# Recruitment dynamics of Hiatella arctica within a high Arctic site (Young Sound Fjord, NE Greenland)

Delphine Veillard, Réjean Tremblay, Mikael Sejr, Laurent Chauvaud,
Emmanuelle Cam, Frédéric Olivier

► To cite this version:

Delphine Veillard, Réjean Tremblay, Mikael Sejr, Laurent Chauvaud, Emmanuelle Cam, et al. Re-cruitment dynamics of Hiatella arctica within a high Arctic site (Young Sound Fjord, NE Greenland). Polar Biology, 2023, 10.1007/s00300-023-03201-0 . hal-04259863

HAL Id: hal-04259863

https://hal.science/hal-04259863v1

Submitted on 5 Feb 2024

**HAL** is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# Recruitment dynamics of Hiatella arctica within a high Arctic site (Young Sound Fjord, NE Greenland)

**Delphine Veillard** (✉ delphine.veillard@uqar.ca)

Institut Universitaire Européen de la Mer, Unité Mixte de Recherche 'Laboratoire des sciences de l'environnement marin' (LEMAR, UMR 6539), Centre National de la Recherche Scientifique, Université de Bretagne Occidentale

**Réjean Tremblay**

Université du Québec à Rimouski

**Mikael K. Sejr**

Aarhus University

**Laurent Chauvaud**

Institut Universitaire Européen de la Mer, Unité Mixte de Recherche 'Laboratoire des sciences de l'environnement marin' (LEMAR, UMR 6539), Centre National de la Recherche Scientifique, Université de Bretagne Occidentale

**Emmanuelle Cam**

Institut Universitaire Européen de la Mer, Unité Mixte de Recherche 'Laboratoire des sciences de l'environnement marin' (LEMAR, UMR 6539), Centre National de la Recherche Scientifique, Université de Bretagne Occidentale

**Frédéric Olivier**

Laboratoire de 'Biologie des Organismes et Écosystèmes Aquatiques' (BOREA), Muséum national d'Histoire naturelle, Sorbonne Université, Centre National de la Recherche Scientifique

## Research Article

**Keywords:** Arctic, Bivalve recruitment, Prodissoconch II, lipids classes, freshwater inputs

**Posted Date:** May 8th, 2023

**DOI:** https://doi.org/10.21203/rs.3.rs-2887024/v1

**License:** © Ⓟ This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License

**Version of Record:** A version of this preprint was published at Polar Biology on October 4th, 2023. See the published version at https://doi.org/10.1007/s00300-023-03201-0.
**Abstract**

Recruitment dynamics including what determines success or failure of Arctic benthic invertebrates are poorly known despite their important role for population dynamics. The main objective of this study was to assess the potential influence of extreme seasonal/physical constraints, in particular freshwater discharge, on the recruitment of a dominant bivalve *Hiatella arctica* within a High Arctic fjord. We collected young recruits over several sampling periods from 2016 to 2018 at two contrasting sites (inner vs middle fjord) for 5-weeks to 12-months and measured their abundance, size at metamorphosis and lipid class composition.

Young stages of *H. arctica* settled from June to the end of October, when trophic conditions are optimal. We hypothesize that growth stops during winter due to poor trophic conditions. Data suggest that abundance of recruits, their total lipid concentration and composition of lipid classes are similar in both sites. By contrast, analyses of size-class show differing temporal and spatial patterns with 6 cohorts detected in middle fjord and only one at the inner station, which can be attributed to discrete spawning events and possible secondary migration. Based on an assessment of their potential age, we hypothesize that spat batches recruiting earlier in the summer exhibit better growth performance probably because of higher food quality and availability.

**Introduction**

The structure of communities and dynamics of populations are strongly influenced by the supply of recruits and their settlement and post-settlement success (Butman 1987; Ólafsson et al. 1994), but these processes are poorly known in polar regions. In the Arctic, bivalves spawn directly after the phytoplankton bloom (Kuklinski et al. 2013), but data on recruitment are limited to few species (Stanwell-Smith and Barnes 1997). According to Thorson (1950), high latitude marine invertebrates exhibit mostly lecithotrophic development (i.e., the larvae rely on energy reserves but not on food sources), due to low and unpredictably phytoplankton biomass. Nevertheless, in recent years, the discovery of more pelagic larvae in polar waters than expected questioned on the general validity of the Thorson model (Pearse 1994). It is becoming increasingly clear that mixed types of development coexist, even within a single species, and that the flexibility and plasticity of life strategies are substantial (Hadfield and Strathmann 1996).

Global warming induces major modifications in the Arctic marine environment, e.g., a decrease in sea-ice cover (extent and thickness) and an increase in freshwater discharge (Kwok and Rothrock 2009; McPhee et al. 2009) leading to the intensification of the stratification (Bridier et al. 2021). In Greenland, warming drives increasing melt of the Greenland ice sheet and results in increased discharge of ice and meltwater (Howat et al. 2007; Kjeldsen et al. 2015). Released icebergs increase the risk of ice scouring (Sejr et al. 2021) while meltwater is likely to impact fjord circulation, surface-water temperature, salinity, and turbidity (Mortensen et al. 2013); low salinity also increases the susceptibility of marine organisms to other stressors (Nielsen et al. 2021). These physical parameters are often suggested to be important
drivers of benthic recruitment in coastal areas (Yakovis et al. 2013), which can influence benthic productivity (Bashevkin et al. 2020; Leal et al. 2022). Increased temperature and turbidity or decreased salinity could lower survival rates of larvae as already documented in the White Sea (Ushakova and Saranchova 2003) or in the Kongsfjorden fjord (Svalbard archipelago, Zajaczkowski and Legezynska 2001).

Because of its high abundance, colonizing various hard-bottom and soft-sediment habitats, *Hiatella arctica* (up to 57 ind.m⁻², Sejr et al. 2002) is a promising marine bivalve model to study recruitment. This species has a long pelagic larval stage (Gordillo 2001). It is a widespread common circumpolar bivalve exhibiting substantial morphological/physiological plasticity and inhabiting shores up to 175 m depth (Ockelmann 1958) in temperate to polar areas (Gordillo 2001). The main objective of this study was to evaluate the seasonal recruitment success of *H. arctica* at two sites in a high Arctic fjord. A site in the inner fjord more influenced by low salinity and high turbidity due to freshwater run-off compared to a site in the outer fjord (Sejr et al. 2022). We expected that recruitment of *H. arctica* would be lower at the site near the Zackenberg River (inner fjord, Pass Hytten) compared to a more marine area (middle fjord, Basalt Island). The specific objectives were therefore 1) to measure the spatial (2 sites) and temporal variability of recruitment (a sampling scheme spanning 12 months) of a dominant marine polar bivalve, 2) to identify the presence of cohorts of recruits and assess their age, 3) to estimate the size at metamorphosis of each cohort, and finally 4) to obtain initial results on the physiological condition of young recruits by their lipid class composition.

# Materials and methods

## 1. Study site and sampling strategy

The study was conducted in the ~ 90 km long and 2–7 km wide deep-sill fjord of 330 m depth with an entrance of 45 m depth (Young Sound, 74°18′N, 20°18′W, NE Greenland, Fig. 1), reducing exchanges with the open sea (Bendtsen et al. 2007). This fjord system is influenced by freshwater inputs from snow and ice melting, especially during summer (Bendtsen et al. 2007), and is covered by sea ice from September to June. Whereas bottom water temperature and salinity at 60 m depth remain relatively steady around ~ 1.8°C and 32 psu respectively, they are much more variable during summer at shallower depths (~ 30 m) (Fig. 1, but also see De Cesare et al. 2017; Bridier et al. 2019; Sejr et al. 2022 for details). Our sampling strategy included several deployment periods of either 5 weeks (August-September 2016), 9 months (August 2016-May 2017) and 12 months (May 2017-May 2018) in two contrasting sites (Fig. 1). Basalt Island (BI) (depth = 21.5 m, 74.33°N, 20.36°W) is located in the most marine part of the fjord whereas Pass Hytten (PH) (depth = 16.5 m, 74.41°N, 20.33°W) is in a more upstream section at the outlet of a river on silted and more turbid bottoms (Bridier et al. 2019; Holding et al. 2019; Sejr et al. 2022). Both sites are influenced by an inflow of nutrient-depleted freshwater, with CO₂-desaturated, lower salinity and higher turbidity measurements (Fig. 1). Settling larvae and subsequent juveniles were collected with *'Tuffy'* traps, extensively used in recruitment studies of invertebrate species (Menge et al. 1994). In each site mooring
lines with subsurface buoys located at 5 m from the bottom were deployed by divers at a depth of 20 m. At each site, 10 replicate traps were collected. The biological material found on traps were directly frozen and preserved at -80°C until laboratory analyses.

## 2. Sample analyses

For each trap/replicate, recruits were retrieved by sieving it gently on a 200-micron square mesh with filtered seawater. Each batch of recruits was examined and counted under a binocular microscope to assess abundance per trap and subsequent settlement rate (see below). Morphometric analysis of ~ 30 randomly selected individuals per trap were performed using the methods described in Martel et al. (1995) under a binocular stereomicroscope. For the examination of Prodissoconch PII (PII), the longest distance was measured along the anteroposterior axis (Fig. 2). All measurements were made using Keyence VHX-2000 Series digital microscope with VH-Z100UR objectives (Osaka, Japan, 1µm and HDR resolution). Lipids were extracted in dichloromethane-methanol as in Parrish (1999) by using a modified Folch procedure (1957). Extracts were separated and analyzed by thin-layer chromatography using flame ionization detection with an latroscan MK-6 (Shell USA, Fredericksburg, VA, USA). This method separates aliphatic hydrocarbons (HCs), ketones (KETs), triglycerides (TAGs), free fatty acids (FFAs), free fatty alcohol (ALCs), free sterols (STs), diglycerides (DGs). acetone mobile polar lipids (AMPLs), and phospholipids (PL). Lipid classes were identified and quantified with the use of standard calibration curves obtained for each lipid class. Lipid classes were evaluated in mg.individual⁻¹, summed and expressed as a percentage of total lipids. Lipids were analyzed only on 9 months samples due to their higher biomass availability, as these analyses required at least 50µg of tissues.

## 3. Data analyses

In *Hiatella arctica*, little is known about larval growth in the Arctic, and we approximated value of post-larval growth by firstly quantifying the difference between total shell length and PII size. Then, to assess days since metamorphosis, we used a growth estimate of 7 µm/day from the study of Flyachinskaya and Lezin (2017) focusing on the larval development of *Mya arenaria* in the White Sea, an environment close to our study area. Differences in the abundances of recruits were investigated by performing permutational univariate analyses of variance (PERMANOVA, 9999 permutations) and matrix of similarity based on the Euclidean Distance using PRIMER 7 /PERMANOVA+. Two sources of variation were tested among treatments including 'Sites' (BI or PH) and 'Period' (2016: 5weeks; 2017: 9 months and 2018: 12 months). The number of replicates was determined by the number of traps used and varied between 9 and 11. To investigate cohort composition and age structure, we performed a length frequency analysis and estimated the proportion of individuals in each cohort. We used a Gaussian mixture model to account for the length distribution and identify the number of cohorts in each sample. The number of components of the finite mixture model was assessed using an information criterion. More specifically, we used the Singular Bayesian Information Criterion (sBIC) (Drton and Plummer 2017). This information criterion is robust in situations where models are irregular, which can be the case for Gaussian mixture
models. This criterion allows determining the model (i.e., the number of components) that best accounts for the data-generating process. The R (R Core Team 2013) package 'sBIC' (Weihs and Plummer 2016) uses the EM algorithm to approximate maximum likelihood estimates of model parameters, estimate the posterior probabilities of cluster membership for each data point, and model posterior probabilities. We considered a maximum of 10 components and the sBIC allowed ranking models including a different number of components and selecting the model that best accounts for the process that generated the data. Once the number of components has been determined, the R package 'mclust' (Scrucca et al. 2016) was used to produce a density estimate for each data point and estimate the mean and standard deviation of each Gaussian component of the mixture model. The analysis associated to one year's data was performed on either 171 and 255 length measurements corresponding to PH or BI respectively. The 5-week period data included 14 (PH) and 80 (BI) length measurements. Lipid classes of young recruits were analyzed on the 9-month period data via 1-way PERMANOVAs (9999 permutations) with 2 fixed levels (BI and PH) and 10 replicates. Non-parametric Wilcoxon test was used to compare mean PII sizes and means of Gaussian components in the size-class distribution. For all statistical tests, α level was set to 0.05.

# Results

## 1. Abundance pattern

*Hiatella arctica* was the dominant species recruiting on traps, its relative abundance exceeding 92% for long sampling periods in both stations. For the 9- and 12-month periods 909 and 937 recruits or 2909 and 3286 recruits were collected in PH and BI, respectively. By contrast, the proportion of *H. arctica* collected over 5 weeks during the fall season was quite low, with 9 and 28% of the total abundance of recruits at PH and BI, respectively (339 and 863 individuals). However, this is mostly due to the high proportion of unidentified post-larvae during this period (60% at BI, 75% at PH). Abundances of *H. arctica* were similar between both stations (P-perm = 0.1983, Pseudo-F = 134.99, Df = 2 and Df residuals = 62), but differences appeared between sampling periods (P-perm = 0.0001, Pseudo-F = 1.7474, Df = 1 and Df residuals = 62), without interaction between 'Site' and 'Period' (P-perm = 0.6093, Pseudo-F = 0.5152, Df = 2 and Df residuals = 62). The increase in the number of recruits as the duration of the sampling period increases reveals a cumulative recruitment starting with fewer than 19 ± 3.0 recruits (± SE) collected during 5-week periods in PH and BI, and mean abundance per trap increasing up to > 83 ± 7.4 during the 9-month period, and to > 302 ± 32.5 individuals for 12-month periods at both sites (Fig. 3).

## 2. Size-class distribution and growth

For the 12-month sampling period, shell length of *H. arctica* recruits collected in PH varied from 485.7 to 976.0 µm (mean value of 721.3 ± 8.1 µm; ± SE, n = 171) and from 495.6 to 971.7 µm (mean value of 687.2 ± 6.3 µm; ± SE, n = 255) in BI (Fig. 4) (Table 1). In BI, five independent cohorts were detected (posterior model probability = 0.79, log(likelihood) = -1514.62, Df = 14, Supplementary File 1) (Fig. 4)
whose mean shell length was equal to 523.1, 607.4, 678.6, 765.3 and 884.4 µm (Table 1), contrasting
with one sole cohort in PH (posterior model probability = 0.61, log(likelihood) = -1038.79, Df = 2,
Supplementary File 1) (Fig. 4). Based on 5-week sampling period, size classes of recruits varied from
311.6 to 617.4 µm (mean value of 464.9 ± 6.4 µm; ± SE, n = 80) in BI and from 347.8 to 949.2 µm (mean
value of 502.6 ± 39.4 µm; ± SE, n = 14) in PH (Table 1, Fig. 4). Whereas no cohorts could be detected in PH
(log(likelihood) = -89.25, Df = 2), six were observed in BI (posterior model probability = 0.33, log(likelihood)
= -425.94, Df = 17, Supplementary File 1) (Fig. 4) with corresponding mean lengths of 326.7, 409.5, 433.6,
468.4, 528.6 and 591.1 µm (Table 1). In fact, size-class distributions were similar for 5-week and 12-
month data in BI, but with a larger size range in the 12-month sample (Fig. 4). Moreover, at BI the mean
PII size of each cohort was very close for the same sampling duration, but not for different durations, with
a mean value of 314.0 µm over 5 weeks and 290.7 µm over 12 months (F-value = 39.23, P-value =
0.000147, Df = 1, Df residuals = 9, Anova) (Table 1). Assuming a mean post-larval growth of 7 µm per day,
approximated ages of *H. arctica* recruits in BI ranged from ~ 33 to 85 days in the 12-month period, and
from ~ 0 to 40 days in the 5-week sampling period, respectively (Table 1).
<table><caption>Table 1
Size distributions for post-larvae of *H. arctica* observed in samples collected during different sampling periods on both sampling sites.</caption><thead><tr><th>Site</th><th>Group</th><th>n</th><th>Mean Full length</th><th>SE</th><th>Mean PII Size</th><th>SE</th><th>Δ (Full length-PII size)</th><th>Approximate age (days)</th></tr></thead><tbody><tr><td rowspan="5">Basalt Island 12 months</td><td>1 [495; 555]</td><td>19</td><td>523.1</td><td>3.4</td><td>287.3</td><td>3.2</td><td>235.7</td><td>~ 33.7</td></tr><tr><td>2 [555; 648]</td><td>78</td><td>607.4</td><td>2.8</td><td>291.2</td><td>1.4</td><td>316.2</td><td>~ 45.2</td></tr><tr><td>3 [648; 712]</td><td>69</td><td>678.6</td><td>2.0</td><td>291.9</td><td>1.9</td><td>386.7</td><td>~ 55.2</td></tr><tr><td>4 [712; 834]</td><td>64</td><td>765.3</td><td>3.8</td><td>291.8</td><td>2.9</td><td>473.5</td><td>~ 67.6</td></tr><tr><td>5 [834; 972]</td><td>25</td><td>884.4</td><td>6.3</td><td>291.3</td><td>4.4</td><td>593.1</td><td>~ 84.7</td></tr><tr><td rowspan="2">Pass Hytten 12 months</td><td>1 [485; 980]</td><td>171</td><td>721.3</td><td>8.1</td><td>282.6</td><td>1.4</td><td>438.7</td><td>~ 62.7</td></tr><tr><td>Basalt Island 5 weeks</td><td>1 [310; 385]</td><td>2</td><td>326.7</td><td>15.1</td><td>326.4</td><td>10.3</td><td>0.3</td><td>~ 0</td></tr><tr><td rowspan="6">Basalt Island 5 weeks</td><td>2 [385; 423]</td><td>19</td><td>409.5</td><td>2.4</td><td>301.4</td><td>2.7</td><td>108.0</td><td>~ 15.4</td></tr><tr><td>3 [423; 445]</td><td>13</td><td>433.6</td><td>1.6</td><td>311.4</td><td>3.3</td><td>122.1</td><td>~ 17.4</td></tr><tr><td>4 [445; 500]</td><td>24</td><td>468.4</td><td>14.0</td><td>314.8</td><td>2.8</td><td>153.6</td><td>~ 21.9</td></tr><tr><td>5 [500; 560]</td><td>18</td><td>528.6</td><td>3.4</td><td>313.8</td><td>2.7</td><td>214.8</td><td>~ 30.7</td></tr><tr><td>6 [560; 618]</td><td>4</td><td>591.1</td><td>10.6</td><td>316.1</td><td>1.3</td><td>275.0</td><td>~ 39.3</td></tr><tr><td>Pass Hytten 5 weeks</td><td>1 [347; 950]</td><td>14</td><td>502.6</td><td>39.4</td><td>306.1</td><td>6.5</td><td>196.5</td><td>~ 28.1</td></tr></tbody></table>

3. Lipid classes
The total concentration of lipids in recruits collected over the 9-month sampling period was similar in both sites (Pseudo-F = 0.0209, P-perm = 0.8755, Df = 1, Df residuals = 18), with a mean value of 0.61 ± 0.15 mg/individual. Seven lipid classes were detected with highest contributions of Phospholipid (PL), representing near 90% of the total lipids, and low values of 1% for Triglycerides (TAG) (Supplementary File 1).

Discussion

Spatial bivalve recruitment patterns in the Young Sound
fjord

*H. arctica* was the dominant species recruiting on the spat traps throughout the surveys at both sampling sites (PH, BI). No difference was observed between sites for the abundance of *Hiatella* recruits with an increasing number of recruits found in samples collected over 5 weeks, 9 months and 12 months, respectively. No spatial differences were observed in 9-month samples for the total lipid concentration and the lipid classes composition. Such results are quite surprising because PH and BI sites are submitted to contrasting freshwater inputs (Bridier et al. 2019, 2021) that could have influenced survival and growth of the young bivalve recruits (Bashevkin et al. 2020). In the White Sea, Saranchova et al. (2006) showed that pediveliger larvae of *H. arctica* display a higher resistance to low salinity than those of *Heteranomia ovata*. The larvae stage *H. arctica* can endure reduced salinity for 2 weeks, with survival rates of 25% at salinities at 12‰ (Saranchova et al. 2006). Such data could explain the dominance of *H. arctica* in the assemblages of recruits observed in spat traps especially if, during their pelagic dispersal, bivalve larvae enter the surface layers of the fjord that exhibit the most variable salinity, temperature, and turbidity during summer (Bendtsen et al. 2007).

The size-class distributions displayed both spatial and temporal differences. At the BI site, we detected 6 cohorts but only one at PH. The single cohort at PH could result from too much overlap of cohort-specific size distributions (i.e., failure to identify cohorts), although a similar size range was observed at both sites 500 to 1000µm and we did find evidence of cohorts in BI. In addition, we hypothesize that the shift between the size ranges of the two sampling periods from 300 to 650µm over 5 weeks and from 500 to 1000µm over 12 months reveals secondary settlement stimulated by trophic conditions, as observed by Forêt et al. (2018, 2020). In YS, recruits would drift from the middle to the inner fjord in response to better food conditions or to density dependence interactions allowing an allochthonous recruitment in PH. As pointed out by Forêt et al. (2018, 2020) secondary dispersal temporal patterns depend on both the physiology of bivalve recruits and the pelagic trophic environment, a phenomenon called "trophic migration trigger" (TMT), analogous to "trophic settlement trigger" (TST) (Toupoint et al. 2012; Androuin et al. 2022).

Trophic constraints on larvae of filter-feeding bivalves
To survive within Arctic Fjords, planktotrophic larvae, feeding on phytoplankton, must be able to respond to short periods of high food availability and prolonged periods of low resources during the polar night (Weslawski et al. 1991). In the present study, we determined that the concentration of total lipids found in *H. arctica* recruits in YS collected in both sites seem to be low with values around 0.6 mg.individual⁻¹ and 1% of TAG at the end of the winter 2017. Gallager et al. (1986) studied the lipid class composition of healthy and starved larvae of *Crassostrea virginica* and *Mercenaria mercenaria* and showed that energetic (TAG) and structural (PL) lipids were roughly in equal proportion throughout development of healthy larvae. TAG content is an indicator of larval quality and is directly affected by exogenous food intake and also influenced by environmental stress increasing metabolic activity and reducing food intake (Fraser 1989). In our study, no data on larvae are available, but the very weak TAG accumulation in the young juveniles sampled seems to indicate starvation in May and potentially poor physiological condition at the end of winter. Another explanation could be the complete use of energy obtained from food to direct transfers to growth without energetic reserve accumulation. However, in optimal laboratory condition, bivalve post-larvae fed ad libitum without environmental stressful conditions showed important TAG accumulation concomitant with substantial shell growth (Gagné et al. 2010).

## Temporal pattern of bivalve recruitment within the YS fjord

The mean PII sizes of *H. arctica* recruits in the YS fjord of 290µm (12-month period) and 314µm (5-week period) are clearly larger than previous values of 200µm observed for the same species in the White Sea by Lyachinskaya and Lezin (2008). Such values of 290–314µm can be compared to PII sizes of post-larvae and juveniles of other bivalves of subarctic areas, which can reach up to 422µm for the blue mussel *Mytilus edulis* (South 2016; Martel et al. 2014). Food supply during the planktonic phase, as well as seawater temperature, are among the most important factors determining the size of veliger larvae at metamorphosis, that is, at the end of the planktonic life (Pechenik et al. 1990; Emlet and Sadro 2006). As hypothesized by Pechenik and Levine (2007) and Martel et al. (2014), a short larval phase associated to small PII size at settlement could decrease larval mortality within the water column due to reduced exposure to predation or dispersal to unsuitable habitats. It is also suggested that a longer larval phase associated to large PII size at settlement could decrease the probability of early mortality of juveniles because of a larger size and a larger pump for more filter-feeding activity (Pechenik 1996). However, data obtained with oysters *Crassostrea gigas* in a Mediterranean lagoon were not consistent with this hypothesis: an inverse relationship between PII size and survival after metamorphosis was observed, showing that recruitment success was associated with smaller PII sizes (Lagarde et al. 2018). The relatively small and uniform size at metamorphosis (PII size < 310µm) observed in this study is probably related to a trade-off between growth and the necessity for pediveliger larvae to undergo metamorphosis rapidly and access to the more rapid post-larvae growth thanks to the development of gills (Gagné et al. 2010). Because of low seawater temperatures throughout the year, metabolism and growth of marine invertebrates are classically slowed down in the polar environment (Clarke 1992). However, Sejr et al. (2004), working on the resource limitations to growth and production of YS *H. arctica* populations, suggested that despite low rates of assimilation and growth at low temperature, *H. arctica* adults were
able to grow much faster in laboratory experiments than observed in YS when provided with food. Thus,
low food availability seems to be the major factor of growth regulation.

In this study, data collection was constrained by difficulties accessing the marine station of the YS fjord. The sampling scheme covers various recruitment periods over several years from 2016 to 2018. While the 5-week period provides a state in late summer / early fall 2016, the 9-month period extends to the end of winter 2016–2017, and the 12-month period includes the summer 2017 and lasts up to the end of winter 2017–2018. The size-class distributions of recruits collected during one year at both sampling sites reveal the occurrence of several successive cohorts with small PII size differences, which suggests several spawning events in *H. arctica*, and a similar size at metamorphosis. As emphasized by Martel et al. (2014), in *Mytilus edulis* recruits mean PII size can be highly variable, which reflects metamorphosis delays later in the recruitment season. Here, with relatively small and constant PII size (< 310µm), we suggest that there is no metamorphosis delay, and that primary settlement is probably controlled by a similar triggering cue. Many studies have focused on such settlement cues, including trophic signals linked to phytoplankton blooms (*Trophic Settlement Trigger* TST, Toupoint et al. 2012; Lagarde et al. 2018; Leal et al. 2018), to water temperature (mussels: Bayne 1964, clams: Lutz and Jablonski 1978; Bayne and Newell 1983) or to chemical cues (Hadfield and Paul 2001). The synchronization between the larval cycle and primary production is related to the 'match/mismatch' theory (Cushing 1990). Thus, an ice breakout earlier in the season could lead to an early phytoplankton bloom, which in turn could lead to a temporal desynchronization between this peak of primary production and that of pelagic secondary consumers such as zooplankton, including meroplankton (Søreide et al. 2010; Leu et al. 2011).

Spawning and larval presence of *H. arctica* in Arctic waters

Based on previous larval growth studies on *H. arctica* (Flyachinskaya and Lezin 2017) and on the size of
the five different cohorts identified at BI (12-months), we estimated that the age of oldest recruits (i.e.,
those that have settled the earliest), could reach 85 days or more. Such an age would indicate the
beginning of spawning in February 2018, a period with ice cover, without light or primary production.
Moreover, a study of the occurrence and abundance of pelagic bivalve larvae within a high Arctic fjord
(Adventfjorden, Svalbard) identified a strong seasonality in the occurrence of bivalve larvae, largely
coinciding with periods of primary productivity (Brandner et al. 2017). The seasonal occurrence of bivalve
larvae shows variation in duration across the biogeographic range of *H. arctica*, with longer duration at
higher latitudes (8 months at 56°N and 78°N) and shorter duration at lower latitude populations (1-2
months at 42°-46°N) (Brandner et al. 2017). Such results contradict those of Ockelmann (1958) who
observed only 2 months of *H. arctica* larval presence at a latitude of 78°N. According to Kulikova et al.
(2013) larvae of *H. arctica* drift in the water column between May and December at all latitudes whereas
the period of presence shifts to Autumn (September-October) at lower latitudes. The spawning season of
*H. arctica* has been determined in the White Sea, lasting from June to November in a study conducted at
a lower latitude (Flyachinskaya and Lezin 2006, 2008). For the data of 5-weeks at BI, we estimated that
the age of oldest recruits (i.e., those that have settled the earliest) could reach 39 days or more. So, it is
possible that the spawning of *H. arctica* in YS starts in June and lasts up to the end of October because
timing of blooms is related to the break-up of ice cover around mid-July (Rysgaard et al. 1999) for a
period of around 80 days.

## Perspectives

Climate change with declining sea ice cover and accelerated melting of glacial ice should increase
freshwater input and turbidity in the water column (Sejr et al. 2022). Whereas the high phenotypic
plasticity of *Hiatella arctica* to freshwater input is well detailed (Saranchova et al. 2006), the primary
production in Young Sound should be more structuring on such a species. Indeed, as sea ice cover (extent
and thickness) will decline under warming, as will the production of sympagic algae, the dynamics of
stratification and light conditions will occur (Søreide et al. 2013). However, the magnitude and direction of
these effects on the various fjord ecosystems around Greenland are still largely unclear. The
desynchronization between an earlier phytoplankton bloom during the season due to advanced ice retreat
and peak of zooplankton (Søreide et al. 2010; Leu et al. 2011; Gaillard 2017) could be important for the
recruitment success of *H. arctica* (growth, size at settlement, physiological state of larvae and post-
larvae). Starvation due to poor trophic conditions, depleting the nutritional reserves, could lead larvae of
*H. arctica* to settle with low energetic contents, which would be consistent with the '*Desperate larvae
Hypothesis*' (Elkin and Marshall 2007). Under climate change, modifications in the composition or
dynamics of the phytoplankton community could lead to asynchronies between different trophic levels
such as an increase in the proportion of dinoflagellates upon diatoms (Hernández-Fariñas et al. 2014).
The trophic regimes and their dynamics that control the functioning of benthic marine invertebrate
communities, including their dominant component such as *H. arctica* in the Arctic, would be deeply
modified. As an example, phytoplankton communities should be dominated by smaller cells such as
picophytoplankton (Holding et al. 2019) that are known to synchronize the primary settlement of mussel
larvae (Toupoint et al. 2012; Androuin et al. 2022), and thus potential risks of mismatches. Because *H.
arctica* is one of the preferred preys of walrus and eider ducks, a reduction in population sizes as well as
energy reserves of adults could impact the benthic-pelagic coupling in polar areas, with cascading effects
on higher trophic levels (Kędra et al. 2015zéquel et al. 2022).

## Declarations

### Acknowledgments

A big thanks to E. Randa Frandsen, E. Amice, J. Grall and J.-M. Warnet for their contributions in the field work. We are greatly indebted to the MarineBasis progamme and the Zackenberg/Daneborg staff for their support in the field. Thanks to J.-B. Nadalini for his help on laboratory analyses. Friendly thanks to J. Jourde, M. Le Duff, V. Le Garrec, G. Droual, C. Uhlir, A.-H. Tandberg, and N. Shunatova for their teaching on species identification. This work was a Master 2 project of first author funded by the Interdisciplinary School for the Blue Planet (ISblue) from the Brittany Regional Council and the Université de Bretagne Occidentale/LIA BeBEST. This research was supported by the Institut Polaire Français Paul-Emile Victor (IPEV - PRIVARC project), the European H2020 INTAROS program, the Observatoire Marin de l'IUEM
(UMS3113) and by Natural Sciences and Engineering Research Council of Canada (NSERC-Discovery Grant no. 299100). Part of the data included in this manuscript was provided by the MarineBasis programme. This study is an international collaboration between the BeBEST International Laboratory (UBO, UQAR, CNRS, and MNHN) and the Aarhus University.

## Author contributions

DV acquired shell length and fatty acid data, performed statistical analyses, and wrote the initial MS draft. RT supervised fatty acid and shell length analyses, contributed to results interpretation and manuscript review. MKS helped in field operations and collecting biological and environmental data, contributed to results interpretation and manuscript review. LC helped in field operations (scuba-diving) and collecting biological and environmental data, contributed to results interpretation and manuscript review. EC supervised modal decomposition analysis of shell length data, contributed to results interpretation and manuscript review. FO conceived and supervised the sampling survey, led the project administration and funding acquisition, contributed to results interpretation and manuscript review.

## Conflict of interest

The authors declare that they have no conflict of interest.

## Informed consent

All authors consent to the publication of this manuscript.

## References

1. Androuin T, Barbier P, Forêt M, Meziane T, Thomas M, Archambault P, Winkler G, Tremblay R, Olivier F (2021) Pull the trigger: interplay between benthic and pelagic cues driving the early recruitment of natural bivalve assemblages. Ecosphere 13:e03672. https://doi.org/10.1002/ecs2.3672

2. Bashevkin SM, Dibble CD, Dunn RP, Hollarsmith JA, Ng G, Satterthwaite EV, Morgan SG (2020) Larval dispersal in a changing ocean with an emphasis on upwelling regions. Ecosphere 11:e03015. https://doi.org/10.1002/ecs2.3015

3. Bayne BL (1964) Primary and secondary settlement in *Mytilus edulis* L. (Mollusca). J Anim Ecol 33(3)513-523. https://doi.org/10.2307/2569

4. Bayne BL, Newell RC (1983) Physiological energetics of marine molluscs. Mollusca 4:407-515. https://doi.org/10.1016/B978-0-12-751404-8.50017-7

5. Bendtsen J, Gustafsson KE, Rysgaard S, Vang T (2007) Physical conditions, dynamics and model simulations during the ice-free period of the Young Sound/Tyrolerfjord system. Bioscience 58:46-59.

6. Brandner MM, Stübner E, Reed AJ, Gabrielsen TM, Thatje S (2017) Seasonality of bivalve larvae within a high Arctic fjord. Polar Biol 40(2):263-276. https://doi.org/10.1007/s00300-016-1950-x
7. Bridier G, Meziane T, Grall J, Chauvaud L, Sejr MK, Menneteau S, Olivier F (2019) Coastal waters freshening and extreme seasonality affect organic matter sources, quality, and transfers in a High Arctic fjord (Young Sound, Greenland). Mar Ecol Prog Ser 610:15-31.
https://doi.org/10.3354/meps12857

8. Bridier G, Olivier F, Chauvaud L, Sejr MK, Grall J (2021) Food source diversity, trophic plasticity, and omnivory enhance the stability of a shallow benthic food web from a high-Arctic fjord exposed to freshwater inputs. Limnol Oceanogr 66:S259-S272. https://doi.org/10.3354/meps12857

9. Butman CA (1987) Larval settlement of soft-sediment invertebrates: the spatial scales of pattern explained by active habitat selection and the emerging role of hydrodynamical processes. Oceanogr. Mar. Biol 25:113-165.

10. Clarke A (1992) Reproduction in the cold: Thorson revisited. Invertebr Reprod Dev 22:175-183.
https://doi.org/10.1080/07924259.1992.9672270

11. Cushing DH (1990) Plankton production and year-class strength in fish populations: an update of the match/mismatch hypothesis. Adv Mar Biol 26:249-293. https://doi.org/10.1016/S0065-2881(08)60202-3

12. De Cesare S, Meziane T, Chauvaud L, Richard J, Sejr MK, Thébault J et al (2017) Dietary plasticity in the bivalve *Astarte moerchi* revealed by a multimarker study in two Arctic fjords. Mar Ecol Prog Ser 567:157-172. https://doi.org/10.3354/meps12035

13. Drton M, Plummer M (2013) A Bayesian information criterion for singular models. arXiv preprint arXiv:1309.0911.

14. Elkin C, Marshall DJ (2007) Desperate larvae: influence of deferred costs and habitat requirements on habitat selection. Mar Ecol Prog Ser 335:143-153. doi:10.3354/meps335143

15. Emlet RB, Sadro SS (2006) Linking stages of life history: how larval quality translates into juvenile performance for an intertidal barnacle (*Balanus glandula*). Integr Comp Biol 46:334-346.
https://doi.org/10.1093/icb/icj023

16. Flyachinskaya LP, Lezin PA (2008) Larval and juvenile shell development in the White Sea bivalve *Hiatella arctica* (Linnaeus, 1767). Zoologia Bespozvon 5:39-46.

17. Flyachinskaya LP, Lezin PA (2017) Embryonic and larval development of the soft-shell clam *Mya arenaria* L., 1758 (Heterodonta: Myidae) in the White Sea. Invertebr Zool 14:14-20. doi: 10.15298/invertzool.14.1.03

18. Folch J, Lees M, Stanley GS (1957) A simple method for the isolation and purification of total lipids from animal tissues. J Biol Chem 226:497-509.

19. Forêt M, Barbier P, Tremblay R, Meziane T, Neumeier U, Duvieilbourg E, Olivier F (2018) Trophic cues promote secondary migrations of bivalve recruits in a highly dynamic temperate intertidal system. Ecosphere 9-12:e02510. https://doi.org/10.1002/ecs2.2510

20. Forêt M, Olivier F, Miner P, Winkler G, Nadalini JB, Tremblay R (2020) Influence of the physiological condition of bivalve recruits on their post-settlement dispersal potential. Mar Ecol Prog Ser 636:77-89. https://doi.org/10.3354/meps13223
21. Fraser AJ (1989) Triacylglycerol content as a condition index for fish, bivalve, and crustacean larvae. Canadian J Fish Aquat Sci 46(11):1868-1873. https://doi.org/10.1139/f89-235

22. Gaillard B, Meziane T, Tremblay R, Archambault P, Blicher ME, Chauvaud L et al (2017) Food resources of the bivalve *Astarte elliptica* in a sub-Arctic fjord: a multi-biomarker approach. Mar Ecol Prog Ser 567:139-156. https://doi.org/10.3354/meps12036

23. Gagné R, Tremblay R, Pernet F, Miner P, Samain JF, Olivier F (2010) Lipid requirements of the scallop *Pecten maximus* (L.) during larval and post-larval development in relation to addition of *Rhodomonas salina* in diet. Aquaculture, 309(1-4):212-221.
https://doi.org/10.1016/j.aquaculture.2010.09.040

24. Gallager SM, Mann R, Sasaki GC (1986) Lipid as an index of growth and viability in three species of bivalve larvae. Aquaculture 56(2):81-103. https://doi.org/10.1016/0044-8486(86)90020-7

25. Gordillo S (2001) Puzzling distribution of the fossil and living genus *Hiatella* (Bivalvia). Palaeogeogr Palaeoclimatol Palaeoecol 165(3-4):231-249. https://doi.org/10.1016/S0031-0182(00)00162-0

26. Hadfield MG, Paul VJ (2001) Natural chemical cues for settlement and metamorphosis of marine invertebrate larvae. Mar Chem 13:431-461.

27. Hadfield MG, Strathmann MF (1996) Variability, flexibility and plasticity in life histories of marine invertebrates. Oceanol acta 19(3-4):323-334.

28. Hernández-Fariñas T, Soudant D, Barillé L, Belin C, Lefebvre A, Bacher C (2014) Temporal changes in the phytoplankton community along the French coast of the eastern English Channel and the southern Bight of the North Sea. J Mar Sci 71(4):821-833. https://doi.org/10.1093/icesjms/fst192

29. Holding JM, Markager S, Juul-Pedersen T, Paulsen ML, Møller EF, Meire L et al (2019) Seasonal and spatial patterns of primary production in a high-latitude fjord affected by Greenland Ice Sheet run-off. *Biogeosciences* 16(19). https://doi.org/10.5194/bg-16-3777-2019

30. Howat IM, Joughin I, Scambos TA (2007) Rapid changes in ice discharge from Greenland outlet glaciers. Sci 315(5818):1559-1561. DOI: 10.1126/science.1138478

31. Jézéquel Y., Mathias D., Olivier F., Amice E., Chauvaud S., Jolivet A., Bonnel J., Sejr M. K., Chauvaud L. 2022. Passive acoustics suggest two different feeding mechanisms in the Atlantic walrus (*Odobenus rosmarus rosmarus*). Polar Biol 45:1157-1162. https://doi.org/10.1007/s00300-022-03055-y

32. Kędra M, Moritz C, Choy ES, David C, Degen R, Duerksen S et al (2015) Status and trends in the structure of Arctic benthic food webs. Polar Res 34(1):23775.
https://doi.org/10.3402/polar.v34.23775

33. Kjeldsen KK, Korsgaard NJ, Bjørk AA, Khan SA, Box JE, Funder S et al (2015) Spatial and temporal distribution of mass loss from the Greenland Ice Sheet since AD 1900. Nature 528(7582):396-400.
https://doi.org/10.1038/nature16183

34. Kuklinski P, Berge J, McFadden L, Dmoch K, Zajaczkowski M, Nygård H et al (2013) Seasonality of occurrence and recruitment of Arctic marine benthic invertebrate larvae in relation to environmental variables. Polar Biol 36(4):549-560. https://doi.org/10.1007/s00300-012-1283-3
35. Kulikova VA, Kolotukhina NK, Omelyanenko VA (2013) Pelagic larvae of the bivalves of Ussuriysky Bay, Sea of Japan. Russ J Mar Biol 39(6):440-446. https://doi.org/10.1134/S1063074013060059

36. Kwok R, Rothrock DA (2009) Decline in Arctic sea ice thickness from submarine and ICES at records: 1958–2008. Geophys Res Lett 36:15. https://doi.org/10.1029/2009GL039035

37. Ushakova OO, Saranchova OL (2003) Changes in resistance to salinity and temperature in some species of polychaetes from the White Sea in early ontogenesis. Russ J Mar Biol 29(4):236-241. https://doi.org/10.1023/A:1025432809672

38. Lagarde F, Richard M, Bec B, Roques B, Mortreux S, Bernard I, Chiantella C, Messiaen G, Nadalini JB, Hori M, Hamaguchi M, Pouvreau S, Roque d'Orbcastel E, Tremblay R (2018) Trophic environments influence size at metamorphosis and recruitment performance of Pacific oysters. Mar Ecol Prog Ser 602:135-153. https://doi.org/10.3354/meps12666

39. Leal I, Bouchard É, Flores AAV, Tremblay R (2018) Trophic cues as possible triggers of mussel larval settlement in southeastern Brazil. Aquat Living Resour 31:26. https://doi.org/10.1051/alr/2018013

40. Leal I, Tremblay R, Flores, AAV (2022) Allochthonous subsidies drive early recruitment of a subtropical foundation species. Oikos :e08991. https://doi.org/10.1111/oik.08991

41. Leu E, Søreide JE, Hessen DO, Falk-Petersen S, Berge J (2011) Consequences of changing sea-ice cover for primary and secondary producers in the European Arctic shelf seas: timing, quantity, and quality. Prog Oceanogr 90(1-4):18-32. https://doi.org/10.1016/j.pocean.2011.02.004

42. Lutz RA, Jablonski D (1978) Larval bivalve shell morphometry: a new paleoclimatic tool? Science 202(4363):51-53. DOI: 10.1126/science.202.4363.51

43. Martel A, Hynes TM, Buckland-Nicks J (1995) Prodissoconch morphology, planktonic shell growth, and size at metamorphosis in *Dreissena polymorpha*. Can J Zool 73(10):1835-1844.
https://doi.org/10.1139/z95-216

44. Martel AL, Tremblay R, Toupoint N, Olivier F, Myrand B (2014) Veliger size at metamorphosis and temporal variability in prodissoconch II morphometry in the blue mussel (*Mytilus edulis*): Potential impact on recruitment. J Shellfish Res 33(2):443-456. https://doi.org/10.2983/035.033.0213

45. McPhee MG, Proshutinsky A, Morison JH, Steele M, Alkire MB (2009) Rapid change in freshwater content of the Arctic Ocean. Geophys Res Lett 36(10). https://doi.org/10.1029/2009GL037525

46. Menge BA, Berlow EL, Blanchette CA, Navarrete SA, Yamada SB (1994) The keystone species concept: variation in interaction strength in a rocky intertidal habitat. Ecol Monogr 64(3):249-286.
https://doi.org/10.2307/2937163

47. Mortensen J, Bendtsen J, Motyka RJ, Lennert K, Truffer M, Fahnestock M, Rysgaard S (2013) On the seasonal freshwater stratification in the proximity of fast-flowing tidewater outlet glaciers in a sub-Arctic sill fjord. J Geophys Res Oceans 118(3):1382-1395. https://doi.org/10.1002/jgrc.20134

48. Nielsen MB, Vogensen TK, Thyrring J, Sørensen JG, Sejr MK (2021) Freshening increases the susceptibility to heat stress in intertidal mussels (Mytilus edulis) from the Arctic. J Anim Ecol 90(6), 1515-1524. https://doi.org/10.1111/1365-2656.13472
49. Ockelmann WK (1958) The zoology of east Greenland: marine Lamellibranchiata (Vol. 18). Bianco Lunos Bogtrykkeri A/S.

50. Ólafsson EB, Peterson CH, Ambrose WG (1994) Does recruitment limitation structure populations and communities of macro-invertebrates in marine soft sediments: the relative significance of pre-and post-settlement processes. Oceanogr Mar Biol.

51. Parrish NM, Kuhajda FP, Heine HS, Bishai WR, Dick JD (1999) Antimycobacterial activity of cerulenin and its effects on lipid biosynthesis. J Antimicrob Chemother 43(2):219-226.
https://doi.org/10.1093/jac/43.2.219

52. Pearse JS, McClintock JB, Bosch I (1991) Reproduction of Antarctic benthic marine invertebrates: tempos, modes, and timing. Am Zool 31(1):65-80. https://doi.org/10.1093/icb/31.1.65

53. Pearse JS (1994) Cold-water echinoderms break 'Thorson's Rule'. Reproduction, larval biology and recruitment in the deep-sea benthos, 27-43.

54. Pechenik JA (1990) Delayed metamorphosis by larvae of benthic marine invertebrates: does it occur? Is there a price to pay? *Ophelia* 32(1-2):63-94.
https://doi.org/10.1080/00785236.1990.10422025

55. Pechenik JA, Estrella MS, Hammer K (1996) Food limitation stimulates metamorphosis of competent larvae and alters postmetamorphic growth rate in the marine prosobranch gastropod *Crepidula fornicata*. Mar Biol 127(2):267-275. https://doi.org/10.1007/BF00942112

56. Pechenik JA, Levine SH (2007) Estimates of planktonic larval mortality using the marine gastropods *Crepidula fornicata* and *C. plana*. Mar Ecol Prog Ser 344:107-118.
https://doi.org/10.3354/meps06887

57. R Core Team (2013) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/.

58. Rysgaard S, Nielsen TG, Hansen BW (1999) Seasonal variation in nutrients, pelagic primary production and grazing in a high-Arctic coastal marine ecosystem, Young Sound, Northeast Greenland. Mar Ecol Prog Ser 179:13-25. doi:10.3354/meps179013

59. Saranchova OL, Ushakova OO, Belyaeva DV (2006) Resistance of larvae of common species of White Sea invertebrates to extreme changes in salinity. Russ J Mar Biol 32(6):369-374.
https://doi.org/10.1134/S106307400606006X

60. Scrucca L, Fop M, Murphy TB, Raftery AE (2016) mclust 5: clustering, classification and density estimation using Gaussian finite mixture models, R J 8:205-233.

61. Sejr MK, Sand MK, Jensen KT, Petersen JK, Christensen PB, Rysgaard S (2002) Growth and production of *Hiatella arctica* (Bivalvia) in a high-Arctic fjord (Young Sound, Northeast Greenland). Mar Ecol Prog Ser 244:163-169. doi:10.3354/meps244163

62. Sejr MK, Petersen JK, Jensen KT, Rysgaard S (2004) Effects of food concentration on clearance rate and energy budget of the Arctic bivalve *Hiatella arctica* (L) at subzero temperature. J Exp Mar Biol Ecol 311(1):171-183. https://doi.org/10.1016/j.jembe.2004.05.005
63. Sejr MK, Mouritsen KN, Krause-Jensen D, Olesen B, Blicher ME, Thyrring J (2021) Small scale factors modify impacts of temperature, ice scour and waves and drive rocky intertidal community structure in a Greenland fjord. Front Mar Sci. doi: 10.3389/fmars.2020.607135

64. Sejr MK, Bruhn A, Dalsgaard T, Juul-Pedersen T, Stedmon CA, Blicher M, Meire L, Mankoff KD, Thyrring J (2022) Glacial meltwater determines the balance between autotrophic and heterotrophic processes in a Greenland fjord. Proc Natl Acad Sci 119:e2207024119.
https://doi.org/10.1073/pnas.2207024119

65. South PM (2016) An experimental assessment of measures of mussel settlement: Effects of temporal, procedural and spatial variations. J Exp Mar Biol Ecol 482:64-74.
https://doi.org/10.1016/j.jembe.2016.05.002

66. Søreide JE, Leu EV, Berge J, Graeve M, Falk-Petersen S (2010) Timing of blooms, algal food quality and *Calanus glacialis* reproduction and growth in a changing Arctic. Glob Change Biol 16(11):3154-3163. https://doi.org/10.1111/j.1365-2486.2010.02175.x

67. Søreide JE, Carroll ML, Hop H, Ambrose WG, Hegseth EN, Falk-Petersen S (2013) Sympagic-pelagic-benthic coupling in Arctic and Atlantic waters around Svalbard revealed by stable isotopic and fatty acid tracers. Mar Biol Res 9(9):831-850. https://doi.org/10.1080/17451000.2013.775457

68. Stanwell-Smith D, Barnes DK (1997) Benthic community development in Antarctica: recruitment and growth on settlement panels at Signy Island. J Exp Mar Biol Ecol 212(1):61-79.
https://doi.org/10.1016/S0022-0981(96)02754-2

69. Thorson G (1936) The larval development, growth, and metabolism of arctic marine bottom invertebrates compared with those of other seas (Vol. 100). CA Reitzel.

70. Thorson G (1950) Reproductive and larval ecology of marine bottom invertebrates. Biol Rev 25(1):1-45.

71. Toupoint N, Gilmore-Solomon L, Bourque F, Myrand B, Pernet F, Olivier F, Tremblay R (2012) Match/mismatch between the *Mytilus edulis* larval supply and seston quality: effect on recruitment. *Ecology*, *93*(8), 1922-1934. https://doi.org/10.1890/11-1292.1

72. Weihs L, Plummer M (2016) sBIC: computing the singular BIC for multiple models. R Package Version 0.2.

73. Weslawski JM, Kwasniewski S, Wiktor J (1991) Winter in a Svalbard fjord ecosystem. Arctic, 115-123.

74. Wilson DP (1937) The influence of the substratum on the metamorphosis of *Notomastus* larvae. J Mar Biolog Assoc 22(1):227-243. https://doi.org/10.1017/S0025315400011978

75. Yakovis EL, Artemieva AV, Fokin MV, Varfolomeeva MA, Shunatova NN (2013) Synchronous annual recruitment variation in barnacles and ascidians in the White Sea shallow subtidal 1999–2010. Hydrobiologia, 706(1):69-79. https://doi.org/10.1007/s10750-012-1340-5

76. Zajaczkowski MJ, Legezynska J (2001) Estimation of zooplankton mortality caused by an Arctic glacier outflow. Oceanologia 43(3):341-351.

Figures
<figure><img src="image_2.png" /><figcaption>Figure 1</figcaption></figure>

See image above for figure legend
<figure><img src="image_3.png" /><figcaption>Figure 2</figcaption></figure>

Picture of a recruit of *H. arctica* taken under a Keyence VHX-2000 Series digital microscope showing the various measurements acquired here (Prodissoconch size (PII): height and width; Shell length; Shell width).
<figure><img src="image_4.png" /><figcaption>Figure 3</figcaption></figure>

Mean abundance per spat trap (± Standard Error) of recruits of *Hiatella arctica* collected at both studied sites over either 5 weeks, 9, or 12 months.
<figure><img src="image_5.png" /><figcaption>Figure 4</figcaption></figure>

Size-class of recruits of *H. arcticacollected in 12 month spat traps (immersed from mid-May 2017 to mid-May 2018) at either a) Basalt Island or b) Pass Hytten and in 5 week samples (immersed from August 2016 to September 2016) at c) Basalt Island and d) Pass Hytten. Lines represent the estimated Gaussian densities (probability density) corresponding to cohorts (Gaussian finite mixture model).

**Supplementary Files**

This is a list of supplementary files associated with this preprint. Click to download.

* Supplementary1Veillardetal.2023.docx