SeaTraIn (beta)

The Seaweed Trait Initiative started in 2016 within the PhD projects of Alizée Mauffrey and Laura Cappelatti, under the supervision of Dr. John Griffin, at Swansea University (Wales). Noting the lack of widely available and organised information on macroalgal traits, they started collecting and measuring species found in the British coast, and to date there are almost 100 species and 10 continuous traits in the dataset. The overarching goal is to inspire researchers and seaweed enthusiasts around the globe to contribute with the expansion of this data, and to have an accessible, comprehensive set of trait data for seaweeds of all groups and forms. Below you can explore the species and the traits that are available at the moment.

Please note that this is a beta version of this website, and in time we hope to expand not only the data but the tools to explore the dataset.

Feel free to contact us.

Download the dataset (.xlsx)

List of species

Ahnfeltiopsis devoniensis
Alaria esculenta
Ascophyllum nodosum
Asparagopsis armata
Asperococcus fistulosus
Bifurcaria bifurcata
Blidingia minima
Bonnemaisonia hamifera traillella
Bostrychia scorpioides
Bryopsis hypnoides
Calliblepharis jubata
Callithamnion tetricum
Carpodesmia tamariscifolia
Catenella caespitosa
Ceramium botryocarpum
Ceramium nodulosum
Chaetomorpha linum
Chondracanthus acicularis
Chondracanthus okamurae
Chondrus crispus
Chorda filum
Chordaria flagelliformis
Chylocladia verticillata
Cladophora rupestris
Cladophora sericea
Cladostephus spongiosus
Codium fragile spp. atlanticum
Codium fragile spp. fragile
Colpomenia peregrina
Corallina officinalis
Cryptopleura ramosa
Cystoclonium purpureum
Desmarestia aculeata
Dictyota sp.
Dilsea carnosa
Ellisolandia elongata
Eudesme virescens
Fucus distichus
Fucus serratus
Fucus spiralis
Fucus spiralis var. nanus
Fucus vesiculosus
Fucus vesiculosus var. linearis
Furcellaria lumbricalis
Gelidium crinale
Gelidium pulchellum
Gelidium pusillum
Gelidium spinosum
Gracilaria gracilis
Grateloupia filicina
Gymnogongrus griffithsiae
Halidrys siliquosa
Halopteris scoparia
Halurus equisetifolius
Halurus flosculosus
Heterosiphonia plumosa
Himanthalia elongata
Hypoglossum hypoglossoides
Jania rubens var. rubens
Laminaria digitata
Laminaria hyperborea
Leathesia marina
Lomentaria articulata
Mastocarpus stellatus
Membranoptera alata
Osmundea hybrida
Osmundea osmunda
Osmundea pinnatifida
Palmaria palmata
Pelvetia canaliculata
Plocamium sp.
Plocamium maggsiae
Polyides rotunda
Porphyra dioica
Pterocladiella capillacea
Rhodothamniella floridula
Rhodymenia holmesii
Rhodymenia pseudopalmata
Saccharina latissima
Saccorhiza polyschides
Sargassum muticum
Taonia atomaria
Treptacantha baccata
Ulva compressa
Ulva intestinalis
Ulva lactuca
Ulva linza
Ulva rigida
Umbraulva dangeardii
Undaria pinnatifida
Vertebrata byssoides
Vertebrata fruticulosa
Vertebrata fucoides
Vertebrata lanosa
Xiphosiphonia ardreana

List of traits

Species Taxonomic identity of the sample, mainly, species. When appropriate, life stage or subspecies is given.
SD This suffix indicates that the standard deviation is given for each corresponding trait value. "NA" applies when a "species" only had a single replicate.
SE This suffix indicates that the standard error is given for each corresponding trait value. "NA" applies when a "species" only had a single replicate.
TDMC Thallus Dry Matter Content (no units): obtained by dividing dry mass (g) by fresh mass (g)
Thickness (mm) Average frond thickness (mm) out of 10 measurements taken haphazardly along the blades of a sample
Max length (cm) Maximum length (cm) of a sample, from the base of the holdfast (or any other anchoring system) to the tip of the longest blade. Measured prior to any subsampling to respect the proportions of the macroalga.
Aspect ratio General shape of the sample, obtained by dividing maximum length (cm) by maximum width (cm).
Branching order Average number of divisions of the main axes of a macroalga from its holdfast to the tip of the blades out of 5 measurements taken haphazardly within the sample; no units
SAV (mm2 mL-1) Surface Area to Volume ratio (mm2 mL-1): obtained by dividing the area (mm2) of a sample by its volume (mL)
STA (mm2 g-1) Specific Thallus Area (mm2 g-1): obtained by dividing the area (mm2) of a sample by its dry mass (g)
SA:P Surface Area to Perimeter ratio (no units): obtained by dividing the area (mm2) of a sample by its perimeter (mm)
C (%) Carbon content (%)
N (%) Nitrogent content (%)
C:N Carbon to Nitrogen ratio (no units): obtained by dividing Carbon content (%) by Nitrogen content (%)
Pneumatocysts (YES/NO) Presence (YES) or absence (NO) of pneumatocysts (or "air bladders") among the blades of the sample
MFGs Morpho-functional groups defined by the functional-form model of Littler and Littler (1980, "The evolution of thallus form and survival strategies in benthic marine macroalgae: field and laboratory tests of a functional form model")
FGs simple Functional groups defined by Steneck and Dethier's (1994, "A functional group approach to the structure of algal-dominated communities") simplified classification (i.e., without sub-groups)
FGs Functional groups defined by Steneck and Dethier's (1994, "A functional group approach to the structure of algal-dominated communities") detailed classification (i.e., including sub-groups based on level of cortication)
Canopy turf Binary classification of vertical space use. 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).
Vertical use Three-level classification of vertical space use adapted from Arenas et al. (2006, "The invasibility of marine algal assemblages: role of functional diversity and identity"). Location along the canopy 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.
EGs k medoids Nine-cluster emergent classification built on the twelve traits of the dataset using k-medoids, a top-down clustering approach
NA Not Applicable
ND No Datum