Intraspecific relationships among wood density, leaf structural traits and environment in four co-occurring species of Nothofagus in New Zealand

Abstract

Plant functional traits capture important variation in plant strategy and function. Recent literature has revealed that within-species variation in traits is greater than previously supposed. However, we still have a poor understanding of how intraspecific variation is coordinated among different traits, and how it is driven by environment. We quantified intraspecific variation in wood density and five leaf traits underpinning the leaf economics spectrum (leaf dry matter content, leaf mass per unit area, size, thickness and density) within and among four widespread Nothofagus tree species in southern New Zealand. We tested whether intraspecific relationships between wood density and leaf traits followed widely reported interspecific relationships, and whether variation in these traits was coordinated through shared responses to environmental factors. Sample sites varied widely in environmental variables, including soil fertility (25-900 mg kg(-1) total P), precipitation (668-4875 mm yr(-1)), temperature (5.2-12.4 °C mean annual temperature) and latitude (41-46 °S). Leaf traits were strongly correlated with one another within species, but not with wood density. There was some evidence for a positive relationship between wood density and leaf tissue density and dry matter content, but no evidence that leaf mass or leaf size were correlated with wood density; this highlights that leaf mass per unit area cannot be used as a surrogate for component leaf traits such as tissue density. Trait variation was predicted by environmental factors, but not consistently among different traits; e.g., only leaf thickness and leaf density responded to the same environmental cues as wood density. We conclude that although intraspecific variation in wood density and leaf traits is strongly driven by environmental factors, these responses are not strongly coordinated among functional traits even across co-occurring, closely-related plant species.

Figure 1. Distribution of four Nothofagus spp. in New Zealand and sampling locations for six functional traits.

Sampling locations (filled circles) are shown relative to modelled distributions in grey shading for (a) N. solandri (b) N. menziesii (c) N. fusca and (d) N. truncata.

Figure 2. Nothofagus spp. wood density and leaf trait relationships in New Zealand.

Relationships between wood density and five leaf structural traits are given for four species of Nothofagus in New Zealand. See Table 1 for correlation tests. LDMC  =  leaf dry matter content; LMA  =  leaf mass per unit area.

Figure 3. Intraspecific variation in functional traits of Nothofagus solandri along environmental gradients in New Zealand.

Intraspecific variation in six functional traits along key environmental gradients is shown for the widespread tree species Nothofagus solandri throughout the South Island of New Zealand. Relationships are shown for each trait and the environmental variable with the highest Pearson correlation coefficient (Table 2). This species is illustrated as an example and all trait-environment relationships for all four species are shown in Figures S1, S2, S3, S4.

Figure 4. Correlation coefficients of environmental variables with wood density and leaf traits.

Biplots show correlation coefficients between wood density and five environmental variables (x-axis), and correlation coefficients between leaf traits and environmental variables (y-axis). Each data point is a pair of correlation coefficients for a species. In each panel, the correlations between wood density and an environmental variable are plotted against the correlation coefficients for a leaf trait and the same environmental variable. There are four points representing each species, for each environmental variable. Open circles are correlations with MAR; filled circles are correlations with Latitude; open triangles are correlations with MAT; filled triangles are correlations with Elevation; open squares are correlations with soil P. Dashed line shows the 1∶1 relationship expected from interspecific trait correlations e.g., that wood density and LMA are positively correlated – and therefore so should their relationships with environment.

Figure 5. Correlation coefficients of environmental variables with leaf mass per unit area (LMA) and leaf traits.

Biplots show correlation coefficients between LMA and five environmental variables (x-axis), and correlation coefficients between leaf traits and environmental variables (y-axis). Each data point is a pair of correlation coefficients for a species. In each panel, the correlations between LMA and an environmental variable are plotted against the correlation coefficients for a second leaf trait and the same environmental variable. There are four points representing each species, for each environmental variable. Open circles are correlations with MAR; filled circles are correlations with Latitude; open triangles are correlations with MAT; filled triangles are correlations with Elevation; open squares are correlations with soil P. Dashed line shows the 1∶1 relationship expected from interspecific traits correlations, e.g., that LMA and leaf size are negatively correlated and therefore so should their relationships with environment.