dataesr/scientific-paragraphs-categorization huggingface dataset:
B). Final library concentrations were measured using a BioAnalyzer (Agilent, Santa Clara, CA, USA) before pooling. Libraries for each sample were then pooled in an equimolar way, diluted to 5nM final concentration and supplemented with 20%PhiX to add sufficient diversity for sequencing on an Illumina MiSeq using a 2x300bp cartridge. Raw data were deposited at the ENA under project accession number PRJEB34906. Raw reads were first trimmed and filtered using the fastx_quality_trimmer script (http://hannonlab. cshl. edu/fastx_toolkit/), assembled using Pandaseq 2. 11  (Masella et al. 2012)  and further processed with mother according to the Miseq SOP (version April 4 th , 2018). testing according to  Benjamini and Hochberg (1995).
were aligned to the non-redundant SSU ref database version 132, chimeric sequences removed usingVsearch (Rognes et al. 2016), and operational taxonomic units (OTUs) defined based on a 97% identity threshold. Rare sequences (<5 reads across all samples) were removed from the final analyses. Taxonomic assignments were generated for both the raw reads and the final OTUs using the RDP classifier method(Wang et al. 2007). Non-metric multidimensional scaling (NMDS) of the OTU matrix was carried out in R 3. 5. 1 using the isoMDS function of the Vegan package and Bray-Curtis dissimilarity as a distance measure. An Analysis of Similarity (ANOSIM) was used to test for difference between the two sites (719 permutations). Alpha diversity was estimated using the Shannon index with e as a base and the diversity() function of the VEGAN package.
Acknowledgments This work was funded partially by  ANR  project  IDEALG  ( ANR-10-BTBR-04 ) " Investissements d'Avenir, Biotechnologies-Bioressources ", the  European Union 's  Horizon 2020 research and innovation Programme  under the  Marie Sklodowska-Curie  grant agreement number  624575  (ALFF), and an internal call for proposals from the  UMR8227 (CNRS, Sorbonne University ). We thank  Cécile Hervé ,  Amandine Simeon , and  Agnieszka P. Lipinska  for helpful discussions;  Gwenn Tanguy  and  Erwan Legeay  from the  GENOMER platform, Roscoff  for support during the library construction and sequencing; and the ABIMS platform for providing the computational facilities for the metabarcoding analyses.
) when the radial distance is large but the right-angle distance is small". Therefore, under the assumption that the detection probability does not depend on the angle of observation, it was much simpler to use only the perpendicular distance. Following Borchers and Cox  [33] , we revive the analysis of 2D distance data. In practice, we customized the functions from the R-package LT2D  [33]  (to simplify the interface, resolve a few typos, and make the framework amenable to extensive simulations). We modelled the behavioural response of animals as a sigmoid function, i. e. duiker abundance gradually increased with distance from the transect (Fig  1A ). Importantly, this implies that the animals always avoid the observers when they are at distance zero (but they may quickly resume normal abundance and availability to detection as soon as their distance is non-zero).
Alternatively, and importantly, we also provide alternative models in the code appendix (S1 Appendix). These alternative models can accommodate the violation of the above assumptions. For example, if the detection probability at distance zero is already imperfect, use detection kernel "h. yTRE" instead of "h. RE" as currently implemented. If the animals do not completely avoid the transect even at distance zero from the observer, use behavioural response function "pi. sigmoI" (for "sigmoid with Intercept") instead of "pi. sigmo" as currently implemented. Note that these two functions h. yTRE and pi. sigmoI are incompatible with each other (cannot be used simultaneously in the same model), and that they feature one more parameter than the functions they replace. The parameters of the two functions (the sigmoid behavioural response function and the detection function with radial exponential detection hazard) were simultaneously estimated by fitting the model to the recorded distance data using a maximum-likelihood approach.
Duiker data collection We surveyed duikers from 5 transects totalling 31km in the Lomako Yokokala Wildlife Reserve (0˚56 0 00@ N, 21˚20 0 00@ E). The five transects were parallel and separated by 1 km. We walked each transect twice in March-April 2012 and twice in November 2013, performing one of the surveys by day for dung piles and the other survey by night with a powerful spotlight for direct observations. We followed the established distance sampling protocol, i. e. , we did not record the forward distance. Upon detection of a duiker or dung pile, we immediately walked to the nearest point on the transect, and recorded the time, GPS coordinates, and perpendicular distance of the initial location of the duiker, or of the dung pile, to the transect. When sampling dung piles, we ignored the piles that were considered older than a day old by experienced trackers upon visual examination.
Artificial data simulation We simulated 150 completely artificial datasets to quantify the bias in population density estimates induced by the behavioural response. We generated the datasets according to the abovedescribed sigmoid-radial exponential model. Our simulations featured 100 animals, which is the order of magnitude of the surveyed population sizes in many real-life applications. We considered 4 scenarios with low or high detectability, and strong or weak behavioural response to the transect. More precisely, the b vector that controls the shape of the detection function (notation as in  [33] ) could be [0. 25,0. 25] or [1. 75, 1. 75]. The log(φ) vector that controls the location and aspect of the sigmoid behavioural response function could be [-4, -1] or [-4, -6]. We applied the standard 1D distance sampling and the 2D distance sampling routines to the 150 datasets.
Bias in abundance estimates In Lomako, there was almost no direct detection less than 2 meters from the transect and a peak in sightings occurred at 5 meters from the transect (Fig  1B ). These patterns are exactly those we expect when the animals avoid the transects or the observers (Fig  1A ). Using the 1D distance sampling methodology, we estimated a density of 119 blue duikers per km 2 (95% CI: 53-180) on average over the two years. The ratio of dung piles per live animal estimated from the 1D distance sampling framework was 1. 8 (95% CI: 0. 7-2. 7). In all three scenarios, the fitted model indicated a behavioural response both in the dung pile data and in the direct observation data (Fig  3 ). The behavioural response was consistently more intense in the direct observation data than in the dung data (Fig 3 : bold black lines).
Therefore, the animals responded both to the transects and to the observers. The response to the observers was almost an order of magnitude stronger than the response to the transects alone (Fig  3 ). In the artificial datasets that did not use the Lomako data, the 1D framework over-estimated the detection rate (for example: Fig 1C, solid black line vs. other lines). This over-estimation of the detection rate propagated into an under-estimation of the abundance by 20 to 70% (Table  2 : "bias 1D"). Applying the 2D method to the same simulated datasets solved the problem for the most part (Table  2 : "bias 2D"). However, some bias remained in the most challenging scenario that combined low detectability and strong behavioural response (Table  2 ). This is because in this scenario the sample size of recorded detections was small compared to other scenarios, especially at short distances which are the ones the model uses to estimate the behavioural response.
These data could be collected either locally by the hunters themselves, or at selling points by researchers. However, at least until the functioning of the population is completely understood, these indicators of body condition are best combined with an indicator of population abundance, with an indicator of food availability, and an indicator of the intensity of herbivory  [11]. For duikers, the population abundance would be tracked through distance sampling as described in this article. The food resource to monitor would be fallen fruit on the forest floor. Lastly, changes in community composition can also be used as an ecological indicator. For example, it has been demonstrated several times that the ratio of blue duikers to larger species of duiker increases along gradients of hunting pressure  [15, 63] , probably under the combined influence of hunter selectivity and of the faster pace of life of the blue duiker relative to larger species.
Acknowledgments We thank the  Campus France program , the  Ministry of Environment and Sustainable Development of the DRC, The Ministry of Higher Education and University of the DRC , the  Program for the Conservation of Forest Ecosystems in Central Africa (PACEBCo) of the Central African Forests Commission (COMIFAC) , and the  Nature Plus program of Belgium. We thank  Cayla Fabien  from the  Embassy of France in the Democratic Republic of Congo-Kinshasa  for his support and help. G. E. 's PhD thesis was supervised by  DieudonneEyul'Anki Musibono  and  Seraphin Ndey Bibula Ifuta.
| Study site and data collection Data come from a previously published study describing a plantpollinator network from pollinator visits to flowers in a dryland ecosystem  (Chacoff et al. , 2018). Data collection was conducted in sites lying at the lowlands of Villavicencio Nature Reserve, Mendoza, Argentina (32°32ʹ0″S, 68°57ʹ0″W, 1,270 m a. s. l. ). Mean annual rainfall ranges 150-350 mm, concentrated during spring and summer  (Labraga & Villalba, 2009). Biogeographically, the study sites are located at the Monte desert ecoregion  (Roig, Roig-Juñent, & Corbalán, 2009). Data were collected weekly during 3 months during the flowering season (Austral spring and early summer, September-December) between 2006 and 2011. Pollinator visits to flowers were recorded in two 1-ha sites separated by c.
5 km, with two additional 1-ha sites sampled in only in 2006. Data from these sites were combined into a single network to improve sampling completeness of species and interactions occurring in the region. Pollinator visits to flowers were recorded between 7. 00 and 14. 00 hr in 5-min observation periods, a representative portion of the daily activity period of pollinators in our study sites. The data include 59 plant species, 196 flower visiting insect species and 28,015 interaction events (flower visits) involving 1,050 different pairs of interacting species. Plant abundance was estimated based on the density of flowers of each plant species, as flowers are the relevant plant structure for this interaction type  (Vázquez, Chacoff, & Cagnolo, 2009). Flower abundance was estimated during the flowering season of all study years using fixed quadrats/transects.
| Building plant-pollinator dependence networks We aggregated the data by pooling the number of visits of any pollinator to any plant into three subseasons of approximately equal length throughout the flowering season of each year (before 1 November, after 30 November and in between). Such level of aggregation allowed us to consider seasonal dynamics at a temporal grain that was not too fine nor too coarse to allow a reasonable seasonal representation of network structure. For any subseason, we built a plant-pollinator dependence network D, a directed weighted network representing the relative dependences among plant and pollinator species  (Bascompte et al. , 2006; Rohe et al. , 2016). From the number of visits in a time interval X ij between any pair of species of plant and pollinator (i, j), we considered two directed and weighted edges (i.
Lastly, the number of visits in cell (5,6) is twice the number in cell (4,5) but the dependence values are comparable (dependence is scale invariant) Furthermore, studying network dynamics requires a method that can handle and model the whole time series of network snapshots (i. e. in a dynamic network). Recently,  Matias and Miele (2017)  proposed an extension of SBM for dynamic networks called dynSBM. Under this approach, the structural position of any species can vary over time. In other words, each structural group (for instance a core group) is inferred using the complete series, but the group membership can vary from any time step to another. Here we rely on a modified version of this approach dedicated to bipartite networks (see Supporting Information) implemented in the r package dynsbm  (Matias & Miele, 2019).
ACK N OWLED G EM ENTS We thank  Sébastien Ibanez  and  Hugo Fort  for their useful comments, and  Benjamín Bender  for insect and flower drawings in Fig. 3. Funding was provided by the  French National Center for Scientific Research (CNRS)  and the  French National Research Agency (ANR)  grant  ANR-18-CE02-0010-01  EcoNet (V. M. ),  CONICYT/FONDECYT  grants  1150348  and  1190173  (R. R. -J. ), a  FONCYT grant   PICT-2014-3168  (D. P. V. ), the  People Programme  ( Marie Curie Actions ) of the European Union's  Seventh Framework Programme  ( FP7/2007-2013 ,  REA  grant agreement  609305 ) (D. P. V. ) and a  Bessel Research Award  from  Alexander von Humboldt Foundation  (D. P. V. ).
Funding information National Center for Scientific Research;  National Research Agency (ANR) , Grant/ Award Number:  ANR-18-CE02-0010-01 ;  CONICYT/FONDECYT , Grant/Award Number:  1150348  and  1190173 ;  FONCYT, Grant /Award Number: PICT-2014-3168; European Union's  Seventh Framework Programme , Grant/Award Number: FP7/2007-2013;  Alexander von Humboldt Foundation.
2017; Tikochinski et al. 2018). However, most of the already published riverscape genetics studies used only a small number of neutral molecular markers. Lately, some studies started to include large Next Generation Sequencing (NGS) datasets to assess gene-environment interactions  (Bourret et al. 2013; Hecht et al. 2015; Brauer et al. 2016 Brauer et al. , 2018;; Hand et al. 2016; Amish et al. 2019). The use of NGS datasets might be helpful for the genetic monitoring of endangered populations, as they provide information of local adaptation processes that is valuable for recovery programmes which could be apply in riverine environments  (Faulks et al. 2017; Kleinman-Ruiz et al. 2017; Hunter et al. 2018; Fan et al. 2018). For riverine species the introduction of exotic species for recreational and aquaculture purposes, might have a detrimental effect on the native populations due to predation, competition, the introduction of diseases and genetic introgression  (Perrier et al.
Population genomics analyses The genetic diversity of trout (Dataset A) was estimated from expected heterozygosity (HE), and observed heterozygosity (HO) using the adegenet R package  (Jombart, 2008). Effective population size (NE) was calculated by applying a molecular co-ancestry method  (Nomura 2008 ) implemented in NeEstimator  (Do et al. 2014). The genetic differentiation between all sample sites (Dataset A) was assessed considering three different approaches. Firstly, pairwise genetic differentiation coefficients (FST) were calculated between all sample sites in Genodive 3. 0  (Meirmans and Van Tienderen 2004) ; this method uses an analysis of molecular variance, performed between each population pair. Secondly, a phenogram was built in adegenet from all individuals using the neighbour-joining algorithm  (Saitou and Nei 1987)  based on Nei's genetic distance  (Tamura and Nei 1993).
Bootstrap confidence intervals were estimated from 10 000 permutations. Thirdly, an unsupervised Bayesian clustering approach implemented in the package fastStructure  (Raj et al. 2014 ) was used to assess the genetic structure of O. chrysogaster and genetic admixture with aquaculture trout. This approach infers population genetic structure for a large number of SNP datasets without assuming predefined populations. Thus, fastStructure was run using Dataset A including all SNPs from all the genotyped individuals and Dataset B including all SNPs from individuals without significant exotic introgression. Based on an approximation of the number of sample sites (total number of sampling sites + 1), K = 30 was considered the maximum value to avoid underestimating the number of clusters.
Riverscape genetics analyses Only Dataset C was considered when exploring the riverscape's influence on genetic diversity, due to the reduced number of sampling sites for the datasets at intra-basin scale (i. e. datasets D and E). Initially, the collinearity between seven predictors was tested using the R package corrplot (Taiyun Wei 2017): latitude, longitude, precipitation of the driest month, temperature of the warmest quarter, river length, altitude, and stream order. Afterwards, to test the effect of both climate and geographical distance, only latitude and precipitation of the driest month were retained because of their low collinearity (-0. 48). Finally, the effect of latitude and precipitation of the driest month on expected heterozygosity was tested by a generalized linear model using the GLM function in R  (Team R.
Detection of SNPs under divergent selection Dataset B was analysed to detect O. chrysogaster SNPs potentially under selection using three different software programs and two different approaches: a population (i. e. sampled sites) outlier detection approach (1) and association tests between genotypes and continuous climatic variables (i. e. riverscape adaptive genomics)  (2). Firstly, the PCAdapt R package  (Luu et al. 2016 ) was used to detect SNPs potentially under divergent selection with approach 1. This method combines principal component analysis and Mahalanobis distances and assumes that molecular markers excessively associated with population structure are candidates for local adaptation. Based on the vector of z-scores, loci which do not follow the distribution of the main cluster of points are considered outliers.
The analysis was run with a threshold of 10% and K=6 based on fastStructure results (observed spatial genetic structure). For approach 2, two gene-environment association software programs were used to test the effect of temperature of the warmest quarter and precipitation of the driest month. These variables had previously been suggested as significant adaptation drivers in salmonids  (Hecht et al. 2015; Hand et al. 2016). Using mixed models, both methods detect outlier loci through allele frequencies exhibiting strong statistical correlations with environmental variables. Initially, Bayenv2 was applied  (Günther and Coop 2013) , using an average of five independent runs (100 000 iterations). Also, the latent factor mixed models (LFMM) algorithm implemented in the R package LEA  (Frichot et al.
2013; Frichot and François 2015)  was run with five repetitions, 10 000 cycles, 5 000 burn-in, and K=6 (based on fastStructure outputs of the spatial genetic structure) as a random factor in the regression analysis. For both LFFM and Bayenv2, a threshold of 1% of total SNPs was defined to select the outlying SNPs with the highest posterior probabilities. A gene ontology analysis was conducted on the nucleotide sequences (64 bp) containing all SNPs obtained from O. chrysogaster, using Dataset B. This analysis is based on a BLAST query (blastn) of the sequences against peptides from the rainbow trout genome database  (Berthelot et al. 2014). Functional categorization by gene ontology terms (GO; http://www. geneontology. org) was carried out using Blast2GO software (version 4. 1, http://www. blast2go.
Population genomics analyses The Culiacán; and Western Fuerte and Western Sinaloa; Fig. 3b  and 4c ) the observed spatial genetic structure does not follow a basin delineating, suggesting human translocations of native trout or potential connectivity among basins during flood events, or both. Similar results were observed while analysing Dataset B, obtaining six native genetic clusters with MGT individuals belonging to the same genetic clusters described above (Online Resource 5). Interestingly, analysing the dataset without outliers (with only neutral SNPs) found the same genetic structure (results not shown).
Additionally, two linear distance matrices were generated for the Río Fuerte populations (Matrix III) and Río Sinaloa populations (Matrix IV) in order to control differences in distance in subsequent analyses (for further details, see Online Resource 2). To define the influence of isolation by riverscape resistance on genetic distances, Partial Mantel tests were applied using the PASSaGE package (Rosenberg and Anderson 2011). Partial Mantel tests were performed between the regression of FST/(1-FST) of Dataset D (Río Fuerte) and Dataset E (Río Sinaloa) and corresponding riverscape resistance matrices at intra-basin scale (either Matrix I, either Matrix II), using geographical linear distance as constant matrices (either Matrix III, either Matrix IV).
Resistance values for riverscape variables: altitude, slope, temperature of the warmest quarter (TWQ) and stream order (SO). Río Sinaloa populations (Matrix II), Isolation by distance among Dataset D for Río Fuerte populations (Matrix III), and Isolation by distance among Dataset E for Río Sinaloa populations (Matrix IV). Matrix I and Matrix II were calculated using the riverscape resistance surface, while Matrix III and Matrix IV were calculated taking into account only the geographical coordinates of the sampling sites (Table S2. 2). LITERATURE ESRI. (2013). ArcGIS, version 10. 2 for Desktop. ESRI, Redlands, California. Escalante, M. A. , García-De León, F. J. , Ruiz-Luna, A. ,Landguth, E. , & Manel, S. (2018). The interplay of riverscape features and exotic introgression on the genetic structure of the Mexican golden trout ( Oncorhynchus chrysogaster ), a simulation approach.
This research was supported under projects funded by The  Mexican Council of Science and Technology (CONACYT ;  Ref. CB-2010-01-152893 ) and  ECOS Nord  ( M14A04 ). Marco A. Escalante  was a recipient of a  Mexican Council of Science and Technology fellowship  for his PhD studies. We are especially grateful to  R. Hernández G. , L. A. Ramirez H. , and other members of  CIAD  staff for all their support in the planning, logistics and conducting of field surveys. We are grateful to  G. Ingle  from the  Mexican Fisheries Institute (INAPESCA)  for facilitating the obtention of trout samples. Thanks also to  T. Durán  for his support during field surveys. We would like to thank all the local guides who helped us during trout sampling, especially  Ricardo Silvas ''El Chalote ''. We are also grateful to the municipal government of Guanaceví, PRONATURA, and  WWF  for their assistance with field surveys.
Journal of Biogeography, 45(7), 1500-1514. https://doi. org/10. 1111/jbi. 13246 van Etten, J. (2012). gdistance: Distances and routes on geographical grids. R package version 1. 1--4. Available a t CRAN. R-Project. Org/Package= Gdistance. Altitude (m) 3009 -1950 1949 -1900 1899 -1850 1849 -1800 1799 -1750 1749 -1700 1699 -1650 1649 -1600 1599 -1550 1549 -1500 ≤ 1499 To calculate isolation by riverscape resistance and linear distance, four matrices among sample sites Slope (° Slope) TWQ (°C) SO Resistance value 0 -6. 74 ≤ 14 1 -3 0 6. 75 -13. 48 15 1 13. 49 -20. 22 16 2 20. 23 -26. 96 17 4 3 26. 97 -33. 70 18 4 33. 71 -40. 44 19 5 40. 45 -47. 18 20 5 6 47. 19 -53. 92 21 7 53. 93 -60. 66 22 6 8 60. 67 -67. 40 23 9 67. 41 -74. 14 ≥ 24 7 10 within basins were generated in gdistance R package (van Etten, 2012): Isolation by riverscape resistance among Dataset D for Río Fuerte populations (Matrix I), Isolation by riverscape resistance FD FE FL FL FL FM FS FC FO FV SB SE SH SL SM SP SS SC SC FEM 0.