Abstract: Contemporary conservation interventions must balance potential trade-offs between multiple ecosystem services. In tropical forests, much attention has focused on the extent to which carbon-based conservation provided by REDD+ policies can also mitigate biodiversity conservation. In the nearly one-third of tropical forests that are community owned or managed, conservation strategies must also balance the multiple uses of forest products that support local livelihoods. Although much discussion has focused on policy options, little empirical evidence exists to evaluate the potential for trade-offs among different tropical forest value components. We assessed multiple components of forest value, including tree diversity, carbon stocks, and both timber and nontimber forest product resources, in forest communities across the trinational frontier of Brazil, Peru, and Bolivia. We installed 69 0.5-ha vegetation plots in local communities, and we characterized 15 components of forest value for each plot. Principal components analyses revealed two major axes of forest value, the first of which defined a trade-off between diversity of woody plant communities (taxonomic and functional) versus aboveground biomass and standing timber volume. The second axis described abundance of commercial species, with strong positive loadings for density of timber and nontimber forest products, including Brazil nut (Bertholletia excelsa) and copaiba oil (Copaifera spp.). The observed trade-off between different components of forest value suggests a potential for management conflicts prioritizing biodiversity conservation versus carbon stocks in the region. We discuss the potential for integrative indices of forest value for tropical forest conservation.

Abstract: Detecting signatures of selection in tree populations threatened by climate change is currently a major research priority. Here, we investigated the signature of local adaptation over a short spatial scale using 96 European beech (Fagus sylvatica L.) individuals originating from two pairs of populations on the northern and southern slopes of Mont Ventoux (south-eastern France). We performed both single and multilocus analysis of selection based on 53 climate-related candidate genes containing 546 SNPs. FST outlier methods at the SNP level revealed a weak signal of selection, with three marginally significant outliers in the northern populations. At the gene level, considering haplotypes as alleles, two additional marginally significant outliers were detected, one on each slope. To account for the uncertainty of haplotype inference, we averaged the Bayes factors over many possible phase reconstructions. Epistatic selection offers a realistic multilocus model of selection in natural populations. Here, we used a test suggested by Ohta based on the decomposition of the variance of linkage disequilibrium. Overall populations, 0.23% of the SNP pairs (haplotypes) showed evidence of epistatic selection, with nearly 80% of them being within genes. One of the between gene epistatic selection signals arose between an FST outlier and a nonsynonymous mutation in a drought response gene. Additionally, we identified haplotypes containing selectively advantageous allele combinations which were unique to high or low elevations and northern or southern populations. Several haplotypes contained nonsynonymous mutations situated in genes with known functional importance for adaptation to climatic factors.

Abstract: Characterisation of forest structure is a major stake for forestry, species conservation, carbon stock estimates and many forest ecology and management issues. At large scale natural forest structure tends to vary according to climate and geomorphomology (Paget, 1999; Steege et al., 2006) while soil characteristics (and notably water regime) and syMgenetic stage add some finer scale variation (Oldeman, 1989; Sabatier et al., 1997). Forest structure characterisation traditionally relies on field-based collection of individual tree dimensions such as stem diameter and stem height sampled across tracks of forest (Hall et al., 1998). However, such field intensive methods are costly, and of low accuracy regarding measures of tree heights. Airborne light detection and ranging (LiDAR) technology provides horizontal and vertical Information at high spatial resolutions and vertical accuracies (Lim et al., 2003; Hyyppä et al., 2004). It has the potential for gathering vegetation structure data over large areas rapidly at moderate cost and hence is of particular relevance for poorly sampled, difficult to access and largely unexplored tropical rainforests. In this study we examined the ability of airborne LiDAR to detect spatial changes in the structure of dense tropical rain forest and we probed this remote sensing approach against local statistics derived from stem diameters (i.e. classical field data information) mapped across a large track of forest at a long term experimental site in French Guyana. The large variability in forest structure occurring at the experimental site is du to natural variation of the soil cover (and notably drainage properties) combined with various logging intensities applied 15 years before the LiDAR data were acquired. On this basis ten different forest types were identified at the site (figure 1 and 3). Various stem based statistics were computed for a series of meshes with cells ranging from 30 by 30 m plots to 250 by 250 m plots. These statistics included basal area, stem density, quadratic mean diameter, and diameter distribution percentiles. Similarly local statistics were extracted either from the Canopy Height Model (e.g. median height, mean height, standard height deviation, height coefficient of variation, height percentiles, frequency of hits below 5 m above ground level). Additionally a wetness index (Böhner et al., 2002) was computed at each node of a 5 by 5 m grid from the Digital Terrain Model also extracted from the LiDAR data set. We used both types of cell statistics to discriminate the various forest types. Comparison between the two approaches for a range of spatial resolution is available from in table 1. Results indicate that LiDAR based statistics are essentially as powerful as field based statistics to discriminate forest types at coarse scale. This reflects the very strong correlation between the CHM and the field based stem diameter data. For example (figure 5) the Pearson correlation coefficient between median height and quadratic mean diameter for cells of 125 by 125 m is 0.945 (n=0.72). When a finer resolution is required however as for the detection of seasonally flooded bottomland forest along thalwegs, then LiDAR technology proves more efficient than field based inventories as it combines information from the DTM and the CHM. The wetness index alone correctly retrieves about 2 thirds of the seasonally flooded areas. All in all, discriminant analysis performance of the LiDAR derived information approaches 80% when classifying forests cover at the finest scale of 5 by 5m into 10 different types and reaches 87% when a coarser classification Into 6 forest types is considered (figure 4).

Abstract: The expansion of selective logging in tropical forests may be an important source of global carbon emissions. However, the effects of logging practices on the carbon cycle have never been quantified over long periods of time. We followed the fate of more than 60 000 tropical trees over 23 years to assess changes in aboveground carbon stocks in 48 1.56-ha plots in French Guiana that represent a gradient of timber harvest intensities, with and without intensive timber stand improvement (TSI) treatments to stimulate timber tree growth. Conventional selective logging led to emissions equivalent to more than a third of aboveground carbon stocks in plots without TSI (85 Mg C/ha), while plots with TSI lost more than one-half of aboveground carbon stocks (142 Mg C/ha). Within 20 years of logging, plots without TSI sequestered aboveground carbon equivalent to more than 80% of aboveground carbon lost to logging (-70.7 Mg C/ha), and our simulations predicted an equilibrium aboveground carbon balance within 45 years of logging. In contrast, plots with intensive TSI are predicted to require more than 100 years to sequester aboveground carbon lost to emissions. These results indicate that in some tropical forests aboveground carbon storage can be recovered within half a century after conventional logging at moderate harvest intensities.

Abstract: Abstract The chemical investigations of Dicorynia guianensis heartwood led to the isolation of four new indole alkaloids for the first time in this plant. Compound (1) identified as spiroindolone 2′,3′,4′,9′-tetrahydrospiro [indoline-3,1′pyrido[3,4-b]-indol]-2-one, and compound (3) described as nitrone 1-methyl-4,9-dihydro-3H-pyrido [3,4-b] indole 2-oxide and were isolated for the first time as natural products. ABTS antioxidant activity guided their isolation. © 2015 Published by Elsevier B.V. on behalf of Phytochemical Society of Europe.