# Meiofaunal diversity across a sharp gradient of fjord oxygenation: Insights from metabarcoding and morphology approaches in foraminifera

Phoebe O'Brien, Magali Schweizer, Filip Morin, Astrid Hylén, Elizabeth K Robertson, Per O J Hall, Sophie Quinchard, Irina Polovodova Asteman

► To cite this version:

Phoebe O'Brien, Magali Schweizer, Filip Morin, Astrid Hylén, Elizabeth K Robertson, et al. Meiofaunal diversity across a sharp gradient of fjord oxygenation: Insights from metabarcoding and morphology approaches in foraminifera. Marine Pollution Bulletin, 2025, 224, pp.119145. 10.1016/j.marpolbul.2025.119145. hal-05444779

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# Meiofaunal diversity across a sharp gradient of fjord oxygenation: Insights from metabarcoding and morphology approaches in foraminifera

Phoebe O'Brien<sup>a,b,*</sup>, Magali Schweizer<sup>c</sup>, Filip Morin<sup>a</sup>, Astrid Hylén<sup>a,d</sup>, Elizabeth K. Robertson<sup>a</sup>, Per O.J. Hall<sup>a</sup>, Sophie Quinchard<sup>c</sup>, Irina Polovodova Asteman<sup>a,b</sup>

## ARTICLE INFO

### Keywords:

Biomonitoring

Foraminifera

Fjord

Ecosystem Quality Status

eDNA

Sediment

Biodiversity

## ABSTRACT

Fjords are important global carbon sinks, yet their ecosystems are increasingly impacted by anthropogenic eutrophication and oxygen depletion. Understanding how benthic meiofaunal communities respond to environmental parameters is essential for environmental impact assessment and biomonitoring. Here, we examine how benthic foraminiferal communities respond to contrasting bottom-water oxygen conditions in three Swedish west coast fjords (long-term anoxic By Fjord, seasonally hypoxic Gullmar Fjord, and permanently oxic Hake Fjord) using parallel morphological identification and environmental DNA (eDNA) metabarcoding methods. Multidimensional scaling revealed site-specific structuring in both datasets. By Fjord showed the lowest diversity in both datasets, dominated by monothalamids and opportunistic, stress-tolerant taxa adapted to anoxic conditions. Gullmar Fjord supported more diverse assemblages, indicated by abundant Globothalamea, notably *Bulimina marginata* and *Stainforthia fusiformis*, reflecting moderate oxygenation conditions. Shallow Hake Fjord supported a slightly lower diversity benthic community and less molecular signal preservation. Both Hake- and Gullmar fjords exhibited occurrence of *Nonionella* sp. T1, a recently established putatively invasive species, indicating ecological competitiveness across variable oxygenation conditions. Complementary use of molecular and morphological analyses highlighted methodological biases: eDNA captured broader taxonomic diversity, particularly elusive monothalamids, but was influenced by variable DNA preservation under different redox conditions and was limited by sequence database resolution. The morphological approach targeted mineralised foraminifera of a certain size (> 63 µm), providing clearer ecological associations related to immediate oxygen conditions and salinity, while metabarcoding of bulk sediment more effectively traced potential bidirectional responses of a wider range of cryptic and soft-bodied foraminifera. We conclude that used together, these two approaches provide critical insights into species ecology necessary for interpreting palaeoecological archives and assessing contemporary environmental status in oxygen-stressed coastal ecosystems.

## 1. Introduction

Fjords and fjord-like estuaries (Arneborg, 2004) characterised as long, narrow coastal inlets with steep slopes formed by tectonic activity and glacial carving, are integral to global carbon sequestration (e.g. Smith et al., 2015; Bianchi et al., 2020; Watts et al., 2024). Despite accounting for only 0.1 % of shelf sediment volume, fjords bury an estimated 11–12 % of global oceanic organic carbon (Nuwer and Keil, 2005;

Smith et al., 2015), driven by high sedimentation and organic input from productive, vegetated catchments (Cui et al., 2016; Wlodarska-Kowalczuk et al., 2019). Specific hydrography and presence of a sill (topographic barrier at the fjord mouth) induce estuarine circulation in fjords, which together with water column stratification may result in oxygen deficiency in deep fjord basins (Svitski et al., 1987; Stigebbrandt, 2012). Deoxygenation in fjords has also been linked to industrial effluents and eutrophication, changes in atmospheric circulation, increased

<footnote><sup>a</sup> Department of Marine Sciences, University of Gothenburg, NATRIUM, Medicinaregatan 7B, 41390, Gothenburg, Sweden</footnote>

<footnote><sup>b</sup> Gothenburg Global Biodiversity Centre, Gothenburg, Sweden</footnote>

<footnote><sup>c</sup> Univ Angers, Nantes Université, Le Mans Université, CNRS, Laboratoire de Planétologie et Géosciences, LPG UMR 6112, 49000, Angers, France</footnote>

<footnote><sup>d</sup> Centre for Research and Teaching in Environmental Sciences, Marseille, France</footnote>

<footnote>* Corresponding author at: Department of Marine Sciences, University of Gothenburg, NATRIUM, Medicinaregatan 7B, 41390, Gothenburg, Sweden.
*E-mail addresses:* phoebeajobrien@gmail.com (P. O’Brien), magali.schweizer@univ-angers.fr (M. Schweizer), hylen@cerege.fr (A. Hylén), elizabeth.robertson@gu.se (E.K. Robertson), per.hall@marine.gu.se (P.O.J. Hall), sophie.quinchard@univ-angers.fr (S. Quinchard), irina.polovodova@marine.gu.se (I. Polovodova Asteman).</footnote>
emissions of greenhouse gases and ocean warming (Salvanes et al., 2025 and references therein; Rosenberg, 1972; Syvitski et al., 1987; Colodey and Wells, 1992; Polovodova Asteman et al., 2015). Because of high sediment build-up, fjords offer a rare opportunity of the highly resolving geological record, making them valuable for reconstructing past marine conditions using traditional palaeoproxies and ancient DNA (e.g. Dolven et al., 2013; Harland et al., 2013; Pawłowska et al., 2014; Pawłowska et al., 2016; Faust et al., 2016; Polovodova Asteman and Nordberg, 2013, Polovodova Asteman et al., 2018; Oksman et al., 2024).

Benthic community composition and diversity can be used as Bio-
logical Quality Elements (BQEs) for biomonitoring within the Water
Framework Directive (WFD) (European Commission, 2000). Fjords and
other transitional environments however pose an issue for environ-
mental assessment, due to naturally heterogeneous conditions promot-
ing tolerant and opportunistic species whilst sensitive species struggle to
find a long-term niche (O'Brien et al., 2021). Consequently, benthic
communities in relatively undisturbed systems can be misclassified as
having bad environmental status. This phenomenon is termed the
'estuarine quality paradox' (Elliott and Quintino, 2007; Munari and
Mistri, 2008), which complicates the decision-making process by
providing misleading estimates of reference conditions (Blanchet et al.,
2008; Salas et al., 2004; McLusky and Elliott, 2007). Hence, BQEs,
essential for the assessment process under the WFD (European Com-
mission, 2000), must be carefully selected and based on well understood
ecological affinities.

Benthic foraminifera (Retaria, Rhizaria) offer the possibility of a suitable BQI in transitional environments such as fjords. Foraminifera are a highly diverse group of unicellular eukaryotes, widely used in marine ecological assessment due to their broad distribution, quick responses to environmental stressors, and role as repositories of geochemical elements (Murray, 2006). Their distribution and species composition are shaped by numerous environmental parameters, and notably by oxygen conditions and food supply (Murray, 2006). In contrast to more sensitive macrofauna, many benthic foraminiferal species are extremophiles and can endure severe hypoxia to anoxia due to unique metabolic strategies, such as mixotrophy via symbiosis or kleptoplastidy (Bernhard et al., 2010, 2012; Jauffrais et al., 2019; Hallock, 1999), denitrification (Piña-Ochoa et al., 2010; Glock et al., 2013, 2025; Woehle et al., 2022), dephosphorylation (LeKieffre et al., 2017), bacterial symbiosis (Orsi et al., 2020), or dormancy (Bernard and Alve, 1996; Jesus et al., 2022; Richirt et al., 2022; Ross and Hallock, 2016). In the benthos, foraminifera constitute over 40–50 % of the eukaryotic biomass (Gooday et al., 1992; Moodley et al., 2000) and possess short life spans of weeks to months, enabling the acquisition of high-resolution environmental data (Murray, 2006). The small size of foraminifera, paired with high abundance facilitates statistically robust analyses and the calculation of biotic indices from small sample volumes. This has been effectively applied across diverse environments in biomonitoring studies using the morphological approach (e.g. Alve, 1995; Alve et al., 2009; Barras et al., 2014; Bouchet et al., 2012; Dimiza et al., 2016; Jorissen et al., 2018; da Silva et al., 2022). Also, biotic indices based on foraminifera responses to organic matter enrichment and foraminiferal diversity are today being increasingly used to reflect environmental changes (O'Brien et al., 2021 and references therein).

In fjord ecosystems, as in other benthic ecosystems, foraminiferal assemblages are primarily modulated by oxygen and organic matter gradients (Murray, 2006). Studies from Scandinavian fjords have shown that opportunistic taxa such as *Stainforthia fusiformis* and *Textularia earlandi* often dominate under hypoxic to anoxic bottom water conditions, while more sensitive species, including *Cassidulina laevigata*, *Hyalinea balthica*, Elphidium spp. and *Bulimina marginata*, occur in well-oxygenated settings (Alve, 1995; Alve et al., 2009; Polovodova Asteman et al., 2015). Here abundance and diversity estimates combined with known species-specific ecological tolerances indicate environmental quality (e.g. Dolven et al., 2013), whilst gradients found in fjord environments provide natural laboratories for assessing how

benthic foraminifera respond to fluctuating oxygenation and other
variables (e.g. O'Brien et al., 2024).

To assess foraminiferal species composition, morphology-based taxonomy is commonly used, but has associated limitations. Sediment sieving often excludes juveniles and smaller taxa, and standard dry-picking techniques are ill-suited to soft-shelled monothalamids, leading to misidentification or exclusion. Rose Bengal staining, commonly used to distinguish live from dead individuals, can be unreliable in low-oxygen settings (Bernhard et al., 2006). All these factors risk misrepresenting true species richness, thereby influencing environmental assessment outcomes (e.g. Dolven et al., 2013).

Recently, advancements in high-throughput sequencing (HTS) technologies have enabled environmental DNA (eDNA) metabarcoding as a tool for foraminiferal diversity assessment (e.g. Brinkmann et al., 2023; Cavaliere et al., 2021; Cordier et al., 2017; Frontalini et al., 2018; Laroche et al., 2016; Pawlowski et al., 2014a, 2014b, 2016a, 2016b; Pochon et al., 2015; Singer et al., 2023). Targeting specific gene regions from bulk sediment, eDNA methods detect both living and recently deceased organisms (Pawlowski et al., 2014a, 2014b). Using foraminifera-specific primers, this approach reduces processing time and improves detection of small taxa and groups rarely captured in morphological approaches like monothalamids (Keeley et al., 2018; Pawlowski et al., 2014a, 2014b, 2016a, 2016b). As a result, metabarcoding is becoming increasingly preferred for environmental monitoring (Laroche et al., 2016; Frontalini et al., 2018; Barrenechea Angeles et al., 2021; Cao et al., 2022; Pochon et al., 2015; O'Brien et al., 2024). Yet, the question remains on how metabarcoding surveys accurately resolve ecological preferences of only-genetically-detected species and how this methodology interacts with varied oxygen regimes. Studies have shown that e.g. low oxygen conditions, fine-grained substrate, eutrophic conditions and low temperatures generally slow down eDNA degradation, which can potentially facilitate its preservation in sediments (e.g. Barnes et al., 2014; Joseph et al., 2022; Mejbel et al., 2022; Morin et al., 2023).

In fjords specifically, the impact of oxygen conditions and sediment geochemistry on the preservation of DNA in the sediments has strong implications for foraminiferal diversity assessment based on eDNA techniques. Whilst previous studies have demonstrated broad congruence between diversity indices derived from morphospecies and molecular datasets in tropical and subtropical regions (e.g. Al-Enezi et al., 2022; Cavaliere et al., 2021; Liu et al., 2025), similar studies in temperate and boreal fjord regions remain few (e.g. O'Brien et al., 2024). The present study was designed to advance this work in temperate and boreal transitional waters. Here we investigate how the benthic foraminifera diversity and community composition vary across contrasting bottom-water oxygen conditions in three Swedish fjords and the associated geochemical parameters. We hypothesise that eDNA metabarcoding will recover higher species richness, particularly of soft-shelled taxa, while both molecular and morphological approaches will converge in detecting the major ecological gradients driven by oxygenation, resulting in comparable environmental status assessments despite differences in taxonomic resolution.

In addition, we evaluate how each method captures the ecological affinities of individual species. Using quantitative (morphospecies) and semi-quantitative (metabarcoding) analyses of foraminiferal communities along ecological gradients, we identify species specific tolerances, including lesser-studied soft-shelled species. This ecological perspective allows us to assess not only method concordance but also the capacity of each approach to resolve functional patterns informing new bio-indication strategies and environmental status assessment.

## 2. Materials and methods

2.1. Study area

This study focuses on three semi-enclosed water bodies located along
the Swedish west coast: By Fjord (B), Gullmar Fjord (G), and Hake Fjord (H; Fig. 1). While Gullmar Fjord is a typical fjord by definition (reference), Hake and By fjords are fjord-like inlets with shallower sills and more estuarine characteristics. These fjords were selected due to their contrasting bottom water oxygen conditions, providing an environmental gradient for examining changes in foraminiferal diversity through a comparison between metabarcoding and morphology-based taxonomic methods. The sampled stations were located at the heads (H), centres (C) and mouths (M) of the fjords (Fig. 1). Following thresholds used in previous fjord and coastal studies (Choquel et al., 2021), we regard bottom-waters with $O_2$ > ~2 mg L$^{-1}$ (~ > 63 µmol L$^{-1}$) as oxic, ~2–0.5 mg L$^{-1}$ (~63-?? µmol L$^{-1}$) as hypoxic, and ≤ 0.5 mg L$^{-1}$ as effectively anoxic.

By Fjord is a small (maximum depth 50 m, sill at 13.5 m), enclosed basin with limited water exchange and strong riverine influence (Fig. 1). Due to its restricted circulation, the fjord experiences long-term anoxia below sill depth (Fig. 2), with basin water residence times of 3–5 years (Viktorsson et al., 2013). Artificial re-oxygenation of bottom waters was carried out through pumping of oxygenated surface water to depth in the fjord during the BOX project (2010–2013) (Stigebrandt et al., 2015). This temporarily restored oxic conditions and altered microbial and benthic communities (Forth et al., 2015; De Brabandere et al., 2015), though anoxia returned post-experiment (Bonaglia et al., 2022). Artificial re-oxygenation in the fjord has been restarted in 2024 by local municipalities (Stiftelsen Byfjordens Framtid, 2024; Uddevalla Municipality, 2024).

Gullmar Fjord (Fig. 1) is the deepest of the three studied fjords (maximum depth 118.6 m, sill at 50 m) and is characterised by a seasonally stratified water column influenced by both riverine input and water exchange with the Skagerrak. Below a thin surface layer, a

brackish intermediate layer (1–15 m) overlies marine waters, with deep basin water (> 50 m) typically becoming hypoxic in late autumn and winter (Filipsson and Nordberg, 2004a, 2004b). Deep-water renewal occurs annually, driven by cascading oxygenated water from the Skagerrak (Arneborg, 2004). These seasonal oxygen fluctuations impact benthic communities and sediment biogeochemistry, making Gullmar Fjord a key site for assessing diversity shifts across oxygen gradients.

Hake Fjord (maximum depth 65 m, sill at 25 m) represents the oxic endmember in this study (Fig. 1). It experiences regular water exchange with the Skagerrak through a wide, shallow mouth, maintaining well-oxygenated bottom waters year-round (Björk et al., 2000). Unlike Gullmar- and By fjords, Hake Fjord lacks significant freshwater inflow at its head and is an open-ended fjord system (Fig. 1).

## 2.2. Sediment sampling

Sediment samples were collected from nine locations in Gullmar Fjord, By Fjord, and Hake Fjord (Table 1) aboard R/V Skagerak between 27th of September and 1st of October 2021. Sampling was done at the head, centre, and mouth of each fjord to cover potential environmental gradients (Watts et al., 2024; Placitu et al., 2024). Prior to sediment collection, CTD (Conductivity, Temperature, Depth) casts were performed at each station to measure temperature, salinity and dissolved oxygen versus depth in the water column.

Surface sediment samples (0–3 cm) of approximately 50 ml were collected using a GEMAX corer, an adapted version of the Niemistö corer, with a 9 cm inner diameter. For eDNA analysis, ca. 20 ml of sediment was collected and homogenised in sterile Falcon tubes using sterile spoons in 2 replicates, which were immediately frozen at –20 °C, and transported to the Laboratory of Planetology and Geosciences,

<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Location of the Gullmar Fjord, By Fjord and Hake Fjord along the Swedish west coast. Studied stations in the fjord mouth, centre and head are indicated by M, C and H, respectively, together with the first letters of the respective fjord names (e.g. GM, GH and GC for Gullmar Fjord). The map is adapted and modified from Morin et al. (2023).</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Dissolved oxygen concentrations (µM) in bottom water for By Fjord at water depths >40 m, Gullmar Fjord >100 m and Hake Fjord >20 m. The red line indicates the hypoxic threshold (63 µmol L⁻¹) and blue shaded area marks when artificial oxygenation was conducted through pumping in the Box project. Datasets recovered from the Swedish Meteorological and Hydrological Institute (SMHI) downloaded for the period 1951–2023. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

<caption>**Table 1**</caption>

Water depth, latitude, longitude and oxygenation regime at each station sampled in three fjords.

<table><thead><tr><th>Fjord</th><th>Stations</th><th>Water depth (m)</th><th>Longitude</th><th>Latitude</th><th>Long-term redox conditions</th></tr></thead><tbody><tr><td>By Fjord (mouth)</td><td>BM</td><td>23</td><td>58°19.645′N</td><td>11°51.525′E</td><td>Anoxic</td></tr><tr><td>By Fjord (center)</td><td>BC</td><td>46.5</td><td>58°19.901′N</td><td>11°52.531′E</td><td>Anoxic</td></tr><tr><td>By Fjord (head)</td><td>BH</td><td>11</td><td>58°20.666′N</td><td>11°54.467′E</td><td>Oxic</td></tr><tr><td>Gullmar Fjord (mouth)</td><td>GM</td><td>57</td><td>58°15.750′N</td><td>11°27.447′E</td><td>Seasonally hypoxic</td></tr><tr><td>Gullmar Fjord (center)</td><td>GC</td><td>118</td><td>58°19.399′N</td><td>11°32.772′E</td><td>Seasonally hypoxic</td></tr><tr><td>Gullmar Fjord (head)</td><td>GH</td><td>26</td><td>58°25.816′N</td><td>11°40.494′E</td><td>Oxic</td></tr><tr><td>Hake Fjord (mouth)</td><td>HM</td><td>41</td><td>57°55.815′N</td><td>11°40.709′E</td><td>Oxic</td></tr><tr><td>Hake Fjord (center)</td><td>HC</td><td>32</td><td>57°57.821′N</td><td>11°43.257′E</td><td>Oxic</td></tr><tr><td>Hake Fjord (head)</td><td>HH</td><td>21</td><td>58°02.843′N</td><td>11°47.315′E</td><td>Oxic</td></tr></tbody></table>

University of Angers, France for further processing. For foraminiferal morphospecies analysis, three replicates per station were collected in wide neck bottles.

Duplicate sediment cores were collected and sliced down to 20 cm core depth for sampling of porewater nutrients and Dissolved Inorganic Carbon (DIC), organic/inorganic carbon and nitrogen, grain size, and pigment (chlorophyll a, zeaxanthin) analyses. All solid phase downcore data and methods, including information on sampling procedures for nutrients, DIC and sediment organic carbon content are published in Watts et al. (2024) and Placitu et al. (2024), whilst herein we only use average surface sediment (0–3 cm) values to compare against foraminiferal morphological and metabarcoding data.

## 2.3. Foraminiferal community analysis

### 2.3.1. eDNA extraction, amplification, sequencing and bioinformatic analysis

Environmental DNA was extracted from sediment samples collected from each site of the three fjords, using two replicates per site. Total DNA extraction from both replicates was performed with 10 g of sediment using the DNeasy PowerMax Soil Kit (Qiagen), following the manufacturer's instructions.

A first PCR amplification was conducted with AccuPrime™ Taq DNA Polymerase High Fidelity (Thermo Fisher Scientific) with foraminifera-specific primers s14F1 (Pawlowski and Lecroq, 2010) and s15r (Lejzerowicz et al., 2014) following the conditions described in Brinkmann et al. (2023) to amplify a fragment of the SSU rDNA. Three PCR replicates were prepared for each sample and pooled after amplification. A negative control was added to each PCR series and also sequenced.

At the ANAN platform (SFR QUASAV, Beaucouzé, France), a second PCR was conducted to incorporate Illumina adapters and barcodes, following the manufacturer's protocol using AccuPrimeTM Taq DNA Polymerase High Fidelity (Invitrogen). The purified samples were quantified using the Quant-iT™ PicoGreen™ dsDNA Assay kit (Invitrogen) and pooled in equimolar concentrations. This pool was mixed with 15 % of phiX phage mix and run on a MiSeq sequencing system (Illumina) with a MiSeq cartridge 300 v2. Raw reads were demultiplexed to samples with DADA2 v.1.26 (Callahan et al., 2016) in R v.4.3.2 (R Core Team, 2023). Miseq overhangs and primers were removed using Cutadapt v.3.5 (Martin, 2011). Clustering of the reads was done using the R package DADA2 (v. 1.16; Callahan et al., 2016). Processed reads were quality controlled by truncating (forward and reverse length of 120 bp) and filtering them to a maximum number of 'expected errors' of two. Amplicon sequence variants (ASV) were dereplicated if identical, clus-tered and pair-end reads merged using a minimum overlap of 12 bp and maximum mismatch of 0 bp. Chimeras were removed using the 'pooled' method (Callahan et al., 2016, p. 2). The ASVs were taxonomically assigned with PR² v. 4.14 (Guillou et al., 2012) in DADA2 v. 1.26. This first assignation allowed removing ASVs that did not belong to foraminifera. In addition, rare ASVs with less than 10 reads, ASVs belonging to planktonic foraminifera and ASVs with more than 0.1 % reads in negative controls (contaminations) were removed. Then, the taxonomic assignment of the most abundant ASVs (> 50 reads) and the ASVs identified at the species level was checked manually by sequence alignment and comparisons with reference databases (BLAST with the GenBank database), and ASVs related to the same taxon were pooled. These pooled ASVs were renamed into Operational Taxonomic Units (OTUs). Finally, tables with retained OTUs were built for further
analyses (Suppl. file 1).

All HTS reads are deposited in the Sequence Read Archive (SRA) at NCBI (PRJNA1238304; https://www.ncbi.nlm.nih.gov/sra/PRJNA1238304).

### 2.3.2. Morphological analysis

Sediment samples (~50 ml) were preserved in a Rose Ben-
gal–ethanol solution (approximately 2 g L⁻¹ in 95 % ethanol) and stored
at 2–6 °C for a minimum of two weeks to allow adequate staining of live
foraminifera, following the Foraminiferal Bio-Monitoring (FOBIMO)
protocol (Schönfeld et al., 2012). Sediment volume was estimated based
on the area of the container holding the sample. Samples were then wet
sieved using 63 µm and 1 mm mesh sizes. Where needed, tetrasodium
pyrophosphate (Na₄P₂O₇) was added to help disaggregate clay particles.

The >63 µm fraction was dried at 50 °C over night and was thereafter
examined under a stereomicroscope using a 45-square grid tray, where
squares were randomly selected for counting. From each sample, at least
300 stained (live) and 300 unstained (dead) individuals were picked to
ensure robust estimates of species composition and density. Identifica-
tion was carried out to the lowest possible taxonomic level, where
possible, by using Polovodova Asteman et al. (2021); Cage et al. (2021);
Feyling-Hanssen (1964); Höglund (1947); Nordberg et al. (2017), and
Qvale and Nigam (1985). For each sample, species richness, diversity
indices, relative abundance (%) and absolute abundance (individuals
per 10 ml sediment) were calculated.

## 2.4. Statistical analyses

All data processing and statistical analyses were conducted in R version 4.3.2 (R Core Team, 2023). The package *dplyr* (Wickham et al., 2023) was used for data wrangling, *tidyr* (Wickham and Girlich, 2023) for dataset restructuring, and *ggplot2* (Wickham, 2016) and *patchwork* (Pedersen, 2020) for figure production.

### 2.4.1. Environmental redundancy analysis and variable selection

To reduce redundancy in the environmental dataset, all variables
were standardised (z-score transformed) and run through a principal
component analysis (PCA). The first two principal components
explained ~65 % of the variance, suggesting strong collinearity across
several variables. Pairwise Pearson correlation coefficients were calcu-
lated using base R, and visualised with *corrplot* (Wei and Simko, 2021)
and *GGally* (Schloerke et al., 2021) to generate both a correlation
 heatmap, variables with *r* > 0.8 were determined as highly collinear.
Raw pair plots were also used to check for outliers or non-linear re-
lationships that might skew correlations, which resulted in several
clusters. For example, bottom-water oxygen conditions, total nitrogen
(TN), chlorophyll *a* (Chla), and DIC; and zeaxanthin and salinity were
found to be highly correlated, but due to their ecological relevance, we
chose to retain the variables. NH₄ and SiO₂ showed moderate correla-
tion; given their distinct geochemical roles, we retained both but noted
the highly correlated relationship.

### 2.4.2. Multidimensional scaling

All foraminiferal data (morphological and eDNA) were subject to a non-metric multidimensional scaling (nMDS) analysis to visualise similarities and differences in foraminiferal diversity among replicates at each station. The MDS analysis was based on Bray-Curtis dissimilarities, using the metaMDS function in the vegan package (Oksanen et al., 2013).

### 2.4.3. Diversity indices

Diversity indices were calculated separately for morphological and genetic datasets to compare taxonomic resolution between methods. For the morphological dataset, Richness was determined as the number of species present per sample, while Shannon (H') and Simpson (1 - D) indices were computed using standard diversity functions using the

vegan package, with a correction to avoid undefined values in samples
with zero richness. The genetic dataset was processed in phyloseq
(McMurdie and Holmes, 2013), with rarefaction applied to account for
sequencing depth variability. The same three diversity indices were
calculated alongside exponential Shannon diversity (expH'_{bc}. Bouchet
et al., 2012) to provide an effective number of species estimate (Jost,
2006).

### 2.4.4. Species and class-level composition

Taxonomic assignments for eDNA data were retrieved from an
external reference database, and species names were standardised to
ensure consistency. The datasets were processed using the package
phyloseq for phylogenetic data handling. To assess the spatial distribu-
tion of foraminiferal communities, species abundances were aggregated
at the station level. The genetic dataset was normalised to relative
abundance by dividing species-specific read counts by the total sum of
reads per station and multiplying by 100. The morphological dataset
was processed similarly, first summing species counts across replicate
samples and then standardised within each station.

### 2.4.5. Correlations with environmental parameters

To explore links between species abundance and environmental
conditions, we calculated Pearson correlation coefficients between the
top 35 most abundant species (based on total abundance across stations)
and environmental variables. Separate analyses were conducted for the
genetic and morphological datasets. Abundance data were log-
transformed (log + 1) to reduce the influence of dominant taxa, and
environmental variables were Z-score normalised to ensure compara-
bility. Species names in the genetic dataset were cleaned to preserve
taxonomic resolution while standardising formatting. Correlation
matrices were visualised using the R package pheatmap (Kolde, 2019),
with hierarchical clustering applied to both species and environmental
variables to reveal structure in species–environment associations. Sta-
tistically significant correlations (*p* < 0.05) computed using the base
stats package (R Core Team, 2023), and multiple testing correction was
applied using the Benjamini–Hochberg False Discovery Rate (FDR)
method. However, as only a small number of species–environment
correlations remained statistically significant after correction, we
retained uncorrected *p*-values in the heatmaps to facilitate ecological
interpretation and identify potential trends.

### 2.4.6. Diversity pattern and distribution comparison

To assess the consistency of diversity patterns between morpholog-
ical and genetic datasets, we computed all pairwise differences (Δ
values) in mean diversity between stations within each fjord, separately
for each dataset and index. These Δ values were then compared across
methods to evaluate two aspects of pattern preservation: (1) the pro-
portion of station pairs with the same direction of difference (i.e., both
positive or both negative), and (2) the Pearson correlation between the
magnitudes of morphological and genetic Δ values. The analysis was
visualised in two heatmaps, one showing correlation coefficients and the
other showing the proportion of same-direction deltas.

## 3. Results

### 3.1. Environmental variables

Environmental conditions varied across the three fjords, with distinct patterns in bottom-water salinity, pore-water concentrations of dissolved nutrients, and sedimentary organic matter contents (Fig. 3, Supplementary Table S1). The lowest bottom water salinity was observed in By Fjord and the highest in the Gullmar Fjord. By Fjord stations (BM, BC, BH) showed the largest variability, with BH displaying the lowest salinity as compared to BC and BM. In contrast, Gullmar- and Hake fjords exhibited relatively stable salinity, with a range of 32.7–34.3 (Fig. 3).
<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Environmental variables along each fjord transect. Pore water and sediment data are averaged from the uppermost 0–3 cm of sediment, whilst dissolved oxygen and salinity data represent bottom water (BW) conditions.</figcaption></figure>

Pore water nutrient concentrations exhibited spatial heterogeneity. The highest NOₓ- concentrations were observed at the BC; however, these values may reflect oxidation during sampling handling, as such elevated NOₓ- levels are unlikely under anoxic conditions. The next highest concentrations were found at HH, while all other stations had values below 3 µM (Fig. 3). Also, PO₄³⁻ concentration varied considerably between stations, the highest was BC at 69.66 µM, followed by >40 µM at the BM, BH, GH and HM, and < 20 µM at GM, GC, HC and HH. NH₄⁺ was elevated at BC and BM, whereas all other stations had concentrations below 50 µM. Silica concentrations were also highest at BC and BM and lowest at GH. Pore water DIC ranged from lowest at GH to highest at BC. The DIC distribution between the fjords was generally opposite to that of the bottom-water oxygen, which were lowest at BC and BM, although the highest value of 209.2 µM was recorded at BH. Gullmar Fjord stations were intermediate, whilst Hake fjord had the highest oxygen concentrations.

Phytopigments in the sediment varied across the stations (Fig. 3). Chlorophyll *a* was highest at BC and BM, while the lowest values were recorded at the deepest site GC. Zeaxanthin, a marker for cyanobacteria, had higher values in all fjord heads, whereas GC again, similar to chlorophyll *a*, exhibited the lowest among all sites. Total nitrogen (TN) and total organic carbon (TOC) showed concurrent variability (Fig. 3), with peaks at BC and the lowest values at BH and GH. Generally, both gradually decreased from By Fjord to Hake Fjord (Fig. 3). The mud content remained consistently high across studied stations, ranging from 87.7 % at GH to 95.4 % at BM and HM.

## 3.2. Foraminiferal community analysis

The total number of reads obtained by metabarcoding from the nine sites in the three fjords with two replicates per site (total of 18 samples) was 716,227. There were 532,081 reads (74.3 %) kept after the cleaning process (DADA2 and removal of non-foraminifera, contaminated, rare and planktonic ASVs). From the 707 ASVs acquired with DADA2, 364 taxa were finally obtained and renamed as OTUs (Suppl. file 1). Among these OTUs, 258 could not be identified at the genus or species levels and were considered as environmental clades named ENV001 to ENV258. These represented about 19 % of the reads. The most abundant foraminifera were the monothalamids with 68.3 % of the reads and 154 OTUs, followed by Globothalamea with 21.2 % of the reads and 119 OTUs, third was the undetermined foraminifera with 10.4 % of the reads

and 87 OTUs. Finally, the last group represented the Tubothalamea/ Miliolida with 0.1 % of the reads and 4 OTUs.

The morphological dataset contained three replicates from nine stations across the fjords, with a total of 5630 individuals counted and identified in 27 samples. Among these the majority belonged to the class Globothalamea, which represented 90.1 % of all individuals. Monothalamids accounted for 7.8 %, while Nodosarita and Tubothalamea were minor components, at 1.6 % and 0.4 %, respectively. At the order level, the most abundant group was Rotaliida (79.2 %), followed by Lituolida (14.6 %) and Textulariida (3.9 %). The remaining orders and subclasses including Miliolida, Spirillinida, and Hormosinina each contributed less than 2 % of the total assemblage.

Euler diagrams were used to visualise the taxa overlapping across replicates (Fig. 4). In the morphological dataset, replicates showed high overlap in species composition, aside from BC due to the low species count overall. Gullmar stations (GM, GC, GH) and HC had the highest overlap relative to total taxa detected and shared 14 (out of a total 30), 12 (out of 25), 17 (out of 29), and 10 (out of 20) species respectively across all three replicates. In the genetic dataset, replicate overlap was relatively high across By Fjord stations, with the highest conserved proportion at BH (37 out of a total 62). Gullmar Fjord stations showed an intermediate amount of crossover taxa, whereas Hake Fjord had the lowest overall (Fig. 4). Sites GH, HM and HC exhibited noticeable variation in replicate-specific richness, with one replicate often containing substantially more OTUs than others.

Non-metric multidimensional scaling (nMDS,) based on Bray-Curtis dissimilarity, was used to visualise the differences in foraminiferal community composition among stations for both the morphological and genetic datasets (Fig. 5). Stress was low in both ordinations, 0.084 and 0.103 for morphological and genetic data sets respectively, indicating good two-dimensional representations. For the morphological dataset, Gullmar Fjord stations and HM clustered tightly indicating high replicate similarity, whilst By Fjord samples were the most dispersed. For HC and HH one replicate was noticeably different from the others. BC was the most dispersed along both NMDS axes, and BH along axis 1. Conversely, in the genetic dataset, By Fjord stations clustered tightly, whilst the dataset exhibited greater dispersion in general, reflecting higher overall diversity and compositional variability. In both ordinations overall the samples clustered primarily by fjord, demonstrating that each technique is able to capture the spatial structuring signature of resident foraminiferal communities across fjord systems.
<figure><img src="image_6.png" /><figcaption>Fig. 4. Proportional Euler diagrams showing overlap of species detected across replicates at each station for genetic (top two rows) and morphological (bottom two rows) datasets. Circle area is scaled to the number of taxa per replicate; overlaps are taxa shared between samples. Station BM from the morphological dataset was excluded from the figure due to replicates barren of foraminifera.</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>Fig. 5. Non-metric multidimensional scaling (nMDS) plots based on Bray–Curtis dissimilarity of detected foraminifera by morphological (left) and genetic (right) approaches. Each point represents individual replicate sample, with stress values in the lower left.</figcaption></figure>

## 3.3. Diversity indices

In the genetic dataset, species richness ranged from 20 to 106 OTUs (Fig. 6). The highest richness was found in Gullmar Fjord (GC = 106 and GM = 92) and the lowest in Hake Fjord (HH and HM = 20). By Fjord stations had a species richness range of 41–51 (Fig. 6). Shannon diversity was again highest in Gullmar Fjord (GC = 3.29; GM = 3.25), and Hake

Fjord (HM = 3.07), and the lowest in By Fjord (BM = 1.08 and BC = 1.19). Hake Fjord showed intermediate Shannon values, with HH (2.62–2.43) and HC (2.25–2.01). Following the trend, Simpson indices were highest at HM (0.93), GC (0.92), and GM (0.92), and lowest at BM (0.33) and BC (0.37). By Fjord had the lowest $\exp H'_{bc}$ values (BM = 2.96 and BC = 3.27), and once again the highest $\exp H'_{bc}$ was found in the Gullmar Fjord (GC = 26.7, GM = 25.7, and GH = 23.7).
<figure><img src="image_8.png" /><figcaption>**Fig. 6.** Genetic (top) and morphological (bottom) foraminiferal diversity along the studied fjords based on indices such as Species Richness, Shannon, Simpson and expH'bc (Bouchet et al., 2012).</figcaption></figure>

In the morphological dataset, richness ranged from a minimum of 0–3 taxa for the By Fjord to a maximum of 28 species in Gullmar Fjord (GM), followed by a generally higher species numbers at GC (25–26) and GH (23–25). Similar trend was observed for Shannon diversity, which was highest at GH (2.67), GC (2.38), and GM (2.58), whilst By Fjord stations remained below 1.42. Interestingly, Simpson index values were lowest at the most diverse stations, including GH (0.093–0.125) and GC (0.139–0.161), while higher values were found at BH (0.31–0.46) and HM (0.34–0.45). Finally, expH'bc followed a similar pattern to richness and Shannon, with highest values being at GH (14.4), GC (10.8), and GM (13.2) and again lowest values were recorded at BM (1.00) and BC (2.83).

Genetic diversity values were consistently higher than those for the morphological dataset, however both datasets largely reported analogous spatial gradients, aside from Simpson (Fig. 6). Based on all indices, genetic diversity was highest in Gullmar Fjord, intermediate in Hake Fjord, and lowest in By Fjord, especially at BM and BC (Fig. 6).

## 3.4. Species distribution

In By Fjord in general, the species composition in both datasets were strongly influenced by the low number of individuals/reads recovered, which resulted in a dominance of a few taxa in relative abundance. Species composition in the genetic dataset was dominated by monothalamids, particularly ICEMON 4, a saccamminid species that does not yet have a formal species name or description (Voltski and Pawlowski, 2015). Here, ICEMON 4 comprised 78.4 % at BM and 79.2 % at BC, while ICEMON 7 was the most abundant taxon at BH (20.6 %) (Fig. 7A). At BH, putatively invasive *Trochammina hadai* (e.g. McGann et al., 2025) was also dominant (15.4 %), along with *Cedhagenia saltata* (22.2 %) and *Vellaria pellucida* (8.56 %). Another alien species *Ammonia confertitesta* (e.g. Richirt et al., 2021) was also present across the By Fjord reaching its highest abundance at BH (7.13 %). In contrast, the morphological dataset in By Fjord was characterised by high relative abundances of *Ammonia* and *Elphidium* species (Fig. 7B). *Ammonia* sp. was dominant at BM (100 %), BC (55.6 %), and BH (55.1 %), while *Elphidium clavatum* was present at BH (9.8 %).

Overall, Gullmar fjord had the highest proportion of Globothalamea in the genetic dataset, and along the fjord their highest proportion was found at the deepest station GC (Suppl. file 3). *Bulimina marginata* became more dominant toward the fjord head, reaching 26.6 % at GH and 8.5 % at GC. Also stress-tolerant taxa such as *Stainforthia fusiformis* were prominent at GM (13.2 %) and GC (16.2 %) in the genetic dataset (Fig. 7A).

The morphological dataset (Fig. 7B) also showed a high relative abundance of *Bulimina marginata* at GC (24.9 %) and was also present at GH (12.2 %) and GM (5.4 %). As well as contributions from opportunistic taxa like *Textularia earlandi*, which was present at GM (3.7 %), GC (6.6 %), GH (1.4 %) and *Stainforthia fusiformis* at 34.4 %, 18.6 %, 16.1 % at GM, GH and GC respectively. Other notable contributions include *Bolivina pseudopunctata*, which accounted for 4.57 % at GM, 8.02 % at GC and 2.82 % at GH and *Cassidulina laevigata* was relatively consistent across stations, contributing 5.16 % at GM and 4.53 % at GC (Fig. 7B).

Both datasets from Hake Fjord hosted a few extremely dominant species, most notably putatively invasive *Nonionella* sp. T1, which accounted for 5.1 to 14.3 % in the genetic dataset and 20.3 to 64.2 % in the morphological data; and generally decreased from fjord mouth to fjord head (Fig. 7A–B). Within monothalamids, *Vellaria pellucida* was consistently detected across Hake Fjord stations at 7 to 11 %, with the greatest abundance at the head of the fjord, whilst *Bathysiphon* sp. was notably abundant at HC (31.0 %). Both latter species however were absent from the morphological dataset. In contrast, *Eggerelloides scaber* was the most abundant taxon at HH (44.7 %) among the morphospecies, despite the lower relative abundance of *Eggerelloides* in the genetic dataset (0.03 % at HH).

## 3.5. Environmental gradients and community responses

### 3.5.1. Genetic species abundance and environmental parameters

The genetic dataset exhibited strong environmental structuring, with distinct species clusters associated with key environmental gradients, particularly salinity, oxygen, and nutrient availability (Fig. 8A). Clustering in the heatmap for the genetic data revealed two primary taxonomic-environmental groupings: one dominated by high-oxygen
<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /><figcaption>**Fig. 7.** Relative abundance of foraminiferal species composition categorised by class. A: Genetic species composition, where blues, pinks, and purples represent Globothalamea, reds and oranges denote monothalamids, greens indicate Tubothalamea, and grey corresponds to non-assigned species. B: Morphological species composition with blue, pink, and purple for Globothalamea, red for Monothalamids, orange and yellow for Nodosariata, and green for Tubothalamea. Species are stacked in the same order as they appear in the key. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

taxa, including *Hippocrepinella hirudinea*, *Vellaria pellucida*, and *ICEMON 7*; and another comprising low-oxygen, high-organic matter taxa, such as *Ovammina* sp., *Reophax* sp. and *ENV011*, which were significantly positively correlated with pore water DIC. Salinity also clustered in the dendrogram together with bottom water oxygen; *Bulimina marginata* and *Nonionella* sp. T3 exhibited strong positive correlations with salinity, whereas *Trochammina hadai*, *Ammonia confertitesta*, and *Cedhagenia saltata* all were negatively correlated (Fig. 7A).

The heatmap also highlighted species-specific responses to nutrient availability and phytopigments (Fig. 7A). *Stainforthia fusiformis* exhibited the strongest negative correlation with phosphate (PO₄³⁻) and *Haynesina germanica* the most positive, while *Hippocripinella hirudinea*, *Psammophaga* sp.13517 and *ICEMON 7* all were negatively correlated with nitrogen (Fig. 7). Concentrations of NOₓ⁻ was only significantly negatively correlated with EV001. *Nonionella* sp. T1 was negatively correlated with NH₄⁺ and positively correlated to NOₓ⁻, although not
Genetic Species Abundance vs Environmental Variables

<figure><img src="image_11.png" /></figure>

Morphological Species Abundance vs Environmental Variables

<figure><img src="image_12.png" /></figure>

**Fig. 8.** Correlation matrix heatmaps comparing genetic (A) and morphological (B) taxa with environmental parameters. Pearson correlation coefficients are shown with colour intensity indicating correlation strength and direction (blue: positive, red: negative), the significant correlations (*p* < 0.05) are indicated by numbers. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
significantly. NH₄⁺ and chlorophyll *a* had the most bidirectional significant correlations. Species-level associations with sedimentary pigments were also evident, where *Ammonia confertitesta* and *Trochammina hadai* positively correlated with zeaxanthin, and *Cedhagenia saltata*, *Ovammina* sp. and *Haynesina germanica* positively correlated with chlorophyll *a*, while many species, including *Bulimina marginata*, *Nonionella* sp. T1 and *Nonionella* sp. T3 showed a strong negative correlation with chlorophyll *a*. Mud content did not show any significant correlations.

### 3.5.2. Morphological species abundance and environmental parameters

The morphological dataset displayed distinct but overlapping patterns compared to the genetic data (Fig. 8B). Only oxygen, salinity and zeaxanthin were significantly positively correlated with foraminifera abundance. Again, salinity was a key structuring factor, with strong positive correlations for *Textularia earlandi*, *Bolivina pseudopunctata* and *Adercotryma glomerata*. In contrast, *Ammonia* sp., *Haynesina germanica*, *Trochammina hadai*, *Elphidium selseyense* and *Elphidium* sp. showed strong negative correlations with salinity.

Oxygen showed significant positive correlation only with *Elphidium clavatum*, whilst no species exhibited a strong negative correlation. In contrast to the genetic dataset, TOC exhibited largely negative correlations overall to the morphological abundance data, the strongest being with *Epistominella vitrea*, *Reophax fusiformis*, and *Leptohalysis catella*.

Like many of the other environmental variables, the porewater nutrients showed only negative significant correlations. A cluster of species including *Stainforthia fusiformis*, *E. clavatum* and *L. catella* exhibited the strongest negative correlations with NH₄⁺, as well as SiO₂ and DIC. PO₄³⁻ was most negatively correlated with *Nonionella* sp., *Textularia earlandi*, *Nonionella* sp. T1 and *Stainforthia fusiformis*. NOₓ⁻ did not show any significant correlations with morphospecies.

The influence of phytopigments is also suggested, based on a significant correlation with many species, e.g. *Ammonia* sp. exhibiting the strongest positive correlation with zeaxanthin, followed by *Haynesina germanica*, *Trochammina hadai*, and *Elphidium selseyense*. The strongest negative correlation for zeaxanthin was observed for *Bolivina pseudopunctata*. Many species also showed negative correlations with chlorophyll *a* e.g. *Bolivina pseudopunctata*, *Stainforthia fusiformis*, *Nonionella* sp. T1, *Textularia earlandi* and *Leptohalysis catella*.

After FDR was applied strong negative correlations were retained between *Ammonia* sp. and salinity (r = -0.93), and positive correlations with zeaxanthin (r = 0.92). Salinity also remained significantly correlated with *Haynesina germanica* (r = -0.92) and *Trochammina hadai* (r = -0.92). Chlorophyll *a* showed strong negative correlations with *Nonionella* sp. T1 (r = -0.91) and *Stainforthia fusiformis* (r = -0.92).

## 3.6. Comparison of diversity gradient patterns between datasets

Alpha and beta diversity trends were broadly similar when comparing molecular and morphological approaches (Fig. 6), with Gullmar Fjord generally reporting the highest diversity, particularly for Globothalamids (Fig. 7), consistent with previous studies conducted in the region (e.g. Brinkmann et al., 2023). A divergence was however recorded at By Fjord, where the molecular assemblage was slightly richer than that of Hake Fjord. This is likely a factor of increased eDNA preservation under anoxic conditions and evidence of the presence of transported propagules.

# 4. Discussion

## 4.1. Methodological comparison and data quality

Both morphological and genetic approaches indicated higher benthic foraminiferal diversity in Gullmar and Hake fjords than in By Fjord. Molecular techniques generated a total of over 700,000 reads across the 18 samples, with 74 % retained after the cleaning steps, consistent with previous metabarcoding studies applying DADA2 (Callahan et al., 2016;

Brinkmann et al., 2023). Though this suggests good overall data quality, the sedimentary environment still influenced eDNA recovery and preservation. The proportion of reads assigned to species level decreased along the oxygenation gradient, from 82 % in persistently anoxic By Fjord (Viktorsson et al., 2013), to 59 % in seasonally hypoxic in Gullmar Fjord (Filipsson and Nordberg, 2004a, 2004b), and only 29 % in consistently oxic Hake Fjord (Björk et al., 2000). Anoxic conditions could favour DNA and other molecule preservation (Corinaldesi et al., 2011; Joseph et al., 2022). Overall only 29 % of OTUs (representing 81 % of sequences) were assigned to the species level, reflecting limited taxonomic resolution in the available reference databases.

There were 15 species in common between datasets: *Ammonia falsobeccarii*, *Bolivina* sp., *Bulimina marginata*, *Elphidium clavatum*, *Elphidium* sp., *Haynesina germanica*, *Nonionella* sp., *Nonionella* sp. T1, *Nonionella turgida* (T3), *Nonionellina labradorica* (T5), *Reophax* sp., *Stainforthia fusiformis*, *Textularia* sp., *Textularia earlandi* (genetically identified as *T. porrecta*) and *Trochammina hadai*. In comparison, another recent study in the region recovered 8 hard-shelled morphospecies via eDNA metabarcoding and single cell DNA barcoding (Brinkmann et al., 2023), although inability to detect full species diversity is expected as a limitation of any sampling technique at any given site (Bouchet et al., 2012; Pawlowski et al., 2022). The absence of monothalamids in the morphological dataset reflects methodological limitations, as many of these taxa are usually not detected using dry picking in morphological approach (Pawlowski et al., 2014a, 2014b; Schönfeld et al., 2012). Euler diagrams showed high within-fjord beta diversity (replicate) similarity in By Fjord, whereas more variability was observed within stations in Gullmar and Hake fjords, reflecting more dynamic oxygenation regimes (Fig. 4). At Gullmar and Hake stations, the eDNA replicates differed considerably in richness, showing that, like morphological techniques, molecular methods are also vulnerable to local sediment heterogeneity or bioturbation effects, leading to eDNA preservation patchiness (Barnes et al., 2014; Guardiola et al., 2015; Corinaldesi et al., 2011; Mejbel et al., 2022); or other methodology-based issues like deterministic amplification bias during PCR or amplicon sampling stochasticity (Gold et al., 2023), which could introduce more compositional noise. An analogous issue can also be encountered with Rose Bengal staining techniques, where recently dead foraminifera with preserved cytoplasm proteins are erroneously stained, however this issue could be circumvented with the combined use of CellTracker Green (CTG) (Bernhard et al., 2006), which was not the case here. On the other hand, presence of recently dead foraminiferal shells is a valid proof for species actually present at sampling sites and not just residing there as dormant propagules. Despite all these biases, which may have potentially affected our results, NMDS ordinations with low stress values revealed distinct clustering by fjord, demonstrating that both the genetic and morphological datasets reliably captured large-scale spatial structuring of benthic foraminiferal communities.

At the Hake Fjord, *Nonionella* sp. T1 emerged as a dominant species in both genetic and morphological datasets, especially at stations HM and HC (Fig. 10). This species, first reported in the Skagerrak-Kattegat region in 2013 (Polovodova Asteman and Schönfeld, 2016), has since established itself in various areas of the region (Charrieau et al., 2019;; Deldicq et al., 2019; Morin et al., 2023). The ecological impact of *Nonionella* sp. T1 remains under investigation, but its successful colonisation and proliferation indicates a competitive advantage in the fjord's environment and high adaptability to local conditions (Deldicq et al., 2019; Choquel et al., 2021). Yet, when comparing numbers of reads for this species detected by eDNA using foraminifera-specific primers with digital PCR (dPCR) assay designed for specific detection of *Nonionella* sp. T1 by Morin et al. (2023), interesting patterns emerge (Fig. 9). Firstly, both molecular methods detected *Nonionella* sp. T1 at all Gullmar and Hake Fjord stations, confirmed by the morphological sampling technique. However, dPCR was able to amplify reads of *Nonionella* sp. T1 at numbers, which were more closely related to morphological data (Morin et al., 2023) than classical PCR method
<figure><img src="image_13.png" /><figcaption>**Fig. 9.** Comparison of diversity gradient consistency between genetic and morphological datasets, the Pearson correlation coefficient (left) and proportion of pairwise station comparisons with matching direction (right) for each diversity index and fjord.</figcaption></figure>

(metabarcoding) in these stations (Fig. 9). Metabarcoding closely reflected the quantity of *Nonionella* sp. T1 reported by morphology in Hake Fjord, but the read number was much lower at GM and GC. The comparatively low metabarcoding read counts for *Nonionella* sp. T1 in

Gullmar Fjord, despite its confirmed presence via morphology and dPCR, may be influenced by PCR primer bias, where foraminiferal primers may fail to efficiently amplify *Nonionella* sp. T1 DNA, or sequence mismatches or competition with other dominant taxa

<figure><img src="image_14.png" /><figcaption>**Fig. 10.** Comparison of sampling methods targeting putatively invasive *Nonionella* sp. T1 across stations using metabarcoding with foraminifera-specific primers (blue), digital PCR (dPCR) with *Nonionella* sp. T1 specific primers (red), and morphological abundances (green). dPCR data is from Morin et al. (2023) and is shown on a log scale to account for a high value at GC. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>
(Guardiola et al., 2015). The relative abundance of *Nonionella* sp. T1 DNA within the total community DNA pool may also contribute to the observed discrepancies, as metabarcoding measures taxon-specific DNA as a proportion of total extracted DNA rather than absolute abundance (Lanzén et al., 2017). A more diverse foraminiferal community in the Gullmar Fjord could thus dilute the relative signal of *Nonionella* sp. T1 in eDNA data, while dPCR, targeting only this species, is unaffected by such biases. Moreover, the detection of *Nonionella* sp. T1 through dPCR in BC (Fig. 10) could be due to a higher sensitivity of dPCR method to dormant propagules preserved in this low oxygen environment, as compared to metabarcoding. It was previously shown that the deep basin in the Gullmar Fjord centre could also act as a propagule trap for species not living at >100 m water depths (Alve and Goldstein, 2010; Brinkmann et al., 2023).

## 4.2. Fjord scale diversity patterns

By Fjord exhibited the lowest diversity (Figs. 5, 7), with the genetic dataset dominated by monothalamous taxa (e.g. ICEMON 4, ICEMON 7), while the morphological dataset was almost entirely composed of shallow-water estuarine species *Ammonia* and *Elphidium* spp., opportunistic, stress-tolerant species capable of thriving in oxygen-depleted and organic-rich sediments (Alve et al., 2009). Diversity increased from BM to BH in both datasets, as did the abundance of *Trochammina hadai*, a putatively invasive species, originating from Asia (McGann et al., 2000; Bouchet et al., 2023; Polovodova Asteman et al., 2025), typically associated with variable oxygen conditions and brackish environments (Bouchet et al., 2023). The lower diversity observed in the By Fjord is consistent with previous studies showing that prolonged anoxia limits foraminiferal assemblages to a few stress-tolerant taxa, whereas more oxygenated environments, such as Gullmar and Hake fjords, sustain more structured and functionally diverse communities (Alve et al., 2016; Alve, 1995; Richirt et al., 2020).

The Gullmar Fjord, in contrast, exhibited the highest proportion of Globothalamea in both datasets, particularly at the deep GC station (Fig. 7). The assemblages in Gullmar Fjord were also the most diverse according to richness and both entropic indices (Shannon and expH'bc), despite its intermediate oxygen concentrations and seasonally hypoxic events (Fig. 6). The genetic dataset also identified a high relative abundance of *Stainforthia fusiformis*, particularly at GM and GC, as well as HC and HH. In its well-established role as an opportunistic species that thrives under fluctuating oxygen conditions (Alve, 1995; Jorissen et al., 2018), high abundance of *S. fusiformis* suggests that these ecosystems are not in 'pristine environmental condition', as stated by the European Commission (2003). Species *S. fusiformis* was also well represented in the morphological dataset, where it formed a substantial proportion of the assemblage at all three stations in both Gullmar and Hake fjords, either indicating seasonal hypoxia (Polovodova Asteman and Nordberg, 2013) or bottom water salinities higher than 29 (Alve, 1995). The presence of *Bulimina marginata* in high abundances in both datasets at all Gullmar Fjord stations, however, suggests better conditions, as this species is a known indicator of moderate oxygen concentrations (Armynot du Châtelet et al., 2018, Filipsson and Nordberg, 2004a, 2004b).

Taking a deeper look at the preservation of diversity patterns across the fjords, we conducted a delta-based pairwise comparison of diversity values from both datasets within each fjord, quantifying the change in diversity along each fjord transect (Fig. 9). By Fjord, the least oxygenated fjord, showed the strongest agreement across all indices (e.g. Shannon: *r* = 0.88, 67 % agreement), possibly due to a strong ecological filtering under anoxic conditions, which may produce more convergent assemblages across methods. In contrast, Gullmar Fjord showed consistently negative correlations for Shannon and expH'bc (*r* ≈ -0.99), in other words a reversal in the detected diversity gradient between methods. This may reflect differences in how the two sampling approaches respond to local environmental structure or the natural

patchiness in heterogenous transitional environments, emphasising the difficulty in selecting appropriate reference conditions for EcoQS assessment in such systems. It could however be possible to further resolve these differences with increased sampling efforts and additional replicates.

Lastly, the results highlight how diversity index selection could affect eventual environmental impact assessment comparison: indices like Simpson, which are particularly sensitive to dominant taxa, showed stronger agreement across fjords and methodologies, while indices that account for rare taxa (e.g. Richness or Shannon) diverged, sometimes in opposite directions. These findings show that both method and metric strongly affect perceived diversity gradients, adding to the value of multi-index, multi-method comparisons for environmental impact assessments.

## 4.3. Environmental and functional associations

For assemblages identified via metabarcoding to be used for bio-monitoring and environmental assessment, it is essential that eDNA diversity and community composition respond to environmental gradients. These positive or negative correlations must align with known morphological associations and established morphology-based indices.

To interpret these correlations, we selected environmental parameters that characterise a range of physical and biogeochemical processes within benthic habitats. Sediment composition (mud content) and organic matter indicators such as TOC, TN, chlorophyll *a*, and zeaxanthin reflect depositional conditions as well as food availability, which strongly influence foraminiferal abundance and diversity (Jorissen et al., 2007). Porewater nutrient (NOₓ-, NH₄, PO₄, SiO₂) and DIC concentrations indicate levels of microbial activity and organic matter degradation (Middelburg and Levin, 2009), while bottom-water oxygen is impacted by respiration rates and ventilation (Glud, 2008; Moodley et al., 1998a, 1998b). Lastly salinity, shows marine influence and stratification, which is highly variable in fjords due to restricted circulation and freshwater inflow (Björk et al., 2000; Stigebrandt, 2012). In combination, these variables provide a framework for understanding which environmental conditions most strongly shape foraminiferal communities across morphology-based and eDNA-based datasets.

The correlation heatmap (Fig. 8A) illustrates the environmentally structured genetic beta-diversity across the fjords, highlighting the abiotic drivers that shape community composition. In both datasets, salinity, organic matter availability, and oxygen concentration emerged as important environmental drivers, consistent with previous studies on fjords (e.g. Aksnes et al., 2019; Brinkmann et al., 2023; Charrieau et al., 2018; Darelius, 2020; Ferraro et al., 2025; Filipsson and Nordberg, 2004a, 2004b; Polovodova Asteman et al., 2025). However, the relative influence of these variables differed, with genetic data showing stronger bidirectional associations with porewater nutrients, TOC and bottom-water oxygen, whereas morphological assemblages were more directly structured by salinity, an indicator of marine influence. This divergence highlights fundamental differences in how each dataset captures foraminiferal diversity; morphological approach reported barren or very low counts in sediments in By Fjord at stations with elevated DIC, NH₄ and SiO₂, hence the correlation between those parameters and the species present at other stations are mostly negative (Fig. 8). Since the morphological assemblages are biased toward hard-shelled, abundant species with more clearly defined niches, it follows that in fjords, characterised by heterogenous bottom water gradients, salinity (the main determinant of water mass structure) emerges as the primary driver of species zonation (Horton and Murray, 2007). Metabarcoding on the other hand, was possibly better able to reflect a time-averaged signal over a wider range of taxa, influenced by DNA persistence and propagule transportation (Barnes et al., 2014; Mejbel et al., 2022; Morin et al., 2023). Heterogeneity in correlation patterns may have also been increased through detection of non-testate foraminifera, which have poorly resolved environmental tolerances. Although without validation
via quantitative single species detection (e.g. dPCR), additional richness either constitutes greater ecological insight or erroneous associations.

Salinity and oxygen have been identified as the primary drivers structuring microbial communities in surface waters, which are the primary source of phytopigments, as pigment production is limited by reduced light availability at greater depths (Tamayo-Leiva et al., 2021). In both the genetic and morphological datasets, taxa positively correlating with salinity also displayed negative correlations with zeaxanthin, a pigment often associated with cyanobacteria (Jeffrey et al., 1997). This inverse relationship suggests that taxa thriving in higher-salinity environments may prefer conditions with reduced cyanobacterial-derived organic matter, reflecting differences in organic matter composition or preservation (Bianchi et al., 2000, 2002; Salonen et al., 2019).

Salinity clustered alongside oxygen in both datasets (Fig. 8). This is consistent with previous studies, where salinity structuring distribution of fjord and estuarine species, based on eDNA, was also reported (Brinkmann et al., 2023; Singer et al., 2023). In our study, stenohaline taxa such as *Bulimina marginata* and *Nonionella* sp. T3 (also known as *Nonionella turgida*) exhibited positive correlations with salinity (Fig. 8), while *Ammonia confertitesta*, *Trochammina hadai*, and *Cedhagenia saltata* were negatively associated with this factor. This pattern aligns with previous studies indicating that *Bulimina marginata* is a stable component of marine-influenced assemblages (Polovodova Asteman and Nordberg, 2013), whereas *Ammonia* and *Trochammina* species are more euryhaline species, often found in shallow-water and intertidal areas (Kitazato and Matsushita, 1996; Alve et al., 2009; Schweizer et al., 2011; Bird et al., 2020a, 2020b; Singer et al., 2023; Polovodova Asteman et al., 2025). Soft-shelled *C. saltata* has been also previously reported in shallow and estuarine waters (Sergeeva and Anikeeva, 2024; Singer et al., 2023). From a purely statistical perspective, strong salinity structuring could once again be due to the low richness at By Fjord, where BH station was the least saline station populated entirely by a few highly stress tolerant species.

In contrast, genetic data exhibited stronger dipolar associations with pore water nutrients and pigments. Phytopigments such as zeaxanthin and chlorophyll *a* are widely recognised as valuable sediment proxies for examining organic carbon flux and oxygenation conditions in marine sediments (Bianchi et al., 2002; Reuss et al., 2005; Jeffrey et al., 1997). In fjord environments, these pigments primarily reflect variations in organic matter input derived from phytoplankton productivity, terrestrial runoff, and microbial decomposition processes (Reuss et al., 2005; Solås et al., 2024). These phytopigments had a structuring role, with the genetic dataset showing some strong positive correlations between *Trochammina hadai* and *Ammonia confertitesta* with zeaxanthin, while no taxa had a strong negative relationship with the latter. The morphological dataset showed some similarities, with *Ammonia* sp., and *Trochammina hadai*, as well as *Haynesina germanica*, *Elphidium selseyense* and *Elphidium* sp., all strongly correlating with zeaxanthin. There were however far more negative correlations between species and both zeaxanthin and chlorophyll *a*, and, for instance, *Stainforthia fusiformis* negatively correlated with chlorophyll *a*. These findings suggest that different species respond to distinct phytoplankton and organic matter sources, or differences in preservation efficiency between zeaxanthin and chlorophyll *a*, illustrating the complexity of benthic-pelagic coupling in fjord systems (Frigstad et al., 2013). Previous studies have experimentally shown that feeding of foraminifera is species and site specific, with some species, including *S. fusiformis*, being mixotrophs, some being detritivores, omnivores or feeding on bacteria (e.g. *Textularia earlandi* and *Bathysiphon* sp.), whilst others such as *Leptohalysis catella* preferring dinoflagellates as food source (Duffield et al., 2014). *Ammonia confertitesta* was shown to be highly opportunistic and having a mixed diet of bacteria, metazoans, fungi, and diatoms, in comparison to *H. germanica* and *Elphidium oceanense* preferring diatoms as food source (Schweizer et al., 2022).

In the morphological dataset, only *Elphidium clavatum* exhibited a

strong positive correlation with oxygen and no taxa showed a strong negative correlation, suggesting that hypoxia-tolerant species were more evenly distributed across the gradient rather than being strongly excluded from oxygenated environments, or alternatively due to mixing microhabitats during sample homogenisation. In contrast, the genetic dataset captured a larger range of taxa associated with both oxic and anoxic conditions. *Hippocrepinella hirudinea*, *Vellaria pellucida* and ICEMON 7 were associated with high oxygen availability, whereas *Ovammina* sp., *Reophax* sp., ICEMON 4, and several environmental clades showed positive correlations with TOC, DIC and chlorophyll *a* (Fig. 8), reflecting their presence in organic-rich, low-oxygen environments. This positive association between chlorophyll *a* (a labile pigment), TOC and low-oxygen taxa suggests that high pigment concentrations are potentially linked to reduced eDNA degradation and eutrophication under anoxic conditions, rather than purely reflecting direct ecological interactions and recent primary productivity (Mejbel et al., 2022). Within the genetic dataset, taxa which positively correlated with oxygen generally exhibited inverse relationships with chlorophyll *a*, except for the opportunistic species *Ammonia confertitesta*. This pattern is consistent with the coupling between oxygen and chlorophyll *a*, where less primary production results in less organic matter deposition and therefore less oxygen consumption. The associations imply that genetically identified taxa, particularly environmental clades and monothalamids, may indicate a preference for environments of lower primary productivity, higher pigment degradation or the capacity to respire anaerobically. Morphological taxa found at shallow By Fjord stations (largely Globothalamea) consistently correlated positively with both zeaxanthin and chlorophyll *a* (Fig. 8), highlighting their reliance on phytoplankton-derived organic inputs and microbial activity, consistent with previous observations of foraminifera's indirect nutritional uptake from sedimentary organic matter (Duffield et al., 2014). Differences in pigment correlations between the genetic and morphological datasets could be due to ecological niche differentiation driven by species-specific microbiomes and capture of monothalamids sequences. Recent studies have emphasised considerable variability in microbiome composition among closely related foraminiferal taxa, strongly influencing their trophic strategies and adaptive capacities (Chronopoulou et al., 2019; Salonen et al., 2019, Schweizer et al., 2022).

Overall, the genetic dataset captured a wider range of taxa as compared to the traditional morphological approach, which likely underpins most of the discrepancies observed between the two approaches.

In particular, detection of monothalamids, which is a group difficult to detect in traditional morphological approaches, showed that some species exhibited contrasting associations with some environmental gradients. Bidirectional environmental variable associations in the genetic dataset may help to resolve the unknown ecological preferences of understudied monothalamids and ecological clades.

Our diversity survey across three fjords detected the presence of several species with known mixotrophic strategies, e.g., denitrification, dephosphorylation and kleptoplastidy. Below we discuss how species occurrences detected by both approaches reflect environmental parameters and biogeochemical gradients in the studied fjords.

Bottom water oxygenation is linked to nitrogen cycling in marine sediments. The permanently oxic bottom waters in Hake Fjord promotes sedimentary nitrification, where microbial oxidation converts $NH_4^+$ to nitrate and nitrite, leading to higher porewater concentrations of $NO_x$-than in the other fjords (aside from possibly erroneous BC). In the Hake Fjord, *Nonionella* sp. T1 was dominant across genetic and morphological datasets, particularly at HM and HC (Figs. 7, 9). *Nonionella* sp. T1 and 18 other species across 9 genera (e.g. *Globobulimina turgida*, *Stainforthia fusiformis*, *Bolivina pseudopunctata*, *Nonionellina labradorica*) have been experimentally shown to denitrify in oxygenated sediments and possess ability to respire nitrate as well as storing it intracellularly (Glock et al., 2019; Choquel et al., 2021). In Hake Fjord, *Nonionella* sp. T1 could be capitalising on its metabolic plasticity, able to respire oxygen in the oxygenated upper levels of sediment and use $NO_x$- as an electron
acceptor for anaerobic respiration deeper in the sediment, potentially contributing by 50–100 % to total benthic denitrification (Choquel et al., 2021).

The strong agreement between metabarcoding and morphological data in Hake Fjord (Fig. 8) further supports the robustness of *Nonionella* sp. T1 populations in particularly in well-oxygenated systems. *Stainforthia fusiformis*, another known denitrifying species, also exhibits the same directional affinities to oxygen, $NH_4^+$ and $NO_x^-$ in both the genetic and morphological datasets, but to a lesser extent. The abundance of this species peaked, however, in Gullmar fjord, which experiences seasonal hypoxia, indicating that *S. fusiformis*' tolerance to low-oxygen conditions is its primary positive selection trait, even within relatively diverse assemblages. The occurrence of *S. fusiformis* is also positively linked to salinity (Fig. 8), once again confirming its occurrence at salinities higher than 29 (Alve, 1995), which likely is why it was not detected at the BH station, with the lowest salinity (Fig. 3). Another known denitrifying species, *Bolivina pseudopunctata*, was detected morphologically but not genetically at GC.

As is the case for other alien species recovered via metabarcoding, the consistent presence of *Ammonia confertitesta* (Fig. 7), a putative invasive species associated with eutrophic and anthropogenically influenced environments (Richirt et al., 2021; Fouet et al., 2024; Polovodova Asteman et al., 2025), across a range of oxygenation regimes, could represent ongoing species introduction via ballast water transport or range shifts due to changing environmental baselines (Blackburn et al., 2011). *Ammonia confertitesta* has been recently shown to have an unusually high intracellular $PO_4^{3-}$ reservoir (Glock et al., 2025). Yet, neither species identification technique in this study was able to detect strong associations between porewater $PO_4^{3-}$ and foraminifera (Fig. 8). The highly variable concentrations of $PO_4^{3-}$ in surface sediment pore-water coupled with low species counts lead to few positive associations with morpho-species. In the genetic dataset, *Stainforthia fusiformis* exhibited the significant negative correlation with $PO_4^{3-}$, whereas *Haynesina germanica* was positively correlated with $PO_4^{3-}$, with both species having been experimentally confirmed to contain relatively high phosphate reservoirs (Glock et al., 2025).

## 5. Conclusions

Our study explored benthic foraminiferal community dynamics along transitional environmental gradients in three temperate fjords, using traditional stereoscopic and novel bulk eDNA metabarcoding techniques. The results show that a combination of morphological and genetic community survey techniques enhances foraminifera species detection and resolves species specific ecological preferences. In both molecular and morphological techniques, the sharp contrast between By Fjord's low-diversity, stress-tolerant assemblages and the more diverse and even communities in Gullmar and Hake Fjords shows that oxygenation regime is one of the primary factors influencing benthic foraminiferal distributions. Although both datasets showed significant structuring by salinity, the response shown by morphological dataset was more pronounced – with strong negative and positive correlations observed only with salinity and zeaxanthin. Filling the large hard shell bias knowledge gap, genetic techniques reported significant bidirectional response to every environmental variable aside from mud, $NO_x$ and zeaxanthin. The notable dominance of putatively invasive *Nonionella* sp. T1 in both seasonally hypoxic Gullmar- and oxygenated Hake fjords highlights the species' metabolic plasticity and adaptability to fluctuating oxygen conditions. Its proliferation in sediments characterised by elevated $NO_x^-$ concentrations suggests a significant ecological advantage through denitrification capacity, potentially influencing nutrient cycling dynamics and competitive interactions within these ecosystems. However, the demonstrated lower sensitivity of eDNA method to detect high abundances of *Nonionella* sp. T1 at stations where observational techniques found it to be dominant (GM, GC, GH) exemplifies the current issues inherent to? metabarcoding, including primer

specificity, DNA preservation biases, and regional gaps in genetic database. However, when combined with morphological data, these differences enhance ecological interpretation rather than detract from it, providing a more robust, multi-faceted perspective of community dynamics.

Overall, this study confirms that environmental gradients influence foraminiferal community composition, with both genetic and morphological sampling strategies able to reflect the unique ecological conditions via benthic foraminiferal community analysis. The strong correlations between genetic data and nutrient dynamics suggest that eDNA metabarcoding can serve as a valuable tool for long-term environmental monitoring, in both oxic and anoxic settings, particularly when used in combination with traditional morphological analyses. Future research should aim to clarify ecological functions of understudied monothalamids and environmental clades, expand sequence databases for improved species identification, and investigate long-term stability and ecological impacts of invasive taxa within these dynamic fjord environments. Overall, the multi-methodological framework used in this study represents a valuable model for assessing benthic community health and ecological quality status in complex coastal systems.

Supplementary data to this article can be found online at https://doi.org/10.1016/j.marpolbul.2025.119145.

## CRediT authorship contribution statement

**Phoebe O'Brien:** Writing – review & editing, Writing – original draft, Visualization, Software, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Magali Schweizer:** Writing – review & editing, Validation, Software, Methodology, Formal analysis, Data curation. **Filip Morin:** Writing – review & editing, Investigation, Data curation. **Astrid Hylén:** Writing – review & editing, Validation, Methodology, Investigation, Data curation, Conceptualization. **Elizabeth K. Robertson:** Writing – review & editing, Methodology, Formal analysis, Data curation. **Per O.J. Hall:** Writing – review & editing, Methodology, Funding acquisition, Data curation. **Sophie Quinchard:** Writing – review & editing, Methodology, Data curation. **Irina Polovodova Asteman:** Writing – review & editing, Writing – original draft, Validation, Supervision, Resources, Methodology, Investigation, Data curation, Conceptualization.

## Declaration of Generative AI and AI-assisted technologies in the writing process

During the preparation of this work the authors used chatgpt in order to streamline analysis and figure coding. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.

## Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Per O. J. Hall reports financial support was provided by Swedish Agency for Marine and Water Management. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgments

We would like to thank the crew of the University of Gothenburg R/V Skagerrak and scientists on board for sampling assistance. The R/V Skagerrak expedition was financed by funding from the Swedish Agency for Marine and Water Management (HaV; Grant 2535-20) to POJH. Coralie Marais, Muriel Bahut, and Sandrine Balzergue are thanked for the high-throughput sequencing (ANAN platform, National Research
Institute for Agriculture, Food and Environment; University of Angers).

## Data availability

All data and code will be made available upon request

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