A critical challenge in calibrating paleoceanographic proxies is ensuring the best possible temporal match between the time period represented by the sediments from which the assemblage data are derived and the length (in years) of the water column environmental parameters considered. To address potential temporal biases, we evaluated ²³⁴Th and ²¹⁰Pb activities. ²³⁴Th activities are the highest in the central Norwegian Trench, whereas the lowest values are in the Fladen Ground and Atlantic Ocean (Fig. S2a). ²³⁴Th and TOC show a positive relationship (R2=0.56, p<0.05; Fig. S2b). ²¹⁰Pb measurements reveal a south-to-north decline in sedimentation rates in the North Sea, from 0.21 cm yr⁻¹ at the southern Norwegian Trench (station N10) to 0.024 cm yr⁻¹ on the northern continental slope (station A31). The inner Baltic Sea has the lowest sedimentation rate of 0.015 cm yr⁻¹ (station B1) (Table S3). Sediments in our dataset reflect modern to recent deposition (see below and Table S3).

Moreover, we consider both seasonal and annual mean water column parameters as input for the CCA analysis and conclude that seasonal data do not significantly improve results as compared to mean annual data, suggesting that dinocyst distribution is generally not distinctively driven by seasonality. Noticeable seasonal differences are only observed for SST and N. However, their overall contributions to the species–environment relationship are weak, as indicated by low eigenvalues (Table 2, -BS). To remain consistent, we use annual means for all water column properties.

Dinocysts, especially heterotrophic ones, are known to be sensitive to oxygen and, hence, selective preservation may affect the assemblages (Versteegh and Zonneveld, 2002; Zonneveld et al., 2007, 2008). In particular, as our dataset also includes stations from the Baltic Sea, assemblage results may potentially be biased by the hypoxic or anoxic conditions characteristic of this basin (Bergman Sjöstrand et al., 2025). Potential preservation biases are evaluated by comparing δ13C_org, MGS (sediment median grain size), BWO (bottom water oxygen), and NPP with TOC (Fig. 2). TOC values range from 0.07 wt % to 5.8 wt %, with the lowest values (< 1 wt %) in the Fladen Ground and Atlantic Ocean and the highest values (> 3 wt %) in the inner Baltic Sea (Fig. 2a). δ13C_org values range from -21.6 ‰ in the Atlantic Ocean to -25.7 ‰ in the inner Baltic Sea (Fig. 2b). MGS varies from smaller grain sizes (down to 5.1 µm) in the Skagerrak, Kattegat, and Baltic Sea to large grains sizes (up to 365.7 µm) in the Fladen Ground and Atlantic Ocean (Fig. 2c). Coarse-grained sediments are consistently characterized by low TOC and high δ13C_org, whereas fine-grained sediments exhibit a broader TOC range and generally lower δ13C_org values (Fig. 2d, e). BWO concentrations range from 14.0 to 301.8 µmol kg⁻¹. When compared to TOC, BWO shows two distinct clusters, with stations B1 and B2 forming one cluster separate from the others (Fig. 2f), while station B3 does not cluster with any group and shows the highest TOC. NPP ranges from 266.1 to 1305.0 gC m⁻² yr⁻¹ and shows a positive correlation with TOC up to ∼ 400 gC m⁻² yr⁻¹, after which the relationship decouples, with high TOC values at moderate NPP values in the inner Baltic Sea (B1–3) and moderate TOC values spanning a wide range of high NPP values (Fig. 2g).

We classified seven palynomorph groups: bisaccate and non-bisaccate pollen, spores, freshwater algae, dinocysts, zooplankton remains, and “other” palynomorphs. In total, 26 pollen taxa, 4 spore taxa, 3 freshwater algae taxa, 35 dinocyst taxa, 7 zooplankton remain taxa, and 6 “other” taxa (e.g., including acritarchs and other undefined or yet undescribed taxa) have been found in the samples (Table S2). Figure 3 presents the palynomorph groups at each location. In general, dinocysts or bisaccate and non-bisaccate pollen dominate the total palynomorph assemblage. Pollen dominates coastal and marginal basins, with the highest percentages in the Baltic Sea, Kattegat, and Skagerrak (14.1 %–71.7 %). Spores and freshwater algae are most abundant in the Skagerrak (4.1 %–12.0 %). Dinocyst and zoomorph remain relative abundances are the highest in the Atlantic Ocean, Fladen Ground, and the Norwegian Trench (56.2 %–92.1 %). In contrast, the Baltic Sea shows the lowest percentages (8.5 %–32.3 %). Zoomorph remains such as tintinnid remains, foraminiferal linings, copepod eggs, and ciliate taxa Halodinium verrucatum (Gurdebeke et al., 2018) are most abundant on the Fladen Ground, whereas ciliate taxa Radiosperma corbiferum (Gurdebeke et al., 2023) dominate in the Baltic Sea. “Other” palynomorphs are generally high in the Baltic Sea (5.4 %–77.2 %).

Due to the low BWO and high TOC at the inner Baltic Sea stations (B1–3; Fig. 2f) and the exceptionally high abundance of “other” palynomorphs (i.e., palynomorph type x, y, and z), these stations were excluded from statistical analyses comparing palynomorph assemblages with environmental variables (Fig. S3). The relative abundance of bisaccate and non-bisaccate pollen in the total palynomorph assemblage correlates highly significantly to NPP, Si, δ13C_org, SSS, DTS, and ΔT, while dinocysts correlate negatively to NPP, and zoomorph remains correlate negatively to TOC and positively to MGS.

We identified 35 dinocyst taxa across all locations (Table 1). The distribution of major taxa (abundance >5 %) reveals distinct regional patterns (Fig. 4). In general, assemblages are dominated by phototrophic taxa, particularly cysts of Pentapharsodinium dalei and Protoceratium reticulatum, which account for up to 84.6 % of the assemblage in the Norwegian Trench (station N14). In contrast, heterotrophic taxa become dominant in nearshore environments and in the Baltic Sea, where Brigantedinium spp. accounts for up to 86.2 % of the assemblage at station B1. Furthermore, the inner Baltic Sea (stations B1–3) is characterized by Pyxidinopsis psilata, a species found exclusively in these samples. Assemblages from Kattegat and Skagerrak show high abundances of Echinidinium spp., cysts of Gymnodinium spp., Lingulodinium machaerophorum, cysts of Polykrikos kofoidii, and Spiniferites bentorii, while on the Fladen Ground, Spiniferites mirabilis and Spiniferites ramosus are highly present. Further north in the Norwegian Trench and Atlantic Ocean, Nematosphaeropsis labyrinthus becomes more abundant (Fig. 4).

We performed a CCA considering all dinocysts in the assemblages (Fig. S4a), and a zoomed-in view is provided in Fig. S4b to show a clearer species–sample relationship. Figure S4a shows that the inner Baltic Sea samples (B1–3) exert a disproportionate influence on the ordination, possibly due to preservation effects related to low BWO as these sample show a strong negative loading with BWO and the unique presence of Pyxidinopsis psilata. This confounding effect, known to influence palynological composition in low-BWO environments (e.g., Reichart and Brinkhuis, 2003; Zonneveld et al., 2007), is reflected in a high eigenvalue (Table 2, +BS). In this full dataset, SSS and NPP have the highest eigenvalues among the environmental variables. To better resolve the environmental drivers across the shelf and open ocean, the three inner Baltic Sea samples were excluded from the final analysis presented in Fig. 5. This CCA reveals that geographically close stations also plot near each other in ordination space, except for the Skagerrak stations, which differ markedly in terms of depositional environment, as reflected by their large difference in terms of water column depth (44–550 m). The first axis, explaining 52.5 % of the canonical variance, clearly separates near-shore (negative scores) from offshore (positive scores) environments. DWC, DTS, δ13C_org, SSS, and BWO load positively on the first axis, while NPP, Si, and ΔT load negatively. The second CCA component explains only 13.3 % of the variance. Excluding the inner Baltic stations reduces the influence of SSS in the ordination (along with SST, N, TOC, and BWO), and NPP becomes the environmental variable with the highest eigenvalue in the marginal CCA (Table 2, -BS). Given that the dominant parameter explaining the dinocyst variability is NPP, we applied MAT to predict NPP. The relationship between the observed NPP and estimated NPP is linear (r = 0.92, p<0.05).

Species-specific abundances reveal strong and statistically significant correlations (|r|≥0.71 and p≤0.05) with key environmental variables (Fig. 6). DWC shows the strongest linear correlation with Nematosphaeropsis labyrinthus (r=0.74). Moreover, NPP shows strong linear correlations with Lingulodinium machaerophorum (r=0.92), the cyst of Gymnodinium spp. (r=0.81), the cyst of Protoperidinium americanum (r=0.76), and Spiniferites bentorii (r=0.74). All of these species also strongly correlate with Si (r=0.61–0.97). Furthermore, the cyst of Protoperidinium americanum shows a strong negative correlation with DTS (r=-0.71) and, similarly to Lingulodinium machaerophorum (r=-0.74), shows a strong negative correlation with SSS (r=-0.71).

The ecological interpretation of species' CCA1 scores reflects their distribution along the productivity gradient, the dominant control on the CCA1 axis (Fig. 5). Species with strongly negative scores are typical of eutrophic, coastal, or upwelling systems. For instance, the cyst of Gymnodinium spp., Lingulodinium machaerophorum, Selenopemphix quanta, Spiniferites bentorii, Xandarodinium xanthum, Votadinium calvum, and the cyst of Protoperidinium americanum are consistently found in stratified, high-productivity environments such as river mouths and fjord systems (Dale et al., 1999; Zonneveld et al., 2013; Lambert et al., 2022). Lingulodinium machaerophorum and the cyst of Protoperidinium americanum are strongly negatively correlated with SSS and δ13C_org (Fig. 6), which indicates that their abundance is related to the presence of freshwater and related nutrients. Other taxa such as Brigantedinium spp., Echinidinium spp., and the cyst of Polykrikos kofoidii similarly reflect nutrient-rich productive coastal conditions (Radi and Vernal, 2008; Zonneveld et al., 2013; Fig. 5).

In contrast, species with high positive CCA1 scores are characteristic of oligotrophic, open-shelf, or deep-ocean environments (Fig. 5). Impagidinium spp. and Ataxiodinium choane prefer low-nutrient waters (Zonneveld et al., 2013). Interestingly, in the literature, Nematosphaeropsis labyrinthus is associated with mesotrophic to oligotrophic oceanic conditions in the cold, deep-ocean environments of the North Atlantic and Nordic seas (de Vernal and Marret, 2007), associated with (winter) deep convection (Wu et al., 2025). Indeed, we find a strong positive correlation of Nematosphaeropsis labyrinthus with DWC and a significant but lower correlation with DTS (Fig. 6). Our data thus confirm the well-known oceanic character of this species (Wu et al., 2025). In our dataset, Nematosphaeropsis labyrinthus can likely be used as a tracer of high-salinity Atlantic water mass influence in the Norwegian Trench.

Other published datasets include samples from locations near to our study area (Grøsfjeld and Harland, 2001; Sildever et al., 2015; de Vernal et al., 2020). While some of the species that, in our dataset, correlate with NPP (cysts of Gymnodinium spp. and Protoperidinium americanum) are not reported in these other datasets, the dominant species (Brigantedinium spp., the cyst of Pentapharsodinium dalei, the cyst of Protoceratium reticulatum, and Nematosphaeropsis labyrinthus) occur in comparable percentages.

High dinocyst concentrations and fluxes have been linked to nutrient-rich conditions and NPP, especially in estuarine and upwelling systems (e.g., Radi et al., 2007; Dale, 2009). The total dinocyst abundances observed in this study are, on average, lower than those reported from surface sediments in nearby fjords (Grøsfjeld and Harland, 2001) yet are comparable to values documented earlier from the Kattegat and Baltic Sea (e.g., Sildever et al., 2015). Total dinocyst concentration and flux correlate positively with NPP, but these relations are not significant (Figs. 6, and7a). While elevated NPP typically coincides with enhanced dinoflagellate populations, resulting in higher dinocyst fluxes to the seafloor (Zonneveld et al., 2009; Ellegaard et al., 2017), the variability in total dinocyst concentration (526 to 70 936 cysts g⁻¹) among stations with similar NPP implies that other environmental parameters (and differences in sediment accumulation rates) must affect this relationship. In our dataset, dinocyst concentrations covary with TOC (R2=0.57, p<0.05; Fig. 7b), a pattern largely driven by preservation (Fig. 2f). Elevated dinocyst concentrations in relatively low-NPP regions suggest the presence of localized “hotspots” of (phototrophic) dinocyst deposition, which might be related to specific conditions or episodic bloom events (e.g., cysts of Protoceratium reticulatum) (Zonneveld et al., 2007; Penaud et al., 2018). These hotspots are particularly evident in the southern Norwegian Trench, Skagerrak, and Kattegat, where seasonal stratification reduces vertical mixing and can support short but highly productive blooms (Zonneveld et al., 2009; Price and Pospelova, 2011; Snoeijs-Leijonmalm et al., 2017). Such short-lived blooms which contribute only little to NPP but result in high fluxes of phototrophic dinocysts may explain why total dinocyst concentrations and flux do not correlate with NPP (p>0.05; Fig. 6). In the sedimentary system, dinocysts behave as fine silt particles, and, thus, finer organic-rich material is related to higher dinocysts abundances. This effect is due to the large surface area of fine particles, which promotes the sorption of organic matter and protects it from degradation (Hedges and Keil, 1995). Calculating total dinocyst flux improves the relationships with both NPP (R2=0.72, p>0.05; Fig. 6) and TOC (R2=0.97, p<0.05; Fig. 7c) as this compensates for differences in sedimentation rates. Note that sedimentation rates are not available for all stations (Table S3), making it more difficult to statistically evaluate these relationships.

The H/A ratio is regularly used to distinguish between eutrophic and oligotrophic conditions as heterotrophic dinocysts are known to be abundant in eutrophic conditions, while phototrophic dinocysts tend to prevail in mesotrophic and oligotrophic conditions (e.g., Pospelova et al., 2008; Zonneveld et al., 2013). However, here, the H/A ratio shows only a relatively weak positive correlation with NPP (R2=0.23, p<0.05; Fig. 7d).

The H/A ratio shows limited use in this region as a proxy for NPP due to the dominant influence of phototrophic species, such as Lingulodinium machaerophorum, the cyst of Gymnodinium spp., and Spiniferites bentorii, on productivity signals, as evidenced by their strong correlations with NPP (Fig. 6). Whereas in other settings this ratio may be used to reconstruct productivity (e.g., Sangiorgi and Donders, 2004; Pospelova et al., 2008), the many confounding factors in the region studied here limit applicability. This relative measure of H/A can be masked if both heterotrophs and phototrophs increase simultaneously; therefore, focusing on only the heterotrophic dinocysts may be a better way to reconstruct NPP.

While individual heterotrophic species, such as the cyst of Protoperidinium americanum, can be used to indicate NPP (strong positive correlation; Fig. 6), their restricted spatial distribution limits their utility across the entire transect (Table 1). Consequently, we combined them with the other heterotrophic dinocyst to form a heterotrophic dinocyst group and to investigate whether this proxy can be applicable in the Baltic Sea to the Atlantic Ocean transect. Both heterotrophic dinocyst concentration (R2=0.55, p<0.05; Fig. 7e) and flux (R2=0.86, p<0.05; Fig. 7f) show a strong relationship with NPP. The relationship between heterotrophic dinocyst concentration and NPP is also valid when considering the inner Baltic Sea stations (B1–3) (Fig. 7e); however, we excluded these stations as the preservation bias may be too high.

Heterotrophic species feed on organic carbon, much of which is produced by primary producers that are linked to chlorophyll a. Heterotrophic dinocyst relative abundance and concentration correlate strongly with Si (Fig. 6). Since diatom blooms rely on Si availability and because the heterotrophic assemblage in our study is dominated by Brigantedinium spp., a known diatom predator (Jacobson and Anderson, 1986), this further supports a trophic connection (Devillers and de Vernal, 2000; Radi and de Vernal, 2008; Price and Pospelova, 2011; Kim et al., 2012; Bringué et al., 2013; Wang et al., 2022). However, Si does not correlate with N and P, indicating different sources and/or uptake. The ratios of nutrients in river water often differ from oceanic averages (Redfield, 1934; Rahm et al., 1996). Depending on the uptake by primary producers and recycling, these ratios change from river and fjord systems to the open ocean. Water column Si correlates strongly to non-bisaccate pollen in the sediment, suggesting that both are transported by rivers (Fig. S3). Si also correlates strongly to phototrophs, such as the cyst of Gymnodinium spp., Lingulodinium machaerophorum, and Spiniferites bentorii (Fig. 6), phototrophic and mixotrophic species that have been linked before to coastal and more nutrient-rich settings (Zonneveld et al., 2013). Given that sedimentation rates vary across sites, flux is the preferred proxy as it better accounts for differences in sediment accumulation and provides a more direct representation of dinocyst production.

The CCA of the dinocyst assemblages allows for the deconvolving of the separate individual environmental factors controlling the dinocyst assemblages. Because each species resulted in a fixed CCA score along axis 1, the sample's CCA1 score is based on a weighted average of the species abundance. 1CCA1sample=∑i=1n(relative abundance of speciesi×CCA1 score of speciesi) The scores, together, show a strong inverse relationship with NPP (R2=0.89, p<0.05; Fig. 7g). Hence, we propose a new regional transfer function based on the CCA1 scores as a proxy for NPP. From the correlation with NPP, we derive the following regression: 2NPPsample=-321.2×CCA1sample+517.9 This two-step approach enables quantitative reconstruction of NPP from dinocyst assemblages and provides a valuable tool for estimating productivity in the region. Previous studies have performed similar multivariate analyses in other regions (Radi et al., 2007; Pospelova et al., 2008), highlighting NPP as a key environmental factor. While NPP is the controlling factor in several dinocyst assemblage datasets, the species most strongly related to the NPP may differ among regions. This underscores the necessity of developing a region-specific calibration.

In addition to the CCA performed on our dataset, we applied a CCA to a combined dataset consisting of our dataset and dinocyst assemblages previously published by Sildever et al. (2015) and de Vernal et al. (2020) from nearby locations using water column parameters (i.e., SST, SSS, N, P, Si, and NPP). The correlation between the resulting CCA1 scores and NPP remains strong considering all datasets (R2=0.71, p<0.05; Fig. S6a). Hence, NPP remains a dominant controlling factor on dinocyst assemblages. Alternatively, MAT can be used to estimate NPP based on both our modern dataset and the other datasets of Sildever et al. (2015) and de Vernal et al. (2020). For our dataset alone, MAT-derived NPP estimates show a strong correlation with observed NPP values (R2=0.84, p<0.05; Fig. 7h), comparable to the performance of the CCA1 approach (Fig. 7g). When datasets from Sildever et al. (2015) and de Vernal et al. (2020) are included, the strength of the correlation decreases (R2=0.53, p<0.05; Fig. S6b) yet remains significant. This suggests that the unimodal model underpinning CCA is better suited for predicting NPP in independent samples such as fossil assemblages used in paleo-environmental reconstructions. The bootstrap validation yields RMSEP values of 113 gC m⁻² yr⁻¹ (based on our dataset alone) and 117 gC m⁻² yr⁻¹ (including other datasets) for the MAT reconstructions, corresponding to an uncertainty of approximately 11 % relative to the maximum observed NPP range. This uncertainty is comparable to previously reported values (Radi and de Vernal, 2008). The difference between the results of our study alone (Fig. 7h–g) and the results that include previously published data from the region (Fig. S6a–b) is mainly due to 25 sediments located in fjords or shallow coastal areas included in de Vernal et al. (2020). In these settings, restricted circulation, freshwater input, and complex nutrient dynamics likely play an important role in (seasonal) primary productivity that is not fully captured by modern analogues (Grøsfjeld and Harland, 2001).