dataesr/scientific-paragraphs-categorization huggingface dataset:
Similarly,  Watanabe and Monaghan (2017)  found deviating relationships between stream macroinvertebrate species and genetic diversity for putatively neutral loci versus loci under selection. However, because both  Bertin et al. (2017)  and  Watanabe and Monaghan (2017)  used AFLP markers, only a few loci putatively under selection were identified (an average of eight across all species' datasets). Because genetic diversity estimates are sensitive to the number of genetic markers  (Dutoit, Burri, Nater, Mugal, & Ellegren, 2017) , such a low number of outlier loci is insufficient to calculate robust genetic diversity estimates of non-neutral diversity. Furthermore, to effectively compare neutral and non-neutral loci patterns, an equal number of both types of loci should ideally be used  (Batista et al.
| Genetic data and bioinformatics The UNEAK GBS pipeline  (Lu et al. , 2013) , a method specially tailored for species that lack a reference genome, was used for SNP discovery. The UNEAK pipeline is a component of the TASSEL 3. 0 bioinformatics package  (Bradbury et al. , 2007)  that calls SNPs after resolving artifacts of problematic data including repeats, paralogs, and sequencing errors. These analyses were carried out by the GDF staff at Cornell University and are summarized in Supporting Information Table  S1. Briefly, sequencing errors were filtered out by retaining only tags that were present at least 3 times, using a maximum error tolerance rate of 0. 03 in the network filter (filters on the identification of reciprocal tag pairs), accounting for 0. 01 average sequencing error rate to decide between homozygous and heterozygous calls, and setting minimum minor allele frequency (MAF) to 0.
After filtering for site depth, we retained all loci with less than 40% missing data. The amount of missing data retained for all SNPs may have serious consequences on values of genetic diversity and calculations of SGDCs; therefore, we reran all analyses with a more stringent dataset, retaining all loci with less than 30% missing data. The more stringent dataset provided very similar results (not shown), and therefore, we chose to retain the 40% missing data filter to provide a greater representation of the genome. To remove potential sequencing errors, we chose a MAF of 0. 04 for all loci across all individuals. Furthermore, we only included loci that were biallelic. We calculated the observed heterozygosity for all remaining sites and excluded sites with observed heterozygosity >0. 5 to exclude potential paralogs  (Hohenlohe, Amish, Catchen, Allendorf, & Luikart, 2011)   using the mgl function of the "poppr" in R  (Kamvar et al.
, 2018). No redundant multilocus genotypes were identified, which indicates that no clones were present in our dataset. All loci were then tested for departure from Hardy-Weinberg equilibrium using the hw. test function of the "pegas" package in R  (Paradis et al. , 2013). Significance was tested using 1,000 random permutations. To identify environmental predictors that were potentially collinear, we calculated Pearson correlations between each environmental variable pair and discarded variables that displayed repeated correlations exceeding 0. 7. The final dataset included MAP, MAWS, MAT, TCI, slope, aspect, and NDVI.
First, a principal component analysis captures the genetic structure of the dataset. Then, the Mahalanobis distance of the z-scores on the first k-components of each locus detects those loci that most relate to population structure  (Luu et al. , 2017). Here, we identified the optimal number of components (i. e. , k-components) from the scree plot and used a 10% false discovery rate to identify outlier loci with significantly larger Mahalanobis distances. For our second approach, we identified SNP loci associated with environmental variables using redundancy analysis (RDA), a genomeenvironment association (GEA) approach to distinguishing candidate   Forester et al. , 2016). The number of components considered in this procedure was determined by examining the inertia scree plot and by verifying that all the selected components were significant.
| The genetic datasets To test for correlations between species and genetic diversity, we created five SNP datasets. The expected composition for these datasets is reported in Table  2. DS1, the original dataset after filtering, included all SNPs and thus a large proportion of neutral loci and some non-neutral ones. The nonoutlier datasets, DS2 and DS3, were formed by excluding either all the outlier loci identified (DS2), or only those jointly identified by the two outlier detection methods (DS3). The two datasets are thus composed for the most part of neutral loci, with a greater number of false negatives (adaptive loci) being expected in DS3 than in DS2. DS4 and DS5 contained the loci putatively under selection identified by at least one outlier detection method and by both detection methods, respectively.
DS5 thus only included SNP loci for which we had convergent evidence of deviations from neutral expectations. In DS4 and DS5, the proportion of non-neutral loci was expected to be much higher than in any other datasets. Some false positives (neutral loci) are likely to be present as well, but much less so in DS5 than in DS4. When species diversity and genetic diversity correlations are neutrally driven, the relative proportion of the neutral and non-neutral loci in the genetic dataset will condition the strength of the SGDC. Based on this rationale, we anticipated neutrally driven SGDCs to be positive for the putatively neutral loci, and to be the strongest in DS2, as DS2 is the more conservatively filtered neutral dataset (Table  2 ). Because the fraction of neutral loci is lower in DS4 and DS5 than in the putatively neutral datasets, these two datasets should display weaker correlations, with either a null or weakly positive association depending on the number of false negatives (neutral loci) in the dataset and the footprint of the neutral processes on the non-neutral loci.
| Genetic diversity and species diversity Before performing genetic diversity estimations, we first investigated the effects of within-population missing data. Genetic estimates of α-diversity were calculated for each population after varying the minimum number of individuals genotyped at each locus from four to eight individuals. Overall, we did not observe an influence of the number of individuals on the genetic diversity estimates (Supporting Information Figure  S1 ). Therefore, we decided to consider all loci with a minimum of four genotyped individuals per population, which allowed us to calculate a genetic diversity estimate for all sites, including site 1, which only had four genotyped individuals. We evaluated within-population genetic diversity for all populations and datasets as the expected heterozygosity (He) over the loci using gstudio  (Dyer, 2017)   Edwards, 1967).
D CH = 2 π √ 2(1 - ∑ l ∑ u √ X u Y u , F I G U R E 2 α-Genetic diversity of Carex gayana plotted against site level species richness (top row) and evenness (middle row) and βgenetic diversity plotted against β-species diversity (bottom row) for the 17 sites included in this study using the complete dataset DS1 (a), the two non-outlier datasets, DS2 (b) and DS3 (c), and the two outlier datasets, DS4 (d) and DS5 (e). α-Genetic diversity is estimated using the expected heterozygosity (He) and α-species diversity by the wetland plant species richness and Pielou's evenness. β-genetic dissimilarity is estimated using Cavalli-Sforza genetic distance and β-species dissimilarity is estimated using Bray-Curtis distance. S6 and S21 are the two outlier sites The plant species assemblage was surveyed in each wetland based on five 30 × 30 cm quadrats.
| Species-genetic diversity correlations between the SNP datasets Before calculating SGDCs, we generated scatterplots to check whether the genetic and species diversity data were linearly related. These scatterplots are provided in the Figure  2. α-SGDCs were calculated as Pearson correlations between the plant αdiversity estimates (i. e. , plant richness and evenness) and He for each population and each of the five SNP datasets. We used t tests to test for the significance of the SGDCs. To test if, as expected, the SGDCs of the outlier loci datasets were significantly different than that of the nonoutlier loci datasets, we calculated the probability of obtaining such extreme SGDCs using an equal number of loci with DS1 and the putatively neutral datasets (DS2 and DS3). To this end, we calculated SGDC with 999 randomized subsets of DS1, DS2 and DS3 containing the same number of loci as DS4 and DS5.
| Genotyping Illumina sequencing produced around 2. 5 million reads per individual. Following basic filtering steps conducted in the UNEAK pipeline, we retained 38,036 SNPs. With the additional, more stringent filtering protocol, we obtained 1,709 high-quality SNPs for our overall dataset (Supporting Information Table  S2 ). This filtering process reduced our number of individuals from 190 to 158 and the number of wetlands from 20 to 17. In our final dataset, the mean depth per individual was 29. 9 reads and the mean depth per site across all individuals was 30. 0 reads. No loci were found to depart from Hardy-Weinberg equilibrium at an α = 0. 05 significance level in more than 50% of the sampling locations. At the population level, no more than 2. 5% of the loci, on average, were found to significantly deviate from Hardy-Weinberg equilibrium.
| Outlier identification and datasets In the scree plot of the PCA conducted in pcadapt, the elbow occurred at the 10th component. With a false discovery rate of 10%, 173 SNPs were identified as outliers. After selecting the predictor variables according to their correlations, none of those included in the RDA analysis showed a VIF greater than 5. The RDA model explained 20. 9% of the genetic variance. Based on the elbow of the eigenvalues scree plot, we retained the first four components for outlier detection. All components were highly significant, with axes 1 through 4 explaining 35%, 22%, 16%, and 9% of the explained variance, respectively, all together accounting for 82% of the variance explained by the RDA model. In total, 95 loci had outlier z-scores on at least one of the four RDA components.
Outliers were most strongly correlated with topographic slope on axis 1, with moisture-related variables (TCI and MAP) on axis 2, moisture and wind speed (TCI and MAWS) on axis 3, and with temperature (MAT) and slope on axis 4. RDA and pcadapt together identified a total of 229 outlier loci, of which 39 were identified in both methods (Table  2 ). They formed the two outlier datasets, DS4 and DS5, respectively. The two nonoutlier datasets, DS2 and DS3, were constructed by removing DS4 and DS5 datasets from DS1, respectively. After removal of outlier loci, DS2 and DS3 contained 1,480 and 1,670 loci, respectively.
| Species and genetic diversity estimates Species richness ranged between 10 and 21 across the sites, and Pielou's evenness between 0. 39 and 0. 82. The average expected heterozygosity for the full dataset (DS1) across all sites was 0. 130. It reached 0. 135 and 0. 132 for DS2 and DS3, and 0. 098 and 0. 050 for DS4 and DS5 (Table  1 ). The α-genetic diversity estimates (He) of DS1, DS2 and DS3 correlated almost perfectly (r > 0. 99, p < 0. 001 in all cases). While the He estimates of DS4 correlated with those of DS1, DS2, and DS3 (range: 0. 63-0. 70, p < 0. 02 in all cases), no such relationships were observed between DS5 and the putatively neutral datasets (range: -0. 23 to -0. 16, p = 1. 00 in all cases). The genetic diversity estimates calculated from the nonoutlier datasets (DS1, DS2, and DS3) and those obtained by  Bertin et al.
| α-Species-genetic diversity correlations with outlier and nonoutlier SNP loci The α-SGDCs estimated from species richness were positive for DS1 and the two nonoutlier datasets (DS2 and DS3). However, the correlations were only moderate in amplitude, ranging from 0. 33 to 0. 37, and marginally nonsignificant (p = 0. 10, 0. 07, and 0. 09 for DS1, DS2, and DS3, respectively, Table  2 ). These correlations were much lower than the SGDCs reported by  Bertin et al. (2017). When excluding sites 6 and 21, however, the SGDC values increased substantially (more than 60%), with correlations ranging from 0. 57 to 0. 60 for datasets DS1-DS3, and became highly significant (Table  2 ). No significant positive correlations were observed for the outlier datasets (Table  2 ); rather, the correlations were negative and not statistically significant.
The results of the randomizations show that it would be extremely rare to obtain α-SGDCs as low as those observed with DS4 and DS5 just by chance with the putatively non-neutral datasets (p ≤ 0. 001 in all cases). None of the α-SGDCs estimated with species evenness were significant (Table  2 ). In spite of that, the randomizations demonstrated that the estimates derived from DS4 differed significantly from those obtained with DS1, DS2, and DS3 (p ≤ 0. 01 in all cases). The putatively non-neutral datasets displayed lower α-SGDCs than the putatively neutral datasets, reaching -0. 24 for DS4 and ranging between 0. 01 and -0. 07 for DS1, DS2, and DS3.
| β-Species-genetic diversity correlations with outlier and nonoutlier SNP loci The β-SGDCs were all positive. For the full and putatively neutral datasets (DS1-3), they ranged between 0. 11 and 0. 16 and were not significant or marginally nonsignificant (p > 0. 08, Table  2 ). For the putatively non-neutral datasets (DS4-5), correlations varied from 0. 21 to 0. 27 and were significant (p < 0. 05, Table  2 ). The results of the randomizations show that the probability of getting such high β-SGDCs by chance would be rare, particularly with DS2 and DS3 (p < 0. 03 in all cases, Supporting Information Table  S3 ).
| Partitioning genetic and species diversity in SGDCs studies Recent studies have stressed the usefulness of partitioning neutral and adaptive genetic diversity  (Bertin et al. , 2017; Watanabe & Monaghan, 2017)  and of simultaneously analyzing various species diversity components  (Lamy et al. , 2017)  to investigate species-genetic diversity relationships. Our results show that combining these two approaches can help to unravel the origin of covariation between these two levels of diversity. As expected, we found contrasting SGDCs between the putatively neutral and non-neutral datasets. Yet, the detected patterns diverged greatly depending on the species diversity component. Indeed, α-SGDCs were detected with species richness but not with species evenness. And, while the α-SGDCs based on species richness were only significant and stronger with the nonoutlier datasets compared to the outlier loci datasets, an opposite trend was observed for the β-SGDCs.
While the SGDCs of the nonoutlier loci datasets were much weaker that those previously reported with AFLP data, they provide relevant information. For the α-diversities, significant and positive SGDCs were only found with the nonoutlier loci datasets and with species richness, confirming that neutral processes were primarily driving the correlations and that the involved processes differentially influenced species richness and evenness. In their study,  Bertin et al. (2017)  examined the effects of wetland size, stability and connectivity on local diversities. They found that connectivity influenced plant species richness and C. gayana genetic diversity. We reproduced this analysis with species evenness but failed to detect such effects (results not shown). This finding is consistent with the expectation that migration more strongly influences species richness than species evenness  (Wilsey & Stirling, 2007)   2017 ), but such an approach requires a large number of sites  (Lamy et al.
| The usefulness and the limits of SNP markers for partitioning neutral and adaptive genetic diversity in SGDC studies Our work extends and refines previous studies that suggested investigating neutral and adaptive genetic diversity simultaneously to reveal neutral signatures in species-genetic diversity relationships  (Bertin et al. , 2017; Watanabe & Monaghan, 2017)  and shows that this framework can be successfully applied in single-species studies when large genomic datasets are available. The efficiency of such approach, however, depends on how much the adaptive and neutral genetic diversity deviate from each other, and may thus be better suited for studies focusing on fragmented ecosystems occurring over environmental gradient, as was the case here. The presence of false positives in the outlier datasets may also be another limiting factor, but this problem is likely to be resolved in the near future since it will be possible to have more genomic resources for nonmodel species (e.
gayana populations due to a past demographic event, whose effects could no longer be detected in AFLPs. The SNP genetic diversity of these two sites reduced the species-genetic diversity relationship with species richness, and once removed, the SGDCs with the full and nonoutlier datasets were much higher (and closer to the value reported in  Bertin et al. , 2017). This suggests that SNP markers responded uniquely to some evolutionary processes in sites 6 and 21. Genes versus species may differ in their rates of response to evolutionary pressures, and the responses of species and genetic diversity may not always be equally strong. Our results suggest that SNPs could strongly respond and show longer-lasting response to diversity-reducing processes such as drift than AFLP markers.
as a measure of α-diversity for the genetic datasets. To check the importance of the neutral signal in the non-neutral loci datasets (due either to the presence of false positives or a strong influence of neutral processes on the whole genome), we calculated the pairwise Pearson correlations between the He estimates of each dataset. Genetic β-diversity was measured as the Cavalli-Sforza genetic distance, calculated as where X and Y represent two populations for which L loci have been studied. X u represents the u th allele at the l th locus (Cavalli-Sforza &.
Expected heterozygosity (He) of Carex gayana populations calculated from the five SNP datasets (DS1-5, see Table2) and from AFLP data, as well as plant species richness at each site. DS1 is the full, original dataset, DS2 and DS3, the two non-outlier datasets, and DS4 and DS5 the two outlier datasets. Populations with conspicuously low SNP genetic diversity in comparison with their AFLP genetic diversity (see Supporting Information FigureS3) appear in bold.
ACK N OWLED G M ENTS We thank  F. Squeo ,  G. Arancio ,  D. Rodriguez ,  R. Inzulza Ayala , L. Cifuentes,  E. Alvarez ,  C. Urqueta ,  R. Hereme , and  A. Troncoso  for their help with field work and/or for their technical assistance. We would like to thank the organizers and participants of the  Distributed Graduate Course in Landscape Genetics 2016  for insightful discussion throughout this project. The authors are grateful to  La Junta de Vigilancia del Elqui, la Comunidad Agrícola los Huascoaltinos, La Sociedad de Parceleros de Coiron, La Sociedad de Parceleros de San Agustín, La Sociedad de Parceleros Hacienda Illapel, la Hacienda Tulahuén Oriente, la Hacienda El Maiten de Pedregal, and la Hacienda El Bosque  for giving us access to private wetlands. This research was funded by a grant from the  Chilean National Research Agency FONDECYT  # 1110514  and the exchange research grant  ECOS-CONICYT C12B02.
Expected heterozygosity (He) of Carex gayana populations calculated from the five SNP datasets (DS1-5, see Table2) and from AFLP data, as well as plant species richness at each site. DS1 is the full, original dataset, DS2 and DS3, the two non-outlier datasets, and DS4 and DS5 the two outlier datasets. Populations with conspicuously low SNP genetic diversity in comparison with their AFLP genetic diversity (see Supporting Information FigureS3) appear in bold TA B L E 1 Population Name n Species richness Expected heterozygosity (He) DS1 DS2 DS3 DS4 DS5 AFLP S1 Cop4 4 11 0. 072 0. 071 0. 073 0. 080 0. 043 0. 070 S5 Cop5 10 14 0. 084 0. 082 0. 084 0. 097 0. 088 0. 068 S6 Cop6 9 19 0. 088 0. 090 0. 089 0. 072 0. 019 0. 131 S7 Hua3 10 16 0. 134 0. 143 0. 136 0. 080 0. 041 0. 095 S8 Hua2 9 17 0. 127 0.
evenness index, and just eight studies used species richness and evenness in calculating SGDCs for the same dataset and genetic measure, reporting in most cases no significant SGDCs for both indices. Our results suggest that it can be profitable to simultaneously analyze the two diversity components as they may reveal different patterns and thus help understand mechanisms behind species-genetic diversity relationships. The β-SGDCs with the putatively neutral datasets were lower than the α-SGDCs obtained with species richness, which is consis- tent with the trends described by Lamy et al. (2017). However, while Lamy et al. (2017) found that β-SGDCs are more often significant than α-SGDCs, none of our β-SGDCs with the putatively neutral datasets were significant (at α = 0. 05). Significant β-SGDCs were detected with the putatively non-neutral loci.
The contrasting pat- terns in β-SGDCs between the putatively neutral and non-neutral datasets thus indicate that selective processes influencing C. gayana genetic diversity are involved in the detected correlation, which does not exclude the possibility that neutral processes are also con- tributing to it. Separating β-genetic diversity into putatively neutral and non-neutral components can help elucidate which processes are influencing the genetic level of the β-SGDCs but does not provide in- formation about the ecological processes involved in community dis- similarity. We see two possible alternatives. First, the concordance in genetic and species composition dissimilarities resulted from common responses to environmental variation. Most of the envi- ronmental predictors that contributed to the detection of outliers in the genotype-by-environment analysis (i.
We compare these datasets with remote CO 2 measurements made at Mace Head (MHD) on the Atlantic coast of Ireland and support our analysis with atmospheric boundary layer height (ABLH) observations made in the center of Paris and with both modeled and observed meteorological fields. The average hourly CO 2 diurnal cycles observed at the regional stations are mostly driven by the CO 2 biospheric cycle, the ABLH cycle and the proximity to urban CO 2 emissions. Differences of several µmol mol -1 (ppm) can be observed from one regional site to the other. The more the site is surrounded by urban sources (mostly residential and commercial heating, and traffic), the more the CO 2 concentration is elevated, as is the associated variability which reflects the variability of the urban sources. Furthermore, two sites with inlets high above ground level (EIF and TRN) show a phase shift of the CO 2 diurnal cycle of a few hours compared to lower sites due to a strong coupling with the boundary layer diurnal cycle.
g. l. ). These three stations (named Montgé-en-Goële -MON; Gonesse -GON; the Eiffel Tower -EIF) were deployed in summer 2010 within the AIRPARIF infrastructure. They ran for several months of the CO 2 -MegaParis project lifetime and delivered almost 1 year of CO 2 concentration datasets for the Paris megacity area. Additional datasets were provided by two long-term stations operated by LSCE named Traînou (TRN)  (Schmidt et al. , 2014)  and GIF  (Lopez et al. , 2012)  that are part of the national monitoring network SO-RAMCES (now called ICOS-France; https://icos-atc. lsce. ipsl. fr/). All the sites are on the same calibration scale (https://www. esrl. noaa. gov/gmd/ccl/ co2_scale. html), use similar analytical procedures and have relatively small uncertainties, as we will further explain in detail.