a= (Cornelissen et al., 2003) ; ab= (Gong & Gao, 2019) ; ac=
(Santamaría, 2002) ; ad= (Freschet et al., 2017) ; b= (Meng et al.,
2015) ; c= (Dostálek et al., 2020) ; d= (Poorter et al., 2009) ; e=
(Lavorel & Garnier, 2002) ; f= (Jung et al., 2010) ; g= (Loughnan &
Gilbert, 2017) ; h= (Violle et al., 2009) ; i= (Butterfield & Callaway,
2013) ; j= (Rooney & Kalff, 2000) ; k= (Wehn et al., 2017); l= (Fu et
al., 2018) ; m= (Bittebiere & Mony, 2015) ; n= (Gray & Brady, 2016) ;
o= (Moles et al., 2009) ; p= (Tao et al., 2016) ; q= (Wang et al.,
2016) ; r= (Martinez‐Almoyna et al., 2020) ; s= (Hutchings et al.,
1997) ; t= (Rusch et al., 2011) ; u= (Louâpre et al., 2012) ; v= (Slade
& Hutchings, 1987b) ; w= (Slade & Hutchings, 1987a) ; x= (Bittebiere
et al., 2019) ; y= (Colom & Baucom, 2020).
Nutrients and phytoplankton quantity
assay
For the measurements of water nutrient and phytoplankton concentrations
(as depicted by the amount of chlorophyll a per mL of water), water
samples were filtered through GF/F filters (0.7µm, Whatman) to remove
coarse and fine particles and phytoplanktonic organisms within 24 hours
of sampling. All samples (filtered water and sediments, and filters)
were stored at -20 °C for several weeks before chemical analyses in
metropolitan France.
Chlorophyll a pigments were quantified from GF/F Whatman filters using
the Unesco method (Vohra, 1966) with a spectrophotometer, and allowed
the quantification of phytoplankton per volume of water in each pond.
N–NH4+,N–NO3- and
P-PO43- concentrations of the water
samples were determined using colorimetric methods with a sequential
analyzer (SmartChem200, AMSAlliance) (Grasshoff et al., 1999). In
sediment samples, organic carbon to nitrogen ratio (C:N) was measured
using the “capsule method” (Brodie et al., 2011). An aliquot of 5 mg ±
10 % of homogeneous sediment sample was acidified into 100 µL of 2M HCl
in silver capsules to eliminate carbonates. Capsules liquids were
evaporated on a 65°C hot plate for 12 hours, before capsules were
oven-dried at 80 °C for two days. Organic carbon and nitrogen total
contents were then measured with an elementary analyzer (FlashEA 1112 NC
Analyzers®, Thermo Fisher Scientific, Waltham, Massachusetts, USA). The
bioavailable P concentration in sediments was determined following the
protocol of (Ni et al., 2016). Available P was extracted from 50 mg of
dry sediment samples with 5 ml of NaOH (1 M). Then, the supernatant was
collected, its pH was stabilized with HCl (3.5 M), and the extracted P
that has been converted into orthophosphate was quantified using the
molybdate/ascorbic acid blue method (Murphy & Riley, 1962); results
were reported in mg of available P per g of dry sediment.
Data analysis
We tested for correlations between all pairs of traits using Spearman
rank correlations to detect possible co-variations among traits. We did
not detect significant strong correlations between any pair of traits
(Spearman’s rho < 0.7, Dormann et al., 2013). Thus, each trait
was indicative for a part of global strategy of growth.
Abiotic and biotic characterization
of
ponds
First, to characterize the abiotic conditions within ponds, the mean of
daily (day and night) water temperatures was calculated from November
2019 to November 2020 for each pond (Douce et al., in prep). Abiotic
variables that have been measured at quadrat scales have been averaged
at pond scale. Depending on the analysis, we performed several
reductions of abiotic variables. Water nutrients dimensions
(N-NH4+,
N-NO3-,
P-PO43-) were reduced to the first
axis of a Principal Component Analysis (PCA), in which data were
centered and standardized by standard deviation (Supporting information
2), hereafter named ‘PCAwater’. PCA Axis 1 (42.06%) was negatively
related to the concentration of N-NH4+and N-NO3-. Similar reduction of
dimensions was done for sediment nutrients (C:N ratio, bioavailable
phosphorus) (Supporting information 3), hereafter named ‘PCAsediments’.
PCA Axis 1 (58.59%) was negatively related to the C:N ratio and
positively related to the bioavailable P concentration.
Second, to characterize the biotic conditions within ponds, we
calculated two non-correlated indices: the macrophyte species richness,
and the Pielou eveness. Biotic dimensions describing species abundance
in each pond were reduced to the first-three axes of a Factorial
Correspondence Analysis (FCA) (Supporting information 4). The first axis
(33.81%) was respectively positively and negatively driven by the
abundances of R. moseleyi and C. antarctica . The second
axis (24.99%) was positively correlated with the abundance of R.
pseudotrullifolius and negatively with J.scheuchzerioïdes . The third axis (16.99%) was related to the
abundances of L. australis (positively) and R. biternatus(negatively). In parallel, to assess the functional characteristics of
pond communities, we calculated the overall functional dispersion (then
referred as FDis) with seven traits (height, SLA, LDMC, internode
length, specific internode mass, maximum root length and specific root
mass), using the fdisp function of package FD (Laliberté &
Legendre, 2010).
Variance partitioning across nested
scales
We used traits measurements from 2017 to 2020 to partition trait
variances across four nested scales. A variance component analysis was
performed using the Parvart procedure (package Cati), for each of
the seven studied traits, across temporal, spatial, and phylogenetic
(including inter- and intraspecific variations) scales introduced in
this order in the model: time (year), space (site), species, and
within-species. For example, the part of variation of the species scale
represents the variance of the species means around the mean of their
site. To that aim, the mean value of each species is calculated, then
the variance of these species means are calculated around the site mean
to which they belong. Thus, the intra-site variation is the sum of the
species and within-species variations, that is the trait means variation
between species in each site, and between individuals in each species in
each site. Trait data were previously normalized (log or square-root
transformations). The significance of the variance components was
assessed by building 95% confidence intervals (CI) through a
bootstrapping procedure. We randomly selected 100 individuals out of the
1430 of our dataset with replacement (Messier et al., 2010), and
calculated the trait variance partitioning between the four nested
scales. We repeated this procedure 500 times to ultimately calculate a
95% CI for each variance component. To get rid of the imperfect nesting
of species within sites, we ran alternative partitioning models by
deleting species scale (Supporting information 5).
Trait responses to biotic and abiotic
filters
We aimed to determine which traits and categories of traits were
involved in their biotic and/or abiotic resistance. On the one hand, we
considered univariate responses and tested for the effects of abiotic
and biotic variables measured or calculated at pond scale, on trait
values measured in November 2020 and averaged at pond scale, on all
ramets of all species. In the full models, we included as explanatory
variables: species identity (categorical variable assessing the species
to which ramets belong to), macrophyte species richness, Pielou eveness,
community FDis, the first three axes of the FCA performed on macrophyte
species abundances (Supporting information 4), and the phytoplankton
concentration for biotic variables; water depth, pH, electric
conductivity, dissolved oxygen, mean temperature, pond area, and the
first axis of the two PCA performed on water nutrients and sediment
nutrients (Supporting information 2 and 3) for abiotic variables. Trait
data were previously normalized (log or square-root transformations)
when needed, and standardized. We applied a stepwise model reduction
based on the AIC criteria (MuMIn package, Bartoń, 2013) on the full
model to select the most parsimonious model and we performed an ANOVA
test (type II).
On the other hand, similar procedure was applied to test for the effects
of the above-listed abiotic and biotic variables on traits grouped into
three categories (multivariate responses): aerial (height, SLA, and
LDMC), clonal (internode length, and specific internode mass), and root
traits (maximum root length, and specific root mass). Trait data were
previously normalized (log or square-root transformations) when needed
and standardized. We selected variables by a backward elimination on the
full model (deleting the non-significant effects), and then performed a
MANOVA test (type II, Pillai statistic) based on the reduced model.
Traits and abiotic variable effects
on species
performance
Ramet total biomass (i.e. performance) and trait values recorded
in November 2020 were averaged per species at the pond level (n= 97),
and standardized separately for each species to get rid of variance due
to species identity. We tested for the direct and indirect effects of
the seven traits on individual performance using a Structural Equation
Modeling (SEM) procedure (Grace et al., 2010). All possible
relationships between traits were included based on the literature
(Supporting information 6). We performed a path analysis with the lavaan
package (Rosseel, 2012), and reduced the full model by variable
selection based on AIC values.
All statistical analyses were carried out with R 4.0.3.