The Arctic Traits Database – A repository of arctic benthic invertebrate traits

The recently increased interest in marine trait-based studies highlights one general demand – the access to standardized, reference-based trait information. This demand holds especially true for polar regions, where the gathering of ecological information is still challenging. The Arctic Traits Database is a freely accessible online 10 repository (https://doi.org/10.25365/phaidra.49; https://www.univie.ac.at/arctictraits) that fulfils these requests for one important component of polar marine life, the Arctic benthic macroinvertebrates. It accounts for 1) obligate traceability of information (every entry is linked to at least one source), 2) exchangeability among trait platforms (use of most common download formats), 3) standardization (use of most common terminology and coding scheme), and 4) user friendliness (granted by an intuitive web-interface and rapid and easy download options, 15 including for the first time the option to download a fuzzy coded trait matrix). The combination of these aspects makes the Arctic Traits Database the currently most sophisticated online accessible trait platform in (not only) marine ecology and a role-model for prospective databases of other marine compartments or other (also nonmarine) ecosystems. At present the database covers 19 traits (80 trait categories) and holds altogether 14242 trait entries for 1911 macroand megabenthic taxa. Thus, the Arctic Traits Database will foster and facilitate trait-based 20 approaches in polar regions in the future and increase our ecological understanding of this rapidly changing system.


Introduction
The interest in trait-based approaches -i.e. such that consider the life history, morphological, physiological and behavioral characteristics (i.e. traits) of species -in the marine realm has been growing tremendously in the last decades (reviewed in Degen et al., 2018) (Fig. 1). Reasons for the increasing popularity of these approaches are 25 that they offer a variety of additional options to solely species-based methods: Traits can be analyzed across wide geographical ranges and across species pools (Bernhardt-Römermann et al., 2011), they can be used to calculate a variety of functional diversity indices (Schleuter et al., 2010), to estimate functional redundancy (Darr et al., 2014), or be used as indicators of ecosystem functioning (Bremner et al., 2006). Given the rapid changes we observe in many marine regions of the world, and especially in the Arctic Ocean (Wassmann et al., 2011), the potential to indicate vulnerability to climate change and biodiversity loss, or to estimate climate change effects on ecosystem functions is another inherent advantage of trait-based approaches (Foden et al., 2013;Hewitt et al., 2016).

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Although the methodical diversity and complexity of trait-based approaches has broadened in the last years (Beauchard et al., 2017;Kleyer et al., 2012), the underlying data are always species traits. Trait information, however, is often not easy to find, and its collation requires a time and labor intensive survey of literature, databases, field data, and expert knowledge. This holds especially true for the polar regions, as ecological information for many polar marine taxa is still scarce, and only few publications supplement traceable resources 40 of trait information (e.g. Kokarev et al., 2017). An additional obstacle is that existing trait repositories focus mainly on species from temperate regions. The increasing variability in terminology that surrounds traits is another challenge, and recent publications stress the importance of standardization in order to facilitate meta-analyses and comparison of results (Costello et al., 2015;Degen et al., 2018). Several online accessible trait databases specialize in specific taxonomic groups such as fish, polychaetes, or copepods, while others cover a wider part of the marine 45 community ( Table 1). The number of traits included and the form of access varies considerably among the different repositories. The database for marine copepods (Brun et al., 2017) contains 14 traits, whereas Fishbase (http://www.fishbase.org), polytraits (Faulwetter et al., 2014) and BIOTIC (http://www.marlin.ac.uk/biotic) contain more than 40 traits. Some repositories allow only for online browsing, while others enable different forms of download that range from spread sheets to different matrix formats (Table 1). No traits repository explicitly 50 comprising polar species exists so far. Table 1. List of marine trait databases or repositories. "Component" indicates the organism group targeted, "Access options" indicates in which forms the data can be accessed. Reference and web links are provided.

Access options Publication, web links Copepoda
Download of excel workbook via PANGAEA, traits provided as original values or binary code (0/1), references per trait provided.  (Table 1). Regarding download options and traceability we follow the successful example given in Faulwetter et al. (2014) and provide download of trait data in different tabular formats (i.e. data in columns, once following a 60 database-specific format and once DarwinCore) (Wieczorek et al., 2012). The use of these formats guarantees that the included trait information can be easily shared between trait repositories and that the content is fully exploitable both by humans and computers. Every trait code is backed up by at least one reference, and where possible the original quote and page number are provided. In addition to above mentioned formats, for the first time trait information is made available also in a fuzzy-coded and ready-to-use matrix format, that can be directly 65 incorporated into appropriate analysis software.
By providing the Arctic Traits Database to the community of benthic ecologists we aim to provide a sound basis for prospective trait-based approaches in polar regions which will in return aid our overall understanding of these unique and rapidly changing ecosystems.

Taxon data
The current taxa in the database are a subset of the dataset compiled in the frame of the "Arctic Traits Project" (Austrian Science Fund FWF, T801-B29), with focus on pan-Arctic benthic invertebrate macro-and megafauna.
This dataset comprises species lists from published studies of collaborators (Blanchard et al., 2013a(Blanchard et al., , 2013bGrebmeier et al., 2015), but also from so far unpublished sampling campaigns (e.g. field courses of the University 4 Center in Svalbard, UNIS, 2007UNIS, -2017). The regional coverage currently comprises the Chukchi Sea and the Svalbard area. At present stage mainly species in the macrofauna size class have been uploaded.

Trait data
Currently we consider 19 traits and 80 trait categories that reflect the morphology, life history, and the behavior of Arctic benthic invertebrates (Table 3). All traits are in categorical format, i.e. belonging to one out of up to six 80 clearly defined trait categories (see Table 3). The three continuous traits included (body size, longevity, and depth distribution) are converted into categories, but the associated text information assures accessibility to users also in their original, numerical or continuous format.
The choice of which traits to include in the database is based on the following considerations: 1) trait information should be available for and applicable to all benthic taxa (Costello et al. 2015), 2) traits used in 85 previous studies and databases should be favored to enable comparisons across studies (Degen et al. 2018), and 3) the traits should be usable across a wide geographical area (Bremner et al. 2006). In order to fulfil this last precondition, the trait body size is provided as "maximum body size as adult" (see also Table 3). While clearly a tradeoff in regard to the detection of intraspecific plasticity, it enables the use of this trait across large spatial scales.
Recent trait-based studies emphasize the importance of standardized traits and trait terminology to ensure that data 90 can be integrated more easily in the future (Costello et al 2015, Degen et al. 2018, Faulwetter et al. 2014. To meet these requirements of the scientific community, the Arctic Traits Database includes seven of the ten traits prioritized in Costello et al. (2015): "depth range", "substratum affinity", "mobility", "skeleton", "diet", "body size" and "reproduction" (  Table 1), one of the most comprehensive databases on biological traits of marine organisms. BIOTIC also includes the trait "salinity". We cover salinity preferences within the trait "tolerance", which accounts also for temperature and pollution tolerance (see Table 3 for details). Traits we include in addition are "skeleton", and "mobility" (i.e. the relative degree of movement).
Although physiological traits are of high interest in trait-based studies, we do not include them as they are not 105 easily retrieved for many (arctic) benthic taxa (one of the preconditions for inclusion in the database as stated above). In addition, physiological traits (e.g. growth rate, respiration rate, ingestion rate) depend on body mass and temperature (Brown et al., 2004), which can vary tremendously among Arctic regions, contradicting that the provided traits information should be usable across a wide geographical area. One common approach to use traits is as indicators of ecosystem functions (effect traits) or of changes in the environment (response traits) (Hooper et al., 2005). An overview of how each of the 19 traits that are currently 115 included in the database may relate to ecosystem functions or respond to environmental changes or pressures is given in Table 3.

Body size
Definition Maximum body size as adult given in mm, as individual or colony and excluding appendages. Can be height in rather upright animals (e.g. corals), body width or diameter in rather round animals (e.g. crabs), or body length in elongated animals (e.g. worms). Categories S1 small < 10 mm S2 small-medium 10-50 mm S3 medium 50-100 mm S4 medium-large 100-300 mm S5 large > 300 mm Function Size has a direct effect on productivity, the amount of habitat structuring and facilitation, and is important for the amount of oxygen and nutrient flux across the sediment-water interface. It correlates with food web structure, trophic levels, and energy flow in ecosystems.

Detail
Smaller animals are faster growing, usually show a higher productivity and are less affected by trawling as they are more likely to fit through the net of trawling gear, thus often replacing larger slow-growing fauna in trawl-impacted areas. A clear majority of small-bodied species may be indicative for environments with high instability or be the result of environmental or anthropogenic disturbances. Larger taxa usually show a lower productivity but higher carbon fixation and have a higher effect on fluxes of nutrients, energy and matter. They usually grow slower, reproduce later, and are more affected by trawling and other disturbances. References Bolam and Eggleton, 2014;Bremner, 2008;Costello et al., 2015;Emmerson, 2012;Micheli and Halpern, 2005;  Ability of a species to disperse, become invasive, or recover from a population decline. Indicator for long-term sensitivity (ability to recolonize disturbed areas). Planktonic stages indicate productivity and elemental transport from benthos to pelagos. References Bolam and Eggleton, 2014;Cardeccia et al., 2018;Törnroos and Bonsdorff, 2012 Life span Definition The maximum reported life span of the adult stage in years. Categories A1 short <2 years A2 medium 2-5 years A3 medium-long 5-20 years A4 long >20 years Function Long lived animals are more susceptible to disturbance and need longer to recover (while shortlived species can recover fast and may increase in richness and abundance as disturbance increases). An indicator for population stability over time, carbon fixation, productivity. Detail Indicates the relative investment of energy in somatic rather than reproductive growth and the relative age of sexual maturity. A proxy for relative r-and k-strategy. References Bolam and Eggleton, 2014;Bremner, 2008;Cain et al., 2014;Costello et  Affects carbon fixation and transport within the sediment, between aerobic and anaerobic layers, or from pelagos to benthos. Can indicate facilitation (e.g. for microbial communities in the sediment) and sensitivity to perturbation (e.g. bottom trawling, infauna less affected than epifauna, hyper-benthic taxa might be able to escape). Endobenthic life style effects the sediment biogeochemistry. Epibenthic and shallow sediment-dwelling taxa are more vulnerable to predation. Hyper-benthic taxa are involved in transport of carbon from benthos to pelagos. References Bolam et al., 2014;Bremner et al., 2008;Frid and Caswell, 2016;Törnroos & Bonsdorff, 2012 Living Indicates the dispersal and recolonization potential, and the invasiveness of an organism. Related to nutrient cycling (burrowing taxa contribute most to nutrient cycling and regeneration, burrows increase the total sediment surface area available for exchange with the water column), carbon deposition (sessile calcifying taxa), facilitation of microbial and other fauna (either via burrowing or via constructing biogenic habitats), and habitat stability. Swimmers may escape predators and trawling gear.

Remark
Closely linked to the trait mobility. References Aller, 1983;Bremner, 2008;Bremner et al., 2006;Costello et al., 2015;Frid and Caswell, 2016 Feeding Can indicate hydrodynamic conditions (suspension feeders in turbulent, deposit feeders in calmer water), carbon transport between pelagos and benthos (suspension feeders) and backwards (predators), and vulnerability (e.g. surface deposit feeders and suspension feeders are more sensitive to trawling). Impacts resource utilization and facilitation (e.g. deposit feeders facilitate microbes that further decompose organic carbon). Effects the depth of oxygen and detritus penetration and can enhance organic matter decomposition and nutrient recycling/regeneration. Control of other species in the assemblage. References Bremner, 2008;Bremner et al., 2006;Dolbeth et al., 2009;Frid et al., 2008;Kröncke, 1994;Oug et al., 2012;Rosenberg, 1995;Tyler et al., 2012;van der Linden et al., 2016 Trophic level Definition Rank of an animal according to how many steps it is above the primary producers at the base of the food web.

Sources of trait information
Sources of trait information are research papers, books, databases and online repositories (Table 1) Arctic species are the only option) we do not restrict source information for arctic-boreal or cosmopolite taxa to stem from Arctic regions. This bears the risk that the assigned trait information is not accurate, as polar taxa might 155 differ in their expression of certain traits from their relatives at lower latitudes (Degen et al. 2018). However, this is an issue for now not resolved, as trait information from the high latitudes is often scarce, and we recommend the user to consider the source of trait information when interpreting results.

Fuzzy coding of traits
The fuzzy coding procedure indicates to which extent a taxon exhibits each trait category (Chevenet et al., 1994).

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This method has the advantage that it enables us to analyze diverse kinds of biological information derived from a variety of sources (as those included in the Arctic Traits Database, see Sect. 2.3), and that also intermediate scenarios (i.e. when a taxon does not clearly fall into one category or the other) can be accounted for (Chevenet et al. 1994). We use the 0-3 coding scheme (details in Table 4 below) as it is the most widely used (which facilitates comparisons and exchange of trait information) and provides a compromise between binary codes and many not 165 clearly delineated graduations (Degen et al. 2018). Taxon has total and exclusive affinity for a certain trait category, all other categories do not apply and must be coded with "0". 2 Taxon has a high affinity for a certain trait category, but other categories can occur with equal (2) or lower (1) affinity. 1 Taxon has a low affinity for a certain trait category. 0 Taxon has no affinity for a certain trait category.  While the coding might for some traits and taxa be pretty straight forward, in some cases a decision might be 175 drawn not so easily. As one of the clearer cases, we point out the coding of the trait "body size" for the star fish Crossaster papposus. A literature reference states that the body size can range "Up to 340 mm in diameter" (Hayward and Ryland, 2012, p. 668). This size fits into the category "large" (S5, > 300 mm), thus the taxon is coded "3" for this size class, and "0" for all other categories (S1 -S4). The trait "mobility" is trickier. A literature reference (Himmelman and Dutil, 1991), p. 68) states the following: "Crossaster papposus and Solaster endeca 180 are highly mobile; large individuals can cover distances of more than 5 meters in 12 hours". Here we have to keep in mind that the particular reference frame in this publication are subtidal sea stars in the northern Gulf of St.
Lawrence (West Atlantic). The reference of the Arctic Traits Database however are all benthic invertebrates, and the trait category "high mobility" is defined here for taxa which are "swimmers or fast crawlers", such as some amphipods and shrimp (see Table 2). Accordingly, the correct coding for C. paposus in the reference system of 185 the Arctic Traits Database is the category "medium" mobility (MO3). Users of the Arctic Traits Database should bear this reference system in mind when downloading only the fuzzy coded trait data and aiming to apply it to another reference system. But as the detailed literature quote that lead to the coding of a trait is always provided (see Sect. 2.3), the trait information can easily be adjusted by the user.
There will always be a certain degree subjectivity related to the fuzzy coding procedure. To find out how 190 strong the coding might differ among scientists a small experiment at the Arctic Traits Workshop in Vienna (December 2016) was performed (Degen et al. 2018). Participants coded 27 trait categories of three common Arctic benthic species, and found the final trait matrices to be to 83% identical. We are confident that the sophisticated structure of the Arctic Traits Database (see Sect. 3) and the provided information and instructions will support a more consistent coding of benthic traits in the future.

3 Database
In order to collect trait information and to disseminate it among users, a web-based database was created. The database features a public interface (Sect. 3.1) and an entry interface that is accessible only for registered collaborators (Supplement). The public interface (Fig. 2, a) allows to browse the traits and references online ("Data per taxon" in the top menu bar), to view background information ("About" and "Trait definitions") and to 200 download either the entire species, trait and literature information or specified subsets in several formats ("Download data") (see Sect. 3.1). Registered collaborators -i.e. those users that actively contribute trait information to the Arctic Traits Database -can access the interactive part of the database via the log in button on the public page (Fig. 2a). This access offers additional options (Fig. 2b): browsing the existing information also per traits ("Traits" in the top menu bar), uploading new taxa, trait and source information, or adding trait 205 information, references and comments to already existing taxa in the database ("Taxa"). As several users can work on the same taxa, a flagging system is used to highlight and discuss potentially conflicting sources and opinions.
The "References", "Statistics", and "Tools" sections are equally accessible only for registered users (Fig. 2, b; Supplement). Every scientist working in the field of Arctic benthic ecology aiming to share trait information can become a registered user by getting in touch with the editor and retrieving a user login. Credit to the registered 210 collaborators is given in the "About" section on the public site and also on taxon pages after each trait entry they conduct. A detailed manual for registered users is provided in the supplementary material to this publication (Supplement), or can alternatively be accessed via the public web interface ("About"). Collaborators who want to share trait information without registering to the database can alternatively be provided with an upload template (.xls).
215 Figure 2. Screenshots of the start page of the Arctic Traits Database. Toolbar of the public page with Login button for the registered user (a), and toolbar in the area for registered users (b).

Public access and download options
The public access enables to browse the database online and to download the complete set of data as well as the 220 bibliography, or specified subsets. Taxon traits can be visually inspected online via the "Data per taxon" button from the top menu bar and "Browse taxa" or "Search taxa". Taxa can be browsed and selected via the taxonomic tree, as indicated for the asteroid Crossaster papposus in Fig. 3. Alternatively, the "Search taxa" panel allows to type in and search a specific taxon. The completeness of trait information can be inspected via "Data completeness" (Fig. 4), equally accessible via "Data per taxon" on the top menu bar. This option shows an alphabetic list of all taxa in the database for which trait information is available. The bar on the right side indicates the information coverage for each taxon and trait, 230 blue color indicates that trait information is present. The download section can be accessed via the "Download data" button on the top menu bar (Fig. 2, a; Fig. 3; Fig.   4). Download is enabled in three different computer readable formats: 1) as data in columns (*.csv) (Table 7), 2) 235 in DarwinCore format (Table 8), and 3) as fuzzy coded trait matrix which some users might prefer (see Sect. 2.4 and Fig. 5). Also, the entire bibliography is available for download. Before the download commences the user is asked whether to download a) all data in the database, b) only data for an uploaded list of taxon names, c) only data for an uploaded list of AphiaIDs, or d) only the data selected from a classification tree. In the last option, entire phyla or sub-groups can be easily selected from the tree. By default, all 19 traits are exported, but if the user 240 is interested only in one or a few specific traits, the option to select these from the total list of 19 traits is available.
As the fuzzy coded trait matrix (download option 3) contains only the fuzzy codes per trait category but no literature sources, we recommend to also download the "Data in columns" (download option 1) for the same taxa, where the detailed source per species and trait category is included. Details on the structure of the first two download options are given below in Table 7 and Table 8. A clipping from a downloaded fuzzy coded trait matrix 245 is shown in Fig. 5. The database can also be accessed programmatically via a REST API (documented at https://www.univie.ac.at/arctictraits/download-api). Table 7. List of fields returned by the Arctic Traits Database when "Data as columns" (*.csv) is chosen as an export option from the download section.

Column label Column description Taxon
The taxon for which the information was recorded. Author The author and year of the Taxon for which the information was recorded.

Rank
Rank of the taxon for which the information was recorded.

Valid taxon
Currently accepted name of the Taxon (as stored in the Arctic Traits Databaseinformation might not be up to date with the WoRMS or the latest taxonomic literature in some cases). Users should check all taxa against WoRMS before use. If Taxon is currently accepted, this field contains the same value as Taxon).

Valid author
Currently accepted name of the Author (as stored in the Arctic Traits Databaseinformation might not be up to date with the WoRMS or the latest taxonomic literature in some cases). Users should check all taxa against WoRMS before use. If Taxon is currently accepted, this field contains the same value as Author.

Taxonomic status
The status of the use of the Taxon (e.g. objective synonym, subjective synonym) as stored in the Arctic Traits database. Source of synonymy Literature reference for synonymy of taxon (if present).

Parent taxon
The Taxon's direct parent in the taxonomic classification (as stored in the Arctic Traits Database). Trait The biological trait for which information is available (e.g. "Feeding habit"). Category The sub-category of the Trait for which information is available (e.g. "Predator").

Category abbreviation
An abbreviated version of the often verbose trait category -useful as a label in further analyses of the data (e.g. "FH(6)"). Traitvalue Describes the affinity of the Taxon to the Category. Values range from 0-3: "0"= no affinity for a certain trait category; "1"= low affinity for a certain trait category; "2"= high affinity for a certain trait category, but other categories can occur with equal (2) or lower (1) affinity; "3"= total and exclusive affinity for a certain trait category.

Reference
Literature reference leading to the assignment of the Text Excerpt modification date Date and time when the Text Excerpt was last modified. If no modification was done since the first entry, this has the same value as Text Excerpt creation date. Table 8. List of fields returned by the Arctic Traits Database when "Darwin Core" is chosen as an export option from the 250 download section. DarwinCore does not provide the same granularity as the "Data as columns" format. The output file consequently contains fewer details.

Column label Column description scientificName
The taxon for which the information was recorded scientificNameAuthorship The author and year of the taxon for which the information was recorded taxonRank Rank of the taxon for which the information was recorded. acceptedNameUsage Currently accepted name and authorship of the scientificName (as stored in the arctictraits database -information might not be up to date with the latest taxonomic literature in some cases.) Taxonomic Status The status of the use of the scientificName (e.g. objective synonym, subjective synonym) as stored in the arctictraits database. Empty if scientificName is the currently accepted name.

MeasurementOrFact
Trait name and trait category, separated by a colon (e.g. Size:small) measurementValue Value from 0-3, describing the affinity of the taxon to a trait category. Coding of values as described in Person who entered the trait information for this taxon into the database. measurementDeterminedDate Date the trait information was entered into the database or last modified.

Figure 5.
A clipping from the fuzzy coded trait matrix returned by the Arctic Traits Database when the "Data in matrix format" 255 is chosen as export option from the download section. Species are rows ("Valid_name" refers to the currently accepted taxonomy in WoRMS), abbreviated trait categories are columns. For abbreviations of trait categories see Table 3. Due to the database structure zero codes ("0") are not displayed (see Table 6).

Database specification
The website runs on an Apache 2.  (Fig. 6). Figure 6. Taxonomic data coverage. "Other ranks" include higher taxonomic levels and intermediate ranks.

Trait data coverage
At present, the database contains 19 traits and 80 trait categories with in total currently 14242 entries of trait 275 information. The trait for which most entries exist is "Skeleton" (1837 entries), followed by "Reproduction" (1328 entries) and "Body form" (1151 entries) (Fig. 7). The phylum with most entries are the Annelida (

Bibliography
The Arctic Traits Database currently includes 394 sources of trait information. Thereof 66 % scientific papers, 11 285 % are books, 10 % webpages, and 4 % are expert communications and personal observation ("Other"). Theses, book sections, and reports each make up around 3 %. Most sources were used for the phylum Echinodermata and Annelida (33 % each), followed by Arthropoda (29 %). Although the Arctic Traits Database is still growing as new taxa and trait information are added, certain trends in data completeness or scarceness, respectively, became apparent (Fig. 7). Thus, the database is not only a valuable tool for collecting and providing information, but also for pointing out where more research might be needed.
Regarding the 19 traits included at the present stage, it shows that our knowledge on e.g. the live span of many 295 Arctic benthic species is still limited (information only for < 5 % of species). This lack of data on species longevity is astonishing, as polar taxa are traditionally depicted as slow growing and long-lived compared to their relatives from lower latitudes. Accordingly, one might have expected that more studies and measurements are available for a variety of Arctic taxa, which is not the case for many groups. Other traits that are currently underrepresented are trophic level (< 8 %) and tolerance ( <13 %).

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Regarding our interest to identify knowledge gaps, a special strength of the database is the implemented flagging system (described in detail in the supplement). As registered users continue to upload trait information, also more "conflicts" -i.e. cases where the sources or observations added by different users point towards different trait categories -may arise. Such cases are then indicated by a red flag and can be easily filtered for. Monitoring and statistical evaluation of these cases will grant important information on where conflicts exist and for which 305 taxa or traits future research is needed. Such evaluation will also aid to identify which traits are more robust (i.e. are never flagged), and which show a higher plasticity (frequent flagging). This kind of information is of tremendous value as it can aid the choice as of which traits to include in prospective trait-based studies. Apart from clearly diverging source information, also different levels of experience or customs in fuzzy coding might lead to red flags in the system. Here the editorial team will take care for consistency by solving the conflicts according to 310 the database standard, by that also fostering a standardized way of coding within the community. In addition, repetitively occurring discrepancies in the coding of certain traits might also point towards a need for revision of these trait categories or their definitions, or maybe even the adding of a new trait, in that way improving the quality of the database.
In addition to the above discussed knowledge gaps surrounding certain traits, also the data coverage 315 among taxonomic groups varies considerable (Fig. 7). This potentially mirrors the sampling design of the underlying datasets. Some taxonomic groups such as the polychaetes clearly dominate many benthic soft-bottom communities, while other taxa such as the shrimp/caridea are highly mobile and might be permanently undersampled with sampling gears like grabs, box corers, or bottom trawls (Eleftheriou and McIntyre, 2007). This points toward the need to include also datasets derived from video and still image analysis in the future 320 development of the database. These methods -despite certain disadvantages (discussed in Degen et al. 2018

Conclusions
The Arctic Traits Database provides an easy accessible and sound knowledge base of traits of Arctic benthic 330 invertebrates and will thus facilitate prospective trait-based studies for a variety of benthic ecologists at all career stages. Its sophisticated structure accounts for the most commonly raised demands to contemporary trait databases: 1) obligate traceability of information (every entry is linked to at least one source), 2) exchangeability among platforms (use of most common download formats), 3) standardization (use of most common terminology and coding scheme), and last but not least 4) user friendliness (granted by an intuitive web-interface and rapid and easy 335 download options). The combination of these aspects makes the Arctic Traits Database a cutting-edge tool for (not only) the marine realm and a role-model for prospective databases.

Author contribution
RD designed the project and performed the trait data collection. SF performed database and webpage development and design. RD prepared the manuscript with contributions from SF.