Abstract: Termites are degradation agents that inflict severe damage on wood. Some long-lasting Amazonian trees can resist these insects by producing toxic secondary metabolites. These metabolites could potentially replace synthetic termiticidal products which are becoming more restricted to use. Results: Sextonia rubra is resistant to termite-induced degradation. It has been demonstrated that this species naturally produces an ethyl-acetate-soluble termiticidal metabolite, rubrynolide, to protect its wood. Assays in the presence of tropical and invasive termites established that both rubrynolide and crude ethyl acetate extract from S. rubra wood can be used as a treatment for the protection of sensitive woods against termites. Conclusion: Rubrynolide and S. rubra extract are promising candidates for the replacement of synthetic termiticides. © 2011 Society of Chemical Industry.

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: Roots have been described as having larger vessels and so greater hydraulic efficiency than the stem. Differences in the strength and stiffness of the tissue within the root system itself are thought to be an adaptation to the loading conditions experienced by the roots and to be related to differences in density. It is not known how potential mechanical adaptations may affect the hydraulic properties of the roots. The change in strength, stiffness, conductivity, density, sapwood area, and second moment of area distally along the lateral roots of two tropical tree species in which the strain is known to decrease rapidly was studied and the values were compared with those of the trunk. It was found that as the strain fell distally along the roots, so did the strength and stiffness of the tissue, whereas the conductivity increased exponentially. These changes appeared to be related to differences in density. In contrast to the distal-most roots, the tissue of the proximal roots had a lower conductivity and higher strength than that of the trunk. This suggests that mechanical requirements on the structure rather than the water potential gradient from roots to branches are responsible for the general pattern that roots have larger vessels than the stem. In spite of their increased transectional area, the buttressed proximal roots were subjected to higher levels of stress and had a lower total conductivity than the rest of the root system. © 2007 The Author(s).

Abstract: 1.Regional above-ground biomass estimates for tropical moist forests remain highly inaccurate mostly because they are based on extrapolations from a few plots scattered across a limited range of soils and other environmental conditions. When such conditions impact biomass, the estimation is biased. The effect of soil types on biomass has especially yielded controversial results. 2.We investigated the relationship between above-ground biomass and soil type in undisturbed moist forests in the Central African Republic. We tested the effects of soil texture, as a surrogate for soil resources availability and physical constraints (soil depth and hydromorphy) on biomass. Forest inventory data were collected for trees ≥20cm stem diameter in 2754 0.5ha plots scattered over 4888km2. The plots contained 224 taxons, of which 209 were identified to species. Soil types were characterized from a 1:1000000 scale soil map. Species-specific values for wood density were extracted from the CIRAD's data base of wood technological properties. 3.We found that basal area and biomass differ in their responses to soil type, ranging from 17.8m2ha-1 (217.5tha-1) to 22.3m2ha-1 (273.3tha-1). While shallow and hydromorphic soils support forests with both low stem basal area and low biomass, forests on deep resource-poor soils are typically low in basal area but as high in biomass as forests on deep resource-rich soils. We demonstrated that the environmental filtering of slow growing dense-wooded species on resource-poor soils compensates for the low basal area, and we discuss whether this filtering effect is due to low fertility or to low water reserve. 4.Synthesis. We showed that soil physical conditions constrained the amount of biomass stored in tropical moist forests. Contrary to previous reports, our results suggest that biomass is similar on resource-poor and resource-rich soils. This finding highlights both the importance of taking into account soil characteristics and species wood density when trying to predict regional patterns of biomass. Our findings have implications for the evaluation of biomass stocks in tropical forests, in the context of the international negotiations on climate change. © 2011 The Authors. Journal of Ecology © 2011 British Ecological Society.

Abstract: The relationship between ants and Philodendron insigne, a trash-basket epiphyte abundant along streams, was studied in French Guiana. Only a few (3%) of the young plants sheltered ants, whereas 90% of the mature individuals did. The most frequent associate was Odontomachus hastatus (Fabricius), an arboreal ponerine ant, and its nests were almost entirely (94.4%) located in P. insigne root clusters. Experimental choice tests conducted on O. hastatus workers confirmed their preference for P. insigne. We propose that the interactions between P. insigne and ants may be intermediate between non-obligatory, reward-based interactions and obligatory, specific ant-myrmecophyte interactions because (1) almost all mature P. insigne individuals are associated with ants; (2) O. hastatus is the most frequent when diverse ants nest in its root clusters; (3) ants colonize mature P. insigne, but rarely young individuals; (4) ants, particularly O. hastatus, protect the foliage of their host; and (5) at least one ant species, O. hastatus, prefers P. insigne over other host plants. The latter relationship is asymmetrical because P. insigne is inhabited by diverse ants whereas O. hastatus nests almost exclusively in P. insigne. (c) 2007 The Linnean Society of London, Biological Journal of the Linnean Society, 2007, 91, 341-346.