dataesr/scientific-paragraphs-categorization huggingface dataset:
However, we suggest to sequence about 45-50 specimens per site, if this number is not too high for routine analyses, to optimize the detection of species and the estimation of species abundance. This new method has excellent potential to replace the morphological method and allow a wide use of oligochaetes as bioindicators for sediment quality, especially because the molecular approach can be applied by non-experts in the systematics of this organism group. To achieve this goal it is important to complement the COI reference database, and to increase the applicability of this approach in routine analyses by facilitating and shortening the different steps of the analysis, in particular DNA extraction and PCR amplification. Table  2. IOBS values obtained with morphological analysis and high-throughput DNA barcoding of 33 and 66 specimens.
Acknowledgements This study is part of the  SYNAQUA  project supported by the  European Regional Development Fund and Swiss Federal  grant in the framework of the European Cross- Border Cooperation Program  ( Interreg France-Switzerland   2014-2020 ). Funding was also provided by the  Office cantonal de l' eau -Service de l' écologie de l' eau of the State of Geneva (Switzerland) ,  Direction générale de l' environnement -Protection des eaux of the State of Vaud (Switzerland)  and  Service de l' environnement -Protection des eaux of the State of Valais (Switzerland).
Data availability All data generated or analyzed during this study are included in the manuscript and its Supplementary Information files. Raw sequences are accessible in the Short Read Archive under the BioProject number PRJNA563268. The COI sequences of 313 bp corresponding to new lineages for our local reference COI database (one sequence per lineage) obtained as part of this study are provided in Supplementary File S1. In the near future, we will Sanger sequence a large segment of COI (658 bp) using the universal primers (HCO and LCO) of the specimens corresponding to these lineages. We will deposit these sequences (658 bp) in the European Nucleotide Archive as part of a future publication on the update of our COI reference database.
PCR products were analyzed on an ABI 3500 genetic analyzer and sized against the LIZ500 internal standard (Applied Biosystems, Foster City, CA, USA). Allele calling was performed using Geneious software v. 9. 1  (Kearse et al. 2012). In order to estimate population differentiation and confirm species identity, two workers per nest were sequenced for a fragment of the cytochrome oxidase 1 marker (COI) using the LF1 and LR1 primer pair  (Herbert et al. 2004; Smith et al. 2005). PCR products were purified using EXOSAP-it PCR purification kit (Applied Biosystems) and sequenced with the ABI BigDye Terminator v. 3. 1 cycle sequencing kit (Applied Biosystems). Base calling and sequence pairing were performed using CodonCode Aligner (CodonCode Corporation, Dedham, MA, USA). Fifty-four new sequences were generated for our samples and combined with 27 additional sequences of Melissotarsus species obtained from GenBank (M.
insularis, M. weissi, M. beccarii and M. emeryi;  Smith et al. 2005). Population and colony structure using FSTAT  (Goudet 1995). Population and colony structure were determined using only worker genotypes. Every analysis was first performed at the entire population scale and successively applied for each locality. For each locality, genotypic frequencies were compared between every pair of nests using log-likelihood (G)-based tests of differentiation using GENEPOP ON THE WEB  (Rousset 2008) , to determine whether they belonged to the same colony. A Bonferroni correction was applied to account for multiple comparisons of all pairs. Significance was determined using a Fisher's combined probability test. The genetic clustering of individuals within nests and populations was visualized by plotting individuals on a Principal Component Analysis (PCA) using Adegenet R package  (Jombart 2008).
The clustering of nests into distinct colonies was also assessed by Bayesian assignments of individuals into genetic clusters using STRUCTURE v. 2. 3. 4  (Pritchard et al. 2000). For each locality, the most likely number of genetic clusters (K) was estimated using simulations ran with values of K ranging from one to the total number of nests encountered within each dataset and repeated 10 times for each value of K. Each run included a 5 × 10 4 burn-in period followed by 1 × 10 5 iterations of the MCMC. The most likely number of genetic clusters was evaluated using the ΔK method  (Evanno et al. 2005)  implemented in Structure Harvester v. 0. 6. 8  (Earl & von Holdt 2012). In addition, the phylogenetic relationship among mtDNA haplotypes was investigated using Maximum Likelihood implemented in the PhyML online web server  (Guindon and Gascuel 2003).
1. 2 (Wang 2004 ). This analysis infers the number of males from the workers' genotypes and assigns each worker to a given patriline. For all colonies, relatedness coefficients (r) among nestmate workers were calculated using the program COANCESTRY v. 1. 0 (Wang 2011) , according to the algorithm described by  Queller & Goodnight (1989). Relatedness coefficients were weighted equally and standard errors (SE) were obtained by jackknifing over colonies. Relatedness coefficients were calculated separately for each locality to account for the strong genetic structure (and differences in allele frequencies) between populations (see Results).
Population and colony structure Overall, eight new haplotypes were found on the 622bp fragment of the COI mitochondrial marker for the 54 samples sequenced in this study. GenBank samples were included, although the species identifications may be ambiguous (Melissotarsus alpha taxonomy awaits a thorough revision). The overall dataset comprised 81 sequences for which 200 nucleotide positions were variable (175 parsimony-informative). Surprisingly, samples from different localities clustered into clearly separated clades (Figure  S2 ). The Cederberg population clusters with the GenBank samples identified as M. emeryi. However, the other localities segregated away from M. emeryi, separated by one (M. insularis, for the localities of Eastern Cape and St Lucia) or even two species (M. insularis and M.
29, 0. 32, 0. 32 and 0. 27 for EC, MK, SL, and CE, respectively; F ST values for each pair of nests are given in Supplementary Information, Figure  S3 ). Therefore, all but three pairs of nests were genetically different from each other (G-tests significant for each pair of nests; P < 0. 001), even those located on the same host trees. The lack of differentiation between the three pairs of nests (EC1 and EC2; MK12 and MK3; SL10 and SL11), most likely results from their genetic similarity than their actual belonging to a common colony, as the nests from each pair were located on different trees, separated by hundreds of meters. When the entire dataset from all locations was analyzed, STRUCTURE revealed the occurrence of at least three genetic clusters (most likely K = 3), roughly corresponding to the different localities sampled (Figure  2 ).
The genotypes of nominal SL11 queens indicate that they are not related (r q-q = -0. 09). Notably, the increased genetic diversity resulting from the presence of multiple unrelated queens in SL11 may explain the apparent lack of genetic differentiation with this colony in the STRUCTURE and PCA analyses. Interestingly, the inbreeding coefficient for the overall dataset was negative (F IS ± SE = -0. 32 ± 0. 02), for the localities analyzed separately (F IS = -0. 23, -0. 33, -0. 34 and -0. 31 for EC, MK, SL, and C, respectively), as well as for all colonies (except SL11; Figure  S4 ). This absence of inbreeding reflects the non-independence of genotypes from a single family. This indicates that mother queens mated with unrelated males, while daughter queens and males do not stay and mate within the nest.
Forest monitoring tools are needed to promote effective and data driven forest management and forest policies. Remote sensing techniques can increase the speed and the cost-efficiency of the forest monitoring as well as large scale mapping of forest attribute (wall-to-wall approach). Digital Aerial Photogrammetry (DAP) is a common cost-effective alternative to airborne laser scanning (ALS) which can be based on aerial photos routinely acquired for general base maps. DAP based on such pre-existing dataset can be a cost effective source of large scale 3D data. In the context of forest characterization, when a quality Digital Terrain Model (DTM) is available, DAP can produce photogrammetric Canopy Height Model (pCHM) which describes the tree canopy height. While this potential seems pretty obvious, few studies have investigated the quality of regional pCHM based on aerial stereo images acquired by standard official aerial surveys.
Our study proposes to evaluate the quality of pCHM individual tree height estimates based on raw images acquired following such protocol using a reference filedmeasured tree height database. To further ensure the replicability of the approach, the pCHM tree height estimates benchmarking only relied on public forest inventory (FI) information and the photogrammetric protocol was based on low-cost and widely used photogrammetric software. Moreover, our study investigates the relationship between the pCHM tree height estimates based on the neighboring forest parameter provided by the FI program. Our results highlight the good agreement of tree height estimates provided by pCHM using DAP with both field measured and ALS tree height data. In terms of tree height modeling, our pCHM approach reached similar results than the same modeling strategy applied to ALS tree height estimates.
Our study also identified some of the drivers of the pCHM tree height estimate error and found forest parameters like tree size (diameter at breast height) and tree type (evergreenness/deciduousness) as well as the terrain topography (slope) to be of higher importance than image survey parameters like the variation of the overlap or the sunlight condition in our dataset. In combination with the pCHM tree height estimate, the terrain slope, the Diameter at Breast Height (DBH) and the evergreenness factor were used to fit a multivariate model predicting the field measured tree height. This model presented better performance than the model linking the pCHM estimates to the field tree height estimates in terms of r² (0. 90 VS 0. 87) and root mean square error (RMSE, 1. 78 VS 2. 01 m). Such aspects are poorly addressed in literature and further research should focus on how pCHM approaches could integrate them to improve forest characterization using DAP and pCHM.
Photogrammetric reconstruction We used Agisoft Metashape 1. 5. 4 in network mode for all the photogrammetric reconstruction steps synthetized in Fig. 1. Agisoft is one of the most used photogrammetric packages using a multiview matching strategy  (Smith et al. , 2016). It also allows to handle large images (200 Mpx and more) generated by large frame sensors like the UltraCam in an easy to set up network processing interface. We choose this software for its relatively user friendly GUI as well as its rather low cost (ca. 549 $ for educational license and 3500 $ for commercial license) compared to other state-of-art photogrammetric package like Trimble Inpho or Imagine Photogrammetry (LPS). These characteristics will ease the reproducibility of the methodology. We set up a processing network of 3-5 (depending on the resources available) computers equipped with GPU processing (NVidia GTX) and 64 Go RAM.
3 -II) of reference ground marked points provided by the National Geographic Institute which consists of various particular points which can be seen on aerial images and have precisely been georeferenced on the ground with precision GNSS (pedestrian crossing, change in road color, …). The total GCP network represents an important reference dataset covering the entire study area (Fig. 3-I  and -II ). The accuracy assessment process is based on a cross-validation using repeated k-fold technique (k = 5, repetition = 50) implemented in Agisoft Metashape through python scripting. For each of the 50 iterations, the GCP dataset is randomly divided into five folds of GCP and five BA processes are successively run (Fig. 2 ). Each BA process is run based on a set of GCP from one fold as checkpoint (not used in the process, i.
The relative position of the individual trees was converted to absolute ones based on the GNSS XY position of the plot center. As the plot centers are located using low-quality GNSS receivers, relocation of the field plot was mandatory and performed in QGIS 3. 0 software (QGIS Delopment Team, 2020) by a trained operator. Using reference GIS layers (orthophoto based on photo. DSM and pCHM time series), the operator looked for the best XY shift and applied it to the entire trees of the FI plot. As the tree XY positions are relative to the stem position (at breast height) and not to tree tops, we used the method developed by  Eysn et al. (2015)  to refine the individual trees location. This processing step also allows identifying trees from the upper canopy envelope as this information is not recorded during the field measurements of the FI.
We performedFI trees XY position and height matching with local maxima detected in the associated pCHM by the tree_detection() function (lmf algorithm, adaptive moving windows based on tree height) of the lidR R package  (Roussel and Auty, 2019). This results in a database of 1850 reference individual trees (Table  2 ) from 489 FI plots presenting an evenly spatial distribution in the study area. To avoid 3D reconstruction issues linked to (partial) leaf-off conditions, FI plots were selected only when they were in leaf-on condition during the associated aerial survey. In even-aged forests (mainly spruce and Douglas fir), the field survey of the tree height is limited to few individuals in order to compute the dominant height. As the evergreen forests are mainly managed as even-aged stands, they occur in a rather low proportion in the reference tree database as compared to deciduous trees.
We performed the same matching between field measured tree heights from FI plots and tree height estimates extracted from the ALS CHM. This database was composed of 1579 trees from 260 FI plots (1144 deciduous, 435 evergreen). The Fig. 3 -III represents the FI plots network used in this study. In order to test the reliability of tree height information provided by pCHM, we performed linear modelling between field measured tree height (from the reference tree database) and tree height estimates from local maxima of the 5 different pCHM's. We applied a similar method with ALS reference dataset to benchmark tree estimates provided by the pCHM with a reference tree height remote sensing data source.
This test highlighted that only the observations from the aerial surveys 2015 and 2016 were significantly different (p-value = 0. 011). Within the set of variables which were proved to have a significant statistical relationship with the pCHM tree height absolute error, we removed the reference year and the tree species factorial variables before running the best subset regression process. This choice was made in order to ease the interpretation (the Species tree factor presents 29 le- vels in our dataset) and the replication of the fitted pCHM error model. Indeed, the aerial survey reference gathers a bunch of environmental parameters at the time of the flight survey (lights conditions, phenology …) with a rather low interest for understanding the drivers of the pCHM tree height estimates error.
The best subsets regression model selected following the BIC a model with 2 variables: the DBH and the evergreenness factor to predict pCHM tree height estimate error. This model presents a rather low r² score (0. 10) and a RMSE of 1. 3 m. Finally, we used the same initial set of variables used to fit the pCHM tree height estimate error model to evaluate their potential income in terms of accuracy improvement of a global model linking the tree height estimates provided with pCHM data (all pCHM used) and the FI tree height estimates. The best subsets regression model selected (using BIC) a model with 4 variables to predict the field measured tree height: the pCHM tree height, the DBH, the evergreenness factor and the terrain slope. This model is significantly different (ANOVA test, pvalue < 0.
These results could be synthetized as the bigger the tree is, the bigger the error of its pCHM height estimate is. The positive correlation between the canopy roughness (assessed here through the pCHM coefficient of variation in the FI plot) can be interpreted by the lower ability of DAP to model high slope variation, especially when considering the rather low overlap of our aerial images dataset (60 % along-track, 30 % across-track) as suggested by  Hirschmugl et al. (2007). The altitude is generally considered as a very good proxy of the ecological gradient in various studies in the study area  (Brogna et al. , 2018; Dufrene and Legendre, 1991; Georges et al. , 2019). Beside the link with an ecological gradient, the positive correlation with the absolute pCHM height estimate error can partially be linked to the tendency of evergreen stands to be located in higher altitude while having a higher height.
Nevertheless, tree height estimates from the ground are both subject to instrumental and measurement errors. As the field height data were collected using ultrasound equipment which tends to have low instrumental errors (if properly maintained), the most important potential errors are suspected to occur in the measurement step itself. Rondeux (1999)  highlighted that tree height measurement errors can be considered as random. They are linked to the shape of the tree and its position, the equipment set-up or even the field operator himself. More recently,  Wang et al. (2019)  highlighted that field measurements tend to overestimate the height of tall trees but also that this trend was more related to non-dominant individuals trees (co-dominant, intermediate, suppressed). Our study design did not allow the evaluation of the field measurement error among pCHM tree height estimate error.
Conclusions Our results allow highlighting the good agreement of tree height estimates provided by pCHM using DAP with both field measured and ALS tree height data. In terms of tree height modeling, our pCHM approach reached similar results than the same modeling strategy applied to ALS tree height estimates. Our results highlight the interest of reprocessing stereo-images acquired during regular national campaigns for orthophotomosaics layers production in order to produce regional pCHM. We tested it at a regional scale (ca. 17,000 km²) using 5 different aerial surveys and a large single tree and FI plots dataset (ca. 3000 trees from 600 FI plots). Our approach presents a great potential as it relies on publically and regularly acquired datasets (aerial images and FI data) and could be thus easily replicated in other countries to build dense time series of pCHM.
These time series can cover time periods prior the ALS survey when the hypothesis of constancy of topography under forest cover can be realized. Our study also identified some of the drivers of the pCHM tree height estimate error and found forest parameters and the terrain slope  to be of higher importance than image survey parameters like the variation of the overlap or the sunlight condition in our dataset. In combination with the pCHM tree height estimate; the terrain slope, the DBH and the evergreenness factor were used to fit a multivariate model predicting the field measured tree height. This model presented better performance than the model linking the pCHM estimates to the field tree height estimates in terms of r² (0. 90 VS 0. 87) and RMSE (1. 78 VS 2. 01 m). As the integration of these environmental parameters is rather straightforward, these results could be further used to improve plot scale forest attributes prediction based on DAP and pCHM.
Acknowledgments Authors thank the  Belgian National Geographic  institute for having shared their reference point database. Authors would also like to thank  Alain Monseur ,  Coralie Mengal  and  Thibault Delinte  for their technical support. Authors express thanks to the Public Service of Wallonia has shared all the dataset and associated expertise from the aerial images (Geomatic Directorate:  C. Schenke ,  N. Stephenne  and  JC Jasselette)  to the forest information from the  Forest Inventory program  ( Forest Resources Department ). Finally, authors would like to thanks the funding agencies who provided support to our research: the  Stereo program  (grants  SR/00/ 347  and  SR/12/383 ) from the  Belgian Scientific Policy Department (BELSPO)  and the  Forest Resources Department of the public service of Wallonia  (accord cadre de recherche et de vulgarization forestière 2014-2019).
The diffraction patterns were collected as 144 × 144 pixels at a camera length of 89 mm. This distance was chosen to optimize the diffraction pattern for indexation. Using the ASTAR ™ software, electron diffraction spot patterns were indexed (providing the local crystal orientations) by comparing individually obtained patterns via cross-correlation matching techniques with pre-calculated electron diffraction templates which are generated every 0. 5 • (orientation resolution). For iron-bearing olivine, templates have been generated from the crystallographic data of  Birle et al. (1968)  for forsterite. From this indexation within the Pnma space group, one obtains crystallographic orientation maps, which display, with a colour-coding, the most probable orientation at each scanned beam position.
The latter is typically the case at grain boundaries where two solutions of co-existing grains overlap. The datasets were processed with MTEX (free and opensource MATLAB ™ Toolbox from  Bachmann et al. , 2011). Grain boundaries were identified where the misorientation to the next pixel is higher than 15 •  (Bachmann et al. , 2011). Groups of pixels below a size of 10 points have been ignored. Note that the orientation dataset was a square-shaped grid for ASTAR, and has been converted to a Channel5 file (i. e. ctf file) for usage in MTEX. To be able to detect deformation details in the microstructures, one must use other tools than only the inverse pole figure colour-coding. In this study, we use the orientation maps to calculate local misorientations as recently developed in the EBSD community (e.
Acknowledgements. We thank  Christophe Nevado  and  Doriane Delmas  for providing high-quality thin sections for TEM. The TEM in Lille is supported by the  Institut National de Sciences de l'Univers (INSU)  du  Centre National de la Recherche Scientifique (CNRS, France) , and by the  Conseil Régional des Hauts-de-France.
Data availability. The raw data (orientation maps for Figs. 2 to 13) supporting this publication can be accessed at https://doi. org/10. 5281/zenodo. 3492179 (Nzogang et al. , 2019). Author contributions. MT and SD provided the specimens. BCN, AM, and PC performed the TEM observations. BCN processed the data. All authors discussed and interpreted the results and implications. PC, SD, and AM wrote the manuscript with contributions from all authors.
In proof-of-concept experiments performed in a MS environment, GS models constructed from MS environment and Marchouch data applied to independent material separated top-performing lines from mid-and bottom-performing ones, and produced actual yield gains similar to PS. The latter result would imply somewhat greater GS efficiency when considering same selection costs, in partial agreement with predicted efficiency results. GS, which exploited drought escape and intrinsic drought tolerance, exhibited 18% greater selection efficiency than MAS (albeit with non-significant difference between selections) and moderate to high cross-population predictive ability. GS can be cost-efficient to raise yields under severe drought.