dataesr/scientific-paragraphs-categorization huggingface dataset:
A complete description of the sequencing process can be found in  [3]. The CO1 sequences were manually edited by using Chromas 2. 6. 4 (DNA Sequencing software. available online: http://technelysium. com. au/wp/chromas/, accessed on 15 May 2017). CO1 sequences with their corresponding specimen images and sampling details (e. g. , location, time, taxonomy) were uploaded into the Barcode of Life Data System database [dx. doi. org/10. 5883/DS-PHDJAO]. In BOLD, each sequence was assigned a barcode index number (BIN) by delineating sequence clusters through single linkage analysis by using a 2. 2% sequence divergence for obtaining the operational taxonomic units  [33]. The resulting OTUs were refined by using Markov clustering and the optimal partition was selected through silhouette criterion (  [33].
Data Analyses Data analyses were implemented with R programming software R3. 5. 1 created by R Core Team in Vienna-Austria  [34]. For providing the expected observed fish species richness and assessing the additional sampling benefits, species accumulation curves of the population from mosquito seine nets and light-trap catches were plotted by using the "vegan" package (Version 2. 5. 4, [35] ). Bootstrap estimators were demonstrated as better estimators of total species richness than jackknife and Chao's estimator  [36]. The "wiqid" package (Version 0. 2. 2,  [37] ) using richboot function was used to obtain the bootstrapped estimation of species richness  [38]. The number of species corresponding to the initial names obtained from morphologicalbased identifications was compared to the resulting OTU from BOLD to highlight the strength of molecular-based identification approach.
Among the 387 species, 238 were observed as post-larvae and 232 as seagrass fishes, 83 species being observed in both sampling gears. Based on the species accumulation curves, the maximum values of species richness were not reached with light traps (Figure  2a ) nor with mosquito seine nets (Figure  2b ). The bootstrap estimator of the total species richness estimated that light traps and mosquito seine nets should catch up to approximately 275 and 264 species, respectively. Among the 387 observed species, 85 (22%) were identified by their morphology only (27 at the species level, 58 as morphospecies which were unsuccessfully sequenced) and 302 (78%) by using CO1 barcoding corresponding to 302 BINs (see Supplementary Material Table  S2 ). Among the 302 obtained BINs, 159 were unambiguously associated with a genus + species name, 115 presented ambiguities in species' name assignment, and 27 BINs had never been recorded before in the BOLD database.
Among the 302 obtained BINs, 159 were unambiguously associated with a genus + species name, 115 presented ambiguities in species' name assignment, and 27 BINs had never been recorded before in the BOLD database. The 115 BINs presenting ambiguities in species' name assignment included 104 identified as "Genus + BIN" (e. g. , Hippocampus [BOLD:ACE6993]), 11 as "Family + BIN" (e. g. , Labridae [BOLD:ACT0048]) and one as "Subclass + BIN" (Actinopterygii [BOLD:ADN1799]) (Figure  3 ). Among the 27 new BINs, 14 were identified as "Genus + BIN" and 13 as "Family + BIN" (see Supplementary Material Table  S2 ). More than half of these 27 new BINs for BOLD belonged to Apogonidae (four BINs), Gobiidae (three BINs), Labridae (four BINs), Lethrinidae (four BINs), and Syngnathidae (three BINs).
Discordances in Species Assignment Among the 141 BINs presenting ambiguities in the species' name assignment, 70, including the 27 new ones, were simply not assigned to species name in the BOLD database. The remaining 71 BINs presented ambiguities in species assignation. These ambiguities came either from BINs associated with several species names in BOLD, or from BINs assigned to one species name to which two or more BIN(s) were associated. The first type of ambiguity concerned three BINs: BOLD:AAA9764 associated with Myripristis hexagona and M. murdjan, BOLD:AAD1777 associated with Pempheris adusta and P. nesogallica, and BOLD: AAD5600 associated with Apogon erythrinus and Ostorhinchus aureus. The second type of ambiguity corresponded to 68 BINs.
DNA Barcoding Effort for Madagascar Fishes Among the 1689 marine or transitional water fish species recorded in Madagascar EEZ  [2] , only 419 (i. e. , about 24. 8%) had already been barcoded and assigned to a BIN in the BOLD database in May 2022 (Table  S3 ). Based on the comparison of these 419 BINs with the obtained BINs from this study, the present work added 120 new BINs for Madagascar (see Supplementary Material Table  S2 ) including the 27 new BINs that were never recorded before for the BOLD database. These 120 new BINs belonged to 41 families, half of them corresponding to species of Pomacentridae (16 species), Apogonidae  (14) , Chaetodontidae (eight), Labridae (eight), Mullidae (eight), and Lethrinidae (six). This study allowed us to identify 10 new records for Madagascar, i. e.
Moreover, the present work also contributed to the enhancement of fish biodiversity knowledge at a global scale as 27 BINs new for the BOLD database were obtained. These new BINs may either correspond to fish species that have never been described due to the rarity of molecular-based studies in the region. This can also correspond to species that have never been barcoded at the adult stage  [42]  as most barcoding studies are still based on the commercial value of the species. The lack of CO1 sequences in BOLD may also be linked to the way specimens are usually selected for DNA analyses. Most of the time, specimens are first grouped by similar morphologies (i. e. , morphospecies) and patterns of colors. As the number of barcoded specimens per morphospecies is often limited, some species that look similar based on their morphology may be ignored.
Supplementary Materials: The following supporting information can be downloaded at: https://www. mdpi. com/article/10. 3390/d14050377/s1. Table  S1 : Dataset of individuals successfully barcoded during the present work. Table  S2 : List of species captured by light traps and mosquito seine nets during austral warm seasons. The species are organized according to  Nelson (2006). Total abundances are reported for each sampling gear as well as the number of successfully barcoded specimens. Table  S3 : List of barcoded fish species from Madagascar in BOLD in 4 May 2022. Institutional Review Board Statement: Ethical review and approval were waived for this study as we work on dead animals retrieved from small-scale fisher catches for juvenile monitoring. For the collected living fish post-larvae from light trap, only one individual per taxa were collected and the remaining we returned to the sea.
Conceptualization, H. J. , J. M. , P. V. , A. C. and D. P. ; Methodology, H. J. , J. -D. D. and D. P. ; Formal analysis, H. J. , J. -D. D. and D. P. ; Investigation, H. J. , J. -D. D. , J. M. , F. C. , P. V. and A. C. ; Data curation, H. J. , J. -D. D. and D. P. ; Writing-Original draft preparation, H. J. , J. -D. D. , J. M. , P. V. , A. C. , F. C. and D. P. ; Writing-review & editing, H. J. , J. -D. D. and D. P. ; Supervision, D. P. ; Project Administration, J. M. ; Funding acquisition, H. J. , J. M. , P. V. , A. C. and D. P. All authors have read and agreed to the published version of the manuscript. Funding: The research was funded by the Critical Ecosystem Partnership Fund under Grant MG 66341; the Rufford foundation under Grant 2ac29a-1; Western Indian Ocean Marine Science Association (WIOMSA) under MARG II Contract 1/2016; the French National Research Institute for Sustainable Development under Grant JEAI-ACOM/IH.
Acknowledgments: The authors would like to thank the field team Duphrehino,  Tsipy Romario , Noelson and Tovondrainy for their help during fish sampling, and the  Institut Halieutique et des Sciences Marines (IH. SM)  for the support of the administrative and technical staff and donations in kind through their materials including their car, boats, and lab facilities used for field and lab work. The DNA extraction and sequencing were performed through the GenSeq technical facilities of the  «Institut des Sciences de l'Evolution de Montpellier»  with the support of  LabEx CeMEB , an  ANR  " Investissements d'avenir" program  ( ANR-10-LABX-04-01 ). This is a contribution of the  IRD-IH. SM-CNRO laboratoire mixte international MIKAROKA.
Author Contributions: Conceptualization, H. J. , J. M. , P. V. , A. C. and D. P. ; Methodology, H. J. , J. -D. D. and D. P. ; Formal analysis, H. J. , J. -D. D. and D. P. ; Investigation, H. J. , J. -D. D. , J. M. , F. C. , P. V. and A. C. ; Data curation, H. J. , J. -D. D. and D. P. ; Writing-Original draft preparation, H. J. , J. -D. D. , J. M. , P. V. , A. C. , F. C. and D. P. ; Writing-review & editing, H. J. , J. -D. D. and D. P. ; Supervision, D. P. ; Project Administration, J. M. ; Funding acquisition, H. J. , J. M. , P. V. , A. C. and D. P. All authors have read and agreed to the published version of the manuscript. Funding: The research was funded by the Critical Ecosystem Partnership Fund under Grant MG 66341; the Rufford foundation under Grant 2ac29a-1; Western Indian Ocean Marine Science Association (WIOMSA) under MARG II Contract 1/2016; the French National Research Institute for Sustainable Development under Grant JEAI-ACOM/IH.
Name in BOLD Current Work Other Projects in BOLD Database BINs Distribution in BOLD Other BINs Distribution in BOLD Amblygobius phalaena BOLD: AAB8727 West IO & PO BOLD: AAB8728 Center PO Antennarius striatus BOLD: AAO6018 East AO & IO, South PO BOLD: AAF2509 West AO, North PO Apogon semiornatus BOLD: AAD2206 West IO BOLD: AAD2207 West PO BOLD: AAC0105/BOLD: Asterropteryx semipunctata BOLD: AAC0108 West IO AAC0106/ BOLD: AAC0107/BOLD: South & North PO, North AO AAC0109 Cheilinus chlorourus BOLD: AAB4186 West IO BOLD: AAB4185/BOLD: ACE8465 PO Corythoichthys flavofasciatus BOLD: AAI8860 West IO BOLD: AAE9785/BOLD: ACR0421 North PO Dendrochirus brachypterus BOLD: AAC9564 West IO BOLD: AAC9563 East IO, West PO Diagramma pictum BOLD: AAD4477 West IO BOLD: AAB9940 North & East IO, West PO Epinephelus rivulatus BOLD: ACZ9919 West IO BOLD: AAF0433 East IO, West PO Fowleria variegata BOLD: AAD8726 West IO BOLD: AAD8727/BOLD: ACC5212 West PO Gazza minuta BOLD: AAB7096 West IO BOLD: AAA9823 North IO & Northwest PO Gerres longirostris BOLD: AAE6359 West IO BOLD: AAE6360 Souther PO Halichoeres nebulosus BOLD: AAC7896 West IO BOLD: AAC7897/BOLD: AAD5811 East IO, North PO Halichoeres zeylonicus BOLD: AAF7654 West IO BOLD: AAF7655 North & East IO Hippichthys spicifer BOLD: AAE5342 West IO BOLD: ADA9516 North PO Hippocampus histrix BOLD: AAE5356 Southwest & North IO BOLD: AAE5355 North PO Lethrinus harak BOLD: AAC1521 West IO BOLD: AAC1520 East IO, West PO Lethrinus lentjan BOLD: AAB0511 West IO BOLD: AAB0508 West PO Lethrinus miniatus BOLD: AAC8078 southwest & north IO BOLD: AAC8077 East IO.
Detection functions and density estimations The effort was divided into 5 km segments of homogeneous detection conditions using the pelaSIG plugin in QGIS  [46] , and further prepared for distance analysis using the in-development pelaCDS and pelaDSM packages in R (v. 4. 0. 4; [47, 48] ). Only segments with good conditions were used for the A CDS procedure was applied to derive density estimates and effective strip half-width (ESW), using the pelaCDS package  [48]. We implemented CDS models, testing the hazard and half-normal distributions on data right truncated at 400 m, to remove extreme distances (visual assessment  [49] ). Model fit was visually assessed and model performance compared with AIC. The model with the smallest AIC was used to estimate the survey-specific ESWs and the density of common dolphins during each survey session and day.
We also derived the current speed (CurrentSpeed; m s -1 ) from the northward and eastward seawater velocities available in the above-mentioned products (computed as ffi p (U 2 + V 2 ), where V is the northward velocity and U is the eastward velocity), and the eddy kinetic energy (EKE; cm 2 s -2 ; computed as 0. 5 × (U 2 + V 2 )). Daily gradient (grad) maps were calculated for Temp, MLD, SSH, Salinity, NPP and Chl using the detectFronts functions (from the grec package  [50] )-large gradient values are indicative of frontal areas. In addition to the gradient maps, we derived daily maps of distance to fronts for Temp, MLD, NPP and Chl. Finally, we considered the bathymetry (GEBCO 2008 database http://www. gebco. net) and two longterm variables: the winter-averaged distance to front of Temp, MLD, NPP and Chl (December-April averages in 2020 and 2021, separately), and the frontal persistence of front over the winter (the number of days each cell includes a front across the study period), also for Temp, MLD, NPP and Chl.
GAMs with one covariate were fitted with the gam function (from the mgcv package  [53] ), using the number of individuals sighted per segment as the response variable, assuming a Tweedie distribution, and using the REML estimation method on thin plate regression splines with a maximum complexity level of 4. The sampled area associated with each effort segment was used as an offset (2 × ESW × segment length). GAMs were chosen in order to benefit from their flexibility: penalized smooths permit the identification of a relationship between the response variable and a predictor without a priori knowledge about its shape, which was the case here. For each variable, we fitted one model per lag (0, 1, 2, 4, 7, 10, 30 days) plus a null model (only the intercept), and retrieved the AIC and the explained deviances of the eight models.
Prediction reliability assessment 2. 7. 1. Principle We used a validation procedure that is commonly used in forecast model assessment to assess the reliability of predictive models  [60, 61]. It is based on simulations, and allows the reproduction and maintains the coherence of the sampling design behind the reference model. The rationale is to simulate alternative aerial surveys following the original survey tracks (segments) on an underlying species distribution that is generated with the distributions predicted by our model. In other words, the aim is to simulate a survey considering the predicted distribution is true and to compare the obtained dataset with what was actually observed; the closer the simulated values are to the observed ones, the more accurate the predicted distribution is.
Challenger seasonal model The seasonal (i. e. non-dynamic) model was built using the same environmental variables as previously (see §2. 4) but averaged over the winter (means). We also included their standard deviations (sd) over the same period to account for their variations during the season. We implemented a model selection procedure similar to the one used to select the reference daily model, with data from the two winters used jointly to calibrate the model. Prediction maps were derived from this model for the winters of 2020 and 2021.
Evaluation of prediction reliability The simulations of aerial surveys and detection process were done at the segment scale and iterated 100 times. For each sample day, we converted the prediction map into an Inhomogeneous Poisson Point royalsocietypublishing. org/journal/rsos R. Soc. Open Sci. 9: 220379 Process (IPPP), using the spatstat package  [62]. This method generates a spatial point process with an intensity proportional to the predicted density, so that the simulated number of common dolphin positions per cell is proportional to the predicted density (individuals per km 2 ). For the daily model, the reference map was different according to the day the segment was sampled (daily predictions), while the reference map was the same for all segments sampled during the same winter for the seasonal model, and the same for all segments whatever the year for the uniform distribution.
However, since the IPPP is stochastic, the distributions generated through every single simulation and for every segment were unique. We then discarded all points further than 700 m from the segment, using the st_is_within_distance function in the sf package  [63] , as this corresponds to the maximum distance to which common dolphins were sighted during our aerial surveys. This approach is conservative, since, in the CDS analysis, we discarded sightings further than 400 m away. The precise distance to the survey track was calculated for all remaining points (using the sf::st_distance function). A point was considered detected if its distance to the track line was shorter than or equal to the survey-specific ESW identified through CDS (see §2. 3). The number of detected individuals for the segment was retrieved as the sum of detected points.
Following this procedure, we obtained, for each segment, 100 simulated values for the number of individuals sighted. Only segments sampled with good observation conditions were considered for the analysis (sea state less than or equal to 3, and medium to good subjective conditions), i. e. 2352 segments. A proper scoring rule was used to compare the distribution of values obtained from the 100 simulations to the observed values, using the quantile decomposition of the continuous ranked probability score (CRPS; crps_sample function in the scoringRules package  [60, 61] ). This method quantifies the distance of a punctual observation (here, the number of individuals obtained from the real survey) from a predicted distribution of this value obtained through simulation. We visually compared the density distributions of scores across the three models to assess the difference in performance (the lower the scores, the better the predictions from a model).
Oceanographic conditions In our study area, two water masses stand out (see electronic supplementary material at https://github. com/CLambert1/WinterBoBDd for day-to-day and season-averaged maps of environmental conditions). The first runs parallel to the coast, stretching from there to the 50 m isobath, and is under the influence of tidal mixing, river plumes and winds. Low temperature and low salinity are characteristics of this water mass, which sustains an important fraction of the phytoplankton biomass produced during the winter. The second water mass extends from the 50 m isobath towards the west, covering the entire shelf and beyond, with a deep MLD, warmer temperature and rather oligotrophic conditions. An important MLD front occurs at the edge of those two water masses, more or less expanded and sinuous depending on the year.
The coastal waters stretched out towards the shelf and were deeper than the 50 m isobath by the end of February, early March, probably through the effects of winds that dispersed the plumes towards the west. By the end of March, the MLD front reached and merged with the shelf edge front before disappearing in April. The late-winter front dispersion coincided with the establishment of spring conditions, with increases in productivity and concentrations of phytoplankton, probably triggered by the establishment of the nutrient-rich water stratification (as shown by the drop in MLD; see variable time series in electronic supplementary material at https:// github. com/CLambert1/WinterBoBDd) combined with increased solar radiance.
Spatial distribution The common dolphin distribution displayed important changes throughout the winter, following the winter dynamics of suitable environmental conditions (the day-to-day predictions can be found in the  royalsocietypublishing. org/journal/rsos R. Soc. Open Sci. 9: 220379 electronic supplementary material at https://github. com/CLambert1/WinterBoBDd, and figure  5  for some selected dates). In the study area, common dolphins were mostly present between the 50 m and 200 m isobaths during both winters. In 2020, common dolphins were distributed preferentially along the frontal systems north of 45. 5°N, associated with the 50 m isobath in January-February before progressively sliding westward in March-April, when the highest densities were predicted north of 45. 5°N. Few common dolphins were predicted close to the coast during this winter and none were predicted south of the Cap Ferret Canyon before mid-March.
In 2021, a similar pattern occurred in January-March, with common dolphins predicted to be associated with the frontal system flourishing along the 50 m isobath, but more widespread than in 2020 and with the highest densities in the central part of the area (45-46°N; figure  5b ) and around the Cap Ferret Canyon. In March-April, common dolphins were predicted throughout the shelf, along the front sliding westward, but also in the outer shelf and close to the Cap Ferret Canyon, with a burst of high densities along the 200 m isobath in early April. The extrapolation level was 27% on average (sd of 0. 12) for the winter 2020 and 35% (sd of 0. 17) for the winter 2021. However, this level was low within the survey area (see electronic supplementary material at https://github. com/CLambert1/WinterBoBDd) but greater outside, with some areas where extrapolation was systematic for all predicted days: along the northern coast and the Gironde estuary in 2020; and along the northern coast and in the central shelf in 2021.
Seasonal model This model used the averaged distance to MLD fronts, the standard deviation of Temp, the standard deviation of gradTemp and the standard deviation of ZEu. Its deviance and AIC weight were 8. 3% and 12%, respectively. The highest densities occurred with short distances to MLD front, lower standard deviation of Temp, intermediate values of gradTemp standard deviation (0. 06-0. 10) and large standard deviations of ZEu (6. 5-13; figure  4b ). In 2020, the model predicted the common dolphins to be distributed throughout the study area, except in the coastal areas below the 50 m isobath. The highest densities were predicted in the western part of the area (figure  6 ). The extrapolation level was 40%. Extrapolations occurred mostly in the north-western part of the area and along the coast (see electronic supplementary material at https:// github.
Statistical methods Data were analyzed using IBM SPSS statistics for Windows version 24 (IBM Corp, Armonk, NY, USA). Normally distributed continuous data were presented as means and standard deviations (SDs). Non-normally distributed continuous data were presented as medians and interquartile ranges (IQRs) or 95% confidence intervals (95%CI). Categorical (binary, nominal, and ordinal) data were presented as frequencies and percentages. Likert-Scale ordinal data were also presented in means and standard deviations, as this allowed more insight into the effect size. Sensitivity analysis regarding views on future implementation was performed for overall annual volume of liver procedures and for center type (i. e. university medical centers, university affiliated centers, or community/independent centers).
We developed two random forest regression models. For each random forest, the number of trees was set at 1000. For each tree, two-thirds of the observations were used. Predictions were derived from the average of samples' estimates in the remaining one-third of the samples, the out-of-bag samples. Regressions were built in a step-wise manner. First, the antigen in the regression was selected based on the importance of that antigen, measured by the mean decrease in accuracy on the out-of-bag samples. Subsequently, all other variables were added one by one to the most important antigen in the regression. The antigen associated with the lowest residual sum of squares was kept in the model. This process was repeated until no further decrease in the lowest residual sum of squares was observed. The randomForest package was used to develop and evaluate the random Forest regression models  [22].