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