Seaweed functional diversity revisited: Confronting traditional groups with quantitative traits

Macroalgal (seaweed) beds and forests fuel coastal ecosystems and are rapidly reorganizing under global change, but quantifying their functional structure still relies on binning species into coarse groups on the assumption that they adequately capture relevant underlying traits. To interrogate this ‘group gambit’, we measured 12 traits relating to competitive dominance and resource economics across 95 macroalgal species collected from the UK and widespread on North‐East Atlantic rocky shores. We assessed the amount of trait variation explained by commonly used traditional groups—(a) two schemes based on gross morphology and anatomy and (b) two categorizations of vertical space use—and examined species reclassification into post hoc, so‐called emergent groups arising from the functional trait dataset. We then offer an alternative, emergent grouping scheme of macroalgal functional diversity. (a) Morphology and anatomy‐based groups explained slightly more than a third of multivariate trait expression with considerable group overlap (i.e. low precision) and extensive mismatch with underlying trait expression (i.e. low accuracy). (b) Categorizations of vertical space use accounted for about a quarter of multivariate trait expression with considerable group overlap. Nonetheless, turf species tended to display attributes of opportunistic forms. (c) A nine‐group emergent scheme provided a highly explanatory and parsimonious alternative to traditional functional groupings. Synthesis. Our analysis using a comprehensive dataset of directly measured functional traits revealed a general mismatch between traditional groups and underlying traits, highlighting the deficiencies of the group gambit in macroalgae. While existing grouping schemes may allow first order approximations, they risk considerable loss of information at the trait and, potentially, ecosystem levels. Instead, we call for further development of a trait‐based approach to macroalgal functional ecology to capture unfolding community and ecosystem changes with greater accuracy and generality.

Traits hold the promise of more predictive ecology that goes beyond case studies of species or taxonomic groups (McGill, Enquist, Weiher, & Westoby, 2006). Functional traits are morphological, physiological or phenological characteristics of individuals that influence their response to the environment and/or effect on ecosystem properties and/or services (Díaz et al., 2013).
Functional traits reflect adaptive strategies and underlying physiological trade-offs. In vascular plants, for example, functional trait variation largely reflects competitive dominance (plant size) and the leaf economics spectrum, which emerges from physiological trade-offs between structural integrity and growth potential (Díaz et al., 2016). As an alternative to direct use of functional trait values, so-called 'emergent' groups have been built through post hoc grouping of functional trait data on the premise that they represent trait variability more closely than traditional grouping approaches (Lavorel, McIntyre, Landsberg, & Forbes, 1997). In turn, functional traits or emergent groups explain species' contributions to ecosystem functions and services. For instance, plant-specific leaf area and leaf nitrogen content have been linked to primary productivity and decomposition (Reich, 2014;Shipley, Lechowicz, Wright, & Reich, 2006). Terrestrial vascular plants have had an inordinate influence on our understanding of functional ecology of autotrophic organisms, but efforts are underway to include a variety of primary producers (de los Santos et al., 2016;Elger & Willby, 2003;Roos et al., 2019).
However, ecologists studying macroalgal beds and forests rarely directly measure functional traits and instead typically assign species to long-established form-based groups. Littler and Littler (1980) first proposed the 'functional-form model', categorizing species into morpho-functional groups based on gross morphology and anatomical features. Over a decade later, Steneck and Dethier (1994) proposed a less subjective grouping scheme based on branching pattern, anatomy and degree of cortication.
Macroalgal ecologists have also employed broad categorizations of vertical space use, such as the binary canopy versus turf scheme, to infer ecosystem consequences of changing assemblage structure, particularly in light of the global rise of turfs over canopy-forming macroalgae (Feehan, Grace, & Narvaez, 2019;Filbee-Dexter & Wernberg, 2018). Traditional grouping methods remain highly influential; for instance, Steneck and Dethier's paper has been cited over 1,000 times, with 60 citations in 2018 alone (Google Scholar, accessed October 2019).
Here we provide the first comprehensive test of the group gambit in macroalgae. Using direct measurements of traits related to two major aspects of ecological variation, competitive dominance and resource economics, we test the assumption that traditional grouping approaches accurately capture underlying trait expression across four commonly used schemes: the form-(i.e. morphology-and anatomy-) based grouping approaches of Littler and Littler (a) and Steneck and Dethier (b), as well as two categorizations of macroalgal vertical space use (i.e. stature), the binary canopy versus turf (c) and a three-level scheme adapted from Arenas, Sánchez, Hawkins, and Jenkins (2006;d). To do so, we quantify the extent of trait variation explained by each traditional grouping scheme and assess the accuracy of traditional groups by examining post hoc group reworking (i.e. species reclassification from traditional to emergent groupings).
A strong correspondence between groups and the underlying functional traits would support their continued application and bolster ecological interpretation. However, substantial mismatch between groups and traits-as well as reclassification of species into emergent groups-would indicate a loss of information and underline the potential gains offered by direct trait measurements. Our study reveals the limitations of traditional grouping schemes and provides screened trait values as well as an alternative nine-group emergent scheme as a platform for further development of trait-based macroalgal ecology.

| Sampling
We measured 11 continuous functional traits and one categorical functional trait (Table 1) at the individual level across 95 erect intertidal macroalgal species, which spanned a great variety of form and function and hence, traditional functional groups (Table S1).
Samples were collected from 12 rocky shores in the UK ranging from very sheltered to very exposed (Table S2): six sites in South Wales, four sites in Orkney (Scotland) and two sites in Cornwall (England).
The collected species are commonly found on North-East Atlantic rocky shores (Ar Gall et al., 2016;Araújo, Sousa-Pinto, Bárbara, & Quintino, 2006;Martínez, Viejo, Carreño, & Aranda, 2012;Martins, Hawkins, Thompson, & Jenkins, 2007), and include species restricted to the region (~25% of species), more broadly distributed across multiple temperate regions (~35%), as well as cosmopolitan and non-native species (~40%). Sampling took place from May to September 2013 and 2015-2018 (Table S3). We collected an average of six replicates per species, ranging from 1 to 45 (mode and median = 6, SD = 7.55; Table S3). Such a large difference in replication was due to the rarity of some species and to our efforts in sampling abundant species across several sites to better capture natural variability. Replicates were sampled more than 2 m apart. Whenever possible, a replicate was made up of a single individual. However, when individuals were too small for the trait measurements, a sufficient quantity was collected for each replicate by pooling several individuals or tufts (Table S3). Whenever distinguishing between individuals was not possible (e.g. for turfs), the samples were collected by isolating tufts (Table S3). Replicates within species were sampled from a variety of microhabitats to better reflect natural variability, but belonged to the same life stage (e.g. the Trailliella intricata stage of Bonnemaisonia hamifera). Samples were kept in seawater in a cooler until brought back to the laboratory. They were then either screened fresh or frozen at −18°C until processed.

| Trait screening
The functional traits measured are hypothesized to capture two fundamental aspects of primary producer variability, (a) the economics spectrum and (b) competitive dominance. We consider multiple indicators (or 'functional markers' sensu Garnier et al., 2004) to provide a more integrated estimation of ecological strategy and TA B L E 1 Functional traits and their physiological significance. 'SLOW' and 'FAST' indicate whether increasing trait values place macroalgae on the so-called slow and fast ends of the resource utilization spectrum respectively. 'COMPETITIVE' indicates that increasing trait values entail increasing competitive dominance; a question mark indicates that the functional trait may not relate to any end of the competitive dominance spectrum in a straightforward way. References are given in superscript and are as follows: (1) Carpenter (1990); (2) Cornelissen et al. (2003); (3) Dromgoole (1981); (4) Elger and Willby (2003); (5) Hay (1981); (6) Littler and Littler (1980); (7) Reich et al. (1999); (8) Roderick, Berry, and Noble (2000); (9) Steneck and Dethier (1994); (10) Taylor and Hay (1984); (11) Veiga et al. (2014); (12) Vile et al. (2005); (13) Weykam, Gómez, Wiencke, Iken, and Klöser (1996)  function. Here we briefly summarize the ecological significance of the traits with regard to photosynthesis, structural integrity, space use and complexity (Table 1; Table S4). The suite of economicsrelated traits indicates the relative investment in resource acquisition versus resistance to (a)biotic stress and therefore resource conservation, tying in with the r-('fast return') to K-('slow-return') selection continuum (Pianka, 1970). Slow-return primary producers tend to display long life spans, low maximum photosynthesis and productivity, reduced palatability and slow decomposition (Littler & Littler, 1980;Smart et al., 2017;Wright et al., 2004). Traits a-g relate to photosynthesis and/or structural integrity, and hence, position on the economics spectrum: (a) Thallus Dry Matter Content (TDMC) is the ratio between dry and wet mass and represents the proportion of structural compounds and water-filled-and therefore mainly photosynthetically active-tissues (Elger & Willby, 2003;Littler & Littler, 1981;Schonbeck & Norton, 1979). (b) Thickness also increases with the amount of structural tissue, providing resistance to physical stress and herbivore grazing (Cappelatti, Mauffrey, & Griffin, 2019;Littler & Littler, 1980;); (c) Carbon (C) content and its ratio with (d) Nitrogen (N) content, (e) C:N, more directly quantify recalcitrant structural compounds relative to N-rich photosynthetically active tissues (Cornelissen et al., 2003;Weykam et al., 1996). Analogously to Specific Leaf Area (Wilson, Thompson, & Hodgson, 1999), (f) specific thallus area (STA), obtained by dividing surface area by dry mass, captures light-and nutrient-absorbing surfaces and increases with the extent of low density, water-filled, photosynthetically active tissues relative to recalcitrant, structural compounds (Littler & Littler, 1980). Finally, because macroalgae absorb nutrients through the blades, (g) the surface-area-to-volume ratio (SA:V) is associated with nutrient acquisition (Littler & Littler, 1980).
Traits h-l are hypothesized to relate to space use and complexity, and hence, competitive dominance. Plant height is a major determinant of competitive dominance (Díaz et al., 2016). Its macroalgal analogue (h) maximum length and, by extension, (i) aspect ratio, or the ratio between maximum length and width, relate to the ability of macroalgae to outcompete surrounding individuals by taking the position of canopy and emerge from a trade-off between light capture and structural integrity (Carpenter, 1990;Littler & Littler, 1980). The presence of (j) pneumatocysts (i.e. air bladders) is another important predictor of macroalgal competitive dominance through canopy occupancy (Dromgoole, 1981).
(k) Branching order, or the degree of branching of a thallus, and (l) the surface-area-to-perimeter ratio (SA:P) relate to three-dimensional complexity and resource acquisition, and hence, both competitive dominance and economics (Steneck & Dethier, 1994;Veiga, Rubal, & Sousa-Pinto, 2014). High complexity allows individuals to maximize light exposure (Stewart & Carpenter, 2003), provides greater nutrient and gas exchange, delays desiccation at low tide (Hay, 1981;Padilla, 1984;Taylor & Hay, 1984) and reduces the impact of herbivory (Padilla, 1984), but increases drag (Starko, Claman, & Martone, 2015). We measured both traits at the whole individual level to capture the complexity of the whole thallus, since all parts of the thallus, from holdfast to fronds, are important habitats for epibiota and nekton (Teagle et al., 2017).
Large or structurally complex individuals were subsampled, ensuring that all parts of the thallus were included at representative proportions (Table S3). We measured the surface area and perimeter of partly microscopic species on subsamples under the microscope and proportionally scaled them up to the whole sample (Table S3). The samples were cleaned of epibiota in seawater and rinsed in deionized water for elemental screening. To obtain TDMC, we recorded sample wet and oven-dried mass We measured frond (when differentiated) or whole-individual surface area (mm 2 ) and whole-individual perimeter (mm) using the software ImageJ (Schneider, Rasband, & Eliceiri, 2012), and calculated SA:V (mm 2 /ml), STA (mm 2 /g) and SA:P (mm). Volume (ml) was measured by water displacement. Maximum length (cm) was measured from the base of the holdfast to the tip of the longest blade. Aspect ratio was obtained by dividing maximum length by maximum width (i.e. largest width of a naturally spread out sample). Branching order was measured as the average number of divisions of the main axes of a thallus from its holdfast to the tip of the blades out of five measurements taken haphazardly within the sample. To obtain C and N content and C:N, ground samples were weighed with a microbalance (Sartorius CPA2P, 0.000001 g) and run through an elemental analyser (PDZ Europa 2020 isotope ratio mass spectrometer interfaced with an ANCA GSL elemental analyser and calibrated with acetanilide).

| Categorization of species into functional groups
We allocated species to the groups defined by Littler and Littler as well as Steneck and Dethier based on a review of the literature (Table S1). The species we screened belonged to five traditional groups: 'articulate calcareous', 'coarsely branched', 'filamentous', 'sheet' and 'thick leathery' for Littler and Littler's functional-form model, and 'filamentous (S)', 'foliose', 'corticated', 'leathery' and 'articulated calcareous', as defined by Steneck and Dethier (1994), in increasing order of cortication. Although both schemes contain groups with similar or identical names, they were originally defined using different approaches and are not assumed a priori to be analogous. We also tested Steneck and Dethier's detailed scheme, which includes two subgroups (Supporting Information, Section 1). We used two common categorizations of vertical space use as follows: the binary canopy versus turf scheme and a three-level canopy/ subcanopy/turf scheme adapted from Arenas et al. (2006). Turfs were considered macroalgae with little to no three-dimensional structure (compared with kelp and other canopy-forming macroalgae) that form a dense layer of fine filaments, branches or plumes on the substratum (Filbee-Dexter & Wernberg, 2018). This broad definition of turf macroalgae allowed categorization of all species within our study. Vertical structure in the water column is somewhat community-dependent, so we categorized species into the three-level scheme based on what we judged was the most common scenario on the rocky shores screened.

| Data analysis
We performed all analyses in r 3.5.3 (R Core Team, 2019) and plotted graphical results using ggplot2 (Wickham, 2016) and ggpubr (Kassambara, 2019 Extensive species reclassification-that is, group reworking-would suggest substantial mismatch between traditional groups and underlying trait variation and hence, rather low accuracy of traditional groups. We created emergent groups from the weighted Gower matrix using k-medoids clustering (k-medoids), a top-down clustering approach whereby species are assigned to a chosen number of groups based on multivariate distance from group medoids, making it rather robust to noise and outliers (Reynolds, Richards, de la Iglesia, & Rayward-Smith, 2006; using 'pam' in package cluster; Maechler et al., 2019).
Clustering to generate emergent groups also provided a tool for generating a more functionally informative alternative to traditional functional groupings. To allow the data to inform not only the assignment of species to groups but also the number of groups, we used k-medoids (as described above) while allowing an increasing number of groups (from five upwards), searching for an emergent grouping scheme that maximized overall explanatory power and parsimony while maintaining statistically significant differ-

| Distributions of individual traits
For both form-based grouping approaches, species-level trait distributions were generally not unimodal and were right-skewed, suggesting that their groups captured underlying trait variation with limited accuracy (Figures 1 and 2). Under both schemes, group overlap was extensive across traits; from a possible 55 cases (11 con- reflecting the position of groups along the continuum of trait variability (Figures 1 and 2). Overall, '(thick) leathery' and 'articulate(d) calcareous' were the two most distinct functional groups (Figures 1   and 2).
Both categorizations of macroalgal stature (i.e. vertical structure in the water column) explained significant differences in most of the traits' distributions (pairwise Wilcoxon rank sum test, p < 0.05; Figure 3, see Figure S1 for the three-level scheme). Specifically, in the canopy versus turf scheme, canopy species had greater thickness, maximum length, aspect ratio and C:N, while turf species had greater SA:V, STA, C and N content values. In the three-level scheme, groups differed in thickness, maximum length, SA:V and STA (pairwise Wilcoxon rank sum test, p < 0.05; Figure S1). Canopy species also had lower values than turf for SA:P and N content, and greater values for C:N. Notwithstanding these differences, staturebased groups spanned wide ranges of trait values and, in most cases, displayed a high degree of overlap, suggesting limited precision. The prevalence of significant differences between groups, compared to the two form-based approaches, should be interpreted in light of the higher within-group sample sizes.

| Distributions in multivariate trait space
Many of the functional traits were entrained along the first PCoA axis. Species positioned further along that axis had lower maximum length, C:N ratio and thickness and higher SA:V, STA and N content (Figures 4 and 5  PCoA axis was clearly most strongly associated with branching order and, to a lesser extent, STA (Figures 4 and 5). This suggests that similarly to the first axis, the second captured some aspects of both resource utilization and competitive dominance. Species located further along that axis displayed lower branching order and higher STA and thus, are expected to favour light acquisition to the detriment of resistance to desiccation, herbivory and water movement ( Table 1).
The first two principal coordinates accounted for 50% of inertia in the distance matrix, while the third (not shown) accounted for an additional 13%.  (Figures 1, 2, 4 and 5). Calcareous species displayed high C content and branching order, and stood out from the rest of the species. Coarsely branched/corticated species were scattered across most of the trait space. The filamentous groups mainly gathered species with high SA:V and branching order. Finally, the 'sheet'/'foliose' group principally represented species with low C:N and high STA (Figures 1, 2, 4 and 5).

| Reclassification across grouping approaches
To evaluate how species are classified under different approaches (including emergent groups), the group membership of individual species can be traced across grouping schemes ( Figure 6). Despite the less sub- Tables S1 and S5). These reclassified species were primarily coarsely branched under Littler and Littler's scheme.
To assess whether traditional grouping schemes accurately capture underlying trait expression, we examined species reclassification-that is, group reworking-from five-group traditional to emergent groupings.
Most species were vastly reworked in their group allocation, with only a few tight nuclei of species left unchanged. Notably, (thick) leathery species became split into two different groups (Figures 5 and 6; Tables S1 and S5). The first group, emergent group 2, corresponded to species

| A proposed nine-group emergent scheme
In order to identify an alternative, trait-based grouping scheme emerging from our dataset, we ran k-medoids with an increasing number of groups. While we observed a monotonic increase in the extent of trait variation explained as well as parsimony, a nine-group emergent scheme maximized explanatory power and parsimony while maintaining significant differences between all groups. This scheme explained about two-thirds of multivariate trait expression (PERMANOVA, R 2 = 0.69, p < 0.001), and all emergent groups were significantly different from each other (pairwise PERMANOVA, p < 0.05; Table S6).
The scheme's explanatory power remained substantial even when we conducted resampling to allow for intraspecific variability in species' traits [PERMANOVA, R 2 = 0.57 ± 0.02 (M ± SD)]. The species composition of the groups emerging from our trait data is largely uncoupled from macroalgal general appearance (i.e. gross morphology, anatomy, and stature; Figure 7; Table S1). Emergent groups 2 and 3 (thick leathery), 4 (coarsely branched) and 6 (articulate calcareous) gather species belonging to a single morpho-functional group; still, all traditional groups but articulate (

| D ISCUSS I ON
As rapid global change dramatically alters community composition, it is imperative that functional schemes-across all major producer groups-are fit-for-purpose. We assessed whether common categorizations of form and stature explained trait differences across macroalgae collected from rocky shores with, to our knowledge, the largest set of macroalgal traits assembled to date. Despite their convenience and prevalence in macroalgal ecology, the traditional grouping schemes all left substantial amounts of interspecific trait variation unexplained. These results highlight the need for a re-evaluation of macroalgal grouping approaches and an increased focus on underlying functional trait variation.

| Traditional groups are incomplete representatives of underlying physiological and ecological variation
Groups established by Littler and Littler (1980) and Steneck and Dethier (1994) were built on the premise that morphology and anatomy capture interspecific differences in physiology and function.

F I G U R E 7
A proposed nine-group emergent scheme. Group composition and locations in macroalgal functional trait space are shown. The divisive k-medoids clustering method was used to create the emergent groups based on post hoc grouping of the trait data. Confidence ellipses (50%) are represented, assuming a multivariate normal distribution. Arrows indicate most strongly related functional traits to each PCoA axis (trait-axis relationships are given in Figure 4). Exact species attribution to the groups of all grouping approaches can be found in Table S1 [Colour figure can be viewed at wileyonlinelibrary.com] Our finding of limited precision, accuracy and stability of such groups suggests that anatomy (mainly, cortication) and morphology (gross form and branching order) alone fail to reliably capture variation in functional traits among species. Assuming functional traits are more direct proxies of physiology and potential contributions to ecosystem functions, Littler and Littler's and Steneck and Dethier's schemes do not provide a strong link between form and function.
In seeming contrast to our results, traditional grouping schemes have been reported to account for functional properties, from photosynthetic rates to susceptibility to grazing (e.g. Littler & Littler, 1980, 1984Steneck & Dethier, 1994). However, closer examination of these studies reveals that large overlap between groups is common and inferences are often drawn from differences between extreme groups, such as articulate(d) calcareous or encrusting against remaining groups (Littler & Arnold, 1982;Littler & Littler, 1984;. Moreover, previous qualitative review found a general mismatch between functional groups and ecological responses and effects across ecosystems (Padilla & Allen, 2000). Our findings also echo work on vascular plants showing that growth form does not accurately represent underlying evolutionary trade-offs between photosynthetic rates, construction costs and longevity and hence, fails to capture global patterns of plant functional traits, nutrient cycling and primary productivity (Díaz et al., 2016;Shipley et al., 2006).
Notwithstanding the overall inadequacy of form-based grouping schemes of macroalgal form in capturing underlying functional trait expression, some of their groups fared worse than others. The groups '(thick) leathery' and 'coarsely branched'/'corticated' were particularly spread out in trait space and were extensively reworked across the groups of the five-group emergent scheme, highlighting their lack of functional distinction. The '(thick) leathery' group was split up between two main emergent groups as follows: an isolated one that consisted entirely of (thick) leathery species, representing what may be considered 'true' (thick) leathery macroalgae (emergent group 3), and one that associated with other morphologies (emergent group 2). In fact, the latter mixed some sheet/foliose and coarsely branched/corticated species with native kelps (orders Laminariales and Tilopteridales). The non-native kelp Undaria pinnatifida was grouped with sheet-like species (emergent group 5), suggesting substantial functional differences with native kelps in accordance to recent findings (reviewed in Teagle et al., 2017). such as reduced C:N and higher STA, correspond to contrasts between 'slower', longer-lived species, and 'faster' opportunistic forms (Airoldi, 1998;Littler & Littler, 1980. These differences may partially explain the global rise of turfs with altered environmental conditions (Feehan et al., 2019;Filbee-Dexter & Wernberg, 2018) as well as the consequences of canopy-to-turf shifts for carbon flow and habitat provisioning (Copertino, Connell, & Cheshire, 2005; Filbee-Dexter & Wernberg, 2018). Therefore, despite risking substantial loss of information and lack of generality, broad categorizations of macroalgal stature may offer quick first-order assessments of the ecological impacts of fast-paced changes in macroalgal assemblages.

| Functional traits and emergent groups offer more ecologically informative alternatives to traditional macroalgal groups
Although traditional functional groups can allow practical and rapid assessments of macroalgal assemblages, functional trait values-as the closest proxies of primary producer functional variation and ecological roles (Díaz et al., 2016;Vile, Shipley, & Garnier, 2006) -provide a more ecologically informative alternative. Our study provides a fresh perspective on the functional differentiation and diversity of seaweed species, and the accompanying trait data provides a platform for researchers to more accurately capture functional identity and diversity (Boyé et al., 2019;Villéger, Mason, & Mouillot, 2008) and therefore more strongly connect changes in community structure to ecosystem processes (Cadotte, 2017;Gagic et al., 2015). As an alternative to direct use of trait values, we also provide a nine-group emergent scheme, potentially simplifying field and analytical methods, while still allowing meaningful measurements of functional diversity (Chapin et al., 1996;Chen et al., 2017;Díaz & Cabido, 2001 Yet, more broadly unleashing the potential of trait-based approaches to macroalgal ecology will require trait data covering other regions and a much greater proportion of the world's seaweed taxa. This could be achieved by geographically distributed research groups undertaking trait-screening using standardized methods and curating data within open-access databases. Because trait screening demands substantial resources, a key challenge is to identify a manageable set of ideally orthogonal, ecologically relevant traits before embarking on globally coordinated efforts. Here maximum length, branching order and surface area to volume ratio emerge as contenders because, collectively, these traits are highly associated with orthogonal axes of variation and relate to both competitive dominance and resource economics. Prioritizing abundant or otherwise functionally important (e.g. habitat-forming) species would further reduce the burden of trait screening.

| CON CLUS IONS
Our analysis using a comprehensive dataset of directly measured functional traits revealed substantial limitations to the group gambit of macroalgae. Despite accounting for up to more than a third of trait expression, traditional groups appeared to inadequately capture macroalgal functional variation; we found substantial group overlap (i.e. low precision) and mismatch between traditional groups and underlying traits values (i.e. low accuracy). Therefore, gross morphology and anatomy as well as vertical position in the community are incomplete representatives of macroalgal functional identity. Conversely, post hoc emergent grouping schemes such as that proposed herein have the potential to more accurately capture macroalgal trait expression than traditional groups while retaining the convenience of species grouping. Ultimately, functional traits are the closest proxies of macroalgal functional variation and hence, should provide the strongest assessments of macroalgal roles under global change. Studies like ours, which emphasize the potential of direct use of functional traits in capturing macroalgal form and function, can act as an incentive to build large, coordinated datasets spanning all main aspects of macroalgal eco-physiology.

DATA AVA I L A B I L I T Y S TAT E M E N T
We have archived our data on Dryad Digital Repository: https://doi. org/10.5061/dryad.nvx0k 6dpn .