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# Trait evolution in tropical rubber (*Hevea brasiliensis*) trees is related to dry season intensity

Kanin Rungwattana<fnref n="1" /> | Poonpipope Kasemsap²,³ | Thitaporn Phumichai⁴ |

Nicha Kanpanon³,⁵ | Ratchanee Rattanawong⁶ | Peter Hietz<fnref n="1" />

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**Correspondence**

Peter Hietz

Email: peter.hietz@boku.ac.at

**Funding Information.**

K.R. is supported by a grant from the Austrian Federal Ministry of Science, Research and Economy (BMWFW) within the framework of the ASEA UNINET. Field sampling of wood was supported by a ASEA UNINET grant to P.H.

Handling Editor: C. E. Timothy Paine

**Abstract**

1. Drought shapes the distribution and survival of trees even in tropical wet forests, and the wood and leaf trait spectra are used to understand drought adaptations. However, trait variation may result from ontogenetic adjustment or be related to tree size and not reflect evolutionary adaptations.

2. Intraspecific variation in adaptations to drought can be an important factor in a species' distribution and response to climate change, but excluding potentially confounding factors and proving adaptive evolution are challenging. Provenance trials can identify hereditary variability.

3. We analysed wood and leaf traits in rubber (*Hevea brasiliensis*) tree clones from 15 locations in the Amazon basin that were planted in northern Thailand, controlled for tree size, tested for genetic relatedness and the phylogenetic signal in traits and compared trait variations with the climate at the location of origin.

4. Correlations between traits and tree size were low. Intraspecific trait variation was similar to relationships in published among-species comparisons, and correlations among wood traits and correlations among leaf traits were stronger than between wood and leaf traits. Genotype explained 30%-70% of the trait variation, and traits differed in how much of this variation was controlled by location or the relatedness among clones.

5. There was no correlation with mean temperature or total annual rainfall. However, rainfall in the driest quarter (19-199 mm) was strongly related to leaf mass per area, carbon isotopic composition and area-based nitrogen content ($r^2$ = 0.54-0.70) and weaker to wood traits (vessel density and vessel lumen fraction). Trees from locations with a stronger dry season also had higher growth rates in Thailand.

6. All traits correlating with climate showed a significant phylogenetic signal. We found no evidence of increased drought tolerance, but the trait spectrum and higher growth in trees from drier locations suggest that deciduous rubber trees have adapted via drought avoidance rather than tolerance. Our study also

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<footnote n="1">¹Institute of Botany, University of Natural Resources and Life Sciences, Vienna, Austria; ²Hevea Research Platform in Partnership, DORAS Centre, Kasetsart University, Bangkok, Thailand; ³Department of Horticulture, Faculty of Agriculture, Kasetsart University, Bangkok, Thailand; ⁴Rubber Research Institute of Thailand, Bangkok, Thailand; ⁵UMR 1137, Ecologie et Ecophysiologie Forestières, Faculté des Sciences, Université de Lorraine, Vandoeure-les-Nancy, France and ⁶Nong Khai Rubber Research Center, Rubber Research Institute of Thailand, Rattanawapi District, Nong Khai, Thailand</footnote>
underlines the importance of looking at a suite of traits rather than individual ones to understand adaptive strategies.

### KEYWORDS

drought resistance, evolutionary adaptation, intraspecific trait variation, provenance trial, tropical tree

# 1 | INTRODUCTION

Drought is a main driver in tropical forest composition (Engelbrecht et al., 2007) and even in evergreen tropical rainforests such as the Amazon affects tree survival and the carbon balance (Phillips et al., 2009). Parts of the tropics and particularly the Amazon basin are expected to become drier with climate change (Davidson et al., 2012). Therefore, predicting the effect of drought on these forests and understanding the effects on and adaptations by individual tree species are important.

A few manipulative experiments with rainfall exclosure have shown that drought mortality is species-specific, related to tree size and can be explained by specific traits that provide drought tolerance (Rowland et al., 2015). The functional trait approach to understand species distribution or demographic rates is attractive because some traits provide a direct functional explanation and some traits are easy to measure and available for many species (Escudero & Valladares, 2016), although these two groups of traits are not necessarily the same. The possible adaptive benefit of functional traits can be inferred from distribution, performance or functional relationships with traits that provide a clear benefit. For instance, cavitation resistance is a direct measure of the functionality of the water transport system at different levels of drought stress (Choat et al., 2018) but is notoriously tricky to measure (Cochard et al., 2013). Wood density also relates to drought stress, but the relationship in this case is likely indirect, possibly due to the correlation between WD and cavitation resistance (Nardini, Battistuzzo, & Savi, 2013), even though there may be no direct causal link and the statistical link is sometimes weak (Lachenbruch & McCulloh, 2014). Leaf mass per area (LMA, a measure of sclerophylly) has also been suggested as a predictor of drought tolerance and at least in evergreen species is related to rainfall (Niinemets, 2001; Wright et al., 2005). Functionally, this may be explained by its correlation with turgor loss point (Bartlett, Scoffoni, Ardy, et al., 2012), which is a good predictor of ecological drought tolerance (Bartlett, Scoffoni, & Sack, 2012). High WD and LMA were also found to be correlated with survival under drought (Greenwood et al., 2017; O'Brien et al., 2017).

Fewer multispecies studies of tropical trees looked at drought resistance and wood traits other than WD. WD, sapwood-specific hydraulic conductivity and vessel length scaled negatively with drought tolerance as measured by minimum leaf water potential (Markesteijn, Poorter, Bongers, Paz, & Sack, 2011) or cavitation resistance (Markesteijn, Poorter, Paz, Sack, & Bongers, 2011). Wood

density is also related to sapwood water content, capacitance and turgor loss point (Santiago et al., 2018). However, looking at a single trait provides an incomplete and sometimes biased picture. A global dataset found species from high rainfall areas having low cavitation resistance, but species from areas with low rainfall range from very high to very low xylem vulnerability (Choat et al., 2012). Even acknowledging that mean annual precipitation is not a perfect measure of drought stress, xylem vulnerability—while clearly significant for drought resistance—is not the only adaptation to drought or the best predictor of drought resistance.

Comparing the relationship between individual traits and species distribution or drought resistance can also be misleading. Average hydraulic conductivity and maximum photosynthesis were higher in dry forest species across a rainfall gradient in Panama (Brenes-Arguedas, Roddy, & Kursar, 2013), which appears to contradict other studies on traits related to drought resistance. Some of these species have adapted to drought by employing desiccation avoidance (high investment in root system) or desiccation delay (deciduousness) strategies rather than desiccation tolerance. Similarly, trees in a Mexican forest with a long dry season scale along a gradient from drought-avoiding species, which tend to shed leaves early in the dry season, have low wood density and cavitation resistance to drought-tolerant species, which are evergreen (Méndez-Alonzo, Paz, Zuluaga, Rosell, & Olson, 2012). Thus, single traits, even with a clear functional importance, can be rather poor predictors of species distribution or survival.

Ignoring other factors may also lead to wrong interpretations. Particularly, wood traits are strongly related to tree size (Lachenbruch, Moore, & Evans, 2011) and trees tend to grow larger in wetter forests. When differences in stem diameter were accounted for, vessel sizes did not differ between angiosperms communities of differing water availability (Olson & Rosell, 2013), which challenges previous interpretations on the functional advantage of smaller vessels. Therefore, if tree size, for whatever reason, is related to the environment, then interpreting traits that scale with the environment and tree size as adaptive is questionable, unless tree size is accounted for.

Trait variation can reflect evolutionary adaptations or the environmental imprint on individuals, and the contribution of inherited factors in trait variation can be studied in provenance trials (Alberto et al., 2013). When populations from places representing an ecological gradient are genetically adapted to the local environment, we can expect to see acline, which is a gradual variation in a character that is related to the environment the plants originate from, when grown in a common garden (Alberto et al., 2013).
Multispecies comparisons are useful to understand adaptations and ecological strategies, but selection works on the levels of individuals and genotypes, not species. Thus, to understand the effect of a changing environment, it is important to also understand the variation within species and the adaptive significance of this.

Within species, potentially adaptive traits differ strongly in the extent they are either genetically controlled or can be adjusted phenotypically (Alberto et al., 2013; Hajek, Kurjak, von Wühlisch, Delzon, & Schuldt, 2016). Distinguishing between the two is important for theoretical and applied questions, from selecting resistant genotypes for reforestation to understanding and predicting the effects of climate change (Aitken, Yeaman, Holliday, Wang, & Curtis-McLane, 2008; Valladares et al., 2014). Most provenance trials of trees have been conducted with northern temperate species across latitudinal or altitudinal gradients where the temperature, growing season length or day length of the place of origin tend to be the main climate factors driving genetic adaptations (Alberto et al., 2013).

As high water use efficiency (WUE, generally expressed as carbon uptake by water transpired, which scales with the carbon isotope signal δ¹³C, Farquhar, Ehleringer, & Hubick, 1989) is seen as an adaptation to drought, trees from more arid regions might be expected to have higher WUE under the same climate conditions. While studies often find differences in δ¹³C among populations, several studies that related WUE with the climate of the place of origin found no correlation with water availability (Klein, Di Matteo, Rotenberg, Cohen, & Yakir, 2013; Peuke, Gessler, & Rennenberg, 2006; Premoli & Brewer, 2007) or a correlation that covaries with a temperature gradient and a higher effect of the latter (e.g., Aranda, Alía, Ortega, Dantas, & Majada, 2010; Dounavi et al., 2016; Gornall & Guy, 2007). A provenance trial of 13 *Nothofagus* species found a strong positive correlation between δ¹³C and rainfall at the site of origin (Read & Farquhar, 1991). This means a lower WUE in species from drier areas, which the authors suggest resulted from mechanisms that permit these species to maintain stomata more open and photosynthesize during periods of mild water deficit. However, δ¹³C in that study also strongly correlated with temperature, which covaried with rainfall. Also, leaf mass per area (LMA) and photosynthesis were related primarily to altitude or temperature rather than rainfall or drought indices (Aranda et al., 2010; Cordell, Goldstein, Mueller-Dombois, Webb, & Vitousek, 1998).

Compared to leaves, still fewer studies looked at intraspecific adaptations of wood traits to drought in provenance trials. Variation in cavitation resistance across the range of *Fagus sylvatica* or *Pinus pinaster* was very low (Aranda et al., 2015; Lamy et al., 2011), but substantially higher and related to precipitation in *Pinus canariensis* (López et al., 2013). Wood density was not related to the climate of origin in a beech trial (Aranda et al., 2015), but another study on beech found a strong positive correlation between vessel size or theoretical hydraulic conductivity with a forest aridity index of the place of origin, and a negative correlation between wood δ¹³C and aridity (Hajek et al., 2016). The latter is surprising

as it suggests a lower WUE in plants from drier locations, although these correlations might be affected by an unexplored collinearity with temperature in that study. Also, results from this provenance trial contrast with an increase in vessel size and xylem vulnerability with rainfall in a field study of beech (Schuldt et al., 2016).

In sum, common garden experiments, which largely eliminate the effect of the growing environment to identify genetic differences in trait adaptation, provide limited and sometimes contrasting evidence of intraspecific adaptations to drought. The few provenance trials of tropical trees mainly looked at potential tree growth for plantations and did not investigate potentially adaptive traits (Evans & Turnbull, 2004).

Using a *Hevea* trial of clones originating from a large area in Brazil, we first ask how high the intraspecific variation in leaf and wood traits is and to what extent the variation is controlled by clone, the place of origin of the clone and tree size. We expected tree size to be related to many traits and prepared to control for size for the analysis of trait: climate correlations. We quantified the correlations among wood and leaf traits and looked for functional correlations and trade-offs. Overall, we expected the leaf and wood economics spectrum in *Hevea* to be similar to interspecific trait patterns, but with lower correlations because trait values span a smaller range within species than among species. We tested which traits were related to the climate the clones came from. Given that this climate spans a substantial rainfall but a very small temperature gradient, we were expecting to find correlations between traits and rainfall, but not with temperature. Specifically, we expected clones from drier regions to have higher wood density, lower hydraulic conductance and higher LMA and higher δ¹³C values, consistent with a drought tolerance or conservative resource use strategy. Finally, we constructed a phylogenetic tree based on single nucleotide polymorphisms (SNPs) and analysed the phylogenetic signal in traits by comparing the similarity in trait values between clones to their genetic relatedness. If populations have adapted to variation in water availability through the evolution of traits, we expected that these traits show a phylogenetic signal.

## 2 | MATERIALS AND METHODS

### 2.1 | Study site, plant materials and sample collection

The rubber tree provenance trial is located in Nong Khai, Thailand, at the Nong Khai Rubber Research Center (18°09'30"N, 103°09'31"E). Average temperature at the site is 27.5°C, and mean annual rainfall is approximately 1,600 mm. The dry season between November and April receives less than 100 mm per month, and the total of the driest 3 months, December to February, is 60 mm (Thai Meteorological Department, 2017). Parent material (known as the International Rubber Research and Development Board IRRDB'81 collection) had originally been sampled from wild populations in forests in the Brazilian Amazon basin in the states of Acre, Rondonia and Mato
Grosso (Fig. 1) and was distributed via collections from Malaysia and Cote d'Ivoire. Between one and 18 individual trees had been collected from different municipalities (subsequently called "locations"), and clones from these trees were planted in 1994 with five replicates per clone in a spacing of 3 × 7 m. Trees were tapped regularly for latex (see Chanroj, Rattanawong, Phumichai, Tangphatsornruang, & Ukoskit, 2017 for details) and fertilized twice a year with 500 g N:P:K (30:5:18) fertilizer per tree, applied in the middle between tree rows.

We extracted climate data for the locations from WorldClim (www.worldclim.org) with 30 arcsec (c. 1 km) resolution. Annual mean temperature (T) ranged between 23.8 and 26°C, annual precipitation (P) between 1,354 and 2,215 mm, precipitation of driest quarter (Pq) between 19 and 156 mm and the coefficient of variation in monthly precipitation (Pcv) between 52% and 75% (Table 1).

In September 2013, sun-exposed leaves were collected from the upper crown of all remaining trees (3–5 per clone, as some had died) of 49 clones to analyse within-clone variation and for one tree per clone for another 101 clones to analyse coordination of traits among clones and correlations with climate. We collected one leaf from four branches per tree and used two leaflets per leaf to measure leaf mass per area (LMA) as dry weight/fresh leaf area. The eight leaflets per tree were pooled and ground to a fine powder, and leaf nitrogen (N<sub>mass</sub>) and carbon (C) concentrations and the carbon isotope signal (δ<sup>13</sup>C) were measured with a elemental analyser (vario ISOTOPE cube coupled to the IsoPrime100, IsoPrime Ltd, Cheadle, UK) at INRA-Nancy, France. The standard deviation of the isotope analyses for repeated measurements of the internal standard during each sample run was below 0.1‰. Some data on the δ<sup>13</sup>C variation in 49 clones were presented previously (Kanpanon et al., 2017). Leaf δ<sup>13</sup>C can be used to calculate air-to-leaf carbon isotope discrimination (Δ<sup>13</sup>C) and intrinsic water use efficiency (iWUE), which is photosynthesis

<figure><img src="image_4.png" /><figcaption>FIGURE 1 Location of the origin of *Hevea* clones from the Brazilian states of Acre (AC), Rondonia (RO) and Mato Grosso (MT). The enlarged map corresponds to the shaded on the overview map. The colour indicates the precipitation (mm) in the driest quarter of the year. See Table 1 for location codes</figcaption></figure>

per stomatal conductance (Osmond, Björkman, & Anderson, 1980). When the atmospheric CO₂ concentration and δ¹³CO₂ are constant, which we assume they were as the leaves were sampled during a short time interval and from the same canopy layer, δ¹³C, Δ¹³C and iWUE will be linearly related and we therefore use the δ¹³C signal as a proxy for time-integrated iWUE. Nitrogen per leaf area (N<sub>area</sub>) was calculated as N<sub>mass</sub> × LMA.

## 2.2 Wood traits

In April 2017, wood samples were collected from 607 trees of 153 clones at c. 195 cm height, which is above the height the bark had been cut for latex collection. Using a 5.15-mm-diameter increment borer (SUUNTO, Vantaa, Finland), 3-cm-long samples of the outer sapwood were extracted and sealed immediately into vials to avoid moisture loss. After cutting off the bark, sample length was measured with a digital calliper to 0.1 mm and volume was calculated as a cylinder from length and the borer diameter. Dry weight was measured after oven drying at 100°C for 72 hr with a digital balance to 0.1 mg, and wood density (WD) was calculated as oven-dry weight divided by fresh volume. Tree circumference was measured at 170 cm height with a tape, and diameter was calculated as circumference/π. Since all trees were planted in the same year, we can use diameter as a measure of tree growth rates.

For wood anatomy, we analysed all trees available (mean 4.3) for 70 clones to evaluate within-clone variation and one tree per clone for the other 83 clones for correlations among clones and with climate. We took the outermost 1-cm segments of the samples to ensure that this represented the most recent wood produced in all trees. Samples were softened with ethylene diamine at 60°C prior to cutting of 30-µm-thick transverse sections with a core microtome (WSL, Switzerland). Sections were double-stained with safranin and astra blue (Carl Roth, Germany), dehydrated with a graded alcohol series and mounted on slides embedded in Euparal (Carl Roth). Photographs of sections, each covering an area of 62.7 mm², were captured with a resolution of 864 pixels/mm, using a DM5500B transmission light microscope with a DMC2900 camera (Leica, Germany).

Vessel lumina (mean 109 per section) were colour-coded manually and then measured automatically with ImageJ 1.49o (http://imagej.nih.gov/ij). We calculated the mean area of individual vessels (VA), vessel density per mm² (VD), the fraction of the cross section occupied by vessel lumina (F) and the theoretical hydraulic conductivity (K<sub>h</sub>) from vessel size and densities according to the Hagen-Poiseuille law (as in Sterck, Zweifel, Sass-Klaassen, & Chowdhury, 2008):

$$K_h = (\pi \rho_w / 128 \eta) \times VD \times D_h^4,$$

where η is the dynamic viscosity of water (1.002 × 10⁻³ Pa·s at 20°C), ρ<sub>w</sub> is the density of water (998.2 kg/m³ at 20°C), VD is the vessel density (m⁻²), and D<sub>h</sub> is the hydraulically weighted vessel diameter (m), which was the average of major and minor axes of the diameter of individual vessels.
## 2.3 | Data analysis

We tested the effects of provenance (location), clone and tree size on functional traits with analysis of variance (ANOVA) where clone was nested in provenance. For this test, we used only clones with at least three replicated trees, that is, 137 clones for wood density and tree size, 70 clones for wood anatomical traits and 49 clones for leaf traits. We calculated the proportion of the variance explained by location, clone within location, tree size and random effects as $\sigma_{\text{effect}}^2/\sigma_{\text{total}}^2$. Since “location” and “clone within location” represent the cumulative effect of the genetic variation, we can use $(\sigma_{\text{location}}^2 + \sigma_{\text{clone/location}}^2)/\sigma_{\text{total}}^2$ as a measure of broad-sense heritability ($H^2$), which is the proportion of the total phenotypic variance attributable to genetic variation among individuals (McKown et al., 2014).

We calculated Pearson correlations to characterize the relationships among traits using the mean trait value per clone, that is, we regard clones as independent data points. Scatter plots did not suggest any of these correlations to be nonlinear. For correlations between traits and climate, we used the mean trait values of all clones collected from one location and weighed the correlation by the number of clones per location (Table 1), that is, regard locations as independent data points. Since diameter was related to some climate parameters, we also tested the relationship between traits and climate in liner models that include tree diameter.

We calculated a principal component analysis (PCA) to evaluate associations among traits and climate to see how the trait spectrum is related to potentially important climatic drivers. Because the climate variables, which are clearly related to the place of origin, will affect the PCA and might result in a stronger clustering by regions, we calculated a second PCA with trait variables only, to

see whether clones from different states cluster by their trait composition. We then correlated the scores along the first two axes of this second PCA with climate to test whether the combination of multiple traits shows a stronger correlation with climate than individual traits. For PCAs, data were mean-centred and scaled to unit variance.

We used 1820 SNPs from DNA extracted from leaves to construct a phylogenetic tree with SplitsTree (www.splittree.org) using the neighbour-joining algorithm to estimate relationships among clones (Fig. 2). These SNPs were filtered from 14155 SNPs from a published dataset of the same clones (Chanroj et al., 2017) using a linkage disequilibrium ($r^2$ threshold 0.4) and a Hardy-Weinberg equilibrium of $p < 0.05$.

We tested for a phylogenetic signal in traits using Pagel's $\lambda$ (Pagel, 1999), Blomberg's K (Blomberg, Garland, Ives, & Crespi, 2003) and Abouheif's $C_{\text{mean}}$ (Abouheif, 1999). $\lambda$ and K compare the pattern of traits across a phylogeny to a Brownian motion model, the most common model for trait evolution (Münkemüller et al., 2012; Revell, Harmon, & Collar, 2008). $\lambda$ scales between zero, when traits are not related to the phylogenetic distance among branch tips, and an expected value of 1.0, when the relationship follows Brownian motion. K is also under Brownian motion evolution, but will be >1 if relatives are more similar than expected under Brownian motion. In contrast to $\lambda$ and K, $C_{\text{mean}}$ is an autocorrelation index that is not based on an evolutionary model and does not include branch length. We used the R package adephylo (Jombart, Balloux, & Dray, 2010) to calculate $C_{\text{mean}}$ and phytools (Revell, 2012) for $\lambda$ and K.

All variables were checked for normal distribution with QQ plots, and VA, VD, F, LMA and $K_h$ were log-transformed. All statistical analyses were calculated with R 3.4.1 (www.r-project.org).

<table><caption>TABLE 1 Geographic origin and habitat characteristics of *Hevea brasiliensis* populations</caption><thead><tr><th>State</th><th>Municipality</th><th>Code</th><th>lat</th><th>lon</th><th>T</th><th>P</th><th>Pcv</th><th>Pq</th><th>Number of clones</th></tr></thead><tbody><tr><td>Acre</td><td>Assis Brasil</td><td>AC_AB</td><td>-10.89</td><td>-69.58</td><td>24.6</td><td>1640</td><td>61</td><td>66</td><td>1</td></tr><tr><td>Acre</td><td>Feijo</td><td>AC_F</td><td>-8.16</td><td>-70.35</td><td>25.9</td><td>2205</td><td>52</td><td>152</td><td>2</td></tr><tr><td>Acre</td><td>Ortet</td><td>AC_I</td><td>-7.86</td><td>-70.68</td><td>25.8</td><td>2254</td><td>47</td><td>199</td><td>5</td></tr><tr><td>Acre</td><td>Sena Madureira</td><td>AC_S</td><td>-9.07</td><td>-68.66</td><td>24.8</td><td>2017</td><td>53</td><td>140</td><td>6</td></tr><tr><td>Mato Grosso</td><td>Aracatuba</td><td>MT_A</td><td>-15.05</td><td>-58.29</td><td>23.6</td><td>1721</td><td>76</td><td>33</td><td>1</td></tr><tr><td>Mato Grosso</td><td>Cotriguaçu</td><td>MT_C</td><td>-9.86</td><td>-58.41</td><td>24.8</td><td>1980</td><td>73</td><td>46</td><td>18</td></tr><tr><td>Mato Grosso</td><td>Itauba</td><td>MT_IT</td><td>-11.06</td><td>-55.28</td><td>25.4</td><td>1885</td><td>75</td><td>19</td><td>16</td></tr><tr><td>Mato Grosso</td><td>Villa Bela</td><td>MT_VB</td><td>-12.97</td><td>-60.11</td><td>21.4</td><td>1943</td><td>70</td><td>57</td><td>6</td></tr><tr><td>Rondonia</td><td>Ariquemenes</td><td>RO_A</td><td>-9.91</td><td>-63.04</td><td>25.4</td><td>2184</td><td>64</td><td>62</td><td>18</td></tr><tr><td>Rondonia</td><td>Calama</td><td>RO_C</td><td>-8.76</td><td>-63.90</td><td>26</td><td>2096</td><td>60</td><td>100</td><td>3</td></tr><tr><td>Rondonia</td><td>Costa Marques</td><td>RO_CM</td><td>-12.45</td><td>-64.23</td><td>26</td><td>1501</td><td>69</td><td>35</td><td>15</td></tr><tr><td>Rondonia</td><td>Jaru</td><td>RO_J</td><td>-10.44</td><td>-62.47</td><td>24.9</td><td>1989</td><td>70</td><td>47</td><td>9</td></tr><tr><td>Rondonia</td><td>Jiparana</td><td>RO_JP</td><td>-10.89</td><td>-61.95</td><td>24.5</td><td>1951</td><td>70</td><td>51</td><td>1</td></tr><tr><td>Rondonia</td><td>Ouro Preto</td><td>RO_OP</td><td>-10.75</td><td>-62.22</td><td>24.4</td><td>1924</td><td>72</td><td>42</td><td>1</td></tr><tr><td>Rondonia</td><td>Pimenta Bueno</td><td>ROBAB</td><td>-11.67</td><td>-61.19</td><td>23.8</td><td>1922</td><td>70</td><td>51</td><td>16</td></tr></tbody></table>

Notes. T: annual mean temperature (°C); P: annual precipitation (mm); Pcv: precipitation seasonality (CV); Pq: precipitation of driest quarter (mm); and n: number of clones analysed.
# 3 | RESULTS

## 3.1 | Trait variation and heritability

The coefficient of variation was highest for wood anatomical traits (20%–36% of the mean); somewhat lower for LMA, $N_{mass}$, $N_{area}$ and diameter (14%–20%); and lowest (<7%) for WD, C content and $\delta^{13}C$ (Table 2).

<figure><img src="image_5.png" /><figcaption>**FIGURE 2** Phylogenetic relationship among *Hevea brasiliensis* clones originating from Brazil. Symbol colour indicates clones from Acre (blue), Rondonia (red) or Mato Grosso (violet), and different symbols denote different populations (municipalities where trees were sampled, see Table 1)</figcaption></figure>

<caption>**TABLE 2** Mean, range, standard deviation and coefficient of variation (SD/ mean * 100) of *Hevea* clones grown in a provenance trial in Thailand</caption>

Heritability ($H^2$, since "clone" was nested within "location," this is the variance explained by "clone" plus "location" in Fig. 3) explained 51%–72% of the variation in traits, except for leaf C, $N_{mass}$ and $N_{area}$, where <42% were explained. Most of the remaining variation was unexplained with very little (<6%) explained by tree diameter. Traits differed substantially in the extent they were explained by clone (nested in location) vs. location. The variation in diameter and vessel size was controlled by clone, but not much by location, while leaf $\delta^{13}C$ and LMA were controlled about equally by clone and location, suggesting a high degree of interpopulation variation for these traits. Even if the proportion of the variation explained was low, the effect of clone was significant for all traits except leaf $N_{mass}$, that of diameter for all except F, $\delta^{13}C$, $N_{area}$ and C (Table 3).

<figure><img src="image_6.png" /><figcaption>**FIGURE 3** Variance explained by location (the municipality in Brazil the germplasm was collected from), clone (nested in location), tree diameter and residual variance</figcaption></figure>

<table><thead><tr><th>Trait</th><th>Unit</th><th>Mean</th><th>Max</th><th>Min</th><th>SD</th><th>CV (%)</th></tr></thead><tbody><tr><td>Diameter</td><td>cm</td><td>24.60</td><td>37.59</td><td>14.56</td><td>4.20</td><td>17.06</td></tr><tr><td>WD</td><td>g/cm<sup>3</sup></td><td>0.55</td><td>0.66</td><td>0.45</td><td>0.03</td><td>6.07</td></tr><tr><td>VA</td><td>µm<sup>2</sup></td><td>20443</td><td>35899</td><td>9439</td><td>4750</td><td>23.23</td></tr><tr><td>VD</td><td>mm<sup>-2</sup></td><td>2.74</td><td>5.77</td><td>1.41</td><td>0.72</td><td>26.41</td></tr><tr><td>F</td><td>%</td><td>5.39</td><td>11.13</td><td>2.64</td><td>1.33</td><td>24.68</td></tr><tr><td>K<sub>h</sub></td><td>kg m·s<sup>-1</sup>·MPa<sup>-1</sup> × 10<sup>5</sup></td><td>9.49</td><td>24.75</td><td>3.48</td><td>3.36</td><td>35.41</td></tr><tr><td>LMA</td><td>g/m<sup>2</sup></td><td>69.65</td><td>117.65</td><td>47.90</td><td>14.34</td><td>20.58</td></tr><tr><td>δ<sup>13</sup>C</td><td>‰</td><td>-30.63</td><td>-26.36</td><td>-33.33</td><td>1.25</td><td>-4.08</td></tr><tr><td>N<sub>mass</sub></td><td>% dry weight</td><td>2.81</td><td>3.93</td><td>1.64</td><td>0.42</td><td>14.78</td></tr><tr><td>N<sub>area</sub></td><td>g/m<sup>2</sup></td><td>1.93</td><td>3.15</td><td>1.23</td><td>0.37</td><td>19.02</td></tr><tr><td>C</td><td>% dry weight</td><td>48.25</td><td>53.30</td><td>43.88</td><td>1.49</td><td>3.09</td></tr></tbody></table>

Notes. WD: wood density; VA: mean area of individual vessels; VD: vessel density; F: the fraction of the cross section occupied by vessel lumina; $K_h$: theoretical hydraulic conductivity; LMA: leaf dry mass per area; $\delta^{13}C$: leaf stable carbon isotope signal; $N_{mass}$: leaf nitrogen concentration; $N_{area}$: N per leaf area; and C: leaf carbon content.
<table><caption>TABLE 3 AANOVA results (left) showing the significance of the effect of location, clone (nested in location) and tree diameter on the variation in tree diameter, wood and leaf properties, and three measures of the phylogenetic signal (right) of these traits in *Hevea* clones</caption><thead><tr><th rowspan="2"></th><th colspan="4">ANOVA</th><th colspan="6">Phylogenetic signal</th></tr><tr><th>Location</th><th>Clone</th><th>Diameter</th><th>n</th><th>λ</th><th>p (λ)</th><th>C<sub>mean</sub></th><th>p (C<sub>mean</sub>)</th><th>K</th><th>p (K)</th></tr></thead><tbody><tr><td>Diameter</td><td>8.9E-06</td><td>3.1E-20</td><td>NA</td><td>137</td><td>0.000</td><td>1.000</td><td>0.131</td><td>0.026</td><td>0.062</td><td>0.298</td></tr><tr><td>WD</td><td>2.6E-28</td><td>1.6E-57</td><td>2.4E-06</td><td>137</td><td>0.660</td><td>0.071</td><td>0.133</td><td>0.016</td><td>0.071</td><td>0.233</td></tr><tr><td>VA</td><td>9.0E-06</td><td>2.5E-14</td><td>0.006</td><td>70</td><td>0.837</td><td>7.9E-11</td><td>0.108</td><td>0.064</td><td>0.180</td><td>0.017</td></tr><tr><td>VD</td><td>1.8E-27</td><td>1.1E-21</td><td>0.008</td><td>70</td><td>0.806</td><td>9.4E-09</td><td>0.196</td><td>0.002</td><td>0.150</td><td>0.028</td></tr><tr><td>F</td><td>7.0E-26</td><td>4.9E-23</td><td>0.966</td><td>70</td><td>0.592</td><td>2.4E-04</td><td>0.184</td><td>0.004</td><td>0.105</td><td>0.067</td></tr><tr><td>K<sub>h</sub></td><td>1.8E-13</td><td>3.5E-23</td><td>0.026</td><td>70</td><td>0.796</td><td>4.1E-09</td><td>0.140</td><td>0.018</td><td>0.168</td><td>0.015</td></tr><tr><td>LMA</td><td>1.9E-17</td><td>2.8E-11</td><td>0.024</td><td>49</td><td>0.685</td><td>2.7E-15</td><td>0.414</td><td>0.001</td><td>0.223</td><td>0.006</td></tr><tr><td>δ<sup>13</sup>C</td><td>2.4E-15</td><td>1.0E-05</td><td>0.124</td><td>49</td><td>0.827</td><td>7.9E-25</td><td>0.468</td><td>0.001</td><td>0.458</td><td>0.002</td></tr><tr><td>N<sub>mass</sub></td><td>6.6E-03</td><td>3.6E-01</td><td>0.001</td><td>49</td><td>0.160</td><td>0.065</td><td>0.032</td><td>0.584</td><td>0.038</td><td>0.729</td></tr><tr><td>N<sub>area</sub></td><td>3.9E-02</td><td>1.1E-03</td><td>0.066</td><td>49</td><td>0.410</td><td>6.1E-06</td><td>0.201</td><td>0.002</td><td>0.076</td><td>0.257</td></tr><tr><td>C</td><td>1.6E-02</td><td>1.1E-02</td><td>0.267</td><td>49</td><td>0.000</td><td>1.000</td><td>-0.102</td><td>0.084</td><td>0.029</td><td>0.865</td></tr></tbody></table>

Notes. *n*: number of clones with 3–5 replicates tested, λ: Pagel's λ, C<sub>mean</sub>: Abouheif's C<sub>mean</sub>, K: Blomberg's K, p: significance. Significant (p < 0.05) traits or phylogenetic signals are printed in bold. The phylogenetic signal was computed for all clones, the ANOVA for those with replicate measurements (n). See Table 2 for abbreviation of traits.

<figure><img src="image_7.png" /><figcaption>FIGURE 4 Correlations among tree size (Diam), wood (WD – K<sub>h</sub>), leaf (LMA – C) traits and climate (T – Pq) at the place of origin of Brazilian *Hevea* clones. For correlations among traits, mean trait values per clone were used, and for clonewise correlations with climate, data were means per location weighed by the number of clones per location. Colour intensity and the size of the circle are proportional to the correlation coefficient with blue showing positive and red showing negative correlations. *p*-Values of Pearson correlations are given in the lower triangle with values <0.05 printed in bold and *p* < 0.001 indicated as 0</figcaption></figure>

All measures of phylogenetic signal were significant for K<sub>h</sub>, LMA and δ<sup>13</sup>C. λ and C<sub>mean</sub> were both also significant for VD, F and N<sub>area</sub>, λ and K for VA, and C<sub>mean</sub> for diameter and WD (Table 3). λ was significantly correlated (p = 0.009) with the variance explained by clone + location, while C<sub>mean</sub> and K were significantly (p = 0.006 and 0.13, respectively) correlated with the variance explained by location alone, but none was related to the variance explained by clone within location.

## 3.2 | Associations among traits and climate

We found strong correlations among wood traits and among leaf traits, but few between wood and leaf traits (Fig. 4). The strongest correlation between VD, VA, F and K<sub>h</sub> were to be expected because these parameters are partially calculated from each other. WD was negatively and weakly correlated with VA and K<sub>h</sub> and positively with VD. LMA was strongly positively correlated with δ<sup>13</sup>C and N<sub>area</sub> and
<table><caption>**TABLE 4** Significance of the climate term in a linear model of the form trait ~ climate + tree diameter. See Table 1 for abbreviation of climate parameters and Table 2 for traits</caption><thead><tr><th rowspan="2"></th><th colspan="2">T</th><th colspan="2">P</th><th colspan="2">Pcv</th><th colspan="2">Pq</th></tr><tr><th>p</th><th>r</th><th>p</th><th>r</th><th>p</th><th>r</th><th>p</th><th>r</th></tr></thead><tbody><tr><td>WD</td><td>0.172</td><td>-0.304</td><td>0.613</td><td>-0.029</td><td>0.142</td><td>0.342</td><td>0.254</td><td>-0.267</td></tr><tr><td>VA</td><td><strong>0.045</strong></td><td><strong>0.549</strong></td><td>0.311</td><td>0.186</td><td><strong>0.026</strong></td><td><strong>-0.249</strong></td><td>0.087</td><td>0.083</td></tr><tr><td>VD</td><td>0.362</td><td>-0.295</td><td>0.651</td><td>-0.003</td><td>0.375</td><td>-0.524</td><td>0.313</td><td>0.551</td></tr><tr><td>F</td><td>0.677</td><td>0.141</td><td>0.866</td><td>0.110</td><td><strong>0.038</strong></td><td><strong>-0.634</strong></td><td>0.060</td><td>0.536</td></tr><tr><td>K<sub>h</sub></td><td>0.574</td><td>0.263</td><td>0.735</td><td>0.130</td><td><strong>0.029</strong></td><td><strong>-0.534</strong></td><td>0.068</td><td>0.388</td></tr><tr><td>LMA</td><td>0.355</td><td>0.377</td><td>0.135</td><td>0.492</td><td><strong>7E-07</strong></td><td><strong>-0.798</strong></td><td><strong>4E-05</strong></td><td><strong>0.762</strong></td></tr><tr><td>δ<sup>13</sup>C</td><td>0.192</td><td>0.419</td><td>0.071</td><td>0.458</td><td><strong>2E-04</strong></td><td><strong>-0.826</strong></td><td><strong>5E-05</strong></td><td><strong>0.839</strong></td></tr><tr><td>N<sub>mass</sub></td><td>0.347</td><td>-0.242</td><td>0.654</td><td>-0.289</td><td><strong>0.017</strong></td><td><strong>0.389</strong></td><td><strong>0.038</strong></td><td><strong>-0.441</strong></td></tr><tr><td>Narea</td><td>0.480</td><td>0.307</td><td>0.081</td><td>0.411</td><td><strong>1E-05</strong></td><td><strong>-0.770</strong></td><td><strong>1E-04</strong></td><td><strong>0.694</strong></td></tr><tr><td>C</td><td>0.596</td><td>-0.296</td><td>0.378</td><td>-0.311</td><td>0.728</td><td>0.092</td><td>0.487</td><td>-0.123</td></tr></tbody></table>

Notes. Values printed in bold are significant at *p* < 0.05.

negatively with N<sub>mass</sub>. LMA and N<sub>area</sub> were the only leaf trait weakly related to wood traits.

LMA (*r* = 0.68), δ<sup>13</sup>C (*r* = 0.70) and N<sub>area</sub> (*r* = 0.55) showed a strong positive correlation with rainfall in the dry season (Pq), N<sub>mass</sub> showed a weaker negative (*r* = -0.26), and VD (*r* = 0.42) and F (*r* = 0.39) showed weak positive correlations with Pq. Rainfall seasonality (Pcv) was strongly negatively correlated with Pq and hence showed the opposite relationship with wood and leaf traits, except for K<sub>h</sub>, where the correlation with Pcv was weakly significant but that with Pq was not. Relationships between traits and climate were very similar when tree diameter was included in models, but the significant relationship between dry season intensity and VD was lost and that with F was weaker (Table 4). No trait was related to total rainfall or mean temperature. Trees from locations with a stronger dry season had grown faster in Thailand (Fig. 4).

Rainfall seasonality scaled strongly positively and dry season rainfall negatively on the first PCA axis along with δ<sup>13</sup>C, LMA, N<sub>area</sub> and VD. WD, VA and K<sub>h</sub> mainly scaled on the second PCA axis and mean temperature weakly along with F (Fig. 5a). When climate variables were omitted from the PCA, the relationships among plant traits remained very similar (Fig. 5b). Pcv and Pq were strongly correlated with the scores of the first axis and weakly with the second axis of the PCA, P was weakly correlated with the first PCA axis, and T was not correlated with the PCA of functional traits (Table 5). However, even the strongest of these correlations (between Pcv and PCA1, r<sup>2</sup> = 0.469) explained less than the correlation between the individual traits LMA, N<sub>area</sub> or δ<sup>13</sup>C and Pq or Pcv (r<sup>2</sup> = 0.53–0.70).

## 4 | DISCUSSION

Looking for traits that may have evolved in adaptation to drought within a species, we sought to avoid or control for potentially confounding factors that would make an interpretation difficult. The common garden experiment avoids the effect of phenotypic adaptations to different environments. Trees were of the same age, and, while size did have a significant effect on some traits, these

effects were small and were accounted for in models looking for correlations between traits and the climate of origin. We report strong phylogenetic signals in several wood and leaf traits and particularly in these traits strong clinal variation with dry season intensity, and finally discuss the mechanisms of drought adaptations.

### 4.1 | Correlations among traits

Although many correlations with size were significant, diameter contributed at the most 6% to trait variation. Consequently, differences in tree size mostly had little effect on the relationships between traits and climate, which was verified by including tree size in models (Table 4). A larger range of tree sizes would likely have resulted in stronger correlations with traits, but for the purpose of our study, tree size was included mainly to avoid a potential size-related bias and not to study the size effect per se.

The pattern of intraspecific trait relationships was similar to that seen in studies investigating trait correlations among species. Correlations among wood traits and among leaf traits were generally stronger than between wood and leaf traits (Fig. 4), although leaf and wood traits were not unrelated (or decoupled) as seen in a large interspecific comparison of tropical trees (Baraloto et al., 2010). Trait variations were lower than in interspecific comparisons because of a greater genetic relatedness of the *Hevea* clones and because phenotypic variation should play a minor role as trees were growing under uniform conditions and were the same age. Consequently, correlations among traits were weaker. For instance, while the relationship between VA and VD was significant, it was much weaker in *Hevea* clones (r<sup>2</sup> = 0.27) than in interspecific comparisons (r<sup>2</sup> = 0.81 in Zanne et al., 2010, 0.88 in Hietz, Rosner, Hietz-Seifert, & Wright, 2017). Similarly, the negative correlation between LMA and N<sub>mass</sub> was much weaker within *Hevea* (Fig. 4, r<sup>2</sup> = 0.19) than across species (r<sup>2</sup> = 0.57; Wright et al., 2004).

We found no correlation between WD and size in trees of the same age, which means that WD is not related to growth rates. This contrasts with across-species comparisons that found WD related to growth and mortality, which is seen as a trade-off
<figure><img src="image_8.png" /><figcaption>**FIGURE 5** Principal component analysis of traits and climate at the location of origin of *Hevea* traits. Symbol colour indicates clones originate from Acre (blue circles), Rondonia (red diamonds) or Mato Grosso (violet squares). Arrows represent the ordination of traits along the first two principal component axes with green arrows for leaf traits, brown arrows for wood traits and blue arrows for climate variables. a: PCA of traits and climate variable to show the relationship with climate, b: PCA with traits only to show to what extent genotypes from different states differ by their trait composition. This PCA without climate was used to correlate the axes with climate variables (Table 5)</figcaption></figure>

between investment in either fast and cheap growth by producing light wood or greater longevity at the cost of slower growth with heavy wood (Wright et al., 2010). While some studies did find negative WD: growth relationships also within species (Schuldt et al., 2016), the forestry literature is full of examples that found no such

<table><caption>**TABLE 5** Correlation between climate of place of origin and scores of the first two PCA axes of the traits PCA</caption><thead><tr><th rowspan="2"></th><th colspan="2">PCA1</th><th colspan="2">PCA2</th></tr><tr><th>P</th><th>r<sup>2</sup></th><th>P</th><th>r<sup>2</sup></th></tr></thead><tbody><tr><td>T</td><td>0.169</td><td>0.017</td><td>0.642</td><td>0.002</td></tr><tr><td>P</td><td>3.0E-04</td><td>0.113</td><td>0.648</td><td>0.002</td></tr><tr><td>Pcv</td><td>8.7E-17</td><td>0.469</td><td>0.006</td><td>0.067</td></tr><tr><td>Pq</td><td>7.8E-15</td><td>0.424</td><td>0.001</td><td>0.101</td></tr></tbody></table>

Notes. T: annual mean temperature; P: annual precipitation; Pcv: coefficient of variation of monthly precipitation; and Pq: precipitation in the driest quarter.

relationship, although this literature tends to be biased towards conifers (Saranpää, 2003). The lack of such a correlation in spite of considerable variation of both is interesting as it suggests that there is no or little trade-off, which raises the question what, if any, disadvantages investing in high wood density might have in the evolution of a tree species. It is also of some practical relevance as it allows to select trees that produce high volumes of high-density wood.

## 4.2 | Genetic variation

The differences in heritability were substantial and ranged from 31% of the variation in $N_{mass}$ explained by clone (including the variation within locations) to c. 70% for WD, VD, F and LMA. Wood anatomy and LMA are thus under strong genetic control also within species. These values are similar to previous reports from temperate trees (e.g., McKown et al., 2014), but comparing $H^2$ between studies and also among traits is somewhat problematic. In theory, there should be no variation in traits measured in genetically identical individuals growing under identical conditions. Therefore, in our and similar studies, $H^2$ and thus the variance apparently not inherited will strongly depend on how well factors including growing conditions, size, sampling and measurement errors are controlled.

Phylogeny-based statistical methods are rarely used for intra-specific variation because populations are not genetically isolated and migration between populations is possible (Stone, Nee, & Felsenstein, 2011). The applicability of phylogenetic comparative methods for intraspecific variation strongly depends on the adequate resolution of the phylogenetic tree, a low migration rate between populations and the number of contrasts (Stone et al., 2011). One study found that the phylogenetic signal, its significance and the effect of sample size were similar for intra- and interspecific contrasts (Ashton, 2004). Our study tests the usefulness of intraspecific phylogenetic signal and compares it to heritability and published interspecific signals for the same traits. Since various processes may result in a low phylogenetic signal, its interpretation is not straightforward and some caution against the use of phylogenetic signals as a measure of evolutionary rates or interpreting the underlying mechanisms (Blomberg et al., 2003; Revell et al., 2008). A strong phylogenetic signal in any case means that a trait has changed through the
phylogeny, whatever the model or rate of evolution. While a phylogenetic signal does not prove that trait evolution is adaptive (the reference evolutionary model is random Brownian motion), we note that for all traits that showed a significant cline, at least one, and for those with the strongest correlations with climate (LAM and δ¹³C) all measures of phylogenetic signal were significant.

Spatially related clones collected from the same municipality or state also tended to be phylogenetically related (Fig. 2). All indices showed a significant phylogenetic signal for LMA and δ¹³C, and λ and C<sub>mean</sub> also for VA and N<sub>area</sub> (Table 3). The variance explained by location or clone plus location was correlated with at least one of the phylogenetic signals tested, but not the variance explained by clone within location. Although there is substantial genetic variation within locations (Fig. 3), individuals grow under the same local climate may face no clear evolutionary pressure to adjust (at least the traits we looked at), and hence a phylogenetic signal is not related to trait variation within locations.

Interestingly, two studies comparing >300 phylogenetically diverse species (Hietz et al., 2017 for wood and Valverde-Barrantes, Freschet, Roumet, & Blackwood, 2017 for leaf traits) report λ very similar to our intraspecific study for WD, VA and VD (0.7-0.8), F (0.5) and K<sub>h</sub> (0.55) and LMA (0.76), but much higher for leaf N<sub>mass</sub> (0.67; vs. 0.16 for *Hevea*). This suggests that, at least for our study system, phylogenetic signals are useful to study intraspecific trait evolution and that the evolution of these traits follows similar patterns across vastly different scales of time and relatedness.

## 4.3 | Evolution of adaptive traits

No trait was significantly correlated with the annual mean temperature of the place of origin, which was expected given that the temperature range was small. Also, no trait was related to annual rainfall (range: 1,354-2,215 mm), but we found strong correlations with dry season rainfall (19-199 mm) and rainfall variability. Leaf traits, particularly N<sub>area</sub>, LMA and δ¹³C, showed the strongest correlations with Pcv and Pq (r² = 0.54-0.70). Wood VD and F were also weakly related to both measures of dry season intensity (r² = 0.34-0.37), though the correlation with Pq may have been biased by tree diameter (VD and F were still significantly correlated with Pcv, Table 4). In two large-scale drought experiments in the Amazon rainforest, where water availability in the control and drought-stressed variants was similar to the wet and dry extreme of the *Hevea* sample locations, mortality was related to wood and leaf traits including xylem vulnerability and turgor loss point (Powell et al., 2017), which are functionally related to the traits we studied (see introduction). It is therefore likely that traits that confer drought resistance have been selected variably across the range of *Hevea*.

Our study minimized the effect of phenotypic plasticity by using trees from a common garden experiment, excluded an effect of age and accounted for a potential effect of tree size, used clones from locations that differed little in a potentially confounding temperature gradient and found a significant phylogenetic signal in all traits strongly related to dry season intensity. These results provide strong

evidence for intraspecific evolutionary adaptation of leaf traits to drought and weaker evidence for wood anatomical traits. However, the trait variation was not what we expected.

## 4.4 | Mechanisms of drought resistance

We expected trees from more intense dry seasons to have higher WD and possibly smaller vessels and lower K<sub>h</sub>. We also expected these trees to have higher LMA and water use efficiency as well as lower growth rate as these are related to a conservative resource use and also drought tolerance. However, WD was not related to any climate parameter and K<sub>h</sub> only weakly and negatively to Pcv. We have no explanation why trees from a less intense dry season should have a higher vessel density and vessel fraction, while vessel size remains constant. This may, however, be the effect of an even small correlation with diameter (these effects were mostly non-significant when diameter was included in models, Table 4), which highlights the importance of accounting for tree size when interpreting wood traits (Hietz et al., 2017), even when the size effect is quite low (Fig. 3).

LMA and δ¹³C both decreased with dry season intensity. These correlations were the strongest of any trait with climate and showed a strong phylogenetic signal. Although few intraspecific provenance studies looked at drought resistance, some do report similar findings. *Pinus contorta* from more mesic locations had higher WUE (based on δ¹³C) than trees from xeric locations (Guy & Holowachuk, 2001), but these provenances also represent an altitudinal (and hence temperature) gradient and the physiological basis as well as the adaptive function of differences in WUE remained unresolved. In a common garden trial of 22 *Nothofagus* species, δ¹³C was also strongly positively related to total rainfall at the places of origin, but again precipitation strongly covaried with temperature and temperature also correlated strongly with δ¹³C (Read & Farquhar, 1991). *Castanea sativa* from drier sites in Turkey had thicker leaves, higher protein per leaf area, stomatal conductance and assimilation rates than provenances from wetter sites (Lauteri, Scartazza, Guido, & Brugnoli, 1997). In that study, the higher δ¹³C from wetter sites was explained by lower mesophyll resistance and photosynthetic capacity. In addition, an experiment in *Betula pendula* found that genotypes from regions with low rainfall had higher stomatal conductance than genotypes from high rainfall when grown in a common garden (Aspelmeier & Leuschner, 2004). We did not measure gas exchange, but similar responses may explain the lower δ¹³C and higher growth we found in *Hevea* from stronger dry seasons.

While many trees in seasonally dry tropical forests drop their leaves in the dry season (Borchert, 1994), evergreen as well as deciduous trees in Amazonian floodplain forests tend to drop their leaves at the height of the flooding season (Schöngart, Piedade, Ludwigshausen, Horna, & Worbes, 2002). *Hevea* grows in non-flooded terra firme and the higher levels of floodplain forests (varzeas) in the central Amazon as well as the seasonally dry south-east. In varzeas (Freitas, Oliveira, & Barros, 1997) as well as terra firme (J. Schöngart, personal communication), it sheds leaves at the
beginning of the dry season, but this coincides with high water-ta-
bles. The forest type of the provenances has not been recorded, but
varzeas are less extensive in the southern region of the Amazon basin
compared to the central and eastern Amazon. Thus, few if any of the
original populations are likely from strongly flooded sites, whereas at
least those from the eastern collection sites would experience sub-
stantial drought (Fig. 2). All provenances drop their leaves in the dry
season at the Thai site as do rubber trees in plantations throughout
the tropics (Priyadarshan, Sasikumar, & Gonçalves, 2001). That said,
*Hevea* and other tropical deciduous trees might drop their leaves op-
portunistically whenever stress levels (by flooding or drought) are
high. Few if any provenances we studied were likely from strongly
flooded forests and we generally found correlations with dry season
intensity (Fig. 4) and not with total rainfall, which would have been a
measure of potential flooding stress for trees growing close to rivers.
We therefore believe that clinal traits reflect adaptations to drought,
and not flooding stress, although deciduousness might serve as an
adaptation to both.

We can thus understand the strong relationship between dry
season intensity and traits as interspecific variation in drought
avoidance rather than tolerance. In seedlings of the tropical dry
forest species *Quercus oleoides*, LMA increased with the moisture
of the location of origin, but plants from drier locations tended to
drop their leaves faster in response to drought (Ramírez-Valiente &
Cavender-Bares, 2017). Adaptations to drought need not be at the
leaf level. *Pinus pinaster* seedlings from a drier region in Morocco
invested more in roots than plants from more mesic climates and
consequently experienced less drought stress (low δ¹³C) under a low
soil water regime (Aranda et al., 2010). Because *Hevea* from drought-
prone areas have adapted to avoid drought, which likely includes
traits in addition to those investigated, when grown under the same
conditions they also experience less drought stress and can realize
higher growth rates (Fig. 4).

Drought is a major limiting factor for tree growth and distribu-
tion, has become a driver of increased tree mortality and will likely
increase in many rainforest areas. This calls for a better under-
standing of drought responses and intraspecific variation in trop-
ical trees. To our knowledge, this is the first study of intraspecific
trait variation of a tropical wet forest tree from across much of
the species range to identify inherited component of traits. Hevea
is untypical among rainforest trees in being deciduous, which is
a strategy of drought avoidance. Drought-avoiding trees do not
have to tolerate negative xylem water potential by high cavitation
resistance, associated with wood anatomical traits (Hoffmann,
Marchin, Abit, & Lau, 2011); thus, finding strong adaptations in
wood traits in Hevea may be unlikely. Instead, drought avoidance is
typically associated with acquisitive traits, low water use efficiency
and higher growth, which was seen in Hevea from drier regions,
although lower wood density is also common in drought avoiders
(Méndez-Alonzo et al., 2012). Our study thus provides strong evi-
dence of interspecific evolutionary adaptation to drought in trees
and suggests that Hevea adapts by drought avoidance rather than
drought tolerance. In other rainforest trees, evolution likely leads

to different patterns of intraspecific drought adaptations, which
remains to be investigated. Drought avoidance is only a rough clas-
sification of the trees' strategies. A more detailed picture might
emerge from directly measuring physiological parameter instead
of interpreting functional traits as proxies, although this is chal-
lenging for a large number of tall trees.

5 | CONCLUSIONS AND FUTURE DIRECTIONS

To assess a species' capacity to adjust to climate change, researcher
has largely looked at traits conferring drought tolerance (Moran,
Lauder, Musser, Stathos, & Shu, 2017) and often focussed on spe-
cific traits such as cavitation resistance rather than the trait spec-
trum. Trees can survive drought with different strategies and failing
to identify drought tolerance, particularly in wood traits, should not
lead to the conclusion that species are not drought resistant or do
not have the genetic diversity to adjust to drought. Drought-avoid-
ing traits that should be looked for are an efficient system of water
uptake, seen in the root/shoot ratio or root morphology, and a con-
servative or efficient control of water loss, seen in stomatal control
and leaf phenology. *Hevea brasiliensis* is especially attractive because
it is one of the few trees with a completely sequenced genome (Tang
et al., 2016), which enables to search for specific genes that are se-
lected and test the association of genetic polymorphisms with adap-
tive traits (Moran et al., 2017). While sequencing is getting faster
and cheaper, few tropical species are grown in established prove-
nance trials of mature trees, although smaller trials with seedlings
can more easily be established. The high genetic variation in drought
resistance should provide *Hevea* with adaptations to cope with a
changing climate. However, this will also depend on the potential
gene flow across large distances, which might be impaired by an in-
creasing isolation of remaining forest areas.

ACKNOWLEDGEMENT

We thank the Rubber Research Institute of Thailand for on-site sup-
port, Ornuma Duangngam and Dokkeaw Chura for help during wood
sampling and Sabine Rosner, Daniel Tholen and anonymous review-
ers for helpful comments on the manuscript.

AUTHORS' CONTRIBUTIONS

P.K. and N.K. contributed to data on leaf traits; T.P. contributed to
data on SNPs; R.R. managed the provenance trial; K.R. analysed the
wood samples; and P.H. and K.R. sampled wood traits, analysed the
data and wrote the manuscript.

DATA ACCESSIBILITY

Data are deposited in the Dryad Digital Repository https://doi.
org/10.5061/dryad.h1nj848 (Rungwattana et al., 2018).
ORCID

Peter Hietz http://orcid.org/0000-0002-0458-6593

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**How to cite this article:** Rungwattana K, Kasemsap P, Phumichai T, Kanpanon N, Rattanawong R, Hietz P. Trait evolution in tropical rubber (*Hevea brasiliensis*) trees is related to dry season intensity. *Funct Ecol.* 2018;32:2638-2651.
https://doi.org/10.1111/1365-2435.13203