dataesr/scientific-paragraphs-categorization huggingface dataset:
This last file was then cleaned for PCR and sequencing errors using the obiclean program. Finally, the ecotag program was used to assign sequences to taxa by using the reference databases built for 12S and cytb (see above). R-scripts and STAMP  [47]  were used to finalize the statistical analyses of the data. STAMP is a software package for analysing taxonomic profiles and providing statistical tests. In particular, its graphical interface allows an easy exploration of the data. Because the number of total reads vary between samples (a sample is considered here as one genetic marker for one given site and one given sampling campaign, or a control), the reads counts assigned by taxa were converted in percentage by sample. Before computing this percentage, all taxa for which less than 5 reads were obtained were removed from each sample in order to avoid possible artefacts.
Intragroup percentage of pairwise sequence differences The genera of 71 fish species found in the NT2 area by surface gillnet capture were considered. When known, all sequences corresponding to each genus were retrieved from the reference sequences databases (12S or cytb). The percentage of pairwise differences between sequences of the same taxonomic group at different taxonomic level (species, genus, family) were computed with MEGA7  [49]. This allowed us to choose a threshold of assignment to the species level.
Fish diversity observed by surface gillnet method Morphological identification of fish captured by surface gillnets allows to identify a total of 11 orders and 93 species in the NT2 area (Table  2 ; S2 Table for detailed dataset). Cypriniformes and Siluriformes are the most represented orders with respectively 62 and 15 species. The remaining orders are all represented by less than 5 species, and by a single species in 5 cases out of 8. The distribution through time shows that 6 orders are detected in all sampling campaigns (S2 Table ): Cypriniformes, Siluriformes, Perciformes, Anabantiformes, Beloniformes and Cichliformes. The others are detected only one year (e. g. Tetraodontiformes in C1 and C2, Gobiiformes in C2 and C3) or during the WD season (Synbranchiformes, e. g. Macrognathus siamensis in the XBF).
Controls and identification of vertebrates by eDNA Controls were performed all along the eDNA metabarcoding study to monitor contamination from the sampling to the sequencing step (SI). No or rare contaminants (human, pig) were detected in extraction and PCR controls. However, sporadic contaminants, usually of a single fish species, was sometimes observed in the water-blank controls. According to their nature or distribution, these sporadic contaminants do not hamper the data analyses or the confidence in the results obtained as detailed in SI. Above all, the strict process followed to perform the experimental analyses has been proved to be efficient to track all possible sources of contaminant, a relevant point when universal primers are used. After removing contaminants, only reads for which a match in the reference databases was obtained with a BLAST identity score higher or equal to 97% and 95% for 12S and cytb respectively were primary considered (SI).
Actually, the cytb fragment is longer and more variable than the 12S one. Thus, the identification of a species usually relies on more discriminating positions, reducing possible taxonomic assignment errors linked to sequencing or PCR artefacts. The database is also more complete for the cytb (around 5 times more sequences than for 12S). Incorrect taxonomic assignment could thus occur more often with 12S than cytb, probably with the identification of close species when the real one is missing. We thus confirmed that multiple marker essays are crucial, to take benefits from their specific advantages or to circumvent their distinctive problems, to improve the biodiversity report  [30, 31]. What can be learnt from the unclassified sequences for eDNA metabarcoding. Although the dataset was restricted to reads with high BLAST identity to the references, it was not possible to assign a part of sequences to all taxonomic levels (called "unclassified" taxa).
As one can expect, the higher the taxonomic level, the lower the unclassified category occurs (Fig 2 ). Therefore, all sequences can be classified at the order level and virtually all at the family level (with 2 exceptions inside the Siluriformes and Cypriniformes groups for the 12S, Table  4 ). For both markers, 11% and 18% reads are unclassified at the genus level. However, this percentage reaches 41-45% when the species level is considered (31/71 and 34/82 for cytb and 12S respectively). Different explanations may be suggested: (i) the sequences of close taxa (species/genus) are not variable enough and cannot be distinguished. The taxonomic assignment is then made to the most recent common ancestor; (ii) the species is unknown. The taxonomic assignment could be done to the upper level (genus or family) if close species/genus are available in the reference database.
Note that a wrong species assignment is also possible if a single taxa is referenced for the whole genus/family and the BLAST identity score is high; (iii) sequence errors could interfere with taxonomic assignment, even after data cleaning and artefact removing. It would possibly increase artificially the number of unclassified reads. Overall, the unclassified data suggest that at least 53 species not yet identified could be present in NT2. This result is not unexpected as the reference databases are not complete. In particular, although 62 and 59 new sequences of 12S and cytb respectively were provided for this study, reference sequences of 88 species described with morphological criteria in the NTH and the XBF are still missing for both markers. Nevertheless, the number of lacking references is different according to the markers: 110 species for cytb and 157 species for 12S compared to species already evidenced in the area  [35]  and this study.
This also explains why some species could be detected with only one marker and not the other. It is worthy to note that missing reference sequences can be generated for species for which tissue samples can still be collected. However, this reveals impossible for species identified before the dam construction and not caught by fishing since that time. This is a major point that needs to be anticipated in the prospect of new dam projects: biodiversity reports in pre-dam surveys should now systematically include tissue sampling for DNA analyses in addition to morphological description. Interestingly, information provided by the morphological study is highly valuable to independently confirm the presence of species detected by the marker genes assays. It could also help in identifying the sequences of some species not yet or badly referenced in DNA databases.
For example, Xenentodon (Beloniformes) appeared as unclassified at the species level with cytb study. This was unexpected because only a single species of this genus, Xenentodon cancila, and of this family (Belonidae), is observed in NT2 area with 12S, fishing and according to previous survey  [35]. Checking the cytb reference database for this group revealed a wrong and imprecisely assigned sequence retrieved from Genbank. Removing this erroneous reference allowed to restore a correct taxonomic assignment to Xenentodon cancila for cytb sequences. Combining results from various sources, i. e. multiple marker assays and morphological study, is an efficient way to improve and curate reference DNA databases, since Genbank is prone to errors. Errors, false negatives or positives and difficulties for eDNA metabarcoding studies when lot of endemic species are unknown.
The choice to sample 6 litres of water by site at the starting of this survey in 2014 proved to be relevant as a similar study in the River Murray in South Australia found that at least 5 litres of water by sample were necessary to obtain a successful and complete report  [30]. Recently proposed to better estimate false-positive errors rate  [58] , statistical analyses based on detection/non detection matrices were however not applied on the results of this study. Indeed, multiscale SODM (site-occupancy-detection modelling) still needs improvements to be used on such complex dataset with multiple samples by site, by time, and using various markers  [48]. Nevertheless, false-positives inducing multiple detection events, that would arise from cross-contamination between sampling campaigns, have been limited by spreading the experiments over time.
Comparison between surface gillnet and eDNA metabarcoding to record species richness Nine out of the 11 orders and 17 out of the 19 families, found by surface gillnet (Table  2 ) were observed by the eDNA metabarcoding approaches (Tables  3  and 4 ) during the entire survey. The two orders and families missed by eDNA are represented by a single species: (i) Brachirus harmandi, a Pleuronectiformes from Soleidae family, and (2) Parambassis siamensis, an Ambassidae representative for which the classification remains unclear and identified sometimes as a Chandidae  [13]. However, this result is not a surprise as no 12S and cytb sequences are available for both species in the reference databases. Checking the 12S sequences identified with low BLAST score, the same Ambassidae sequence is found in almost all sites of NTH or XBF (25 sampling points covering all sampling campaigns).
The low BLAST identity score (around 90%) observed against the reference in the database coming from a different genus (Chanda sp. ) explains why it was not retained in our restrictive dataset. This sequence could probably come from Parambassis siamensis, since it is the unique species from this group detected in the area, but this should be confirmed independently. Nevertheless, the same is not true for Brachirus harmandi since no Soleidae, or even Pleuronectiformes, sequences with BLAST score below the threshold fixed were observed although some Soleidae taxa are present in the 12S and cytb reference databases. Finally, a supplementary order and family (Clupeiformes, Clupeidae, Tables  3  and 4 ) is detected with both DNA markers but is not recorded by fishing. At the genus level, 56% of the genera are identified overall by the 3 surveys taken independently.
In particular for NTH, the reservoir appears with the poorest taxonomy variety (Fig  2 ) with only few species present at high frequency (notably H. macrolepidata (12S) and Oreochromis niloticus (cytb), similarly observed as highly abundant by fishing  [12]  at this sampling site). Despite missing data in DNA reference databases, more than half the species were confirmed on their specific location in a single river by at least one marker assay. Details about the ecological implications of these differences are beyond the scope of this methodological paper and will be discussed elsewhere.
Acknowledgments The eDNA metabarcoding study was possible thanks to the  CNRS (Mission pour l'interdisciplinarite, Defi Enviromics  projects  2014-2016 ) and was entirely performed at the IGFL sequencing platform. A part of the sequence analyses were possible thanks to the use of the ENS Galaxy platform maintained by  Emmanuel Quemener  ( Centre Blaise Pascal, ENS Lyon ). The gillnet monitoring and water sampling was coordinated by the  NTPC. Samples (water and fish) were collected by the  Environment Department of Nam Theun 2 Power Company in Lao PDR whose shareholders are E ´lectricitede France, Lao Holding State Enterprise and Electricity Generating Public Company Limited of Thailand. Authors would like to thank all the teams that make this research possible with the support of the Lao local authorities and the  French Embassy in Lao PDR.
Theoretical work has shown that context-dependent dispersal has important consequences in the context of intraspecific competition,  17, 18  predator-prey interactions  19, 20  and species coexistence,  21  to name but a few examples. The challenge is to uncover fundamental proximate drivers of dispersal, which are relevant to population and community dynamics, while simultaneously maintaining generality and tractability. We argue that dispersal is best understood and investigated within the relevant community setting where it is likely a function of the fundamental ecological forces that determine local population dynamics, including bottom-up (resource availability) and top-down (predation risk) impacts that regulate the focal species demography. To investigate this hypothesis as well as to provide a general test of the ubiquity of context-dependent dispersal (CDD), we need synthetic datasets covering multiple species.
Such datasets should be obtained using comparable methodology and, most importantly, should include responses to multiple drivers of dispersal simultaneously as these may interact, which can lead to non-additive effects. 9, 22   uch datasets have hitherto been largely lacking for dispersal. 5, 7   herefore, we conducted a coordinated distributed experiment  23, 24  involving 7 laboratories across Europe and 21 species ranging from protozoa to vertebrates to test for bottom-up and top-down effects on dispersal, more specifically on the emigration phase of dispersal,  25  in experimental two-patch systems. By designing the two-patch systems with connections between them to be 'hostile matrices', incompatible with sustained population survival, we test emigration decisions rather than routine movement (see Supplementary Information for details).
Data collection Data on dispersal, more specifically emigration, that is, the number of residents (individuals in the patch of origin at the end of the experiment) and dispersers (individuals that had left their patch of origin and were in the target patch at the end of the experiment) after the dispersal phase in each replicate, were either collected using video recording and analysis  36  or by direct observation. Using data from further analyses or literature surveys (specified in the Supplementary Information), we collected species specific information for the focal species, resources and predators including: movement, space use, feeding strategy, body size, predator specialization and focal species escape strategies. The latter information was either used directly or in relevant focal species to predator ratios as potential explanatory variables for understanding the modulators of resource and predator impacts on emigration (see Supplementary Table  1 ).
Statistical analysis All statistical analyses were performed using the R Language and Environment for Statistical Computing (version 3. 4. 4) and occurred in two steps. We analysed overall treatment effects on all species together using generalized linear mixed effects models (GLMM) on proportion counts of residents and dispersers (aggregate binomial regression; binomial error structure with logit link function; 'glmer' function of the 'lme4' package using the 'bobyqa' optimizer). As random effects we included experimental block within species within taxon. We used taxon as a random effect to account for potential phylogenetic nonindependence and included the levels 'protists', 'algae', 'arthropods', 'molluscs' and 'vertebrates' (see Supplementary Table  1 ). We further included the laboratory in which the experiment was performed as a random effect in order to account for potential experimenter effects.
Dispersal and data collection After the opening of corridors, we monitored dispersal daily for 15, 5, 15 and 1 days for woodlice, crickets, spiders and newts, respectively. To do so, we captured dispersers in the target patches without disturbing the origin patches. At the end of the dispersal assay, we captured all individuals of each species in origin and target patches. We recaptured 114 woodlice, 266 crickets, 153 spiders and 177 newts. We only used recaptured ones in our analysis to prevent confounding dispersal with recapture probability and survival. Protists -Chilomonas sp. , Colpidium sp. , Tetrahymena pyriformis and Desxiostoma sp. Authors Emanuel A. Fronhofer, Frank Pennekamp and Florian Altermatt.
Dispersal and data collection After 1 h acclimation phase and 4 h dispersal we closed the clamps and took 20 s videos in origin and target patches using a Leica M205 C stereomicroscope at a 16-fold magnification and a Hamamatsu Orca Flash 4 video camera (imaged volume: 34. 4 µL; height: 0. 5 mm). Population densities of residents and dispersers were obtained using video analysis and the 'BEMOVI' package for the R Language and Environment for Statistical Computing. 43 For specific settings see GitHub (https://github. com/efronhofer/analysis script bemovi).
Data collection Both patches were sampled after the dispersal phase. 1. 2 mL per patch was removed and stored in 1. 5 mL Eppendorf tubes. To preserve the samples and fixate the algae, lugol was added. Densities were determined using a 1 mL counting chamber. Moreover, a sample of 8 µL from the origin patch was taken for video analysis (concentrations were too low to take samples of the target patch for video analysis). For measuring dispersal, cell numbers were acquired using a counting chamber.
Dispersal and data collection We allowed fish to disperse for 7 days during the first block and 10 days during the second block. The difference of time was statistically controlled through a block random intercept. All tanks were then emptied out to count the number of fish in each tank. We considered the residents as the fish having been caught in the origin patch, while dispersers were those in the farthest tank from origin patch. It resulted into 125 fish used in the analyses. Large white butterfly -Pieris brassicae, White-legged damselfly -Platycnemis pennipes Authors Delphine Legrand, Lieven Therry, Alexandre Vong, Staffan Jacob and Julien Cote License 09-2016-02.
Dispersal and data collection After the opening of corridors, we monitored dispersal daily for 4 and 5 days for butterflies and damselflies respectively. To do so, two people entered and walked quietly for 5 min in each cage. When a butterfly or a damselfly was located, the observer recorded the individual number. This procedure is commonly used on these species 1, 108, 109 and we did not observe any individuals leaving the enclosure during dispersal monitoring. We attributed a disperser status to individuals which moved at least once between patches and a resident status to individuals which never left the origin patch. We retained individuals which were alive after three days and excluded individuals which were never observed as their dispersal status is unknown. Tetrahymena sp. Authors Staffan Jacob, Estelle Laurent and Nicolas Schtickzelle.
Data collection We used a standardized procedure to measure cell density and morphology based on automatic analysis of digital images. 123 7] [118] [119] [120] 122  Digital pictures were analysed using ImageJ software (version 1. 47, National Institutes of Health, USA, http://imagej. nih. gov/ij) to obtain the overall number of cells on the picture, from which the abundance in each culture tube was recomputed. 123 wo-spotted spider mite -Tetranychus urticae Authors Stefan Masier and Dries Bonte.
This procedure has been repeatedly shown to efficiently elicit antipredator responses. 34, 137, 138  D ersal and data collection After the opening of corridors, we monitored dispersal daily for 9 days. To do so, we captured lizards in the target patches without disturbing the origin patches. At the end of the dispersal assay, we captured all lizards in origin and target patches. Ten lizards were found in the intermediate tanks (i. e. fake road) and, as we were unsure of their dispersal status, we excluded them from the analysis Supplementary Figures  for each study species individually. We here show species' trends in dispersal responses (log odds ratios extracted from species specific GLMMs) with respect to species specific dispersal modulators (see Tab. S1). Note that relative space use implies focal species space use relative to the respective predator's space use.
The focal species in patch 1 disperses less due to CDD (low predator densities) which makes the focal species increase in densities more rapidly in patch 1 while preventing rescue of patch 2. This can be seen in the dynamics illustrated in  If not indicated otherwise in the respective methods section, data contained within this table was measured during the experiment. For qualitative information, such as relative space use or mobility, we were conservative, ignoring differences roughly within one order of magnitude.
Acknowledgments FA thanks the  Swiss National Science Foundation  (Grant No. PP00P3 150698 ). DB and SM thank the  FWO (Fonds Wetenschappelijk Onderzoek  -grant n. 11T7518N LV). SJ, EL and NS thank  UCL and F. R. S. -FNRS ; SJ acknowledges a " Move-In Louvain" postdoc position at UCL ;  NS is Research Associate of F. R. S. -FNRS. DL and  MD thank Fyssen Foundation  for funding. JDR thanks the  FWO (Research Foundation Flanders  -grant n. FWO14/ASP/075 ). FrP was financially supported by  Swiss National Science Foundation  Grant  31003A 159498. JC was supported by an  ANR-12-JSV7-0004-01 , by the  ERA-Net BiodivERsA , with the national funder ONEMA, part of the 2012-2013 BiodivERsA call for research proposals and by the  French Laboratory of Excellence  project " TULIP"  ( ANR-10-LABX-41 ).
This work was supported by an ' Investissements d'avenir' programme  from the  Agence Nationale de la  recherche number  ANR-11-INBS-0001AnaEE -Services. DL and JC thank  Audrey Trochet  and  Olivier Calvez  for their valuable input in experiments involving newts, toads and snakes. DL and JC thank  Lucie Raymond  for providing butterflies. MD, AA and LM are especially grateful to  Christelle Van Gheluwe  for running the experiments, and to  Maryvonne Charrier  for providing Deroceras reticulatum slugs. We thank  Mridul Thomas  for valuable input on statistical analyses.
On this subject, we previously performed a microarray analysis of foetal muscle to identify candidate genes for piglet maturity, which revealed genes that were differentially expressed between the 90 th and the 110 th day of gestation  21. Using Pearson correlation a relevance gene co-expression network was built from these differentially expressed genes (DEGs) for four gestational ages. The network revealed and confirmed that: (i) genes involved in muscle development were up-regulated at the 90 th day of gestation, (ii) at the 110 th day, the enriched biological functions were involved in energy metabolism. An increasing number of studies use gene co-expression networks to deal with large gene expression datasets in order to decipher biological processes  [22] [23] [24]. Modelling co-expression with network models is useful for providing a global overview of the co-expression relationships between genes and enables a set of genes to be analysed globally with specific network tools.
Results Data selection. The 44,368 probes from the expression dataset of the muscle transcriptome study from Voillet et al. 21 were found to correspond to 13,855 unique annotated genes, among which 1,131 unique genes were found to be differentially expressed between the two gestational ages and for the four genotypes characterizing the establishment of piglet maturity. Among them, 359 DEGs (Supplementary Table  S1 ) were selected for being highly correlated with IGF2, DLK1 and MEG3 (R² ≥ 0. 84), also identified as DEG, and were used in all subsequent network inferences (see further details in "Materials and Methods", the section on "Microarray data description and pre-processing").