DOI: 10.5061/dryad.1vhhmgr5z
title: Functional traits are moderate predictors of above- and belowground biomass in multispecies seagrass habitats

description: Seagrasses are important ecosystem engineers that maintain biodiversity
 and modify the abiotic and biotic environment. At present, we are lacking
 a wider understanding on the functional traits that predict seagrass
 biomass stock, whether trait-biomass associations vary across multispecies
 seagrass habitats and which biodiversity mechanisms explain variation in
 ecosystem functions in seagrass ecosystems. To explore which traits
 predict biomass, we conducted a field survey along 1500 km coastline of
 Western Australia, where species-rich seagrass meadows are common. We
 sampled multispecies meadows at 14 sites in coastal embayments or
 estuarine habitats and measured seven morphological and biochemical traits
 of multiple species. Our aim was to explore the functional structure of
 seagrass communities in coastal embayments and estuaries and investigate
 how various components of diversity (species richness, community-weighted
 mean traits (CWM), and functional dispersion (FDis)) predict above- and
 belowground biomass in multispecies seagrass habitats by using piecewise
 structural equation modelling.  Trait-biomass associations ranged
 from strong (standardized path coefficient 0.5) to weak (&lt; 0.2).
 More traits predicted belowground than aboveground biomass, and the total
 explained variance was higher when conducting separate analysis for
 coastal embayments compared to including both seagrass habitats.
 Site-level variation accounted for the largest part of the explained
 variation in biomass stock as the overall explanatory power of traits to
 biomass was low (r2 &lt; 0.3). For individual traits, mass ratio
 effects (CWM) primarily predicted biomass in both coastal embayments and
 estuaries, and species were functionally similar (low FDis).  Our
 study concludes that functional traits act as moderate predictors of
 biomass stock across multispecies temperate seagrass habitats, but
 environmental context is of more importance. Our results further
 demonstrate that the main biodiversity mechanism driving biomass
 allocation in multispecies seagrass communities is through dominance
 rather than complementarity, and co-existing species show similarity in
 their functional traits. The predictive strength of individual traits to
 biomass varied between different seagrass habitats, indicating
 context-dependency in trait-biomass associations. More research is needed
 to understand how patterns in functional diversity are regulated by the
 environment and how such patterns relate to other ecosystem properties and
 services sustained by these important ecosystems.
description: # Functional traits are moderate predictors of above- and belowground
 biomass in multispecies seagrass habitats Dataset DOI:
 [10.5061/dryad.1vhhmgr5z](10.5061/dryad.1vhhmgr5z) ## Description of the
 data and file structure Data was collected in a field survey along 1500 km
 coastline of south-west Australia in November 2016–January 2017. 14 sites
 were sampled of which ten were coastal embayment and four estuarine sites.
 At each site, 12 samples were collected (using a core, ⌀ 25 cm) with 168
 samples in total. All above- and belowground material from each sample was
 harvested and later dried in the laboratory. Trait measurements were
 weighted by the shoot densities of each species to retrieve the
 community-weighted mean trait value and standardized to have a mean of 0
 and variance of 1. See article for more information on the trait
 measurements.   ### Files and variables #### File: Traits\_allsites.csv
 **Description:** Data used in piecewise Structural Equation Modeling (SEM)
 for second hypothesis that species richness and community-weighted mean
 (CWM) of various morphological and biochemical seagrass traits would
 predict biomass stock through direct and indirect associations across and
 within different habitats. Standardized CWM-values were used in the
 analyses. ##### Variables * site ID: ID for each site on map shown in
 Figure S2 in the Supporting Information - site: Site number (same as ID
 but without letter) - rep: replicate number of samples from each site
 (1-12) - Habitat: Two habitat types sampled; coastal embayment (C) and
 estuarine site (E) - SpRich: Species richness, unit: number of seagrass
 species - CWM.Hmax_raw: Community-weighted canopy height (Hmax),
 non-standardized, unit: cm - CWM.LDMC_raw: Community-weighted leaf dry
 matter content (LDMC), non-standardized, unit: mg g-1 - CWM.SLA_raw:
 Community-weighted specific leaf area (SLA), non-standardized, unit: mm2
 mg-1 - CWM.RDMC_raw: Community-weighted root dry matter content (RDMC),
 non-standardized, unit: mg g-1 - CWM.RhizDMC_raw: Community-weighted
 rhizome dry matter content (RhizDMC), non-standardized, unit: mg g-1 -
 CWM.LeafN_raw: Community-weighted leaf nitrogen concentration (LeafN),
 non-standardized, unit: mg g-1 - CWM.RootN_raw: Community-weighted root
 nitrogen concentration (RootN), non-standardized, unit: mg g-1 - CWM.Hmax:
 Community-weighted canopy height (Hmax), standardized to have a mean of 0
 and variance of 1 - CWM.LDMC: Community-weighted leaf dry matter content
 (LDMC), standardized to have a mean of 0 and variance of 1 - CWM.SLA:
 Community-weighted specific leaf area (SLA), standardized to have a mean
 of 0 and variance of 1 - CWM.RDMC: Community-weighted root dry matter
 content (RDMC), standardized to have a mean of 0 and variance of 1 -
 CWM.RhizDMC: Community-weighted rhizome dry matter content (RhizDMC),
 standardized to have a mean of 0 and variance of 1 - CWM.LeafN:
 Community-weighted leaf nitrogen concentration (LeafN), standardized to
 have a mean of 0 and variance of 1 - CWM.RootN: Community-weighted root
 nitrogen concentration (RootN), standardized to have a mean of 0 and
 variance of 1 - Shootbiom: Aboveground biomass (shoots, sheaths, vertical
 rhizomes/stems) gram dry weight per sample, unit: g dwt 0.049 m-2 -
 LOGShootbiom: natural log of Aboveground biomass - Belowbiom: Belowground
 biomass (horizontal rhizomes, roots) gram dry weight per sample, unit: g
 dwt 0.049 m-2 - LOGBelowbiom: natural log of Belowground biomass ####
 File: Traits\_all\_CWM\_FDis.csv **Description:** Data used in piecewise
 SEM for third research hypothesis on how individual traits may mediate the
 effect of species richness on biomass through different biodiversity
 mechanisms (mass ratio effect/dominance, quantified using CWM and niche
 complementarity, quantified using Functional dispersion, FDis). The
 computation for FDis requires at least two species to co-occur, and it was
 calculated for samples with &gt;2 sp. for each trait separately (infers
 niche complementarity for individual traits) and on all measured seven
 traits (multivariate FDis). The number of samples with &gt;2 sp. was
 unequal across all sites, and we randomly selected 5 samples from each
 site to ensure equal sample size between sites. Variable descriptions same
 as above (other file), but description for FDis added (only computed using
 this dataset, please refer to article for more information). Standardized
 CWM-values were used in the analyses. ##### Variables * FDis.Hmax:
 Functional dispersion of canopy height (Hmax), unitless * FDis.LDMC:
 Functional dispersion of leaf dry matter content (LDMC), unitless *
 FDis.SLA: Functional dispersion of specific leaf area (SLA), unitless *
 FDis.RDMC: Functional dispersion of root dry matter content (RDMC),
 unitless * FDis.RhizDMC: Functional dispersion of rhizome dry matter
 content (RhizDMC), unitless * FDis.LeafN: Functional dispersion of leaf
 nitrogen concentration (LeafN), unitless * FDis.RootN: Functional
 dispersion of root nitrogen concentration (RootN), unitless *
 FDis_multivar: Multivariate functional dispersion calculated on all
 measured seven traits, unitle ## Code/software Community Weighted Mean
 (CWM)  values and Functional Dispersion (FDis) were calculated with the
 *FD* package in R (Laliberté et al. 2014). For piecewise structural
 equation modeling (SEM), Site was included as a random effect to model
 variation in the intercepts among the sites. The responses were then fit
 with linear mixed models using the function lme from the *nlme* package in
 R (Pinheiro et al. 2013). Piecewise SEM was then used to fit the path
 models using the package *piecewiseSEM* in R (Lefcheck 2016). Laliberté E,
 Legendre P, Shipley P. 2014. FD: measuring functional diversity from
 multiple traits, and other tools for functional ecology. R package version
 1.0-12. Lefcheck JS. 2016. PiecewiseSEM: Piecewise structural equation
 modelling in R for ecology, evolution and systematics. Methods in Ecology
 and Evolution 7: 573-579. Pinheiro J, Bates D, DebRoy S, Sarkar D, and R
 Core Team. 2013. nlme: Linear and Nonlinear Mixed Effects Models.