Abstract: By harvesting scattered large trees, selective logging increases light availability and thereby stimulates growth and crown expansion at early-life stage among remnant trees. We assessed the effects of logging on total and merchantable bole (i.e., lowest branch at crown base) heights on 952 tropical canopy trees in French Guiana. We observed reductions in both total (mean, −2.3 m) and bole (mean, −2.0 m) heights more than a decade after selective logging. Depending on local logging intensity, height reductions resulted in 2–13 percent decreases in aboveground tree biomass and 3–17 percent decreases in bole volume. These results highlight the adverse effects of logging at both tree and stand levels. This decrease in height is a further threat to future provision of key environmental services, such as timber production and carbon sequestration.

Abstract: Canopy height is a key variable in tropical forest functioning and for regional carbon inventories. We investigate the spatial structure of the canopy height of a tropical forest, its relationship with environmental physical covariates, and the implication for tropical forest height variation mapping. Making use of high-resolution maps of LiDAR-derived Digital Canopy Model (DCM) and environmental covariates from a Digital Elevation Model (DEM) acquired over 30,000 ha of tropical forest in French Guiana, we first show that forest canopy height is spatially correlated up to 2500 m. Forest canopy height is significantly associated with environmental variables, but the degree of correlation varies strongly with pixel resolution. On the whole, bottomland forests generally have lower canopy heights than hillslope or hilltop forests. However, this global picture is very noisy at local scale likely because of the endogenous gap-phase forest dynamic processes. Forest canopy height has been predictively mapped across a pixel resolution going from 6 m to 384 m mimicking a low resolution case of 3 points·km − 2 . Results of canopy height mapping indicated that the error for spatial model with environment effects decrease from 8.7 m to 0.91 m, depending of the pixel resolution. Results suggest that, outside the calibration plots, the contribution of environment in shaping the global canopy height distribution is quite limited. This prevents accurate canopy height mapping based only on environmental information, and suggests that precise canopy height maps, for local management purposes, can only be obtained with direct LiDAR monitoring.

Abstract: Cephalotes are herbivorous ants (>115 species) feeding on low-nitrogen food sources, and they rely on gut symbionts to supplement their diet by recycling nitrogen food waste into amino acids. These conserved gut symbionts, which encompass five bacterial orders, have been studied previously for their primary nitrogen metabolism; however, little is known about their ability to biosynthesize specialized metabolites which can play a role in bacterial interactions between communities living in close proximity in the gut. To evaluate the biosynthetic potential of their gut symbionts, we mine 14 cultured isolate genomes and gut metagenomes across 17 Cephalotes species to explore the biodiversity of biosynthetic gene clusters (BGCs) producing specialized metabolites. The diversity of BGCs across Cephalotes phylogeny was analyzed using sequence similarity networking and BGC phylogenetic reconstruction. Our results reveal that the conserved gut symbionts involved in the nutritional symbiosis possess 80% of all the 233 BGCs retrieved in this work. Furthermore, the phylogenetic analysis of BGCs reveals different patterns of distribution, suggesting different mechanisms of conservation. A siderophore BGC shows high similarity in a single symbiont across different ant host species, whereas a BGC encoding the production of non-ribosomal peptides (NRPs) found different symbionts within a single host species. Additionally, BGCs were abundant in four of the five bacterial orders of conserved symbionts co-occurring in the hindgut. However, one major symbiont localized alone in the midgut lack BGCs. Because the spatial isolation prevents direct interaction with other symbionts, this result supports the idea that BGCs are maintained in bacteria living in close proximity but are dispensable for an alone-living symbiont. These findings together pave the way for studying the mechanisms of BGC conservation and evolution in gut bacterial genomes associated with Cephalotes. This work also provides a genetic background for further study, aiming to characterize bacterial specialized metabolites and to understand their functional role in multipartite mutualisms between conserved gut symbionts and Cephalotes turtle ants.

Abstract: Changes in global and regional precipitation regimes are among the most pervasive components of climate change. Intensification of rainfall cycles, ranging from frequent downpours to severe droughts, could cause widespread, but largely unknown, alterations to trophic structure and ecosystem function. We conducted multi-site coordinated experiments to show how variation in the quantity and evenness of rainfall modulates trophic structure in 210 natural freshwater microcosms (tank bromeliads) across Central and South America (18°N to 29°S). The biomass of smaller organisms (detritivores) was higher under more stable hydrological conditions. Conversely, the biomass of predators was highest when rainfall was uneven, resulting in top-heavy biomass pyramids. These results illustrate how extremes of precipitation, resulting in localized droughts or flooding, can erode the base of freshwater food webs, with negative implications for the stability of trophic dynamics. © 2020, The Author(s).

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).