dataesr/scientific-paragraphs-categorization huggingface dataset:
In the present study, living benthic foraminifera and macrofauna were investigated in fjordic systems along the Norwegian Skagerrak coast to assess to which degree their responses to major driving environmental factors correspond. This work is part of a comprehensive project (PES), which includes both foraminifera and macrofauna collected at the same sites at the same time. An understanding of the driving forces of the benthic foraminifera communities, expressed as species diversity, was partly achieved in a previous analysis of the same data set  (Bouchet et al. 2012 (Bouchet et al. , 2013)). The present paper aims (i) to determine the driving environmental factors of both the benthic foraminiferal and macrofaunal community structures, (ii) to identify indicator species of both normal and poor environmental conditions, (iii) to compare species assignment in EG from this study to the existing Foram-AMBI and AMBI species lists, and (iv) to assess the correlation between benthic foraminiferal and macrofaunal communities patterns to identify the potential of benthic foraminifera to serve as a proxy for benthic macrofauna in environmental assessments.
Community data were log(x + 1) transformed prior to analysis; taxa occurring at only one station were removed. Environmental variables were selected using forward selection with 999 permutations. Procrustes analysis (Peres-Neto & Jackson 2001) was used to compare unconstrained ordinations of the foraminiferal (PCA) and macrofaunal (correspondence analysis) community data. The m 2 statistics, which is analogous with the r 2 of a correlation, is reported. Co-correspondence analysis (Co-CA, Ter  Braak & Schaffers 2004 ) was used to relate the foraminiferal and the macrofaunal communities in a direct way. All calculations were performed using the statistical language R version 3. 4. 1 (R Core Team, 2017). Ordinations were run with the vegan version 2. 4-3  (Oksanen et al. 2010 ). R code is archived at https://github.
Discussion In the present study, patterns of distribution of foraminiferal and macrofaunal species are best explained by the variables of TOC and DBT. Along the same lines, deepwater renewal and oxygenation in a fjord depend on DBT as well as on the supply of organic matter (i. e. , impacts oxygen consumption). DBT will, however, also represent the influence of other variables, e. g. pigments, different organic components and temperature, that increase or decrease due to the gradually changing conditions with depth. In the following discussion, however, it will be made reference to oxygen rather than of DBT sensu stricto that may incorporate several unknown factors. This is further supported by  Bouchet et al. (2012)  who showed on the same dataset that benthic foraminiferal diversity is correlated to bottom-water dissolved water oxygen.
The same concerns arose about T. cf. sarsii which behave like an opportunistic species in this study and is assigned to tolerant EG in the AMBI-list. Occurrencies of T. equalis, S. kroeyeri and S. inflatum in several Norwegian fjords and coastal areas  (Rygg & Norling 2013)  showed tolerance levels corresponding to EG3 (tolerant), thus supporting the classification in the AMBI system. These discrepancies may be due to the fact that indices' scores come from large dataset collections covering large geographical regions and subregions and not local scales  (Borja et al. 2000 , Rosenberg et al. 2004 ). Hence, they integrate the "experience" from a species behavior over a large span of data or literature data. Local adapation of species ecological requirement may lead to wrong interpetation of the species indicative value  (Dauvin et al.
Benthic foraminifera as indicator of macrofaunal community structure The assessment and quantification of cross-taxon congruence i. e. similar response along an environmental gradient of species belonging to different taxa, in space and time, represents a critical step in the identification of suitable indicator taxa for biodiversity. In this study, Procrustes rotation and co-correspondence analysis (Co-CA) were used to quantify the strength of the congruence between foraminiferal and macrofaunal communities along the studied environmental parameters gradient. Procrustes rotation and Co-CA are powerful and robust methods to evaluate the degree of congruence between two community datasets  (Peres-Neto & Jackson 2001 , Gioria et al. 2011). Patterns of foraminiferal species distribution closely resembled those of the macrofauna, as is showed by significant correlation between the groups.
Acknowledgment We are grateful to the crew of the R/V Trygve Braarud; S Holm, J. Sundøy and T. E. Baade. Nina Reuss is warmly thanked for the analysis of pigment samples. Thanks are also due to M. Hollerbach, and J. Håvardstun for assistance during fieldwork, and Y. Descatoire for graphics. This study was supported by the Norwegian Research Council-funded project PES (no. 184870) "Paleoecological reconstructions of marine soft-bottom Ecologic Status and in situ reference conditions: calibrating benthic foraminifera with macrofauna and hydrographic data". V. M. P. B. was supported through the PES project by a post-doctoral fellowship. Thanks to Fede who waited few more days with the birth of Clelia for the first version of this manuscript to be written. ). Definition of low (<2. 7%), medium (2.
However, for Firmicutes species, 35% of putative gyrB reads were actually identified as sequences of a gyrB paralog, parE, which encodes subunit B of topoisomerase IV; we therefore constructed a reference database of published sequences of both gyrB and pare for use in all subsequent analyses. Despite this co-amplification, the deviation between relative sequencing quantification and absolute qPCR quantification was comparable to that observed for 16S rDNA for all the tested species. This confirms that gyrB can be used successfully alongside 16S rDNA to determine the species composition (richness and evenness) of food microbiota. The major benefit of gyrB sequencing is its potential for improving taxonomic assignment and for further investigating OTU richness at the subspecies level, thus allowing more accurate discrimination of samples.
5% of the final concentration. From this mixture, 600 μl were loaded onto the Illumina MiSeq cartridge according to the manufacturer's instructions using the MiSeq Reagent Kit v3 (2x300 bp paired-end reads, 15 Gb output). FastQ files were generated at the end of the run (MiSeq Reporter software, Illumina, USA) for quality control. The quality of the run was checked internally using PhiX Control and then each paired-end sequence was assigned to its sample of origin using the multiplexing index tag. Raw read sequences were deposited at the Sequence Read Archive under the accession numbers SAMN09070427to SAMN09070506.
Quality filtering, definition of OTUs, and taxonomic assignment The quality of the sequencing was first evaluated using FastQC  [48]. Individual reports were merged into a single one with MultiQC  [49]. The 16S rDNA and gyrB paired-end sequences were merged into contigs with PEAR v0. 9. 10  [50]. Adapters were trimmed with cutadapt v1. 12  [51]. Low-quality bases at the extremities of sequences were removed using Sickle v1. 330  [52]. Data were subsequently imported into the FROGS (Find Rapidly OTUs with Galaxy Solution) pipeline  [53]. Sequences were dereplicated before being clustered using SWARM  [54]  with a local clustering threshold with a distance of 3. Chimeras were removed with vsearch  [55]. The resulting sequences were filtered for spurious OTUs likely arising from sequencing artifacts (OTUs with low abundance and low frequency) by keeping only those appearing more than 10 times in the whole dataset  [56].
Taxonomic assignment of OTUs that corresponded to 16SrRNA sequences was performed using Silva 128 SSU  [57]  as reference database, while a homemade databank was created for the gyrB and parE sequences (see below), using in both cases the Blastn+ algorithm  [58]. Our Bioinformatic report for these initial steps can be found here: http://genome. jouy. inra. fr/analyses/REDLOSSES-gyrB/report. html.
Construction of gyrB and parE database A total of 44,494 genomes were downloaded from Ensembl Bacteria (release 38, January 2018)  [59]. Then, embl files were parsed with a BioPython library to obtain only sequences of gyrB and parE in FASTA format. To be sure we kept only sequences of interest, we processed all sequences with cutadapt v1. 12  [51]. If 5' or 3' extremity primers were not found, the sequences were discarded. The resulting 44,572 gyrB and 30,549 parE sequences were combined with another 30,525 sequences extracted from IMG (release 4. 3) and with 389 additional sequences from strains and species known to be important in food microbiota that were not represented in Ensembl and IMG  [60]. The final database contained 106,035 sequences which were submitted to FROGS analysis for taxonomic assignment.
Analysis of alpha and beta diversity Bacterial diversity was analyzed using the R package Phyloseq  [61]. To facilitate comparative analysis between 16S rDNA data and gyrB data, a common Phyloseq object was created which comprised a single otu_table, sample_data, and tax_table (the whole dataset is available at (DOI:10. 6084/m9. figshare. 7083209). OTU abundance was normalized using the median sequencing depth of all samples (for both 16S rDNA and gyrB). Analyses of alpha and beta diversity were then carried out using standard or custom Phyloseq command lines. Our R script (redlosses_phyloseq_custom. R), which includes all commands performed to create our figures, is available for download at (DOI:10. 6084/m9. figshare. 7083254).
Study design and sample collection Food samples. Five different food products (ground beef burgers, pork sausages, poultry sausages (turkey), cod fillets, and salmon fillets), all packaged under modified atmosphere, were selected for this study. For each food item, three batches (biological replicates) were purchased in supermarkets one week apart and stored at 8˚C until the product's use-by date. The nomenclature of the food samples and their associated dataset items are described in Table  2. These products were chosen because the composition of their microbiota has already been well described  [1, 47]. In addition, the different communities offered a good system with which to study variations in abundance between the two main bacterial phyla (Firmicutes and Proteobacteria) and the various species therein.
Both gyrB and parE genes are amplified within Firmicutes In order to assign taxonomic identifications to OTUs obtained with gyrB sequencing, we constructed a gyrB database (see Materials& methods section). However, our first attempt at assignment revealed that 72 OTUs within the whole gyrB dataset were not assigned to any species; these OTUs accounted for 19. 5% of the total diversity and 35% of the total read in the dataset. Further investigation revealed that these OTUs demonstrated affinity to sequences of parE. The parE gene encodes subunit B of topoisomerase IV, an enzyme involved in chromosome segregation in bacteria (Interpro accession IPR005740). Because the ParE and GyrB proteins are considered to be paralogs, their respective genes may show significant sequence similarity. To check whether the confusion between parE/gyrB occurred for all types of taxa, we analyzed the proportions of gyrB and parE sequences assigned to the fifty most-abundant genera from our dataset (Fig  2 ).
Interestingly, with the exception of Clostridium algidicarnis for which the total number of reads remained below 50 in the whole dataset, the relative abundance of gyrB and parE sequences was relatively stable within each of the twenty Firmicutes species from one sample to another. Indeed, the standard deviation for these values from all Firmicutes species varied from 0 to ±13%. It can thus be hypothesized that, for a given species, the ratio between reads of gyrB and parE across samples is relatively constant. We also noticed that after chimera filtering none of the filtered OTUs were chimeras between gyrB and parE. Furthermore, the use of gyrB only induces the formation of 1. 2% of chimera (corresponding to 10. 6% of the clusters) while 16S rDNA generates 9. 5% of chimera (corresponding to 17.
9% of the clusters). Therefore, chimera formation was not a valid explanation for this phenomenon. We decided to further investigate the reason why amplification of gyrB also recovered parE genes only from Firmicutes and not from Proteobacteria. To do this, we performed a phylogenetic analysis that estimated the distances between gyrB and parE sequences of the species that were introduced in the mock communities (Fig  3 ). As we expected, gyrB sequences of different members of phylum Firmicutes appeared to be more similar to parE sequences from those same Firmicutes genomes than to gyrB sequences extracted from Proteobacteria genomes. This result confirms why the parE paralog gene was amplified only within strains belonging to phylum Firmicutes in our experiment. These results highlight that, within ecosystems containing Firmicutes species, gyrB assignment databases must also take parE sequences into account.
The estimated absolute number of16S reads and gyrB reads were thus compared separately to the qPCR results, which were expressed in CFU. g -1 at the taxonomic level of phylum as described in Fig  6. A linear regression model was constructed for each of the four datasets obtained from food samples and mock communities (Firmicutes 16S, Firmicutes gyrB, Proteobacteria 16S, and Proteobacteria gyrB). For all tested species, there was no evidence of additional bias in the comparison between qPCR results and gyrB reads with respect to the comparison between qPCR and 16S rDNA. However, some discrepancies in quantification between qPCR and amplicon sequencing were found for both markers; these inconsistencies appeared to be linked with the presence of rare species in certain samples, indicating that either qPCR quantification or amplicon sequencing was more difficult or was subject to inhibitory effects for less-abundant species.
Accuracy of gyrB/parE-based OTUs in determining subspecies-level bacterial richness To investigate the discriminatory power of using gyrB at the subspecies level, we further focused on the 10 most-abundant phylogenetic clades recovered by 16S rDNA amplicon sequencing, which comprised~80% of the reads in the 16S dataset. As mentioned in  Fig 6,  we refer here to phylogenetic clades because most of the thirteen 16S-based OTUs (named 16S_Cluster_XX, for example) representing these 10 clades could not be assigned a specieslevel taxonomic identification. Instead, we defined our OTUs for this analysis as groups of phylogenetically related species that cluster together at a threshold of 97% identity; these clades thus represent an intermediate situation between genus and species. To these ten clades, we added three additional genera (Brochothrix, Weissella, and Acinetobacter) because they had been included as controls in the mock communities (MC1 to MC4).
We then constructed a heatmap that showed the abundance of these sixteen OTUs assigned through 16S rDNA amplicon sequencing of the food and mock communities samples (Fig  7A ). These phylogenetic clades corresponded to 44 gyrB-based OTUs (named, e. g. , gyrB_Cluster_XX), for which a heatmap was also constructed  (Fig 7B). To facilitate comparison between these two analyses, a phylogenetic tree was constructed that included all gyrB-based OTUs along with the gyrB or parE sequences (extracted from public databases) of the different species and strains identified in the various samples or used in the mock communities  (Fig 8). In general, it appeared that the gyrB-based approach was able to resolve broad phylogenetic clades to the species-or subspecies-level with a high degree of accuracy. There were two notable examples of this.
One solution could be to design several sets of primers for each major taxonomic level (phyla or orders), as was performed in the human microbiome study  [24]. Even this approach, though, cannot be guaranteed to prevent the unwanted amplification of parE from some species (notably, within families Leuconostocaceae and Carnobacteriaceae). We noticed that for the species in which parE reads represented a significant proportion of recovered amplicons (>50%), relative quantification was slightly underestimated (around 10%) compared to the absolute quantification measured in CFU. g -1. Therefore, the quantitative estimates based on gyrB amplicon sequencing were less reliable for Firmicutes species compared to estimates based on 16S rDNA amplicon sequencing. A further challenge will be the need to include in a computing pipeline two different databases (gyrB and parE) for taxonomic assignment.
Therefore, we would not recommend the use of gyrB-based methods to de novo analyze microbiota that are completely unknown; indeed, our data showed that some species that are not yet well characterized (e. g. , the unknown genus found in cod fillet) might be missed. Generally speaking, gyrB sequencing still needs to be tested in many different types of complex microbiota and especially in those that contain phyla other than Firmicutes and Proteobacteria.
Acknowledgments We are grateful to the  INRA MIGALE bioinformatics platform  (http://migale. jouy. inra. fr)for providing computational resources and data storage and to the  INRA @BRIDGe platform  for handling the MiSeq sequencing run. We thank  M. Barret  ( INRA Angers ) for sharing initial gyrB amplification protocols and for providing suggestion on the construction of the gyrB database.
The smoothing that is built into paleolimnological data sets apparently interfered with the signal of rolling window indicators, especially autocorrelation. We thus turned to time-varying autoregressions (online dynamic linear models, DLMs; and time-varying autoregressive state-space models, TVARSS) to evaluate the possibility of detecting regime shifts and critical transitions in simulated and real core records. For the real cores, we examined both varved (annually laminated sediments) and nonvarved cores, as the former have limited mixing issues. Our results show that state-space models can be used to detect regime shifts and critical transitions in some paleolimnological data, especially when the signal-to-noise ratio is strong. However, if the records are noisy, the online DLM and TVARSS have limitations for detecting critical transitions in sediment records.
2013 , Frossard et al. 2015). As such, sediment core time series are not strictly analogous to instrumental data sets, and there is a growing need for the development of quantitative methods to identify regime shifts and critical transitions in paleolimnological records  (Spears et al. 2016). In this work, we examined three sediment core archives displaying either a gradual change in trophic state, an abrupt state change, or plausible flickering between two alternate states, to (1) show that regime shifts are discernible in real sediment cores and (2) evaluate whether we could use these cores to test for evidence of critical transitions between alternate states. To address the latter, we built a model of core formation (simulated core records) to help define the conditions under which resilience indicators of critical transitions can be detected with paleolimnological data.
g. , isotope of an element, frustules of a diatom species, and carapaces of a zooplankton species). For the varved stratigraphies, we assumed that the increase in the yearly volume of hypolimnetic hypoxia, reconstructed from the multiple cores, was directly related to the eutrophication process To represent a 400-yr paleo-archive of a lake that was eutrophied ~100 yr ago, we used the Self Exciting Threshold Autoregressive model (SETAR; package tsDyn in R;  Stigler 2010, Di Narzo, Aznarte and Stigler 2014)  to estimate the time point when the system shifted to a eutrophic regime in the 1000-yr time series and selected a window of 300 yr prior to and 100 yr post this SETAR estimate of the switchpoint. All SETARs were modeled with only one threshold, which was estimated by a search over a reasonable grid.
The adaptability of the online method to nonstationary data is advantageous, but the arbitrary choice of d is a disadvantage of the online method. In addition, the DLM does not account for errors in the observations (elements of the vector F). Therefore, we also estimated the AR parameters in time-varying autoregressive statespace form by maximum likelihood estimation using TVARSS  (Ives and Dakos 2012). The TVARSS method estimates the time-varying autoregression coefficients by fitting the model y t ¼F 0 tÀ1 / tÀ1 þ e t / t ¼I/ tÀ1 þ x t x t ¼y t þ v t (8) by maximum likelihood. The first two equations of (  8 ) are analogous to equations (  6 ) for the DLM. The third equation models the errors in the observations, assumed to be normally distributed with individual errors υt having mean 0 and standard deviation P.
Thus, TVARSS accounts for the dynamics of the estimated system, changes in the autoregression parameters, and observation errors in the data. The advantages and disadvantages of the online DLM and TVARSS methods are complementary in some ways, and therefore, we used both methods. For both the online DLM and TVARSS, we chose the optimal AR order p using Akaike's information criterion (AIC). We calculated eigenvalues from the AR parameters according to  Box et al. (2008). R functions for the online DLM, example data sets, and R programs demonstrating calculations are downloadable from https://github. com/ SRCarpen/Demo_DynamicLinearModels.
2009). Interestingly, this could be examined further with TVARSS which can handle covariates and potentially help tease apart the role of each driver. In general, with field data, the inference of critical transitions depends on convergence of several sources of information. For example: Is there a sensible ecosystem model that can generate critical transitions? Is this model consistent with diverse kinds of data (mechanistic field studies, experiments, comparative studies, etc. )? Does the model generate data that resemble the observed time series using various statistical diagnostics? Does the observed time series exhibit characteristics consistent with critical transitions? In the case of lakes, are there other, similar, lakes that seem to exhibit the same critical transitions and do their time series have similar For paleolimnological studies, we offer the following advice: (1) More samples (closer-interval sampling) are better, and (2) time series with high signal to noise is especially valuable for identifying a threshold.
ACKNOWLEDGMENTS A significant portion of this paper was made possible thanks to the valuable input, statistical modeling, and R code provided by  Dr. Anthony R Ives , to whom we are most grateful. We are very grateful to  Drs. Mark Stevenson  and  Suzanne McGowan  for providing data on Lake Anarry. We also thank  Dr. Irene Gregory-Eaves  for fruitful discussions and comments on earlier drafts of the manuscript. We are also very thankful to  Drs. Peter R. Leavitt ,  Lynda Bunting ,  Teresa Buchaca ,  Jordi Catalan ,  Isabelle Domaizon ,  Piero Guilizzoni ,  Andrea Lami ,  Heather Moorhouse ,  Frances R. Pick ,  Patrick L. Thompson ,  Rolf D. Vinebrooke , and the late  Giuseppe Morabito  for providing sediment core archive data that helped parametrize the compression model. This paper is a contribution for the  PAGES Aquatic Transition working group , from which the original idea emerged.