Unravelling the architecture of functional variability in wild populations of Polygonum viviparum L

Abstract

Functional variability (FV) of populations can be decomposed into three main features: the individual variability of multiple traits, the strength of correlations between those traits and the main direction of these correlations, the latter two being known as 'phenotypic integration'. Evolutionary biology has long recognized that FV in natural populations is key to determining potential evolutionary responses, but this topic has been little studied in functional ecology.Here we focus on the arctico-alpine perennial plant species Polygonum viviparum L.. We used a comprehensive sampling of seven functional traits in 29 wild populations covering the whole environmental niche of the species. The niche of the species was captured by a temperature gradient, which separated alpine stressful habitats from species-rich, competitive sub-alpine ones. We seeked to assess the relative roles of abiotic stress and biotic interactions in shaping different aspects of functional variation within and among populations, that is, the multi-trait variability, the strength of correlations between traits, and the main directions of functional trade-offs.Populations with the highest extent of functional variability were found in the warm end of the gradient whereas populations exhibiting the strongest degree of phenotypic integration were located in sites with intermediate temperatures. This could reveal both the importance of environmental filtering and population demography in structuring FV. Interestingly, we found that the main axes of multivariate functional variation were radically different within and across population.Although the proximate causes of FV structure remain uncertain, our study presents a robust methodology for the quantitative study of functional variability in connection with species' niches. It also opens up new perspectives for the conceptual merging of intraspecific functional patterns with community ecology.

Keywords: alpine plants; ecological niche; functional traits; intraspecific variation; lines of least resistance; phenotypic integration; variance-covariance matrix.

Figure 1. Graphical representation of the functional variability of a population as an ellipsoid

Each of the three characteristics of FV translates into different kinds of ellipsoids, as exemplified by the pictures. Statistical measures of each characteristic are presented. P is the variance-covariance matrix of the selected traits. P′ is their correlation matrix.

Figure 2. Response of functional traits to the temperature gradient

Individual trait measures for all traits except SEX are plotted in grey. Black lines show the regression lines (quadratic regression in the case of H_max).

Figure 3

Left-panel: Relation between overall trait variability (FV extent) and mean annual temperature. Black dots represent each of the 29 populations sampled. The regression line is drawn in grey (p=0.012). Overall trait variability increases with temperature. To get an idea of the unit, the extent of FV across the 29 populations equals 7. Right panel: Relationship between the strength of phenotypic integration (FV shape) and mean annual temperature. The parable represents the quadratic regression for the 75% percentile (p=0.022). The most strongly integrated populations are found on the middle of the gradient. All values are above 0.75, and thus represent significant integration.

Figure 4. Results of a PCA on the P_max of the 29 populations studied as well as the general P_max for all populations combined (PV_total).

The ‘line of least phenotypic resistance’ for each population is projected on the plane defined by the two first PCA axes. The top-left plot shows the different trait variances in relation to the PCA axes. Note that most of the P_max for populations are directed towards high variance in LNC and low variance in C:N, while the general P_max is orthogonal to most of them and directed towards high variance in LDMC.

Figure 5. FV structure in populations located in different parts the environmental niche

This illustration is meant to summarize the main findings of our study and differences between populations have been exaggerated for clarity. The environmental niche of P. viviparum can be symbolically represented along the temperature gradient, the grey Gaussian curve representing values of habitat suitability. The niche has been cut into three main parts for simplicity, according to the results: the niche centre and the ‘cold’ and ‘warm’ edges. The size and shape of the ellipsoids represent respectively FV extent and shape: smaller volumes meaning low FV, and volumes close to spheres representing less integrated populations.