dataesr/scientific-paragraphs-categorization huggingface dataset:
Blastx and Blastn analysis of putative differentially expressed sequences against the nr database retrieved few homology with gene databases, underlying their probable specificity to brown algal sequences (S7 and S8 Tables). In many cases (i. e. 97/122 for L. digitata and 84/132 for L. spicata) Blast results retrieved non coding sequences, likely corresponding to 3'UTR sequences of the cDNA library. Indeed the brown algal genes have been shown to feature very long 3'UTR  [2] , leading to their over-prevalence in cDNA libraries. To confirm the results obtained from in silico analyses of EST libraries, we selected 15 unigenes in L. digitata and 13 in L. spicata among the top putative up-regulated expressions under grazer pressure (S7 and S8 Tables). Expression patterns of these unigene candidates were followed in juvenile individuals of both species challenged with their specific limpet grazers P.
Acknowledgments We are very grateful for assistance and technical help of Karine Cahier during mass spectrometry data collections on Corsaire-MetaboMer core facility at the  Station Biologique de Roscoff. We would like to thank our colleagues from the  Marine Service of Roscoff  and from the  Estacion Costera de Investigaciones Marinas (ECIM) at Las Cruces  for their help during the material collection and the implementation of the experiments in both laboratories.
This work was supported by the  'Centre National de la Recherche Scientifique ' and by the  'UniversitePierre et Marie Curie ,  Paris 06 '. The Las Cruces experimental access was supported by the  European Community  through the  ASSEMBLE programme  no. 227799 , and the sequencing costs through  Marine Genomics Europe NoE 7  ( EU  contract  n˚GOCE-CT-2004-505403 ). The project was also partly founded by the  IDEALG  project (grant  ANR-10-BTBR-04-02 ), the  GDRI DBMA , the  GDR MediatEC , the  Region Bretagne  (L. C. 's PhD, allocation no. J13U220/ARED ), and the  CONICYT of Chile  (A. R. 's  Post-doc fellowship ).
The growing colonies on our homemade culture medium were orange, circular, entire, smooth and convex, with a diameter of 1. 0 to 2. 0 mm. The complete 16S rRNA gene was sequenced using fD1-rP2 primers as previously described and a 3130-XL sequencer (Applied Biosciences, Saint Aubin, France)  [4]. The strain Saudii DR2 exhibited a 98. 3% sequence similarity with Halomonas xianhensis (GenBank accession no. NR116016. 1), which was the phylogenetically closest species with standing in nomenclature (Fig. 1 ). Consequently, it putatively classifies the strain Saudii DR2 as a new member of the genus Halomonas within the family Halomonadaceae in the phylum Proteobacteria. H. xianhensis was first described by Zhao et al. [5]  in 2012 as an aerobic, Gram-negative, short rod-shaped, moderately halophilic bacterium.
(2010). Operational taxonomic units (OTUs) were delineated at the 97% similarity threshold, and taxonomically affiliated using the BLAST algorithm (http://blast. ncbi. nlm. nih. gov/). To ascertain the phylogenetic position of Sebacinales OTUs, one longer sequence per each OTU was obtained with primers ITS3seb and TW13 as in  Selosse, Dubois, and Alvarez (2009). One representative sequence per OTU was deposited in GenBank (GB accession numbers KF871201-19). Agilent Technologies, Santa Clara, CA, USA) with a flame ionization detector (FID) was used. One microlitre of each scent sample together with 1 μl of octadecane (10 ng/μl) as internal standard was injected splitless (closed split vent) at 50°C (1 min), followed by a programed increase in oven temperature to 300°C at a rate of 4°C/min. The GC was equipped with an Agilent 19091J-431 column (15 m × 0.
| Flower odor sampling and chemical analysis For identification of compounds, 60 scent samples (30 of each species) were additionally run on an Agilent GC with mass selective detection (Agilent 5975C; Agilent Technologies, Santa Clara, CA, USA). As above, 1 μl of the natural sample and 1 μl octadecane solution (1 ng/μl) as an internal standard were injected into the GC-MS (gas chromatography-mass spectrometry) system. For tentative identification of natural compounds, their mass spectra were compared with data reported in the NIST library and by  Francke et al. (2000). For unequivocal structure assignments, mass spectra and gas chromatographic retention times of natural products were successfully compared with the following standards: tricosane, tetracosane, pentacosane, hexacosane, heptacosane, nonacosane (all purchased from Sigma-Aldrich); (Z)-9pentacosene, (Z)-9-heptacosane, (Z)-9-nonacosene (all from the stock collection of WF); octyl palmitate and nonyl palmitate (synthesized by WF through the reaction of palmitoyl chloride and the two primary alkohols following standard procedures).
| Phylogenetic analysis To uncover the phylogenetic relationships within the O. insectifera group, we Sanger-sequenced three nuclear markers (BGP, LACS, and Bayesian analysis was conducted with a single runs of a Markov-chain Monte Carlo (MCMC) chain for one million generations with tree sampling every 500 generations (CBSU BioHPC, Cornell University). Runs converged at split frequencies below 0. 01 after 600,000 generations. LFY The combined dataset had a length of 957 base pairs and Bayesian inference produced a single tree.
selected as the most variable regions from the original dataset of Breitkopf et al. (2015)  for 18 Ophrys specimens belonging to the O. insectifera lineage (namely 3 O. aymoninii, 3 O. subinsectifera, and 12 O. insectifera) plus one accession of O. garganica used as outgroup. The combined alignment was analyzed using Bayesian inference (MrBayes v. 3. 1. 2), with the un-partitioned dataset and by employing the GTR+Γ model of molecular evolution according to Breitkopf et al. (2015).
ACKNOWLEDGMENTS We thank  Ed Connor  and  Shuqing Xu  for helping with the scent analysis,  Karin Gross  for performing the ploidy level analysis, and Philipp Schlüter helpful discussions. The research was carried out with the formal approval of the " Parc Naturel des Grands Causses " authorities. Mycorrhizal sampling complies with the arrêté 2012-06 from 16 may 2012 from  Préfecture de Midi Pyrénées. This work was supported by grants to FPS from the  European Union 's  Seventh Framework Program  ( FP7/2007-2013 ,  FP7/2007-2011 ) under grant agreement no. 281093  and by the  Swiss National Science Funds (SNF) ProDoc Program  (grant no. PDAMP3-127227/1 ). We also thank the  Eugen-Baumann-Fond  and the  Fondation de France  for financial support.
For example, if the population index in a year is two for a particular species, it means that the population size of that species is twice the value in the reference year. Number of bird species per country ranged from 58 to 170, mean number of years with data in every country from 7 to 33. 3, and last year with information on population size from 2004 to 2008  [29]. Population indices were obtained for a total of 231 bird species and 1189 populations (i. e. , country x bird species combinations). Number of countries with information for each species differed greatly, ranging from only one country in 55 species to all 12 countries in 22 species  [29]. The search for information on population indices was finished by 8 th June 2009. Once population indices were compiled, they were regressed on years for every population, and the SEE (calculated as the square root of the residual mean square from the regression) was considered our estimate of population fluctuations around the trend.
e. , the distance (in km) between the locations where birds were ringed as nestlings (or fledglings) and recovered as breeders, derived from  [79]. These data were based on information recorded by the British Trust for Ornithology in the British Isles during an extended period of time. Geometric mean distances (for detailed information on calculations, see  [79] ) were used in the present study following  [80]. Adult survival. Annual adult survival rate was obtained from  [64]. If multiple estimates were provided, information from the UK was used because those estimates were generally based on the largest sample sizes. Predation. Susceptibility to predation by one mammalian (domestic cat (Felis catus)) and two avian (goshawk (Accipiter gentilis) and sparrowhawk (Accipiter nisus)) predators was estimated as the log 10 -transformed abundance of prey minus the log 10 -transformed expected number of prey, both referred to Denmark.
FID was assessed by one of us (APM) during 2006-2008 using a standardized technique. Briefly, when a bird had been located with binoculars, the observer moved at a normal walking speed towards the bird, and the distance (in meters) at which it took flight was recorded as FID. If the bird was positioned in the vegetation, the height above ground was recorded to the nearest metre, and FID was estimated as the square root of the sum of the squared horizontal distance and the squared height above ground level  [86]. The distance at which the observer started walking towards the bird when first observed was at least 30 m and relatively constant across species  [87]. For further details on the methods and reliability of FID estimates across spatial and temporal scales, see  [88] [89] [90]. Nest predation rate was obtained from  [64]  and  [91]  combined with data from the primary literature.
Body mass was calculated as the mean of male and female body mass. Brain size. Relative brain mass was estimated as the residuals after regressing log 10 -transformed brain mass on log 10 -transformed body mass while simultaneously controlling for phylogenetic relationships among species (see Statistical analysis below for details). Brain size was obtained from an extensive database recorded by a taxidermist (J. Erritzøe) on post-mortem examination of dead birds collected in Denmark. Brains were weighed to the nearest milligram, always excluding damaged heads. Further information can be found elsewhere  [87, 95, 96]. Sexual dichromatism. Bird species were classified as sexually dichromatic or monochromatic depending on whether males and females were readily distinguishable based on plumage coloration according to field guides  [97, 98].
Acknowledgments We are grateful to all the people who kindly replied to our messages during the search for information on population indices of European bird species, particularly to  Martin Flade ,  Magne Husby ,  Jaroslaw Krogulec ,  Robert Kwak ,  Jiřı ´Reif ,  Norbert Teufelbauer ,  Tibor Sze  ´p and  Dansk Ornithologisk Forening. We thank  Juan Soler  for helping with statistical analysis and  Bernt-Erik Saether  for providing data on process variance for bird species in the UK.
•Choice of markers: -Nuclear marker: 29S 5F5R -Mitochondrial markers: COI I: ICO 1490, HCO 2 198  [7] , cyt b: CP1 (Harry et al 1998), CB2  [8]. •Sequence analysis Sequencing results: nuclear marker; mitochondrial markers in concatenation COI I / cyt b were analysed using two matrices: -The genetic distance (K2P) and the number of different base pairs. This one records all the substitutions whatever is their nature. -The matrix K80 of Kimura, taking into account the transition-transversion processes. Sequences were analysed using the Geneious software Version 10. Multiple alignments were performed with the algorithm MAFFT  [9] ; and the phylogenetic trees were constructed using the algorithm PHYML  [10]  with the substitution model IC69.
Certain forms of reichei with appendages, antennae and legs entirely of yellow colour, could be confused with alpigrada (figure  3, 4 ). However, reichei often holds 2 bristles at the first article of the antenna and the triangular ala shape avoids any confusion (alpigrada having an S-shaped ala). Her geographical distribution and possibly her infraspecific variability (see data of molecular biology) remain to be specified. The prospecting should be carried out in the localities mentioned for transsylvanica alpigrada Csiki. 1837 ), also cited from Ukraine  [26, 22, 23, 24]  and Austria in Carinthia Südsteiermark  [47]. Its availability in Ukraine, is rather doubtful (PANIN Ruslan 2017 personal communication). Size 10-12 mm. Shiny black elytra. Head normally without red stain to vertex, except immature individuals.
MOLECULAR DATA Some molecular biology studies have been dedicated in Europe to: -species with wide distribution: jockischi, gyllenhali with an identification of the most appropriate markers -Nebria brevicollis Fabricius 1792 with characterization of the entire mitochondrium  [51]  -some other species (helwigi [52], salina [53], and GenBank). To our knowledge, no studies have been carried out on the Nebria species form Carpathian Mountains.
, 2014). Here we examine the behavioral responses of wandering albatrosses from the Crozet Islands to the longline fishing fleet operating near the colony (seven boats in total). We used GPS-tracking data collected over three consecutive breeding seasons on incubating birds of known age and sex, together with GPS positions of boats as recorded for the vessel monitoring system (VMS). This fine-scale resolution data allowed us to define encounter events (birds remaining within attraction range of a boat, beyond on-board observation scope) and attendance behavior (sitting within very close range of a boat), hence to evaluate encounter rates, probability to join an encountered boat compared to simply fly past, and several parameters of attendance behavior. We investigated first the extent of overlap and attendance of wandering albatrosses with the fishing fleet operating close to the colony, second whether these responses to boats differed between sex, age, and/or breeding season, and third what could be the consequences of attendance behaviors and their variation for the net mass gain of birds at sea.
| Bird data The study was carried out on Ile de la Possession (Crozet Archipelago 46°S, 52°E). In total, 160 incubating adult birds were equipped with GPS tags (IgotU Mobile Action Technology) in  2011, 2012, and 2013  between mid-January and mid-March. Birds were caught on their nest and the GPS tags, encased in heat shrink tubing, were attached onto back feathers using adhesive Tesa tape. The total mass of attached devices (<32 g including the final package, 0. 3%-0. 5% of the bird body mass) was well under the 3% recommended threshold  (Phillips, Xavier, Croxall, & Burger, 2003). Birds were recaptured on the nest after they left for at least one foraging trip. All GPS tags had a recording frequency of 15 min. In addition, 45 females  (18 in 2011, 13 in 2012, and 14 in 2013) and 44 males (20 in 2011, 10 in 2012, and 14 in 2013)  were weighed during both equipment of logger and recapture.
| Vessel data We used data from VMS (boat GPS locations recorded every 1 hr) and fishing events (GPS points taken at the start and end of each line setting or hauling), both made available from the Pecheker database hosted at the Museum National d'Histoire Naturelle de Paris  (Martin & Pruvost, unpublished data; Pruvost, Martin, Denys, & Causse, 2011). Following  Collet, Patrick, and Weimerskirch (2015) , we merged VMS data with fishing activity data to recreate trajectories, that were then linearly interpolated to estimate one point every 10 min. This 10-min resolution means that all bird locations would fall within 5 min of a vessel location, while keeping a large proportion of noninterpolated vessel GPS positions (~1/3-1/5). All VMS points were categorized either as "transit" or "fishing" according to fishing operation records.
| Mass gain analyses We modeled the mass gained at sea (g) in function of year, sex, and level of attendance to boats (the latter proportional to total trip duration). To test the hypothesis that females would obtain less offal at boats because of competition with larger males, we also included the interaction between sex and level of attendance to boats. Following results from  Cornioley, Börger, Ozgul, and Weimerskirch (2016) , we also included mass at departure as a covariate (using within-sex anomalies in mass at departure to account for size dimorphism). As we had no repeat measures for individuals, we used a linear model without random effects. Similar results were obtained when looking at mass gain rates (g/day at sea). All analyses were conducted in the R environment. Codes are publically available at https://github.
ACKNOWLEDGMENTS We are grateful to the administrators of the Pecheker database ( Alexis Martin ,  Patrice Pruvost ,  Nicolas Gasco , and  Charlotte Chazeau ) who kindly provided access to the VMS and longline data. Logistical and financial support was provided by the  Institut Paul Emile Victor (IPEV)  and the  Terres Australes et Antarctiques Françaises. This work would not have been possible without the contribution of all fieldworkers of IPEV Programme 109. We also thank two anonymous reviewers who provided constructive comments to help improve the manuscript.
In Siberia, the group of leaf mining insects remains poorly studied. For example, the Catalogue of the Lepidoptera of  Russia (2008)  contains little data on the taxonomic diversity and distribution of leaf mining moths in Siberia as compared to western and eastern regions of the country. Data on the distribution of leaf mining beetles, sawflies, and flies in Siberia are even scarcer. The goal of this work was to study the taxonomic diversity and distribution of leaf mining insects on birches in Siberia, based on the literary data and our records. The trophic specialization of leaf mining insects was studied, pests and invasive species were listed, and comparative analysis of the faunas of insects mining birch leaves in Siberia and Europe was performed.
, 1994). The conditions of amplification and sequencing were described in our earlier papers  (Kirichenko et al. , 2015 (Kirichenko et al. , , 2016)). Samples of three insect species were sequenced in the Canadian Centre for DNA Barcoding (Biodiversity Institute of Ontario, University of Guelph) using the standard protocol  (Ivanova et al. , 2006). All sequences were edited in CodonCode Aligner V. 3. 7. 1. and compared with references in the BOLD database  (Ratnasingham and Hebert, 2007). Data on the distribution of insects mining birch leaves in Siberia were extracted from the literature  (Forest Pests, 1955; Ilyinsky, 1962; Tomilova, 1962 Tomilova, , 1973;; Tibatina and Stroganova, 1966; Tibatina, 1970; Dovnar-Zapolsky and Tomilova, 1978; Barannik, 1981; Gershenzon, 1981; Kuznetsov, 1981; Gusev, 1984; Yanovsky, 1996 Yanovsky, , 2003;; Kuznetsov and Baryshnikova, 1998, 2003 ; Insects and Acarines…, 1999;  Tarasova and Sanina, 2004; Tarasova et al.
, 2004; Krivets, 2007; Catalogue…, 2008; Legalov, 2010; Baryshnikova, 2014)  and supplemented with our observations. The Latin names and trophic associations of species are given according to the modern databases (De Prins and De Prins, 2015;  Edmunds, 2015; Ellis, 2015; Fauna Europaea, 2015). In our paper the leaf miners are divided into mono-, oligo-, and polyphagous according to the accepted classification  (Hering, 1951; Ellis, 2015) : monophagous insects damage plants of one genus; oligophagous, plants of one family or, less frequently, plants of related families of the same order; polyphagous, a wide range of plant species from unrelated families and orders. Data on the richness of leaf mining insects on birches in Europe were borrowed from  Hering (1951)  and  Ellis (2015). The leaf mining species richness in Siberia and Europe was compared using the Chekanovsky-Sørensen similarity index: K S = 2C/(A + B), where C is the number of species common to the lists compared, and A and B are the numbers of species in each list  (Magurran, 2004).
, Profenusa MacGillivray, and Sco- lioneura Konow (Fig. 2 ). Leaf mining flies are represented on birches in Siberia by a single species Agromyza alnibetulae. Our collections included over 2500 mined leaves and 400 specimens of leaf mining larvae and pupae as well as adults reared from mines. In all, 25 leaf mining species were identified among this material, which makes up a little more than half the total number of leaf miners known on birches in Siberia according to the published data (Table  2 ). The species which we identified are marked with † in Table  2 ; the photographs of larvae and mines of most of these species, and also the cases of casebearer moths are shown in Fig. 3. The larvae and pupae of 15 leaf mining species were identified using DNA barcoding. Their sequences were published in the BOLD database and can be accessed at dx.
doi. org/10. 5883/DS-BETULEAF. Thirteen species were identified with 99. 2-100% match. The BOLD database showed a considerable similarity of the Siberian sequences to those from Europe (Finland, Germany, France, Norway, the Netherlands, Austria, and Czech Republic). In many cases, sequences of the Siberian sawflies were found to be close not only to the European but also to the Cana-dian ones (Table  2 ), which could be expected keeping in mind the history of invasion of these insects into North America (see the section Pests and invasive species below). Two moth specimens from Siberia were identified as Heringocrania unimaculella and Lyonetia clerkella by their mitochondrial COI gene fragments with 97. 46 and 98. 02% match, respectively. Differentiation of the Lepidoptera species using DNA barcoding is most commonly based on a 2% threshold  (Hebert et al.
About 30% of the leaf mining species are polyphagous; this group includes only micro-moths and beetles (Table  2 ). Besides birch and alder, they may feed on plants of the family Rosaceae and, less frequently, on Salicaceae and Ulmaceae. For six insect species mining birch leaves in Siberia, the Russian publications listed some host plants which are not typical of these insects according to the modern databases (De Prins and De Prins, 2015; Edmunds, 2015; Ellis, 2015; Fauna Europaea, 2015). In particular, maple (Acer) was mentioned as a host plant for the casebearer Haploptilia serratella  (Barannik, 1981) , alder (Alnus) and poplar (Populus), for the moth Caloptilia populetorum  (Tomilova, 1962 (Tomilova, , 1973;; Kuznetsov, 1981) , poplar and willow (Salix), for the weevils Orchestes jota and Tachyerges stigma  (Yanovsky, 2003) , poplar for the sawfly Scolioneura betuleti  (Tomilova, 1962) , and, finally, alder for the leafminer fly Agromyza alnibetulae (Forest Pests, 1955; Dovnar-Zapolsky and  Tomilova, 1978; Gusev, 1984).
Distribution in Siberia. The distribution of leaf mining insects on birches in Siberia remains poorly known. In this respect, Novosibirsk and Irkutsk Provinces, where over a half of the known birch leaf miners have been recorded (Table  3 ), are the best explored regions of Siberia. Our surveys provided new data about the distribution of leaf mining insects trophically associated with birches in Siberia. New distribution records were made for 20 species in Siberia (Table  3 ). Most of these species were for the first time recorded in Krasnoyarsk Territory (Krasnoyarsk) and Novosibirsk Province (Novosibirsk), which could be expected since material was collected repeatedly in these regions. For instance, some species of leaf mining micro-moths of the families Gracillariidae, Eriocraniidae, and Incurvariidae, and also sawflies earlier known from other Siberian regions were recorded in Krasnoyarsk for the first time.
CONCLUSIONS Analysis of the Russian literature and our data allowed us to revise the list of birch leaf miners, to study their distribution, and also to clarify their trophic relations with the native and introduced Betula species in Siberia. Altogether, 44 leaf mining insect species were recorded on birches in Siberia. Two-thirds of them are micro-moths; some of them are known as pests of Betula and cultivated Rosaceae in the Palaearctic. Four out of five sawfly species damage birches not only in the Palaearctic but also in North America where they invaded several decades ago. As the result of our study, new distribution records were made for 20 leaf mining species; besides, the micro-moth Stigmella continuella (Stainton, 1856) from the family Nepticulidae, previously known only from the extreme western and eastern regions of Russia, was found for the first time in Central Siberia.