Introduction

Trees may produce millions of seeds over their lifespan (Moleset al. 2004), yet the vast majority of those seeds will never become adults. Most of them will disappear in early life stages, when mortality is high (Valen 1975; Petit & Hampe 2006)⁠, through herbivory, disease, lack of resources like water (Slot & Poorter 2007),⁠ and maladaptation to their environment (Donohueet al. 2010). Such high mortality rates should provide ample opportunity for the action of natural selection (Petit & Hampe 2006; Donohue et al. 2010). Even though one must expect that any given seedling has much higher chances to die than to survive, identifying signals of adaptation to local micro-environmental conditions in young seedlings is paramount to understand species and phenotype distribution patterns. However, the causal links between environmental heterogeneity, spatial distribution of species and phenotypes, and local adaptation remain elusive in trees, mainly due to the lack of long-term studies, low statistical power, and insufficient understanding of the environmental factors determining local adaptation.
The study of seedling survival and growth is a straightforward way to make inferences on performance differences among tree species and populations (Baralotoet al. 2005)⁠. The analysis of common gardens, with or without the experimental application of environmental stresses meant to assess genetically driven phenotypic response variations in populations, is common practice. They make possible to compare the performance of individuals from different habitats (Valladares & Sánchez-Gómez 2006; Brousseau et al. 2013; Barton et al. 2020), populations (Ramírez-Valiente et al. 2009; Carsjens et al. 2014; Barton et al. 2020), or species (Lopez & Kursar 2003; Poorter & Markesteijn 2008; Queenborough et al. 2009) to test for local adaptation signals in traits of interest (de Villemereuil et al. 2016 for a review). In several cases however, adaptation is not proven (Lopez & Kursar 2003; Lópezet al. 2009; Queenborough et al. 2009) or give counter-intuitive (Poorter & Markesteijn 2008; Pineda-García et al. 2011). Common gardens and experimental stresses may nevertheless not exactly replicate conditions experienced by seedlings in the wild, leading to possible biases in the conclusions drawn about divergence and trait/survival response to stress.
Reciprocal Transplant Experiments (RTE), whereby seeds or seedlings are translocated between sites in the field and grow in the same conditions as natural regeneration, may be a better option to observe adaptive processes as they occur in wild conditions, albeit sometimes with higher implementation costs and larger variance in the estimation of parameters as experimental conditions are less strictly controlled. RTEs are an elegant way to test the hypothesis of a link between habitat variation and species or phenotype distribution, because they allow observing performance variance directly across an array of environmental factors (Morris et al. 2007). These differences in performance components (i.e. survival, growth, and/or reproduction) between populations in different environments can be interpreted directly in terms of adaptation, based on straightforward theoretical expectations: local adaptation is found when local populations perform better than transplanted ones (the ’local vs.foreigner’ condition) and any given population performs better in its own provenance than elsewhere (the ’home vs. away’ condition) (Kawecki & Ebert 2004). Local adaptation can be further nuanced depending on the generality of patterns across the system: if ‘local’ individuals always outperform ‘foreign’ individuals for all populations and conditions, then genetic trade-offs are orchestrating the patterns of local adaptation; if, however, some populations perform better at ’home’, but are not penalised elsewhere, then conditional neutrality may underpin the local adaptation (Anderson 2013). The two processes are not mutually exclusive and may coexist for different traits within a given system (Wadgymaret al. 2017).
In trees, conclusions from RTEs on local adaptation, mostly tested in seedlings due to the long lifecycle, are diverse: evidence of local adaptation to particular environmental parameters has been identified for a wide range of tree species (Fine et al. 2006; Wright 2007; Pizano et al. 2011; Pluess & Weber 2012; Smith et al. 2012; Baltzer & Davies 2012; Nagamitsu et al. 2015; Pluesset al. 2016; Mathiasen & Premoli 2016; Rellstab et al.2016; Barton et al. 2020); while the expected signals of local adaptation to, at least some environmental conditions, were not observed in other studies (Boshier & Stewart 2005; Eichhorn et al. 2006; Vizcaíno-Palomar et al. 2014; Latreille & Pichot 2017); In addition, RTEs have also shown that stark morphological differences between tree ecotypes may be due to purely phenotypic plasticity with no evidence of genetic influence (Fanget al. 2006). These results show the complexity of the relationship between environmental conditions and phenotypic traits through plasticity and local adaptation processes. The main constraint in interpreting the results obtained from RTEs is that one can only observe differential adaptation to a bundle of ecological factors that differ between habitats. By designing RTEs that cross more than one factor (e.g. differences in climate, soil properties, competition, and predators) one may be able to tease apart at least subsets of covarying ecological factors (Wadgymaret al. 2017). Such type of approach is thus extremely useful for understanding the mechanisms for spatial heterogeneity, which in turn is essential for the construction of predictive models of spatial distribution of species/phenotypes.
Lowland Neotropical forest, such as those in French Guiana, show a highly variable and complex mosaic of microhabitats linked to variations in topography and soil characteristics. Differences in water drainage has been long identified as a main ecological factor driving the tree community composition on the Guiana shield (Barthes 1991; ter Steege et al. 1993; Sabatier et al. 1997), allowing to position the species along a gradient of tolerance to prolonged water saturation of soil porosity and a gradient of tolerance to temporary water saturation (Pélissier et al. 2002). All these studies show that the most striking variations in tree species distribution at local scale result from the widespread gradient between seasonally flooded (SF) habitats along streams and all other surrounding habitats on slopes and hilltops (HT). These studies also showed that congeneric species often display opposite niche preferences across such gradients, as was pointed out by Allié et al. (2015). A typical example is provided by the genus Symphonia of African origin, with the species S. globulifera, widespread across the Neotropics, and a morpho-taxon of yet undetermined status, currently identified asS. sp1 , which is known to occur in the Guiana shield. Adults of the two taxa, referred hereafter as ’ecotypes’, are found in sympatry, often with intermingled crowns, but are environmentally segregated, withS. globulifera being strongly linked to SF habitats, whileS. sp1 is found on both HT and SF (Alliéet al. 2015; Schmitt et al. a. in review ;), The two ecotypes are differentiated by the size of their leaves, flowers, and fruits, the texture of their bark, the presence of pneumatophores or prop roots (; Baraloto et al. 2007 Schmitt et al. b. in review ), as well as differences in maximum diameter at breast height and growth rates (Héraultet al. 2011). Whether these morphological differences are the product of environmentally driven phenotypic plasticity or genetically determined is not yet known. The system constitutes an extreme case of microgeographic differentiation, and potentially, adaptation (Richardson et al. 2014), as trees of the two ecotypes are distributed in mosaic patches smaller than the pollen dispersal potential (20-50m) (Degen et al. 2004). The two ecotypes are genetically differentiated based on nuclear microsatellites (FST =0.086), although less so than African and South American population of S. globulifera(FST =0.31) (Torroba-Balmori et al.2017), or even than Neotropical population of S. globulifera(FST =0.138) (Dick & Heuertz 2008)The two ecotypes follow thus different evolutionary paths and may show genetic variants associated with adaptations to different habitats, but, genetically intermediate individuals (F1, F2, etc.) have been observed (N. Tysklind – unpublished data) suggesting that some mixing of the two ecotypes occurs in the field, leading us to believe thatSymphonia operates as a syngameon in French Guiana. Syngameons were defined by botanists during the XXth century (Lotsy 1917; Grant 1971) to describe closely related plant species that are interfertile despite the presence of morphologically distinct groups classified as different species. This term covers today the most inclusive interbreeding evolutionary unit (Suarez-Gonzalez et al.2018), of which oak is one of the best representatives (Cannon & Petit 2019). The genetic differentiation between Symphonia ecotypes is smaller than among members of the oak syngameon (FST (SNPs)=0.13 (Lang et al. 2018).
Previous shadehouse common garden experiments (Baralotoet al. 2007) could not pinpoint differential physiological responses to drought and flooding between the two ecotypes, and thus the heterogeneous spatial distribution of Symphonia is not explained by these alone, at least as far as experiments in artificial conditions can tell. Nevertheless, mortality was higher in S . sp1after six weeks of controlled flooding, and wild S. globuliferaseedlings tended to survive better in SF areas in the wild (Baraloto et al. 2007). To dissect the causes of the observed association of the two ecotypes with habitats, we established an RTE, considering both habitat and provenance region, in the field under the natural canopy. We hypothesise that germination, growth, herbivory defence, and survival, or a combination of these factors, are better for each ecotype and provenance in its habitat of origin than in other habitats; we argue that such differences contribute to explain the niche distribution patterns observed in the wild.

Materials and Methods

Experimental design and data collection:

The experimental design aimed to collect seeds from mother trees from two contrasting habitats and the associated ecotypes: SF-S. globulifera and HT-S. sp1; and from two broad regions with markedly different rainfall patterns: ’east’, with the highest rainfall in French Guiana, and ’west’, the driest part of French Guiana (Fig. 1). Seeds were transplanted onto experimental gardens installed on HT and SF habitats at two field sites in the ’east’ and ’west’ regions, respectively, which were not among the sampled sites for the seeds.
Seeds were collected between September 2008 and April 2009, due to largely unequal flowering times, from nine mother trees belonging to both ecotypes in the ’western’ region and from five mother trees in the ’eastern’ region, composing the variables “Provenance region” and “Ecotype” (Table 1). From each mother tree, 35-39 seeds were collected and sown in polypropylene germination plates with soil in a common shadehouse at the Kourou Agronomic Campus prior to transplantation into field sites between May and July 2009. This meant that seeds spent between 27 and 315 days in the shadehouse, depending on the seed collection and transplant dates. This introduced substantial differences among groups in the number of days spent in the shadehouse (Fig. 2a), the proportion of seeds that had germinated (i.e. at least cotyledons emerged from ground), as well as in the developmental stage reached by the germinated seedlings, at transplantation time. Although such differences among groups are likely to originate from ecological differences in flowering time, they can be viewed as biases in analyses. First of all, comparisons of germination trends among groups must be interpreted according to this bias; secondly, germination rates themselves must be included as an important cofactor, in its turn carrying information about amount of time spent in shadehouse, in the life history and growth-associated traits analyses. See below for how biases in seed collection were incorporated into the analysis of germination rates, and for how differences in germination status were considered in the analyses of other traits. Whether seeds had germinated at the moment of transplant or not was stored in the variable “transplant status”. The moment at which individual seeds were first recorded as seedlings (e.g. moment of transplant, year 1, year 2, year 3, etc.) was stored as “germination timing”. To avoid confusion, we hereafter refer to “seed” when discussing aspects regarding the phase prior to germination, “seedling” for those aspects regarding the phase after germination, and “individual” for those aspects regarding both seed and seedling phases.
Individuals (i.e. seed or seedling) were transplanted to gardens established at each field site (i.e. east vs. west) and habitat: three in SF and three in HT conditions in each site, totalling 12 gardens. Field site plantation and habitat compose the variables “Plantation region” and “Habitat”, respectively (Table 2; Fig. 1). Each garden was fenced from large herbivores with chicken wire. Prior to transplanting, all understory vegetation (i.e. up to 5 cm D.B.H.) was removed; the canopy was left undisturbed. Regeneration other than the transplanted seedlings was removed yearly by hand. Individuals were distributed over the twelve gardens as follows: each garden was arranged in 44 ten-seedling slots. In each garden, six slots were randomly attributed to Symphonia (the remaining slots were used for other experiments), and then three individuals per mother plant were assigned to random positions within those slots in each garden. Individuals were allocated to different gardens depending on their provenance, ecotype, plantation region, and habitat without differentiating between those that had germinated in the shadehouse and transplanted as germinated seedlings or those transplanted as ungerminated seed (Table 2). Data for each individual (i.e. germination status at transplantation, germination year, survival, growth-associated traits, and herbivory) were collected at transplant date and then yearly in September from 2009 until 2014, except for 2012 (Suppl. Table 1). Individual survival was recorded as follows: 1 for seedlings found alive and 0 for ungerminated seeds transplanted to field sites and yet to germinate, and for seedlings previously living but found dead. Seedling height was measured in centimetres between the apical bud and the collar. Stem diameter was measured in millimetres at the collar in two orthogonal directions and estimated as the mean of the two measures. As an architectural trait, we selected “total number of leaves”, an indicator of seedling leafiness. Herbivory was determined as follows: each leaf was assigned one of five classes of percentage of damaged area (0-20%; 20-40%; 40-60%; 60-80%; 80-100%) and then seedling herbivory attack level was estimated as the average of the percentage of damaged area of all its leaves (Suppl. Table 1).

Data analyses:

Two complementary analytical strategies were applied to the data to extract the biological significance of individual germination, survival, and performance depending on the studied predictor variables: a) random forest methods were used to explore variable importance, untangle and understand the structure of interactions among the covariates, and graphically visualise their effects on the dependent variables; and b) Linear model and Generalized linear model (GLM) were used to find general effects of predictor variables on the dependent variables. More specifically, we introduce a test based on the least-squares means, also named adjusted means (Searleet al. 1980), which allows us to compare the effect of growing in their ‘home’ habitat vs. ‘away’ habitat and of being ‘local’vs. ‘foreigner’ in a given habitat, while averaging for other potential effects to make such comparisons meaningful and reduce the confusion of effects.
Random forest analyses:
Random forest methods and classification trees were applied to evaluate relative variable importance in explaining germination, survival, growth, leafiness, and herbivory on individuals at the end of the experiment (year 6) and the average yearly relative growth rate (RGR), untangle interactions among the predictor variables, and graphically visualise their effects on each of the responses. Random forest methods (Breiman 2001) are particularly suited for data where nonlinear relationships and complex interactions among variables are expected (Cutleret al. 2007). Classification trees visualize predictive models of responses significantly dependent on predictor variables. This is achieved by recursive binary classification of the data, where independence of the response (i.e. here germination, survival, growth, leafiness, and herbivory) and the covariates (i.e. here provenance region, ecotype, plantation region, habitat, and transplant status) is tested; then, if a significant dependence is found, the best split value for the predictor variable with the strongest effect is retained and used to divide the response in two groups. The process is then repeated with each of the groups, recursively, until no significant dependence between covariates and response can be found (Hothornet al. 2006). In a random forest, the above classification tree is performed on a bootstrap subset of the data and a reduced number of predictor variables to obtain response predictions based on a majority vote of the whole forest. Such methodology allows assessing relative variable importance, by identifying those covariates which, when removed, ensue a significant drop of prediction power (Stroblet al. 2007). In our case, it allows us to identify if certain combinations of variables (e.g. S. globulifera in SF) lead to significant improvement of performance of ‘home’ or ‘local’ individuals.
We repeated the analyses including all the predictor variables as explanatory variables and removing transplant status (i.e. seed or seedling) to check if transplant status confounded the analysis of the impact of the covariates of interest (i.e. provenance region, ecotype, plantation region, and habitat). Furthermore, we evaluated the effects of provenance region and ecotype on shadehouse germination rates, and of all predictor variables on the germination rates of individuals planted as seeds in the field (Supplementary material 1).
For the random forest analyses of growth performance and herbivory, only individuals germinated in 2009, whether in the shade-house or in the field and having a final measure in 2014 were analysed. Therefore, each individual has a unique discrete value for each trait in 2014, that is at age 5 (i.e. year 6). The average yearly relative growth rate (RGR) in height, diameter, and leafiness as well as the average herbivory were also summarised over the life of the individual, giving each individual a single value over the course of the experiment.
All analyses were run in the R statistical environment (R Core Team 2020) with the package ‘party’ (Hothornet al. 2006; Strobl et al. 2009). Conditional inference trees were also grown in party with an α = 0.05 and a minimum of 2 observations in each branch.
Linear models and general linear model analyses:
Linear model (LM) and generalized linear model (GLM) were additionally used to test the potential effects of different predictor variables on traits (i.e. germination, survival, growth, architecture, and herbivory), and most importantly to define an ad-hoc procedure to test local adaptation by subsuming in a single test both the ‘home vs. away’, and the ‘local vs.foreigner’ tests of Kawecki and Ebert (2004), while averaging over all possible other effects to disentangle the effect of the unbalanced final design. Our approach, as described below, aims at synthetically observing the effect of having been planted in the environment of provenance or in a different environment, in itself, on traits taken as proxies for individual performance. This contrasts with previous strategies for the detection of local adaptation, which rest on the separate analysis of the “home vs. away” and “localvs. foreigner” effects and deduce the presence of the effect from slope comparisons (i.e. , they test for population x environment interactions). In the wording of Kawecki and Ebert (2004), our method is tantamount to comparing the means of “‘sympatric’ and ‘allopatric’ deme-habitat combinations”. While confounding the effects of “true” local adaptation and of global superiority of one population relative to all others (Kawecki & Ebert 2004), our LM/GLM approach has the comparative advantage of better coping with unbalanced design and, possibly, having greater power.
We denote e the ecotype (e = 1 for S. globuliferaand e = 2 for S. sp1 ), h the habitat (h = 1 for SF, and h = 2 for HT, so that individuals growing at ’home’ are specified by 11 or 22), o for provenance region (o = 1 for east and o = 2 for west), r for the plantation region (r = 1 for east and r = 2 for west), s for the transplantation status (s = 1 if transplanted as a seed ors = 2 for transplanted as a seedling), and finally, a for the age of the considered individual (a = 1, …, 5). The full model for growth-associated traits (i.e. seedling height, stem diameter, leafiness, and herbivory) might be expressed as a normal response Yehorsa ~ N( μehorsa , σ2), while binary responses like the life history traits, germination and survival, are expressed as a Bernoulli distribution with probability of successpehorsa. In the following text, we show the development of formulas for the ecotype/habitat case, with subscriptse for ecotype and h for habitat, as described above; formulas for the provenance region / plantation region case are identical, except that they bear subscripts o for provenance region and r for plantation region, as described above, and will not be further described here.

Germination success in the shadehouse and overall germination (G ):

In the evaluation of the difference in germination success (G ) in the shadehouse, individuals have not yet been transplanted, therefore, the only effects to account for were genetic effects: ecotype and provenance region. However, the time spent in the shadehouse depends on the collection and should be accounted for. The logit of the probability of germination (G ) of the kth seed is given by:
\begin{equation} \text{logit}\left(p_{\text{eok}}^{G}\right)=u^{G}+\alpha_{e}^{G}+\delta_{o}^{G}+\varepsilon_{\text{eo}}^{G}+\nonumber \\ \end{equation}
Where t stands for the time spent in the shadehouse.
To correct the effects of unbalanced design and difference in time in the shadehouse, the effect of ecotype (e ) on the difference in germination success (G ) in the shadehouse, has been studied by a comparison of the classical least square means of ecotype:
\begin{equation} {\text{logit}\left(p\middle|\middle|adj,G\middle|\middle|e\right)}=u^{G}+{\alpha^{G}}_{e}+\left(\beta\middle|\middle|G+{\gamma^{G}}_{e}\right)\acute{t}+\frac{1}{2}\sum_{o}{\delta^{G}}_{o}+{\xi^{G}}_{\text{eo}}+{\eta^{G}}_{o}\acute{t},\left(2\right)\nonumber \\ \end{equation}
where (\(\acute{t}\)) is the average time spent in the shadehouse.
The same approach using least square means is used to study the overall germination except that we don’t account for the time in the shadehouse. In case of germination success, a GLM approach is used to study the time of germination. The response is modelled through a geometric distribution and the log link function. Least Square Means are used to compare the expected time of germination.

Survival (S ):

To evaluate differences in survival (S ) among ’home’ and ’away’ groups, we developed the following approach: assuming the survival probability is constant over a year, the observed maximal age might be modelled as a geometric distribution whose probability of success,pSehors , depends on ecotype (e) , habitat (h ), provenance region (o ), plantation region (r ), and the transplantation status (s ). The least-squares means for the log odd ratio of such probability is given by:
\begin{equation} \text{logit}\left(p\middle|\middle|adj,S\middle|\middle|\text{eh}\right)=u^{S}+{\alpha^{S}}_{\text{eh}}+\frac{1}{8}\sum_{o,r,s}{\delta^{S}}_{\text{ots}}+{\zeta^{S}}_{\text{ehors}},\left(3\right)\nonumber \\ \end{equation}
where αSeh stands for the joint effect of ecotype and habitat,δSors stands for all other effects like the provenance region, plantation region, and the transplantation status, and \({\zeta^{S}}_{\text{ehors}}\)the interaction between those different effects on the survival probability.

Growth-associated traits:

As the aim of the analysis of growth-associated traits is identifying the potential effect of the growing ’home’ vs. ’away’, the meanμehorsa has to reveal the joint effect of ecotype and habitat; the joint effect of provenance region, plantation region, and transplantation status; and the age effect as well as all interactions between any two of these variables. Therefore, for any growth-associated trait (Y), μehorsa might be expressed as:
\({u^{Y}}_{\text{ehorsa}}=u^{Y}+{\alpha^{Y}}_{\text{eh}}+{\delta^{Y}}_{\text{ors}}+\left(\beta\middle|\middle|Y+{\gamma^{Y}}_{\text{eh}}+{\theta^{Y}}_{\text{ors}}\right)a\)(4)
where
- \({\alpha^{Y}}_{\text{eh}}\) stands for the joint effect of ecotype and habitat, with a total of 4 possible different combination of ecotype x habitat
- \({\delta^{Y}}_{\text{ors}}\)stands for all other controlled effect like the provenance region, the plantation region, and transplantation status for a total of 8 possible different levels,
- \(\beta^{Y}\) is the effect of age,
- \({\gamma^{Y}}_{\text{eh}}\) is the differential effect of age according to the ecotype/habitat level,
- \({\theta^{Y}}_{\text{ors}}\) is the differential effect of age according to the provenance/plantation region/transplantation status levels.
Interaction between main effects of interest (i.eecotype/habitat) and other controlled effects have not been incorporated as not all combinations have been observed.
To detect signals of local adaptation in growth-associated traits, we compared the effects of growing at the ecotype’s (or provenance’s) ’home’ habitat vs. growing in the ’away’ habitat. Such comparison was achieved by defining least-squares means at age aμehadj(a) (Russell V. Lenth, 2016) to account for the unbalanced design:
\begin{equation} {u^{adj,Y}}_{\text{eh}}\left(a\right)=u^{Y}+{\alpha^{Y}}_{\text{eh}}+\left(\beta\middle|\middle|Y+{\gamma^{Y}}_{\text{eh}}\right)a+\frac{1}{8}\sum_{o,t,s}\left({\delta^{Y}}_{\text{ors}}+{\theta^{Y}}_{\text{ors}}a\right)\left(5\right)\nonumber \\ \end{equation}
The comparison between ‘home’ and ‘away’ habitat-ecotype pairs is performed age by age by forming the following contrast:
\(C_{a}=\left(u_{11}^{adj,Y}\left(a\right)+u_{22}^{adj,Y}\left(a\right)\right)-\left(u_{12}^{adj,Y}\left(a\right)+u_{21}^{adj,Y}\left(a\right)\right)\)\(\left(6\right)\)
which quantifies the difference of an average individual growing at ’home’ (combination 11 or 22) and an average individual growing ’away’ (combination 12 or 21).
All GLM analyses were run in the R statistical environment with the packages ‘car’ (Fox & Weisberg 2010)⁠, ‘multcomp’ (Hothornet al. 2008)⁠, ‘emmeans’ (Lenth 2016, 2018)⁠,⁠ and visualized using ‘ggplot2’ (Wickham 2009)⁠.

Results

Description of the data:

A total of 510 individual Symphonia were followed over the course of 6 years. Of these, 36.1% had germinated at the time of transplant and were transplanted as seedlings. The remainder were transplanted as seeds. Overall germination reached 61.2% by the end of the experiment (Table 1). At the end of the experiment 37% of seedlings were alive, and survival was lowest in western SF gardens (Table 2). Summary statistics of growth-associated traits (i.e. height, diameter, total number of leaves) and average herbivory over the course of the 6 years for those individuals germinated in 2009 and still alive at the end of the experiment are reported in Table 3.

Impact of the transplant status on survival: seeds vs. seedlings.

The classification tree analysis of the success of germination (Supplementary material 1) indicated strong effects of the provenance region independently of the transplant status: seeds having germinated in the shadehouse or seeds having germinated in the field. The same result was observed using GLM when considering the global success of germination (i.e. whatever the transplant status, all the seeds that have germinated at some point, during the 6 years of the experiment). Since the main effect tested was observed in both the seeds that have germinated in shadehouse and the seeds that have germinated in the field, the two transplant statuses were merged together for the rest of the analyses.

Effects of covariates on germination and survival of Symphonia seedlings

To overcome the bias in duration of time at the shadehouse among the different ecotype x provenance groups (Fig. 2a), we compared the probability of germination success in the shadehouse of the two ecotypes depending on the region of provenance after the average time in the shade house (i.e. 89 days). A significantly lower probability of germination success in the shadehouse was identified for westernS. sp1 compared to all other groups (Fig. 2b), even when accounting for the difference in time in the shadehouse. However, the analysis of overall germination (i.e. shadehouse and field combined) indicated that provenance and plantation habitat, rather than ecotype, were the main drivers of the variance in germination success (Fig. 2c and Fig. 3). The analysis of the delay on germination showcases that provenance also had a strong impact on the timing of germination (Fig. 2d), where seeds from the west germinate significantly later than those from the east (west: 13 months vs. east: 3 months).
In terms of overall germination and survival, classification trees identified that provenance region, plantation region, habitat, and ecotype as significant variables explaining overall germination and survival of individuals at the 5% threshold, exposing the complexity of the interactions among predictor variables (Fig. 3). Provenance had the strongest impact, where most individuals from the east (Fig. 3, nodes 9, 10, 11) had germinated, while those from the west systematically suffered from lower germination rates (Fig. 3, nodes 2, 3, 4, 5, 6, 7 and 8). Eastern provenance individuals planted in the east plantations survived significantly better than when planted in the west (Fig. 3, node 9). Western provenance individuals survived better in the east than in the west (Fig. 3, node 2). Germination and survival were lowest for western provenance individuals, planted in the west in SF habitats, regardless of ecotype (Fig. 3, node 4). Finally, for western provenance individuals, planted in the west, in HT habitats, there is a significant ecotype effect, where S. sp1 (i.e. ‘local’) germinates and survives significantly better (Fig. 3, node 6) than S. globulifera (i.e. ‘foreign’) (Fig.3, node 7).

Effects of predictor variables on growth-associated traits of Symphonia seedlings

According to the classification trees, ecotype had the strongest significant effect on 5 out of 7 growth-associated traits and herbivory: average RGR height, diameter, total number of leaves, and average RGR in number of leaves, and average relative herbivory, separating S. globulifera from S. sp1 (Fig. 4). S. sp1 grew faster (Fig. 4b, 5a,b), was thinner (Fig. 4c, 5e,f) but leafier (Fig. 4e, 5i,j), and suffered less herbivory than S. globulifera (Fig. 4g, 5n). Habitat also had significant effects on four growth-associated traits, revealing that: individuals were taller on average in SF than in HT (Fig. 4a), that S. globulifera grew faster and was leafier in SF than in HT (Fig. 4b node 3, 4e node 3), and that S. sp1suffered the least herbivory when planted in SF habitats (Fig. 4g, 5n). Finally, plantation region had an impact on the height of individuals planted in HT (Fig. 4a node 3) and on the RGR in diameter (Fig. 4d), with individuals planted in the west being taller and thicker than those in the east.
The Linear Model analyses, where LS Means were used to correct for unbalanced design, of the growth-associated traits comparing ‘home’vs. ‘away’ (i.e. ecotype x habitat and provenance x plantation combinations) corroborated the RF results but focused on a yearly comparison to provide a contrasting view. Figure 5 illustrates the significantly better growth performances (e.g. height, diameter, and TNL, as well as their relative growth compared to reference measures at age 0) of individuals grown in their ‘home’ ecotype x habitat combination compared to those in ‘away’ ecotype x habitats combination, and that the differences increase with age (Fig. 5a,b,e,f,i,j).
The provenance x plantation analyses showed significant, or near significant, effects in the early ages for height, diameter, and total number of leaves, where ‘home’ individuals outperformed ‘away’ individuals, but the significance disappeared in later years (Fig. 5c,g,k). Individuals grown in their ‘home’ provenance x plantation combination, had significantly larger diameters at ages 3-5 relative to their diameter at age 0 compared to individuals in ‘away’ provenances x plantation combination (Fig. 5h).

Discussion

Local genetic differentiation and its adaptive significance are widely recognised in plants and evidence of microgeographic adaptive processes is accumulating for trees (Wright 2007; Ramírez-Valienteet al. 2009; Brousseau et al. 2013, 2015, 2018; Carsjenset al. 2014; Pluess et al. 2016; Rellstab et al.2016; Barton et al. 2020). Differences in seedling performances along ecological gradients are typically interpreted as underlying observed interspecific differences in the distribution of mature trees; in a way, our two ecotypes in the Symphonia syngameon behave in a species-like way relative to habitat preferences, given that they can grow in mixed or neighbouring stands, where morphological and genetic hybrids are occasionally found, and yet they retain their respective ecological properties.
Superior performances of ’home’ and ’local’ individuals in life-history and growth-associated traits suggest that Symphonia trees have locally adapted to different environmental conditions across French Guiana. We find both ‘home’ vs. ‘away’, and ’local’ vs.’foreigner’ examples of local adaptation (sensuKawecki & Ebert 2004)). The patterns are, however, complex; revealing that the measured traits are not exclusively caused by genetic trade-offs in the underlying genes coding for the patterns of local adaptation.

Patterns of germination:

The classification trees and the estimated shadehouse and overall probability of germination (Fig. 2 and Suppl. Fig. 3, 4, 5) exposed some unexpected patterns: Western S. sp1 seeds have a significantly lower probability of germination under the controlled conditions of the shadehouse after 89 days than all other groups (Fig. 2b). However, overall germination at the end of the experiment of western S. sp1 planted in HT in the west was relatively high (~70%, Fig. 2c), indicating that germination for western S. sp1 in western HT recovered while in the field. The germination of western S. sp1 in other habitat-region combinations remained very low till the end of the experiment (Fig. 2c). In stark contrast, all four eastern provenance combinations of ecotype and habitat planted in the east had nearly 100% overall germination success. Western seeds germinated significantly later than eastern seeds (Fig. 2d: 1 year later on average). Such differential success rate and timing of germination among ecotype-provenance combinations could be due to differences in local adaptation to germination timing and cues. Matching germination with the best possible conditions for seedling growth is paramount for seedling survival, and the timing and environmental cues underpinning such favourable conditions may vary across a species range. Variation in seed dormancy duration, and the genetic basis for such variation, has been reported as evidence for local adaptation among populations ofArabidopsis thaliana(Donohue 2009; Postma et al. 2015, 2016). Similarly, the regional differences in germination success over the course of our study may indicate evolutionary advantages for delayed germination ofSymphonia in the west or rapid germination of Symphonia in the east (Fig. 2d). A higher seed quiescence or dormancy level, or tighter environmental requirements for germination, may have emerged as a local adaptation in western Symphonia , especially in S. sp1¸ as a means to cope with a drier environment (Dallinget al. 2011), and spreading seedling mortality risk across several years (Gremer & Venable 2014). Supporting such hypothesis, we observe reduced survival of eastern provenance-individuals in the west (Fig. 3) indicating that the western plantations are in a harsher environment overall.  Conversely, a quicker germination time may be a local adaptation in response to differences in seed mortality rate (e.g. herbivory, disease, or ageing) between regions or ecotypes (Dallinget al. 2011; Postma et al. 2015)

Patterns of growth-associated traits:

The LSMeans analyses exposed how individuals in their home habitat significantly outperformed individuals in away habitats in terms of growth-associated traits, and the classification trees pinpointed how that signal was dominated by significant decreases in growth-associated traits for S. globulifera when planted in HT, indicating a reduction in competitive growth performance of individual S. globulifera in HT. Conversely, we did not detect any significant effect of habitat for S. sp1 , suggesting a capacity to perform well regardless of habitat. Individual tree vigour, as in the difference between observed and expected growth, has been shown to have a pervasive effect on Neotropical tree survival, where variance in individual vigour along the tree’s life was the most important variable predicting survival, well above ontogenetic status or species membership (Aubry-Kientzet al. 2015). Variance in growth performance, such as seen inS. globulifera, can be interpreted as variance in vigour, which could be one of the mechanisms explaining the variance in survival patterns we observed among Symphonia seedlings. The observed variance in growth is indeed in accordance with the rarity of adultS. globulifera in HT habitats, and the more generalist distribution of S. sp1 across habitats (Alliéet al. 2015; S. Schmitt, unpublished data).

Patterns of survival and contributions towards understanding the patterns of species distribution:

The classification trees of combined germination and survival reveal complex interactions among the predictor variables, not only explaining the patterns of survival ofSymphonia individuals in our experiment, but also potentially explaining patterns of species distribution in French Guiana and the maintenance of ecological differences between ecotypes within theSymphonia syngameon.
The groups with the highest survival (>50% after 6 years) were western-provenance S. sp1planted in western HT, and eastern-provenance individuals planted in the east regardless of ecotype and habitat (Fig. 3). These two high survival groups also had high germination rates (Fig. 2c). The first group is suggestive of very specific local adaptation. It is the only group planted in the west with a relatively high survival (i.e.>50% survival at age 5, compared to <25% for the rest of groups transplanted in the west). S. sp1 is common in hilltops throughout French Guiana and may therefore be able to cope better in the drier west. Furthermore, it is only western S. sp1which significantly separate from all others in terms of survival, perhaps indicative not only of an ecotype adaption to drier HT, but also a regional effect where eastern S. sp1 are particularly drought tolerant. This case constitutes a double example of a ‘local vs.foreign’ evidence of local adaptation across two variables (both habitat and regional), stressing the efficacy of the selection pressures in eastern HT habitats. Variations in survival and germination in S. sp1 are furthermore accompanied with an overall lower performance ofS. globulifera seedlings when planted in HT; both probably contributing to contrasted distributions between the two ecotypes. We did not detect an adaptive cost for S. sp1 in the form of lower survival in either eastern gardens nor in SF habitats, suggesting that either 1) conditional neutrality (i.e. whereby an adaptation conveys a performance advantage in one environment without costs in alternative environments) is at play in the genetic basis underlying its improved performance in dryer conditions (Anderson 2013; Wadgymar et al. 2017), or that 2) our experimental design did not capture the selective pressures penalising S. sp1 in wetter conditions, such as those found in SF habitats or eastern gardens. The latter could be related to environmental variables we did not account for in the experiment or related to stages we missed across the trees’ life history (Migliaet al. 2005).
The second group with comparatively high survival confirms a better performance of eastern individuals in eastern field sites compared to western field sites, indicative of either local adaptation at the regional level to heavier rainfalls, or alternatively, poor drought tolerance, as the survival of eastern-provenance individuals, regardless of ecotype, planted in the west drops significantly. This constitutes an example of ‘home vs. away’ pattern of local adaptation, but not ‘local vs. foreign’ as western-provenance individuals have a high survival in the east once germinated. Given the general high survival of individuals in east plantation gardens, and the non-appearance of other factors significantly affecting survival in these gardens, we infer a less stressful environment in the eastern field site forSymphonia in general. We did not capture evidence of differences in survival between ecotypes or habitats in the east, which exemplifies the potential confusion between divergent selection and differences in habitat quality.
Overall, we find evidence that S. sp1 has better survival in the driest conditions, suffers less herbivory, and has no penalisation on other environments, which suggests a habitat generalist behaviour, and matches the extant species distribution. Conversely, S. globulifera is triple penalised out of SF (i.e. lower TNL, lower RGR in height, and higher herbivory), suggesting that it is a habitat specialist limited to SF habitats. The apparent limitation in habitat availability for S. globulifera could be compensated by the faster adult growth rate and a potentially higher reproductive output because of the larger maximum sizes in S. globulifera thanS. sp1 allowing the coexistence of both ecotypes. The variance in juvenile performance in the two habitats also helps explain the maintenance of the ecological differences between ecotypes, which could contribute to the preservation of the genetic integrity of each ecotype even when occasional hybridisation occurs between them.

Selective pressures behind the signals of local adaptation:

The experimental setup was designed to detect adaptation patterns to the combined effects of the contrasted habitats, with a focus on soil factors influencing hydric regime. Our results show a pattern consistent with an adaptive advantage of westernS. sp1 to the driest conditions included in the experiment (i.e. western HT), potentially mediated through improved water use efficiency (Baltzeret al. 2005).
Beyond the sharp variations in water regime, many other variables covary across the microhabitats presented here (i.e. east vs.west, SF vs. HT): access to resources such as light and soil nutrients, the risk of death, the floristic and soil microbiota community, and presence of herbivores vary significantly between HT and SF.
SF habitats have a higher fertility and access to light than HT habitats, however, trees living in SF habitats double their risk of death through tree fall (Ferryet al. 2010), creating a high-risk high-gain environment. Species specializing in SF habitats must therefore adapt their resource allocations accordingly. S. globulifera seedlings in SF were the tallest (Fig. 5a) and had the largest diameters after 6 years (Fig. 5e), potentially indicative of an ecotype adaptation towards a strategy maximising growth in a risky environment.
Arthropod assemblages in French Guiana SF and HT habitats are significantly different, where leaf feeders in particular are more abundant in HT than in SF (Lamarreet al. 2016). Herbivory was highest for S. globuliferaregardless of all other covariates, and lowest for S. sp1 in SF habitats, suggesting different predator avoidance strategies between ecotypes. Our herbivory analyses are in agreement with those of previous studies, where similar patterns are observed in RTEs between species specializing in high and low herbivory pressure environments: species which are normally exposed to a higher herbivory environment (i.e. similar to S. sp1 ), experienced reduced herbivory in low herbivory environments (i.e.similar to SF) compared to their ‘home’ environment and species from low-herbivory environments (Fineet al. 2004, 2006; Baltzer & Davies 2012). S. globuliferatissues are rich in secondary metabolites of the b is-xanthone family, known to have insecticidal properties in other organisms (Ondeyka et al. 2006; Wezemanet al. 2015); the leaves are particularly rich in globulixanthone E (Cottetet al. 2014), which has strong anti-microbial activity (Nkengfacket al. 2002). S. globulifera populations from Cameroon and French Guiana differ for their content in anti-microbial and anti-parasitic compounds (Cottetet al. 2017), suggesting that chemical differences may also occur between S. globulifera and S. sp1. Under this hypothesis,S. sp1 may have adapted to a higher herbivory environment (HT) by increasing the production of unpalatable and toxic compounds to compensate for a potential limitation in resources in HT habitats (Bryantet al. 1985; Fine et al. 2004, 2006). Such scenario would also explain the lower herbivory rate of S. sp1 in SF habitats compared to HT habitats.

Limitations of the study:

The power of our tests and the meaning of their results may suffer from multiple biases. Differences in germination successes lead to unbalances. While the analytical method we developed is meant to compensate them, they may still affect the results. Maternal effects (e.g. maternal provision to seeds) may still influence seedling growth and resources, because seedling mass is probably still in the same order of magnitude as seed mass. Epigenetic inheritance may also contribute to differences in seedling reactions to environmental cues. In the absence of precise information about genetic divergence and gene expression / regulation differences between ecotypes, it is hard to tell which mechanism is at play in theSymphonia system. Finally, as stressed by Miglia et al.(2005), to gain a comprehensive understanding of the ecological factors driving survival and performance of related taxa across environmental variables, multi-life-stage comparisons including germination, som
atic growth, and reproduction should be included.

Conclusion:

The critical importance of natural selection on genetic differences expressed during early life stages, when trees experience the highest mortality, has been recognised (Donohueet al. 2010; Postma & Ågren 2016). Our RTE experiment has given us insights into the ecological mechanisms governing differential germination and survival of cohorts of individuals in their own and foreign natural environments. We have revealed significant life-history and growth-associated trait differences between ecotypes and between provenances, that match with known environmental constraints (i.e. hydric regimes, nutrient availability, death risk, and herbivory risks), and may be the result of coevolution of germination phenology and seedling survival. S. globulifera seedlings were penalised in HT habitats with reduced growth and higher herbivory, however, in SF habitats they outgrew other such groups (ecotypes x habitat), a pattern also observed in adults Symphonia , suggesting that S. globulifera has a specialized competitive advantage in SF habitats, which may results in higher reproductive output if greater adult biomass is attained, and thus, allowing the coexistence for both ecotypes and the maintenance of the syngameon. Our results therefore suggest a link between differential growth and survival in seedlings, on the one hand, and adult tree distribution, on the other hand, and indicates that processes occurring at early life stages, far from being of an exclusively stochastic nature, contribute in a significant way to the selective processes and ecological filters that determine a species’ pattern of distribution across habitats. Furthermore, our results suggest that even relatively small environmental differences, such as those between HT and SF, can lead to the evolutionary differentiation and maintenance of distinct taxa in sympatry with different life-history traits to suit such mosaic environmental heterogeneity despite occasional geneflow. Overall, the Symphonia model furthers our comprehension of the eco-evolutionary processes underpinning the diversity and the spatial structuring of Neotropical tree communities as well furthering our understating of the processes involved in the maintenance of syngameons in sympatry.

Acknowledgements:

This manuscript is posthumously dedicated to MC, who started the RTE experiment and analysed the first years of measurements in part of his thesis “Genetic of the divergence within closely related species of tropical trees”. This manuscript also includes part of AT’s thesis. We thank the many AgroParisTech master students from the “Forets Tropicales Humides” module that helped measure the seedlings (years 2009-2014). We are thankful to Sylvain Schmitt and Myriam Heuertz for comments on previous drafts that have greatly improved this manuscript. We kindly thank the Agence National de la Recherche grant “Investissement d’Avenir” (Labex CEBA, ref. ANR-10-LABX-25-01) and the European Union (PO-FEDER ENERGIRAVI) for financial support. MC Thesis was financed by the CNRS (BDI) and the Region Guyane, AT thesis was financed by UE FEDER and Labex CEBA. Both theses were carried out at the Université de Guyane.

Author contributions:

IS and CSS conceived the idea and designed the experiment. MC, VT and SOC established the RTE experiment. MC, AT, LB, VT and SOC coordinated the field measurement campaigns. BF contributed with soil and habitat characterisation. NT and MPE conducted the data analyses; NT, IS, CSS, and MPE wrote the manuscript. All authors contributed critically to previous drafts and gave their final approval for publication.

Data availability:

The data will be submitted to the TRY plant database upon acceptance of the manuscript.

Bibliography:

Allié E, Pélissier R, Engel Jet al. (2015) Pervasive local-scale tree-soil habitat association in a tropical forest community. PLoS ONE , 10 , 1–16.
Anderson RP (2013) A framework for using niche models to estimate impacts of climate change on species distributions. Annals of the New York Academy of Sciences , 1297 , 8–28.
Aubry-Kientz M, Rossi V, Boreux J-J, Hérault B (2015) A joint individual-based model coupling growth and mortality reveals that tree vigor is a key component of tropical forest dynamics. Ecology and evolution , 5 , 2457–65.
Baltzer JL, Davies SJ (2012) Rainfall seasonality and pest pressure as determinants of tropical tree species’ distributions. Ecology and evolution , 2 , 2682–94.
Baltzer JL, Thomas SC, Nilus R, Burslem DFRP (2005) Edaphic specialization in tropical trees: Physiological correlates and responses to reciprocal transplantation. Ecology , 86 , 3063–3077.
Baraloto C, Goldberg DE, Bonal D (2005) Performance trade-offs among tropical tree seedlings in contrasting microhabitats. Ecology ,86 , 2461–2472.
Baraloto C, Morneau F, Bonal D, Blanc L, Ferry B (2007) Seasonal water stress tolerance and habitat associations within four neotropical tree genera. Ecology , 88 , 478–489.
Barthes B (1991) Influence of soil conditions on the distribution of two Eperua species (Caesalpiniaceae) in the rain forests of French Guiana.Revue d’Ecologie (Terre et la Vie) , 46 , 303–320.
Barton KE, Jones C, Edwards KF, Shiels AB, Knight T (2020) Local adaptation constrains drought tolerance in a tropical foundation tree.Journal of Ecology .
Boshier D, Stewart J (2005) How local is local? Identifying the scale of adaptive variation in ash (Fraxinus excelsior L.): results from the nursery. Forestry: An International Journal of Forest Research ,78 , 135–143.
Breiman L (2001) Random Forests. Machine Learning , 45 , 5–32.
Brousseau L, Bonal D, Cigna J, Scotti I (2013) Highly local environmental variability promotes intrapopulation divergence of quantitative traits: An example from tropical rain forest trees.Annals of Botany , 112 , 1169–1179.
Brousseau L, Fine PVA, Dreyer E et al. (2018) Genomics of microgeographic adaptation in the hyperdominant Amazonian tree Eperua falcata Aubl. (Fabaceae). bioRxiv , 312843.
Brousseau L, Foll M, Scotti-Saintagne C, Scotti I (2015) Neutral and adaptive drivers of microgeographic genetic divergence within continuous populations: The case of the neotropical tree Eperua falcata (aubl.).PLoS ONE , 10 , 1–23.
Bryant JP, Chapin FS, Coley PD (1985) Resource Availability and Plant Antiherbivore Defense. Science , 230 , 895–899.
Cannon CH, Petit RJ (2019) The oak syngameon: more than the sum of its parts. New Phytologist .
Carsjens C, Nguyen Ngoc Q, Guzy J et al. (2014) Intra-specific variations in expression of stress-related genes in beech progenies are stronger than drought-induced responses. Tree Physiology ,34 , 1348–1361.
Cottet K, Genta-Jouve G, Fromentin Y et al. (2014) Comparative LC-MS-based metabolite profiling of the ancient tropical rainforest tree Symphonia globulifera. Phytochemistry , 108 , 102–108.
Cottet K, Kouloura E, Kritsanida M et al. (2017) Comparative metabolomic study between African and Amazonian Symphonia globulifera by tandem LC–HRMS. Phytochemistry Letters , 20 , 309–315.
Cutler DR, Edwards TC, Beard KH et al. (2007) Random forests for classification in ecology. Ecology , 88 , 2783–2792.
Dalling JW, Davis AS, Schutte BJ, Elizabeth Arnold A (2011) Seed survival in soil: Interacting effects of predation, dormancy and the soil microbial community. Journal of Ecology , 99 , 89–95.
Degen B, Bandou E, Caron H (2004) Limited pollen dispersal and biparental inbreeding in Symphonia globulifera in French Guiana.Heredity , 93 , 585–591.
Dick CW, Heuertz M (2008) THE COMPLEX BIOGEOGRAPHIC HISTORY OF A WIDESPREAD TROPICAL TREE SPECIES. , 6 .
Donohue K (2009) Completing the cycle: Maternal effects as the missing link in plant life histories. Philosophical Transactions of the Royal Society B: Biological Sciences , 364 , 1059–1074.
Donohue K, Rubio de Casas R, Burghardt L, Kovach K, Willis CG (2010) Germination, Postgermination Adaptation, and Species Ecological Ranges.Annual Review of Ecology, Evolution, and Systematics ,41 , 293–319.
Eichhorn MP, Compton SG, Hartley SE (2006) Seedling species determines rates of leaf herbivory in a Malaysian rain forest. Journal of Tropical Ecology , 22 , 513–519.
Fang W, Taub DR, Fox GA et al. (2006) Sources of variation in growth, form, and survival in dwarf and normal-stature pitch pines (Pinus rigida, pinaceae) in long-term transplant experiments.American Journal of Botany , 93 , 1125–1133.
Ferry B, Morneau F, Bontemps JD, Blanc L, Freycon V (2010) Higher treefall rates on slopes and waterlogged soils result in lower stand biomass and productivity in a tropical rain forest. Journal of Ecology , 98 , 106–116.
Fine PV, Mesones I, Coley PD (2004) Herbivores Promote Habitat Specialization by Trees in Amazonian Forests Author ( s ): Paul V . A . Fine , Italo Mesones and Phyllis D . Coley. Science ,305 , 663–665.
Fine PVA, Miller ZJ, Mesones I et al. (2006) The growth-defense trade-off and habitat specialization by plants in Amazonian forests.Ecology , 87 , 150–162.
Fox J, Weisberg S (2010) An R Companion to Applied Regression (Google eBook) . Thousand Oaks: Sage.
Grant V (1971) Plant Speciation . Columbia University Press, New York, NY, USA.
Gremer JR, Venable DL (2014) Bet hedging in desert winter annual plants: Optimal germination strategies in a variable environment. Ecology Letters , 17 , 380–387.
Hérault B, Bachelot B, Poorter L et al. (2011) Functional traits shape ontogenetic growth trajectories of rain forest tree species.Journal of Ecology , 99 , 1431–1440.
Hothorn T, Bretz F, Westfall P (2008) Simultaneous inference in general parametric models. Biometrical Journal , 50 , 346–363.
Hothorn T, Hornik K, Zeileis A (2006) Unbiased recursive partitioning: A conditional inference framework. Research Report Series 8, Department of Statistics and Mathematics, WU Wien, 2004. Journal of Computational and Graphical Statistics , 15 , 651–674.
Kawecki TJ, Ebert D (2004) Conceptual issues in local adaptation.Ecology Letters , 7 , 1225–1241.
Lamarre GPA, Hérault B, Fine PVA et al. (2016) Taxonomic and functional composition of arthropod assemblages across contrasting Amazonian forests. Journal of Animal Ecology , 85 , 227–239.
Lang T, Abadie P, Léger V et al. (2018) High-quality SNPs from genic regions highlight introgression patterns among European white oaks (Quercus petraea and Q. robur). bioRxiv , 388447.
Latreille AC, Pichot C (2017) Local-scale diversity and adaptation along elevational gradients assessed by reciprocal transplant experiments: lack of local adaptation in silver fir populations. Annals of Forest Science , 74 , 77.
Lenth R V. (2016) Least-Squares Means: The R Packagelsmeans . Journal of Statistical Software , 69 , 1–33.
Lenth R V. (2018) emmeans: Estimated Marginal Means, aka Least-Squares Means.
Lopez OR, Kursar TA (2003) Does flood tolerance explain tree species distribution in tropical seasonally flooded habitats? Oecologia ,136 , 193–204.
López R, Rodríguez-Calcerrada J, Gil L (2009) Physiological and morphological response to water deficit in seedlings of five provenances of Pinus canariensis: potential to detect variation in drought-tolerance. Trees , 23 , 509–519.
Lotsy JP (1917) La quintessence de la théorie du croisement.Archives Neerlandaises des Sciences Exactes et Naturelles Séries III , 3 , 351–353.
Mathiasen P, Premoli AC (2016) Living on the edge: adaptive and plastic responses of the tree Nothofagus pumilio to a long-term transplant experiment predict rear-edge upward expansion. Oecologia ,181 , 607–619.
Miglia KJ, Mcarthur ED, Moore WS et al. (2005) Nine-year reciprocal transplant experiment in the gardens of the basin and mountain big sagebrush (Artemisia tridentata: Asteraceae) hybrid zone of Salt Creek Canyon: the importance of multiple-year tracking of fitness.Biological Journal of the Linnean Society , 86 , 213–225.
Moles AT, Leishman MR, Falster DS, Westoby M (2004) Small-seeded species produce more seeds per square metre of canopy per year, but not per individual per lifetime. Journal Of Ecology , 92 , 384–396.
Morris WF, Hufbauer RA, Agrawal AA et al. (2007) Direct and interactive effects of enemies and mutualists on plant performance: A meta-analysis. Ecology , 88 , 1021–1029.
Nagamitsu T, Shimada K, Kanazashi A (2015) A reciprocal transplant trial suggests a disadvantage of northward seed transfer in survival and growth of Japanese red pine (Pinus densiflora) trees. Tree Genetics & Genomes , 11 , 813.
Nkengfack AE, Mkounga P, Meyer M, Fomum ZT, Bodo B (2002) Globulixanthones C, D and E: Three prenylated xanthones with antimicrobial properties from the root bark of Symphonia globulifera.Phytochemistry , 61 , 181–187.
Ondeyka JG, Dombrowski AW, Polishook JP et al. (2006) Isolation and insecticidal/anthelmintic activity of xanthonol, a novel bis-xanthone, from a non-sporulating fungal species. Journal of Antibiotics , 59 , 288–292.
Pélissier R, Dray S, Sabatier D (2002) Within-plot relationships between tree species occurrences and hydrological soil constraints: An example in French Guiana investigated through canonical correlation analysis.Plant Ecology , 162 , 143–156.
Petit RJ, Hampe A (2006) Some Evolutionary Consequences of Being a Tree.Annual Review of Ecology, Evolution, and Systematics ,37 , 187–214.
Pineda-García F, Paz H, Tinoco-Ojanguren C (2011) Morphological and physiological differentiation of seedlings between dry and wet habitats in a tropical dry forest. Plant, Cell and Environment ,34 , 1536–1547.
Pizano C, Mangan SA, Herre EA, Eom A-H, Dalling JW (2011) Above- and belowground interactions drive habitat segregation between two cryptic species of tropical trees. Ecology , 92 , 47–56.
Pluess AR, Frank A, Heiri C et al. (2016) Genome-environment association study suggests local adaptation to climate at the regional scale in Fagus sylvatica. New Phytologist , 210 , 589–601.
Pluess AR, Weber P (2012) Drought-Adaptation Potential in Fagus sylvatica: Linking Moisture Availability with Genetic Diversity and Dendrochronology (HYH Chen, Ed,). PLoS ONE , 7 , e33636.
Poorter L, Markesteijn L (2008) Seedling Traits Determine Drought Tolerance of Tropical Tree Species. Biotropica , 40 , 321–331.
Postma FM, Ågren J (2016) Early life stages contribute strongly to local adaptation in Arabidopsis thaliana. Proceedings of the National Academy of Sciences , 113 , 7590–7595.
Postma FM, Lundemo S, Ågren J (2015) Seed dormancy cycling and mortality differ between two locally adapted populations of Arabidopsis thaliana . Annals of Botany , 117 , mcv171.
Postma FM, Lundemo S, Ågren J (2016) Seed dormancy cycling and mortality differ between two locally adapted populations of Arabidopsis thaliana.Annals of Botany , 117 , 249–256.
Queenborough SA, Burslem DFRP, Garwood NC, Valencia R (2009) Taxonomic scale-dependence of habitat niche partitioning and biotic neighbourhood on survival of tropical tree seedlings. Proceedings of the Royal Society B: Biological Sciences , 276 , 4197–4205.
R Core Team (2020) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
Ramírez-Valiente JA, Lorenzo Z, Soto A et al. (2009) Elucidating the role of genetic drift and natural selection in cork oak differentiation regarding drought tolerance. Molecular Ecology ,18 , 3803–3815.
Rellstab C, Zoller S, Walthert L et al. (2016) Signatures of local adaptation in candidate genes of oaks ( Quercus spp.) with respect to present and future climatic conditions. Molecular Ecology , 25 , 5907–5924.
Richardson JL, Urban MC, Bolnick DI, Skelly DK (2014) Microgeographic adaptation and the spatial scale of evolution. Trends in Ecology and Evolution , 29 , 165–176.
Sabatier D, Grimaldi M, Prévost M-F et al. (1997) The Influence Of Soil Cover Organization on the floristic and structure heterogeneity on a Guianan rain forest. Plant Ecology , 131 , 81–108.
Schmitt S, Hérault B, Ducouret É et al. Topography consistently drives intra- and inter-specific leaf trait variation within tree species complexes in a Neotropical forest. OIKOS . in review .
Schmitt S, Tysklind N, Derroire G, Heuertz M, Herault B. Topography shapes the local coexistence of tree species within species complexes of Neotropical forests. Oecologia . in review .
Searle SR, Speed FM, Milliken GA (1980) Population Marginal Means in the Linear Model: An Alternative to Least Squares Means. The American Statistician , 34 , 216.
Slot M, Poorter L (2007) Diversity of Tropical Tree Seedling Responses to Drought. Biotropica , 39 , 683–690.
Smith DS, Schweitzer JA, Turk P et al. (2012) Soil-mediated local adaptation alters seedling survival and performance. Plant and Soil , 352 , 243–251.
ter Steege H, Jetteer VG, Polak AM, Werger MJA (1993) Tropical rain forest types and soil factors in a watershed area in Guyana.Journal of Vegetation Science , 4 , 705-716.
Strobl C, Boulesteix A-L, Zeileis A, Hothorn T (2007) Bias in random forest variable importance measures: illustrations, sources and a solution. BMC bioinformatics , 8 , 25.
Strobl C, Hothorn T, Zeileis A (2009) Party on! A new, conditional variable-importance measure for random forests available in the party package. R Journal , 1 , 14–17.
Suarez-Gonzalez A, Lexer C, Cronk QCB (2018) Adaptive introgression: A plant perspective. Biology Letters , 14 .
Torroba-Balmori P, Budde KB, Heer K et al. (2017) Altitudinal gradients, biogeographic history and microhabitat adaptation affect fine-scale spatial genetic structure in African and Neotropical populations of an ancient tropical tree species. PLoS ONE ,12 , 1–23.
Valen L Van (1975) Life, Death, and Energy of a Tree. Biotropica ,7 , 259.
Valladares F, Sánchez-Gómez D (2006) Ecophysiological Traits Associated with Drought in Mediterranean Tree Seedlings: Individual Responses versus Interspecific Trends in Eleven Species. Plant Biology ,8 , 688–697.
de Villemereuil P, Schielzeth H, Nakagawa S, Morrissey M (2016) General methods for evolutionary quantitative genetic inference from generalized mixed models. Genetics , 204 , 1281–1294.
Vizcaíno-Palomar N, Revuelta-Eugercios B, Zavala MA, Alía R, González-Martínez SC (2014) The Role of Population Origin and Microenvironment in Seedling Emergence and Early Survival in Mediterranean Maritime Pine (Pinus pinaster Aiton) (S Delzon, Ed,).PLoS ONE , 9 , e109132.
Wadgymar SM, Lowry DB, Gould BA et al. (2017) Identifying targets and agents of selection: innovative methods to evaluate the processes that contribute to local adaptation. Methods in Ecology and Evolution , 8 , 738–749.
Wezeman T, Bräse S, Masters KS (2015) Xanthone dimers: A compound family which is both common and privileged. Natural Product Reports ,32 , 6–28.
Wickham H (2009) ggplot2 by Hadley Wickham. Media , 35 , 211.
Wright JW (2007) Local adaptation to serpentine soils in Pinus ponderosa. Plant and Soil , 293 , 209–217.
Anderson RP (2013) A framework for using niche models to estimate impacts of climate change on species distributions. Annals of the New York Academy of Sciences , 1297 , 8–28.
Aubry-Kientz M, Rossi V, Boreux J-J, Hérault B (2015) A joint individual-based model coupling growth and mortality reveals that tree vigor is a key component of tropical forest dynamics. Ecology and evolution , 5 , 2457–65.
Baltzer JL, Davies SJ (2012) Rainfall seasonality and pest pressure as determinants of tropical tree species’ distributions. Ecology and evolution , 2 , 2682–94.
Baltzer JL, Thomas SC, Nilus R, Burslem DFRP (2005) Edaphic specialization in tropical trees: Physiological correlates and responses to reciprocal transplantation. Ecology , 86 , 3063–3077.
Baraloto C, Goldberg DE, Bonal D (2005) Performance trade-offs among tropical tree seedlings in contrasting microhabitats. Ecology ,86 , 2461–2472.
Baraloto C, Morneau F, Bonal D, Blanc L, Ferry B (2007) Seasonal water stress tolerance and habitat associations within four neotropical tree genera. Ecology , 88 , 478–489.
Barthes B (1991) Influence of soil conditions on the distribution of two Eperua species (Caesalpiniaceae) in the rain forests of French Guiana.Revue d’Ecologie (Terre et la Vie) , 46 , 303–320.
Barton KE, Jones C, Edwards KF, Shiels AB, Knight T (2020) Local adaptation constrains drought tolerance in a tropical foundation tree.Journal of Ecology .
Boshier D, Stewart J (2005) How local is local? Identifying the scale of adaptive variation in ash (Fraxinus excelsior L.): results from the nursery. Forestry: An International Journal of Forest Research ,78 , 135–143.
Breiman L (2001) Random Forests. Machine Learning , 45 , 5–32.
Brousseau L, Bonal D, Cigna J, Scotti I (2013) Highly local environmental variability promotes intrapopulation divergence of quantitative traits: An example from tropical rain forest trees.Annals of Botany , 112 , 1169–1179.
Brousseau L, Fine PVA, Dreyer E et al. (2018) Genomics of microgeographic adaptation in the hyperdominant Amazonian tree Eperua falcata Aubl. (Fabaceae). bioRxiv , 312843.
Brousseau L, Foll M, Scotti-Saintagne C, Scotti I (2015) Neutral and adaptive drivers of microgeographic genetic divergence within continuous populations: The case of the neotropical tree Eperua falcata (aubl.).PLoS ONE , 10 , 1–23.
Bryant JP, Chapin FS, Coley PD (1985) Resource Availability and Plant Antiherbivore Defense. Science , 230 , 895–899.
Cannon CH, Petit RJ (2019) The oak syngameon: more than the sum of its parts. New Phytologist .
Carsjens C, Nguyen Ngoc Q, Guzy J et al. (2014) Intra-specific variations in expression of stress-related genes in beech progenies are stronger than drought-induced responses. Tree Physiology ,34 , 1348–1361.
Cottet K, Genta-Jouve G, Fromentin Y et al. (2014) Comparative LC-MS-based metabolite profiling of the ancient tropical rainforest tree Symphonia globulifera. Phytochemistry , 108 , 102–108.
Cottet K, Kouloura E, Kritsanida M et al. (2017) Comparative metabolomic study between African and Amazonian Symphonia globulifera by tandem LC–HRMS. Phytochemistry Letters , 20 , 309–315.
Cutler DR, Edwards TC, Beard KH et al. (2007) Random forests for classification in ecology. Ecology , 88 , 2783–2792.
Dalling JW, Davis AS, Schutte BJ, Elizabeth Arnold A (2011) Seed survival in soil: Interacting effects of predation, dormancy and the soil microbial community. Journal of Ecology , 99 , 89–95.
Degen B, Bandou E, Caron H (2004) Limited pollen dispersal and biparental inbreeding in Symphonia globulifera in French Guiana.Heredity , 93 , 585–591.
Dick CW, Heuertz M (2008) THE COMPLEX BIOGEOGRAPHIC HISTORY OF A WIDESPREAD TROPICAL TREE SPECIES. , 6 .
Donohue K (2009) Completing the cycle: Maternal effects as the missing link in plant life histories. Philosophical Transactions of the Royal Society B: Biological Sciences , 364 , 1059–1074.
Donohue K, Rubio de Casas R, Burghardt L, Kovach K, Willis CG (2010) Germination, Postgermination Adaptation, and Species Ecological Ranges.Annual Review of Ecology, Evolution, and Systematics ,41 , 293–319.
Eichhorn MP, Compton SG, Hartley SE (2006) Seedling species determines rates of leaf herbivory in a Malaysian rain forest. Journal of Tropical Ecology , 22 , 513–519.
Fang W, Taub DR, Fox GA et al. (2006) Sources of variation in growth, form, and survival in dwarf and normal-stature pitch pines (Pinus rigida, pinaceae) in long-term transplant experiments.American Journal of Botany , 93 , 1125–1133.
Ferry B, Morneau F, Bontemps JD, Blanc L, Freycon V (2010) Higher treefall rates on slopes and waterlogged soils result in lower stand biomass and productivity in a tropical rain forest. Journal of Ecology , 98 , 106–116.
Fine PV, Mesones I, Coley PD (2004) Herbivores Promote Habitat Specialization by Trees in Amazonian Forests Author ( s ): Paul V . A . Fine , Italo Mesones and Phyllis D . Coley. Science ,305 , 663–665.
Fine PVA, Miller ZJ, Mesones I et al. (2006) The growth-defense trade-off and habitat specialization by plants in Amazonian forests.Ecology , 87 , 150–162.
Fox J, Weisberg S (2010) An R Companion to Applied Regression (Google eBook) . Thousand Oaks: Sage.
Grant V (1971) Plant Speciation . Columbia University Press, New York, NY, USA.
Gremer JR, Venable DL (2014) Bet hedging in desert winter annual plants: Optimal germination strategies in a variable environment. Ecology Letters , 17 , 380–387.
Hérault B, Bachelot B, Poorter L et al. (2011) Functional traits shape ontogenetic growth trajectories of rain forest tree species.Journal of Ecology , 99 , 1431–1440.
Hothorn T, Bretz F, Westfall P (2008) Simultaneous inference in general parametric models. Biometrical Journal , 50 , 346–363.
Hothorn T, Hornik K, Zeileis A (2006) Unbiased recursive partitioning: A conditional inference framework. Research Report Series 8, Department of Statistics and Mathematics, WU Wien, 2004. Journal of Computational and Graphical Statistics , 15 , 651–674.
Kawecki TJ, Ebert D (2004) Conceptual issues in local adaptation.Ecology Letters , 7 , 1225–1241.
Lamarre GPA, Hérault B, Fine PVA et al. (2016) Taxonomic and functional composition of arthropod assemblages across contrasting Amazonian forests. Journal of Animal Ecology , 85 , 227–239.
Lang T, Abadie P, Léger V et al. (2018) High-quality SNPs from genic regions highlight introgression patterns among European white oaks (Quercus petraea and Q. robur). bioRxiv , 388447.
Latreille AC, Pichot C (2017) Local-scale diversity and adaptation along elevational gradients assessed by reciprocal transplant experiments: lack of local adaptation in silver fir populations. Annals of Forest Science , 74 , 77.
Lenth R V. (2016) Least-Squares Means: The R Packagelsmeans . Journal of Statistical Software , 69 , 1–33.
Lenth R V. (2018) emmeans: Estimated Marginal Means, aka Least-Squares Means.
Lopez OR, Kursar TA (2003) Does flood tolerance explain tree species distribution in tropical seasonally flooded habitats? Oecologia ,136 , 193–204.
López R, Rodríguez-Calcerrada J, Gil L (2009) Physiological and morphological response to water deficit in seedlings of five provenances of Pinus canariensis: potential to detect variation in drought-tolerance. Trees , 23 , 509–519.
Lotsy JP (1917) La quintessence de la théorie du croisement.Archives Neerlandaises des Sciences Exactes et Naturelles Séries III , 3 , 351–353.
Mathiasen P, Premoli AC (2016) Living on the edge: adaptive and plastic responses of the tree Nothofagus pumilio to a long-term transplant experiment predict rear-edge upward expansion. Oecologia ,181 , 607–619.
Miglia KJ, Mcarthur ED, Moore WS et al. (2005) Nine-year reciprocal transplant experiment in the gardens of the basin and mountain big sagebrush (Artemisia tridentata: Asteraceae) hybrid zone of Salt Creek Canyon: the importance of multiple-year tracking of fitness.Biological Journal of the Linnean Society , 86 , 213–225.
Moles AT, Leishman MR, Falster DS, Westoby M (2004) Small-seeded species produce more seeds per square metre of canopy per year, but not per individual per lifetime. Journal Of Ecology , 92 , 384–396.
Morris WF, Hufbauer RA, Agrawal AA et al. (2007) Direct and interactive effects of enemies and mutualists on plant performance: A meta-analysis. Ecology , 88 , 1021–1029.
Nagamitsu T, Shimada K, Kanazashi A (2015) A reciprocal transplant trial suggests a disadvantage of northward seed transfer in survival and growth of Japanese red pine (Pinus densiflora) trees. Tree Genetics & Genomes , 11 , 813.
Nkengfack AE, Mkounga P, Meyer M, Fomum ZT, Bodo B (2002) Globulixanthones C, D and E: Three prenylated xanthones with antimicrobial properties from the root bark of Symphonia globulifera.Phytochemistry , 61 , 181–187.
Ondeyka JG, Dombrowski AW, Polishook JP et al. (2006) Isolation and insecticidal/anthelmintic activity of xanthonol, a novel bis-xanthone, from a non-sporulating fungal species. Journal of Antibiotics , 59 , 288–292.
Pélissier R, Dray S, Sabatier D (2002) Within-plot relationships between tree species occurrences and hydrological soil constraints: An example in French Guiana investigated through canonical correlation analysis.Plant Ecology , 162 , 143–156.
Petit RJ, Hampe A (2006) Some Evolutionary Consequences of Being a Tree.Annual Review of Ecology, Evolution, and Systematics ,37 , 187–214.
Pineda-García F, Paz H, Tinoco-Ojanguren C (2011) Morphological and physiological differentiation of seedlings between dry and wet habitats in a tropical dry forest. Plant, Cell and Environment ,34 , 1536–1547.
Pizano C, Mangan SA, Herre EA, Eom A-H, Dalling JW (2011) Above- and belowground interactions drive habitat segregation between two cryptic species of tropical trees. Ecology , 92 , 47–56.
Pluess AR, Frank A, Heiri C et al. (2016) Genome-environment association study suggests local adaptation to climate at the regional scale in Fagus sylvatica. New Phytologist , 210 , 589–601.
Pluess AR, Weber P (2012) Drought-Adaptation Potential in Fagus sylvatica: Linking Moisture Availability with Genetic Diversity and Dendrochronology (HYH Chen, Ed,). PLoS ONE , 7 , e33636.
Poorter L, Markesteijn L (2008) Seedling Traits Determine Drought Tolerance of Tropical Tree Species. Biotropica , 40 , 321–331.
Postma FM, Ågren J (2016) Early life stages contribute strongly to local adaptation in Arabidopsis thaliana. Proceedings of the National Academy of Sciences , 113 , 7590–7595.
Postma FM, Lundemo S, Ågren J (2015) Seed dormancy cycling and mortality differ between two locally adapted populations of Arabidopsis thaliana . Annals of Botany , 117 , mcv171.
Postma FM, Lundemo S, Ågren J (2016) Seed dormancy cycling and mortality differ between two locally adapted populations of Arabidopsis thaliana.Annals of Botany , 117 , 249–256.
Queenborough SA, Burslem DFRP, Garwood NC, Valencia R (2009) Taxonomic scale-dependence of habitat niche partitioning and biotic neighbourhood on survival of tropical tree seedlings. Proceedings of the Royal Society B: Biological Sciences , 276 , 4197–4205.
R Core Team (2020) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
Ramírez-Valiente JA, Lorenzo Z, Soto A et al. (2009) Elucidating the role of genetic drift and natural selection in cork oak differentiation regarding drought tolerance. Molecular Ecology ,18 , 3803–3815.
Rellstab C, Zoller S, Walthert L et al. (2016) Signatures of local adaptation in candidate genes of oaks ( Quercus spp.) with respect to present and future climatic conditions. Molecular Ecology , 25 , 5907–5924.
Richardson JL, Urban MC, Bolnick DI, Skelly DK (2014) Microgeographic adaptation and the spatial scale of evolution. Trends in Ecology and Evolution , 29 , 165–176.
Sabatier D, Grimaldi M, Prévost M-F et al. (1997) The Influence Of Soil Cover Organization on the floristic and structure heterogeneity on a Guianan rain forest. Plant Ecology , 131 , 81–108.
Searle SR, Speed FM, Milliken GA (1980) Population Marginal Means in the Linear Model: An Alternative to Least Squares Means. The American Statistician , 34 , 216.
Slot M, Poorter L (2007) Diversity of Tropical Tree Seedling Responses to Drought. Biotropica , 39 , 683–690.
Smith DS, Schweitzer JA, Turk P et al. (2012) Soil-mediated local adaptation alters seedling survival and performance. Plant and Soil , 352 , 243–251.
ter Steege H, Jetteer VG, Polak AM, Werger MJA (1993) Tropical rain forest types and soil factors in a watershed area in Guyana.Journal of Vegetation Science , 4 , 705-716.
Strobl C, Boulesteix A-L, Zeileis A, Hothorn T (2007) Bias in random forest variable importance measures: illustrations, sources and a solution. BMC bioinformatics , 8 , 25.
Strobl C, Hothorn T, Zeileis A (2009) Party on! A new, conditional variable-importance measure for random forests available in the party package. R Journal , 1 , 14–17.
Suarez-Gonzalez A, Lexer C, Cronk QCB (2018) Adaptive introgression: A plant perspective. Biology Letters , 14 .
Torroba-Balmori P, Budde KB, Heer K et al. (2017) Altitudinal gradients, biogeographic history and microhabitat adaptation affect fine-scale spatial genetic structure in African and Neotropical populations of an ancient tropical tree species. PLoS ONE ,12 , 1–23.
Valen L Van (1975) Life, Death, and Energy of a Tree. Biotropica ,7 , 259.
Valladares F, Sánchez-Gómez D (2006) Ecophysiological Traits Associated with Drought in Mediterranean Tree Seedlings: Individual Responses versus Interspecific Trends in Eleven Species. Plant Biology ,8 , 688–697.
de Villemereuil P, Schielzeth H, Nakagawa S, Morrissey M (2016) General methods for evolutionary quantitative genetic inference from generalized mixed models. Genetics , 204 , 1281–1294.
Vizcaíno-Palomar N, Revuelta-Eugercios B, Zavala MA, Alía R, González-Martínez SC (2014) The Role of Population Origin and Microenvironment in Seedling Emergence and Early Survival in Mediterranean Maritime Pine (Pinus pinaster Aiton) (S Delzon, Ed,).PLoS ONE , 9 , e109132.
Wadgymar SM, Lowry DB, Gould BA et al. (2017) Identifying targets and agents of selection: innovative methods to evaluate the processes that contribute to local adaptation. Methods in Ecology and Evolution , 8 , 738–749.
Wezeman T, Bräse S, Masters KS (2015) Xanthone dimers: A compound family which is both common and privileged. Natural Product Reports ,32 , 6–28.
Wickham H (2009) ggplot2 by Hadley Wickham. Media , 35 , 211.
Wright JW (2007) Local adaptation to serpentine soils in Pinus ponderosa. Plant and Soil , 293 , 209–217.

Tables:

Table 1 : Symphonia individual information: theMother tree from which the seed was collected, thesampling site and latitude and longitudewhere the mother tree was found. Two general provenance regionsare indicated (e.g. east and west), as well as the ecotype to which the mother belonged (S. globulifera or S. sp1 ) and the type of habitat the mother tree was found: seasonally flooded (SF ) and hilltops (HT ).  The number of seeds collected (Ni ), the number of seeds germinated at the time of transplant (Gt ), and that had germinated overall at the end of the experiment (Go ), and the number of seedlings alive at year 6 (Alive Y6 ) are also tabulated.