Partial contributions to Observed vs. Fitted tree occurrences within the simplified BRT model.

<p>The graphs show Observed vs. Fitted tree occurrences (A) and smoothed partial contributions within the simplified BRT model for (B) mean annual temperature (C) percentage woody cover in a 25 m radius and (D) distance to nearest forest. The smoothed partial contribution plots reflect the influence of a predictor variable when all other variables are held constant. CVROC is the cross-validated receiver operator curve (ROC) for the final boosted regression tree model. ROC is a measure of discrimination accuracy when predicting a binary response.</p

Functional Traits Reveal Processes Driving Natural Afforestation at Large Spatial Scales

<div><p>An understanding of the processes governing natural afforestation over large spatial scales is vital for enhancing forest carbon sequestration. Models of tree species occurrence probability in non-forest vegetation could potentially identify the primary variables determining natural afforestation. However, inferring processes governing afforestation using tree species occurrence is potentially problematic, since it is impossible to know whether observed occurrences are due to recruitment or persistence of existing trees following disturbance. Plant functional traits have the potential to reveal the processes by which key environmental and land cover variables influence afforestation. We used 10,061 survey plots to identify the primary environmental and land cover variables influencing tree occurrence probability in non-forest vegetation in New Zealand. We also examined how these variables influenced diversity of functional traits linked to plant ecological strategy and dispersal ability. Mean annual temperature was the most important environmental predictor of tree occurrence. Local woody cover and distance to forest were the most important land cover variables. Relationships between these variables and ecological strategy traits revealed a trade-off between ability to compete for light and colonize sites that were marginal for tree occurrence. Biotically dispersed species occurred less frequently with declining temperature and local woody cover, suggesting that abiotic stress limited their establishment and that biotic dispersal did not increase ability to colonize non-woody vegetation. Functional diversity for ecological strategy traits declined with declining temperature and woody cover and increasing distance to forest. Functional diversity for dispersal traits showed the opposite trend. This suggests that low temperatures and woody cover and high distance to forest may limit tree species establishment through filtering on ecological strategy traits, but not on dispersal traits. This study shows that ‘snapshot’ survey plot data, combined with functional trait data, may reveal the processes driving tree species establishment in non-forest vegetation over large spatial scales.</p> </div

Map of survey plots used in boosted regression tree modeling.

<p>Vegetation classes for New Zealand survey plots are mapped based on a reclassification of Dymond and Shepherd [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0075219#B58" target="_blank">58</a>]. ‘Other’ is all non-forest vegetation except subalpine scrub.</p

Map of predicted tree occurrence probability in non-forest vegetation in New Zealand.

<p>The map shows model-predicted probability of tree occurrence in non-forest vegetation (the ‘other’ and ‘subalpine scrub’ classes of Dymond and Shepherd [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0075219#B58" target="_blank">58</a>]) in New Zealand. Grey areas are covered by indigenous (from [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0075219#B58" target="_blank">58</a>]) and planted forest (from Land Cover Database 2).</p

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

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

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

Intraspecific variation in functional traits of <i>Nothofagus solandri</i> along environmental gradients in New Zealand.

<p>Intraspecific variation in six functional traits along key environmental gradients is shown for the widespread tree species <i>Nothofagus solandri</i> throughout the South Island of New Zealand. Relationships are shown for each trait and the environmental variable with the highest Pearson correlation coefficient (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0058878#pone-0058878-t002" target="_blank">Table 2</a>). This species is illustrated as an example and all trait-environment relationships for all four species are shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0058878#pone.0058878.s001" target="_blank">Figures S1</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0058878#pone.0058878.s002" target="_blank">S2</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0058878#pone.0058878.s003" target="_blank">S3</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0058878#pone.0058878.s004" target="_blank">S4</a>.</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

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

<p>Biplots show correlation coefficients between LMA 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 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 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0058878#pone.0058878-Pickup1" target="_blank">[31]</a> and therefore so should their relationships with environment.</p