dataesr/scientific-paragraphs-categorization huggingface dataset:
Amplicon sequence data analysis For both 16S rRNA gene and 18S rRNA gene amplicon sequences, we used the DADA2 R package, v1. 15. 1  (Callahan et al. , 2016)  to construct Amplicon sequence variant (ASV) tables by the following steps: prefiltering "filterandtrim" function with truncL = 50 and default parameters (S1_code_archive/dada2). Primer sequences were cut using the Cutadapt software implementation (v1. 18) in the DADA2 pipeline, removing a fixed number of bases matching the 16S forward (515F-Y, 19 bp) and reverse (926R, 20 bp) primers and the 18S forward (TA-Reuk454FWD1, 20 bp) and reverse (TAReukREV3, 21 bp) primers (S1_code_archive/dada2/dada2_16S. R L88:104; S1_code_archive/dada2/dada2_18S. R L92:104). Primertrimmed fastq files were quality trimmed with a minimum sequence length of 50 bp and checked by inspection of the average sequence length distribution (for both the 16S rRNA gene and 18S rRNA gene sequences).
Samples within forward and reverse fastq files were dereplicated and merged with a minimum overlap of 20 bp. ASV tables were constructed, and potential chimeras were identified de novo and removed using the "removeBimeraDenovo" command. Sequencing statistics for removed reads and sequences in each step can be found in Table  S3. Taxonomic assignment was performed using the SilvaNGS (v1. 4; Quast et al. , 2013)  pipeline for 16S rRNA gene data with the similarity threshold set to 1. Reads were aligned using SINA v1. 2. 10 (  Pruesse et al. , 2012)  and classified using  BLASTn (v2. 2. 30; Camacho et al. , 2009)  with the Silva database (v132) as a reference database (Supplement File C). For taxonomic assignment of 18S rRNA gene amplicons, we used the plugin "feature-classifier" (from package "q2-feature-classifier", v2019.
7. 0) in QIIME2  (Bokulich et al. , 2018)  and the pr2 database (v4. 12; Guillou et al. , 2013). We removed ASVs annotated to mitochondria and chloroplasts from 16S rRNA gene ASV tables and ASVs annotated as metazoans from 18S rRNA gene ASV tables (S1_code_archive/import/import_16S. R L35:38; S1_code_archive/import/import_18S. R L29). ASV tables of 16S rRNA gene amplicon (Table  S4 ) and 18S rRNA gene amplicons (Table  S5 ) were used for further statistical analyses.
Ecological data and statistical analysis A combination of temperature, salinity, dissolved oxygen concentrations, and dissolved inorganic nutrient concentrations (NO - 3 , NO - 2 , NH + 4 , Si, and PO 3- 4 ) were used to characterize the physical and biogeochemical environment of the study region. All statistical tests were performed in R version 3. 6. 3 (R Core Team, 2020). Statistical documentation, package citations, and scripts are available in S1. Microbial alpha diversity was calculated with Hill numbers (richness, Shannon entropy, inverse Simpson, q = 0-2;  Chao et al. , 2014)  using the iNEXT package v2. 0. 20 in R with confidence set to 0. 95 and bootstrap = 100 (S1_code_archive/alpha_diversity). Accordingly, rarefaction curves are shown in Fig. S6. Pearson correlations between microbial richness (q = 0), inverse Simpson diversity (q = 2), environmental parameters, and biological rates were calculated and plotted (ggplot2) (Fig.
S7 ). The p values were adjusted for multiple testing using Holm adjustment  (Holm, 1979) , and residuals were checked for normal distribution (Fig. S8 ). For comparability and statistical downstream analyses, we performed the following transformations to the ASV table and the environmental metadata: to account for the compositionality of sequencing data (see  Gloor et al. , 2017) , we performed a centered log ratio (CLR)-transformation for redundancy analysis (RDA). We used Hellinger transformation (decostand() function in vegan) of the ASV pseudocount data (minimum pseudocount per ASV cutoff was 3) for PERMANOVA analyses. Environmental data were z scored for comparable metadata analysis (S1_code_archive/transformations). For multivariate analyses of microbial beta diversity and environmental parameters, we performed redundancy analyses (RDA) of the CLR-transformed ASV tables (S1_code_archive/RDA).
Differences of microbial beta diversity (based on Hellingertransformed ASV tables), phytoplankton community composition (based on pigment concentrations), and water masses were tested with permutational ANOVA (PERMANOVA;  Anderson, 2001)  using the adonis2() function in vegan along with a beta dispersion test to evaluate the homogeneity of the dispersion (Fig. S9 ). To investigate where differences of environmental variables have an impact on microbial community dissimilarity, we performed a general dissimilarity model (GDM) of the community dissimilarity and environmental variables, and we checked for the influence of geographic distance based on spline magnitude (gdm package; S1_code_archive/GDM). As differences in microbial beta diversity were significant in PERMANOVA between provinces and water masses, we performed a similarity percentage (SIMPER) analysis in R using the vegan package to assess which ASVs contribute most to the observed variance of microbial community composition (Table  S6 ; S1_code_archive/taxonomy_analyses).
Primary productivity (PP) Maximum primary productivity (PP) values within our dataset were measured near the Kerguelen Plateau in the Polar Front Zone (PFZ) at station 9 (3236. 8 and 3553. 3 µmol C L -1 d -1 , respectively) and station E (2212. 4-2688. 1 µmol C L -1 d -1 , n = 6). Comparing all PP measurements across water masses, we found relatively high PP in other stations of the PFZ (stations 6, 7; Fig. 3a ; Table  1 ) and in the Subantarctic Front (SAF) (stations 4, 15). Lowest PP values  (190. 4-642. 6  µmol C L -1 d -1 ) were measured at the stations in the Indian South Subtropical Gyre (ISSG). While stations in the ISSG showed very little variations within one station (e. g. , 226. 09-371. 07 µmol C L -1 d -1 , n = 6, station 18), variation within SO stations was relatively high (e.
02, n = 12; Fig. S7e ) and significantly and positively associated with N 2 fixation (r = 0. 74, p = 0. 01, n = 12; Fig. S7g ). However, a strong correlation between rate measurements (PP, N 2 fixation) and eukaryotic diversity was only apparent in the ISSG, and no significant trend across other provinces (Pearson correlation after removal of ISSG samples from dataset: for PP r = 0. 47, p = 0. 24 and for N 2 fixation r = -0. 48, p = 0. 23, n = 8). Our RDA constrained 81 % of the variance in the ASV table, with a p value of 0. 095 (permutations = 999, n = 12). Sites were well separated between Longhurst provinces along the first two RDA axes (capturing 52. 67 % constrained variance, Fig. 4a ). Our PERMANOVA, which tested the province-based separation, produced moderate but significant results (permutations = 999, R 2 = 0.
Acknowledgements. We thank  Nicolas Metzel  as well as the captain and crew of the Marion Dufresne. We thank  Gaute Lavik  from the  Max Planck Institute of Marine Microbiology in Bremen  for the guidance and for allowing us to use membrane inlet mass spectrometry. We thank  Stefan Neuhaus  for his knowledge on the bioinformatics pipeline. We thank  Vladimir Benes  and his team from the  Genomics Core Facility ,  European Molecular Biology Laboratory, Heidelberg, Germany , for their kind guidance and support with the 16S rRNA gene sequencing. We thank the  Leibniz Institute on Aging (FLI) in Jena (Germany)  for their support in 18S rRNA gene sequencing. We thank  Allison Fong  and  Matthias Ullrich  for their comments on this study. Financial support. The article processing charges for this openaccess publication were covered by the  Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research (AWI).
Code availability. All code is available under S1_code_archive. zip in the Supplement and additionally publicly archived under https://doi. org/10. 5281/zenodo. 5000001 (Hörstmann, 2021). Data availability. All HPLC data; environmental and rate measurement data, including PN, MIMS data, PP, and N 2 fixation; and minimum quantification rate calculations are stored in the PANGAEA database  (Hörstmann et al. , 2018). All sequences are archived in the European Nucleotide Archive (primary accession: PRJEB29488). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/bg-18-3733-2021-supplement.
Aim: The abundant centre hypothesis (ACH) predicts a negative relationship between species abundance and the distance to the geographical range centre. Since its formulation, empirical tests of the ACH have involved different settings (e. g. the distance to the ecological niche or to the geographical range centre), but studies found contrasting support for this hypothesis. Here, we evaluate whether these discrepancies might stem from differences regarding the context in which the ACH is tested (geographical or environmental), how distances are measured, how species envelopes are delineated, how the relationship is evaluated and which data are used.
In recent years, several studies found stronger support for an environmental declination of the ACH  (Martínez-Meyer et al. , 2013; Osorio-Olvera et al. , 2020; Yañez-Arenas et al. , 2012 ; but see  Dallas, Pironon, et al. , 2020). However, this assessment is often based on an unclear characterization of species envelopes (and thus niches). For instance, envelopes are often characterized by projecting the geographical coordinates of species' observations (e. g. occurrence or abundance) in a two-dimensional environmental space characterizing variation in the global climate  (Dallas et al. , 2017; Santini et al. , 2019). If these observational data were collected over the full species range, then envelopes could be considered as an estimate of the realized (also called ecological) niche, that is, the set of suitable abiotic conditions that are accessible to the species  (Barve et al.
Most ACH studies mainly focused on the distance to centres with little attention to margins  (Santini et al. , 2019). While these two distance metrics convey the same information for circular envelopes, this is not the case for non-circular envelopes where populations close to the centre are not necessarily far from margins  (Blonder et al. , 2014; Broennimann et al. , 2021; Santini et al. , 2019). In such cases, the distance to margins might be a better descriptor of the suitability gradient  (Broennimann et al. , 2021). The Mahalanobis The latter calls for further testing on other datasets using the same range of settings and methodological framework.
This aspect is particularly important as the use of different datasets could yield varying support for the ACH. Here, we aim to re-analyse the dataset published by  Dallas et al. (2017)  for birds, mammals, fishes and trees, while accounting for the reported biases, to assess whether different conclusions could be obtained when using different methodological settings (see below). For birds, we also consider another dataset collected under a robust sampling protocol [North American Breeding Bird Survey (BBS) dataset;  Osorio-Olvera et al. , 2020]  to evaluate the effect of the quality of abundance data on the distance-abundance relationship. We evaluate the support for the ACH both globally and for each species at the same time, using two different statistical frameworks: the traditional one based on correlation coefficients and another one based on hierarchical Bayesian nonlinear mixed-effect models.
| Abundance data We used the data described in  Dallas et al. (2017) , in which estimates of population density for mammals, birds, fish, and tree seedlings were extracted from different databases based on published work  (Knouft & Anthony, 2016; Sullivan et al. , 2009; Thibault et al. , 2011; Woudenberg et al. , 2010). For all groups, species abundance was estimated as the number of individuals within a sampling area, standardized by either sampling area or sampling intensity, thus representing a measure of population density. Owing to concerns expressed regarding the bird dataset  (Soberón et al. , 2018) , which is based on a citizen science programme (eBird), we also tested the ACH using the BBS dataset, which follows a standardized survey protocol. This evaluation allowed us to assess the effect of the quality of the sampling protocol on the estimated distance-abundance relationship.
The BBS dataset was extracted from  Osorio-Olvera et al. (2020). For further details, see  Dallas et al. (2017)  and  Osorio-Olvera et al. (2020). Similarly, because for trees, the measure of density only included one age class that may not be representative of the whole population density, we also report global results with tree seedlings excluded. For all species, when several populations fell within the same climatic pixel (c. 340 km²), we averaged abundances across populations so that only one population was considered per pixel (i. e. we thinned the data;  Knouft, 2018; Soberón et al. , 2018). After this thinning procedure, we only retained species presenting at least 10 populations. Our analyses are thus based on 801 species, including 626 species of birds (599 species for the eBird dataset, 389 species for the BBS dataset -362 species in common between the two datasets), 28 species of fish, 27 species of mammals and 120 species of trees.
| Geographical range maps The above-mentioned abundance data (including the BBS dataset) do not encompass the full geographical range for most species, potentially leading to niche truncation issues and biased location of both the centres and the margins of geographical and environmental spaces  (Dallas, Pironon, et al. , 2020; Knouft, 2018; Soberón et al. , 2018). Thus, contrary to previous studies that built envelopes using the location of observed populations, we here delineated species envelopes using the entire distribution of species. For birds, mammals and fish, we used International Union for Conservation of Nature (IUCN) range maps (https://www. iucnr edlist. org/), only considering the resident range for mammals and fish, but also including the breeding range for birds owing to the presence of migratory species in both the eBird (n = 246 species; 41% of the original number) and the BBS (n = 196 species; 50% of the original number) datasets.
| Distance-abundance relationship We evaluated the distance-abundance relationship both globally and individually for each species using two different statistical frameworks. For the traditional framework, we computed Spearman (to account for nonlinear effects) correlation coefficients for each species and then averaged the coefficients across species to obtain a global estimate of the relationship at the scale of the taxonomic group for each setting  (Dallas et al. , 2017; Osorio-Olvera et al. , 2020). We then tested whether averaged values differed from zero using Wilcoxon one-sample signed-rank tests. While this approach is expected to produce better species-specific estimates than a hierarchical approach when using sound datasets (e. g. standardized sampling protocol;  Chevalier & Knape, 2019) , this is not the case when using heterogeneous and noisy datasets that are prone to small sample sizes and measurement errors.
, 2013). Mixedeffect models are composed of both fixed and random effects and are commonly used when measurements are made on clusters (e. g. taxonomic group) of related statistical units (e. g. species). This approach is particularly useful in settings with noisy data because it makes it possible to reduce the effect of sampling noise and to refine individual estimates by borrowing information across statistical units  (Gelman et al. , 2013; Harrison et al. , 2018; Kruschke, 2014). This borrowing of information is made possible by using higher-level distributions with means representing commonalities shared by all species (fixed effects) and standard deviations representing departures from the means (random effects) that allow the model to partly adapt to features specific to the species.
Thus, while each species is characterized by its own set of parameters (providing species-specific relationships), the relationships are assumed to come from the same higher-level distribution and therefore mutually inform each other. This hierarchical structure implies that species with a large number of observations have a stronger influence on the estimation of fixed effects and that species estimates tend to be pulled toward the fixed effect, a process known as shrinkage  (Gelman et al. , 2013; Kruschke, 2014). In general, the shrinkage is strongest for species with small sample sizes, as the paucity of within-species information to estimate fixedeffects is counteracted by the model using data from other species to improve the precision of the estimate. For each group, we investigated the distance-abundance relationship under the 12 different settings using Bayesian nonlinear mixed-effect models including species-specific effects (denoted s) on intercepts (α s ), slopes (β s ) and quadratic terms (γ s ).
| Influence of the statistical framework on ACH evaluation Using the traditional framework (i. e. correlation coefficients averaged across species), we found little support for the ACH, regardless of the methodological setting or the taxonomic group ( mammals =. 015, eBird = -. 01, fish =. 03 and trees =. 005; Figure  2  -note that the signs of coefficients for margins were reversed before averaging in order to be comparable to the two other measures of distance). While the correlations were larger for the BBS dataset and were all statistically different from zero, they were still weak ( BBSbirds = -. 09; Figure  2 ). Regarding the alternative framework (i. e. mixed effect models), all models fitted the data well (Bayesian p-values close to. 5), thus suggesting that the data are equally plausible under the different settings (Supporting Information Table  S1 ).
Importantly, we found strong evidence for non-null relationships under most settings with this framework. For all groups, the proportion of variance explained was rather high (conditional R 2 mammals =. 23, R 2 eBird =. 41, R 2 fish =. 61, R 2 trees =. 54, R 2 BBSbirds =. 86) and was comparable across models (except for the tree and the eBird dataset), indicating that all models performed well in explaining abundance variations across species (Supporting Information Table  S1 ). However, the models greatly differed regarding their support for the ACH. Similar results were obtained with generalized additive mixed-effect models (Supporting Information Figure  S1 ).
| Influence of methodological settings on ACH evaluation Over the 60 relationships tested (5 datasets × 12 settings), 23 (38%) showed support for the ACH (expected relationships, Figure  3a ), 18 of which were related to the two bird datasets. In contrast, seven relationships (11. 6%) presented trends opposite to ACH expectations (i. e. unexpected relationships, Figure  3a ). When excluding tree seedlings, the percentage of expected relationships increased to 45. 8% whereas the percentage of unexpected relationships dropped to 6. 2% (Supporting Information Figure  S2a ). A similar pattern was observed when removing migratory birds (Supporting Information Figure  S3a ). Overall, the proportion of expected relationships was larger in the geographical space (46%) than in the environmental space (30%) while the proportion of unexpected relationships was lower in the former (10%) than in the latter (13%) (Figure  3b ).
| Influence of the dataset (taxonomic group) on ACH evaluation All taxonomic groups showed support for the ACH under some settings. However, the relationships were not always consistent and were sometimes unexpected (Figure  3e ). For mammals, we found support for the ACH for three out of the twelve settings but also found evidence for one unexpected relationship (Figures  3e, 4 , Supporting Information Table  S1 ). The number of expected (unexpected) relationships were one (two) for the fish dataset, one (four) for the tree dataset, ten (zero) for the eBird dataset and eight (zero) for the BBS dataset (Figures  3e, 4 , Supporting Information Table  S1 ). For the two bird datasets, removing migratory species yielded stronger support for the ACH under most settings (Supporting Information Figures  S3e  and S4 , Table  S2 ).
| Among-species variation in distanceabundance relationships Despite evidence for global patterns, large variations were visible across species (Figure  5a , Supporting Information Figures  S5-S9 ). Over the 60 relationships tested, 33% of species displayed expected relationships whereas 8. 4% displayed unexpected relationships and 58. 6% presented undetermined relationships. These proportions greatly varied across settings and datasets but confirmed the observed trends. Only marginal changes were observed when excluding tree seedlings (Supporting Information Figure  S10a ). Larger changes were detected after removing migratory birds with, for example, the proportion of expected and unexpected relationships increasing to 40. 5 and 9. 7%, respectively (Supporting Information Figure  S11a ).
On average (considering all settings), 52% of the birds displayed the expected relationship with the BBS dataset (8% of unexpected relationships), whereas this proportion only reached 26% for the eBird dataset (3. 3% of unexpected relationships; Figure  5b ). When excluding migratory species, the proportion of expected relationships strongly increased for both the BBS (59%) and the eBird (43. 8%) datasets whereas the proportion of unexpected relationships remained similar (8% for the BSS dataset and 2. 3% for the eBird dataset) (Supporting Information Figure  S11b ). For mammals, fish and trees, these proportions were 17 (5. 8), 12 (12. 8) and 12% (35%), respectively. A higher proportion of species presented expected under some settings than others in both the geographical (e. g. distance to the margins of the KDE) and the environmental (Euclidean distance to the centre of the MVE) space (Figure  5c ), particularly after removing tree seedlings (Supporting Information Figure  S10c ) or migratory birds (Supporting Information Figure  S11c ).
, 2017; Santini et al. , 2019)  but contradicting others  (Martínez-Meyer et al. , 2013; Osorio-Olvera et al. , 2020; Yañez-Arenas et al. , 2012). Only when considering the BBS dataset did we find a tendency toward support for the ACH, but the correlations were still weak. This low support can either be explained by all species showing no signal (scenario 1) or by large variations across species where positive trends are cancelled out by negative trends, resulting in a low signal after averaging coefficients (scenario 2). In the first scenario, we would not expect results to show any difference between correlation coefficients and mixed-effect models because all species are showing no signal, regardless of how much they are affected by sampling noise. Differences between methods are however expected in the second scenario owing to the shrinkage procedure where the estimate of species affected by sampling noise is pulled toward the main effects  (Gelman et al.
, 2019; Sagarin et al. , 2006). However, an alternative explanation for this inconsistent pattern could be that the data used here and in previous studies  (Dallas et al. , 2017; Osorio-Olvera et al. , 2020)  are inappropriate to test the ACH  (Dallas, Pironon, et al. , 2020; Knouft, 2018; Santini et al. , 2019; Soberón et al. , 2018; Yañez-Arenas et al. , 2020). Five features of the data seem worth further assessment. First, abundance data collected under a standardized sampling protocol should be favoured. We here tested the ACH using two different datasets for birds: eBird data, which does not follow any sampling protocol, and BBS data, which follows a standardized sampling protocol  (Sauer et al. , 2005). Using BBS data increased the overall support of the ACH under most settings owing to a larger proportion of variance explained and a larger number of species displaying the expected relationship.