dataesr/scientific-paragraphs-categorization huggingface dataset:
Here we present a multi-cruise overview of the basin-scale variability in the slope of phytoplankton size-abundance (SAS) spectra in the Atlantic Ocean covering coastal, shelf, and open-ocean environments over the 50 • N-50 • S latitudinal range. The analysis is based on a dataset of > 700 phytoplankton samples collected during > 30 cruises over a 27year period. Our aim is to identify general patterns in the large-scale geographical variability of phytoplankton biomass and SAS slopes, provide an observational benchmark for size-based plankton models, and gain insight into the mechanisms and drivers underlying changes in phytoplankton size structure across a wide productivity range.
Data collection Phytoplankton abundance and cell size, together with complementary oceanographic variables, were measured during > 30 oceanographic cruises conducted within the period 1995-2022 in coastal and open-sea waters of the Atlantic Ocean, including the central, subtropical and tropical Atlantic, the Equatorial region, the continental shelf from Mar del Plata to South Brazil, the Angola continental shelf, the Benguela upwelling region, and the coastal upwelling region of the NW Iberian Peninsula (Table  S1 , Fig. S1 ). The resulting dataset contains phytoplankton abundance and size data for a total of 706 samples.
, 2018 , 2012; Sal et al. , 2013). A specific cell volume value was assigned to each phytoplankton species, resulting in a taxonomic assessment of phytoplankton cell abundance and biovolume. When biovolume measurements were not available for the identified species, cell volumes were assigned based on the data given by  Sal et al. (2013)  and other databases of phytoplankton species biovolume  (Harrison et al. , 2015 , Karlson et al. , 2020; Olenina et al. , 2006). Phytoplankton size-abundance spectra (SAS) were constructed by calculating the total cell abundance in each of 26 size classes, covering the size range from the smallest picophytoplankton (0. 5 µm in equivalent spherical diameter, ESD) to the largest observed microphytoplankton (160 µm ESD). Size classes were defined according to an octave (log 2 ) scale of cell volume, where size class width equals the value of the size class lower limit and nominal volume.
Persistence of the size-abundance relationship Heavy departures from linearity in phytoplankton SAS, while virtually absent in oligotrophic, open-ocean environments, have been reported for highly dynamic and productive coastal and shelf waters  (Rodríguez et al. , 2002; Reul et al. , 2006) , often during transient blooms in which most of the biomass is contributed by a few species within a small number of size classes. However, most observations of strong nonlinearity in phytoplankton SAS have limited temporal and spatial coverage, focusing on specific geographic areas or particular time periods. Surveys with a wide temporal and geographical coverage are required to assess the persistence of the inverse relationship between phytoplankton cell size and abundance. Using a dataset of monthly phytoplankton observations during a 10-yr period in a productive, shelf ecosystem,  Huete-Ortega et al.
Changes in SAS slope across a wide trophic gradient We have found a basin-scale relationship between increasing resource supply and progressively less steep SAS slopes, reflecting increasing importance of large cells. This pattern confirms earlier results of studies in which size structure was assessed with measurements of size-fractionated Chl a  (Marañón et al. , 2012; Marañón et al. , 2015; Poulton et al. , 2006)  or phytoplankton carbon biomass in a few size classes  (Brotas et al. , 2022). It must be highlighted that the resource supply index used here includes also a term (the ratio between the depths of the euphotic layer and upper mixed layer) representing light availability. Although in our dataset most of the variability in resource supply was driven by changes in nutrient supply, irradiance has also been shown to be a relevant factor for phytoplankton size structure, explaining the increased contribution of small cells under low-light, high-nutrient conditions in both temperate  (Cermeño et al.
, 2006)  and polar environments  (Clarke et al. , 2008). Seawater temperature varied widely across the sampled regions but we found that SST explains a negligible amount of the variability in SAS slopes. This result agrees with the findings of  Barnes et al. (2011) , who found no relationship between SST and the slope of the normalized biomass size spectra in a dataset covering multiple coastal and openocean environments. While it is well established that temperature affects the mean population cell size of aquatic protist populations  (Atkinson et al. , 2003) , the variability in phytoplankton size structure observed across the ocean mostly results from changes in the relative abundance of different species, rather than changes in mean intrapopulation cell size  (Marañón, 2015 , Sommer et al.
Conclusions A large, multi-cruise dataset demonstrates that the inverse relationship between phytoplankton cell size and abundance is pervasive even in nutrient-rich, productive waters. Our analysis reveals a resource-driven, biogeographic pattern in phytoplankton size structure that is common to both surface and deep waters within the euphotic layer over large spatial scales in the Atlantic Ocean. We have also characterized the latitudinal variability in phytoplankton biomass and size structure, which reflects changes in nutrient supply as represented by the nutricline depth. The overall non-linear relationship between trophic status and SAS slope underscores the pivotal role of intermediate-size cells for primary productivity and energy transfer in the most productive pelagic ecosystems.
Acknowledgements We thank all the researchers who contributed to the collection and analysis of samples and data as well as the captain and crew of all oceanographic vessels involved in the different surveys and programs. This research was supported by the  European Union  through  H2020   TRIATLAS project  ' Tropical and South Atlantic climate-based marine ecosystem predictions for sustainable management'  (Grant agreement No. 817578 ) and by the  Spanish Ministry of Science and Innovation  through research grants  CTM2008-03699 ,  CSD2008-00077 ,  CTM2004-05174-C02  and  REN2000-1248. The  Atlantic Meridional Transect  is funded by the  UK Natural Environment Research Council  through its  National Capability Long-term Single Centre Science Programme,   Climate Linked Atlantic Sector Science  (grant number  NE/R015953/1 ).
This study contributes to the international  IMBeR  project and is contribution number 388 of the  AMT programme. The  RADIALES program  is supported by  Instituto Español de Oceanografía (Spain)  and additional funds from  Xunta de Galicia (Spain)  through grant  IN607A2022-05. The  ACEx /SIMTECO cruise was supported by the  Brazilian National Council for Scientific and Technological Development (CNPq , grant  558108/2009-1 ) and the  Funding Authority for Studies and Projects (FINEP). The PATEX cruise was supported by the  Brazilian National Council for Scientific and Technological Development (CNPq , grant  520189/ 2006-0 ). The Abraços 2 cruise was funded by the the  French oceanographic fleet.
Data availability statement The phytoplankton size-abundance data from the Ría de Vigo annual surveys are available at https://doi. org/10. 5281/zenodo. 8138107. Phytoplankton data from the RADIALES program are available at https://doi. org/10. 1594/PANGAEA. 777384. Data from the ACEx cruise are available at https://doi. org/10. 1594/PANGAEA. 855752. Data from the AMT and the Benguela cruises are available at the British Oceanographic Data Centre (https://www. bodc. ac. uk/). Data from other programs and surveys listed in Table  S1  can be obtained from the authors upon request.
In microemulsions with the hydrophobic isopropyl myristate (IPM), the optimal microemulsion (Winsor III and IV), was achieved by a small amount of rhamnolipid, whereas the optimal solubilization of the more hydrophilic limonene required more than twice the amount of rhamnolipid. In 2011, Nguyen and Sabatini provided an overview of microemulsions formulated with rhamnolipids and/or sophorolipids and discussed the use of such microemulsions in various applications  (Nguyen and Sabatini, 2011). Guo et al. investigated the effect of styrene added to an aqueous rhamnolipid solution consisting of an equimolar ratio of mono-and di-rhamnolipid on aggregate morphology using SANS adjusting two contrasts (deuterated and hydrogenated styrene)  (Guo et al. , 2011). Analysis of the data recorded for the aqueous rhamnolipid solution showed that the cylindrical micelles formed at low concentration changed into a mixture of the latter with vesicles as the concentration increased.
For instance, Hamburger et al. (2013)  found that LAMP detection of schistosome-infected snails was 10 times more sensitive than PCR. We have found that LAMP also works with products of rapid and cheap extraction method (Q&D DNA extraction) where PCR is not working. Combination of Q&D extraction and LAMP allows large-scale molecular sexing in a limited time (~2 h for 32 samples). Another important difference between PCR and LAMP is the nature of false negatives: while sexing PCR can use M-and F-specific primers in tandem, hence significantly reducing the incidence of false negatives due to DNA quality or quantity, LAMP relies on the amplification of a single, small locus (212 bp-long in our case). While a higher incidence of false negatives is a possibility, we found none in our dataset. In fact, the small size of the LAMP amplification region probably contributes to its sensitivity.
In the current study, we estimated the stability of targeted pharmaceutical compounds (ACM, antibiotics, and steroid hormones) under various sample storage conditions and maximum holding times after their preconcentration involving solid phase extraction (SPE) cartridges. Given the focus on marine life, saltwater serves as the focal point. Tis choice is rooted in environmental, regulatory, and ecological considerations, acknowledging the signifcant infuence of medicinal compounds on coastal ecosystems and public health. Te outcomes of this study hold profound relevance, particularly in regions with limited access to necessary laboratories and analytical instruments such as Indonesia, where the time span between sampling and analysis is extended. By addressing these challenges, the study aims to contribute invaluable insights to uphold data validity and reliability in environmental analysis.
Acknowledgments We express gratitude to the  Directorate General of Higher Education (DIRJEN DIKTI)  of the  Ministry of Education, Culture, Research, and Technology, Republic of Indonesia  (Contract no. 116/E5/PG. 02. 00. PT/2022 ) and the  Indonesian Endowment Fund for Education (LPDP) , who provided the funding for this research. Te frst author extends thanks to the  Laboratoire de Chimie de L'Environnement (LCE)-AMU  for their invaluable support, without which this study would not have been possible. Tis endeavor would not have been possible without the generous support received from this laboratory.
DNA Extraction, PCR, and Sequencing DNA extractions were performed using a ZymoBIOMICS DNA Miniprep Kit (ref. D4300) according to the manufacturer's protocol. The variable V3V4 loops (341F: 5 -CCTACGGGNGGCWGCAG-3 ; 805R: 5 -GACTACHVGGGTATCTAATCC-3 )  [16]  of the 16S rRNA gene of the bacterial communities were amplified and sequenced. In addition, the 18S rRNA gene of the eukaryotic communities (microeukaryotes and metazoans) was amplified and sequenced using the variable V4 loop (TAReuk454FWD1: 5 -CCAGCASCYGCGGTAATTCC-3 ; TAReukREV3: 5 -ACTTTCGTTCTTGATYRA-3 )  [17]. Paired-end sequencing (250 bp) was performed at McGill University (Génome Québec Innovation Centre, Montréal, Canada) using the v2 chemistry of the MiSeq system (Illumina). Raw sequence data are available in the Sequence Read Archive database (BioProject ID PRJNA945393).
Sequence Analyses Trimmomatic  [18]  (default parameters, except MINLEN = 100) and the DADA2 package  [19]  (truncLen = c(245,245); maxN = 0; maxEE = c(2,2); truncQ = 2) were used to define the amplicon sequence variant (ASV) and computed taxonomic affiliations using the Silva database (release 138, December 2019). The taxonomic affiliations were used to generate three datasets: bacteria, microeukaryotes, and metazoa. Each dataset was filtered for singletons. Lastly, the tax_glom function was used to obtain the abundances at different taxonomic ranks (from genus to phylum).
Multivariate and Statistical Analyses All statistical analyses were performed using R v4. 0. 4 (R Development Core Team, 2008). The {phyloseq} R package and the ggrare function were used to compute rarefaction curves of species richness for the metabarcoding dataset. The rarefy_even_depth function was used to subsample both the metabarcoding and metabolomic datasets. The alpha diversity metrics (Chao1 and Shannon) were obtained using the estimate_richness function. We performed one-way ANOVA or non-parametric Kruskal-Wallis tests (when the normality of residuals was rejected (Shapiro test)) to compare alpha diversity metrics between plant species. When the ANOVA or Kruskal-Wallis tests were significant, we computed pairwise comparisons between group levels (post hoc analyses) using pairwise t-tests or Dunn tests, respectively.
Metabolite Annotation Molecular networks were generated in negative modes via MzXml files using the online workflow on GNPS (http://gnps. ucsd. edu) (accessed on 19 February 2023)  [36]. MS/MS spectra were filtered by choosing only the top six peaks in a +/-50 Da window throughout the spectrum. Data were clustered for some analyses using MSCluster (parent mass tolerance and MS/MS fragment ion tolerance of 0. 02 Da) to create consensus spectra  [37]. The edges between two nodes in a network were kept if each of them appeared in each other's respective top 10 most similar nodes. The library spectra were filtered similarly to the input data. All matches kept between the library spectra and a network were required to show a cosine score above 0. 7 and at least six matched peaks. The generated similarity networks were compared to a set of databases proposed by GNPS (CASMI Spectral Library, GNPS library).
The AllegroLayout representation mode was used to organize the spatial arrangement of the arrays in Cytoscape. Molecular formulae of significant features were calculated using Sirius 5. 6. 2 [39]. In order to reduce the number of potential candidates, various parameters were used, such as the element selection exclusively including C, H, and O. The isotopic ratio tolerance was set to 20%, and the mass tolerance was fixed at 5 ppm. Only natural product databases of plants were selected. A list of compounds sorted according to the score value of the match was obtained. This value encompassed the uncertainty of the isotopic pattern score, the accurate mass, and the experimental MS/MS fragmentation mirrored in in silico matches. Chemical classes were kept for identified features with scores above 5 and structures with scores above 70% for similarity.
The Alle-groLayout representation mode was used to organize the spatial arrangement of the arrays in Cytoscape. Molecular formulae of significant features were calculated using Sirius 5. 6. 2 [39]. In order to reduce the number of potential candidates, various parameters were used, such as the element selection exclusively including C, H, and O. The isotopic ratio tolerance was set to 20%, and the mass tolerance was fixed at 5 ppm. Only natural product databases of plants were selected. A list of compounds sorted according to the score value of the match was obtained. This value encompassed the uncertainty of the isotopic pattern score, the accurate mass, and the experimental MS/MS fragmentation mirrored in in silico matches. Chemical classes were kept for identified features with scores above 5 and structures with scores above 70% for similarity.
Developmental Effect Then, we compared the rhizosphere compositions of seedlings and trees to identify potential developmental effects. Using a PERMANOVA, significant developmental effects were found for INO on bacterial composition; for CYC, INO, MIC, and SYZ on microeukaryotes; and for INO, MIC, and SYZ on metazoans (Table  1 ). As a consequence, INO had a strong developmental effect on the three types of assemblages (bacteria, microeukaryotes, and metazoans). In addition, while MIC and SYZ had different effects on microeukaryotes and metazoans during development, CYC only had different effects on microeukaryotes. No developmental changes were highlighted for the other plant species and the metabolomic dataset.
Tree Effects on Soil Diversity Significant effects were found in the tree stage using the Shannon index for bacteria and microeukaryotes but not for metazoans (Figure  3 ). In addition, significant effects were identified for microeukaryotes and the negative mode using the Chao1 index. In particular, this analysis highlighted that MIC contained higher metabolite richness than the other plant species. Significant effects were also obtained for the three types of taxa and the negative mode when the beta diversity was analyzed using PCoA and PERMANOVA (Figure  6 ). The significances seemed to be mostly due to INO, but the effects were still significant when INO was discarded from the datasets of microeukaryotes, metazoans, and the negative mode but not the dataset of bacteria (Figure  S2 ). In particular, MIC formed an isolated cluster for microeukaryotes and the negative mode.
particular, this analysis highlighted that MIC contained higher metabolite richness than the other plant species. Significant effects were also obtained for the three types of taxa and the negative mode when the beta diversity was analyzed using PCoA and PERMANOVA (Figure  6 ). The significances seemed to be mostly due to INO, but the effects were still significant when INO was discarded from the datasets of microeukaryotes, metazoans, and the negative mode but not the dataset of bacteria (Figure  S2 ). In particular, MIC formed an isolated cluster for microeukaryotes and the negative mode.
Phylogenetic Analyses of MIC-Associated Taxa As significant associations were identified between MIC and soil taxa (microeukaryotes and metazoans) in the seedling and tree stages, phylogenetic analyses were computed with the best hits from the NCBI database to better describe their diversity. While microeukaryotes were similar to uncultured Cryptomycota (Figure  8A ), metazoans were close to Axonchium sp. (MG921264. 1) and Dorylaimellus virginianus (AY552969. 1) (Figure  8B ). Despite a lack of significant associations with bacteria, a similar phylogenetic analysis was computed to describe the diversity of the ten most abundant ASVs of MIC trees (Figure  S3 ).
Phylogenetic Analyses of MIC-Associated Taxa As significant associations were identified between MIC and soil taxa (microeukaryotes and metazoans) in the seedling and tree stages, phylogenetic analyses were computed with the best hits from the NCBI database to better describe their diversity. While microeukaryotes were similar to uncultured Cryptomycota (Figure  8A ), metazoans were close to Axonchium sp. (MG921264. 1) and Dorylaimellus virginianus (AY552969. 1) (Figure  8B ). Despite a lack of significant associations with bacteria, a similar phylogenetic analysis was computed to describe the diversity of the ten most abundant ASVs of MIC trees (Figure  S3 ). The 10 ASVs were similar to cultured strains of Catellatospora tritici (NR_181780. 1), Desulfovibrio subterraneus (NR_179335. 1), Embleya scabrispora (NR_112597.
The 100 best exometabolite VIPs were also manually checked and were then compared to the metabarcoding dataset in order to identify correlations between metabolite and taxa abundances. While no correlations were obtained between the exometabolites and abundances of both bacteria and metazoans, several links were highlighted with microeukaryotes. In particular, the highest correlations (above 98%) were found between four ASVs (ASV_778, ASV_1008, ASV_1052, and ASV_1296) and 37 exometabolites (Figure  9 ). All ASVs were affiliated with Cryptomycota, and 9 out of 37 metabolites were classified as terpenoids (Table  S9 ). The 25 best endometabolite VIPs were manually checked, and 15 of them were annotated using Sirius and MzMine2 (Table  S9 ). Most belonged to the terpenoid class (Figure  S4 ), except five, which were benzenoids, a flavonone, and a phenolic acid.
The 100 best exometabolite VIPs were also manually checked and were then compared to the metabarcoding dataset in order to identify correlations between metabolite and taxa abundances. While no correlations were obtained between the exometabolites and abundances of both bacteria and metazoans, several links were highlighted with microeukaryotes. In particular, the highest correlations (above 98%) were found between four ASVs (ASV_778, ASV_1008, ASV_1052, and ASV_1296) and 37 exometabolites (Figure  9 ). All ASVs were affiliated with Cryptomycota, and 9 out of 37 metabolites were classified as terpenoids (Table  S9 ).
Trees Had Stronger Effects Than Seedlings on Soil Diversity Our study revealed a weak effect of seedlings on soil diversity in comparison to trees. This result was expected and might be explained by a short influence in terms of time period as well as by the small rhizosphere surfaces of seedlings. Similar results were already obtained, for example, for arbuscular mycorrhizal fungi associated with the large tree Swietenia macrophylla (Meliaceae)  [40]. Although our analyses highlighted the stronger effect of trees on soil diversity, we did not identify significant differences between seedlings and trees for each plant species and each dataset when we compared the matrix compositions using a PERMANOVA (Table  1 ). This observation may reflect a lack of statistical power due to a limited number of replicates.
Miconia calvescens Strongly Influenced Assemblages of Microeukaryotes and Metabolites Although no significant associations were identified between MIC and bacteria, a phylogenetic analysis revealed that the 10 most abundant ASVs of MIC trees were similar to cultured strains, and several were isolated from tropical and/or warm sites (among them, some strains were described as thermophilic)  [44] [45] [46]  (Figure  S3 ). This suggested that the most abundant ASVs of MIC trees might be adapted to the tropical climate of Mo'orea. In contrast, the specific effects of MIC on the composition of microeukaryotes and metabolites (negative mode) were visible in the PCoA (Figure  6 ) and were even larger when the INO samples were removed from the dataset (Figure  S2 ). In particular, MIC showed specific interactions with several Cryptomycota (Figure  7B ), and all of them were close to uncultured strains that were already identified in terrestrial soil  [47] [48] [49] [50]  or freshwater sediment  [51].
Both observations suggest that terpenoids were produced by MIC and that they favored the presence of Cryptomycota. Indeed, terpenoids might be involved in communication between plants and fungi  [58]. However, we cannot reject the hypotheses that exoterpenoids were directly produced by Cryptomycota or other rhizospheric organisms or that endoterpenoids were synthesized by endophytic fungi  [59]. The presence of meroterpenoids reinforces these hypotheses [60]. In addition, terpenoids are not only molecules involved in communication; they are also involved in inhibitory activities against bacteria and fungi  [59]. As a consequence, more studies are needed in the future to identify the producers of these metabolites, the role of terpenoids, and whether they are linked to MIC fitness. In particular, future studies should be conducted on MIC trees using more replicates from more sites and focusing on the most important datasets represented by microeukaryotes, metazoans, and metabolites identified with the negative ionization mode.