Reconstructing pico- and nanophytoplankton assemblages from long-term coastal thermohaline observations

Couteyen Carpaye, Mathilde; Nerini, David; Garcia, Fabrice; Lagadec, Véronique; Nunige, Sandra; Pecqueur, David; Salmeron, Christophe; Buniak, Léa; Didry, Morgane; Feuerstein, Jean-Marc; Grégori, Gérald

doi:10.5194/egusphere-2026-3454

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https://doi.org/10.5194/egusphere-2026-3454

Preprints

01 Jul 2026

| 01 Jul 2026

Reconstructing pico- and nanophytoplankton assemblages from long-term coastal thermohaline observations

Mathilde Couteyen Carpaye, David Nerini, Fabrice Garcia, Véronique Lagadec, Sandra Nunige, David Pecqueur, Christophe Salmeron, Léa Buniak, Morgane Didry, Jean-Marc Feuerstein, and Gérald Grégori

Abstract. Over the past 30 years, the Gulf of Lions in the northwestern Mediterranean Sea has undergone various environmental changes that have impacted marine ecosystems. At the beginning of the 21st century, shifts were observed in small pelagic fish and zooplankton communities. However, little is known about the impact of these changes on phytoplankton communities, especially pico- and nanophytoplankton (hereafter referred as PicoNano), which play a crucial role in the northwestern Mediterranean Sea. One major limitation is the lack of data on these size fractions during the observed shifts. In this study, we show that vertical profiles of temperature and salinity can serve to infer PicoNano assemblages from the thermohaline properties measured in a coastal area, the Bay of Marseille (BoM). We use functional data analysis and clustering methods to identify recurring vertical thermohaline structures over 20 years of temperature and salinity data (1994–2024), measured at low and high frequencies. To address the issue of truncated vertical profiles, we employ a reconstruction method based on functional principal component analysis. Using flow cytometry data collected over the past 11 years (2014–2024), we characterize PicoNano assemblages and the nutrient concentrations associated with each recurring thermohaline structure. We identify three thermohaline structures associated with the most oligotrophic conditions observed in the BoM, all involving a thermally stratified water column. The occurrence of these stratified conditions does not show any significant trend over the past 20 years. However, vertical structures indicative of Rhône River intrusions have been less frequently observed since 2004, while mixed water columns are more frequently highly homogeneous over the same period. We show that highly homogeneous water columns favour different PicoNano assemblages compared to mixed but less homogeneous water columns. Overall, these results suggest that conditions favouring larger nanoeukaryotes have become less frequent since 2004. This work highlights that easily measurable variables, such as vertical profiles of temperature and salinity, can provide valuable insights into more complex variables—such as nutrient concentrations and PicoNano assemblages—when direct observations are unavailable, a situation commonly encountered in long-term and high-frequency monitoring.

Received: 12 Jun 2026 – Discussion started: 01 Jul 2026

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Mathilde Couteyen Carpaye, David Nerini, Fabrice Garcia, Véronique Lagadec, Sandra Nunige, David Pecqueur, Christophe Salmeron, Léa Buniak, Morgane Didry, Jean-Marc Feuerstein, and Gérald Grégori

Status: final response (author comments only)

RC1:
'Comment on egusphere-2026-3454', François Ribalet, 21 Jul 2026
This manuscript links two decades of thermohaline profiles to phytoplankton assemblage shifts at a single Mediterranean coastal station. The dataset is impressive, the analytical approach is interesting, and the writing is clear. However, I have a few concerns, mainly that the authors often use a causal language that doesn’t match what the data show, and several analytical decisions lack justifications, which I explain below:
My first concern is that main conclusion covers a decade with no biological data. The abstract states that conditions favoring larger nanoeukaryotes have grown less frequent since 2004. The PicoNano time series begins in 2014. Ten years of the claimed shift were never observed. The paper shows that thermohaline structures changed after 2004. It shows that different structures associate with different assemblages after 2014. From these two facts it infers a third: that assemblages probably changed between 2004 and 2014. My issue is the language in the abstract and discussion. It reads as observed fact rather than inference. The causal chain: wind shift, then mixing, then smaller nanoeukaryotes, then reduced copepod lipids, then sardine decline. Each link rests on correlation across separate time series. None are modeled together. The chain's foundation is a single Wilcoxon test at p = 0.031, uncorrected, out of four tests run.
Second, the clustering pipeline rests on undocumented choices. The classification of 1,619 TS profiles into nine vertical structures is the analytical backbone of the paper, yet several decisions that directly determine the result are presented without validation. First, the authors report that BIC selected 9 clusters using a VVV model, but BIC has a well-known tendency to favor a larger number of clusters as sample size grows, and it is not guaranteed to identify ecologically meaningful groupings. No sensitivity analysis is provided showing how the biological or temporal results change under 7 or 11 clusters. Second, the clustering is performed on the first 4 MFPCA scores, justified by a “plateau” in explained variance, but the proportion of total variance actually retained by 4 components is never reported. This information is needed to assess whether the low-dimensional representation is adequate. Third, the uncertainty threshold of 0.25 (posterior probability > 0.75) is cited to Scrucca et al. (2023) as a general guideline, not validated for this specific dataset or application. With ~25% of profiles unclassified, the “NC” category is large enough to affect temporal trend estimates; the sensitivity of GAM results to this threshold needs to be tested.
Finally, the Prochlorococcus signal may be an instrument artifact rather than ecology. The paper acknowledges, briefly, that surface Prochlorococcus counts likely miss high-light-adapted ecotypes. These cells lose pigment under strong light and drop below detection. This matters because the paper's central summer-autumn pattern is exactly what that artifact would produce. Stratified oligotrophic water brings high light and photo-inhibition, pushing cells below detection. Mixed water brings low-light-adapted cells to the surface, where the instrument sees them. The paper treats the resulting abundance pattern as ecology, but no evidence is given to rule out instrument bias as the source. The authors should run a sensitivity analysis that removes Prochlorococcus from the analysis and report whether the assemblage separation holds.
Minor comments:
Wind direction is circular (angular) data. Applying a regression tree (CART) to degrees treats the 30° difference between 320° and 350° as equivalent to any other 30° difference, while the 40° gap between 350° and 10° becomes 340°. Please discuss or justify the use of linear CART on circular wind direction data.

Please describe the gating strategy and how did you manage consistency of gate boundaries across operators and years, over a multi-decade series.

Zero-inflated counts (RedPicoProk, OraNano) enter a DDA that assumes normality. Please describe how zeros where handled.

Four Wilcoxon tests are conducted comparing wind intensity between well-mixed and mixed conditions at 320°, 330°, 340°, and 350°. Did the author correct for multiple comparisons? The p = 0.031 result for 330°, the only result highlighted, corresponds to a false discovery rate that is not controlled. With four tests at α = 0.05, the familywise error rate is ~18.5%.

The mean-normalized nutrient index (sum of concentrations each divided by its temporal mean) gives equal weight to each nutrient regardless of its ecological role or dynamic range, so a nutrient with high absolute concentration and low variance will dominate the index. Please justify this choice.

The COAST-HF profiler operates only from April to November, creating a systematic seasonal gap in the high-frequency portion of the combined dataset. Please discuss whether this seasonal truncation introduce a potential source of bias in the cluster distribution or temporal analysis.

The paper identifies three structures associated with freshwater intrusions but is unable to classify Northern Current intrusions at all. The Discussion acknowledges this limitation but does not address its consequences for the completeness of the temporal trend analysis or the NC category composition.

The link between small nanoeukaryotes and reduced copepod lipid content rests on a separate study, different organism, different years. The two series are never formally compared, and confounders (e.g., temperature effects on copepod lipid metabolism, prey switching) are not considered.
Citation: https://doi.org/10.5194/egusphere-2026-3454-RC1
- AC1: 'Reply on RC1', Mathilde COUTEYEN, 24 Aug 2026
  
  We would like to thank the reviewer for their constructive comments.
  Please find attached our detailed responses to each of the comments.
  Best regards,
  Mathilde Couteyen Carpaye and co-authors
  
  Citation: https://doi.org/10.5194/egusphere-2026-3454-AC1
RC2:
'Comment on egusphere-2026-3454', Anonymous Referee #2, 04 Sep 2026
Here, Couteyen Carpaye et al. analyzed and categorized the vertical structure of the water column, based on temperature and salinity profiles, at a coastal observing station in the Bay of Marseille over a 20-year period. The authors then investigated the relationships between the different types of vertical structures identified and surface nutrient concentrations, as well as pico- and nanophytoplankton community structure, characterized using flow cytometry at the same coastal station over the past 11 years.
The authors successfully characterized some significant patterns in the surface PicoNano assemblages and the associated surface nutrient concentrations for most recurring thermohaline structure. They used several statistical approaches to reconstruct truncated vertical profiles, allowing them to better characterize the occurrence and temporal variability of the different types of vertical structures over the 20-year period and to investigate long-term changes in the frequency of occurrence of specific structures.
The relationships observed between surface PicoNano assemblages, nutrient concentrations, and the different thermohaline structures during the past decade were then used to extrapolate these relationships to periods for which direct biological and nutrient observations were not available, including periods when shifts in small pelagic fish and zooplankton communities had been documented in the region.
The authors ultimately aimed to link the observed long-term changes in the occurrence of different water-column vertical structures, and the associated changes in PicoNano assemblages, to documented changes at higher trophic levels of the food web. They hypothesized that shifts in small pelagic fish and zooplankton communities may have been driven, at least in part, by changes in PicoNano assemblages resulting from changes in the physical and biogeochemical structure of the water column.
General Comments
This is a very well-written, concise, and clearly structured manuscript that presents a substantial amount of work and analysis. I was particularly impressed by the clarity with which the results and main outcomes are presented. I have to acknowledge that I do not have sufficient expertise to fully assess the statistical workflow and the different statistical approaches employed in this study. However, despite the complexity of these approaches, the results remain remarkably accessible and relatively easy to follow. The authors have done an excellent job of presenting the results in a clear and coherent manner and of highlighting the main outcomes of the analyses. The approach presented here is very promising and provides an excellent case study of how readily available oceanographic observations, such as temperature and salinity profiles, can be leveraged to infer more complex environmental parameters and investigate long-term changes within an ecosystem.
I have two major concerns about the study, which are somewhat interconnected.
The title does not fully reflect the results presented in the manuscript.

My first concern relates to the title, which, in my opinion, does not fully reflect the main results presented in the study. In particular, the manuscript does not actually reconstruct pico- and nanophytoplankton assemblages over the 20-year period. Rather, the title may lead the reader to expect a time series describing long-term variability and trends in pico- and nanophytoplankton community structure, whereas the main reconstruction presented in the manuscript concerns the vertical structure of the water column at the coastal observatory over the past 20 years. The potential implications of these changes in vertical structure for surface nutrient concentrations and PicoNano assemblages are subsequently inferred based on relationships established during the period for which direct observations are available.
This distinction is particularly important because the statement at the end of the Introduction—“This study […] enables us to empirically infer surface nutrient concentrations and phytoplankton assemblages when these data are unavailable”—seems to suggest that the study directly reconstructs these variables over periods for which observations are missing. I do not think this is fully representative of what is actually presented in the manuscript. The study instead reconstructs the long-term variability in water-column vertical structure and uses the observed relationships between vertical structure, nutrient availability, and PicoNano assemblages to infer the potential changes in these variables during periods when direct observations are unavailable. I therefore think that both the title and the framing of the study in the Introduction could be revised to more accurately reflect the scope and nature of these reconstructions and inferences.
The manuscript does provide valuable information regarding the potential implications of these long-term changes in vertical structure for phytoplankton community structure and nutrient availability. However, these biological and biogeochemical changes are inferred or projected based on relationships established during the more recent period for which direct observations are available, rather than being directly reconstructed over the full 20-year period.
This does not diminish the value of the study. On the contrary, I think the results provide an important contribution to the scientific community, and the approach presented here is very promising for investigating long-term changes at the base of the planktonic food web in this ecologically and economically important region, particularly given the limited availability of direct observations of phytoplankton community structure prior to 2012.
In this regard, I see this study perhaps more as a compelling example or case study demonstrating the potential of novel and sophisticated statistical approaches to infer or reconstruct complex biological and biogeochemical variables—such as nutrient availability and PicoNano assemblages—when direct observations are unavailable. This is a situation commonly encountered in long-term, high-frequency monitoring programs. I therefore encourage the authors to consider whether the title could be revised to more accurately reflect what is actually reconstructed in the study and what is inferred from these reconstructions.
Limited presentation of the underlying biogeochemical and biological observations

My second major concern is the limited presentation of the observations underlying the statistical analyses, particularly with respect to the measured biogeochemical and biological variability captured by the coastal observatory time series.
For example, no actual nutrient concentration data are presented in the manuscript; instead, the authors rely on a nutrient index. I find the use of such an index particularly interesting and potentially very useful for this type of study, as it provides a synthetic representation of multiple nutrient concentrations. However, relying exclusively on this index makes it difficult for the reader to assess the actual biogeochemical variability represented in the dataset. In particular, it is not possible to determine which individual nutrients may have been limiting, or how nutrient limitation may have varied seasonally or over the course of the time series, nor which type of nutrient is associated with which type of vertical structure.
A similar issue applies to the PicoNano assemblages. The manuscript does not provide information on the abundances or SSC values of the different flow-cytometry-resolved groups, and only limited information is provided regarding the FCM dataset used in the analyses. As a reader, I therefore find it difficult to fully assess the magnitude and temporal variability of the biological observations that underpin the statistical relationships presented in the study.
I fully understand the authors' need to synthesize and simplify a complex dataset in order to focus the manuscript on its main objectives and outcomes. Nevertheless, I feel that the reader would benefit from having access to a more complete description of the underlying observations, either in the main text or, preferably, in the supplementary material. In particular, I would encourage the authors to provide additional information on the temporal variability of the measured nutrient concentrations and PicoNano assemblages, as well as more details on the FCM data and how these observations were incorporated into the statistical workflow.
Providing these additional data and methodological details would, in my opinion, greatly improve the transparency of the study and help the reader better understand the relationships between the original observations, the statistical reconstructions, and the resulting interpretations. I have included several specific comments below addressing different aspects of this issue, which I hope will help the authors clarify these points.

Specific Comments
Taxonomy and References: Species names lack proper citations. For fish and plankton species, please provide references in the bibliography.
Page 2, Line 47 Suggested revision: "to monitor pico- and nanophytoplankton populations"
Page 2, Line 48 "This dataset provides a reliable time series beginning in 2012, though FCM measurements were initiated in 2009.” Please clarify the distinction between measurement initiation (2009) and the establishment of reliable time series protocols (2012).
Page 2, Line 48 Suggested revision: "the pico- and nano-sized fractions of phytoplankton"
Integration of Longer Time Series Have the authors considered incorporating the extended SOMLIT time series of chlorophyll-a and nutrient data (available since 2004) into the long-term biogeochemical variability analysis? While FCM data are limited to 2012 onwards, applying the same statistical approach to the pre-2012 period could provide valuable insights into ecosystem variability over a longer timeframe.
Page 3, Line 61 More recent references should be cited (e.g., Cheng et al., 2025; Li et al., 2020; Yamaguchi & Suga, 2019) rather than older citations like Sarmiento et al. (1998).
Page 3, Line 63 Please clarify the relationship between increased upwelling and primary production. The current statement appears to contradict the arguments presented elsewhere in the manuscript. Does increased upwelling necessarily lead to increased primary production in this system?
Page 3, Lines 66-67 Add supporting references for this statement.
Extreme Events Discussion (3rd paragraph of Introduction and throughout) The manuscript would benefit from explicit acknowledgment of extreme but brief events (e.g., flooding, intense upwelling) that significantly impact phytoplankton community structure in the Bay of Marseille and Mediterranean Sea generally. Given that climate change is expected to increase the frequency of such events, it is important to note that while these events are ecologically significant, the biweekly sampling resolution may be insufficient to fully characterize their effects on biogeochemical and biological parameters. Consider citing Fuchs et al. (2023) and Thyssen et al. (2026).
Surface Data Specification (General comment) Please consistently clarify throughout the manuscript that only surface nutrient and FCM data are analyzed. This distinction should be explicit to prevent reader confusion.
Page 4, Lines 101-103 Suggested revision: "Whenever possible, […]. When this was not possible, […]."
Section 2.2.1 A map showing the location of the Bay of Marseille within the Gulf of Lions, the Rhone River discharge point, and the observing station location (including meteorological station) would strengthen the methods section. This could be included in supplementary materials if space is limited.
Page 4, Line 119 Salinity units can be omitted if preferred by the journal. Chlorophyll-a fluorescence from in situ probes should be interpreted cautiously without post-calibration against discrete chlorophyll-a samples.
Page 5, Line 124 Suggested revision: "with the additional contribution of fluorescence induced by phycoerythrin"
Section 2.3 It would be interesting to provide more detailed descriptions of the flow cytometry methods and instrumentation used throughout the study period, to specify how side scatter (SSC) is expressed (linear vs. logarithmic scale) and to clarify whether SSC was normalized to standardized reference beads.
Page 16, Lines 377-379 Suggestion: Include boxplots of each nutrient concentrations for each type of vertical structure type would be interesting.
Page 17, Lines 383-384 Have chlorophyll-a concentrations been log-transformed for statistical analysis? Given that chlorophyll-a typically varies over two orders of magnitude, log-transformation is standard practice and would improve normality assumptions.
Section 3.3.2 Currently, the PicoNano assemblages appear as a "black box"; visualization is necessary for reader comprehension. Please provide figures displaying FCM data (abundance and SSC) for each phytoplankton group. It would also be interesting to include supplementary material showing seasonal variability of abundance and SSC for each group. Finally, please clarify whether abundance and SSC data were log-transformed in the analysis (essential given the typical 1-2 order of magnitude variation in abundance and the log-normal distribution of SSC)
Page 17, Lines 395-397 According to Figure 5a, the statement appears contradictory. Suggested revision: "Big intrusion events were characterized by higher SSC intensity of RedNano, greater abundance of OraNano, and lower abundance of OraPicoProk." Please apply similar clarity to the following paragraph.
Page 17, Line 404 "27 time points"—does each point represent one year of data?
Page 19, Lines 441-443 This passage is unclear. Please rephrase for clarity.
Density Profiles (General comment) Have the authors considered analyzing density profiles? Density could effectively integrate temperature and salinity information and provide additional insights into water column vertical structure differences.
Page 20, Line 447 This statement requires clarification. The authors demonstrate that their statistical approach requires >10 years of biweekly sampling and associated physical data. How could these methods be applied to ecosystems lacking such extensive observation histories? Addressing the practical limitations of this approach would strengthen the manuscript.
Page 20, Line 450 Please clarify what is meant by "longitudinal nature of vertical profiles."
Pages 21, Lines 459-464 Explicitly state that in this study, vertical profile classifications were linked exclusively to surface biogeochemical and biological observations.
Page 22, Lines 467-468 It makes total sense, but not sure to see any data supporting that in this study.
Page 22, Lines 469-470 Define "dominance" precisely (absolute abundance, relative abundance, or biomass). When comparing organisms with one or two orders of magnitude difference in abundance (e.g., Prochlorococcus vs. Picoeukaryotes), comparisons must account for these disparities.
Page 22, Lines 474-476 Thyssen et al. (2026) and Marrec et al. (2018) are also relevant references for this discussion.
Page 22, Lines 479-481 The stated mechanism appears reversed. In well-mixed waters, reduced encounter rates between predators and prey are expected, potentially lowering grazing pressure. When combined with higher nutrient availability, this could decouple grazing from growth rates, allowing phytoplankton biomass accumulation as growth rates exceed grazing rates. Please revise accordingly.
Page 22, Line 481 “Another possibility is that different and smaller species of nanoeukaryotes are favored by mixing." While interesting, this hypothesis lacks substantial supporting evidence in the current analysis.
Page 22, Line 484 Revise to "high light and UV stress can negatively affect certain Prochlorococcus strains and ecotypes."
Page 22, Lines 485-489 Given advances in flow cytometry since the historical SOMLIT analyses, have recent data from your laboratory detected high-light-adapted Prochlorococcus populations? Previous generations of flow cytometers may have had limited resolution for these populations. It would be valuable to report whether contemporary FCM instrumentation and analytical approaches now resolve these populations in recent samples.
Page 22, Line 495 Have the authors considered employing metrics such as Brunt–Väisälä frequency (N²) to quantitatively characterize water column stability.
Page 23, Line 500 As Minidiscus are small diatoms that can dominate the nanoeukaryote population seasonally, have the authors examined links between small nanoeukaryote abundance and silicate concentrations?
Page 23, Line 503 This is not so challenging anymore. We are in 2026. Molecular techniques using size-fractionated omics studies are now readily available and could effectively characterize phytoplankton composition of different size classes.
Page 23, Line 523 Larger Picoeukaryotes during downwelling may reflect photoacclimation responses, as reduced surface light availability may necessitate larger cell size for increased light capture. Access to standardized red fluorescence measurements for each group would facilitate investigation of photoacclimation processes.
Page 23, Lines 516-518 Rather than inviting readers to conduct analysis, consider completing this analysis within the manuscript if data are available.
Page 23, Lines 519-522 Consider discussing the potential impact of extreme local river runoff or intense upwelling events on phytoplankton community composition.
Page 24, Lines 544-545 Higher summer Picoeukaryote abundances during mixing events may reflect their superior competitive ability for the slight (albeit barely detectable) nutrient pulses that accompany mixing, as small cells are the first responders to nutrient availability (see Thyssen et al., 2026).
Page 24, Lines 547-549 During mixing events, combined effects of reduced predator-prey encounter rates and elevated remineralized nutrient availability typically favor Picoeukaryotes initially due to their superior nutrient assimilation capacity. Subsequently, as predator abundance increases and populations stabilize, Nanoeukaryotes respond by exploiting reduced predator-prey interactions.
Page 24, Line 549 "Phycoerythrin-rich nanoeukaryotes (e.g., cryptophytes)"
Section 4.2 Discuss the potential impacts of extreme and brief events (e.g., flooding, strong upwelling) on phytoplankton community structure in the Bay of Marseille. Emphasize that despite biweekly sampling resolution, such extreme events and their ecosystem responses may not be fully captured.
Page 25, Lines 573-574 A significant limitation exists in comparing abundance and size independently. The authors should clarify whether observed conditions favor increased abundance of large nanoeukaryotes or simply alter the mean size of existing nanoeukaryote populations."
Page 25, Lines 577-578 Literature demonstrates that copepod feeding preferences include microzooplankton (heterotrophic and mixotrophic protists; Sommer, 2008; Saiz & Calbet, 2011) and some Synechococcus species (Shoemaker & Moisander, 2017). Note that "nanoplankton" encompasses both autotrophic (phytoplankton) and heterotrophic (nanoflagellates) components, with considerable mixotrophic representation.
Section 4.3 Consider repositioning the discussion of technical limitations earlier in the discussion or condensing it, as the preceding section linking physical structure, biogeochemistry, biology, and higher trophic levels (e.g., sardines) provides more substantive insights for the conclusion.
Page 25, Lines 595-596 This passage requires rephrasing for clarity.
Page 26, Lines 624-625 Not sure what the authors mean here. It seems that it would be great to have more data during winter, when high-frequency data are not so available.
Page 26, Lines 624-626 The potential for coupling observational data with model output should be explicitly highlighted as a future direction. Such integration could significantly enhance understanding and modeling of water column vertical structure in the ecologically important Bay of Marseille.
References
Cheng, L., Li, G., Long, S. M., Li, Y., von Schuckmann, K., Trenberth, K. E., ... & Yuan, H. (2025). Ocean stratification in a warming climate. Nature Reviews Earth & Environment, 6(10), 637-655.
Li, G., Cheng, L., Zhu, J., Trenberth, K. E., Mann, M. E., & Abraham, J. P. (2020). Increasing ocean stratification over the past half-century. Nature Climate Change, 10(12), 1116-1123.
Marrec, P., Grégori, G., Doglioli, A. M., Dugenne, M., Della Penna, A., Bhairy, N., ... & Thyssen, M. (2018). Coupling physics and biogeochemistry thanks to high-resolution observations of the phytoplankton community structure in the northwestern Mediterranean Sea. Biogeosciences, 15(5), 1579-1606.
Saiz, E., & Calbet, A. (2011). Copepod feeding in the ocean: scaling patterns, composition of their diet and the bias of estimates due to microzooplankton grazing during incubations. Hydrobiologia, 666(1), 181-196.
Shoemaker, K. M., & Moisander, P. H. (2017). Seasonal variation in the copepod gut microbiome in the subtropical North Atlantic Ocean. Environmental microbiology, 19(8), 3087-3097.
Sommer, U. (2008). Trophic cascades in marine and freshwater plankton. International Review of Hydrobiology, 93(4‐5), 506-516.
Thyssen, M., Marrec, P., Taupier-Letage, I., Ismail, S. B., Ismail, M. A. B., Denis, M., ... & Sammari, C. (2026). Early winter pico-nanophytoplankton bloom in the Bonifacio cyclonic gyre after an extreme flooding. Progress in Oceanography, 103790.
Yamaguchi, R., & Suga, T. (2019). Trend and variability in global upper‐ocean stratification since the 1960s. Journal of Geophysical Research: Oceans, 124(12), 8933-8948.
Citation: https://doi.org/10.5194/egusphere-2026-3454-RC2

Mathilde Couteyen Carpaye, David Nerini, Fabrice Garcia, Véronique Lagadec, Sandra Nunige, David Pecqueur, Christophe Salmeron, Léa Buniak, Morgane Didry, Jean-Marc Feuerstein, and Gérald Grégori

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Short summary

Monitoring of phytoplankton over long periods and at high frequency is challenging. Yet, it is essential because these organisms form the base of marine food webs, and changes in their communities can affect higher trophic levels. Here, we address the challenge of missing biological data by using vertical profiles of temperature and salinity as proxies for environmental conditions influencing nutrient availability and shaping pico- and nanophytoplankton communities at a coastal site.


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