dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Analysis We used numerous methodologies to analyse the data gathered and acquired; they are listed below. The potential evapotranspiration was calculated using Thornthwaite method  (Thornthwaite, 1948)  for evolution since 1966 and with Penman method  (Monteith, 1965; Penman, 1948, with CropWat 8. 0)  and for monthly values between 1990 and 2021, using available weather data from Höfn weather stations. Effective rainfall was then calculated, with the following equations:  if Pi + Si >= PETi , RETi = PETi and if Pi < PETi , SWi = SWi-1-(PETi-Pi) if Pi + Si < PETi , RETi = max(Pi + Si ; PETi ) and SWi = SWi-1-(PETi-Pi) if Pi > RETi, EFi = Pi -RETi otherwise EFi =.
8 Leptosol and Andosols that compose the area  (Arnalds, 1999; Arnalds, 2015). As rainfall and snow events are not completely distinguished in the precipitation chronicle provided by the IMO, the effective rainfall we calculate does not represent the effect of the snow melt (delayed recharge to the aquifers). We used estimates of glacier melt to deduce a recharge rate to the underlying till and glacio-fluvial deposits or basalt aquifers. To estimate the glaciers melt, we used two datasets: (i) the estimated summer mass balance based on direct glaciological methods from 2010 to 2021  (Björnsson et al. , 1998; Björnsson et al. , 2013; Pálsson et al. , 2022) ; and (ii) melt calculated from 1980 to 2016 by  Schmidt et al. (2020)  forcing the Parallel Ice Sheet Model (PISM)  (Bueler and Brown, 2009)  with output from the Regional Climate Model HARMONIE-AROME reanalysis-forced simulations.
, 2015). In this study a 67. 3 km 2 catchment of the glacier is studied, with daily surface runoff measurements in a river south of Skálafellsjökull, discharge which is assumed equal to the discharge emerging 500 m upstream from under the glacier  (Young et al. , 2015). The surface runoff measured and extrapolated (for days without data) accounts for 50 % (± 10%) of the volume of ice melt during the same period. The thicknesses of the geological formations were estimated combining the thicknesses extracted from the existing geological logs and literature. We calculated hydraulic conductivities with two different methods: from grain size data for the till and glacio-fluvial deposits and from slug tests for both aquifers. For the grain size data method, we used d10 from samples collected in the Skálafellsjökull area.
, 2018)  and lidar  (Jóhannesson et al. , 2013)  from the National Land Survey of Iceland (IslandsDEMv1, Landmaelingar Íslands), with a 2×2 m resolution and a vertical accuracy better than 0. 5 m. The basal topography of the 4 studied outlet glaciers is known using radio-echo sounding measurements interpolated at a resolution of 200×200 m  (Björnsson and Pálsson, 2020). We used several free and open software packages: the Geographic Information System QGIS (QGIS. org, 2022) to update and create maps, as well as perform mathematical operations on maps; LibreOffice for statistical treatments and graphics drawing (Foundation, T. D. , 2020); and Python (Python. org, 2022) for data analysis and graphics drawing.
Introduced species constitute a critical bio-security issue worldwide and the precise monitoring of their spread is crucial for their management. For species forming cryptic complexes this may remain difficult. Using integrative taxonomy, we formally report for the first time, well-established populations of the cosmopolitan introduced bryozoan Watersipora subatra in the French Mediterranean Sea and compile worldwide existing genetic data for Watersipora species alongside newly acquired data to establish the most complete phylogeny of the genus to date. This revealed pervasive erroneous identifications in Genbank, which in turn perpetrate further errors in recent studies, primarily misidentifying W. subatra as W. subtorquata. High abundance and geographic spread of W. subatra in our Mediterranean sampling sites suggest that this species has been present for some time but has been misidentified until now.
2006; Mackie et al. 2012; Vieira et al. 2014; Mastrototaro et al. 2020; Salonna et al. 2021) , or for which taxonomic expertise is rare, such as for many marine invertebrates. Identifying the precise species at a certain location is however important for subsequent evaluation of spread and invasion  (Vieira et al. 2014; Golo et al. 2023). Especially when morphological criteria are lacking to identify a species, genetic methods have been increasingly used to help with the identification of introduced species. DNA barcoding of individual specimens, metabarcoding of communities in bulk (from sediments, scrapings, or other substrates) and more recently environmental DNA (eDNA) are now all regarded as useful techniques for detecting introduced species. However, the efficiency of molecular species identification highly depends on the quality of reference sequences, particularly how well species identification was carried out for the reference sequences before being submitted to public databases  (Couton et al.
2022). Incorrect species identification of reference sequences can have important effects on subsequent studies which will base molecular identifications on imprecise species names. The morphology of the bryozoan genus Watersipora is notoriously complicated, and the redescription of the genus by  Vieira et al. (2014)  has reattributed individuals of introduced populations in multiple localities to Watersipora subatra (Ortmann, 1890) (identified as "Watersipora subtorquata" (d'Orbigny, 1852) in  Mackie et al. 2012; Ryland et al. 2009 ) and others to W. subtorquata (identified as "W. subvoidea" (d'Orbigny, 1852) in  Mackie et al. 2006 Mackie et al. , 2012)). This has led to confusion regarding the identity of populations of several localities around the world, most notably in Europe. Many species identifications of reference sequences in Genbank have not been updated since the redescription by  Vieira et al.
(2014). Nevertheless, these sequences continue to be used for e-DNA monitoring. As an example, individuals that were resolved as W. subatra are still listed as "W. subtorquata" on GenBank (accessed June 2022, NCBI,  Benson et al. 2013). This probably induces subsequent errors in the literature, falsely detecting W. subatra as "W. subtorquata", hindering the accurate e-DNA detection of several Watersipora species. In the Mediterranean Sea, the presence of W. subtorquata has been reliably confirmed  (Vieira et al. 2014 ) and this species is the most frequently described introduced Watersipora species in this area  (Vieira et al. 2014; Harmelin et al. 2016; Rosso and Di Martino 2016; Tempesti et al. 2020) , even though Watersipora arcuata Banta, 1969 seems to be spreading rapidly in recent years  (Ulman et al.
2017 (Ulman et al. , 2019;; Reverter-Gil and Souto 2019). Here however, we identified W. subatra as the dominant Watersipora species in several French Mediterranean harbors, with only anecdotal presence of W. subtorquata (and no observations of W. arcuata). This points towards an inherent identification problem, as the high abundance and persistence on many artificial substrates makes it impossible to miss the species (see Fig. 1a ) and suggests it has been present some time already. The present study has a two-fold objective. Firstly, we wish to declare the first formal record of W. subatra as an already well-established introduced species in the French Mediterranean Sea. Secondly, we provide a phylogenetic analysis of existing COI sequences of Watersipora from Genbank, including new sequences from individuals that were carefully identified according to morphological criteria, in order to improve molecular identification and detection of non-indigenous Watersipora species, particularly the spread of W.
Morphological analysis Approximately 200 g of Watersipora colonies from the studied locations were sampled for the present study and scanned for different species. Individuals were first identified alive in the laboratory using a ZEISS SteREO Discovery. V12 microscope coupled to a ZEISS Axiocam 506 mono camera and visualized and measured with ZEISS Zen 3. 0 software. Operculum structure (see figures 65-68 in  Vieira et al. 2014 ) and general individual characteristics (Zooid Length, Zooid Width, Orifice Length, Orifice Width, Sinus Length, Sinus Width, Pseudopore Diameter, Intrazooidal Septula Presence and Diameter; see tables  1, 3 in Vieira et al. 2014)  were used as first identification criteria. A total of 15 high-quality fragments (no epibionts, not epibionts themselves, clean, alive…) were chosen for detailed analysis and description (3 for each of the three areas of the Toulon Bay, 3 for Marseilles and 3 from Brittany).
Phylogenetic analysis Sequence chromatograms were trimmed for low quality bases and visually inspected for errors in 'Genieous Prime  ' (v. 2020. 2. 4; Dotmatics). Forward and reverse fragments were aligned to generate a consensus sequence. High quality sequences were obtained for three individuals from Marseilles, two individuals from Toulon and three individuals from Brest. They were deposited on GenBank (NCBI;  Benson et al. 2013) , under the accession numbers OQ918440-OQ918447. All sequences attributed to the genus Watersipora were downloaded from 'GenBank' (accessed on 01/11/2022) by using the search term "Watersipora" in the Nucleotide search engine. This returned 264 sequences from which we chose only COI sequences (229). Two sequences of W. platypora Seo, 1999 were excluded from our database as they were identical, and one sequence from eDNA detection from  Portas et al.
(2022)  was added. Finally, we added eight sequences acquired for the present study. An alignment was generated for a total of 236 individuals using 'Sequencher' (v. 5. 3; Gene Codes Corp). The alignment was then trimmed manually to remove sections with high levels of missing data in the 5' and 3' ends. Identical sequences were removed from the dataset using 'DAMBE'  (v 7. 5. 3; Xia 2018) , to generate a non-redundant dataset composed of 99 unique sequences. A re-alignment of this final dataset was conducted with 'MAFFT' (v7. 490; Katoh and Standley 2013), using the '--localpairs' algorithm and a maximum number of 1000 iterations. Models of sequence evolution were tested with 'modeltest-ng' (v0. 1. 6; Darriba et al. 2020 ), and the model with the highest probability score was selected by considering the Bayesian information criterion (BIC), Akaike information criterion (AIC) and the corrected AIC (AICc).
e. , the number of samples to be discarded at the start of the run, of 6000 trees (20%). Phylogenetic trees were visualized with 'FigTree' (v1. 4. 4; Rambaut 2010) and rooted on the W. arcuata clade. All datasets, tree files and the code of our analyses can be consulted at https://gitlab. ifremer. fr/lebco/fnunes/watersipora. git. Sequence accession numbers were color coded according to the species identification indicated in the Genbank record. For clarity, in this manuscript identifications from Genbank will appear in double quotation marks (ex: "W. subtorquata") as some were already resolved to other species by  Vieira et al. (2014). Exceptions to this are proven reliable identifications (ex:  Couton et al. 2019; McCann et al. 2019). For redundant sequences, only one accession number was listed per species name on the phylogenetic tree, with the number of individuals having an identical sequence indicated in brackets, however a detailed phylogenetic tree can be accessed in the Suppl.
2018)  or to detect species in an area via metabarcoding  (Leray and Knowlton 2015; Miralles et al. 2016; Ardura and Planes 2017; Couton et al. 2019 Couton et al. , 2022;; Azevedo et al. 2020). These tools can be quite powerful to detect NIS, especially when combined with morphological analyses  (Azevedo et al. 2020; Couton et al. 2022) , however the efficiency of the method depends on the quality of reference sequences  (Viard et al. 2019; Couton et al. 2022). Species identification based on molecular methods still require detailed morphological identifications to be carried out when reference sequences are generated. Integrative taxonomy, where genetics and morphology are both carefully considered, is required in order to ensure accurate species identifications  (Dayrat 2005). Monitoring programs that use eDNA or other molecular approaches require reliable databases, with reference sequences generated from specimens that have been carefully identified or updated once errors are detected.
The lack of genetic references for some species can result in missing or misidentified NIS and other species using metabarcoding  (Couton et al. 2022). Even more problematic however, are reference sequences with misidentifications, as they provide a false sense of certitude to authors without taxonomic expertise  (Viard et al. 2019; Cognato et al. 2020). Vieira et al. (2014)  first noted that high numbers of Watersipora identifications (ex. Mackie et al. 2006; Mackie et al. 2012)  were erroneous and our phylogeny has since revealed that 49% of Watersipora sequences on Genbank were incorrectly identified, a percentage that rises to 65% when excluding sequences identified only to the genus level. Most misidentifications concern W. subatra being listed as "W. subtorquata". This causes subsequent identification errors in publications using these reference sequences (ex:  Duncan et al.
However, this would be very time consuming. A new way of genetic database management, similar to the WoMRS database, might compensate for the time-consuming nature of follow-up corrections on sequences. Taxonomists and geneticists dedicated to a family/genus could have a right to modify scientific names associated with sequences following the recommendations of recent peer-reviewed papers. Changes (by whom, references, etc. ) should be tracked for transparency. This could help avoid the perpetuation of errors and improve the monitoring of both the spread of NIS and species distributions in general. Our new sequences, as well as the table updating the identity of almost all existing Watersipora sequences (Suppl. material 1: table  S1 ) may help with future identification of different Watersipora species.
Acknowledgements We wish to thank the harbor authorities of the  Toulon Darse Nord marina ,  La Seyne sur Mer  marina and  Old Harbor of Marseilles  for authorizing access for sampling of Watersipora colonies. We also wish to thank  Jasmine Ferrario  ( University of Pavia ) and  Leandro Manzoni Vieira  ( University of São Paulo ) for discussions on Watersipora spp. We wish to also thank the reviewers of this article.
As explained in further detail in the next section, because there is no requirement to fit a joint probability model, the D-IFORM method can be applied in higher dimensions, without suffering from the curse of dimensionality. The method for calculating D-IFORM contours has been implemented in open-source code in MATLAB and Python, available from https: //github. com/edmackay/Direct-IFORM, together with the data used in this study. The remainder of the paper is organised as follows. Section 2 presents the motivation for the D-IFORM method, and compares this to alternative methods for deriving contours. Section 3 describes the implementation of the D-IFORM method in an arbitrary number of dimensions. An example application is presented in Section 4, where we consider four-dimensional contours of wind and wave parameters, and the potential application for the design of offshore wind turbines.
6 ). A check is then performed to see if there are any peaks which are separated by less than ℎ hours, which can happen if there are two equal maxima in the same window. In this case, we discard the second equal maxima in the window. For most metocean variables this is unlikely to occur, so this final check is quick to make. This algorithm also works with time series with gaps (often the case for measured datasets), as gaps do not affect the moving maxima envelope. This declustering algorithm has the advantage that it does not require definition of a threshold or a definition of peak prominence. It only requires a separation time, specified in terms of the length of the moving maxima window. A rigorous selection of peak separation time can be determined by looking at decorrelation times, using visualisations such as the extremogram  (Davis and Mikosch, 2009).
This is defined as the limiting conditional probability lim 𝜇→∞ Pr(𝑋 𝑡+𝜏 > 𝜇|𝑋 𝑡 > 𝜇), which estimates the extent of asymptotic dependence in the time series at lag 𝜏. For stationary series, the quantity Pr(𝑋 𝑡+𝜏 > 𝜇|𝑋 𝑡 > 𝜇) -Pr(𝑋 𝑡 > 𝜇) will tend to zero when 𝑋 𝑡 and 𝑋 𝑡+𝜏 are independent. This quantity can be plotted as a function of 𝜏 for various thresholds 𝜇, to select de-correlation timescales used in declustering routines (see  Mackay and Johanning (2018) ). Typical choices for peak separation times are in the range of 2-5 days. The efficiency of the declustering algorithm is strongly dependent on the implementation. Looping over the time series, moving the window and calculating maxima can be very slow. MATLAB has an  efficient native function for calculating a moving maximum of an array, movmax, which is several orders of magnitude faster than looping through the time series.
An example of the effect of neglecting serial correlation on estimated return periods is shown in Fig. 7 , using significant wave height (𝐻 𝑠 ) and wind speed (𝑈 10 ) data for the example presented in Section 4. Return periods calculated under the incorrect assumption that all hourly observations are independent are shown in red. Return periods for the declustered peaks are shown in blue. It is clear that neglecting the serial correlation in the observations leads to a positive bias in the return value at a given return period. However, since the largest hourly observation and largest observed peak must coincide in any dataset, the empirical return periods for the largest hourly value and largest peak are equal. This is an effect of finite sample size. To mitigate for this, return periods inferred from fitted GP models (see following subsection) are also shown.
The choice of 𝜁 is a biasvariance trade-off between the asymptotic GP model being appropriate for the exceedances (bias) and having a sufficient number of observations to estimate the model (variance). In practice, we found that setting 𝜁 = 0. 1 gave reasonable results for our datasets. The fit of the model for a given threshold can be assessed using standard diagnostic plots (see e. g. Coles (2001) ). The scale and shape parameters are estimated using a constrained maximum likelihood method. When the shape parameter, 𝜉, is nonnegative, the distribution is unbounded from above. For most metocean variables, this is physically unrealistic, and we would expect the distribution to have some finite upper bound. We therefore constrain the estimate ξ < 0. A GP distribution with 𝜉 = -1 is a uniform distribution.
Following this reasoning, we see that the intersection of all such non-exceedance regions is the set of points that is closer to the origin than to the set of reflections, 𝑆. That this, the Voronoi cell of the origin with respect to 𝑆, given in (7). Fig. 8  provides an illustration of the equivalence of the Voronoi definition of a contour, with the 'standard' definition in terms of intersections of non-exceedance regions. For the MATLAB implementation, the native function voronoin is used to calculate Voronoi cells in arbitrary numbers of dimensions, which utilises the Quickhull algorithms 1   (Barber et al. , 1996). In the present work, we have set the origin as the joint median of the sample. However,  Hafver et al. (2022)  showed that contours defined in terms of Voronoi cells are invariant to the location of the origin, provided that it is within the contour.
We then note that the closed convex hull of a set is the intersection of all closed half-spaces containing it  (Rockafellar, 1970). So the projected contour in dimensions 𝑋 𝑗 , 𝑋 𝑘 and 𝑋 𝑙 can be defined as the convex hull of 𝐶 𝑗𝑘𝑙. In MATLAB, this is implemented in the native function convhulln, which also uses the Quickhull algorithms  (Barber et al. , 1996). This function outputs a triangulation of the vertices of 𝐶 𝑗𝑘𝑙 , which form the convex hull, so that a mesh of the 3D surface can be plotted. A 2D slice through the contour for fixed values of certain variables can be calculated as follows. First, we calculate a triangulation of the hyper-surface into (𝑑 -1)-simplices, using the function convhulln. For each simplex which intersects with the values of the 𝑑 -2 fixed variables, we find coordinates of the intersection.
Using reflection to obtain direction vectors in each of the 16 orthants and removing duplicates (i. e. those vectors where the distance from the reflection hyperplane is zero), results in 2720 direction vectors. The declustering procedure used a peak-separation time of 48 hours, and identified between approximately 2100 and 2600 peaks at each direction. The threshold exceedance probability was fixed at 𝜁 = 0. 1, which resulted in between 210 and 260 exceedances used to fit the GP model at each direction. For the fitted GP distributions, the upper bound for the shape parameter estimate, of ξ = 0, was reached for 5 out of 2720 directions, and the lower bound of ξ = -1 was reached once. This relatively low occurrence is likely due to the use of a reasonably long dataset of 31 years. If a shorter dataset had been used, then the upper bound would likely have The non-constant spacing of direction vectors is due to the method used to create an approximately regular spacing of direction vectors in higher dimensions.
The main objective of the EMERGTOX network is to acquire data on the main groups of unregulated lipophilic and hydrophilic toxins listed at the international level in order to contribute to risk assessment. The analytical approach used also allows the monitoring of regulated toxins and consequently the possible identification of hazards related to the presence of these toxins in shellfish outside favorable periods of toxic algal blooms (e. g. , winter), as a complement to the official monitoring. Indeed, three analytical approaches using mass spectrometry were optimized and in-house-validated to monitor all targeted lipophilic toxins: (i) okadaic acid/dinophysistoxins, azaspiracids, yessotoxins, domoic acid, in addition to lipophilic toxins not regulated in Europe (pectenotoxins, spirolides, gymnodimines, pinnatoxins, ovatoxins/palytoxins, brevetoxins, microcystins, and nodularin); (ii) hydrophilic toxins: saxitoxin group and unregulated toxins (tetrodotoxins, anatoxins, cylindrospermopsins); and (iii) β-N-methylamino-L-alanine (BMAA), 2,4-diaminubutyrique acid (DAB) and N-(2-aminoéthyl)glycine (AEG), which are also not regulated.
Using the SCOPUS database and searches of article titles, abstracts and keywords, we found an increasing number of publications (Figures  8A-C ) except for chlordecone, which presented a more erratic pattern of publication with peaks in the midseventies corresponding to contamination issues at Hopewell and since 2010 due to issues in the French West Indies (Figure  8D ). In contrast, in the local media, the interest was rather different, with a high number of publications focusing on Sargassum (Figure  8G ) and chlordecone (Figure  8H ) and barely any reports on arsenic or glyphosate (Figure  8F ). For Sargassum, we found two peaks of publication in 2015 and 2018 corresponding to years with high levels of stranding, and potentially to the presence of an international conference on Sargassum that occurred in Guadeloupe during the year 2018.
to thank the Labex DRIIHM; the French program "Investissements d'Avenir" (ANR-11-LABX-0010), which is managed by the ANR. This work was supported in part by the Agence de l'environnement et de la maîtrise de l'énergie (ADEME). The authors thank the anonymous referees for their helpful comments that improved the quality of the manuscript. We are also grateful to our colleagues working within the OHM Caribbean Coast, and to Oscar for his support.