dataesr/scientific-paragraphs-categorization huggingface dataset:
In France, HAB monitoring is carried out by the REPHY (French Observation and Monitoring program for Phytoplankton and Hydrology in coastal waters) and the RE-PHYTOX (French Monitoring program for Phycotoxins in marine organisms) monitoring networks. The objectives of REPHY are mainly environmental, whereas the objectives of REPHYTOX are strictly sanitary. However, these two monitoring networks, based on in situ observations, remain closely associated for better monitoring and understanding of contamination events in marine organisms  [10]. Although REPHY and REPHYTOX data have been available since 1984 for environmental data and DSP toxins, 1988 for PSP toxins and 1999 for ASP toxins  [10] , there are no historical records on the commercial bans and closures related to HABs. Since 2016, the management authorities have digitalized some of the closure decrees, but the information is still scattered and uncompleted, and there are no available data on older periods.
Guillotreau et al. (2021)   [9]  have created a database of closures based on legal decrees regarding HAB-related closures, but this dataset concerns only shellfish farming activities and it is only related to the coastal area along the western French coasts (Finistère, Morbihan, Loire-Atlantique and Vendée). Data on administrative fishing restrictions are still very few and sparse. This lack of data limits the understanding of decision-making processes and the analysis of the associated impacts. In addition, existing data from legal decrees are not only attached to closures due to sanitary reasons (i. e. , exceedances of regulatory thresholds) but also to closures related to resource management as well as precautionary closures, and this can lead to biases in the impact assessments. To deal with all these constraints, this work aims to simulate closures due to HAB events using REPHYTOX data through the case study of the king scallop (Pecten maximus) fishery in the French area of the eastern English Channel.
The resulting dataset only covers closures associated with regulatory threshold exceedances of phycotoxins in scallop and reflect the real risk of HABs. This provides valuable insight for impact assessments and enables a better understanding of the management system and adaptation strategies developed by authorities to cope with HAB events. This data paper presents the dataset of the estimated closures of scallop (Pecten maximus) fishing areas due to HAB toxicities. These data were produced within the framework of the French (Channel) English (FCE) Interreg project S-3 EUROHAB (Sentinel-3 products for detecting EUtROphication and Harmful Algal Bloom events), which aims to better understand the dynamics of harmful algal blooms and to assess their impacts over the English Channel area. The simulated closure data were issued from an algorithm  [11]  which calculates the number and duration of bans for each scallop fishing area.
These data are also useful for the assessment of HAB impacts on human activities through the analysis of an objective ecological risk associated with HABs (theoretical closures). Many studies are now focusing on the early detection of HABs and the possibilities of their prediction based on remote sensing and modelling. The algorithm and the resulting dataset presented in this paper can be integrated to those models to predict the economic impacts if the early warning of HABs and their toxicities are ever possible. Finally, although the methodology and the script developed to provide these data concern the scallop fishery of the English Channel, they can be reused and applied to other species, other areas and other economic activities. HAB monitoring data are available for all impacted species and areas and, with a simple adaptation in terms of species and areas, the script could be reproduced and applied to any other case study.
Data Description The dataset described in this article includes the number and duration of closures due to HABs simulated for each scallop production area between October 2011 and May 2020. It concerns the scallop beds of the eastern Channel, from the Pas-de-Calais Strait (50 • 57 N, 1 • 51 E) in the east to the Pointe de Barfleur, to the north-western limit of the Bay of Seine (49 • 41 N, 1  This CSV file includes theoretical closures of scallop production areas. The table contains 4199 rows and 13 variables. The first variable (Week) contains an accumulation of the numbers of weeks starting from the 1 January 2011. The time series concerns only the fishing season of scallop; the weeks outside of this period are excluded from the dataset. The second variable (FishingZone) describes the 17 scallop production areas that are likely to be closed in the case of scallop contamination by phycotoxins.
These areas are described in detail in the data file n • 2, of which a description can be found in Section 3. 2. The third variable of the dataset (Season) represents the fishing season of scallop in the eastern Channel, from 2011-2012 to 2019-2020. Each season ranges from October to May of the next year, which represents the fishing season of scallop as indicated in the regulations. The fourth, fifth and sixth variables, named ClosureNbrDSP, ClosureNbrASP and ClosureNbrPSP, respectively, contain the number of closures by type of phycotoxin (reason of closures). This means closures due to threshold exceedances of toxins in shellfish causing lipophilic shellfish poisoning (DSP), amnesic shellfish poisoning (ASP) and paralytic shellfish poisoning (PSP), respectively. These values were calculated for each fishing area and each fishing season.
Extraction of REPHYTOX Data Data on phycotoxin concentration in shellfish was extracted directly from the Quadrige database (the Quadrige database is a part of the French Water Information System; it includes several data collected by all coastal monitoring networks and REPHYTOX data have been banked in this database since 1987) to ensure data quality and reliability, after a preselection based on a range of criteria. Only regulated toxins responsible for ASP, DSP and PSP were considered and only data used for regulatory monitoring were taken into account in this processing. The objective of this selection was to extract only the regulatory data used for the implementation of management measures (closures of areas attached to contaminated shellfish), and thus, to avoid estimation bias.
Data Preprocessing This step represents the preparation of the input data. After the extraction, the data were filtered using three filter criteria: the shellfish species on which the phycotoxin concentration analysis was carried out, the monitoring area and the period. The filtered data concern, therefore, scallops (Pecten maximus), the eastern Channel area and the period ranging from October 2011 to May 2020. These data represent the first input data used for the processing. The second input data which contain the phycotoxin thresholds were extracted from the regulations (Table  1 ).
Data Processing The developed script consists of two main steps. A calendar of closures/openings was created by comparing toxin concentration values to the regulatory thresholds: it consists of a binary coding (0 and 1) for each production area and each sampling date in the input data. The 0's represent the values below or equal to regulatory limits of toxicity which correspond to being open to fishing, and the 1's indicate values above the regulatory thresholds which correspond to temporary bans on harvesting or marketing of contaminated shellfish. In the case of the fishing sector, the toxin limit exceedences lead to the closure of the production areas. This calendar was then crossed with the scallop fishing calendar, which is used for resource management purposes that involve specific fishing periods over an annual campaign.
• 16 W) in the west. The dataset is available in a CSV format under the SEANOE data repository (https://doi. org/10. 17882/71912, accessed on 22 May 2022). Data are shown by fishing zones (management areas), weeks and fishing seasons. Data presented by week (Table 91595) and by fishing season (Table 91596) are separately displayed in each CSV table. A shape file showing the location of scallop fishing areas is associated with this data article as supplementary data and it is presented in the zip file 91597 in the SEANOE repository. The input data (phycotoxin monitoring data) used to calculate the number and duration of closures presented in this article are also available in SEANOE and are publicly accessible (https://doi. org/10. 17882/47251, accessed on 22 May 2022). The R code developed to generate this dataset is also available on GitHub and Zenodo (https://doi.
Data Availability Statement: The data presented in this paper are available in the SEANOE repository (https://doi. org/10. 17882/71912, accessed on 22 May 2022). The input data and the R code developed to create this dataset are available on GitHub and Zenodo following this link (https://doi. org/10. 5281/zenodo. 6320765, accessed on 22 May 2022). All these repositories are publicly accessible.
7, 18, 19 Here, we conduct the first comprehensive study to reconstruct the drivers of shape variation, disparity (morphological diversity), and evolutionary rate in the cetacean cranium throughout their entire evolutionary history. To do so, we gathered the most expansive 3D scan dataset ever for Cetacea, spanning 88 living species (representing 95% of extant cetacean species  20  ) and 113 fossil species. We extracted high-density 3D geometric morphometric data and used these to quantify morphology, disparity, and evolutionary rate of the cetacean cranium in unprecedented detail. Because the tetrapod cranium is known to evolve in a modular fashion, with individual skeletal elements exhibiting distinct evolutionary histories,  10, 21, 22  we characterized variation in the skull as a whole and in each skull element, identifying the major factors influencing shape and reconstructing evolutionary rates and disparity over 50 million years of cetacean evolution.
The Eocene Mystacodon selenensis, which is not only the oldest mysticete in this dataset but also the current oldest known neocete,  23, 24  sits near to the Eocene archaeocetes, while Oligocene mysticetes, such as aetiocetids, bridge the space between archaeocetes and modern baleen whales. The nares are further posteriorly positioned in mysticetes than in their archaeocete ancestors that occupy the lower right quadrant of the morphospace, reflecting the long rostra and anteriorly placed nasals of the latter. It is noteworthy that extinct toothed mysticetes and archaeocetes overlap substantially in this cranial morphospace.
The archaeocetes show a peak in maxilla, premaxilla, and nasal rates in the middle Eocene (Figure  4C ). The flatline in the data thereafter is due to there being only one archaeocete in the dataset (Kekenodon) that lived into the late Oligocene. The mysticetes show a peak in the evolutionary rates of several bones toward the end of the Eocene after the two suborders had diverged, with a further peak in the late Oligocene (Figure  4D ). Thereafter, rates remain conservative. High evolutionary rates in the frontal, maxilla, and nasal are found on the branch leading to the earliest diverging odontocetes (Figure  S5 ). As well as high rates in their early divergence, the odontocetes show several peaks in evolutionary rate (mostly in the maxilla, frontal, and nasal) in the mid-late Oligocene (Figure  4E ), with smaller peaks in the Miocene.
74) and coastal-pelagic (s 2 mult = 27. 78) environments, with the lowest rates seen in freshwater (riverine) inhabitants (s 2 mult = 0. 49). Similar patterns are supported for individual bones, with some of the highest rates of evolution in the nasal (the highest at s 2 mult = 31. 64 in coastal cetaceans), frontal, premaxilla, and maxilla of coastal and coastal-pelagic inhabitants and the lowest rates in the riverine taxa (Figure  S6C ). See additional supplemental information at https://github. com/EllenJCoombs/ Cetacean_cranial_evolution for whole cranial rates, binned by ecological categories. Differences in cranial disparity among ecological groupings largely, but not entirely, reflect patterns observed for evolutionary rates (Figure  7 ). When categorized by diet, no group is consistently more disparate across the whole skull; however, feeders on zooplankton and fish and benthic invertebrates and fish generally have some of the lowest levels of disparity in individual bones, and fish, cephalopod and fish eaters, and tetrapod and fish eaters tend to show some of the highest levels of disparity (Figure  7D ).
Echolocating cetaceans (the odontocetes except Odobenocetops) have the highest disparity across each bone, with lower disparity in taxa that do not echolocate (the archaeocetes and mysticetes) (Figure  7F ). Finally, some of the highest disparity is observed in the skulls of pelagic cetaceans, which includes all ziphiids and some oceanic dolphins. The lowest disparity is seen in the bones of the skull of riverine cetaceans (Figure  S6D ), which comprises the three extant freshwater species in this study (Inia geoffrensis, Lipotes vexillifer, and Platanista gangetica). See additional supplemental information at https://github. com/ EllenJCoombs/Cetacean_cranial_evolution for whole cranial disparity, binned by ecological categories.
DISCUSSION Cetaceans transitioned from being land-based to wholly aquatic in just 8 million years. 3 Here, we collected and analyzed the first morphometric dataset to quantify cetacean cranial morphology throughout their entire evolutionary history, spanning 50 million years, to reconstruct the processes and factors that drove their     (legend continued on next page) morphological diversification. Using high-dimensional landmark data to comprehensively represent the entire cranium for 201 living and fossil whales, we reconstructed the drivers of cranial shape variation, disparity, and evolutionary rates, recovering three key waves of morphological diversification that reflect the distinct ecological and evolutionary trajectories of the cetacean suborders. Early archaeocetes (47. 8-42 Mya) Archaeocete cranial morphology evolved rapidly, both across the whole skull and in individual elements of the face (Figures  3,  4,  and 5 ), possibly due to a lack of competition or to high productivity allowing or promoting rapid change.
Morphometric data collection We placed 123 landmarks and 124 curve sliding semi-landmarks on each specimen using Stratovan Checkpoint (Stratovan, Davis, CA, USA) (Tables  S2  and S3 ; Figure SA: https://github. com/EllenJCoombs/Cetacean_cranial_evolution). Landmarks were defined as Type I (biology) or Type II (geometry)  64, 65  and were chosen to capture clear definitions e. g. , tripartite sutures. Sliding semi-landmark curves (hereafter termed 'curves') define key structures such as the margins of bones and anatomical ridges, representing a significant increase in shape capture compared to landmark-only data sets. 65, 66 rchaeocetes have prevalent asymmetry in the rostrum and in the squamosal, jugal, and orbit, and, although some of this may represent preservational deformation, it is possible that some is biological.
17, 63 Asymmetry in odontocetes is predominant in the naso-facial region, whilst mysticetes show a high degree of symmetry similar to terrestrial artiodactyls. 17, 67 Due to these differences in asymmetry among suborders, asymmetric elements were manually landmarked on both sides of the skull, while symmetric elements were mirrored using the using the mirrorfill function in the R package 'paleomorph' v. 0. 1. 4 (for methodological details and code, see Coombs and Felice  68  ). The resulting data set is identical in terms of landmarks and curves across all suborders, allowing unified analyses across the whole of Cetacea 68 (Figure SA, as above). Following manual placement, semi-landmark curves were each resampled to a consistent number across specimens (Table  S3 ; Figure SB: https://github.
com/EllenJCoombs/Cetacean_cranial_evolution) and then slid to minimize bending energy. 66, 69 Following sliding, all morphometric data were subjected to Procrustes superimposition to remove variation due to orientation (both rotation and translation) and isometric size. 70 Log centroid size, extracted from cranial shape data during Procrustes superimposition, was used in further analyses as a proxy of overall size (Table  S4 ). Although sampling was limited to specimens that were undeformed and mostly complete, there were some instances of missing elements or structures. Where structures were missing due to preservation, rather than true biological absence, the relevant landmarks and semi-landmarks were estimated with fixLMtps function from the R package 'Morpho,'  71  which uses weighted averages from the three most morphologically similar, complete, configurations to estimate missing landmarks for the incomplete missing specimen.
Bone ) e cFigure 5. Figure 6. 56 Figure 5. Evolutionary rate and disparity per bone for each suborder (assemblage) (A) Evolutionary rate (s 2 mult ) per bone for each suborder (assemblage). (B) Disparity (Procrustes variance) per bone, per suborder (assemblage). Disparity calculated using the whole cetacean dataset. Archaeocetes (dark brown), mysticetes (light brown), and odontocetes (light gray).
An experimental setup involving nine scenarios was conceived to study the impact of the number of flight lines (scenarios fl1, fl2 and fl3) and predominant scan angle (scenarios A, B or C) or combination of scan angle directions (scenarios A and B, or A and C, or B and C), on area-based approach (ABA) models. We built ABA models for the same forest plots for 5000 resampled datasets in each scenario to predict three forest attributes, i. e. , stem and total volume (V st and V tot ) and basal area (BA). Three goodness-of-fit criteria were computed for each model (coefficient of determination (R 2 ), relative root mean square error (rRMSE) and mean percentage error (MPE). We compared the distributions of the goodness-of-fit criteria between scenarios to assess the behaviour of the predictive models when: 1) the number of flight lines (i.
e. , scan angles) increases (fl1, fl2 or fl3); 2) lidar datasets comprise specific scan angle (A, B or C) or combination of scan angles (AB, AC or BC); 3) voxelisation is used to compute P f and CV PAD. The results show that models built with point clouds scanned from multiple flight lines were more robust, with a lower standard deviation of their goodness-of-fit criteria. On average, across all forest types, compared to fl1, the standard deviations of R 2 distributions were lower for fl2 and fl3 by 42 % and 77 %, respectively. We also observed that a dataset with a predominantly nadir configuration (i. e. , scenario A) did not always result in better predictions (mean R 2 higher by 0. 08, 0. 07, 0. 04 for scenario B for broadleaf, coniferous and mixed, respectively). For a set of calibration plots, the resulting forest attribute models depend on the acquisition geometry over the plots, as observed in this study, which could result in unreliable wall-to-wall predictions.
Secondly, we conceived an experimental setup wherein standard lidar datasets were resampled to create multiple datasets made up of lidar observations corresponding to different scanning scenarios to build ABA models. The resampling was done at the level of the flight lines by randomly picking flight lines to make different combinations. The experimental setup comprises lidar scanning scenarios with different inclinations (scan angle), with (multiple flight lines), and without (single flight line) overlap. Finally, we used voxel-based metrics to demonstrate their potential to normalise the effects of scan angle and improve predictions. We proposed these new metrics based on the underlying hypothesis that voxel-based tracking of the extinction of a laser pulse can improve the accuracy of forest variables such as LAD/PAD (used in this study)  (Vincent et al.
Study sites and field plot measurements We used two sites representative of complex forest environments for this study. The first study site in southwest France is a riparian zone along the river Ciron, a tributary of the Garonne, and three of its subtributaries. It is rich in biodiversity and not subject to intense forest management. Field data were collected at 29 locations along the river Ciron and its tributaries. These sites were chosen to represent a gradient in width and density of the riparian forest. The species present on this site include the common oak (Quercus robur), Pyrenean oak (Quercus Pyrenaica), common alder (Alnus glutinosa), and maritime pine (Pinus pinaster). Several locations are also characterised by shrub species such as common hawthorn (Crataegus monogyna) and alder buckthorn (Frangula alnus).
Firstly, the number of small trees was extrapolated from 10 m  radius plots. Secondly, the nationwide tree inventory database (NFI) generated by IGN (Institut National de l'Information Géographique et Forestière), containing measurements of trees with DBHs in the 7. 5 cm to 17. 5 cm range, was used to extrapolate DBH and height values for non-measured trees. All NFI plots located in the ecoregion that includes the study site were selected to have forest plots with similar climatic and growing conditions to those measured on the study site. For trees with DBH ranging from 7. 5 cm to 17. 5 cm, the median DBH value in the NFI database is around 11. 1 cm. This value was used to compute the basal area of the trees with DBHs lower than 17. 5 cm. Using NFI measurements, allometric relationships were established for each species (or group of species when the number of trees was not high enough) to estimate the heights of all the trees when there were no available height measurements.
Lidar data acquisition and processing At the Ciron study area, INFOGEO (France) acquired lidar data using a VQ580 laser scanner (Riegl, Austria) on an ultralight aircraft platform in early October 2019. The flying height was approximately 250 m, which enabled data acquisition at an overall point density of around 68 pts/m 2. Overlap of 35 % -40 % and several passes over any given area ensured that most of the area was sampled using multiple scanning configurations. Data pre-processing was carried out by INFOGEO, which involved the classification of ground points using TerraScan (Terrasolid ltd. , Finland). For the Bauges site, the company Opsia (France) carried out airborne lidar data acquisition and processing in September 2018. The flying height was around 1050 m with several passes of flight lines to ensure most of the area was scanned using multiple scanning configurations.
The fundamental 'unit' in our experiments is the point cloud for a plot acquired from only one flight line. We assessed pulse densities for each of the point clouds. For the Bauges dataset, 99 % of the constituent point clouds had a pulse density greater than 1 pulse per m 2 , and for the Ciron dataset, all the point clouds had a pulse density greater than 14 pulse m 2. These values were higher than the thresholds below which pulse densities are known to influence lidar metrics and forest attribute predictions. (Bouvier et al. , 2019; Jakubowski et al. , 2013; Magnussen et al. , 2010; Pearse et al. , 2019).