dataesr/scientific-paragraphs-categorization huggingface dataset:
1 ). Both methods allow for discrete sampling of a known volume of seawater from a well-constrained depth. Sampling varied across campaigns and days, following AUV surveys laid out in order to transition from nearshore to offshore environments. Sample depths ranged from the surface to ∼40 m targeting zones above, at, and below the predominant thermocline, which was often associated with a band of high fluorescence. Samples were enumerated by microscopic examination. Both dinoflagellates and zooplankton (bioluminescent or not) were fully enumerated; orders of magnitude were estimated for diatoms and converted into approximate numerical values. This paper focuses on two autonomous datasets and matching in situ counts: (1) bioluminescence measured by a BP attached to a shipboard Schindler trap (all campaigns), and (2) fluorescence and bioluminescence measured by a Dorado-class AUV  (AOSN-2003).
The first dataset is used to validate the bioluminescence proxies using plankton counts from all field campaigns (Section 3. 1), and the second dataset is used for h-dino/a-dino/a-other proxy calculation (Section 3. 2) and further analysis of the entire set of proxies (Section 3. 3). Even though validations of the AUV proxies using shipboard samples is complicated by space and time lags, the AUV dataset was chosen because the h-dino/a-Fig. 2. Example of simulated bioluminescence time series. Flash kinetics (a) were chosen as typical of dinoflagellates (low intensity, short duration) and zooplankton (high intensity, longer duration) following Table  2  and calculations based on Eq. (1) (see Appendix A). The concentrations of bioluminescent sources ([BLS]) correspond to dinoflagellate and zooplankton concentrations in Fig.
) dino/a-other proxies require both fluorescence and bioluminescence to be simultaneously measured. This rules out shipboard datasets because the CTD (fluorescence) and Schindler trap (bioluminescence) were sequentially deployed. Different generations of MBBPs were used across campaigns, the main difference being the temporal resolution: 60 Hz for AOSN-2003, 1 Hz for  MUSE-2000 and SPOKES-2002 (see Appendix B). This is a significant difference as typical flash durations can be 100-200 ms (Table  2 ), thus flashes are fully resolved with the 17 ms time step of the 60 Hz sensor but not with the 1 s time step of the 1 Hz sensors. The proxy method was directly applied to AOSN-2003 but had to be adapted for  MUSE-2000 and SPOKES-2002. The min_bg bioluminescence was estimated using a similar sliding window technique.
Across the entire nighttime dataset, their correlation is very high for bg_BL (r = 0. 95) and lower for flashes (r = 0. 76 for flashes per liter averaged over 15 sec windows), suggesting that 1 Hz is sufficient to resolve dinoflagellates but less so zooplankton. Consistent with this assessment, for AOSN-2003 the correlation between bg_BL and dinoflagellates only slightly decreased by using 1 Hz data (0. 52 instead of 0. 60, Tables  4  and 5 ). Even without flash resolution, 1 Hz bioluminescence proxies significantly correlated with the dinoflagellate and zooplankton concentrations during all three campaigns (Table  5 ). Correlations between 1 Hz bg_BL and dinoflagellate concentrations range from 0. 44 to 0. 65, and reach 0. 49 when considering all three campaigns together. Correlations between zooplankton concentrations and the 1 Hz flash proxy ranged from 0.
34 to 0. 49, decreasing to 0. 29 for the combined dataset. These low correlations are not surprising since 1 Hz time series do not resolve individual flashes. With the exception of zooplankton for MUSE-2000, zooplankton correlations with flashes are stronger than with bg_BL, and dinoflagellate correlations with bg_BL are always stronger than with flashes. The scaling factor is identical for a-dinos and h-dinos, but different for diatoms, which were only qualitatively estimated. Plankton dominance in (j) is attributed to whichever has the highest proxy, and the value for the color mapping is the difference between the dominant and the next highest proxy (colorbars range: 0-0. 4). When the value of the dominant proxy is < 0. 2, dominance is set to 0 (low/mixed). Blue triangles in the top panels identify the two vertical profiles displayed in Fig.
Fluorescence / bioluminescence proxies during the AOSN-2003 field campaign H-dino, a-dino and a-other proxies were computed from the AUV Dorado nighttime dataset collected during the AOSN-2003 field campaign  (August 10-16, 2003). For this dataset, fluorescence and bioluminescence are decoupled (r = 0. 38). Fluorescence and background bioluminescence, while more strongly correlated, share less than half of their variance (r = 0. 65) suggesting that the plankton community alternated between h-dinos, a-dinos and a-other (diatoms). The only parameter needed to define the proxies, ratioAdinos, was determined from the Dorado dataset by analyzing the ratio of the 60 Hz background bioluminescence and fluorescence, both regridded at a 1 Hz resolution. A histogram of the ratio clearly separates low (a-other), high (h-dinos) and intermediate (a-dinos) ratios (Fig.
Analysis of the AOSN-2003 Dorado dataset Combining all the Dorado surveys provides a picture of plankton communities in Monterey Bay during upwelling conditions and highlights the potential of the method (Fig. 8 ). A single map was built because winds were consistently upwelling-favorable  (Shulman et al. , 2011; Fig. 8a) , suggesting that spatial variability was likely stronger than temporal variability. The proxies indicate that dinoflagellates dominated near shore while other phytoplankton (here diatoms) were more abundant inside the bay (Fig. 8b ). This plankton distribution mirrors the surface temperature measured by the vehicle (Fig. 8c ) with dinoflagellates dominating in warm waters and diatoms in colder waters. This is consistent with the model presented by  Smayda and Trainer (2010)  in which diatoms bloom first when upwelling intensifies, then dinoflagellates bloom when upwelling relaxes.
AOSN-2003 correlations supports this hypothesis by showing a increase in performance for the larvacean proxy when excluding samples with larvacean concentrations > 3. 5 L -1 , also corresponding to bioluminescent zooplankton > 4 L -1 ("high BLzoo samples", 7% of the samples, Table  4 ). The validity of the dinoflagellate proxy can also be estimated from Fig. 10d  as a function of concentration. Measured bioluminescent dinoflagellate concentrations were most often > 100 L -1 , a situation where both min_bg and med_bg are proportional to [BLS] (Fig. 10d , noting that in dinoflagellate simulations with shorter duration flashes, min_bg becomes > 0 above ∼ 60 L -1 instead of ∼ 20 L -1 ). Either min_bg or med_bg can be used as a dinoflagellate proxy since both are highly correlated (r = 0. 96 in the AOSN-2003 AUV dataset).
In addition, which platform is used and how the BP is mounted can impact the signal as well, notably for zooplankton because of avoidance behavior. The bioluminescence proxies were primarily developed based on data measured by a 60 Hz BP deployed during AOSN-2003 and later commercialized as the WET Labs UBAT (http://www. seabird. com/ubat). The method could thus be applied to other UBAT datasets, although parameters such as the minimum envelope and flash threshold would need to be adjusted. While the proxies could be adapted to different BP/fluorometer combinations, direct quantitative intercomparisons of resulting proxies would be difficult. Within the same dataset, the proxies are expected to be proportional to the concentration of their target species, with a few caveats: First, zooplankton proxies are only representative of the water being sampled by the BP, and may not be representative of local conditions because of organism patchiness and low concentrations.
This assumption is supported by the correlation between bioluminescent and total dinoflagellate species in our dataset (r = 0. 52, p < 0. 01 over the three field campaigns). Kim et al. (2006)  also reported a synchronous increase in both bioluminescent and non-bioluminescent dinoflagellate species during a bloom, and  Marcinko et al. (2013b)  identified a number of studies that found positive associations between bioluminescence and total dinoflagellate concentration (bioluminescent or not). However, dinoflagellate communities can evolve over time, and ratioAdinos could for instance increase when the adino population shifts towards more bioluminescent dinoflagellates leading to an overestimate of h-dinos relative to a-other. Particularly in large datasets spanning very diverse communities, deviations from the average relationship are possible and would result in unreliable proxies.
, 2006)  but there is at present no known method that can determine whether a dinoflagellate bloom is harmful or not from their bioluminescent emissions. Zooplankton research is currently largely limited to sampling by net tows and to imaging and acoustic surveys. In recent years, acoustic sensors have been integrated onboard AUVs (e. g. ,  Powell and Ohman, 2012; Moline et al. , 2015)  but these datasets remain limited. By contrast, bathyphotometers have been deployed for years if not decades. The Dorado AUV has been operating in and near Monterey Bay since 2003, now representing a 15-year time series of concurrent fluorescence and bioluminescence measurements. The corresponding proxies could shed light on zooplankton temporal and spatial patterns, interactions with phytoplankton, and environmental forcing.
While proxies do not represent a direct measure of zooplankton concentration nor biomass, they can provide valuable information on the community's spatio-temporal variability. The method presented here represents a novel effort to go beyond direct measurements to visualize zooplankton, in line with other studies attempting to infer its distribution from space  (Messié and Chavez, 2017) , from coupled physical/biological models  (Santora et al. , 2013) , and from next generation DNA sequencing methods  (Harvey et al. , 2017). To help make the proxies more readily tested and adopted by the community, example datasets and the toolbox of programs necessary to compute the proxies are available online at https://bitbucket. org/messiem/toolbox_BLprocess. series. The median background (med_bg) is similarly obtained using a median function.
The mean dinoflagellate bioluminescence is better represented by the median than mean bioluminescence, as the mean would be strongly contaminated by high values (zooplankton flashes i. e. nondinoflagellate). It is assumed that dinoflagellate bioluminescence oscillates symmetrically around its mean, based on observations for high-concentration dinoflagellate populations  (Latz and Rohr, 2013, their Fig. 2 ). The envelope is thus defined by using min_bg as the bottom limit, and defining the top limit symmetrically around med_bg (equal to 2 × med_bg -min_bg). Flashes are identified using the Matlab function peakfinder. m (https://mathworks. com/matlabcentral/fileexchange/25500-peakfinder); only flashes above the envelope are considered. The number of flashes per second is computed from the time series, and divided by the BP flow rate to obtain the number of flashes per liter.
Pearson correlation coefficients between bioluminescent plankton concentrations and 1 Hz bioluminescence proxies from the Schindler trap dataset. "n. s. " indicates correlations non-significant at the 5% level, * correlations significant at the 1% level, ** at the 0. 1% level, bold expected correlations (dinoflagellates with bg_BL, zooplankton with flashes). Numbers of samples are given for each campaign. The AOSN-2003 bioluminescence was adjusted using a 2. 63 factor in the "combined" correlations to account for a change in BP (see Appendix B).
Acknowledgements This work was supported by the  David and Lucile Packard Foundation , and by  ONR  Grant  N00014-00-1-0842  to S. H. I. S. was supported through the  US Naval Research Laboratory  under program element 61153 N. We appreciate the support of  Hans Thomas  and the AUV operations team,  SeaBird WET Labs  for the loan of the instrument, and  John Ryan  and  Jodi Brewster  for early assistance with data processing toolboxes.
Pearson correlation coefficients between bioluminescent plankton concentrations and 1 Hz bioluminescence proxies from the Schindler trap dataset. "n. s. " indicates correlations non-significant at the 5% level, * correlations significant at the 1% level, ** at the 0. 1% level, bold expected correlations (dinoflagellates with bg_BL, zooplankton with flashes). Numbers of samples are given for each campaign. The AOSN-2003 bioluminescence was adjusted using a 2. 63 factor in the "combined" correlations to account for a change in BP (see Appendix B). Background bioluminescence (ph L -1 ) Low intensity flashes (nb L -1 ) High intensity flashes (nb L -1 ) Maximum intensity (ph s -1 ) Dinoflagellates (N=15) 0. 60 n. s. n. s. n. s. Larvaceans (N=89) 0. 48 ** 0. 46 ** n. s. n. s. Larvaceans < 3. 5 L -1 (N=83) 0.
We examined the rRNA genes of over 10,000 publicly available complete bacterial and archaeal genomes to identify which taxa have unlinked rRNA genes and to determine if there are any genomic characteristics shared across taxa with this feature. As complete genomes are not typically available for the broader diversity of Bacteria and Archaea found in environmental samples  (13) , we also characterized rRNA gene arrangements using long-read metagenomic datasets obtained from a range of environmental samples, which together encompassed over 17 million sequences (≥1000 bp). With these long-read metagenomic datasets, we were able to determine whether unlinked rRNA genes are common in environmental populations and how the distributions of unlinked rRNA genes differ across prokaryotic lineages and across distinct microbial habitats.
Analyses of complete genomes We downloaded all bacterial and archaeal genomes in the RefSeq genome database  (14)  classified with the assembly level "Complete Genome" from NCBI in January 2019 (12539 genomes). We removed genomes from consideration that had rRNA genes that were split across the genome start and end (96 genomes), >20 reported rRNA genes (2 genomes), or an unequal number of 16S and 23S rRNA genes (219 genomes). This left us with a set of 12222 genomes. We used gene ranges associated with each open reading frame (ORF) to pair the 16S and 23S rRNA genes that were closest to each other in each genome. We then checked for gene directionality (sense/antisense) and calculated the distance between each pair, taking directionality into account (see Supplemental Figure  S1  for more detail and a visual representation).
Long-read metagenomic analyses To investigate the prevalence of unlinked rRNA genes among those Bacteria and Archaea found in environmental samples (including many taxa for which genomes are not yet available), we analyzed long-read metagenomic datasets generated from soil, sediment, activated sludge, anaerobic digesters, and human gut samples. These metagenomic datasets were generated using either the Oxford Nanopore MinION/PromethION (6 samples) or the Illumina synthetic long-read sequencing technology (also known as Moleculo, first described in  (16) , 9 samples). The Moleculo sequences originated from four previously published studies covering: the human gut  (17) , prairie soil  (18) , sediment  (19) , and grassland soils (MG-RAST project mgp14596,  (20). The Nanopore sequences originated from four unpublished studies spanning a diverse range of environment types: anaerobic digesters, activated sludge, sediment, and lawn soil.
Instead, we relied on our downstream filtering steps to remove sequences of poor quality. Metaxa2 version 2. 1  (21)  was run on all sequences with default settings to search for SSU (16S rRNA) and LSU (23S rRNA) gene fragments. Taxonomy was assigned to the partial rRNA sequences using the RDP classifier  (22)  and the SILVA 132 SSU and LSU databases (both clustered at 99% sequence identity, 23). If a sequence contained both 16S and 23S rRNA genes we used the taxonomy with the highest resolution (if the 16S was annotated to family level while the 23S was genus level, we used the 23S taxonomy for both rRNAs). Details on each sample, including number of reads and median read lengths, are available in Supplemental Table  S1. We next used a number of criteria to filter the reads included in downstream analyses and to identify taxa with unlinked rRNA genes.
We only included those reads in our final dataset that met the following criteria: 1) Contained a 16S rRNA gene (to avoid potentially double counting organisms with unlinked 16S and 23S rRNA genes), 2) Included the last two domains of the 16S rRNA gene (V8|V9) (Metaxa2 uses multiple Hidden Markov Model (HMM) profiles targeting conserved regions of rRNA genes, each of these regions is referred to as a domain), 3) The length of the 16S rRNA gene was ≤4000 bp and the length of the 23S rRNA gene (if present) was ≤ 6800 bp. These thresholds were chosen to remove erroneously long rRNA genes while accommodating insertions within rRNA genes such as those that occur in Candidate Phyla Radiation (CPR) taxa  (24) , Nostoc, Salmonella, and others (25), 4) Could be classified to at least the phylum level of taxonomic resolution.
Of the subset of reads that met these criteria (112 -878 per Moleculo sample, 3817 -28056 per Nanopore sample, see Supplemental Table  S1  for details), we classified reads as containing unlinked rRNA genes if there was >1500 bp between the 16S and 23S rRNA genes, or if there was no 23S domain found 1500 bp after the end of the 16S rRNA. We note that, unlike the NCBI gene ranges, Metaxa2 takes strand information into account and translates start and stop locations into sense orientation for SSU and LSU. For our final analyses, we removed reads that could not be classified as linked or unlinked rRNA genes (for instance a sequence with only 300 bp after the 3' end of the 16S rRNA gene). All analyses were done in R version 3. 5. 1  (15). Information on all long-read sequences included in these analyses (including classification of rRNA genes) is available in Supplemental Dataset S2.
Phylogenetic tree combining long-read and NCBI datasets A phylogenetic tree was created from full-length 16S rRNA gene sequences by combining both the NCBI complete genomes and representatives of the long-read metagenomic datasets. For the NCBI genome sequences, we selected one 16S rRNA gene sequence per unique species. For the long-read datasets, we first matched the partial 16S rRNA genes recovered by metaxa2  (21)  to full-length 16S rRNA gene sequences in the SILVA 132 SSU database  (23)  using the usearch10 version 10. 0. 240 command usearch_global (settings: -id 0. 95 -strand both -maxaccepts 0 -maxrejects 0; 26). The fulllength SILVA 16S rRNA genes sequences that matched to the long-read sequences ≥95% percent identity and ≥500 bp alignment length were used as representatives of their longread sequence match.
Genomic attributes associated with unlinked rRNA genes All tests for genomic attributes were done with a subset of our complete genome dataset -we reduced the dataset to include only one representative genome per unique species and operon status. For example, if a species had 24 genomes with linked rRNA genes and 3 genomes with unlinked rRNA genes, we retained two genomes total, one linked and one unlinked. Species with heterogeneous rRNA gene status accounted for only 0. 71% of species and we found that the presence of unlinked rRNA genes was strongly conserved at the species and genus level (Supplemental Figure  S2 ). With this set of reduced genomes (3967 genomes in total), we first calculated Pagel's lambda  (30)  to determine whether there was a phylogenetic signal associated with unlinked rRNA genes using the phylosig function of the phytools package version 0.
6. 60  (31). The results of this test indicated there was a strong phylogenetic signal (lambda = 0. 96, p < 0. 0001), so we controlled for phylogeny in all of our subsequent tests by using a Phylogenetic Generalized Linear Model for continuous variables (with the function phyloglm in the phylolm package version 2. 6; 32). To determine if taxa with unlinked rRNA genes have a lower predicted growth rate, we calculated the codon usage proxy ΔENC'  (33, 34) , which provides an estimate of minimum generation times  (35). We calculated ΔENC' with the program ENCprime  (33)  with default options, on both the concatenated ORF sequences and concatenated ribosomal protein sequences for each genome following Vieira-Silva and Rocha (2009). To determine if RNaseIII was present in each genome, we used HMMER version 3.
This 1500 bp threshold is in some ways conservative, as the distance between genes in an operon is usually quite low -peaking between 20 and 30 bp in most genomes  (37). Additionally, tRNA are the most common genes found in the space between the 16S and 23S rRNA genes, and range from only 75 to 90 bp in length  (38). After classifying each rRNA gene pair as linked or unlinked based on the distance between the 16S and 23S rRNA genes, we found that 3. 65% of the genomes in our dataset had exclusively unlinked rRNA genes, 0. 62% had mixed rRNA gene status (i. e. genomes with multiple rRNA copies that had at least one set of unlinked rRNA genes and at least one canonical, linked rRNA operon), and 95. 73% had exclusively linked operons (these numbers do not match up with the per rRNA gene dataset as each genome has a variable rRNA copy number).
We found unlinked genomes to be relatively common (present in ≥5% of members) in taxa characterized as having an obligate intracellular lifestyle within the phyla Spirochaetes (genus Borrelia), Epsilonbacteraeota (family Helicobacteraceae), Alphaproteobacteria (order Rickettsiales), and Tenericutes (species Mycoplasma gallisepticum). However, we also found high proportions of unlinked rRNA genes in phyla that are generally considered to be free-living, such as Deinococcus-Thermus (families Thermaceae and Deinococcaceae), Chloroflexi (family Dehalococcoidaceae), Planctomycetes (families Phycisphaeraceae and Planctomycetaceae), and Euryarchaeota (class Thermoplasmata). Phyla with at least 5% of genomes having exclusively unlinked rRNA genes are shown in Figure  2C. Unlinked rRNA genes are widespread in environmental metagenomic data While the results from our complete genome dataset demonstrate that unlinked rRNA genes are common in some putatively free-living phyla, databases featuring complete genomes do not capture the full breadth of microbial diversity and are heavily biased towards cultivated organisms relevant to human health  (13).
Just three phyla (Proteobacteria, Firmicutes, Actinobacteria) accounted for >83% of the genomes in our NCBI dataset -even though recent estimates of bacterial diversity total at least 99 unique phyla  (39). To investigate the ubiquity of unlinked rRNA genes among those taxa underrepresented in 'complete' genome databases, we analyzed long-read metagenomic data from a range of distinct sample types. Focusing exclusively on long-read sequences allowed us to span the 1500 bp distance required for classification of rRNA genes without the need for assembly. This is important as the repetitive structure of rRNA genes makes it difficult to assemble a mix of non-identical rRNA genes from the short reads typical of most current metagenomic sequencing projects  (40). From our initial long-read dataset encompassing 15 unique samples (~890,000 Illumina synthetic long reads (also known as Moleculo) and ~19 million Nanopore reads, with median read lengths of 8858 bp and 5398 bp, respectively), only 15855 sequences contained rRNA genes and met the criteria we established for the classification of rRNA genes as linked or unlinked (see Methods).