dataesr/scientific-paragraphs-categorization huggingface dataset:
However, we did not estimate stronger interactions despite having quite long time series, which suggests that interspecific competition at least may in fact not exist in many (phyto)plankton datasets. Are there other datasets corroborating our findings? Our inference of interactions using MAR models on field data, at a fine taxonomic scale, can to our knowledge only be compared to the detailed freshwater study of  Huber and Gaedke (2006). Distinguishing 4 diatom species, 2 cryptophyte groups and 1 dinoflagellate species, they could infer a negative effect of a dinoflagellate on one species of diatom. However, a different species of the same diatom genus was not affected by the dinoflagellate. Species-specific interactions could also be found between diatoms and cryptophytes. One of our main results was the absence of interactions between pennate and centric diatoms.
Acknowledgements This study could not have been performed without the dedicated, long-term data collection of the REPHY programme undertaken by Ifremer, for which we are deeply grateful. We would like to thank in particular  Nadine Neaud-Masso ,  Myriam Rumèbe ,  Claire Méteigner  for phytoplankton counts and  Florence d'Amico  for hydrological measurements. We also thank  Météo France  for meteorological data. Grégoire Certain ,  Florian Hartig  and  Jarad Mellard  provided constructive comments on the manuscript. This study was supported by the  French ANR  through  Labex COTE  ( ANR-10-LABX-45 ). FB and CP have equally contributed to this work.
g. ,  Guillou et al. , 2008; Bråte et al. , 2012). The development of NGS (Next Generation Sequencing) tools has suggested that marine parasites are more frequent than the literature has reported (e. g. ,  Lefèvre et al. , 2008). Parasitism is without a doubt a relation, which enhances the density and extend foodwebs. This in turn enhances the connectivity between species and makes carbon transfers more efficient, particularly because parasitic taxa have complex life cycles and infect organisms that belong at different trophic levels (e. g. ,  Laferty et al. , 2008; Niquil et al. , 2011). Thus, the effect of protistan parasites in the marine plankton varies greatly depending on parasite species  (Skovgaard, 2014). Taking into consideration the complexity of most microbial interactions, there are substantial limitations in our efforts for direct insight into relating taxonomic datasets to rates of discrete microbial groups exploitable in biogeochemical cycles.
They addressed this issue directly by associating specific functions to individual organisms, and indirectly, by investigative the statistical relationships among organisms and environmental variables measured over different temporal and spatial scales (e. g. , multivariate and network analysis). Several recent metagenomic studies of marine microbes have utilized network analysis to understand ecological processes. For example,  Gilbert et al. (2012)  with the use of networks described the connectivity between different eukaryotic and prokaryotic taxonomic groups, and between bacteria and environmental variables through a 6-year time-series dataset in a coastal area. Logares et al. (2014)  used networks to identify patterns of rare and abundant marine unicellular eukaryotes in surface coastal waters of six distinct sites in Europe.
, 2014). The objective of the present study was to focus on the putative Symbiotic/Decomposer/Parasitic community (SymbDec) in the EEC over a 2. 5 years period (32 dates). The molecular signatures of protists belonging to SymbDec are included in numerous NGS datasets, indicating a major complementary role in topdown population control  (Edgcomb, 2016). Thus, the two main objectives were (1) to examine connections between SymbDec taxa and other taxa using network analysis within the seasonal microplankton succession, and (2) to identify the underlying environmental parameters that affect specific taxonomic and trophic groups of the microplankton community; both in the temperate coastal waters of the EEC.
Tag Pyrosequencing Quality Filtering All samples produced between 12,581 and 29,712 reads. Downstream read processing was performed using the mothur 1. 28. 0 software following the standard operating procedure  (Schloss et al. , 2009 (Schloss et al. , , 2011)) , described in  Genitsaris et al. (2015). Briefly, the flowgrams of each sample were extracted, separated and denoised using the mothur implementation of PyroNoise  (Quince et al. , 2009). Primer sequences, tag and key fragments were removed, and only reads above 200 bp long with homopolymers shorter than 8 bp were included. The dataset were dereplicated to the unique reads and aligned against the SILVA 108 database. The reads suspected of being chimeras were removed using the UCHIME software  (Edgar, 2010). The dataset was normalized to the sample with the lowest number of reads using the subsample command in mothur, so that all samples contained 12,581 reads.
Data Analysis After tag pyrosequencing filtering and normalization, 1,303 OTUs were produced. Taxonomic classification was assigned using BLASTN  (Altschul et al. , 1990) , according to the PR2 curated database  (Guillou et al. , 2013). All reads affiliated to metazoan were removed from the dataset, thus 1,242 OTUs belonging exclusively to protists, remained. These OTUs, belonging to nine taxonomic super-groups were sorted into six groups according to their trophic status (Table  1 ), as in  Genitsaris et al. (2016). The six trophic groups that were formed were: SymbDec, including symbionts, decomposers, and parasites; autotrophs; heterotrophs and mixotrophs (based on their metabolism); and micro-, nano-, and picograzers (based on the size of their prey). Briefly, the strategy applied in order to sort the OTUs into trophic groups, was to individually examine the 1,242 OTUs and annotate them to a trophic group using the highest level of information.
01, based on pre-computed P-values of various MIC scores at different sample sizes, was used (MIC > 0. 6 in this case). Second, the Local Similarity correlations (LS), were determined by LS analysis (LSA; https://bitbucket. org/charade/elsa/wiki/Home), using a delay of one time point in order to identify time-lag correlations  (Ruan et al. , 2006; Steele et al. , 2011; Xia et al. , 2011 Xia et al. , , 2012)). The correlations without any delay were also checked. The correlations with P < 0. 001 and R > 0. 6 were taken into consideration. LS analysis is optimized to detect non-linear, time-sensitive relationships and is used to construct correlation networks from time-series data, similar to the present dataset. The networks of significant OTU-OTU pairs, or OTUenvironmental parameter pairs as identified by MIC associations, and by both time-lag LS correlations and correlations without time-lag, were constructed using Cytoscape v3.
5  (Smoot et al. , 2011). Network Randomizer 1. 1. 2 (Tosadori et al. , 2016)  was used to generate random networks of the dataset. The "smallworld" coefficient (σ) of the network was calculated according to  Humphries and Gurney (2008) , as follows: σ = C/Cr L/Lr (1) where C and Cr are the observed and random Clustering Coefficients and L and Lr the observed and random Characteristic Path Lengths. If σ > 1 (C>>Cr and L≈Lr), then the network has "small world" characteristics  (Watts and Strogatz, 1998; Humphries and Gurney, 2008). In an ecological context, small-world networks have specialized nodes or regions within themselves (e. g. , a parasite on a certain microbe), and at the same time they can exhibit shared or distributed processing across all of the connected nodes (all connected microbes have trophic interactions, contributing to energy transfer in the community).
Protistan Community and Potentially Parasitic Community In the present study we identified 1,242 OTUs. Rarefaction curves of the samples were calculated, reaching a plateau in all cases, suggesting a good coverage of the protistan diversity in all samples (Figure  S1 ). All OTUs were classified into 9 taxonomic super-groups, which were further sorted into 32 highlevel taxonomic groups (Table  1 ). The microscopic counts of P. globosa, diatoms, and dinoflagellates during the sampling period showed a similar succession with read data (Figures  2A-C ). The most abundant OTUs in the dataset belonged to the above 3 groups. Among the 99 diatom-related OTUs, an OTU identified as Leptocylindrus danicus (100% similarity; OTU003) showed an increase during late spring and summer (05/ 2011, 07/2012 and 05 to 07 2013, Figure  2B ).
and Perkinsus sp. , and Apicomplexa were represented by 13 OTUs, including several Gregarines, but as chytrids above, they all were found at very low abundances (<0. 1%). The plot of the cumulative number of OTUs indicated that the number of newly introduced OTUs was always higher during summer and autumn for each year, and the highest number FIGURE 3 | Relative abundance of major high-level taxonomic groups of protists that were detected in the study, based on comparisons against the Protist Robosomal Reference (PR 2 ) database using the "blastn" function (Table  1 ). Groups that predominantly include symbionts, parasites and decomposers (see Table  1 ) are shown with shades of gray, and separated pie pieces. To facilitate reading, only groups with relative abundance >1% are presented. was recorded in July 2011 (98 OTUs, Figure  5A ).
Network Analyses Two correlation techniques, MIC and LSA, that detect ecological and time-series relationships between 18S rRNA gene reads were selected in order to implement network analysis on the present time-series dataset. According to the MIC, a total of 93 out of the 142 most abundant OTUs (i. e. , >0. 1% abundance) were found to have 254 strong correlations (edges) with each other. Fifty-seven of the correlations were negative (Table  S1 ). The environmental parameters found to have strong correlations with OTUs were O 2 , N, T, and N/P (data not shown), however, only 11 OTUs were found to be connected with them. The 60 OTUs present in the MIC network when only connections of SymbDec OTUs were included were involved in 86 connections (12 negative, Table  S1 ). LSA with no lag showed a relatively higher level of participation of OTUs in the respective networks than MIC (Table  2 ).
The detection of ecological relationships between microbial taxa is challenging for sparse datasets and filtering out the rare biosphere in microbial datasets is recommended prior to utilizing these tools  (Weiss et al. , 2016). The comparison of performance of eight correlation techniques commonly used to describe microbial interactions inferred from highthroughput sequence data suggested that the best tools to address sparsity of data were MIC and LSA  (Weiss et al. , 2016). The authors concluded that although these tools reflect relationships that involve commensalism and mutualism, they have better fidelity in reflecting parasitic relationships than the other correlation techniques tested. Furthermore, in the case of time series data, LSA best describes the ecological relations between taxa.
Chytrids play also an important role in the food web since they recycle dead organic matter which can be further used by other organisms (see  Gleason et al. , 2011 for a review). In this study, chytrids accounted for about the 25% of fungal OTUs (24 OTUs) but, as also observed before at the same station their number of sequences were low and without apparent relation to the phytoplankton blooms in the area  (Monchy et al. , 2012; Christaki et al. , 2014). Most of the OTUs found in the data set are currently reported as freshwater and soil inhabiting chytrids and it cannot be excluded that they arrived from rain or freshwater inputs at this coastal station. However, four OTUs were affiliated to the genus Rhizophydium, which is reported to include parasites of chlorophytes and diatoms (  (Gleason et al.
globosa biomass buildup. Furthermore, the P. globosa-related OTU, albeit being one of the 5 most abundant OTUs in the entire dataset, did not show any significant correlation with parasitic or/and other, possibly indicating no parasitic or antagonistic regulation. Also, at the best of our knowledge there have been no reports about eukaryotic P. globosa parasites. However, it is well-documented that viruses play an important ecological role in population regulation of free P. globosa cells  (Brussard et al. , 2007). The presence of a "skin, " which effectively protects from grazing and infection by pathogens, is most likely the key reason why P. globosa colonies have reduced mortality relatively to singular cells and can create large blooms in various areas of the world's oceans  (Hamm et al.
ACKNOWLEDGMENTS This study was supported by the  "Nord-Pas de Calais" FRB-DEMO  ( FRB 2013 ), the  ANR-ROME  ( ANR 12 BSV7 0019 ) and the SOMLIT network. The authors thank  Eric Lecuyer  for his help during the sampling. We thank www. englisheditor. webs. com for English proofing. We are thankful to three reviewers for their constructive comments that have helped improve the original manuscript.
Benavides, M. , Moisander, P. H. , Daley, M. C. , Bode, A. , & Arístegui, J. (2016). Longitudinal variability of diazotroph abundances in the subtropical North Atlantic Ocean. Journal of Parameter number project Name Dataset-specific Instrument [ table of contents | back to top ] Description Sample ID number PI issued project name PCR Units unitless unitless -Added ISO_DateTime_UTC column date Date of sampling; YYYY/mm/dd Generic -Filled in blank cells with nd -Reformatted date to YYYY/MM/DD lon Longitude; E is positive Description [ table of contents | back to top ] time Time of sampling; HH:MM Instrument PCR Thermal Cycler station Station where sampling occurred Name lat Latitude; N is positive Dataset-specific Used to process PCR samples unitless decimal degrees unitless unitless decimal degrees Related Publications group_B Group B gene copy concentration Plankton Research, 38(3), 662-672.
Main results: The rate of successful identification of populations among the different species was very high (> 94 %) for P. nigra, P. sylvestris and P. uncinata, high (81 %) for P. pinaster, and low (< 65 %) for P. halepensis. Research highlights: Chloroplast DNA markers from extensive population datasets can be used to assign the origin of the forest reproductive material in some pine species. Additional keywords: genetic distance; region of provenance; fingerprinting. Abbreviations used: cpSSR (chloroplast microsatellite); F CT (variance components of the population permutated among groups); F SC (variance components of genotypes permutated among populations within groups); F ST (variance components of genotypes permutated among populations and among groups); k (number of groups).
, 2010) , as an example for the use of extensive marker datasets of populations that are becoming available (e. g. : (GD) 2 Database: https://gd2. pierroton. inra. fr/gd2/login/login, Demiurge database: http://www. demiurge-project. org/). The species considered were Pinus halepensis Mill. , Pinus pinaster Ait. , Pinus nigra Arn. subsp. salzamanni (Dunal) Franco, Pinus sylvestris L. , and Pinus uncinata Ram. Forest tree species play an essential role in determining many ecosystem properties and also influence the genetic diversity of associated organisms  (Whitham et al. , 2006). These species are highly relevant in Europe and along the Mediterranean region as they are broadly used in afforestation, restoration and conservation programs. Moreover, extensive studies on the genetic variation of the species have been done (P.
We performed a STRUCTURE version 2  (Pritchard et al. , 2000) , without prior information on the locality of origin, allowing the allele frequencies to be correlated among them for the rest of the species. This method is the recommended configuration to be used in the case of limited population structure  (Falush et al. , 2003). The number of groups (K) was set from a minimum of one to a maximum of 10, and ten simulations were run for each K-value with a burnin of 100,000 and with 100,000 iterations each. Theron the mean value of the posterior probability was calculated from the ten simulations for each K, and the most likely number of clusters was selected following the methodology proposed by  Evanno et al. , (2005) , implemented in the software STRUCTURE HARVESTER  (Earl & von Holdt, 2012).
Conclusions We demonstrated the usefulness of extensive markers datasets that are becoming available for identification of gene pools at the population level in different species. Nevertheless, results depend on factors such as the genetic diversity and the differentiation within and between populations. The rate of successful identification of populations among the different species was very high (> 94 %) for P. nigra, P. sylvestris and P. uncinata, high (circa 85 %) for P. pinaster, and low (< 79 %) for P. halepensis. More information is needed in P. halepensis and for some areas in P. pinaster.
Acknowledgements We are grateful to the  INIA marker database GENFODAT  ( A. Gómez ,  A. Soto de Viana ,  S. C. Gonzalez-Martínez ,  J. J. Robledo-Arnuncio ,  P. Jimenez ,  D. Barba ,  C. Garcia ) for providing the information of the populations. The text has been revised by a professional in English grammar and style, P. C. Grant.
ACKNOWLEDGEMENTS We are grateful to  Knut Helge Jensen  for comments on an earlier version of this manuscript. Many thanks to  Olav Moberg  for help with collecting the Frøya and Scottish lice, and for discussions and input. We are grateful to  Thor Mo  for help with data collection. The salmon used in our experiments was treated according to Norwegian regulations. Approval (application ID 5549, Forsøksdyrutvalget) and necessary licenses were obtained before experiments were conducted. The authors declared no conflict of interest.
In our case, we directly linked fecundity with owner profit maximization. In the absence of reliable macroeconomic data, this microeconomic approach allowed us to model the owner's decision-making process and study its impact on species' demography. However, our elephant price dataset was derived from data for a population between 530 and 900 individuals. Therefore, the estimated price of elephants in a population of less than 200 individuals remains questionable, being far from the original data range. This was one reason we limited our projections to 100 years. As shown by our model, changes in handling practices have long-term effects on population structure and dynamics. In 2012, the population was already transient and unstable, with a strong bias in favour of adults over 20 years old (see the age pyramid in Methods -Model calibration).
Population data. Since 2006, the Lao Care and Management Program (LECMP) of the Department of Livestock and Fisheries of Lao PDR has implemented a national campaign to register all captive elephants. Captive elephants are microchipped by the LECMP. Their records, including name, microchip number, sex, age, physical description, medical history, ownership details and location, are saved in a database. Data are updated twice-yearly during LECMP veterinary care visits. We used the data on captive populations structured by sex and age extracted from the database with the year 2012 as a reference  21. Data on the Nam Pouy wild elephant population from 1990 to 2012 were extracted from the literature  [23] [24] [25]. More details on the data used throughout are provided in Supplementary Note 4. Trade in captive elephants.
Interviews were conducted in Lao language by the first author, recorded on a Zoom H1 handy recorder, and fully translated into French and English. All methods were carried out in accordance with the guidelines for anthropological surveys  35  , and in accordance with the legal requirements of France and the National Center for Scientific Research (CNRS) institutional protocols for research. Research protocol has been approved by Montpellier University. All participants provided informed verbal consent before participation. Sampling was done using a stratified method based on the district of residence and ownership status of the respondent. We coded answers using MaxQDA Analytics Pro 12 software for qualitative data analysis.
We select three different growth rates to explore how wild elephant conservation policies could impact the captive dynamics: an increasing wild pool at a rate of 3% per year, a stable wild pool, and a declining wild pool at a rate of 8% per year. This annual decrease of 8% reflects the sharp decline of this population over the last 20 years (see data in Supplementary Note 4). Economic decision-making. According to microeconomic theory, owners tend to maximize profit. Because breeding is not compatible with female elephants working, elephant owners make an economic choice between having their female elephants breed or work. Thus, our economic model assumes owners maximize expected profits by choosing whether or not to breed their females, where the expectation arises because owners do not know future elephant prices or logging income with certainty.
155 10 , f min = 0. 02  21  , and σ = 3. Sensitivity and elasticity analyses. The sensitivity analysis allows studying the effect of variation in the model parameters on the population dynamics as measured by their asymptotic growth rate (λ). The asymptotic population growth rate, calculated as the dominant eigenvalue of the matrix, is calculated using the R popbio package  46. We used elasticities instead of sensitivities because these metrics take into account the unit in which the demographic parameters are measured and thus make comparisons possible between these parameters. The elasticity (EL) of parameter α is given by: EL( ) ( ) (  1 ) α λ λ λ = ′ - + Δα where λ′ is the new value of λ after applying a variation Δ to the parameter α. Model calibration. To calibrate our model, we projected the population over 20 years starting from a stable structured population of one thousand individuals in 1992 and compared its demographic structure with the observed structure in 2012.
The PLS regression is suitable when sample size is low relative to the number of variables and when collinearities occur among predictor variables  [22]. The loads, weights (i. e. relative contribution of each variable to the derived factors), and cross-correlations (e. g. the correlation between each X variable and the whole Y component, i. e. PLS' scores, and vice versa) were also estimated. Finally, the Stone-Geisser test (Q 2 ) at a threshold of 0. 0975 was used to assess the fit of the PLS model. The Q 2 statistic can be viewed as a jackknife version of the R 2 statistic. Analyses were performed with the "plspm" package  [23]  in R, version 3. 3. 2 [24].
Acknowledgements We would like to thank  Mr. Jordi Xifra  of the  National Game Reserve of Freser-Setcases  and all the game keepers from the  Cos d'Agents Rurals  for their support during the Pyrenean chamois sampling. Funding JMG holds a  FPI pre-doctoral scholarship  ( BES-2015-072206 ), funded by the  Spanish Ministry of Economy and Competitiveness. ES was supported by the postdoctoral programme ( SFRH/BPD/96637/2013 ) of the Fundação para a Ciência ea Tecnologia, Portugal. We would like to thank the  University of Aveiro (Department of Biology)  and  FCT/MEC  for the financial support to  CESAM RU  ( UID/AMB/50017 ) through national funds and, where applicable, co-financed by the  FEDER , within the  PT2020 Partnership Agreement. This study was partly funded by the  scientific programme  " Approche Intégrée de la Démographie des Populations d'Isard " sponsored by the  ONCFS  and the SEFaS.
The climatic drivers include a minimum of three meteorological inputs: precipitation, air temperature, and fraction of bright sunshine hours. Indices, such as the moisture index, the climatic water deficit, and the Priestley-Taylor coefficient, are also defined. The SPLASH code is transcribed in C++, FORTRAN, Python, and R. A total of 1 year of results are presented at the local and global scales to exemplify the spatiotemporal patterns of daily and monthly model outputs along with comparisons to other model results.