Abstract: Unveiling the genetic basis of local adaptation to environmental variation is a major goal in molecular ecology. In rugged landscapes characterized by environmental mosaics, living populations and communities can experience steep ecological gradients over very short geographical distances. In lowland tropical forests, interspecific divergence in edaphic specialization (for seasonally flooded bottomlands and seasonally dry terra firme soils) has been proven by ecological studies on adaptive traits. Some species are nevertheless capable of covering the entire span of the gradient; intraspecific variation for adaptation to contrasting conditions may explain the distribution of such ecological generalists. We investigated whether local divergence happens at small spatial scales in two stands of Eperua falcata (Fabaceae), a widespread tree species of the Guiana Shield. We investigated Single Nucleotide Polymorphisms (SNP) and sequence divergence as well as spatial genetic structure (SGS) at four genes putatively involved in stress response and three genes with unknown function. Significant genetic differentiation was observed among sub-populations within stands, and eight SNP loci showed patterns compatible with disruptive selection. SGS analysis showed genetic turnover along the gradients at three loci, and at least one haplotype was found to be in repulsion with one habitat. Taken together, these results suggest genetic differentiation at small spatial scale in spite of gene flow. We hypothesize that heterogeneous environments may cause molecular divergence, possibly associated to local adaptation in E. falcata. © 2012 European Society For Evolutionary Biology.

Abstract: Plant tissues shrink and swell in response to changes in water pressure. These strains can be easily measured, e.g., at the surface of tree stems, to obtain indirect information about plant water status and other physiological parameters. We developed a mechanical model to clarify how water pressure is transmitted to cell walls and causes shrinkage of plant tissues, particularly in the case of thick-walled cells such as wood fibers. Our analysis shows that the stress inside the fiber cell walls is lower than the water tension. The difference is accounted for by a stress transmission factor that depends on two main effects. The first effect is the dilution of the stress through the cell wall, because water acts at the lumen border and is transmitted to the cuter border of the cell, which has a larger circumference. The second effect is the partial conversion of radial stress into tangential stress. Both effects are quantified as functions of parameters of the cell wall structure and its mechanical properties.

Abstract: The anatomy of tension wood and opposite wood was compared in 21 tropical rain forest trees from 21 species belonging to 18 families from French Guyana. Wood specimens were taken from the upper and lower sides of naturally tilted trees. Measurement of the growth stress level ensured that the two samples were taken from wood tissues in a different mechanical state: highly tensile-stressed wood on the upper side, called tension wood and normally tensile-stressed wood on the lower side, called opposite wood. Quantitative parameters relating to fibres and vessels were measured on transverse sections of both tension and opposite wood to check if certain criteria can easily discriminate the two kinds of wood. We observed a decrease in the frequency of vessels in the tension wood in all the trees studied. Other criteria concerning shape and surface area of the vessels, fibre diameter or cell wall thickness did not reveal any general trend. At the ultrastructural level, we observed that the microfibril angle in the tension wood sample was lower than in opposite wood in all the trees except one (Licania membranacea).

Abstract: Line transect surveys are widely used in Neotropical rainforests to estimate the population abundance of medium- and large-sized vertebrates. The use of indices such as encounter rate has been criticized because the probability of animal detection may fluctuate due to the heterogeneity of environmental conditions among sites. In addition, the morphological and behavioral characteristics (biological traits) of species affect their detectability. In this study, we compared the extent to which environmental conditions and species’ biological traits bias abundance estimates in terra firme rainforests in French Guiana. The selected environmental conditions included both physical conditions and forest structure covariates, while the selected biological traits included the morphological and behavioral characteristics of species. We used the distance sampling method to model the detection probability as an explicit function of environmental conditions and biological traits and implemented a model selection process to determine the relative importance of each group of covariates. Biological traits contributed to the variability of animal detectability more than environmental conditions, which had only a marginal effect. Detectability was best for large animals with uniform or disruptive markings that live in groups in the canopy top. Detectability was worst for small, solitary, terrestrial animals with mottled markings. In the terra firme rainforests that represent ~80% of the Amazonia and Guianas regions, our findings support the use of relative indices such as the encounter rate to compare population abundance between sites in species-specific studies. Even though terra firme rainforests may appear similar between regions of Amazonia and the Guianas, comparability must be ensured, especially in forests disturbed by human activity. The detection probability can be used as an indicator of species’ vulnerability to hunting and, thus, to the risk of local extinction. Only a few biological trait covariates are required to correctly estimate the detectability of the majority of medium- and large-sized vertebrates. Thus, a biological trait model could be useful in predicting the detection probabilities of rare, uncommon, or localized species for which few data are available to fit the detection function.