dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analyses Spearman's rank correlation was computed between individual admixture q-values and h-index obtained by STRUCTURE software and Introgress package software, respectively. Metazoan parasite abundance and averaged prevalence were log-transformed prior to statistical analyses. Kolmogorov-Smirnov test was used for normality data assessment. Subsequently, Bonferroni post-hoc tests following General Linear Model (GLM) were used to compare the estimated marginal means of total abundance, prevalence and species richness adjusted for fish body length, water temperature, and sampling years between BB, BM and H from different river basins. Since all fish individuals were infected with at least one parasitic species (i. e. the overall prevalence was 100% in each fish population), the average of prevalences for each parasite species across the fish populations was used in our study (further referred to as averaged prevalence).
Genetic composition of Barbus spp. populations Based on microsatellite markers, STRUCTURE analysis confirmed the existence of one allopatric BB population in the Loire River basin (Site 1) and one allopatric BM population in the Argens River basin (Site 14). A low level of admixture between the populations of two Barbus species was revealed in the Rhône River basin (q < 0. 12). In contrast, the extent of population admixture in the Argens River basin was high except for the abovementioned the Argens allopatric BM population (Fig. 2 ). The values of hindex obtained by the Introgress software package resembled the overall picture of the individual admixture obtained by STRUCTURE (Fig. 2 ) and correlated significantly with the q-values (Spearman's rank correlation, r (373) = 0. 97, P < 0. 001).
Acknowledgements This study was funded by  GACR  project No. P505/12/0375. We thank  Radim Blažek ,  Rémi Chappaz ,  Jean-Pierre Cornec ,  Caroline Costadoat ,  Martina Dávidová ,  Kateřina Francová ,  Milan Gelnar ,  Lucka Havlátová ,  Šárka Mašová ,  Petra Navrátilová ,  Markéta Ondračková ,  Jarek Piňos ,  Eva Řehulková ,  Tomáš Scholz , and the representatives of  Maison Régionale de l'Eau, France  for help in the field and with parasite determination, and  Kristína Civáňová  and  Kristýna Koukalová  for their assistance in the laboratory. We kindly thank  Matthew Nicholls  for English revision of the final draft.
, 2001; Wilke and Drummond, 2006; Brion et al. , 2015). No specific selection pressure on the positive ESR was identified (Figure  3C ). To evaluate sequence conservation of the ESR genes at the interspecies scale, we calculated the percentage identity between the protein sequences of L. kluyveri and S. cerevisiae orthologues. We observed that conservation is high among negative ESR genes, with half displaying >80% identity, whereas positive ESR genes were less conserved, as the majority shared ∼65% identity (Figure  3D ). Moreover, only five genes without an orthologue in S. cerevisiae  Lachancea-specific genes and 70 ORFans in L. kluyveri  (Vakirlis et al. , 2016). This high number of uncharacterized genes is a unique characteristic of L. kluyveri. Our large data set is a promising tool that can be used to enhance gene annotation.
Consistent with this, protein domain detection using Pfam  (Finn et al. , 2014)  highlighted a mitochondrial domain in this gene (PF08213, p = 5. 9 × 10 -12 ). The expression of the gene SAKL0H23584g is correlated with genes expressed in MATa strains. Therefore we attempted to test mating efficiency with or without this gene. We immediately noticed that the agglutination generally induced after mixing MATa and MATα strains was not observed after the deletion of SAKL0H23584g (Supplemental Figure  S7 ). This agglutination has been thoroughly a potential GO term for 301 of the 1176 genes that previously had no reliable annotation (26%; Figure  5B ), which includes various biological processes (respiration, ion resistance, mating, expression; Figure  5C ). Among the 74 Lachancea-specific genes and 70 ORFans  (Vakirlis et al.
RNA sequencing and data processing RNA sequencing was completed as previously described  (Brion et al. , 2015). Briefly, sequencing was performed at the Gene Core Facility, EMBL (Heidelberg, Germany), using the Illumina HiSeq2000 platform with 50-base pair nonoriented single-end reads. Reads were cleaned, filtered, trimmed, and aligned using FastQC report, CutAdapt  (Martin, 2011) , and TopHat, version 2. 0. 8b (Trapnell et al. , 2009). The alignment rate ranged from 98. 4 to 99%, and the number shares only 39% of protein identity, and no orthologue has been detected for MSN4. Of the 23 STRE genes in the S. cerevisiae ESR, only four have been consistently conserved in the L. kluyveri ESR. The action of PKA, which participates in the control of the stress response in S. cerevisiae, is attributed to a set of three isoforms  (Santangelo, 2006).
Automated assignment of biological processes to genes For each gene, we generated a list of other genes that are coexpressed (Spearman correlation >0. 65) across conditions and strains, using previously obtained data  (Brion et al. , 2015). From that list, reliable S. cerevisiae orthologues were extracted (only those with a similarity >50%). By performing 100 permutations of expression levels across conditions for each of the 5600 genes before correlation calculation, we determined that only 2. 1% of the genes had >2 coexpressed genes by chance. From the list of orthologues, we performed a GO term enrichment using topGO of the R Bioconductor package, with the classic algorithm and Fisher statistical test  (Alexa and Rahnenfuhrer, 2010)  with org. Sc. sgd. db as the database. The three GO terms with the lowest p values were obtained and assigned to the initial gene and were generally involved in related pathways.
By 10 permutations of the pairwise correlation values for each of the 5600 genes before enrichment analysis, we determined that a p threshold of 10 -4 allowed for a false discovery rate of <2% and disregarded all cases of enrichment based on fewer than three genes. When the initial gene had a defined orthologue in S. cerevisiae, we extracted the annotated GO terms for the orthologue and compared them to the three we proposed, if significant. To do so, we used GOsim from the R Bioconductor package, with the relevance method and using org. Sc. sgd. db as the database. Similarity scores ranged between 0 and 1, and we arbitrarily chose a threshold of 0. 60 to denote significant similarities. We evaluated the reliability of this approach by applying this method to S. cerevisiae expression data previously obtained for 23 strains  (Skelly et al.
Raw data are available from the European Nucleotide Archive (www. ebi. ac. uk/ena) under accession number PRJEB10946. We filtered out genes with low expression levels (number of reads was <32 for all samples) and performed intersample normalization using Bioconductor DEseq2  (Love et al. , 2014)  on R (R Core Team, 2014). We verified data stability by obtaining the Spearman correlation coefficient between biological replicates, which was consistently >0. 96. The average values of gene expression between replicates were used for all subsequent analyses. The final data set is given in Supplemental Table  S4. We also calculated log 2 FC for the expression of each gene in the 19 stress-inducing media compared with that in the reference medium (YPD).
ACKNOWLEDGMENTS We thank the two anonymous reviewers for helpful suggestions and comments, as well as  Kelle Freel  for invaluable advice. We also thank  Gilles Fischer ,  Ingrid Lafontaine , and  Nikolaos Vakirlis  for providing the list of Lachancea-specific genes and ORFans. We are most grateful to the GeneCore sequencing team ( EMBL, Heidelberg, Germany ) and the  Plateforme BioImage (IBMP-CNRS, Strasbourg, France). This work was supported by  Agence Nationale de la Recherche  Grant  2010-BLAN-1606-05  and  ANR   Young Investigator Grant   2011-JSV6-004-01  (J. S. ). We also thank the  Université de Strasbourg (IdEx 2012 Attractivité)  for financial support.
Many experiments have shown that local biodiversity loss impairs the ability of ecosystems to maintain multiple ecosystem functions at high levels (multifunctionality). In contrast, the role of biodiversity in driving ecosystem multifunctionality at landscape scales remains unresolved. We used a comprehensive pan-European dataset, including 16 ecosystem functions measured in 209 forest plots across six European countries, and performed simulations to investigate how local plot-scale richness of tree species (α-diversity) and their turnover between plots (β-diversity) are related to landscape-scale multifunctionality. After accounting for variation in environmental conditions, we found that relationships between α-diversity and landscape-scale multifunctionality varied from positive to negative depending on the multifunctionality metric used.
Most present-day European forests and almost all forest plantations worldwide are dominated by only one or a few tree species  (15, 37) , although their diversity could be promoted relatively easily by planting more species or by encouraging natural regeneration. This fact makes the understanding of diversitymultifunctionality relationships in these ecosystems highly relevant for forest management. We therefore assessed the importance of αand β-diversity of tree species in driving γ-multifunctionality in mature European forests. To do so, we used data taken from a pan-European forest dataset consisting of 209 forest plots, specifically selected to investigate relationships between tree diversity and ecosystem functioning by maximizing variation in dominant "target" species richness and minimizing (i) variation in other potential drivers of ecosystem function (e.
ACKNOWLEDGMENTS. We thank the  Hainich National Park  administration as well as  Felix Berthold  and  Carsten Beinhoff  for support of this study and  Gerald Kaendler  and the  Johann Heinrich von Thünen-Institut  for providing access to the  German National Forest Inventory data. The research leading to these results received funding from the  European Union Seventh Framework Programme  ( FP7/2007-2013) under Grant Agreement 265171.
Animal movement is a fundamental eco-evolutionary process yet the behaviour of juvenile animals is largely unknown for many species, especially for soaring seabirds which can range widely over the oceans at low cost. We present an unprecedented dataset of 98 juvenile albatrosses and petrels (nine species), tracked for the first three months after independence. There was a startling diversity within and among species in the type and scale of post-natal movement strategies, ranging from area-restricted to nomadic patterns. Spatial scales were clustered in three groups that ranged from <3000 km to >6000 km from the natal nest. In seven of the nine species, the orientation of flight paths and other movement statistics showed strong similarities between juveniles and adults, providing evidence for innate orientation abilities.
Some of the juveniles were sexed using a molecular sexing method, others are of unknown sex. After data filtering and homogenization (Supplementary Methods 1) we obtained a total of 83 tracks, with 53 lasting at least 3 months and with an average of 0. 65 ± 0. 12 (SD) locations per day. Adults. Adults from the same nine species and same sites were tracked during the non-breeding season, when they are no longer central place foragers. Birds were fitted with light-recording geolocators (GLS) weighting less than 3 g and attached to a leg band, beginning in 2000. GLS give positions twice per day with a low accuracy (median error of 180 km)  63. To compare the overall large-scale movement patterns of juveniles and adults we randomly chose 10 complete adult tracks per species starting at the end of a breeding season and ending at the beginning of the next one.
To compensate for the low accuracy of locations given by the GLS system we smoothed the trajectories by averaging locations on a 3-days' time scale. Due to the low accuracy and low frequency of sampling we used adult data only for the comparative analysis of overall displacements and a global assessment of habitats used but not for the detailed trajectory analysis. Movement Analysis. We used two types of approach for analysing the movement data, both implemented using the R Software Environment  64. 1: The squared displacement method was used to identify and compare movement types within and between age classes and species  36. 2: Movement statistics commonly used for animal trajectory analysis were calculated to quantify detailed juvenile movement parameters and test the innate navigation hypothesis  65.
Trajectory parameters analyses. To quantify detailed juvenile movement parameters and test the innate navigation hypothesis, we calculated movement statistics commonly used for animal trajectory analysis  65  , i. e. orientation, daily distance travelled, sinuosity and range. For consistency, all parameters were averaged over 15 day time periods for the three first months of the trips for each individual. To aid visualization, however, we present only the first 15 days of each month in our figures, i. e. values for three periods of 15 days. Circular statistics for orientation analyses were computed with the R package "circular"  69. For other parameters, we used linear mixed effect models (lme within 'nlme' R package  70  ) with individual as random factor to take into account individual variability, followed by post-hoc Tukey tests ('multcomp' R package  71  ) to test for temporal and interspecific differences (see lme & Tukey test results in Supplementary Results 1 & 2 and Supplementary Table  2 ).
To determine the conditions where juveniles settled in specific areas we examined the bathymetric characteristics of their last locations (neritic waters when depth > -3000 m, i. e. from lower continental slope to continental shelf, or oceanic area when over deeper waters), and compared this with adults' broad habitat types (Supplementary Methods 2. 3). Although it was not possible here to obtain accurate information on adult habitat, we extracted bathymetry, daily sea surface temperature (SST) and monthly chlorophyll a (CHLa) values for all juveniles' trajectories. Environmental data were downloaded from the NOAA coast watch website (http:// coastwatch. pfeg. noaa. gov/erddap/index. html). To allow comparison between species, bathymetry, SST and CHLa values have been summarised in Supplementary Fig.
Acknowledgements The study at Crozet, Amsterdam and  Kerguelen Islands  was supported by  IPEV  (programme N°  109 ), with additional funding from the  Prince Albert II de Monaco Foundation. We thank the many field workers involved in the field programmes, and  Dominique Besson ,  Karine Delord  and  Maud Berlincourt  for help with data management as well as  Rory Wilson ,  Tim Coulson  and  E. J. Milner-Gulland  for helpful comments on earlier version of the manuscript and  Emily Shepard  for English language corrections. We thank the two anonymous referees for extensive comments and suggestions on earlier version of the manuscript. The study is a contribution to the  Programme EARLYLIFE  funded by a  European Research Council   Advanced Grant  under the European Community's  Seven Framework Programme   FP7/2007-2013 (Grant Agreement ERC-2012-ADG_20120314  to  Henri Weimerskirch).
In this paper, we present a new, semi-automated methodology for mapping hydromorphological indicators of rivers at a regional scale using multisource remote sensing (RS) data. This novel approach is based on the integration of spectral and topographic information within a multilevel, geographic, object-based image analysis (GEOBIA). Different segmentation levels were generated based on the two sources of Remote Sensing (RS) data, namely very-high spatial resolution, near-infrared imagery (VHR) and high-resolution LiDAR topography. At each level, different input object features were tested with Machine Learning classifiers for mapping riverscape units and in-stream mesohabitats. The GEOBIA approach proved to be a powerful tool for analyzing the river system at different levels of detail and for coupling spectral and topographic datasets, allowing for the delineation of the natural fluvial corridor with its primary riverscape units (e.
g. , water channel, unvegetated sediment bars, riparian densely-vegetated units, etc. ) and in-stream mesohabitats with a high level of accuracy, respectively of K = 0. 91 and K = 0. 83. This method is flexible and can be adapted to different sources of data, with the potential to be implemented at regional scales in the future. The analyzed dataset, composed of VHR imagery and LiDAR data, is nowadays increasingly available at larger scales, notably through European Member States. At the same time, this methodology provides a tool for monitoring and characterizing the hydromorphological status of river systems continuously along the entire channel network and coherently through time, opening novel and significant perspectives to river science and management, notably for planning and targeting actions.
This is especially the case for modern river management in Europe, where the Water Framework Directive (WFD)  [4]  acknowledges the major importance of fluvial geomorphology, requiring Member States to evaluate and monitor these types of forms. Recent advances in Remote Sensing (RS) techniques are transforming the way we look at and analyze river systems and provide a new source of spatially distributed, multi-dimensional information at wider spatial and temporal scales compared to the past  [5, 6]. Thanks to RS datasets, hydromorphological indices nowadays can be estimated in a quantitative rather than descriptive way for multiple spatial scales-from reach and sub-reach units up to the entire basin-with continuous information that is more objective and less exposed to operator subjectivity  [7] [8] [9].
They both used eCognition Definiens ® software, GEOBIA and decision trees on RGB orthophotos to map water, gravel bars and vegetation classes within the total active channel but did not focus on distinguishing the active channel from other floodplain units. At that time, the near-infrared band was not available for large coverages and the spatial resolution was not high enough to be suitable for this type of river application, making it difficult to distinguish vegetation patches from other channel corridor units. In the study by Bertrand et al. [8] , water, vegetation, and gravel patches were mapped with an object-oriented classification approach, but based on band ratio and thresholding developed with expert-based rule-sets built on near-infrared and red bands of available orthophotos. Sediment bars within the channel were distinguished from arable crops, urban areas and mining sites found within the floodplain, according to their distance to the floodplain centerline and neighboring objects (e.
In this paper, we investigate the potential of very high resolution (VHR) near infrared aerial imagery (0. 4 m) and LiDAR-derived products for mapping riverscape units and in-stream mesohabitats. For this purpose, we employed a database composed of VHR and LiDAR-derived products that were simultaneously acquired in 2009-2010 and available at the regional scale for the Piedmont Region in Italy (25. 387 km 2 ). We present a new, semi-automated methodology, based on the integration of the spectral and topographic information within a multilevel geographic, object-based image analysis (GEOBIA). The ability of GEOBIA to couple the high spatial resolution of VHR imagery and the high-resolution topography of LiDAR were tested along a 45-km section of a gravel-bed alpine river (Orco River, tributary of the Po River).
High resolution multi-spectral datasets and LIDAR derived topography are starting to be accessible to most countries in Europe, covering large areas and opening the possibility to attempt basin, and beyond regional scales hydromorphological characterization  [33]. Being able to semi-automatize this task deriving continuous, objective and repeatable measures would support the monitoring of active channel changes and associated dynamics of riverscape units, fostering managers and scientists to interpret river processes with an unprecedented level of accuracy and therefore providing novel opportunities to understand fluvial processes and to support their management.
AC, TAC and FP can be further subdivided into a number of sub-units, each associable to specific hydromorphological processes and forms. The Sparsely-Vegetated units (SV) can be subdivided into riparian (RSV) and island (SVI), if surrounded by WC and/or US, while within the FP it is possible to identify Riparian Densely-Vegetated units (RDV) and Densely-Vegetated units of Islands (DVI) from Other Floodplain Units (OFU) (Figure  2 ). SVI and DVI are usually found within the active channel boundaries and therefore are entirely surrounded by WC and/or US. On the other hand, RSV and RDV are vegetation units adjacent to the active channel, but not entirely surrounded by it (Figure  2 ). The major challenge of mapping riverscape units composing AC and TAC automatically from remotely sensed data lies in distinguishing them from other landscape features found within the floodplain (FloodPlain units class).
It is a crucial fluvial unit in the bio-geomorphic characterization of stream networks  [43]  because it represents the deposition zone of alluvium, including both riverbed and floodplain areas. All analyses performed within this study are focused within the boundaries delineated by the Valley Bottom shapefile and therefore all raster-based data described in Figure  4  were accordingly clipped, ignoring everything that fell outside of these boundaries. The "Fluvial corridor" toolbox was also employed for the calculation of the Detrended Digital Terrain Model (DDTM), by using the Orco River centerline shapefile and the DTM. As a first step, the stream elevation was extracted along the fluvial network with a constant spatial step (disaggregated network). The DDTM was then obtained by subtracting the stream elevation from the original DTM (7, Figure  4 ) for each pixel within the floodplain.
The DDTM is necessary to represent the elevation of all floodplain pixels compared to the river network, and if it is well-fitted, it can be essential in distinguishing different geomorphic features (see Section 4. 1). Remote Sens. 2016, 8, 97 8 of 24 within the boundaries delineated by the Valley Bottom shapefile and therefore all raster-based data described in Figure  4  were accordingly clipped, ignoring everything that fell outside of these boundaries. The "Fluvial corridor" toolbox was also employed for the calculation of the Detrended Digital Terrain Model (DDTM), by using the Orco River centerline shapefile and the DTM. As a first step, the stream elevation was extracted along the fluvial network with a constant spatial step (disaggregated network). The DDTM was then obtained by subtracting the stream elevation from the original DTM (7, Figure  4 ) for each pixel within the floodplain.
Multilevel GEographical Object-Based Image Analysis (GEOBIA) The primary aim of GEOBIA is to generate geographic information from RS data that enables users to tackle general environmental issues such as global climate change, natural resource management, landuse/land-cover mapping and others  [44]. GEOBIA's ambition as a discipline is to develop theory, methods and tools sufficient to replicate human interpretation of RS imagery in automated/semi-automated ways, allowing for more accurate and repeatable information, less subjectivity, and reduced labor and time costs. GEOBIA relies on RS data and generates GIS outputs, providing a critical bridge between the raster domain of RS and the vector domain of GIS. The "bridge" linking both sides of these domains is the generation of polygons, created by grouping connected pixels with similar characteristics into meaningful image objects, an analysis technique akin to the way humans conceptually organize the landscape to comprehend it  [44].