A pan-Arctic pigment database for phytoplankton and sea–ice algae

Climate change has dramatically altered the Arctic seas with significant decrease in sea ice extent and thickness and warming water temperature. The ecological impacts of such change have been described for many parts of the Arctic Ocean, but long-term records of biological indicators are still missing. Among those, photosynthetic and accessory pigments are one of the key tools that aid quantification of phytoplankton and sea–ice algae biomass and characterisation of community composition. To address this gap, we present the first pan-Arctic compilation of in situ algal pigment data obtained exclusively by High-Performance Liquid Chromatography (HPLC), containing 10 798 samples collected across 77 Arctic research cruises between 2000 and 2024. As a result of large-scale collaborative effort, this database covers both open water and sea–ice environments across coastal, shelf and open domains. The database (https://doi.org/10.11583/DTU.29445104, Heidemann et al., 2026) includes measures of up to 26 pigments, with 8 major marker/accessory pigments being considered in this study, namely Alloxanthin (Allo), 19'-Butanoyloxyfucoxanthin (But-fuco), Chlorophyll a (Chl a), Chlorophyll b (Chl b), Fucoxanthin (Fuco), 19'-Hexanoyloxyfucoxanthin (Hex-fuco), Peridinin (Peri), and Zeaxanthin (Zea). This publicly available database provides crucial data that can be used to assess phytoplankton dynamics, validating remote sensing observations and can serve as a resource for future Arctic ecological- and modelling studies.

1 Introduction

Climate change has disproportionately affected the Arctic, shown by drastic increases in temperature and ongoing sea–ice decline (England et al., 2021; Stroeve and Notz, 2018). Changing conditions in the Arctic have already altered the food web, with studies indicating further impacts in the near future (Ardyna and Arrigo, 2020; Arrigo and van Dijken, 2015; Flores et al., 2023; Freer et al., 2022; Quinlan et al., 2005). As the foundation of marine food webs, phytoplankton and sea–ice algae are critical for sustaining ecosystems and contribute to large parts of global primary production, leading to energy transfer across trophic levels and ultimately carbon sequestration (Falkowski, 1994; Serra-Pompei et al., 2022). Environmental heterogeneity and changes may alter community composition, with the potential to impact large-scale ecological processes. For example, in the Arctic and sub-Arctic regions, increased open water areas has led to a longer phytoplankton growing season likely associated with changes in community composition. Studies have demonstrated that climate change may favor increased biomass of smaller flagellated phytoplankton species, leading to reduced dominance of large diatoms species (Blais et al., 2017; Coupel et al., 2012; Vonnahme et al., 2025). This can impact the structure of the food web and overall carbon cycling by having an immediate effect on higher trophic levels, potentially influencing the abundance, distribution and feeding preferences of zooplankton (Campbell et al., 2009; Negrete-García et al., 2024).

Characterising phytoplankton community composition is therefore essential for better understanding ecosystem structure, dynamics and services, especially in the under-sampled Arctic seas, which have experienced the greatest impact of climate change in recent years (England et al., 2021). It is clear that long-term data on microalgae physiology and biomass are vital to enhance our ability to assess and detect spatial and temporal patterns in phytoplankton and sea–ice algae community structure and physiology in response to environmental change. Many studies have used pigment-based measurements, but datasets are often scattered across many sources. This makes it difficult to obtain, access and synthesize into a larger spatial and temporal scale. To address this gap, our paper provides baseline information regarding phytoplankton and sea–ice algae biomass and photosynthetic pigments in the Arctic Ocean aiming at providing a framework to support such large-scale Arctic algae studies across two decades of data.

Phytoplankton pigments, particularly chlorophyll a (Chl a), drives photosynthesis by harvesting light and protecting the cells (Falkowski and Kiefer, 1985; Pereira and Gonçalves, 2022). Advances in pigments analysis through High-Performance Liquid Chromatography (HPLC) and ultra HPLC (UHPLC), have enabled precise identification and quantification of a suite of photosynthetic and photoprotective microalgal pigments. These pigments can serve as chemotaxonomic markers for major functional and taxonomic phytoplankton groups, including the commonly used diagnostic pigments, Alloxanthin (Allo), 19'-Butanoyloxyfucoxanthin (But-fuco), Chlorophyll b (Chl b), Fucoxanthin (Fuco), 19'-Hexanoyloxyfucoxanthin (Hex-fuco), Peridinin (Peri), and Zeaxanthin (Zea) used in this study. Additionally and when available, the database presented in this paper also includes α-/β-carotene (αβ-Car), Bacteriochlorophyll a, Chlorophyllide a (Chlide a), Chl c1, Chl c1+c2, Chl-c3, Diadinoxanthin (Diadino), Diatoxanthin (Diato), Divinyl Chlorophyll a and b (DV-Chl a and DV-Chl b), Lutein (Lut), Neoxanthin (Neo), Pheophorbide a (Phide a), Pheophytin a (Phytin a), Prasinoxanthin (Pras) and Violaxanthin (Viola) (Jeffrey et al., 1999) (Table 1). These additional accessory pigments, some of which are also commonly used as marker pigments (Coupel et al., 2015; Fragoso et al., 2017a), enable phytoplankton to adapt and/or acclimate to various light regime changes that occur in the environment (Brunet et al., 2011). Variability in pigment composition can reflect strategies such as photoprotection, which involves the production of pigments that mitigate damage from excessive light and photo acclimation that refers to the ability of algae to adjust their pigment composition in response to light intensity changes that hereby can optimise photosynthetic efficiency (Bonilla et al., 2009; Gosselin et al., 2017).

Table 1 Alphabetically ordered list of the major diagnostic pigments and additional accessory pigments considered in this study (including abbreviations, their full name and associated phytoplankton groups) (Fragoso et al., 2017a; Jeffrey et al., 1999). Notice that Dinoflagellates contains two types, reflecting different major pigments.

Absorption and scattering of light by phytoplankton pigments and other cellular constituents influence the reflectance, which allow their detection by radiometers, making them an invaluable source for remote sensing applications (see overview in Bracher et al., 2017). Multispectral satellite sensors such as SeaWiFS, MODIS, MERIS and OLCI have paved the way and demonstrated the ability to distinguish phytoplankton functional types on large scales (Miller, 2004; Nieke et al., 2015). Recent development have focused on hyperspectral sensors, capable of measuring more narrow absorption and reflectance propertied, that can be missed by multispectral sensors (Hu, 2022). Successful applications include ENVISAT's Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) using the PhytoDOAS method (Bracher et al., 2009; Sadeghi et al., 2012) and the DLR Earth Sensing Imaging Spectrometer Mission (DESIS) using the WebAssembly System Interface (WASI) (Gege, 2004, 2014) by Bracher et al. (2021). Most recently NASA's Plankton, Aerosol, Clouds, ocean Ecosystem (PACE) mission have been launched (Werdell et al., 2024) and the European Space Agency (ESA) is preparing the CHIME hyperspectral to be launched in the coming decade (Nieke et al., 2023).

Since satellites provide long-term synoptic observations of phytoplankton groups high-quality in situ pigment data is invaluable for product validation and algorithm refinement (Cetinić et al., 2024). Similarly, several studies based on self-organizing maps, spectral composition or machine-learning-based methods applied to satellite data have leveraged phytoplankton pigments to understand taxonomic shifts in phytoplankton communities (El Hourany et al., 2019, 2024; Hayward et al., 2025; Xi et al., 2020).

While satellite data is becoming increasingly important, it remains limited by its ability to capture only the first optical depth under cloud- and ice-free conditions.

In situ pigment data from the Arctic have compared to other parts of the world historically been limited due to the region's remoteness, harsh weather conditions and logistical challenges of conducting fieldwork in such remote regions. Advances in HPLC technology, together with standardised protocols (Hooker et al., 2005) and round-robins intercomparison of methodologies (Canuti, 2023), have contributed to the growing availability of pigment data globally, which also includes data from the Arctic and Sub-arctic regions (Kramer et al., 2022; Losa et al., 2017; Mattei and Scardi, 2021; Swan et al., 2016; Xi et al., 2023). Related efforts to obtain pigment data, such as those using UPLC methods (Hwang, 2025), also represent a valuable contribution, though they cannot be incorporated into the current database for inter-comparability reasons with HPLC-based measurements. Despite advances, spatial and temporal coverage across the Arctic remains uneven, underscoring persistent knowledge gaps, highlighting the need for coordinated, collective efforts to compile and expand existing data in this rapidly changing region.

To address this gap, we present the first pan-Arctic overview of phytoplankton and sea–ice algae pigment distributions, based on a comprehensive database of Arctic and sub-Arctic in situ pigment measurements collected between 2000 and 2024. The database is compiled from 77 cruises conducted across the Arctic Ocean, covering coastal, shelf, open-ocean and sea–ice domains (see Table 2). Importantly, this database represents a collective effort involving multiple institutions, research teams and international collaborations. By consolidating data from diverse sources, it provides a baseline for assessing long-term trends in phytoplankton pigments, communities and their ecological responses to environmental change. Additionally, such a database can support a pan-Arctic taxonomic characterisation of phytoplankton communities by employing pigment-based taxonomy techniques (Hayward et al., 2023, 2024; Vidussi et al., 2001; Wright and Jeffrey, 2006).

Table 2 Alphabetically ordered summary of Arctic and sub-Arctic cruises (=77) with key dataset information such as year, sampling region(s) (as defined in Sect. 3.1.), environment, sample count and associated DOI, where possible. Note that few samples were from melt-ponds, here assigned as ice samples.

2 Methodology

2.1 Database compilation

Phytoplankton and ice algae pigment data was compiled from 77 research cruises conducted between 2000 and 2024. The datasets were sourced from a combination of publicly available repositories (e.g., PANGAEA), institutional databases (e.g., NASA) and personal correspondence with scientists. Datasets were accompanied by relevant ancillary metadata, including information on sampling time, location and depth. A total of 10 798 pigment datapoints were compiled (Table 2). Where possible, cruise reports and original publications were consulted to verify the context and methods of data collection. Part of the data has already been published individually for the respective research cruises and/or projects, however, the compiled database presented in this article (including datasets not yet publicly available) has been stored in a public repository (https://doi.org/10.11583/DTU.29445104, Heidemann et al., 2026).

2.2 Data quality measures

This dataset integrated HPLC pigment measurements from 77 research cruises, each carried out by laboratories using their own established laboratory procedures, including different HPLC set-ups, calibration standards and water filtration volume. Methodological descriptions associated with each dataset can be accessed through the provided DOIs in Table 2 or by contacting the original data providers. To ensure consistency and comparability across sources, we applied a set of inclusion/exclusion criteria and harmonisation procedures to ensure data consistency and comparability across sources. Only datasets with measurements of seven out of the eight marker pigments described in Table 1 were included in the database, allowing for reliable inter-sample comparison of pigment composition (criterion 1, see Fig. 1). Although zeaxanthin is normally included in the analysis of phytoplankton functional types, it was excluded as a formal requirement in this database compilation because one source lacked measurement of this pigment and reliable distinction of green algae from other phytoplankton groups remain possible without zeaxanthin (see available pigments for each cruise in Table S1 in the Supplement). Zeaxanthin is also a major diagnostic pigment for Synechococcus, which is largely absent from the Arctic region due to its thermal dependence on warmer waters (Six et al., 2021). Samples were excluded from the final database if they reported Chlorophyll (Chl a) concentration of zero (criterion 2, see Fig. 1), lacked measurements of the required marker pigments and/or were missing essential metadata such as geographic coordinates (criterion 3, see Fig. 1).

Figure 1 Workflow diagram displaying the major decisions in the data compilation process.

In addition to data filtering, multiple data harmonisation steps were also undertaken: longitudes reported in degrees East (0 to 360° E) were converted to signed longitude (−180 to 180° E/W) to ensure spatial compatibility with standard mapping tools; datasets were also checked for true 0 s and NaNs and that the distinction between these were standardised across datasets. This process required manual inspection of each dataset: If a pigment was entirely absent from a dataset, it was treated as NaNs (empty cells), whereas if a pigment was present but only had a single non-zero value reported, the remaining empty cells were interpreted as true 0s; finally, concentration units for accessory pigments and Chl a were also standardised to the common unit, µg L⁻¹. There was considerable variability in methods used to calculate total Chl a was observed across the database, and these methods were not always reported. Consequently, this parameter was excluded. In addition, only a limited number of melt pond samples were available. These were therefore treated as part of the ice sample category, instead of a category by itself.

2.3 Limitations and uncertainties of the dataset

Despite efforts to minimize both limitations and uncertainties, this dataset consists of HPLC measurements from multiple laboratories. We acknowledge that methodological variability between laboratories exists and will introduce variability to the dataset. The following sections are provided to summarise the main known sources of limitations and uncertainties associated with compiling HPLC data from various laboratories, as well as general uncertainties inherent to pigments measurements.

2.3.1 Analytical

All datasets presented in this paper have followed standard protocols for HPLC analysis of phytoplankton pigments (see Table S2). However, analytical uncertainties can arise from multiple stages of the workflow. As outlined by Wright and Jeffrey (2006), these can broadly be categorised into:

• Collection and storage: liltration volume, filter type and size, storage temperature and storage time

• Extraction procedures: sample disruption, duration and temperature, and choice of solvents

• HPLC methods and techniques(see review of HPLC pigment methods in Roy et al., 2011): variations of column type and flow rate etc.

• Peak detection, identification, integration and quantification: differences in baseline correction, peak threshold, co-elution handling and pigment standards used for calibration

To minimize bias in each of these steps, substantial effort has been made through inter-comparability studies. Most notably are the SeaWiFS HPLC Analysis Round-Robin Experiments (SeaHarre-1 to SeaHarre-5) (Hooker et al., 2012) and HPLC/DAD intercomparison on phytoplankton pigments (HIP-1 to HIP-4) (Artuso et al., 2016; Canuti et al., 2016), which efforts has been to eliminate differences in pigment quantification across laboratories and quantify these uncertainties in terms of what can be expected, even when conducting analysis according to strict protocols.

2.3.2 Pigment-specific

In addition to the analytical sources of uncertainties, pigment-specific sources also exist because of a varying degree of sensitivity to the above-described workflow. Any chosen HPLC method can therefore increase or reduce the variance experienced for a specific pigment. However, defining robust “uncertainty values” for each individual pigment remains a challenge across analytical methods. Recent inter-laboratory comparisons highlight this challenge, while also underscoring the need of such estimates in applications such as remote sensing validation (e.g., Canuti, 2023).

A main source of pigment-specific behavior comes from differences in the chemical stability of the pigments. This will affect their susceptibility to degradation, co-elution and extraction efficiency. For example, Chlorophylls (a, b, c₁ and c₂) are prone to degradation, particularly during storage and extraction, producing degradation products such as pheophytins and chlorophyllides. This can lead to biased and unprecise chlorophyll estimations as well as co-elution issues. This have often been reported for chlorophyll c₁ and chlorophyll c₂ as well as for the Fuco derivatives (But-fuco and Hex-fuco) (Roy et al., 2011; Simmons et al., 2016; Wright and Jeffrey, 2006; Zapata et al., 2000). Additionally, some chlorophyll degradation products, for example Phide a and Phytin a, can co-elute with carotenoids due to polarity changes (Mendes et al., 2007).

Finally, pigment concentration itself can be a major source of uncertainty. This is particularly true for low-abundance accessory pigments, which often approach the instrumental limits of detection (LOD) (Bidigare et al., 2002; Canuti, 2023; Canuti et al., 2016; Claustre et al., 2004; Hooker et al., 2005). For instance, when pigment concentrations fall below 0.01 mg m⁻³, the relative standard deviation can reach up to nearly double that of pigments present at higher concentrations (Claustre et al., 2004).

3 Data description

3.1 Sampling coverage

To classify the sampling efforts across the Arctic and sub-Arctic seas into common groups, the study area was subdivided into 15 regions (Fig. 2). A total of 10 095 water samples and 703 sea–ice samples were compiled and mapped according to their geographic location. Water samples displayed extensive sampling across most regions, while ice samples were comparatively sparse and primarily concentrated in the Central Arctic Ocean (CAO), Canadian Archipelago and Hudson Bay. These samples originate from a limited number of cruises and are not consistently represented throughout the years. Regional sampling frequencies are summarised in Fig. 2, highlighting the dominance of water samples and the uneven spatial distribution of sea–ice samples.

Figure 2 Map showing the spatial distribution of water and sea–ice samples across 15 defined regions of the Arctic and sub-Arctic seas. Each point represents an individual sampling location.

Figure 3 Leftpanel: yearly sampling frequency for water column and sea–ice samples, middle panel: monthly sampling frequency and right panel: number of water samples observations per depth bin. Note that for sea–ice samples, the vertical distribution is not presented due to inconsistencies in the ice core sampling methods among the different campaigns.

Overall, the water sample collection also varied substantially across years, with higher coverage in 2002, 2009, 2016 and 2020 (Fig. 3). These periods correspond to large-scale, multidisciplinary campaigns such as MALINA (Coupel et al., 2015), GreenEdge (Bruyant et al., 2022a; Massicotte et al., 2020), the AR7W transect during the MOSAiC cruise and others, including global initiatives such as the International Polar Year (IPY). Additionally, the MAREDAT pigment database represents a major synthesis effort (Peloquin et al., 2013a) (see Table 1).

Seasonally, sampling frequency was the highest in spring and summer (May–August). Sampling frequency was lower in September, followed by a notable secondary drop in December. The lowest sampling frequency is observed during winter and early spring (December–March), when there is extensive sea ice extent. Sampling frequency also varied vertically in the water column (Fig. 3). Most samples were collected within the upper 1–15 m range. Sampling frequency consistently declines from 21–30 m onward, before a slight increase in the 91–100 m range.

3.2 Pigment distribution

Pigment concentration in water and sea–ice samples exhibited considerable variability in the biomass and composition of algal communities as well as in the spatiotemporal structure of the database (Table 3). In addition, the observed variability can also be attributed to adaptive pigment strategies.

Table 3 Summary of 5th, 95th, mean, median and std statistics across all cruises (=77), for (a) pigment concentration categorized by sample type and (b) relative ratio of accessory pigments vs. Chl a, For detailed pigment information by individual cruises, see Table S1. For detailed information about all pigments across environments, see Table A1.

Figure 4 Display of the seasonal variation of individual accessory pigment concentration to Chl a ratio. Water samples are shown as monthly ratios in blue and sea–ice samples are shown overall across months per pigment in green. Only values within 5th to 95th percentile are included. Data points are displayed as open circles, the mean ratio for each month is shown as a red dot and an orange line connects means.

Since direct comparison of absolute pigments concentrations between sea–ice and water samples should be interpreted with caution, only a brief description of absolute pigment concentration is provided for context (Table 3a). Chl a, the primary indicator of phytoplankton and sea–ice algae biomass, had a mean concentration of 0.997 µg L⁻¹ in water samples and 0.639 µg L⁻¹ in sea–ice samples although the highest individual values in both environments point to episodic blooms. These values represent the mean of all individual concentration measurements in the compiled dataset. The highest concentration across the database, 32.76 µg L⁻¹, was recorded in the Chukchi Sea originating from the Ice Scape dataset in August 2010. In general, such high concentrations are rare, with the majority of samples falling below 0.5 µg L⁻¹ across seasons. Overall, concentrations were highly variable with 90 % of values falling between 0.017 and 3.789 µg L⁻¹ in water samples and between 0.022 and 0.292 µg L⁻¹ in sea–ice samples (Table 3a). It should be recognised that there is a potential bias when comparing open-water and bulk–ice samples due to neglect of the dilution of pigments when bulk sea–ice cores are melted, which can underestimate concentrations from sympagic algae (Chamberlain et al., 2022; Gradinger, 2009; Miller et al., 2015).

In terms of the relative ratios to Chl a (Table 3b and Fig. 4) the database also showed marked seasonal variability. Among the eight analysed marker pigments, Fuco consistently had the highest ratios, particularly during winter months, followed by a decline through summer and early autumn. The Fuco-derivatives, But-fuco and Hex-fuco, both considered diagnostic markers of haptophytes, had a stable distribution throughout the year, aside from notable peaks in November.

The ratio of Allo, a distinct marker pigment of cryptophytes, started to increase in late winter, prior to the main growing season. Peri, which is a marker pigment of dinoflagellates, had low overall ratios. These ratios increased during summer, peaking in October, before they exhibited a sharp decline toward the onset of December. As expected, Zea and Chl b were relatively aligned throughout most of the year, consistent with their shared role as a marker pigment of green algae. However, a marked dissimilarity in their patterns occurred in November, when Chl b declined while Zea increased.

For the sea–ice samples, the only accessory pigments with high ratios are Fuco and Chl b.

These differences suggest distinct pigment signatures between sea–ice and water samples, underscoring the importance of expanding sea–ice sampling efforts to better capture algae dynamics in this environment.

3.2.1 Spatial and temporal dynamics

In our dataset, Chl a exhibited strong spatial and temporal (seasonal) and inter-annual variability, reflecting the dynamic nature of phytoplankton and sea–ice algae in polar and subpolar environments. Elevated concentrations were observed in areas such as the Fram Strait, Barents Sea, Beaufort Sea, Chukchi Sea and Labrador Sea (Fig. 5). However, as the database is opportunistic by nature, covering only discrete regional samplings, spatial and seasonal comparisons should be made with caution.

Figure 5 (a) Spatial distribution of all measured Chl a water samples in each season, shown as subplots for spring, summer, autumn and winter. Color mapping uses logarithmic scale, but the values themselves are not log-transformed. (b) Mean Chl a concentration as function of depth range (rows) and season (columns). Color indicates the average value for each depth/season bin. Missing color indicates that data is not available. (c) Spatial distribution of all measured Chl a for sea–ice samples.

Figure 6 Frequency histograms displaying log-scaled pigment concentrations. Water samples are shown in blue and sea–ice samples are shown in grey. x axis represents actual pigment concentrations. Note the different ranges for y axis comparing water and sea–ice samples.

In spring, the Labrador Sea recorded the seasonal maximum (18.75 µg L⁻¹), with the highest values occurring mostly in surface waters (0–5 m). As previously mentioned, summer exhibited the maximum database record for Chl a. Contrary to spring, highest averages were found between 16–30 m. By autumn, Chl a had declined across most regions. However, localised high biomass persisted in some areas such as Beaufort Sea, Chukchi Sea and Fram Strait. Winter Chl a was generally low.

The concentration frequency distribution plots of water samples (Fig. 6, blue plots) showed that both Chl a, Chl b and Fuco exhibited approximately log-normal distribution, with peak concentrations near central values and tails on either side. This interpretation was supported by the log-normal distribution of data. However, formal normality tests such as Kolmogorov–Smirnov (K–S test) indicated significant deviations from perfect log-normality, likely due to large sample size and/or the presence of outliers (see Fig. A1). In contrast, pigments such as Zea, Peri and the Fuco derivatives exhibited more variable distributions and greater heterogeneity among samples. The histograms for the sea–ice samples (Fig. 6, grey plots), had much more variability in the distribution of concentrations. This is likely also related to smaller sample size for sea–ice samples.

4 Data availability

All datasets used in this paper have been compiled into a single repository, accessible using https://doi.org/10.11583/DTU.29445104 (Heidemann et al., 2026).

5 Conclusions

Photosynthetic and accessory pigments serve as indicators of community structure and biomass, and are important oceanographic variables for understanding ocean ecology, as they can also support assessment of biogeochemical cycling. Yet, datasets reporting concentrations of those pigments remain sparse in the Arctic region. To address this gap, we compiled, filtered, standardised and harmonised 10 798 pigment samples from HPLC, including water (n=10 095) and sea–ice samples (n=703), collected during 77 research cruises spanning from 2000 to 2024 across 15 defined Arctic and sub-Arctic regions. Sampling frequency varied by year, with peak efforts in 2002, 2009, 2016 and 2020. These years correspond to major multidisciplinary research projects and international initiatives such as the International Polar Year (IPY). Seasonally, most samples were collected during spring and summer months in the upper water column.

Within the available dataset, Chl a was found in higher concentrations in sea–ice samples compared to water samples, on average. However, for accessory pigments, concentrations were generally found to be lower in sea–ice samples. Despite this, the highest concentration of Chl a across the dataset, 32.76 µg L⁻¹, was recorded for a water sample in the Chukchi Sea. Other areas that exhibited elevated Chl a concentrations were spread across the Arctic marginal seas such as the Fram Strait, Barents Sea, Beaufort Sea and Labrador Sea. Interestingly, among the eight marker pigments, Fuco consistently had the highest pigment to Chl a ratios, particularly during winter months, followed by a decline through summer and early autumn. For the sea–ice samples, the only accessory pigments with high ratios were Fuco and Chl b.

Our comprehensive phytoplankton and sea–ice algal HPLC-based pigment dataset provides valuable insights into the research effort in the Arctic Ocean. Furthermore, it offers important observations on spatial and temporal patterns of primary producers supporting large-scale environmental research in the Arctic across over two decades of sampling efforts.

Appendix A

Table A1 Pigment concentration statistics for all included measured pigments, separated by sample type and summarised across all cruises. For each pigment and environment, the table reports the 5th percentile, 95th percentile, mean, median and standard deviation (Std).

Figure A1 Histogram and Q–Q plots performed for each marker pigment on standardised and log-transformed data. For each pigment, a histogram and a Q–Q plot are visualized to assess normality. All Kolmogorov–Smirnov (K–S) p values were below 0.05 test, indicating that the data deviates from a normal distribution.