dataesr/scientific-paragraphs-categorization huggingface dataset:
The list of species and related literature from Jim's catalogue were made available on WoRMS. The traits defined were scaled by priority, on the basis of availability in the literature of information related to the trait for most of the species considered; ecological relevance of the trait; link of the trait to other WoRMS datasets and working groups (e. g. , parasites). Qualitative body size was defined as the first trait to be curated, given its availability for all species, and is now completed. A set of additional traits (such as behavioural traits), were indicated for a future focus.
VLIZ-WoRMS Philanthropy grants 2019 and 2021 More recently, two VLIZ-WoRMS Philanthropy Grants were awarded within the VLIZ Philanthropy project "Support the WoRMS editors", enabling focussed work to target particular gaps within the whole WAD. Within the Amphipoda, 1004 accepted taxa remained without original names documented (as of 24 June 2019). This gap was targeted, with an aim to complete the original name information for all accepted species within the World Amphipoda Database, and to reduce considerably the total number of amphipod species without an original name linked (a further 1198 unaccepted species) at the same time. As a result of this grant, all of the 1004 accepted species without an original name linked were completed. A total of 882 taxon names were added to the World Amphipod Database and 2785 edits were made to taxa in the database during the grant period, including linking the original name, linking the original description and correcting the environment and fossil status  (Horton & Valls Domedel, 2019).
Of the 1325 taxa without an environment documented, 519 remained. Additionally, 115 edits were made to the sources (including creation, addition of PDF documents to existing sources, or addition of links to journals when PDF versions of the articles were not available). The 2021 Philanthropy grant allowed further work on the remaining gaps to be completed. All of the 167 accepted species and sub-species level taxa without an original name linked were completed, and all 2108 genera (accepted and unaccepted) in the WAD now have a type species linked (> 400 of these were edited during the grant period). 1436 accepted species-level taxa had the original description source linked (more than half of the 2621 which were missing an original description at the beginning of the grant). More than 1100 edits were made to existing entries in the database including linkage to the original name, to the original description and edits of the habitat information or the fossil range  (Horton & Valls Domedel, 2019).
Current Status of the World Amphipoda Database Despite the considerable amount of work to complete gaps for the agreed priorities, the editing of the WAD is not yet complete. Work remains to complete the linking of original names (964 unaccepted taxa remain) and linking of original descriptions (780 unaccepted taxa remain). In addition, 234 species (of which 203 are unaccepted) remain without an environment flag (marine, brackish, fresh or terrestrial). Filling of these gaps will remain high on the priority list and will be managed on an ad-hoc basis until completed. Once these gaps are filled, the work will continue to input information on the new taxa described annually, which is currently an average of 137 per year (see Table  1 ), and focus will turn to completing the remaining priorities, particularly the input of type localities.
They were placed outside the Amphipoda by  Lowry & Myers (2017)  and raised to the rank of order Ingolfiellida. They continue to be maintained as part of the World Amphipoda Database owing to their historical placement. The ingolfiellidans are now a sister group to the amphipods and comprise 51 species (of which 19 are freshwater species), and about 100 taxon names (including species names and higher taxa). The taxonomy displayed in the WAD cannot always incorporate all the newest aspects of systematic relationships due to conflicting views, but does strive to provide information on alternative classifications and names used. An example of this is provided by the recent works on the systematics and diversity of freshwater gammaroid amphipods  (Hou & Sket, 2016; Sket & Hou, 2018). We present a list of accepted infraorders, superfamilies and families within each of the six suborders, providing counts of the number of genera and species within each family in three tables (Table  3 : Amphilochidea; Table  4 : Hyperiidea, Hyperiopsidea, Pseudingolfiellidea & Colomastigidea; Table  5 : Senticaudata) to give an overview of this information.
This provides a stable reference for the current state of amphipod systematics following the extensive higher-level revisions in recent years. The majority of taxa are in the suborders Amphilochidea (688 genera, 4196 species) and Senticaudata (974 genera, 5813 species). (Hughes et al. , 2023). The WAD is an ever-expanding database, with an average of 137 newly described taxon names entered into the database annually (Table  1 ), with edits to and additions of older names being made all the time. The database still contains residual errors and there are certain taxa that have received less focussed attention and will need targeted improvement in the future. Amphipoda systematics are in a state of flux, particularly due to new insights from molecular data and the fact that this information is not yet available for many of the relevant taxa  (Copilaş-Ciocianu et al.
, 2020). Users of the database are encouraged to point out errors to the Data Management Team using the single contact email info@marinespecies. org and queries will be passed to the relevant editor. Contributors are acknowledged annually on the WAD webpages. Table  5. Suborder Senticaudata: Alphabetical list of infraorders, superfamilies and families, providing counts of the number of genera and species within each family; data from the World Amphipoda Database  (Horton et al. , 2022;.
Acknowledgements. The authors are indebted to  Jim Lowry , for the immense legacy of taxonomic work he has provided. Without Jim, the World Amphipoda Database would not be what it is today. His legacy will live on. We would also like to thank the editors of the World Amphipoda Database, past and present, and all amphipod taxonomists describing taxa and revising the systematics of the group. The authors would like to acknowledge the important contributions of past members of the  WoRMS Steering Committee , and the past and present members of the  WoRMS Editorial Board  in the development of the  World Register of Marine Species. The creation and continuous further development of the WoRMS database has been funded by numerous projects including LifeWatch (2012-present). This work makes use of data and/or infrastructure provided by VLIZ, and funded by  Research Foundation-Flanders (FWO)  as part of the  Belgian  contribution to LifeWatch.
The initiation and coordination of the  World Amphipoda Database project  was funded jointly by  INDEEP (International network  for  scientific investigation of deep-sea ecosystems)  and BP. Tammy Horton  was funded by  Climate Linked Atlantic Section Science ( CLASS) programme  ( NE/R015953/1 ) supported by  UK Natural Environment Research Council (NERC) National Capability  funding to the  National Oceanography Centre. The work of  Leen Vandepitte ,  Wim Decock  &  Bart Vanhoorne  were funded by  Research Foundation-Flanders (FWO)  as part of the  Belgian  contribution to LifeWatch. Cene Fišer was partially supported by  Slovenian Research Agency  through core program  P1-0184. Sabrina Lo Brutto  was partially supported by the  NBFC  ( PNRR ,  Missione 4 Componente 2 , " Dalla   ricerca all'impresa ",  Investimento 1.
Bioinformatics And Statistical Analysis Bioinformatics analyses were conducted in RStudio (v. Rarefaction curves were used to analyse the expected number of amplicon sequence variants (ASVs) detected per sample according to the sequencing depth. To account for differences in sequencing depth (Fig. S1 ), diversity indices were computed through rarefaction of the dataset at a subsampling depth of 12,300 reads and thus, one sample with < 12,300 reads was excluded from the rare ed dataset and downstream analyses. Accumulation curves were obtained for rare ed data, including all samples analysed, to visualise the expected number of taxa detected according to the sampling size (Fig. S2 ).
Results In total, muriqui faecal samples from 53 uniquely identi ed individuals (inferred from microsatellite genotyping) were successfully sequenced and the curated 16S rRNA dataset included 651,900 reads after all quality control ltering, classi cation, and rarefaction. Across the microbiota of all muriqui sampled in this study, 1339 ASVs were retrieved, representing at least nine Phyla, 18 Classes, and 56 Orders. The bacterial microbiota pro le obtained for all analysed groups revealed Bacteroidetes as the most abundant phylum accounting for 58. 33% of all sequences, Firmicutes were about half as abundant with 25. 77% of the reads, followed by Proteobacteria (9. 55%), Tenericutes (2. 71%) and Cyanobacteria (1. 95%) (Fig. 2 , Tables  S3-S5 ). A similar pattern of phyla compositional abundance was obtained across most of the individuals analysed, with only four individuals showing a read abundance > 20% for Proteobacteria, from which two individuals showed a higher abundance of this phylum over Bacteroidetes/Firmicutes (MC104, MC112 -Fig.
3. 3, 2020), following the DADA2 work ow (Callahan et al. 2016, please see Supp. Material for detailed information on the ltering steps). The 16S rRNA gene amplicon sequencing yielded a total of 2,502,995 raw sequence reads, with a contig length of ~ 253bp after merging paired-end reads. Sequence variants (SVs) with lengths > 260 bp (0. 004% of total sequences) along with chimaeras (0. 031% of total reads) were removed. Taxonomic assignment was based on the SILVA v132 database (Quast et al. , 2013; Yilmaz et al. , 2014). DADA2 identi ed 10 unique SVs in the sequenced mock community sample including the 8 bacterial isolates (Pseudomonas aeruginosa, Escherichia coli, Salmonella enterica, Lactobacillus fermentum, Enterococcus faecalis, Staphylococcus aureus, Listeria monocytogenes, Bacillus subtilis) and 2 yeasts (Saccharomyces cerevisiae, Cryptococcus neoformans).
), from which regions is there most risk of debris beaching at Seychelles, and can we therefore predict high-risk fisheries and shipping channels? • What are the physical drivers of debris accumulation at Seychelles, and are variations in accumulation rates (seasonal and interannual) predictable, allowing for more targeted cleanup efforts? All datasets generated by this study, as well as associated documentation, results that are not discussed in detail in the main text, and code to repeat analyses under different parameters, can be found in the Supplementary Materials.
In this study, we store results for 27 beaching sites, representing islands or island groups. 18 of these sites are within Seychelles (Aldabra, Assomption, Cosmoledo, Astove, Providence, Farquhar, Alphonse, Poivre, St Joseph, Desroches, Platte, Coëtivy, Mahé, Fregate, Silhouette, Praslin, Denis, and Bird). For the terrestrial-sourced debris experiments only, we included an additional 9 sites from the wider western Indian Ocean (Comoros, Mayotte [France], Lakshadweep [India], Maldives, Mauritius, Réunion [France], Pemba [Tanzania], Socotra  [Yemen] , and the Chagos Archipelago). A grid file with the labelled grid cells for all sites can be found in Supplementary Dataset 3. For brevity, we focus on Seychelles in this paper, specifically islands on the Seychelles Plateau, and the Aldabra Group as representative of the Outer Islands.
We focus on these two island groups as the population of Seychelles almost entirely resides within the Seychelles Plateau, whilst Aldabra is a World Heritage Site and the site of a major observational marine debris study  (Burt et al. , 2020). Together, these groups span the geographical range of Seychelles. Analyses and figures for other island groups that could not be included in this paper can be produced using the Supplementary Datasets  (Vogt-Vincent, 2022; Vogt-Vincent and Johnson, 2022a,b). By efficiently choosing which data to store during particle tracking simulations (see Supplementary Text 2), it is possible to compress all data required to reconstruct almost all beaching events from the over 2 × 10 11 particles used across all our simulations in ~1. 2 TB, whilst allowing key parameters to be varied through postprocessing.
In the absence of good constraints on this parameter, we take f c = 0. 25, corresponding to a total flux of debris from coastal and riverine sources of 3. 1 Mt. y -1 and 1. 0 Mt. y -1 respectively (r = 3. 1, between r = 1. 9 in  Kaandorp et al. (2020)  and r = 4. 9 in  Lebreton et al. (2018) ). The parameter f c can, however, be modified during postprocessing and, if it becomes better constrained in the future, it is straightforward to regenerate our results for another value of f c , or even an entirely different debris input distribution, using the Supplementary Datasets. To minimise the cost of simulations, we only consider coastal cells for countries that could reasonably act as a source of marine debris for islands in the western Indian Ocean, identified from a preliminary backward particle tracking experiment (Supplementary Text 3, Supplementary Fig.
To derive these classifications, we use guidance on windage coefficients from  Duhec et al. (2015)  and  Domon et al. (2012) , sinking rates from  Fazey and Ryan (2016) , and beaching rates from our own analysis (Supplementary Text 4, Supplementary Figs. 3 4 ) and  Kaandorp et al. (2020). However, we stress that windage coefficients and sinking rates for different types of marine debris remain poorly constrained, and the classes we have defined are suggestions only. Practitioners can recompute predictions for parameters of their own interest using the Supplementary Datasets.
Countries of origin for debris beaching across For several countries of origin, there is agreement between the two datasets, particularly for Class C debris. Of the 5 largest sources of Class C debris predicted by the model, bottles were found on Aldabra from 3 (Indonesia, India, and South Africa). Indonesia was the largest source of Class C debris in our model, and was the second largest country of manufacture in the sample from Aldabra. However, there are some significant differences. This in itself is not unexpected. The sample size (45) of PET bottles in  Burt et al. (2020)  is small, and the sample is likely biased against bottles with longer drift times, as only bottles with intact labels could have their country of manufacture identified. Additionally, the country of manufacture of a bottle is not necessarily the same as the country where a bottle entered the ocean.
However, the particular countries associated with model-observation disagreement provide interesting insights into the sources of debris for Aldabra. The most obvious discrepancy between the two datasets is China. In our analysis, China was responsible for a negligible proportion of debris of any class accumulating at Aldabra (<0. 1 %), but was responsible for the manufacture of almost half of all bottles actually found on Aldabra. Although our debris classes may be an imperfect representation of the physics driving PET bottle transport, no realistic combination of μ * b , μ s , or physical scenario results in a significant flux of marine debris from China to Aldabra. More likely is an explanation suggested by  Duhec et al. (2015) , that a large proportion of labelled items from Asia accumulating at beaches in Seychelles were thrown overboard or lost from shipping activities (commercial, leisure, and fishing) in the vicinity of Seychelles.
We do not have data on the proportion of bottled drinks sold in Indonesia (or other identified source countries) which are foreign imports, but imports would have to account for almost all PET bottles sold in these countries to explain the discrepancies in Table  3. We therefore suggest that disposal at sea is the most likely explanation for the discrepancies we have found. Disposal at sea cannot, however, explain the absence of bottles from the Philippines amongst PET bottles found at Aldabra relative to our predictions. We suggest the most likely explanation is that the value for μ b * diagnosed from drifters based on a global dataset is inappropriate for   (Imzilen, 2021). Supplementary Fig. 22  is the analogous plot for the Seychelles Plateau. the complex archipelagic coastline and bathymetry around the Philippines, resulting in our analyses underestimating the local beaching rate for debris of Philippine origin, and therefore overestimating the quantity of debris entering the Indian Ocean.
, 2014; Suanda et al. , 2018; Deschepper et al. , 2019). Finally, considerable uncertainty remains in the input function of marine debris into the ocean. Nevertheless, a strength of this study is that our results can be easily recomputed for different combinations of sinking rate, beaching rate, and windage so, if improved constraints in the future demonstrate that our classification of debris (into our four classes A-D) is inappropriate, it will be straightforward to recompute results with the dataset and scripts provided in the Supplementary Datasets. Supplementary data to this article can be found online at https://doi. org/10. 1016/j. marpolbul. 2022. 114497.
Acknowledgements This work was funded by  NERC  grant  NE/S007474/1 , and used the ARCHER2 UK National Supercomputing Service (https://www. archer2. ac. uk) and JASMIN, the UK collaborative data analysis facility. We thank  Dr. Kay Critchell  and an anonymous reviewer for their comments and suggestions, which all greatly improved the clarity, accuracy, and utility of the manuscript. Data analyses in this study made use of a wide range of python modules, most significantly OceanParcels (Lange and Sebille, 2017;Delandmeter and van Sebille, 2019), numpy (Harris et al. , 2020), xarray (Hoyer and Hamman, 2017), scipy (Virtanen et al. , 2020), cmasher (van der Velden, 2020), and matplotlib (Hunter, 2007). We are particularly grateful to all developers of OceanParcels, who have enormously improved the accessibility of Lagrangian particle tracking as a research tool.
Data availability statement All data required to recompute results for arbitrary parameters are archived at the British Oceanographic Data Centre as Supplementary Dataset 1. The matrices described in this manuscript corresponding to the four debris classes are archived as Supplementary Dataset 2. Code required to reproduce figures in this manuscript, and more figures for further parameter combinations, are archived as Supplementary Dataset 3, with documentation available here. The dFAD deployment data cannot be published due to a confidentiality agreement. Requests for access to dFAD deployment and tracking data should be addressed directly to the Ob7 pelagic ecosystem observatory using the following email address: adm-dblp@ird. fr.
Participants Participants were adult volunteers (UK resident in studies 1 and 2; US resident in studies 3 and 4) recruited via research participation platform Prolific. Prolific members sign up to be notified of research studies, which they complete remotely for small payments. Use of platforms like Prolific is widespread in experimental psychology. They can rapidly produce large datasets. These have been shown in controlled comparisons to be of high quality and to replicate the results of in-person studies in a number of cases  [17] [18] [19]. Of US and UK Prolific members active in last 90 days (6 April 2023), 57. 7% had declared at registration that they had university degrees, against 42. 3% who did not; and 56. 3% placed themselves on rungs 6-10 of the Macarthur ladder of subjective socioeconomic status  [20] , against 43.
Data analysis Data and analysis code are available at: https://osf. io/kf87e/. Data were analysed using Bayesian generalized linear mixed models using the brms R package  [23]. Priors on parameters were N(0, 1). We report parameter estimates, their 95% credible intervals (CI), and Bayes factors (BFs) for each parameter (Savage-Dickey density ratios). Pre-registered predictions are identified with the letter P. We interpret strength of evidence from BFs using the categories given by Andraszewicz et al. [24]. We also repeated all analyses using frequentist generalized linear mixed models, which produced very similar conclusions. Depending on the prediction, the unit of analysis was either the individual decision, or the set (i. e. counting behaviours and averaging trust across the eight players).
Funding. This research was supported by the  EUR FrontCog  grants  ANR-17-EURE-0017  and  ANR-10-IDEX-0001-02  to Université PSL;  ANR  grant  ANR-21-CE28-0009 ; a  MathWorks Fellowship  to S. R. ; and by grants from the  Patrick J. McGovern Foundation  and the  Guggenheim Foundation  to R. S.
Briefly, a solution of 15 N-AA (98 ‰ 15 N algal amino acid mix, Cambridge Isotopes), calculated as approximately 20% of the original free amino acid concentration, was added to two aliquots of fresh soil (stopped 15 min and 45 min after tracer addition and extracted with 1 M KCl). In the extracts, ammonium was removed  (Noll, Zhang, & Wanek, 2019)  and amino acids were converted to N 2 O gas, then analysed by purge-and-trap isotope ratio mass spectrometry  (Lachouani et al. , 2010; Noll, Zhang, & Wanek, 2019). Using the change of 15 N: 14 N amino acids over time, soil organic N cycling rates were calculated  (Kirkham & Bartholomew, 1954). All statistical analyses were done using R software (version 4. 1. 0). Linear mixed effects models from the 'nlme' R package  (Pinheiro & Bates, 2020)  were used to assess the effect of tree species composition (birch, pine, mixed birch-pine), water availability (low, high), and their interaction on the measured variables (rows in Table  1 ).
Superblock was included as a random effect, in which plot identity was nested. Variance homogeneity and normality of the distribution of residuals were verified visually. Biomass-specific rates were log-transformed to satisfy model assumptions. Pearson's correlations were run separately for each water availability treatment (Figure  2  and Figure  S4 ). For further site, analytical, and statistical details please refer to the Supplementary Information S1, published code  (Maxwell et al. , 2023) , and to  Maxwell (2021).
ACKNOWLEDGEMENTS First, we would like to thank  Bastien Castagneyrol ,  Hervé Jactel ,  Bernard Issenhuth , and the  UEFP team  for maintenance of the ORPHEE experiment. We thank  Sylvie Niollet  and  Pierre Pujos  for help in the field, along with  Léa Danies ,  Léa Delmas ,  Sylvie Milin , and  Maëva Pastoret  for help in the laboratory in Bordeaux, and  Michaela Bachmann  and  Grete Watzka  for help in the laboratory in Vienna. We thank  Pierre Trichet  for providing precipitation data from the XyloSylve platform. This work was supported by the  Agence Nationale de la Recherche (ANR) ,  France  project  DiPTiCC  ( 16-CE32-0003 ), the ' Thèses en partenariat international ' project,  Excellence Initiative ,  Université de Bordeaux, France  to  Tania L. Maxwell , and the  WTZ Austria/Campus France PHC  Amadeaus  2020-2021   InTEGRaTE  project ( 44082TM ) to  Laurent Augusto ,  Tania L.
| Data and methods Guided by previous large-scale mapping of SMCs beneath the FIS and LIS  (Dewald et al. , 2022; Lewington et al. , 2020; McMartin et al. , 2021; Vérité et al. , 2022) , our approach is to study the distribution and assemblages of subglacial bedforms found within and in between SMCs, with a focus on the spatial transitions between the different types of bedforms. This involves mapping, morphometric description and interpretation of bedforms within portions of SMCs across northern Canada and Fennoscandia selected to capture the most exhaustive range of SMCs features (Figure  1 ). High-resolution DEMs (2-m in spatial resolution), including national LiDAR datasets in Sweden (https://www. lantmateriet. se) and Finland (https://www. maanmittauslaitos. fi/en) and the ArcticDEM mosaic v7  (Porter et al.