Notes: * p < 0.05; ** p < 0.01; *** p
< 0.001; ‘.’ 0.05 < p < 0.1; ‘n.s.’ not
significant (p > 0.1). ‘n’ number of points to calculate
relationship. To normalize data: log transformation on all traits,
expect for height and maximum root length that have been square-root
transformed. ‘C. ant’ Callitriche antarctica, ‘L. aus’ Limosella
australis, ‘R. bit’ Ranunculus biternatus, ‘R. mos’ Ranunculus moseleyi.
Within ponds, growth strategies relied on aerial, clonal, and root
categories of traits. Aerial traits as a whole responded to water
nutrients only, while clonal and root traits were impacted by several
biotic and abiotic variables (Table 2). Clonal traits responded to
species abundance, water depth, electric conductivity and nutrients,
while root traits were impacted by species abundance, phytoplankton
concentration, and water temperature.
Four traits responded to either pond abiotic (height, SLA and internode
length, respectively 3.15%, 14.31% and 5.03% of explained variance)
or biotic (specific internode mass, 10.28% of explained variance)
variables only (Table 2). Nevertheless, variations in the three other
traits i.e. LDMC, maximum root length, and specific root mass
were influenced as expected, by both biotic and abiotic variables (Table
2). Our results showed that the responses of these three traits to the
different tested variables were trait dependent, with a higher
cumulative influence of biotic variables (Table 2). LDMC and maximum
root length were rather predicted by species abundances (5.90% and
4.37% of explained variance respectively), whereas specific root mass
was mostly predicted by phytoplankton concentration (10.49%). To a
lesser extent, water physico-chemistry (i.e. temperature, pH, and
nutrients) influenced both root traits, while LDMC was impacted by
sediment nutrients.
Plant traits directly and indirectly affected individual
performance