A dated phylogeny shows Plio-Pleistocene climates spurred evolution of antibrowsing defences in the New Zealand flora

Some plant traits may be legacies of coevolution with extinct megafauna. One example is the convergent evolution of ‘divaricate’ cage architectures in many New Zealand lineages, interpreted as a response to recently extinct flightless avian browsers whose ancestors arrived during the Paleogene period. Although experiments have confirmed that divaricate habit deters extant browsers, its abundance on frosty, droughty sites appears consistent with an earlier interpretation as a response to cold, dry Plio-Pleistocene climates. We used 45 protein-coding sequences from plastid genomes to reconstruct the evolutionary history of the divaricate habit in extant New Zealand lineages. Our dated phylogeny of 215 species included 91% of New Zealand eudicot divaricate species. We show that 86% of extant divaricate plants diverged from non-divaricate sisters within the last 5 Ma, implicating Plio-Pleistocene climates in the proliferation of cage architectures in New Zealand. Our results, combined with other recent findings, are consistent with the synthetic hypothesis that the browser-deterrent effect of cage architectures was strongly selected only when Plio-Pleistocene climatic constraints prevented woody plants from growing quickly out of reach of browsers. This is consistent with the abundance of cage architectures in other regions where plant growth is restricted by aridity or short frost-free periods

Intraspecific Relationships among Wood Density, Leaf Structural Traits and Environment in Four Co-Occurring Species of <em>Nothofagus</em> in New Zealand

<div><p>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 <i>Nothofagus</i> 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.</p> </div

Multiple regressions predicting intraspecific variation in six plant functional traits from five environmental variables in four <i>Nothofagus</i> species.

<p>A full regression model with all five environmental variables was run for each trait and species. This model was reduced to significant terms through backwards selection. The direction of significant terms in each model is shown. Non-significant terms, removed from each model, are shown with NS. All trait data were corrected for variation in tree size (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0058878#s2" target="_blank">Methods</a>) and log₁₀-transformed</p

Intraspecific variation in wood density and five component leaf traits for four <i>Nothofagus</i> species.

*<p>LMA  =  leaf mass per unit area.</p>†<p>LDMC  =  leaf dry matter content.</p><p>Values are Pearson correlation coefficients. All trait data were corrected for variation in tree size (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0058878#s2" target="_blank">Methods</a>) and log₁₀-transformed before analysis. Correlations in bold are significant at α = 0.05 after Bonferroni-Holm correction for the number of tests.</p

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

<p>Biplots show correlation coefficients between wood density and five environmental variables (<i>x</i>-axis), and correlation coefficients between leaf traits and environmental variables (<i>y</i>-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 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0058878#pone.0058878-Kitajima1" target="_blank">[37]</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0058878#pone.0058878-Ishida1" target="_blank">[39]</a> and therefore so should their relationships with environment.</p

Intraspecific variation in six plant functional traits for five environmental variables in four <i>Nothofagus</i> species.

<p>Values are Pearson correlation coefficients. All trait data were corrected for variation in tree size (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0058878#s2" target="_blank">Methods</a>) and log₁₀-transformed before analysis. Correlations in bold are significant at α = 0.05 after Bonferroni-Holm correction for the number of tests. Number of individuals per species follows <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0058878#pone-0058878-t001" target="_blank">Table 1</a>.</p

Distribution of four <i>Nothofagus</i> spp. in New Zealand and sampling locations for six functional traits.

<p>Sampling locations (filled circles) are shown relative to modelled distributions <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0058878#pone.0058878-Leathwick3" target="_blank">[71]</a> in grey shading for (a) <i>N. solandri</i> (b) <i>N. menziesii</i> (c) <i>N. fusca</i> and (d) <i>N. truncata</i>.</p