dataesr/scientific-paragraphs-categorization huggingface dataset:
Funding This work was supported by the  Institut Pasteur; Center for Research and Interdisciplinarity (CRI) ,  Centre National de la Recherche Scientifique (CNRS) ; the  University Paris Diderot ; the  Fondation pour la Recherche Médicale  [DBI20141231310]; the  European Commission Horizon 2020  [COSYN]; The  Human Brain  Project; the  European Commission Innovative Medicines Initiative  [ AIMS2-TRIALS ; No. 777394 ]; the Cognacq-Jay foundation; the Bettencourt-Schueller foundation; the Orange foundation; the FondaMental foundation; the Conny-Maeva foundation; and the  Agence Nationale de la Recherche (ANR)  [SynPathy]; the  Laboratory of Excellence GENMED (Medical Genomics)  grant no. ANR-10-LABX-0013 ,  Bio-Psy ; by the  INCEPTION program   ANR-16-CONV-0005 , managed by the  ANR part of the Investment for the Future program.
J. -B. P. was partially funded by  NIH-NIBIB   P41 EB019936 (ReproNim) NIH-NIMH R01 MH083320  (CANDIShare) and  NIH   5U24 DA039832  (NIF), as well as the  Canada First Research Excellence Fund , awarded to  McGill University for the Healthy Brains for Healthy Lives initiative. The Study of Health in Pomerania (SHIP) is part of the  Community Medicine Research net (CMR)  (http://www. medizin. uni-greifswald. de/icm) of the  University Medicine Greifswald , which is supported by the  German Federal State of Mecklenburg -West Pomerania. MRI scans in SHIP and SHIP-TREND have been supported by a joint grant from  Siemens Healthineers, Erlangen, Germany  and the Federal State of 29.
Shaded area corresponds to 5%-95% confidence regions. All data are from CNRM-CM5, including all runs available from this model (10 historical, 5 RCP8. 5). year-to-year independence and therefore does not account for potential interannual correlation. d Nonparametric distributions, assuming that the quantile of order a at time t, q a (t), satisfies q a (t) 5 m a 0 1 m a 1 X all (t): (9) In this case, the parameters g 5 (m a 0 , m a 1 ) can be estimated, for a collection of a, using quantile regression  (Koenker and Bassett 1978; Koenker and Hallock 2001). Given typical sample sizes (a few hundreds to thousands of data) and the computational cost of resampling, a fast algorithm is needed, and we used the Frisch-Newton approach after preprocessing (see  Portnoy et al. 1997 , implemented in R under ''pfn'').
2. Fit of a nonstationary Gaussian distribution. Illustration of the nonstationary fit for data from the CNRM-CM5 model, assuming Gaussian distribution. Black points: data [X all (t), Y t ], where X all (t) has been estimated following section 3a, and Y t is the raw Y data, that is, August mean surface air temperatures from CNRM-CM5 for the 58 3 58 grid square that includes Paris. Several simulations are considered, leading to several values of Y t at each time t. Many points lie in the bottom-left corner, corresponding to the quasi-stationary climate of the period before 1980. Red lines: change in the mean of Y t (solid) or 5%-95% quantiles (dashed lines) as estimated from the nonstationary fit. magnitude of the event (now defined by its occurrence probability) increases by about 68C. These diagnostics suggest that recent changes are large in terms of probability ratios (PR near 10 in 2003) while remaining limited in terms of magnitude  (near 18C in 2003)  in the CNRM-CM5 model.
Acknowledgments. We acknowledge  Geert Jan van Oldenborgh ,  Francis Zwiers , and one anonymous reviewer for their constructive and insightful comments on the previous versions of this paper. Part of this work was supported by the  French Ministère de la Transition Énergétique et Solidaire (through Extremoscope and Convention pour les Services Climatiques grants) , the  European Research Area for Climate Services  (through the  Eupheme  project, Grant  690462 ), and the  French National Research Agency (ANR , through the  REMEMBER  project, Contract  ANR-12-SENV-001 ). The authors are supported by  Météo France (AR) ,  CEA (ST) , and  CNRS (JC). The R scripts used to make the analysis described in this paper will be made available on the personal worldwide web page of author  Aurélien Ribes.
Data availability statement: All data used in this study are publicly available. 1) HadCRUT4 observations are available at https://crudata. uea. ac. uk/cru/ data/temperature/. 2) Outputs of CMIP5 simulations are available at https://esgf-node. llnl. gov/projects/cmip5/. Parameters of the EBMs were taken from  Geoffroy et al. (2013). Time series of natural forcings used to compute EBMs response were taken from IPCC AR5, Annex II (IPCC 2013).
Aquatic ecologists face challenges in identifying the general rules of the functioning of ecosystems. A common framework, including freshwater, marine, benthic, and pelagic ecologists, is needed to bridge communication gaps and foster knowledge sharing. This framework should transcend local specificities and taxonomy in order to provide a common ground and shareable tools to address common scientific challenges. Here, we advocate the use of functional trait-based approaches (FTBAs) for aquatic ecologists and propose concrete paths to go forward. Firstly, we propose to unify existing definitions in FTBAs to adopt a common language. Secondly, we list the numerous databases referencing functional traits for aquatic organisms. Thirdly, we present a synthesis on traditional as well as recent promising methods for the study of aquatic functional traits, including imaging and genomics.
g. , size in  Andersen et al. (2016)  or stoichiometric traits in  Meunier et al. (2017)). A network analysis of key words associated with the aquatic trait-based literature highlights differences between studies, both in the terminology used to characterize traits and in the application of trait-based approaches in studies of freshwater and marine systems (Fig. S1 ). The goal of this review is to facilitate exchanges of functional trait-based approaches (FTBAs) and their products across different aquatic fields. To do so, we propose (1) a table compiling the main definitions of traits that are commonly used in trait-based studies, in addition to recommendations for using a common and unambiguous vocabulary; (2) a unified typology of 40 aquatic functional traits that could be used in multicompartment studies (including several biological compartments, or different habitats, for example, sediment and water); (3) a summary of existing databases that contain information on functional traits; (4) a review of traditional and emerging methods for estimating and using traits of aquatic organisms; and (5) The main challenges that aquatic ecologists can now address using FTBAs and that should inspire future studies.
Adopting common definitions for aquatic FTBAs Trait definitions vary between scientific communities, from the individual organism (e. g. , life-history traits) to the population (e. g. , demographic traits), community (e. g. , response traits), and the ecosystem scale (e. g. , effect traits;  Hébert et al. 2017). Traits can also be directly measured in situ (e. g. , realized traits) or inferred from the literature (e. g. , potential traits). Realized traits are ultimately one of the sources for potential traits found in databases (see section "Imaging and acoustic techniques"). To establish a unified framework and avoid subjectivity in these definitions, we recommend the use of the definitions that focus on the individual level only. These are the ones proposed by  Violle et al. (2007) , by  Litchman and Klausmeier (2008) , and by  Reu et al.
These traits are water content, color, breeding type, life cycle, life span, diapause, reproduction strategy, salinity preference/tolerance, chemical compounds for mating or detecting congeners, diet/food preference, allochemical compounds, dissemination potential, substrate relation (plankton/benthos, including substrate specific relation for benthos), ecosystem engineering, including bioturbation/irrigation for benthos, and finally perception/production of sounds. Most of the 40 functional traits presented in this typology can be estimated quantitatively  (Costello et al. 2015) , making them good candidates for comparative studies. In addition, a dynamic representation of this typology is proposed as an online mental map (http://doi. org/10. 5281/zenodo. 3635898), which links to associated research articles.
Empirical studies of traits as a source for trait databases The investigation of functional traits has been largely based on empirical studies. Such studies rely on three complementary approaches that can be described by (1) measurements of traits in situ, (2) measurements of traits under controlled laboratory conditions, and (3) metadata analyses of databases and literature (Fig. 2A ). The metadata approach has been undoubtedly the most developed across aquatic ecosystems  (Degen et al. 2018; Kiørboe et al. 2018 ) and the literature has been the basis of a number of reviews describing functional traits. For example, in freshwater ecology,  Kolkwitz and Marsson (1909)  pioneered a compilation of types of organisms in relation to the presence of various pollution levels. In marine ecosystems, metadata compilations allowed mapping of key traits of marine copepods at a global scale and evaluation of their relationships with environmental conditions  (Brun et al.
This technique of coding is robust enough to compensate for different types and levels of information available for different taxa. In recent years, numerous open-access databases recording functional traits have been developed to document traits included in existing databases (Table  2 ). This diversity of databases gathers trait information not only for widely studied traits (e. g. , body size or feeding strategy), but also for less common traits or for those that are more difficult to measure (e. g. , age at first reproduction, migration mode, or nutrient affinities). Some large trait databases were published online and open access (e. g. ,  Herring 1987; Barnett et al. 2007; Benedetti 2015; Hébert et al. 2016; Degen and Faulwetter 2019) , thus allowing for follow-up studies that compare and merge trait data across taxa, species, and environments.
In some instances (e. g. , freshwater invertebrates), published databases rapidly became foundational for environmental assessment procedures (e. g. ,  Mondy et al. 2012; Mondy and Usseglio-Polatera 2013; Larras et al. 2017). The main caveat of these FTBAs is that only a limited number of species and/or traits have been reported so far, thus not yet allowing for a generalization of findings across taxa, the definition of fitness landscapes, and/or the characterization of ecological niches or responses to environmental change. Indeed, these databases often focus on the dominant and most easily sampled or cultivable species. Moreover, metadata associated with trait measurement methods are usually lacking. Until now, shortfalls in the knowledge of many aquatic taxa  (Troudet et al. 2017)  restrict the application of trait databases at the community scale and remain a limiting factor for the integration of FTBAs into macroecology  (Tyler et al.
2012; Borgy et al. 2017 ). However, the main limit so far to provide and share trait data remains the lack of an ecological standard for data  (Schneider et al. 2018). Attempts to increase unification are currently emerging on various fronts such as the terminology of traits (e. g. ,  Schmera et al. 2015  for stream ecology), the cross-taxa compatibility of functional traits (e. g. , Weiss and Ray 2019 for plants and animals) or the actual measurements of such traits (e. g. ,  Moretti et al. 2017  for terrestrial invertebrates). Large efforts are still needed to combine and integrate all these various trait databases  (Degen et al. 2018 ), but applying Open Science principles should accelerate trait-based science (see, e. g. , the Open Traits Network initiative,  Gallagher et al. 2019). Such databases are already numerous, large-sized, and of increasing complexity.
Therefore, their manipulation requires strong computational abilities  (Durden et al. 2017 ). As a result, aquatic research is evolving into a more biostatistical-and bioinformatical-based field, enabling the extraction of largescale information on traits and putting to full use taxonomic surveys recorded over time. Despite this, naturalist taxonomic knowledge per se remains critical and future challenges in ecology will undoubtedly benefit from a combination of modern FTBAs and a modern integrative taxonomic knowledge. The traits documented by these databases originate from direct measurements of realized traits in the laboratory or in situ (Fig. 2A ). Laboratory experiments allow for the quantification of functional traits of model species within a large range of controlled environmental conditions.
2011; Valentini et al. 2016 ) can be used as a first step for fast and automatic taxon recognition, prior to the attribution of traits to the respective taxa using trait databases (Fig. 2C ; Table  2 ). This was recently done to describe the biogeography of mixotrophic traits of marine protists at global scale  (Faure et al. 2019) , or to estimate the functional diversity of coastal protist communities  (Ramond et al. 2019). In freshwater systems, metabarcoding of benthic diatoms was used to assess the water quality status of rivers  (Vasselon et al. 2017)  and metabarcoding was combined with text-mining or phylogenetic inference of ecological profiles and traits for biomonitoring  (Compson et al. 2018; Keck et al. 2018 ). Yet, metabarcoding is inherently biased in multiple ways, such as its lack of quantitative link between the number of copies of barcodes (targeted DNA sequences) and the biomass or abundance distribution of organisms, the risk of gene amplification from dead material (not currently influencing ecosystem function), or the use of universal barcodes that may not be adapted to distinguish taxa for all lineages (e.
g. ,  Deiner et al. 2017 ). However, the main obstacle to using metabarcoding data for FTBAs is the low number of taxa for which barcodes have been documented (in addition to the low number of taxa for which trait information is available). This limitation precludes a full assessment of ecosystem structure from metabarcoding (e. g. ,  de Vargas et al. 2015; Le Bescot et al. 2016). Thus, a strong effort remains to be made to supplement existing genomic databases with more taxonomically referenced sequences and trait information to allow the metabarcodingbased monitoring of aquatic functional traits (e. g. ,  Ramond et   Beyond metabarcoding, -omics approaches are of particular interest to identify or measure functional traits linked to metabolic pathways (e. g. , photosynthesis, nitrification, diazotrophy, calcification, etc.
), using either (meta-)genomic or (meta-)transcriptomic approaches (Fig. 2C ). When combined with databases like Kyoto Encyclopedia of Genes and Genomes or KEGG  (Kanehisa and Goto 2000) , which includes the genes (for genomics/transcriptomics), proteins (for proteomics), and metabolites (for metabolomics) implied in a specific pathway, -omics approaches open up the possibility of monitoring functional traits (defined at the individual level) across different levels of biological organization (from organisms to communities). For example, approaches that report the expression level of genes, proteins, and metabolites are increasingly used in ecotoxicology to assess functional traits (e. g. , photosynthesis, chemical degradation) in response to stressor(s) via targeted approaches (e. g. , q-PCR on preidentified candidate genes,  Pesce et al.
3. Main opportunities for trait-based approaches in aquatic ecology. These four opportunities are described in section "Future opportunities for aquatic FTBAs. " taxa according to the speed at which they are able to take up nutrients and invest in leaf biomass  (Wright et al. 2004). In this vein, some work was done with aquatic organisms by considering trade-offs among lotic insects (e. g. ,  Poff et al. 2006) , fishes (e. g. ,  Winemiller et al. 2015) , and phytoplankton (e. g. ,  Edwards et al. 2013a). By considering trait syndromes, FTBAs are likely to better predict competitive outcomes as well as distributions of traits across environmental gradients. We thus encourage the aquatic ecology community to engage with the vast array of accessible trait databases provided in Table  2  and to take the next steps to characterize trait syndromes across the different groups of aquatic organisms.
2016a). More recently, the study of taxonomic and functional diversity of fish communities between two different regions (Caribbean and Great Barrier Reef) and among three habitats (coral reef, seagrass, and mangrove) revealed that traits and functional groups varied among habitats, whereas taxonomic composition varied between regions  (Hemingson and Bellwood 2018). Similar relationships should now be tested across ecosystems, geographical regions and trophic levels to verify whether these findings can be generalized to other aquatic organisms/ecosystems (Fig. 3 (2)). The trait databases now available for many groups of aquatic organisms (see Table  2 ) should provide relevant information to explore this direction.
Trait response to global changes Temporal dynamics of traits and their response to climate change FTBAs can be used to estimate the temporal response of aquatic organisms and ecosystems to environmental forcing (Fig. 3 (3)). For example, functional traits have been shown to explain community structure and seasonal dynamics of marine phytoplankton  (Edwards et al. 2013b). It is also possible to combine classical data sets, and especially time series of species abundance, with trait databases described at the species level (see section "Empirical studies of traits as a source for trait databases") to apply a FTBA to in situ observations and/or monitoring data sets previously collected. In such reanalyses, key traits could be targeted (see section "Going further towards a trait-based aquatic ecology by identifying key traits") to compare their temporal changes, identify tipping points, and reveal trade-offs among traits.
2019). Conversely, climate change may have contrasting impacts on stream fishes  (Buisson and Grenouillet 2009)  or decrease their functional diversity  (Buisson et al. 2013). By combining climate change scenario modeling with species distribution modeling and functional trait databases, the impact of climate change on the functional diversity of aquatic ecosystems can be assessed at broader scales and across biological compartments and ecosystem boundaries. For aquatic insects, such a combined modeling-FTBA study revealed the spatial patterns of vulnerability to climate change, which also opens opportunities for biomonitoring  (Conti et al. 2014 ). However, limitations remain in the use of trait-based approaches for the assessment of the effects of multiple stressors in the context of climate change, as emphasized recently by  (Hamilton et al.
Trait-based biomonitoring Traditionally, the ecological health or "good environmental status" of aquatic ecosystems has been assessed in terms of species composition or relative abundance/biomass of specific indicators, initially within the context of the European Water Framework Directive (WFD-2000/60/CE). However, trait-based approaches offer new opportunities for the monitoring of aquatic ecosystems  (Culp et al. 2011) , since they can provide new tools that transcend taxonomical denomination, directly related to ecological functions, and exploit the traits available in open databases  (Usseglio-Polatera et al. 2000a,b; Baird et al. 2011 , see also Table  2 ). To date, trait-based biomonitoring has been mainly applied to freshwater ecosystems (cf. the corresponding cluster in Fig. S1 ).
This observation is explained by a high demand for P-rich ribosomal RNA, but also by the shorter lifespan of faster growing organisms, which prevents large investments into reserve structures  (Elser et al. 1996 (Elser et al. , 2003)). Consumers stoichiometry, in addition to metabolic characteristics, also gives important information on consumers driven nutrient recycling (Allen and Gillooly 2009). Better documenting the stoichiometric ratios of aquatic organisms in existing trait databases would help to identify their drivers and thus improve our understanding of the impact of stoichiometric traits on food web dynamics and ecosystem functioning.
From aquatic functional traits to global biogeochemical cycles Finally, studying aquatic food webs following a FTBA should improve predictions of nutrient and carbon fluxes at the ecosystem scale  (Vanni and McIntyre 2016). For example, trait-based models of food webs could be constructed to infer trophic interactions influencing ecosystem stocks and fluxes  (Woodward et al. 2005). In addition to size and stoichiometry, several other functional traits could be taken into account in these models, such as predator foraging and prey vulnerability traits  (Boukal 2014 ). To do so, one promising pathway is to increase the exploitation of trait databases. For example, global datasets of marine plankton abundances and biomass were recently coupled with a trait-based model used to predict dominant feeding strategies in pelagic ambush predators and to estimate the effects of these feeding traits on energy and biomass transfer efficiency  (Prowe et al.
Yet, distinct questions and methods are often specific to each identified habitat (i. e. , benthic and pelagic) or even to each biological compartment (i. e. , invertebrates and diatoms for the freshwater benthic habitat). Here, we proposed functional trait-based pathways across multiple ecological components. As a first step, we (1) homogenized the terminology used in FTBAs and provided a common typology for aquatic functional traits that can be used across various aquatic systems and for multi-compartment studies, (2) listed the currently available databases dedicated to (aquatic) functional traits, (3) described classical and emerging methods for estimating traits of marine and freshwater organisms, and (4) highlighted some key traits that could be used for multicompartment and trans-ecosystem studies.
Establishing such a common ground among aquatic ecologists is required to further encourage and stimulate collaborative research across disciplines. The next step would be to create a common ontology dedicated to FTBAs, such as the Open Traits Network initiative  (Gallagher et al. 2019) , in order to improve the sharing of trait information in databases. The recent methodologies we described offer new opportunities to study traits at various scales, from -omic sequences to whole-ecosystem approaches and biogeochemical cycles. Imaging, -omics and modeling tools are among the most promising emerging approaches to work with traits across the tree of life. We propose extending discussions within aquatic ecologists, including freshwater, marine, benthic, and pelagic fields, to better share expertise in these tools, thereby improving our knowledge on potential and realized functional traits.
Acknowledgments This paper emerged from informal discussions about trait-based ecology between aquatic ecologists, especially through visit of SDA at TAKUVIK, ISYEB, and LIEC during her sabbatical periods. S. M. was funded by a postdoctoral grant from  Sorbonne Université  while SDA sabbaticals were funded by  Sorbonne Université  and  CNRS. Additional support was provided by the  Institut des Sciences du Calcul et des Données (ISCD) of Sorbonne Université  through the support of the sponsored junior team FORMAL ( From ObseRving  to Modeling oceAn Life). We thank  Thomas Kiørboe  and three anonymous reviewers for their constructive comments on previous versions of the manuscript. Finally, french co-authors wish to thank public taxpayers who fund their salaries.