Abstract: We examined the limitations of parataxonomic inventories for developing management plans for woody plant resources in tropical rain forests of southwestern Amazonia. Using compilations of herbarium labels, forest personnel interviews and published species descriptions, we assessed the accuracy of common names as parataxonomic units (PUs). We identified 384 common names for 310 harvested woody plant species in the Brazilian state of Acre, of which only 50% were unique to a single taxonomic species. About 10% of common names referred to more than one species, more than half of which included multiple genera. For the 106 species from the Acre sample common to the MAP region including Madre de Dios, Peru and Pando, Bolivia, we identified 198 common names. Splitting was much more frequent in this sample, with more than 80% of species having more than one common name. When the Acre sample was expanded to 131 species from the Brazilian Amazon region, including the states of Amazonas and Para, we identified 740 common names, with nearly 90% of species being represented by more than one common name. Errors and inaccuracy of parataxonomy may contribute to market instability if product orders can not be homogenized within regional markets, and to unsustainable harvests if species are mistakenly lumped into single parataxonomic units. We discuss several programs currently being implemented by our collaborative team in the region to address this issue, including field guides based on digital photography, field courses, and workshops featuring discussions between regional inventory personnel and botanists.

Abstract: Nodulated legume trees comprised 43% of the stand basal area in the low, most frequently flooded microsites, and 23% in higher, drier microsites in a tropical freshwater swamp forest in French Guiana. Dinitrogen fixation in Pterocarpus officinalis, Hydrochorea corymbosa and Inga pilosula was confirmed by acetylene reduction assay (ARA), presence of leghaemoglobin in nodules and the N-15 natural abundance method. The results for Zygia cataractae were inconclusive but suggested N-2 fixation in drier microsites. Nodulated Inga disticha had a N-15-to-N-14 ratio similar to non-N-2-fixing trees, but ARA indicated nitrogenase activity and leghaemoglobin was present in nodules. All bacterial strains were identified as Bradyrhizobium spp. according to the partial 16S rDNA sequences, and they were infective in vitro in the model species Macroptilium atropurpuretan. About 35-50% of N in the leaves of P. officinalis, H. corymbosa and I. pilosula was fixed from the atmosphere. Dinitrogen fixation was estimated to contribute at least 8-13% and 1728% to whole-canopy N in high and low microsites, respectively. Symbiotic N, fixation appears to provide both a competitive advantage to legume trees under N-limited, flooded conditions and an important N input to neotropical freshwater swamp forests.

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: Myrmecophiles are species that usually have developed specialized traits to cope with the aggressiveness of ants enabling them to live in their vicinity. Many coccinellid species are predators of Hemiptera; the latter is also often protected by ants. Therefore these ladybirds frequently interact with ants, and some species have become myrmecophilous. In this paper, we aim to provide an overview of the evolution of myrmecophilous traits in ladybirds. We then discuss the costs and benefits of myrmecophily and the dietary shift to myrmecophagy observed in a few species. Copyright © 2012 Amélie Vantaux et al.

Abstract: In the context of an ongoing monitoring study on the impacts of gold-mining activities on critical ecosystem processes, we explored the use of soil Denitrifying Enzyme Activity (DEA) and Substrate Induced Respiration (SIR) as ecosystem indicators in tropical rainforests of French Guiana. We also propose DEA/SIR ratio as ecosystem attribute able to describe the state of an ecosystem and to reflect changes in ecological processes. With this purpose, we measured SIR, DEA and DEA/SIR ratio in five gold-mining areas and five surrounding natural reference rainforests. We also measured indicators in two conditions of spontaneous regeneration of vegetation (stratified or not) and two conditions of soil rehabilitation (prior preparation of soils or not). We showed a high variability of DEA, SIR and DEA/SIR ratio in the natural reference forests. This pointed out the necessity to identify relevant reference systems – i.e. proving a close match in all relevant ecological dimensions – to compare with closed perturbed systems in order to assess the levels of alterations after disturbances. Results showed a high impact of gold mine on microbial processes with a strong decrease of DEA (10-fold lower), SIR (2-fold lower) and DEA/SIR ratio (8-fold lower) in perturbed areas in comparison with natural reference forests. The type of spontaneous vegetation (stratified or not) influenced the values of indicators as well as prior rehabilitation of soils, demonstrating the capacity of DEA, SIR and DEA/SIR ratio to respond in proportion to the perturbation (robustness) and to the different levels of restoration (sensitivity). The systematic decrease of the ratio DEA/SIR observed in the studied perturbed situations demonstrates clearly that the structure of microbial communities has been also modified. The ratio DEA/SIR proved to be robust and sensitive, and able to describe in fairly fine way changes of soil microbial communities in terms of structure and function in gold mine areas and during processes of restoration. We propose to use DEA, SIR and DEA/SIR ratio as bioindicators of both structural and functional aspects of C and N cycling in soils. Together with others bioindicators based on key supporting functions in soils, these indicators should accurately evaluate the ecological potential of natural ecosystems and the levels of degradation in case of land-use changes. © 2012 Elsevier Ltd.