dataesr/scientific-paragraphs-categorization huggingface dataset:
Adding a new crop If a crop is to be added to the list of 53 existing crops in floodam. agri, two options should be considered. First, it is necessary to determine whether the crop can be assigned to a vulnerability category. If so, it is necessary to calibrate the damaging and action functions with the physiological stages, crop management sequence, yield and price of the crop. If not, it will be necessary to create a new crop category (step 5 on figure  8 ) and to develop new damaging and action function. For this, data collection from agricultural experts will be necessary. Moreover, agroeconomic data will have to be collected to calibrate the functions.
T2 : Has the model been transferred to another context ? To date, some adjustments have been done to adjust resolutions (step 1) or to adjust local data (step 2) in the frame of the mandatory CBA of flood management projects. In  Mao (2019) , an adaptation a flood damage functions has been done at regional level (step 2) using regional data. Work is underway to adapt floodam. agri to coastal flooding (step 6).
agri is a standard behavior. Collecting data from agricultural experts who have witnessed flooding on a large number of farms allows us to model this standard behavior. However, we must be aware and vigilant that this average view does not reflect the diversity of individual vulnerability situations at the farm level. Thus, at the individual scale, decisions, especially those concerning long term issues such as replanting, will depend on individual parameters such as investment dynamics, the age of the farm manager, the farm's trajectory. Furthermore, floodam. agri does not take into account adaptation decisions that could be made at the time of reclamation, such as changing the crop. Understanding the internal and external determinants of adaptation implementation would require a different approach and investigation at the individual level.
Capitalise over time with updatability The proposed methodological framework requires the specification of all the data used, their source and their vintage. This makes it possible to consider updating the models produced for a given context over time. This is the case, for the damage functions produced thanks to floodam. agri. On the other hand, this effort makes it possible to consider the transfer by comparison of existing databases from one context to another. A difficulty persists for data that are not tracked over time, and in this case we recommend either updating the data on the basis of expert opinions, or using a discount rate whose value must be specified.
g. related to fishing resources and social communityrelated aspects. However, to date, little published research has targeted the uses and management of reef passages by local authorities including coastal communities, or has looked at which marine animals actually dwell in, visit or transit through these spaces, across temporal scales (with valuable exceptions, e. g. by  Johannes and Hviding, 2000; Aswani and Hamilton, 2004). As a first step, we will screen and characterize reef passages based on their physiography using satellite data, to develop a typology of reef passages, which can then be built on to assess how the different functions and roles of reef passages may vary according to their geographical and other characteristics (e. g. distance from coastline). Using remote sensing data does not require ground-truthing, which is relevant for ocean areas with many islands and reefs scattered over large areas (e.
METHODS This work focused on nine islands of different sizes and shapes in the Southwest Pacific, and belonging to the archipelagoes of Fiji, New Caledonia and Vanuatu (Table  1 ), study sites of the project SOCPacific (A Sea of Connections; socpacific. net; Fache and Breckwoldt, 2019). A number of the project scientists, and coauthors of this Brief Research Report, have field expertise on at least one of these islands, and presumably, rich local knowledge exists here on the importance of reef passages (e. g. Sadovy de  Mitcheson, 2011; Gonson et al. , 2017; Mangubhai et al. , 2019. The locations of reef passages were determined using satellite imagery and GIS Software (QGIS version 3. 16 1  ). Criteria relevant to develop a first typology of reef passages were derived from GIS-based visual interpretations of satellite imagery (Figure  S1 , Table  S1 , see Supplementary Material).
Island outlines from the Database of Global Administrative Boundaries (GADM) and basemaps within QGIS were used for visualization and analyses. Consistent with the definition of reef passages in the Introduction, these were identified as being all clearly visible breaks in the reefs surrounding each of the islands or island lagoons that could be detected and located by visual interpretation of Google Earth imagery. Their locations were then exported from GoogleEarth in KMZ format and imported as shapefiles into QGIS. QGIS maps were created to display the distribution of the identified passages on the islands. The relevant island outlines were then selected from GADM data and measured. This allowed for comparing the number of reef passages around an island. The minimal width or minimum distance between the two sides of a passage was measured using the QGIS ruler tool and high resolution satellite imagery basemaps (Google Satellite Hybrid and ESRI Satellite tiles).
In QGIS, the native tools, integrated SAGA tools, QuickMapServices and Point Sampling Tool plugins were used. Apart from passage types, the minimal width of the passage and its calculated distance to the nearest coastline were added as attributes to the database  [Nozik et al. (2022)  to PANGAEA]. The complete attribute table of the reef passage shapefile was then imported into R (version 3. 5. 3. , www. r-project. org), to plot parameters and their relationships and to compute descriptive statistics.
Equally important for characterizing a specific passage will be measurements of both its maximum depth and the depth of its respective edges (e. g. for those within submerged barrier reefs), as well as passage length. However, due to a lack of resolution, there are no reliable estimates for passage depth relative to reef depth in global satellite datasets. Many shelf systems may have a barrier reef on the outside that is largely submerged -and breaks within these sections of reef should also be considered a passage. So far, only clearly visible features of passages surrounded by shallow reefs were included. With the widths of passages, their depths, and their (relative) length, hydrodynamic features such as speed (cf. tidal amplitude), amount and (relative) temperature of water passing (in both ways) could be used to refine the typology as a next step.
The nine study locations of this report, ranked by size (island surface area), with length of coastline, number of reef passages identified by visual interpretation of satellite imagery, density of reef passages per 100 km coastline, and dominant reef passage type(s); in New Caledonia (NC), Vanuatu (VU), and Fiji (FJ). Source for 'Surface area', 'Coastline length' and 'Density of reef passages per 100km coastline' values (calculated in QGIS): GADM database (gadm. org).
ACKNOWLEDGMENTS This paper is based on research conducted as part of the project ' A Sea of Connections: Contextualizing Fisheries in the South Pacific Region ' (SOCPacific, https://socpacific. net/),co-funded by the  Agence Nationale de la Recherche  (grant number  ANR-17-FRAL-0001-01 ) and the  Deutsche Forschungsgemeinschaft  (grant number  389654580 ). We are especially grateful for the valuable knowledge shared by all co-authors in this study. This work contributes to Future Earth Coasts, a  Global Research Project of Future Earth.
The spectra were recorded at room temperature, in the range of 500-4000 cm -1 with a resolution of 2 cm -1 and, evaluated by Agilent software. Diffraction data of the EPS gold sensor membrane was collected with an X-ray diffractometer CICECO Empyrean (JDX 3532; Japan) equipped with CuKα radiation (λ = 1. 54184A), a 4-circle kappa goniometer, and a CCD Detector. Data collection and processing were carried out using CrysAl-isPro Software System. The diffraction angle range of observation was 5° to 50°.
The frequency was scanned in a range of [10 -3 Hz, 10 6 Hz]. Many Factors induce destabilization of the response of the cell. A delay time is always needed to achieve a steady state. This stabilization was detected by the reproducibility of the response. In our case, a delay of 90 min was sufficient to minimize the drift in the system. The response of the EIS was investigated by Nyquist diagrams according to the microplastics. All our measurements were done at the negative bias of -0. 3 V which allows a great definition of the Nyquist plot and is not too high to induce the corrosion phenomenon. The equivalent circuit parameters for the electrolyte interface of the functionalized surface of EPS were directly deduced from the experimental data by applying the software Nova Autolab 1. 5, after testing several models of the equivalent circuit.
Functional traits offer a rich quantitative framework for developing and testing theories in evolutionary biology, ecology and ecosystem science. However, the potential of functional traits to drive theoretical advances and refine models of global change can only be fully realised when species-level information is complete. Here we present the AVONET dataset containing comprehensive functional trait data for all birds, including six ecological variables, 11 continuous morphological traits, and information on range size and location. Raw morphological measurements are presented from 90,020 individuals of 11,009 extant bird species sampled from 181 countries. These data are also summarised as species averages in three taxonomic formats, allowing integration with a global phylogeny, geographical range maps, IUCN Red List data and the eBird citizen science database.
International initiatives for data sharing and synthesis are relatively advanced for plants, and plant trait data are therefore readily available to the scientific community at a global scale  (Kattge et al. , 2020). However, while these datasets have provided insightful tests of theory, including the mechanisms underlying community assembly  (Mayfield & Levine, 2010) , species coexistence  (Kraft et al. , 2008)  and the scaling from traits to ecosystems  (Enquist et al. , 2015) , it is difficult to know whether these results can be applied more generally to non-plant systems. The same caveat applies to many fundamental trait-based concepts designed from a plants-only perspective  (Suding et al. , 2008). Plant functional ecology has flourished, yet trait datasets of plants are nonetheless patchy in terms of species and trait coverage at a global scale.
Out of roughly 352,000 flowering plant species, the TRY database currently contains fewer than 60,000 species (17%) with 10 or more traits  (Kattge et al. , 2020). This creates problems for analytical approaches that assume coverage is complete, including phylogenetic comparative analyses and evolutionary models. Missing species in partially sampled trait datasets can radically alter the trait structure of communities and the fit of evolutionary models, reducing predictive power and restricting studies to a biased sample of well-known clades  (Kohli & Jarzyna, 2021; Weiss & Ray, 2019). The main obstacle to the completion of species sampling for plant traits is the sheer diversity of plants themselves. One solution for the next generation of trait-based models in ecology and evolutionary biology is to switch some attention to vertebrate clades containing a more manageable number of species.
In any case, a catch-up phase for animal trait databases is a high priority because this would open up research into a range of novel questions. For example, integration across animal and plant trait data may provide the key to understanding complex trophic interaction networks at the heart of ecological processes, such as nutrient and energy transfer, seed dispersal, pollination and predation  (Bartomeus et al. , 2016; Harfoot et al. , 2014; Schleuning et al. , 2015).
A brief history of animal functional ecology To date, the most prominent species-level trait datasets published for animals at a global scale-PanTHERIA  (Jones et al. , 2009) , EltonTraits  (Wilman et al. , 2014)  and the amniote database  (Myhrvold et al. , 2015) -have focused mainly on life history and ecological traits for the world's mammals, birds and reptiles. These datasets have been highly influential, yet the only complete continuous morphological trait data presented in each case is body mass. Body mass has long been the mainstay of ecological and evolutionary research, but it provides only limited information about ecological niches and trophic interactions  (Bender et al. , 2018; Pigot et al. , 2020). Moving beyond body mass in animal functional ecology has proved challenging, with most progress made in the validation and compilation of avian traits  (Tobias et al.
, 2016; Dehling et al. , 2016; Pigot et al. , 2016). All datasets presenting compilations of avian morphological traits were similarly restricted in geographical or taxonomic sampling until 2017 (Figure  1 ). Since then, a series of global analyses (e. g. Chira et al. , 2018 Chira et al. , , 2020;; Cooney et al. , 2017)  have included data from many more species, but relatively few measurements per species of a limited number of traits (Figure  1 ). The recent publication of macroevolutionary analyses by Pigot et al. (2020) marked another step-change in scale, with comprehensive species-level sampling and deeper individual sampling of multiple morphological traits. However, all these datasets have limited utility for research because they present measurements summarised as principal components aligned with the BirdTree taxonomy  (Jetz et al.
, 2012). In this paper, we provide a global overview of the AVONET database, a compilation of individual-level trait measurements for all the world's bird species. AVONET contains the raw trait data used by  Pigot et al. (2020) , focusing on the same set of phenotypic traits with well-established connections to diet, dispersal and locomotion  (Pigot et al. , 2016 (Pigot et al. , , 2020;; Sheard et al. , 2020 ; Figure  2 , Supplementary Dataset 1). To improve intraspecific sampling, we added measurements taken from a further 37,150 individual birds-a 71% increase (Figure  1 ). To improve interoperability with external datasets, we also averaged all traits at the species level according to three alternative taxonomic treatments-BirdLife International, eBird and BirdTree (Supplementary Dataset 1).
We hope this removes a major obstacle to future analyses, allowing integration with IUCN Red List data and geographical range maps, as well as eBird citizen science data  (Sullivan et al. , 2014)  and the global bird phylogeny  (Jetz et al. , 2012). The AVONET database represents a collaborative effort by specimen collectors, museum workers and field ornithologists over many decades. In the spirit of the Open Traits network  (Gallagher et al. , 2020) , the data are released for use by the wider research community, in conjunction with additional information describing the ecology and geographical context of all bird species.
M AT ER I A L S A N D M ET HOD S Morphological traits External morphological traits were measured from live individuals and preserved museum skins. For each individual, we measured nine traits (generally to the nearest 0. 1 mm): four beak measurements, three wing measurements, tarsus length and tail length (see Figure  2  and Supplementary Dataset 1 for details). Traits were selected for the information they provide about ecological niches. The beak is the primary apparatus used by birds to capture and process food, while morphological differences in wings, tails and legs are related to locomotion, providing insight into the way birds move through their environment and forage for resources  (Leisler & Winkler, 1985; Miles & Ricklefs, 1984; Pigot et al. , 2020; Ricklefs & Travis, 1980). We targeted four individuals (two of each sex) as our minimum sample wherever possible.
Further data for these traits were added by merging independent datasets, excluding data measured in a substantially different way. The final version contains measurements from 90,020 individual birds with an average of 8. 1-9. 0 individuals per species (variable depending on taxonomy; Table  S1 ). All raw data and species averages are provided in Supplementary Dataset 1. F I G U R E 2 In addition to direct measurements, we also calculated the hand-wing index (HWI), a metric of flight efficiency and dispersal ability in birds  (Claramunt, 2021). Although a global HWI dataset following BirdTree taxonomy has been published previously  (Sheard et al. , 2020) , we update HWI scores to reflect additional sampling under the BirdTree taxomomy, and provide species averages aligned with BirdLife and eBird species lists.
Finally, we also update published body mass data for 1985 species, either by inferring from close relatives or, in the case of 716 species, from literature, fieldwork and museum specimen labels (Supplementary Material). In many of these cases, previously published mass data had been inferred with low accuracy on the basis of predictive models  (Wilman et al. , 2014) , so AVONET provides a much-improved body mass dataset for the world's birds.
Data curation We included a series of checks to identify and remove errors before merging trait datasets (Supplementary Material). To assess the potential influence of observer biases, we collected duplicate measurements from 4799 individual specimens of 3421 species measured by 64 different people. We then used mixed effects models to assess concordance between independent measures of the same trait. To assess whether trait data were significantly different in museum specimens versus live samples, we compared measurements for 962 species with both types of data.
Taxonomic classification Taxonomic classification is in constant flux, particularly in birds  (Garnett & Christidis, 2017) , causing major problems for compilers and users of functional trait datasets. To facilitate integrative analyses, we average our trait data according to three different taxonomic treatments differing by roughly 1000 species: BirdLife International (2020), 10,999 species; eBird  (Clements et al. , 2021) , 10,661 species; and BirdTree  (Jetz et al. , 2012) , 9993 species. Most extant BirdLife species (n = 8949, 81. 4%) are one-to-one matches with BirdTree species. This leaves roughly a fifth of species with imperfect matches across these two datasets. In many cases, BirdTree species have been split into multiple BirdLife species, such that traits are averaged across smaller numbers of specimens-per-taxon, with specimens reallocated among newly split species.
Geographical sampling All individual birds measured by field teams were assigned to the country in which sampling localities were situated. For museum data, we identified the country where each specimen was collected based on the label. For specimens with no locality data, we assigned them to their respective countries in the case of single-country endemics. Where possible, we matched the transcription with country names and boundary data aligned with country shapefiles published by the World Resources Institute (https://github. com/wri/wri-bounds, accessed on 14/10/2020). Further details of methods for identifying localities and assigning specimens to countries are given in Supplementary Material.
This is a separate dimension to diet inasmuch as species eating fish may be aquatic (e. g. pelican), aerial (e. g. tern), terrestrial (e. g. heron) or insessorial (e. g. kingfisher). Insessorial denotes a perching lifestyle, including arboreal species, but also any species habitually perching on other substrates, including cliffs or manmade structures. Further explanations of all ecological categories are given as metadata (Supplementary Dataset 1).
Biogeographical variables For the BirdLife dataset, species geographical range maps were obtained from Birdlife International. We selected breeding and resident ranges in areas where the species is coded as extant and either native or reintroduced. Mapping species under the BirdTree taxonomy dataset often required the combination of maps for multiple BirdLife species to form an expanded range map for a single BirdTree species (Supplementary Dataset 1). We did not generate new maps for the eBird dataset which can be integrated directly with citizen-science locality data and a suite of spatial research tools  (Sullivan et al. , 2014). Using BirdLife and BirdTree maps, we extracted range size (km 2 ), longitude (of range centroid) and three latitudinal variables (maximum, minimum and centroid).
Repeatability of morphological measurements When all repeated trait measurements were pooled regardless of measurement protocol used, data were highly concordant among individual measurers (Figures  S1  and S2 ), indicating that different methods produce comparable measurements of the traits included in our dataset. Repeatability (R) scores differed across traits (GLM: F = 523,963, Df = 7, p < 0. 0001; Table  S4 ) suggesting that some trait measurements are more repeatable than others. For example, estimated variation in beak width was relatively high (6. 6%; Figure  S1 ), suggesting low repeatability arising from the use of different trait definitions by contributors (see Supplementary Material). Nonetheless, the overall concordance of trait data collected by different measurers was high for all traits (R = 0.
Species coverage Discounting duplicate measurements, we compiled morphological trait data from 90,020 individual birds, including previously unpublished raw data from 75,287 museum specimens and 11,424 living individuals. We added further measurements from 556 museum specimens and 2753 living individuals published in previous data papers (Supplementary Dataset 1). Overall, morphological trait sampling was conducted by 153 researchers, of which 88 used the AVONET protocol (see Supplementary Material). In addition, we integrated several unpublished trait datasets including morphological traits of 37,150 individual birds measured using alternative protocols by 65 researchers. In total, 99. 4% (n = 89,434) of measured individual birds were included in all three datasets (BirdLife, eBird and BirdTree), with the remaining 0.
6% (n = 586) appearing in 1-2 datasets. The AVONET database greatly increases coverage and availability of morphological trait data for birds in comparison with previous studies (Figure  1 ; Table  S1 ). All morphological traits are now sampled for 95. 7-96. 8% species under BirdLife; 96. 4-97. 1% under eBird; and 99. 4-99. 7% species under BirdTree (percentages varying by trait; see Table  S2 ). Regardless of taxonomy, 100% of avian families are sampled, with species-level sampling near-complete for most families, particularly for the BirdTree dataset (Table  S1 ; Figure  3 ). The few remaining undersampled families are either small (low species richness), particularly when subject to recent taxonomic splitting (e. g. ostriches), or difficult to collect (e. g. owls; Figure  3 ; Table  S3 ).
At the species level, sampling was uneven according to specimen availability, with some common species sampled intensively and rarer species falling short of the targeted four individuals (Figure  3 ). The AVONET dataset currently lacks direct measurements of any trait for 351 species under BirdLife, 308 species under eBird and 26 species under BirdTree. An additional 56-193 species have data missing for one or more traits, depending on the taxonomy used (Table  S3 ). We fill these gaps using inference by identifying the closest relative with the most similar ecology and morphology (often this was the parent species in the case of daughter species arising from taxonomic splits). We highlight inferred traits and the surrogate species from which inferences were made (Supplementary Dataset 1) to allow future users to decide whether to include inferred trait data.