DOI: 10.5061/dryad.nvx0k6dpn
title: Seaweed functional diversity revisited: confronting traditional groups with quantitative traits

description: 1. Macroalgal (seaweed) beds and forests fuel coastal ecosystems and are
 rapidly reorganising 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. 2. 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 – (i) two schemes based on gross morphology and anatomy
 and (ii) two categorisations 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. 3. (i) 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).
 (ii) Categorisations 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. (iii) A nine-group emergent scheme provided a highly explanatory
 and parsimonious alternative to traditional functional groupings. 4.
 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.
description: <b>2.1.
 Sampling</b> We measured
 eleven continuous and one categorical functional traits (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 twelve 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 6
 replicates per species, ranging from 1 to 45 (mode and median = 6, S.D. =
 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
 <i>Trailliella intricata</i> stage of <i>Bonnemaisonia
 hamifera</i>). 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.   <b>2.2. Trait
 screening</b> The functional
 traits measured are hypothesised to capture two fundamental aspects of
 primary producer variability, (1) the economics spectrum and (2)
 competitive dominance. We consider multiple indicators (or ‘functional
 markers’ <i>sensu</i> Garnier et al., 2004) to provide a more
 integrated estimation of ecological strategy and function. Here, we
 briefly summarise the ecological significance of the traits with regard to
 photosynthesis, structural integrity, space use and complexity (Tables 1,
 S4). The suite of economics-related traits indicates the relative
 investment in resource acquisition versus resistance to (a)biotic stress
 and therefore resource conservation, tying in with the<i>
 r</i>- (‘fast return’) to <i>K</i>- (‘slow-return’)
 selection continuum (Pianka, 1970). Slow-return primary producers tend to
 display long lifespans, low maximum photosynthesis and productivity,
 reduced palatability, and slow decomposition (Littler & Littler,
 1980; Smart et al., 2017; Wright et al., 2004). Traits
 <i>a-g</i> relate to photosynthesis and/or structural
 integrity, and hence, position on the economics spectrum:
 (<i>a</i>) 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). (<i>b</i>) Thickness
 also increases with the amount of structural tissue, providing resistance
 to physical stress and herbivore grazing (Cappelatti, Mauffrey, &
 Griffin, 2019; Littler & Littler, 1980; Littler, Taylor, &
 Littler, 1983); (<i>c</i>) Carbon (C) content and its ratio
 with (<i>d</i>) Nitrogen (N) content, (<i>e</i>)
 C:N, more directly quantify recalcitrant structural compounds relative to
 N-rich photosynthetically-active tissues (Cornelissen et al., 2003;
 Weykam, Gómez, Wiencke, Iken, & Klöser, 1996). Analogously to
 Specific Leaf Area (Wilson, Thompson, & Hodgson, 1999),
 (<i>f</i>) 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, the surface-area-to-volume ratio, or
 (<i>g</i>) SA:V, is associated with nutrient acquisition
 (Littler & Littler, 1980). Traits
 <i>h-l</i> are hypothesised 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, (<i>h</i>) maximum length, and by extension
 (<i>i</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 emerges from a tradeoff
 between light capture and structural integrity (Carpenter, 1990; Littler
 & Littler, 1980). The presence of (<i>j</i>)
 pneumatocysts (i.e. air bladders) is another important predictor of
 macroalgal competitive dominance through canopy occupancy (Dromgoole,
 1981). (<i>k</i>) Branching order, or the degree of branching
 of a thallus, and (<i>l</i>) 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 maximise 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 thallus parts, 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
 thallus parts 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 deionised water for elemental screening. To obtain TDMC, we
 recorded sample wet and oven-dried mass (g; Ohaus Scout Pro SP402, SPU602
 and Pioneer Analytical PA114, Parsippany, NJ, USA). Thickness (mm) was
 averaged from ten measurements taken haphazardly along the fronds (Digital
 Micrometers Ltd, DTG03 0.005, DML3032 0.001 mm, Sheffield, UK), avoiding,
 when applicable, the midrib. Individuals were scanned (Epson Perfection
 V600, V39, Suwa, Japan) or displayed on a lightbox (MiniSun A1,
 Manchester, UK) and photographed directly (Pentax K3 digital camera, SMC
 DA L 18-55 mm, Tokyo, Japan) or <i>via</i> an imaging
 microscope (Leica S8AP0, Wetzlar, Germany, affixed with GT Vision
 GXCAM-H3, Sudbury, UK). We measured frond (when differentiated) or
 whole-individual surface area (mm<sup>2</sup>) and
 whole-individual perimeter (mm) using the software ImageJ (Schneider,
 Rasband, & Eliceiri, 2012), and calculated SA:V
 (mm<sup>2</sup> mL<sup>-1</sup>), STA
 (mm<sup>2</sup> g<sup>-1</sup>), 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 5 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,
 Göttingen, Germany) and run through an elemental analyser (PDZ Europa 2020
 isotope ratio mass spectrometer interfaced with an ANCA GSL elemental
 analyser and calibrated with acetanilide).  
 <b>2.3.
 Categorisation of species into functional groups</b> 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: ‘filamentous’, ‘sheet’,
 ‘coarsely branched’, ‘thick leathery’ and ‘articulate calcareous’ for
 Littler and Littler’s functional-form model, and five groups defined by
 Steneck and Dethier (1994), ‘filamentous (S)’, ‘foliose’, ‘corticated’,
 ‘leathery’ and ‘articulated calcareous’, 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 <i>a priori</i> to be analogous. We also
 tested Steneck and Dethier’s detailed scheme, which includes two subgroups
 (Supplementary information, section 1). We used two common categorisations
 of vertical space use: the binary canopy vs. 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 categorisation of all species within our study.
 Vertical structure in the water column is somewhat community-dependent, so
 we categorised species into the three-level scheme based on what we judged
 was the most common scenario on the rocky shores screened.   <b>2.4. Creation of the
 emergent grouping schemes</b> We performed
 all analyses in R 3.5.3 (R Core Team, 2019). Species trait averages were
 transformed to bring their distribution as close to normality as possible
 and to reduce differences in scale across traits (Table S4). We imputed
 the four percent of average trait values that were missing from the
 dataset (function ‘mice’ in eponymous R package; van Buuren &
 Groothuis-Oudshoorn, 2011) to then reduce the dimensionality of the data
 using a Principal Coordinate Analysis (PCoA; function ‘cmdscale’). We
 favoured a PCoA over a Principal Component Analysis because it allowed us
 to include pneumatocyst presence. The PCoA was run on a weighted Gower
 matrix (function ‘daisy’ in ‘cluster’; Maechler, Rousseeuw, Struyf,
 Hubert, & Hornik, 2019), with equal weighting to traits associated
 with photosynthesis, structural integrity, space use, and complexity
 (Table S4). We created emergent groups from the weighted Gower matrix
 using <i>k</i>-medoids clustering
 (<i>k</i>-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 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 ran <i>k</i>-medoids while
 allowing an increasing number of groups (from five upwards), searching for
 an emergent grouping scheme that maximised overall explanatory power and
 parsimony while maintaining statistically significant differences
 (<i>P</i> &lt; 0.05) between all pairs of groups. Finally,
 to evaluate the robustness of the resulting grouping scheme to
 intraspecific trait variability, we computed the explanatory power
 (PERMANOVA; ‘adonis’ in ‘vegan’; Oksanen et al., 2019) of the new grouping
 across 999 iterations of the species x traits matrix, randomly assigning
 1/3 of rows to each of species-level mean, mean + 1 S.E., and mean - 1
 S.E. in each iteration. While we
 observed a monotonic increase in the extent of trait variation explained
 as well as parsimony, a nine-group emergent scheme maximised explanatory
 power and parsimony while maintaining significant differences between all
 groups. This scheme explained about two thirds of multivariate trait
 expression (PERMANOVA, <i>R</i><sup>2</sup> =
 0.69, <i>P</i> &lt; 0.001), and all emergent groups were
 significantly different from each other (pairwise PERMANOVA,
 <i>P</i> &lt; 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,
 <i>R</i><sup>2</sup> = 0.57 ± 0.02 [mean ±
 S.D.]).