Hg burden increases were detected at chla low and high extremes in Arctic seabirds, likely as a result of bottom-up nutrients effects on fish communities [START_REF] Tartu | A U-Turn for Mercury Concentrations over 20 Years: How Do Environmental Conditions Affect Exposure in Arctic Seabirds?[END_REF]. In addition, MeHg transfer to food webs can vary with the depth of the ocean mixed-layer [START_REF] Dietz | A risk assessment review of mercury exposure in Arctic marine and terrestrial mammals[END_REF]Heimbürger et al., 2015), with potential cascading effects on Hg contamination of consumers, including those feeding at relatively shallow depths like little auks [START_REF] Fort | The feeding ecology of little auks raises questions about winter zooplankton stocks in North Atlantic surface waters[END_REF]. However, interactions between these environmental variables and the ecological processes that lead to Hg exposure in little auks are complex, likely work at different temporal scales, and might vary at a high rate under climate change [START_REF] Stern | How does climate change influence Arctic mercury?[END_REF][START_REF] Tartu | A U-Turn for Mercury Concentrations over 20 Years: How Do Environmental Conditions Affect Exposure in Arctic Seabirds?[END_REF][START_REF] Wang | How closely do mercury trends in fish and other aquatic wildlife track those in the atmosphere? -Implications for evaluating the effectiveness of the Minamata Convention[END_REF]. Further studies on larger sample sizes associating wintering movements of seabirds and their Hg contamination are necessary to gain more insights on Hg hotspots, their environmental drivers, and their interannual variation in the North Atlantic. This is particularly important given the hotspot of seabird biodiversity found within this oceanic region, which sustains millions of birds every year [START_REF] Davies | Multispecies tracking reveals a major seabird hotspot in the North Atlantic[END_REF].

IV.2. Carryover effect of winter Hg exposure on concentrations in summer

Previous studies have shown that Hg exposure at distant migrating grounds can affect summer Hg concentrations in aquatic birds (e.g., [START_REF] Bertram | Mercury contamination level is repeatable and predicted by wintering area in a long-distance migratory seabird[END_REF][START_REF] Lavoie | Contamination of Mercury during the Wintering Period Influences Concentrations at Breeding Sites in Two Migratory Piscivorous Birds[END_REF]. In agreement with this, the correlation between head feather and blood Hg concentrations found here supports that part of Hg bioaccumulated during winter is still circulating in blood during summer. Hg contamination is higher over wintering than breeding grounds in several Arctic breeding seabirds, including little auks [START_REF] Albert | Seasonal variation of mercury contamination in Arctic seabirds: A pan-Arctic assessment[END_REF]. Hg excretion into feathers is an efficient mechanism [START_REF] Bond | Total and methyl mercury concentrations in seabird feathers and eggs[END_REF], but winter moult only affects head feathers in little auks, i.e., a small proportion of the whole plumage. In addition, excretion in feathers is known to be up to 90% of the body burden [START_REF] Bond | Total and methyl mercury concentrations in seabird feathers and eggs[END_REF][START_REF] Braune | Mercury levels in Bonaparte's gulls (Larus philadelphia) during autumn molt in the Quoddy region, New Brunswick, Canada[END_REF]. This can limit the total amount of Hg that can be eliminated from the organism. Furthermore, the biological half-life of Hg in seabirds can be longer than 65 days in non-moulting individuals [START_REF] Monteiro | Kinetics, Dose-Response, and Excretion of Methylmercury in Free-Living Adult Cory's Shearwaters[END_REF]. A correlation of Hg concentrations in head feathers and blood could also result from individuals consistently targeting prey of similar Hg content in both winter and summer. However, between-and within-individual variation in diet is small in this copepod specialist, at least in summer [START_REF] Amélineau | Where to Forage in the Absence of Sea Ice? Bathymetry As a Key Factor for an Arctic Seabird[END_REF][START_REF] Jakubas | Flexibility of little auks foraging in various oceanographic features in a changing Arctic[END_REF]. In addition, little auk prey and its Hg content are poorly known at wintering grounds [START_REF] Rosing-Asvid | Winter diet of the little auk (Alle alle) in the Northwest Atlantic[END_REF], further studies are thus necessary to confirm this hypothesis. Individuals may also differ in their physiological capacity to assimilate, bioaccumulate and excrete Hg, resulting in some birds consistently retaining more Hg than others, irrespective of exposure. Importantly, prey

Hg content explains a large part of inter-annual variations in summer Hg concentrations in this population [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]. Carryover effects of winter Hg exposure appear to be another important driver of summer Hg concentration, and should be considered when using little auks as bioindicators of temporal variations of Hg in Arctic food webs [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]. The sexrelated and inter-annual differences in summer Hg exposure can be related to trophic and abiotic factors, respectively [START_REF] Carravieri | Wandering albatrosses document latitudinal variations in the transfer of persistent organic pollutants and mercury to Southern Ocean predators[END_REF][START_REF] Hitchcock | Effects of Maternal Reproductive Investment on Sex-Specific Pollutant Accumulation in Seabirds: A Meta-Analysis[END_REF][START_REF] Robinson | Sex differences in mercury contamination of birds: testing multiple hypotheses with meta-analysis[END_REF].

IV.3. Carryover effects on fitness proxies

IV.3.1. Telomere length and body condition

Telomere length integrates the effect of multiple stressors and can predict reproductive performance and survival in a variety of taxa [START_REF] Angelier | Is telomere length a molecular marker of individual quality? Insights from a long-lived bird[END_REF][START_REF] Chatelain | The association between stressors and telomeres in non-human vertebrates: a meta-analysis[END_REF][START_REF] Louzon | Telomere dynamic in humans and animals: Review and perspectives in environmental toxicology[END_REF][START_REF] Salmón | Telomeres and anthropogenic disturbances in wildlife: A systematic review and meta-analysis[END_REF]. Exposure to environmental contaminants can disrupt telomere dynamics, e.g., through molecular oxidative damage, disruption of antioxidant defences, and/or impact on telomerase, the enzyme responsible for telomere elongation (reviewed in [START_REF] Louzon | Telomere dynamic in humans and animals: Review and perspectives in environmental toxicology[END_REF]. In vitro and studies in humans have shown a general trend of decrease in telomere length after contaminant exposure [START_REF] Chatelain | The association between stressors and telomeres in non-human vertebrates: a meta-analysis[END_REF][START_REF] Louzon | Telomere dynamic in humans and animals: Review and perspectives in environmental toxicology[END_REF]). Yet, the few studies on Hg-related variation of telomere length in birds showed contrasted results. A female-specific positive association between Hg contamination and telomere length was observed in black-legged kittiwakes Rissa tridactyla (Blévin et al., unpublished data in Angelier et al., 2018), while no association was found in Mandt's black guillemots Cepphus grylle mandtii [START_REF] Eckbo | Individual variability in contaminants and physiological status in a resident Arctic seabird species[END_REF]. A potential indirect effect of Hg on telomere dynamics through impacts on corticosterone has been suggested in red kite Milvus milvus nestlings [START_REF] Powolny | Corticosterone mediates telomere length in raptor chicks exposed to chemical mixture[END_REF]. Other studies have shown no clear evidence of Hg effects on corticosterone (e.g., Chastel et al., 2022 and references therein), the latter result should thus be taken with caution. Recently, [START_REF] Bauch | Higher mercury contamination is associated with shorter telomeres in a long-lived seabird -A direct effect or a consequence of among-individual variation in phenotypic quality?[END_REF] showed a clear, male-specific decreasing trend of telomere length with increasing winter Hg contamination in Cory's shearwaters Calonectris borealis. In our study, and irrespective of sex, little auk telomere length was not clearly related to winter Hg contamination, even though there was a negative, nonsignificant trend. Winter Hg concentrations are 1.5 times lower in little auks than Cory's shearwaters [START_REF] Bauch | Higher mercury contamination is associated with shorter telomeres in a long-lived seabird -A direct effect or a consequence of among-individual variation in phenotypic quality?[END_REF], suggesting that Hg contamination might be too low to have a similarly strong and significant effect. This is consistent with low Hg risk identified for little auks when compared to other Arctic seabird species [START_REF] Chastel | Mercury contamination and potential health risks to Arctic seabirds and shorebirds[END_REF]. On the other hand, telomere length increased with sampling date, and varied significantly between years. These results could be explained by a confounding effect of age. Telomere length has been associated

with biological and chronological age in birds [START_REF] Bize | Telomere dynamics rather than age predict life expectancy in the wild[END_REF][START_REF] Young | Age, sex, and telomere dynamics in a long-lived seabird with malebiased parental care[END_REF]. Breeding phenology and performance can also vary with age, with older, more experienced seabirds breeding earlier than younger ones (e.g., [START_REF] De Forest | The Effect of Age on Timing of Breeding and Reproductive Success in the Thick-Billed Murre[END_REF][START_REF] González-Solís | Individual changes underlie agespecific pattern of laying date and egg-size in female common terns Sterna hirundo[END_REF]. The observed increase in telomere length across the season could thus indicate that birds sampled later were younger. In addition, telomeres were on average shorter in birds sampled in 2018 than 2019, potentially because a larger proportion of old birds was sampled in 2018.

Environmental conditions can drive annual changes in telomere length of adult birds [START_REF] Mizutani | Environmental perturbations influence telomere dynamics in long-lived birds in their natural habitat[END_REF][START_REF] Young | Age, sex, and telomere dynamics in a long-lived seabird with malebiased parental care[END_REF]. Environmental conditions were likely challenging in 2018, as both hatching success and chick survival were lower in 2018 than 2019. Furthermore, chick growth rates were lower in 2018 compared to 2019 (Fig. S3), and to the long-term average [START_REF] Amélineau | Arctic climate change and pollution impact little auk foraging and fitness across a decade[END_REF]. The more challenging year 2018 possibly allowed only older and experienced birds to breed, and thus be sampled, while several young birds may have failed early or skipped reproduction. Good conditions in the winter and/or summer of 2019 might also have promoted telomere restoration (i.e., through telomerase activity, [START_REF] Louzon | Telomere dynamic in humans and animals: Review and perspectives in environmental toxicology[END_REF].

Further investigation in birds of known age and quality are necessary to confirm or refute these interpretations. Quantifying the intrinsic and extrinsic drivers of telomere length and Hg contamination, which could co-vary [START_REF] Bauch | Higher mercury contamination is associated with shorter telomeres in a long-lived seabird -A direct effect or a consequence of among-individual variation in phenotypic quality?[END_REF][START_REF] Salmón | Telomeres and anthropogenic disturbances in wildlife: A systematic review and meta-analysis[END_REF], is essential to rule out whether telomere length and/or dynamics are useful biomarkers of Hg health effects.

Body mass tended to decrease with increasing winter Hg contamination. This is consistent with one previous investigation showing a negative association between summer Hg concentrations and body condition in this population [START_REF] Amélineau | Arctic climate change and pollution impact little auk foraging and fitness across a decade[END_REF]. Current (summer) Hg exposure may be more important than past contamination over wintering grounds for effects on body condition. Alternatively, the lack of statistical significance could be linked to small statistical power. A recent meta-analysis showed that Hg contamination effects on body condition are weak, can be confounded by several ecological and physiological factors, and are difficult to detect in wild birds [START_REF] Carravieri | Quantitative meta-analysis reveals no association between mercury contamination and body condition in birds[END_REF].

IV.3.2 Short-term reproductive performance

Reproduction is a sensitive endpoint of Hg toxicity in birds (reviewed in [START_REF] Evers | The effects of methylmercury on wildlife: a comprehensive review and approach for interpretation[END_REF][START_REF] Whitney | Impacts of sublethal mercury exposure on birds: a detailed review[END_REF]. Effects on hatching success can result from Hg embryotoxicity [START_REF] Whitney | Impacts of sublethal mercury exposure on birds: a detailed review[END_REF][START_REF] Yu | Acute embryotoxic effects but no long-term reproductive effects of in ovo methylmercury exposure in zebra finches (Taeniopygia guttata)[END_REF], impacts on egg quality [START_REF] Brasso | Effects of mercury exposure on the reproductive success of tree swallows (Tachycineta bicolor)[END_REF][START_REF] Evers | Common loon eggs as indicators of methylmercury availability in North America[END_REF][START_REF] Fort | Spatial ecotoxicology: migratory arctic seabirds are exposed to mercury contamination while overwintering in the Northwest Atlantic[END_REF]but see Pollet et al., 2017), but also from disruption of parents' incubation behaviour, for example through egg neglect [START_REF] Tartu | Corticosterone, prolactin and egg neglect behavior in relation to mercury and legacy POPs in a long-lived Antarctic bird[END_REF][START_REF] Tartu | Mercury exposure, stress and prolactin secretion in an Arctic seabird: an experimental study[END_REF] see [START_REF] Blévin | Organochlorines, perfluoroalkyl substances, mercury, and egg incubation temperature in an Arctic seabird: Insights from data loggers[END_REF]. Overall, Hg effects on hatching success are contrasted, with some studies showing declines (e.g., [START_REF] Albers | Effects of methylmercury on reproduction in American kestrels[END_REF][START_REF] Henny | Nineteenth Century Mercury: Hazard to Wading Birds and Cormorants of the Carson River, Nevada[END_REF], no associations (e.g., [START_REF] Carravieri | Trophic and fitness correlates of mercury and organochlorine compound residues in egg-laying Antarctic petrels[END_REF][START_REF] Weech | Mercury exposure and reproduction in fish-eating birds breeding in the Pinchi Lake region, British Columbia, Canada[END_REF] present study), or increases [START_REF] Heinz | Enhanced reproduction in mallards fed a low level of methylmercury: an apparent case of hormesis[END_REF].

Stronger evidence exists on the association of Hg with breeding success in multiple avian taxa (e.g., [START_REF] Brasso | Effects of mercury exposure on the reproductive success of tree swallows (Tachycineta bicolor)[END_REF]Goutte et al., 2014ab, 2015 as disruption of the hormone prolactin [START_REF] Tartu | Corticosterone, prolactin and egg neglect behavior in relation to mercury and legacy POPs in a long-lived Antarctic bird[END_REF][START_REF] Tartu | Mercury exposure, stress and prolactin secretion in an Arctic seabird: an experimental study[END_REF]. Here, we showed that parental winter Hg contamination had no effect on egg volume, hatching success and chick survival, while chick growth was negatively affected. Potential confounding effects of embryonic Hg on chick health without impact on hatching success cannot be ruled out by the present study.

However, there was no correlation between Hg concentrations in maternal head feathers and their eggshell membrane, and in parent head feathers and in their chicks' feathers (Table S2).

This indicates that the association of parental winter Hg contamination with chick growth is not solely linked to Hg transfer to the egg. However, parental age and diet quality, or other factors not considered here, could also confound this association, calling for caution in the interpretation. Even though further evidence is needed, we hypothesise that trans-generational effects of Hg could act indirectly through parental care during chick-rearing.

V. Conclusions

The present study shows spatial variation in Hg transfer to mid-trophic consumers in the North Atlantic Ocean in winter, and calls for further investigations to identify the drivers of this. Importantly, we detected carryover effects of winter Hg exposure on summer breeding concentrations of adults and on the quality of their offspring. This suggests that whole-year exposure should be considered to quantify effects of Hg contamination on reproductive performance in migrating seabirds. Our results did not provide conclusive evidence of a negative effect of winter Hg contamination on telomere length. However, further studies on a larger sample size of known-age birds, and in other species, are warranted to confirm this. In addition, nutritional factors such as varying exposure to selenium, an essential metal necessary to contrast Hg deleterious effects [START_REF] Manceau | In vivo formation of HgSe nanoparticles and Hg-tetraselenolate complex from methylmercury in seabird -Implications for the Hg-Se antagonism[END_REF][START_REF] Scheuhammer | Relationships among mercury, selenium, and neurochemical parameters in common loons (Gavia immer) and bald eagles (Haliaeetus leucocephalus)[END_REF], should also be taken into account to understand Hg effects in the wild. Finally, variation in the association between Hg and reproductive performance could be driven by the interplay of multiple stressors [START_REF] Bårdsen | Multiple stressors: modeling the effect of pollution, climate, and predation on viability of a sub-arctic marine bird[END_REF], which merits further investigation, in particular in the context of rapid environmental change.

the graphical abstract. During data collection, CA and MC were supported by a PhD fellowship from the French Ministry of higher education and research. Thanks are also due to the CPER (Contrat de Projet Etat-Région) and the FEDER (Fonds Européen de Développement Régional)

for funding the CEBC and the AMA of LIENSs laboratory. We thank three anonymous reviewers for helpful suggestions to improve the manuscript. 2 in the main text).

  resources.marine.copernicus.eu/products). Bathymetry was extracted using the NOAA dataset https://www.ncei.noaa.gov/maps/bathymetry/ (GEBCO). We calculated the median of latitude and longitude, and extracted all environmental variables at this location, for each individual separately. These unique values per individual were used to test the influence of location and environmental characteristics on head feather (i.e., winter) Hg concentrations (see Section II.7.).II.4. DNA extraction and molecular sexingGenomic DNA was extracted from calamus tips of plucked feathers stored at room temperature using NucleoSpin Tissue Kit (Macherey-Nagel EURL, Hoerdt, France). DNA concentration was determined using a Nanodrop 2000. Molecular sexing was performed using primers 2550F and 2718R[START_REF] Fridolfsson | A simple and universal method for molecular sexing of non-ratite birds[END_REF] that amplify portions of the CHD genes present on the Z and W sex chromosomes.PCR reactions included 0 to 90 ng of DNA, 0.4 μM of each primer, 1X reaction buffer (biotechrabbit, Hennigsdorf, Germany), 1.5 mM MgCL2, 0.2 mM of dNTPs, 0.32 mg/μL of bovine serum albumine and 1.2 U of Taq polymerase (biotechrabbit, Hennigsdorf, Germany) in a 30 μL final volume. The following PCR profile was performed on a TC-5000 (Techne) thermocycler: 94°C for 1 min, followed by 35 cycles of 30 s at 94 °C, 45 s at 53 °C, and 45 s at 72 °C, and a final extension of 5 min at 72 °C. PCR products were separated by electrophoresis on a 2.5% agarose gel stained with GelRed. No-template negative controls were included in DNA extractions and PCR reactions.

  ) further studies should look at similar potential Hg effects in other Arctic species.IV.1. Spatial differences in Hg exposure and potential environmental drivers Head feather Hg concentrations in little auks breeding in East Greenland were driven by longitude of wintering position, but, contrary to our hypothesis and to results in other species[START_REF] Albert | Seasonal variation of mercury contamination in Arctic seabirds: A pan-Arctic assessment[END_REF] AMAP 2021), increased eastward. Namely, head feather Hg concentrations decreased eastward in the North Atlantic in wintering little auks from multiple Arctic colonies in 2015 and 2016[START_REF] Renedo | Contrasting Spatial and Seasonal Trends of Methylmercury Exposure Pathways of Arctic Seabirds: Combination of Large-Scale Tracking and Stable Isotopic Approaches[END_REF]. Results from the latter study included little data from longitudes farther west, i.e., shelf waters off the United States and south Greenland, which were visited by birds of the present study. This could indicate that winter Hg exposure is low on shelf waters east of the United States and Greenland, and high in oceanic waters in the central North Atlantic This is consistent with empirical and modelling studies on MeHg content in different marine matrices, which show a similar longitudinal trend in zooplankton MeHg content in

Figure 1 .

 1 Figure 1. Left panel: median positions of little auks wintering in the North Atlantic Ocean in 2017-18 and 2018-19 (1 st December -31 st January), coloured by the Hg concentrations in their head feathers. The blue cross indicates the breeding colony at Ukaleqarteq, East Greenland. Right panel: Head feather Hg concentrations increase with decreasing longitude (eastward) in little auks wintering in the North Atlantic in 2017-18 and 2018-19 (1 st December -31 st January) (β ± SE on unscaled values: slope 0.028 ± 0.008, intercept 2.621 ± 0.343, N = 28, ΔAICcModel-Null = 7.93, see Table1).

Figure 2 .

 2 Figure 2. Red blood cell (RBC) Hg concentrations increased with increasing head feather Hg concentrations in little auks breeding at Ukaleqarteq, East Greenland, in 2018 and 2019 (β ± SE: slope 0.145 ± 0.022, intercept 0.362 ± 0.080, N = 79, ΔAICcModel-Null = 40.8, see Table2, sex and year effects are not represented for readability).

Figure 3 .

 3 Figure 3. Chick growth rate decreases with increasing head feather Hg concentrations of their parents in little auks breeding at Ukaleqarteq, East Greenland, in 2018 (slope -0.555 ± 0.215, intercept 6.615 ± 0.838, N = 18, ΔAICcModel-Null = 2.93, see Table2).

Figure S2 .

 S2 Figure S2.Telomere length (TS ratio) increases with sampling date (Julian day) in little auks breeding at Ukaleqarteq, East Greenland, in 2018 and 2019. The two lines represent yearspecific models, as telomere length was significantly larger in 2019 than 2018, and were obtained by using averaged coefficient estimates (Table2in the main text).

  . As Hg can decrease time spent in energy-demanding activities (reviewed in[START_REF] Whitney | Impacts of sublethal mercury exposure on birds: a detailed review[END_REF], chick survival effects are thought to be mediated by disruption of parental care. Evidence of this comes from both behavioural observations (e.g., decrease in chick provisioning rate,Merrill et al., 2005, increase in egg neglect behaviour, Tartu et al., 2015 at higher Hg exposure), and endocrine effects, such

Table 1 .

 1 Akaike tables including model specification and sample size of the models used to address the different aims of this study (see Introduction, and TableS1). This table includes response variables showing significant effects, while response variables showing insignificant effects are reported in TableS2. Only the five best ranked models and the null model are presented. When more than two models had similar support (ΔAICc < 2), model averaging was applied, and averaged estimates (β) reported, to determine the explanatory variables with the clearest effect (in bold). Abbreviations: Lat and Lon, median latitude and longitude of wintering position; PC1 and 2, scores on axis 1 and 2 of a PCA of environmental variables (SST, seasurface temperature; chla, sea-surface chlorophyll a concentration; MLD, mixed-layer depth); Hg_HF, head feather (i.e., winter) Hg concentrations; AICc, Akaike's information criterion corrected for small sample sizes; ΔAICc, difference between AICc of the specific model and the best model; k, number of parameters; wi AICc weight; Exp. Dev., explained deviance.

	Aim 1) Head feather Hg concentrations in GLS-tracked birds (N = 28)
	Initial model: Head feather Hg ~ Lat + Lon + PC1 + PC2 + Year + Sex
	GLM Gaussian (log)	k	AICc	ΔAICc	wi	Exp. Dev.
	Lon	3	90.3	0	0.29	0.30
	Lon + Lat	4	92.1	1.78	0.12	0.29
	Lon + PC2	4	92.7	2.32	0.09	0.28
	Lon + Year	4	92.7	2.34	0.09	0.28
	Lon + PC1	4	93.0	2.64	0.08	0.27
	Null	2	98.3	7.93	0.01	0.00
	Aim 2)

Red blood cell Hg concentrations (N = 79)

  Initial model: Red blood cell Hg ~ Hg_HF + Year + Sex + Hg_HF:Sex

	GLM Gamma (identity)	k	AICc	ΔAICc	wi	Exp. Dev.
	Hg_HF + Year + Sex	5	-28.11	0	0.69	0.43
	Initial model	6	-26.03	2.09	0.24	0.42
	Hg_HF + Sex	4	-22.89	5.22	0.05	0.38
	Hg_HF + Sex + Hg_HF:Sex	5	-20.74	7.37	0.02	0.37
	Hg_HF + Year	4	-10.99	17.12	0.00	0.28
	Null	2	12.66	40.77	0.00	0.00

Aim 3) Telomere length (TS ratio) (N = 77)

  Initial model: TS ratio ~ Hg_HF + Julian + Year + Sex + Hg_HF:Sex

	GLM Gamma (identity)	k	AICc	ΔAICc	wi	Exp. Dev.
	Julian + Sex + Year	5	-98.50	0.00	0.28	0.22
	Julian + Year	4	-98.04	0.46	0.22	0.20
	Hg_HF + Julian + Year	5	-97.70	0.81	0.19	0.21
	Hg_HF + Julian + Sex + Year	6	-97.60	0.91	0.18	0.22
	Julian + Sex	4	-95.23	3.27	0.05	0.22
	Null	2	-83.27	15.24	0.00	0.00
	Model averaging (full average) β ± SE [95% CI]
	Intercept 0.955 ± 0.021 [0.912-0.997]
	Julian 0.049 ± 0.014 [0.021-0.078]
		SexM 0.024 ± 0.030 [-0.036-0.083]
	Year2019 0.078 ± 0.030 [0.017-0.138]
	Hg_HF -0.007 ± 0.012 [-0.032-0.017]

Acknowledgments. This study was funded by the projects MAMBA (ANR-16-TERC-004), ILETOP (ANR-16-CE34-0005), and ADACLIM . This is also a contribution to the international initiative ARCTOX (arctox.cnrs.fr), the project ARCTIC-STRESSORS (ANR-20-CE34-0006-01) and to the Excellence Chair ECOMM funded by the Region Nouvelle Aquitaine (France). The IUF (Institut Universitaire de France) is acknowledged for its support to PB as a senior member. DNA sample preparation and molecular sexing were conducted at the Molecular Core Facility of La Rochelle University. The authors are grateful to Maud Brault-Favrou and Carine Churlaud for their assistance with Hg analyses, and the SEATRACK project for analysing the GLS data, and to Julie Charrier for drawing the adult and chick little auk of

Conflict of interest

The authors declare that there are no conflicts of interest.


Thomas Haevermans

Jessica Tressou

Joon Kwon

Roseli Pellens

Anne Dubéarnès

Simon Veron

Liliane Bel

Stéphane Dervaux

Juliette Dibie-Barthelemy

Myriam Gaudeul

Rafaël Govaerts

Gwenaël Le Bras

Serge Muller

Germinal Rouhan

Corinne Sarthou

Lydie Soler

Services S Díaz

J Settele

E S Brondízio

H T Ngo

M Guèze

J Agard

A Arneth

P Balvanera

K A Brauman

S H M Butchart

K M A Chan

L A Garibaldi

K Ichii

J Liu

S M Subramanian

G F Midgley

P Miloslavich

Z Molnár

D Obura

A Pfaff

S Polasky

A Purvis

J Razzaque

Bennun L Mair

Da Fonseca

Navarro, L. M Nic Lughadha

Hawkins, F & Mcgowan

A Metric

Global Plant Extinction Risk Assessment Inform Novel Biodiversity Hotspots

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

Introduction.

Assessing the threat status of all known species is a stated objective to guide conservation actions across the world (UNEP (United Nations Environment Programme) & CBD (Convention on Biological Diversity), 2010) and enable the sustainable management of life on Earth [START_REF] Newbold | Has land use pushed terrestrial biodiversity beyond the boundary? A global assessment[END_REF]. Recent assessments indicate that the window to reverse the current trends of biodiversity loss, in a serious commitment to intergenerational justice, is narrowing fast (IPBES, 2019b(IPBES, , 2019a; Secretariat of the United Nations Convention on Biological Diversity, 2021). These assessments emphasize that the importance of rapidly obtaining high quality data for guiding global conservation policies. As a result, the United Nations Convention on Biological Diversity (CBD) is now working on a new generation of targets, goals and indicators aimed at curbing species extinction and reversing biodiversity loss (Díaz & al., 2020a; Secretariat of the United Nations Convention on Biological Diversity, 2021).

Extinction risk and biodiversity distribution data form the baseline for tracking our progress within the CBD's post-2020 biodiversity framework. The Global Strategy for Plant Conservation (GSPC), included within the CBD in 2002, is a central project for documenting and conserving plant diversity. Through its actions, massive datasets of occurrences are now available for assessment and analysis. Unfortunately, due to the sheer scale of the task, the evaluation of plant conservation statuses by the International Union for the Conservation of Nature (IUCN) and other national and international bodies is lagging behind, and a global threat status is still lacking for the vast majority of organisms. Indeed, despite the recognized importance of species extinction risk evaluation, the assessment data available is still far from sufficient to enable the establishment of clear goals and track progress within the post-2020 framework, which aims to promote science-based targets for biodiversity conservation (Mair & al., 2021). Currently, only 138,374 species have a global status on the IUCN Red List (IUCN, 2021), representing only ca. 7% of the estimated number of described species, with a strong bias towards certain geographical regions or flagship groups, for which datasets are the most complete, such as vertebrates and trees [START_REF] Bachman | Progress, challenges and opportunities for Red Listing[END_REF], and whose permanent loss is perceived as having an impact on humanity (IPBES, 2019a). This figure includes ca. 56,000 plant species, which represents less than 15% of the total number of plants known to science. So far, most global-scale conservation assessments have focused on vertebrates [START_REF] Ceballos | Vertebrates on the brink as indicators of biological annihilation and the sixth mass extinction[END_REF]Robuchon & al., 2021), even though global conservation planning should be informed by more species-rich groups such as invertebrates (Mace & al., 2000) and vascular plants, which are of outstanding and economic importance for human well-being [START_REF] Molina-Venegas | Maximum levels of global phylogenetic diversity efficiently capture plant services for humankind[END_REF]. The overwhelming number of species with insufficient data along with the difficulties in reassessing species in the field hamper any rapid progress and make it currently impossible to assign a formal threat status to an important fraction of existing species, which will, at best, remain classified as Data Deficient (Mair & al., 2021).

Machine learning methods (AI) can find predictive patterns that humans would not have been capable of spotting or identifying as relevant within huge and highly dimensional datasets [START_REF] Walker | Evidence-based guidelines for developing automated assessment methods[END_REF]. Natural history accessions and their associated information (locality, time of collection), as well as the taxonomic history of each species represent a as of yet unexplored source of information and may be potential predictors of frequency, distribution range and period during which a species was observed [START_REF] Albani Rocchetti | Reversing extinction trends: new uses of (old) herbarium specimens to accelerate conservation action on threatened species[END_REF]. The recent digitization of major global natural history collections (Le Bras & al., 2017) has given access to a wealth of information (Paton & al., 2020) and has contributed to authoritative expert-based lists of known plant life (Le Bras & al., 2017;[START_REF] Borsch | World Flora Online: Placing taxonomists at the heart of a definitive and comprehensive global resource on the world's plants[END_REF]Govaerts & al., 2021). We used machine learning methods to explore an innovative, objective and reproducible method for assigning a threat status to all known vascular plant (VP) species in one go using massive datasets [START_REF] Pelletier | Predicting plant conservation priorities on a global scale[END_REF][START_REF] Nic Lughadha | Extinction risk and threats to plants and fungi[END_REF]. Our results provide the first global assessment of VP conservation status and define a new set of science-based Plant Conservation Hotspots (PCH2.5 and PCH5, see Methods), taking into account three commonly used biodiversity facets [START_REF] Myers | Biodiversity hotspots for conservation priorities[END_REF][START_REF] Forest | Preserving the evolutionary potential of floras in biodiversity hotspots[END_REF].

Results.

Performance (test scores) of the AI algorithm in Extinction Risk (ER) prediction for n = 345,697 species (Supplementary Table 4) was 82.7%, 70%, and 53% for the two, three and five classes, respectively. The importance of each of the 535 features in the prediction models is given in Supplementary Table 2. Results were distributed as follows and were converted into threatened (Þ) / non-threatened (NÞ) status using the thresholds defined by the IUCN (i.e. LC.NT = NÞ and VU.EN.CR = Þ, see Methods): two classes (41% LC.NT, 59% VU.EN.CR, corresponding to 41% NÞ / 59% Þ), three classes (30% LC, 20% NT, 50% VU.EN.CR, corresponding to 50% NÞ / 50% Þ), and five classes (26% LC, 20% NT, 8% VU, 9% EN, 37% CR, corresponding to 46% NÞ / 54% Þ). When considering specific plant groups such as trees (here, 'Phanerophytes'), we find that our results are congruent with the most recent global estimates (Botanic Gardens Conservation International, 2021). Indeed, one third of trees are threatened worldwide (44% when including the 'possibly threatened' category), and up to two thirds are threatened in Madagascar. Those figures match our own results (59% of trees are threatened in Madagascar, 41% globally (n = 89,151)). The percentage of species assigned to the DD category in the training dataset that were predicted to be threatened ranged between 65-73% (2 classes: all 59% Þ / DD 73% Þ, 3 mod.: all 50% Þ / DD 65% Þ, and 5 mod.: all 54% Þ / DD 71% Þ), a proportion that is in between the "best" and the "high" estimates from the IUCN (IUCN, 2021) (i.e. best = DD species are predicted to be threatened in the same proportion as data sufficient species; high = DD species are always predicted to be threatened). The top 2.5% and 5% hotspot areas (using botanical countries normalized for size, see Methods) for each facet (current: SPR, PD, END; threatened: ÞSPR, ÞPD, ÞEND and/or expected losses 50 years from now: ℓSPR, ℓPD, ℓEND) are listed in Extended Data Table 4. Analyses of the correlation between facets revealed two main groups (Figure 1, rankings given in Supplementary Table 3). Figure 1 illustrates the existence of two different types of areas, which is confirmed by the PCA rankings of botanical countries (Supplementary Table 3) following those biodiversity facets clusters. The Andes were confirmed to be an important PD+SPR hotspot (Extended Data Fig. 5a), whereas Old World insular areas were found to be crucial threatened diversity and endemicity hotspots (Extended Data Fig. 5b). The SPR and PD hotspots, see Extended Data Table 4, strongly overlapped (only differing by one country) and were mostly located in continental areas in the Neotropics, with Colombia having the highest SPR/PD (Extended Fig. 1a and1c; Supplementary Table 3). The END hostpots were mostly insular and paleotropical areas, with Madagascar (MDG) having the highest END in the world (0.825) (Extended Data Fig. 1b, Supplementary Table 3). Hotspots of threatened diversity (ÞSPR, ÞPD, and ÞEND) and with expected losses of diversity (ℓPD, ℓPD and ℓEND) overlapped, albeit not completely, with END hotspots, being mostly paleotropical with almost half occurring on insular territories (Extended Data Figs. 2 and3).

Priority Conservation Hotspots (PCH) identified from PCA ordination with 2.5% and 5% thresholds (PCH2.5 and PCH5) capture most of the contribution of each facet to global plant biodiversity (Fig. 2). PCH capture a similar proportion of each facet than the top 2.5% and 5% area identified for each facet (Extended Data Table 2), but with a notable minimal taxonomic overlap as indicated in Extended Data Table 3. PCH thus capture top endemics from SPR and PD hotspots, as well as top endemics from END hotspots (by definition containing nonoverlapping taxa strictly restricted to distinct areas).

Our results show that Madagascar, China South-Central, the Philippines, New Guinea, Peru, Borneo, Colombia, Brazil Southeast and Ecuador, identified as our PCH2.5, are of extreme importance for their global contribution to plant biodiversity (Fig. 2, see complete rankings in Supplementary Table 3). PCH5 also included Turkey, Hawaiʻi, Vietnam, Cuba, Iran, India, China Southeast, the Cape Provinces and Thailand. The total area covered by PCH2.5 represents ca. 5% of emerged areas (n = 114,577 distinct taxa, i.e. ca. 30% of VP) and PCH5 represents up to 10% (n = 164,142 taxa, i.e. ca. 50% of VP) (Extended Data Tables 3 and4). Overall, our analyses suggest that PCH5 contain 50% of the world's species richness, 43% of the plant tree of life and 53% of the world's highly endemic species. These areas also contain a quarter of threatened species (ÞSPR), almost half of the threatened tree of life (ÞPD) and a third of the threatened endemics (ÞEND). If no conservation measures are taken to reverse current trends, we estimate that 14% of the global SPR, 27% of endemics and 47% of the plant tree of life may be lost during the next 50 years (ℓSPR, ℓPD and ℓEND in Extended Table 2).

AI and the estimation of the proportion of threatened plants worldwide.

Predicting extinction risk is a challenge that has been tackled recently [START_REF] Humphreys | Global dataset shows geography and life form predict modern plant extinction and rediscovery[END_REF][START_REF] Nic Lughadha | Extinction risk and threats to plants and fungi[END_REF][START_REF] Walker | Evidence-based guidelines for developing automated assessment methods[END_REF], and this endeavor is facilitated when local floras are well known and documented, which is not the case for most regions of the world harboring biodiversity hotspots [START_REF] Myers | Biodiversity hotspots for conservation priorities[END_REF]. Our study is the most comprehensive threat pre-assessment ever carried out for a large clade (n = 345,697 vascular plant (VP) species), representing almost all of VP diversity. The large number of features used here (535) ensures that we can identify the features that may be relevant for performing the prediction task (the importance of each feature is given in Supplementary Table 2). The use of sophisticated AI methods is especially relevant for this kind of projection by efficiently managing raw biodiversity data [START_REF] Walker | Evidence-based guidelines for developing automated assessment methods[END_REF]. Climate features, in particular those linked to aridity ('Climate_Desert', 'Climate_Consistently_Dry') were important in two of our prediction schemes. However, the most highly ranked features in the final models were distribution arearelated parameters, including several artificial features created by a neural network (Supplementary Tables 1 and2). Indeed, we expected that some of these artificial features would account for the quantity of occurrence data as well as the geographical range of species, and therefore be correlated to the threat level, since the IUCN Red List criteria are based on such parameters. The IUCN takes into account low population size and the tendency to decline (criteria A, C, D), geographical range (B), or uses a quantitative analysis (Mace & al., 2008) indicating a probability of extinction in the wild (E). However, in practice most of the Red List assessments strongly rely on distribution data due to the difficulty in obtaining reliable measurements for most criteria, resulting in threat pre-assessments for plants being de facto based on criterion B [START_REF] Pelletier | Predicting plant conservation priorities on a global scale[END_REF][START_REF] Stévart | A third of the tropical African flora is potentially threatened with extinction[END_REF][START_REF] Nic Lughadha | Extinction risk and threats to plants and fungi[END_REF].

By including geospatial data to account for habitat reduction, previous studies of trees (ter Steege & al., 2015) and birds [START_REF] Ocampo-Peñuela | Incorporating explicit geospatial data shows more species at risk of extinction than the current Red List[END_REF] suggested that 57% of Amazonian tree species (ter Steege & al., 2015) and nearly 50% of birds [START_REF] Ocampo-Peñuela | Incorporating explicit geospatial data shows more species at risk of extinction than the current Red List[END_REF] may be threatened, highlighting the fact that earlier methods may have largely underestimated the figures. Other studies of larger datasets [START_REF] Pelletier | Predicting plant conservation priorities on a global scale[END_REF][START_REF] Stévart | A third of the tropical African flora is potentially threatened with extinction[END_REF][START_REF] Nic Lughadha | Extinction risk and threats to plants and fungi[END_REF] of local or global significance provided lower estimates (e.g. 30% of plant life may be threatened based on spatial data only [START_REF] Pelletier | Predicting plant conservation priorities on a global scale[END_REF][START_REF] Stévart | A third of the tropical African flora is potentially threatened with extinction[END_REF], and 40% based on a MPR (multilevel regressions and post-stratification) method [START_REF] Nic Lughadha | Extinction risk and threats to plants and fungi[END_REF], i.e. not based on individual assessments). These discrepancies may be explained by the way DD species were accounted for or not, as we showed here that a majority of these species should be considered as threatened (see results). Besides distribution parameters, other features proved to be important in our analyses, in particular taxonomic features (e.g. number of synonyms, description date, family) and the sampling frequency of a given species (e.g. number of specimens, sampling date). Our analyses showed that factors other than distribution are relevant for predicting species threat status and should be taken into account when estimating extinction risk.

Post-2020 targets and priority setting for the conservation of various biodiversity facets

One of the main recommendations of the scientific community for curbing biodiversity loss and reducing the risk of losing many of Nature's Contributions to People (NCP) (Díaz & al., 2020b) is to set ambitious goals in the post-2020 Global Biodiversity Framework. It is essential to identify hotspots of biodiversity, in its different facets, to define clear targets for reducing species extinctions (Goal A) and conserving ecosystem services and NCP (Goal B). The hotspots we identified (Extended Data Table 1 and4) grouped according to the correlations between facets of biodiversity (Fig. 1), illustrating the fact that two main aspects of biodiversity: hyper-diversity (SPR/PD hotspots) and irreplaceability (END/allÞ/allℓ), are not nested [START_REF] Orme | Global hotspots of species richness are not congruent with endemism or threat[END_REF]Díaz & al., 2020b). A high proportion of areas with high endemicity, containing irreplaceable species [START_REF] Veron | High evolutionary and functional distinctiveness of endemic monocots in world islands[END_REF], correspond to insular ecosystems, whereas PD and SPR hotspots occur in neotropical continental areas. Surprisingly, the latter harbor a lower proportion of global endemicity (Extended Data Table 2). Biodiversity hotspots were first identified by Myers et al. from levels of species endemism (with particular focus on vascular plants) and degree of threat (approximated through habitat loss) [START_REF] Myers | Biodiversity hotspots for conservation priorities[END_REF]. Twenty hotspots are shared (at least partially) between Myers et al. and our PCH5 analysis (Extended Data Table 1), which is a significant overlap but which nevertheless reveals key differences. The main differences are found in the California Floristic Province, Central Chile, the West African Forests, the Mediterranean Basin, the Caucasus, Southwest Australia, New Zealand and New Caledonia, which are listed in Myers et al. [START_REF] Myers | Biodiversity hotspots for conservation priorities[END_REF] but are absent from our list. Conversely, we identified Iran, Turkey, India, Southeast China and New Guinea, whereas these areas were not retained by Myers et al. or only very partially. Some of these discrepancies might be due to the fact that we used proportions instead of raw counts or to a difference in the way geographical units were defined (we did not separate coastal areas from our botanical countries, unlike Myer et al.). Most notable, perhaps, is the absence of New Zealand from the PCH5 list and the inclusion of New Guinea. It is possible that New Guinea would make it on the list if Myers et al.'s study was conducted today, due to the catastrophic decline of its Wilderness Areas [START_REF] Watson | Catastrophic declines in wilderness areas undermine global environment targets[END_REF] and the richness and irreplaceability of its flora (Cámara-Leret & al., 2020). Some of these metrics are already used in research programs that aim to highlight priority areas for conservation: species extinction risk is one of the criteria for designating Key Biodiversity Areas (KBA) and Important Plant Areas (IPA) (Brooks & al., 2015b;IUCN, 2016;[START_REF] Darbyshire | Important Plant Areas: revised selection criteria for a global approach to plant conservation[END_REF]. Thirteen of the PCH5 biodiversity hotspots are in fact already being monitored under the IPA scheme, and numerous KBAs have been identified in all our PCH5 hotspots (BirdLife International, 2021).

Our study fulfill the highly ambitious goals of the CBD for biodiversity and sustainability (Díaz & al., 2020b) by helping optimize the safeguarding of the tree of life and the conservation of a high number of irreplaceable species with globally or locally critically restricted range [START_REF] Daru | Endemism patterns are scale dependent[END_REF], and areas where species diversity is high. Conservation efforts targeting the regions highlighted by PCH5 would likely benefit other branches of the tree of life (twenty of our top 25 PCH overlap with areas that have been shown to be important for the conservation of phylogenetic diversity in mammals (Robuchon & al., 2021)). The individual species threat assessments and the megaphylogeny provided here may also be used at scales other than the one considered here, and may meet other or complementary CBD goals, such as protecting NCPs and ecosystem services [START_REF] Possingham | Biodiversity: Turning up the heat on hotspots[END_REF].

Extended Data and Figures. Extended Data

Figure 1 |

 1 Figure 1 | Correlogram (R package Corrr) of all biodiversity facets obtained using Kendall's τ. (a) weak correlation (>0.3). (b) moderate correlation (>0.5). (c) high correlation (>0.7). The correlation between variables is indicated by clustering and color intensity. Clustering: (i) SPR and PD are strongly correlated (0.89) while only weakly correlated (0.39-0.40) with endemicity (END), (ii) the various measures of threat (Þ and ℓ) are all strongly correlated (0.78-0.93) and are correlated with END (0.71-0.75) but only weakly with SPR or PD (0.32-0.38) (Extended Data Fig. 4).

Figure 2 |

 2 Figure 2 | Map of Priority Conservation Hotspots. PCH ranked according to values of axis 1 of the PCA analysis of all variables (see Methods), taking into account both the hyperdiversity and irreplaceability of the species harbored by botanical countries.

Figure 1 |

 1 Current distribution of facets of plant diversity (SPR, END, PD) | (a) polygons with the highest number of species (SPR). (b) polygons with the highest proportion of endemics (END) compared to the total number of species in that polygon. (c) polygons with the highest phylogenetic diversity (PD). Extended Data Figure 2 | Distribution of plant diversity (ÞSPR, ÞEND, ÞPD) that is under threat | (a) polygons with the highest proportion of threatened species (ÞSPR). (b) polygons with the highest proportion of threatened endemics (ÞEND) compared to the total number of species in that polygon. (c) polygons with the highest proportion of threatened phylogenetic diversity (ÞPD). Extended Data Figure 3 | Expected biodiversity loss 50 years from now for each facet of plant diversity (ℓSPR, ℓEND, ℓPD) | (a) polygons with the highest proportion of expected loss of species richness (ℓSPR). (b) polygons with the highest proportion of endemics expected to be lost (ℓEND) compared to its known flora. (c) polygons with the highest proportion of expected phylogenetic diversity loss (ℓPD). Extended Data Figure 4 | Correlation graphs for all variables. Current (SPR, END, PD), threatened (ÞSPR, ÞEND, ÞPD) and expected losses (ℓSPR, ℓEND, ℓPD), using Kendall's τ, matching the correlogram presented in Fig. 1. (significance *** = 0.001, ** = 0.01, * = 0.05, . = 0.1, ' ' = 1). Extended Data Figure 5 | Top 2,5% and 5% hyperdiverse (HYP) and irreplaceable (IRR) areas, identified from groupings of biodiversity facets (see correlogram; Fig. 1)). (a) areas ranked by level of hyperdiversity, using values from axis 1 of the PCA analysis of PD+SPR. (b) areas ranked by level of irreplaceability and threat, using values from axis 1 of the PCA analysis of the other variables (ÞSPR, ℓSPR, END, ÞEND, ℓEND, ÞPD, ℓPD).

Extended Table 1 | Top 25 PCH expressed by area

	TDWG3 Hotspots SPR PCH	ÞSPR ÞEND ÞPD ℓSPR ℓEND ℓPD Area PD END ÞSPR ÞPD ÞEND ℓSPR	ℓPD	ℓEND
	MDG SPR2.5 34.1 14.238619	0.6 30.5	0.6 34.2	0.7 14.4	0.4 28.6	0.4 19.0	0.3 Madagascar 7.8 26.5	14.4
	CHC SPR5 50.3 11.947092	0.4 44.1	0.3 55.5	0.5 23.5	0.3 45.8	0.3 31.9	0.3 China South-Central 11.9 45.2 24.3
	PHI PD2.5 32.9 11.475855	0.5 32.2	0.5 34.3	0.5 15.0	0.4 29.9	0.4 20.1	0.3 Philippines 8.1 29.5	15.3
	NWG PD5 50.5 11.199821	0.4 44.9	0.4 56.1	0.5 24.3	0.3 47.1	0.3 33.0	0.3 New Guinea 12.5 47.4	25.4
	PER END2.5 26.0 10.472048	0.4 26.2	0.3 28.2	0.5 11.0	0.3 22.2	0.2 15.2	0.2 Peru 7.6	23.0	11.8
	BOR END5 50.4 10.457607	0.4 43.7	0.4 53.0	0.5 22.8	0.3 43.3	0.3 30.7	0.3 Borneo 14.0 43.2	23.9
	CLM PCH2.5 34.6 9.709279	0.3 33.6	0.2 35.8	0.4 16.8	0.2 32.5	0.2 22.7	0.2 Colombia 10.0 32.8	17.1
	BZL PCH5 49.5 9.537902	0.3 42.9	0.3 52.9	0.4 24.8	0.3 46.3	0.2 33.4	0.2 Brazil Southeast 14.4 47.3 26.5
	ECU	9.273073	0.4	0.3	0.4	0.2	0.2	0.2 Ecuador
	TUR	9.004472	0.4	0.4	0.4	0.4	0.3	0.3 Turkey
	HAW	8.974727	0.5	0.5	0.4	0.4	0.4	0.3 Hawaii
	VIE	8.692707	0.4	0.3	0.5	0.3	0.2	0.2 Vietnam
	CUB	8.534072	0.4	0.4	0.4	0.3	0.3	0.2 Cuba
	IRN	7.852140	0.4	0.3	0.4	0.4	0.3	0.2 Iran
	IND	7.458381	0.4	0.3	0.4	0.3	0.3	0.2 India
	CHS	7.207854	0.3	0.2	0.4	0.3	0.2	0.2 China Southeast
	CPP	7.117899	0.3	0.3	0.4	0.2	0.2	0.2 Cape Provinces
	THA	6.759573	0.3	0.2	0.4	0.2	0.2	0.2 Thailand
	SUM	6.502656	0.3	0.3	0.4	0.2	0.2	0.2 Sumatera
	MXS	6.488629	0.3	0.2	0.4	0.2	0.2	0.2 Mexico Southwest
	VEN	6.346893	0.2	0.2	0.4	0.2	0.1	0.1 Venezuela
	NWC	5.624424	0.3	0.3	0.4	0.2	0.2	0.2 New Caledonia
	MLY	5.359497	0.3	0.2	0.4	0.2	0.2	0.1 Malaya
	SUL	5.345428	0.3	0.3	0.4	0.2	0.2	0.2 Sulawesi
	BOL	5.123567	0.2	0.2	0.3	0.2	0.1	0.1 Bolivia

. 'TDWG3' = TDWG level 3 area code, 'Area' = TDWG level 3 botanical country name (full ranking in Supplementary Table

3

).

Extended Data Table 2 | Respective contribution (percentage) of each biodiversity facet.

  Total contribution to current diversity measures (SPR, END, PD), threatened diversity (ÞSPR, ÞPD, ÞEND), and expected losses in 50 years time (ℓSPR, ℓPD, ℓEND) of the top 2.5% and 5% hotspots for each facet (SPR, PD, END), and for the top 2.5% and 5% PCH (calculations described in the Methods).

	Hotspots SPR2.5 SPR5 PD2.5 PD5	END2.5 END5 PCH2.5 PCH5 ANIDS
	SPR2.5 100.0	67.8	80.1	67.6	41.8	55.6	67.5	56.5	113141
	SPR5	100.0	100.0 100.0 96.9	79.1	75.0	95.5	83.3	166852
	PD2.5	77.2	65.3	100.0 65.1	49.0	59.2	86.1	60.2	109005
	PD5	100.0	97.2	100.0 100.0 85.1	78.1	100.0	86.4	167391
	END2.5 31.9	40.9	38.8	43.8	100.0	51.6	50.8	45.5	86245
	END5	82.1	75.1	90.7	77.9	100.0	100.0 100.0	90.4	167005
	PCH2.5 68.3	65.6	90.5	68.4	67.4	68.6	100.0	69.8	114577
	PCH5	82.0	82.0	90.7	84.7	86.6	88.8	100.0	100.0 164142
	Extended Data

Table 3 |

 3 Percentage of species in common. Percentage of species in common between each of the top 2.5% and 5% hotspots for each facet (SPR, END and PD) and PCH. 'ANIDS' = n species included (calculations described in the Methods).

		Current	Þ	ℓ
		Colombia, Peru,	Madagascar, Hawaiʻi,	Hawaiʻi, Madagascar,
		Ecuador, China South-	Philippines, Borneo,	Philippines, Turkey, Iran,
	2.5%	Central, Brazil Southeast, Cape	China South-Central, Iran, Turkey, Cuba,	New Guinea, Cuba, China South-Central,
		Provinces, Venezuela,	New Guinea	Tadzhikistan
		Mexico Southwest,
	SPR	Bolivia New Guinea, Vietnam,	Peru, India, Ecuador,	Borneo, India, Vietnam,
		Madagascar, Borneo,	Somalia, Vietnam,	Afghanistan, Uzbekistan,
	5%	Costa Rica, Thailand, Mexico Northeast,	Brazil Southeast, China Southeast,	Peru, Fiji, Brazil Southeast, Kirgizistan
		China Southeast,	Sumatera, Tadzhikistan
		Panamá
		Colombia, Ecuador,	Madagascar, China	Madagascar, Philippines,
		Peru, Venezuela,	South-Central, New	China South-Central,
	2.5%	China South-Central, Brazil Southeast, New	Guinea, Borneo, Philippines, Vietnam,	Turkey, Hawaiʻi, New Guinea, Borneo, Iran,
		Guinea, Bolivia,	Peru, Socotra, Brazil	Cuba
		Borneo	Southeast
	PD	Mexico Southwest,	China Southeast,	Vietnam, Brazil
		Vietnam, Costa Rica,	Somalia, Cuba, India,	Southeast, India, Peru,
	5%	Thailand, Panamá, Philippines, China	Sumatera, Turkey, Thailand, Ecuador,	Juan Fernández Is., New Caledonia, China
		Southeast, Cape	Iran	Southeast, Thailand
		Provinces,
		Madagascar
		Madagascar, New	Madagascar, Hawaiʻi,	Hawaiʻi, Madagascar,
		Guinea, Cape	Philippines, Borneo,	Philippines, Turkey, New
	2.5%	Provinces, Western Australia, New	New Guinea, Cuba, Turkey, China South-	Guinea, Cuba, Iran, Borneo, India
		Caledonia, Borneo,	Central, Iran
	END	Philippines, Brazil Southeast, Hawaiʻi
		Peru, China South-	Peru, India, Brazil	China South-Central, Fiji,
	5%	Central, Ecuador, Cuba, Chile Central,	Southeast, Ecuador, New Caledonia,	Peru, Juan Fernández Is., Brazil Southeast, New
		Colombia, Turkey,	Somalia, Sumatera,	Caledonia, Sulawesi,
		Queensland, Iran	Sulawesi, Fiji	Vietnam, Marquesas
	Extended Data

Table 4 |

 4 Hotspots for each facet. Botanical countries are ordered by decreasing rank (see Supplementary Table 3 for values and full ranking).

Acknowledgements.

We thank Hélène Citerne for her help with language editing.

Data availability

The data sources used to build the features used in this study, the taxonomic and distribution backbone, and the training dataset are detailed in the article; the megaphylogeny along with details of the results are provided in Supplementary Tables 1234and Supplementary File 1.

Code Availability

The R and Python codes used for calculations and analyses are available from the corresponding authors on request.

Author Contributions. TH, JT, JK and RP conceived the study and analyzed the data; SV and TH performed the expected PD loss and PD calculations; TH, JT, JK, RP, AD wrote a first draft of the paper; All authors acquired the data, interpreted the results of the analyses and contributed to writing the final manuscript.

Competing interests. The authors declare no competing interests.

Additional Information

Supplementary Information is available for this paper.

Correspondence and requests for materials should be addressed to TH and JT.