Trait-based approaches, focusing on diversity of organismal characteristics rather than species diversity, are an effective data source to answer important biological questions (Costello et al., 2015; Beauchard et al., 2017; Degen et al., 2018). Traditionally, traits have been and still are of significant value for taxonomic research (Voultsiadou et al., 2017). Moreover, as they enable the description and investigation of complex ecosystems in relatively simple ways without having to laboriously study each individual component species, they are also integral to the study of the ecological and evolutionary dynamics of populations, species, communities, and ecosystems (Violle et al., 2007; Kattge et al., 2011; Degen et al., 2018; Schleuning et al., 2020). For example, the analysis of trait data can assist in estimating responses to multiple stressors, including anthropogenic and climate change impacts (Degen et al., 2018; Schleuning et al., 2020), and may provide crucial information needed to develop effective management strategies to counter negative consequences of climate change (Bremner, 2008).

Although the importance of trait-based approaches is widely recognised, there remains a general lack of accessible, standardised, and harmonised trait data for aquatic organisms (Costello et al., 2015; Beauchard et al., 2017; Degen et al., 2018; Martini et al., 2021). Recent initiatives have intended to resolve this gap and made significant progress for some marine animals, including fishes, polychaetes, copepods, and macroinvertebrates (reviewed in Martini et al., 2021), but aquatic photoautotrophs, including marine macroalgae (or seaweeds), remain underrepresented. Seaweed traits and functional forms have been used for at least one century as a tool to answer various ecological and evolutionary questions. Since the early 1900s, traits have been explored to formulate life-form classification schemes for algae which intended to reflect habitat requirements and responses to environmental fluctuations (Chapman and Chapman, 1976; Feldmann, 1966) or describe different reproductive strategies (De Wreede and Kinger, 1988; Bell, 1994) used to explain various evolutionary questions (Heesch et al., 2021). In the 1980s, Littler and Littler (1980) and Littler et al. (1983) used the adaptive value of morphological traits to categorise algal species into morpho-functional groups, which cluster species with similar ecological functioning. As the concept of morpho-functional groups is relatively easy to apply and can limit processing time and associated costs, it has been frequently applied in seaweed ecology (Veiga et al., 2013; Vélez-Rubio et al., 2021). As a result, multiple variations of the original six morpho-functional groups have been developed and applied in a wide range of contexts. For example, they have been applied to understand the distribution of communities along spatial (Steneck and Dethier, 1994; Wieters et al., 2012; Gaspar et al., 2017) or environmental scales (Balata et al., 2007; Gaspar et al., 2017; Gómez et al., 2019; Gómez and Huovinen, 2020), to understand the potential of communities to resist invasion (Arenas et al., 2006), and to evaluate the ecological status of coastal waters (Orfanidis et al., 2011). Yet, other recent research indicates that current morpho-functional groups may not capture enough variation to describe ecological functioning (Mauffrey et al., 2020; Ryznar et al., 2020) and call for further development towards a broader trait-based approach that includes non-morphological and other traits (Mauffrey et al., 2020).

Seaweed trait information remains largely scattered, not widely available, and not semantically standardised, which has hampered the development and application of phycological trait-based approaches over broad taxonomic, spatial, or temporal scales. To overcome such drawbacks and enhance the development of a common ontology favouring the use and comparability of trait-based approaches on seaweeds, we present AlgaeTraits, a seaweed trait database for 1745 species occurring along the Atlantic and Mediterranean coasts of Europe. We expand the spectrum of commonly used functional traits (i.e. measurable or quantifiable properties of individuals; McGill et al., 2006) to include data on habitat, seasonality, morphology, life cycle, life history, and biogeographical range (Fig. 1, Table A1). In total, we describe 21 traits linked to taxonomic information, covering 9 of the 10 previously prioritised traits in Costello et al. (2015). These data are available on the AlgaeTraits data portal 10.14284/574 (AlgaeTraits, 2022), which is a subregister of the World Register of Marine species (WoRMS, 2022; Marine Species Traits, 2022). Here we introduce AlgaeTraits by (i) presenting the methodologies used to collect data, (ii) highlighting trait coverage for the current and first version of the database, and (iii) exploring trait variability for a selected subset of traits.

Twenty-one traits were documented resulting in 45 195 entries for 2830 accepted taxa according to AlgaeBase, among which 681 were at the genus level and 1742 at the species level. Fifty-six percent of the trait entries for species were inherited from the genus level. Environment and cytomorphology were the traits most documented for species, while the least information was provided for the traits that show substantial variation within genera and that were not inherited from genus to species level (Fig. 3).

AlgaeTraits includes 21 traits and 1742 European seaweed species, and it is structured within a general framework and ontology aiming to describe all marine species (Costello et al., 2015). With a mean coverage of ∼60% per trait, this database will be a solid tool for a variety of biological research and related fields, including marine conservation, nature-based solutions, and aquaculture. For example, traits can be used to monitor community and ecosystem changes (McGill et al., 2006; Vélez-Rubio et al., 2021) or to identify conservation priorities (Albouy et al., 2017; Cardeccia et al., 2018; Esmaeili et al., 2022). In addition, they can be incorporated into predictive modelling to assess eco-evolutionary consequences of climate change (Schleuning et al., 2020), can contribute to predicting the invasiveness of species (Nyberg and Wallentinus, 2005; Quell et al., 2021), can be used in research aiming to better understand the driving forces of evolutionary trait history (Heesch et al., 2021) or the mechanisms of community assembly (Weiss and Ray, 2019), and can even help to assess ecosystem service vulnerability (Díaz et al., 2013; Stevenson, 2014).

The AlgaeTraits database complements recent efforts to collect and publicly provide well-structured and organised seaweed trait data. Just as MarLIN (2006) and SeaTraIn (2022) we focussed on biologically important traits. But contrary to these other initiatives, AlgaeTraits includes all European seaweed species and presents a different, yet overlapping, set of traits. MarLIN (2006) covers over 40 traits, of which 12 overlap with AlgaeTraits, but it is limited to only 30 seaweed species. SeaTrain (2022) covers around 10 traits, of which 2 traits overlap with AlgaeTraits, and includes 96 seaweed species. As to taxonomic coverage (1742 species) and inclusion of functional important traits as prioritised by Costello et al. (2015), AlgaeTraits is the most extensive seaweed trait database published so far.

In this first version of AlgaeTraits, not all traits are available for all species yet, and the current list of traits is not yet complete. For example, other key eco-evolutionary traits that might be considered to be included in the database are tolerance to light, tolerance to temperature, tolerance to grazing, tolerance to sedimentation, or tolerance to epiphytism. There is currently no possibility to describe parasitic algal life forms in the database, ignoring a relatively diverse group of red algae. Completion and further refining and expanding the database will be an ongoing effort of the AlgaeTraits editor community. AlgaeTraits currently has 30 trained thematic editors who actively update the trait information on a voluntary basis through the online editing interface (https://www.algaetraits.org, last access: 10/09/2022) and gradually work on the expansion of this European database to a global database. The AlgaeTraits editorial community will also need to report to the WoRMS steering committee. As the database expands, adding new traits or trait values can be considered under thorough consideration and discussion with the WoRMS steering committee and data management team.

For now, traits are included at the species level in AlgaeTraits. However, multiple traits can exhibit substantial intraspecific variation (Kattge et al., 2011). For example, morphological seaweed traits can differ among populations depending on underlying genetic patterns (Serisawa et al., 2003) or depending on environmental conditions such as wave exposure with rather smaller and more slender individuals in exposed than sheltered localities (Ruuskanen et al., 1999; Fowler-Walker et al., 2006; Kim et al., 2022). But also habitat-preference, life-history (Araújo et al., 2011), or life-cycle traits can vary intraspecifically. Some seaweeds reproduce sexually under normal conditions but change to asexual reproduction under specific environmental conditions (Demes and Graham, 2011; Murúa et al., 2017) or at the limits of their distribution (i.e. geographic parthenogenesis: Oppliger et al., 2014; Hoshino et al., 2021). Intraspecific trait variation is thus caused by both genetic variation and phenotypic plasticity and can be a response to improve performance under specific environmental biotic or abiotic conditions (Kattge et al., 2011). At the moment, we considered intraspecific variation by assigning localities (or life stages) to the trait values when possible or specifying detailed information in the note field. However, complementary approaches measuring trait information at specimen level (e.g. Mauffrey et al., 2020; Cappelatti et al., 2019) are useful, especially for quantitative traits, to fully capture intraspecific trait variability and allow more in-depth analysis.

The data can be accessed from the AlgaeTraits portal, 10.14284/574 (AlgaeTraits, 2022).

We provided a consolidated database of important traits of European seaweeds that is distinct in its completeness and taxonomic coverage. Because the database is standardised and fits within the broader framework of WoRMS that aims to include all taxa described, it serves not only as a significant resource for phycological research focusing on trait-based ecology or evolution, but also for general macro-ecological and macroevolutionary research in general. The database can help to explore ecological questions about relations among traits or help to unravel taxonomic and evolutionary patterns of traits in seaweeds. Because some trait values vary geographically and some traits values were documented at the genus level, the database may not always provide the highest variation at the species level. However, an active thematic editorial community is currently committed to further updating and improving the quality of this European database and expanding it to a global level to facilitate thorough and broad-scale trait-based analysis.