dataesr/scientific-paragraphs-categorization huggingface dataset:
Although there are species with broader distributions extending, e. g. , from Japan to Taiwan (A. hinomotoensis) or Indonesia, the Strait of Malacca, Vietnam and the China Sea (A. mamillata)  (Gratecap 2016b) , such distributions should be taken with caution, as exemplified by A. miriky sp. nov. , previously considered a form or subspecies of A. hilgendorfi, but shown by molecular data to be a distinct although morphologically similar, species. With our dataset being very patchy and relying on a single genetic marker, having limited validity of phylogenetic inferrence at the supraspecific level, we did not address the validity of the subgenera recognized within Amalda. Nevertheless, our observations are in line with the conclusions made by  Gemmel et al. (2020) , who analyzed the Amalda from New Zealand and several Indo-Pacific locations based on the entire mitogenome and long nuclear rRNA gene cassette data.
Amalda australis (type species of Baryspira) is the sister species to A. novaezelandiae (type species of Gracilispira), while other species, attributed to Baryspira (A. mucronate) and to Gracilispira (A. benthicola) form a polytomy. Thus we agree with  Gemmel et al. (2020)  that the status of at least these two subgenera is not resolved. Both European Journal of Taxonomy 706: 1-59 (2020) in  Gemmel et al. (2020)  and our trees, A. bellonarum occupies a basal position in the first offshoot in the datasets analyzed. Kilburn & Bouchet (1988: 292)  concluded that it "closely resembles (in miniature) its probable sister-species A. rosea  (Macpherson, 1956)  of the Queensland and New South Wales littoral" and that "Amalda rosea, together with a number of similar Australian species, is generally referred to [the] subgenus Alocospira, but probably belongs (with A.
MNHN registration Genus Species Cruise Station Country Coordinates Depth BOLD ID GenBank accession KANTOR Yu. I. et al. , The Indo-Pacific Amalda (Neogastropoda, Olivoidea, Ancillariidae) Source This study This study This study This study This study This study This study This study This study This study This study This study This study This study This study This study This study This study This study This study This study Kantor et al. 2017 This study Kantor et al. 2017 This study Kantor et al. 2017 Japan, S Honshu appr. 34°30′ N, 138°00′ E 70-100 m NEOGA1495-20 MT556071 Japan, S Honshu appr. 34°30′ N, 138°00′ E 70-100 m NEOGA1496-20 MT556043 CP3183 Madagascar 12°37′ S, 48°14′ E 420-436 m NEOGA1437-20 MT556150 CP3183 Madagascar 12°37′ S, 48°14′ E 420-436 m NEOGA1418-20 MT556065 CP3183 Madagascar 12°37′ S, 48°14′ E 420-436 m NEOGA1419-20 MT556052 CP3188 Madagascar 12°31′ S, 48°22′ E 298-301 m NEOGA1432-20 MT556053 CP3183 Madagascar 12°37′ S, 48°14′ E 420-436 m NEOGA1420-20 MT556126 CP3188 Madagascar 12°31′ S, 48°22′ E 298-301 m NEOGA1421-20 MT556140 DW3212 Madagascar 12°34′ S, 47°54′ E 367-369 m NEOGA1439-20 MT556103 CP3188 Madagascar 12°31′ S, 48°22′ E 298-301 m NEOGA1438-20 MT556099 CP3188 Madagascar 12°31′ S, 48°22′ E 298-301 m NEOGA1433-20 MT556041 DW3216 Madagascar 12°34′ S, 47°52′ E 296-350 m NEOGA1434-20 MT556143 DW3216 Madagascar 12°34′ S, 47°52′ E 296-350 m NEOGA1435-20 MT556087 DW3216 Madagascar 12°34′ S, 47°52′ E 296-350 m NEOGA1422-20 MT556048 DW3216 Madagascar 12°34′ S, 47°52′ E 296-350 m NEOGA1423-20 MT556040 WB28 Western Australia 33°40′12′′ S, 114°56′36′′ E 0-15 m NEOGA1476-20 MT556110 New Caledonia, Prony Bay 12-15 m NEOGA1492-20 MT556159 CP3130 Mozambique Channel 25°53′ S, 33°07′ E 112-127 m NEOGA1431-20 MT556070 DW3694 Papua New Guinea 02°10′ S, 147°16′ E 200-219 m NEOGA1487-20 MT556032 Japan, appr.
MNHN registration Genus Species Cruise Station Country Coordinates Depth BOLD ID GenBank accession KANTOR Yu. I. et al. , The Indo-Pacific Amalda (Neogastropoda, Olivoidea, Ancillariidae) Source Kantor et al. 2017 This study Kantor et al. 2017 Kantor et al. 2017 This study Kantor et al. 2017 Kantor et al. 2017 Kantor et al. 2017 Kantor et al. 2017 Kantor et al. 2017 25°01. 05′ S, 2-4 m 47°00. 00′ E OLIV011-16 KX233307 8°52′ N, 569-583 m NEOGA465-10 MT556117 123°37′ E 19°50′ S, 490-580 m OLIV066-16 165°33′ E KX233317 9°38′ N, 123°44′ E 584-596 m NEOGA459-10 KX233370 appr. 32°34′ N, NEOGA1497-20 MT556136 132°22′ E 16°21. 3′ N, 1 m 61°44. 92′ W OLIV063-16 KX233349 4°43′14′′ S, 8-9 m 11°49′4′′ E OLIV074-16 KX233272 15°39′ S, 970-1060 m OLIV008-16 146°56′ W KX233316 20°21′ S, 345-351 m NEOGA673-10 158°46′ E KX233379 15°34′36.
(2011). We obtained digital images of the dorsum for each male by scanning with a high-resolution computer scanner (Canon ® , CanoScan Lide 110; image size: 2,550 × 2,600 pixels). We gently held the lizard motionless by placing a patch of soft, black moss on the ventrum of image acquisition reduces potential biases in the estimates of dorsal darkness due to differences in luminosity between pictures. Using ImageJ software  (Schneider, Rasband, & Eliceiri, 2012) , we selected a section of each individuals' back, going from the fore to the hind legs, and transformed it into a 32-bit black and white picture. The score of dorsal darkness was calculated as the proportion of black pixels in this section by using the grayscale threshold option.
ACK N OWLED G M ENTS We thank  P. Dufour ,  L. Duhalde ,  A. Faure ,  J. Rense  and  Q. Wu  for helping in lizard capture, field, and laboratory work. We are very thankful to  E. Bestion  for advice on the methodology used and for her help in dorsal darkness computation and to  K. E. Cain  and  S. Meylan  for their valuable comments on previous versions of the manuscript. We are grateful to the  Agence Meteo France  that provided us access to their database.
Research method Part of this work is based on a conceptual and theoretical approach and its purpose is to provide a framework from literature review, analyzing 33 selected articles available from the databases Scopus, Web of Science, Science Direct and Scielo. According to  Nakano (2012)  the conceptual and theoretical study provides a literature review, develops conceptual models and promotes theoretical discussions. The theoretical study is conducted jointly with the empirical research, characterized by case studies. This research has a qualitative approach whose focus is to explore the elements related to the object in more depth. Taking into account the notes from  Voss, Tsikriktsis & Frohlich (2002)  and  Eisenhardt (1989) , the multiple case study with the aim of broadening/refining a theory that was developed in this study following the proposal of Yin (2010) (Figure  2 ).
Measuring PS Curves The saturation of the nonwetting phase S nw is extracted by image analysis from the sequence of captured images. The saturation increases linearly with time, in accordance with the constant flow rate imposed on the system. The pressure drop across the model is at any time such that it maintains the constraint by the flow rate. The pressure ΔP is the difference between the atmospheric pressure P a and the pressure in the wetting phase at the outlet ΔP = P a -P outlet. (6) Combining the extracted saturation with the pressure measurements, a series of PS curves are obtained, as shown in Figure  4. The equivalent measurement is done on the IP simulations and shown in Figure  5. The first data point on these curves of pressure difference between the two phases, in this system is nonzero, which is related to the varying hydrostatic pressure ΔP h across the system, which scales as the gravitational term g sin  for different inclinations.
( 23 ) Data in 3-D with detailed capillary-pressure relationships and systematically changing Bond number are more scarce than in 2-D, as it is easier to change the orientation of the 2-D cell than it is to perform 3-D experiments where the gravitational field is changed and the wetting properties are kept fixed. Doing so would require to perform the experiments in a given medium and with a given fluid pair in a centrifuge, which is an interesting perspective for future work. To check nonetheless the level of consistency or inconsistency between the predicted 3-D relation from Equation  23 and the experiments, the proposed prediction was compared with measurements conducted by  Nouri et al. (2014). Here saturation and pressure were measured using a hanging column apparatus for four different soil types: namely, persian gulf sand (PGS), sandy loam (SL), silty clay (SiC), and silty loam (SiL).
For every soil type drainage experiments were performed for four different wetting phases. We determined the final volumetric content of each wetting fluid as function of the soil texture by digitizing the presented data and extracting the resulting final air saturation. We note that the displacement of petroleum by air was extremely low and resulted in very small saturation variations even by applying very high capillary pressure values. The reason for this abnormal behavior is not yet known; we therefore used only the well-documented experimental data obtained for kerosene, diesel, water, and gasoline. The Bond number was calculated for each fluid pair and soil type, using the given surface tension for each of the four wetting fluids and the density contrast between the wetting fluid and air, the mean grain size diameter (d50, the median value of the diameters) as the typical pore size, and setting  = ∕2.
Data Availability Statement The simulation code can be found online (https://zenodo. org/badge/latestdoi/273205082 [https://doi. org/ 10. 5281/zenodo. 3899977]). The experimental raw data are found online (https://doi. org/10. 5281/zenodo. 3900005 [https://doi. org/10. 5281/zenodo. 3900004]).
Data availability. The compiled datasets used to produce each figure within this paper are available as Igor Pro files upon request. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/acp-20-13069-2020-supplement. Author contributions. YR wrote the paper with input from all authors. BS and GS analyzed the MARGA and dew data and wrote the paper. YR and BG conducted the HONO measurements using LOPAP. TT and AW were responsible for the particle measurements. AM and HH designed the experiments and led the campaign. All coauthors commented on the paper. Competing interests. The authors declare that they have no conflict of interest. Financial support. This research has been supported by the German Federal Environment Agency (UBA) research foundation (grant nos.
351 01 093 and 351 01 070) and the European Union (EU) for the transnational access (TNA) under ACTRIS-2. The comparison of HONO measurements with MARGA and LOPAP at TROPOS research site Melpitz (MARLO) is part of the project that has received funding from the European Union's Horizon 2020 research and innovation program (grant no. 654109). The CNRS team (Orléans-France) acknowledges the support from Labex Voltaire (grant no. ANR-10-LABX-100-01) and the ARD PIVOTS program (supported by the Centre-Val de Loire regional council). Europe invests in Centre-Val de Loire through the European Regional Development Fund. Review statement. This paper was edited by Hang Su and reviewed by two anonymous referees.
Based on a workshop involving international ICLS scientists and practitioners we analyzed secondary data and case studies to draw general conclusions on the factors influencing ICLS prevalence and trajectories within commercial agricultural systems. We ask the following: (1) What are current trends in ICLS adoption or retirement? (2) What are the causes of ICLS persistence, adoption, or retirement? (3) What policy levers could contribute to greater adoption of ICLS? We use a multilevel perspective of system innovations, i. e. , a means of explaining how technological transitions come about in a given multilevel context,  (Geels 2011)  to understand current ICLS prevalence and trajectories.
Data and Working Methods This study relies on data from 27 Sahelian weather stations in three countries (Burkina Faso, Mauritania, and Senegal). Annual data for the period between 1947 and 2014 has been used for the chronological graphic method of information processing (MGCTI) of "Bertin matrix" (this choice was prompted by data availability-Figure  1 ). The mean monthly values were available only for stations in Senegal and Burkina Faso. Available monthly rainfall data were extracted between 1948 and 2017 for each country for the continuous wavelets transform (CWT) analysis. The data used have not been interpolated but come from measurements from observation stations that appear on the map in Figure  1. The data were collected from the weather services in Senegal (ANACIM) (http://www. anacim.
sn/ meteorologie/), Mauritania (http://www. onm. mr/). Rainfall data from Burkina Faso were collected from the KNMI Climate Explorer (http://climexp. knmi. nl/selectstation. cgi?id=someone @somewhere). Some missing data were found on "TuTiempo. net" (https://fr. tutiempo. net/climat/afrique. html). This site uses the National Climatic Data Center's Global Database (NCDC-https://www. ncdc. noaa. gov/dataaccess/quick-links). The data were collected from the weather services in Senegal (ANACIM) (http://www. anacim. sn/meteorologie/), Mauritania (http://www. onm. mr/). Rainfall data from Burkina Faso were collected from the KNMI Climate Explorer (http://climexp. knmi. nl/selectstation. cgi?id=someone @somewhere). Some missing data were found on "TuTiempo. net" (https://fr. tutiempo. net/climat/afrique.
html). This site uses the National Climatic Data Center's Global Database (NCDC-https://www. ncdc. noaa. gov/data-access/quick-links). Three regionalized indices of surface temperatures of the North Atlantic and South Atlantic Tropical, freely accessible on the site (https://www. esrl. noaa. gov/psd/data/climateindices), were used: The AMO index (25-60° N, 7-75° W), the TNA anomaly of the average of the monthly SST from 5. 5° N to 23. 5° N and 15° W to 57. 5° W), and the TSA anomaly of the average of the monthly SST from Eq-20° S and 10° E-30° W). The analysis of rainfall variability employs two working methods: - The chronological graphic method of information processing of "Bertin matrix" type. The MGCTI is an analytical method based on a statistical analysis and on a graphical representation of results.
Rainfall Variability in Senegal and Burkina Faso The continuous wavelets transform is a good method to study the relation between rainfall and the climate index AMO, TNA, and TSA. This method was used by some authors to identify the non-stationary behavior of North Atlantic Oscillation (NAO) evolution. Similar researches were carried out by  [34, [40] [41] [42] [43] [44] [45] [46]. In this research, CWT was performed on monthly dataset aiming to identify the spectral components of the signals which can be assigned to different modes of variability characterizing the geophysical signal and, eventually, the time scales involved. The mean monthly values were available only for stations of Senegal and Burkina Faso used in the first part of this research  (Rainfall, 1948 (Rainfall, -2017)). Available monthly rainfall data were extracted between 1948 and 2017 for each country.
To identify such cities and departments, we ran an internet search (using google. fr) with the key words "subvention", "destruction", "nid" and "frelon asiatique" or "vespa velutina" (i. e. "subsidy", "destruction", "nest" and "Asian hornet"). All cities and departments, identified as subsidising the yellow-legged hornet nest destruction, were then contacted to obtain data regarding the total yearly cost of nest destruction, as well as the number of nests that were destroyed.
Extrapolating the cost of nest destruction spatially To take into account invaded areas with no subsidy of nest destruction, we aimed at spatially extrapolating this cost by identifying potential correlates of the cost of nest destruction. As potential correlates, we chose to investigate the surface area and the human population size of the spatial unit for which we were able to gather cost information. As we could only gather a reduced dataset, potential correlations were investigated through simple models -a linear model and a log-log linear model: for each potential correlate, we fitted the two following models (1) y~x and (2) log(y)~log(x). Spatial extrapolation to countries other than France, need to be adjusted to per capita GDP (in purchase power parity terms), i. e. to the cost of living in a given country.
Modelling the potential distribution of the yellow-legged hornet Presence data of the yellow-legged hornet in its native and invaded ranges Presence data of the yellow-legged hornet from the native Asian range was obtained by gathering information on museum specimens, published records and hornet sampling performed in China  (Villemant et al. 2011). As for the invaded range in Europe, data from the French part of the invaded range came from the INPN database that aggregates all validated French records (https://inpn. mnhn. fr/). To this French database, we added the recent locations reported in other European countries (Spain, Portugal, Italy, Belgium and Germany)  (López et al. 2011; Rome et al. 2013; Porporato et al. 2014; Witt 2015; Goldarazena et al. 2015; Bertolino et al. 2016). Overall, we obtained 10,395 records in the European invaded range observed from 2004.
Climate data We used the same eight climatic variables as in previous studies for the niche modelling of the yellow-legged hornet  (Villemant et al. 2011; Barbet-Massin et al. 2013 ). We considered: (1) annual mean temperature, (2) mean temperature of the warmest month, (3) mean temperature of the coldest month, (4) temperature seasonality, (5) annual precipitation, (6) precipitation of the wettest month, (7) precipitation of the driest month and (8) precipitation seasonality. The seasonality is the coefficient of variation of the monthly means. Current data were downloaded from the worldclim database  (Hijmans et al. 2005 ) (http://www. worldclim. org/) as 2. 5 arc-min grids (subset of the 19 bioclim variables). These data are interpolations from observed data representative of current climatic conditions.
Climate suitability modelling Climate suitability of the yellow-legged hornet was modelled by running eight different modelling techniques implemented within the biomod2 package (3. 3-7 version)  (Thuiller et al. 2009 ) in R (R Core Team 2015): three regression methods (GLM, GAM and MARS), two classification methods (CTA and FDA) and three machine learning methods (ANN, BRT and RF). As no absence data were available for the species, pseudo-absences were randomly drawn (Barbet-Massin et al. 2012) from the South-East part of Asia and from Europe. We used 10,000 random pseudo-absences, with the total weight of presences being equal to the total weight of pseudo-absences  (Barbet-Massin et al. 2012). As results might depend on the choice of pseudo-absences, models were replicated three times (with different pseudo-absences selection)  (Barbet-Massin et al.
Acknowledgments This work was supported by the  INVACOST  project ( ANR & Fondation BNP Paribas ),  Biodiversa Eranet (FFII). We are most thankful to  Quentin Rome ,  Claire Villemant  and all persons and organisations that provided records of yellow-legged Asian hornet nests in France and in Europe (their list is available on the INPN website). We thank all persons who provided cost data regarding nest destructions.
Quistadrilus multisetosus is recognizable by the presence of prominent light sensory papillae arranged in a transversal row in every segment on the chaetal line, by the presence of foreign particles irregularly arranged in some parts of the body and by characteristic ventral and dorsal chaetae  [11] [12] [13]. The species can be confounded with two other Tubificinae species, Embolocephalus velutinus (Grube, 1879) and Spirosperma ferox Eisen, 1879 also covered by foreign particles and especially S. ferox that has a similar shape of chaetae. In the present work, we mention the presence of Quistadrilus multisetosus in Lake Geneva, report its current distribution and abundance in this lake, complement the existing data concerning its ecology and determine the invasive potential of this species in this lake.
Construction of a COI Phylogenetic Tree The obtained sequences of Q. multisetosus were added to a database including Tubificinae lineages found in Switzerland  [23]  using the Muscle automatic alignment option as implemented in SeaView vs. 4. 3. 3. [33]. The alignment contains 35 sequences with 658 sites of which 351 are without polymorphism. Nucleotide frequencies are 0. 37 (A), 0. 21 (C), 0. 10 (G) and 0. 32 (T). A phylogenetic tree was constructed using maximum likelihood phylogeny (PhyML 3. 0) as implemented in ATGC: PhyML  [34]. An automatic model selection by SMS  [35]  based on Akaike Information Criterion (AIC) was used yielding in a GTR + G + I substitution model being selected for the analysis. The initial tree is based on BioNJ. An additional tree was constructed using FastMe 2.
0, a distance-based phylogeny inference program as implemented in ATGC: FastMe  [36]. F84 was used as substitution model with gamma distributed rates across sites and tree refinement with Subtree Pruning and Regrafting (SPR). Bootstrap values (BV) are based on 100 replicates for PhyML and FastMe analyses. A 10% threshold of COI divergence was applied to distinguish between species (species = lineage) (cf. Section 2. 5). The intra-and inter-lineage distances were calculated using the K2P model in MEGA 5. 1  [37].
Equimolar concentrations of PCR products were pooled into a single tube that was purified using High Pure PCR Product Purification kit (Roche Diagnostics, Risch-Rotkreuz, Switzerland). The library preparation was performed using a TruSeq ® DNA PCR-Free Biology 2020, 9, 436 6 of 17 Library Preparation Kit (Illumina, San Diego, CA, USA) and was quantified with qPCR using KAPA Library Quantification Kit (Roche). Finally, the library was sequenced on a MiSeq instrument using paired-end sequencing for 500 cycles with Standard kit v2. Raw sequences of the 10 samples are accessible in the Short Read Archive under the BioProject number PRJNA678609.
Sequence Analysis Bioinformatics analyses were performed using the web application SLIM  [40]. Raw fastq reads were first demultiplexed using the dtd algorithm implemented in SLIM. Then, they were quality-filtered by removing any sequence with a mean quality score of 30 and also removing all sequences with ambiguous bases or any mismatch in the tagged primer. Paired-end reads were then assembled using simple bayesian algorithm implemented in pandaseq  [41]. Chimera removing and the OTUs clustering at 97% was performed using vsearch  [42]. All the sequences were taxonomically assigned using the assignment function of vsearch tool  [42]  against a local COI oligochaete database  [23] , to which we had added the COI sequences of Quistadrilus multisetosus obtained during the present work. The sequences of our Swiss database are deposited in the European Nucleotide Archive and directly available in Vivien et al.
Sequence Analysis Bioinformatics analyses were performed using the web application SLIM  [40]. Raw fastq reads were first demultiplexed using the dtd algorithm implemented in SLIM. Then, they were qualityfiltered by removing any sequence with a mean quality score of 30 and also removing all sequences with ambiguous bases or any mismatch in the tagged primer. Paired-end reads were then assembled using simple bayesian algorithm implemented in pandaseq  [41]. Chimera removing and the OTUs clustering at 97% was performed using vsearch  [42]. All the sequences were taxonomically assigned using the assignment function of vsearch tool  [42]  against a local COI oligochaete database  [23] , to which we had added the COI sequences of Quistadrilus multisetosus obtained during the present work. The sequences of our Swiss database are deposited in the European Nucleotide Archive and directly available in Vivien et al.