Abstract: Increasing evidence shows that the functioning of the tropical forest biome is intimately related to the climate variability with some variables such as annual precipitation, temperature or seasonal water stress identified as key drivers of ecosystem dynamics. How tropical tree communities will respond to the future climate change is hard to predict primarily because several demographic processes act together to shape the forest ecosystem general behavior. To overcome this limitation, we used a joint individual-based model to simulate, over the next century, a tropical forest community experiencing the climate change expected in the Guiana Shield. The model is climate dependent: temperature, precipitation and water stress are used as predictors of the joint growth and mortality rates. We ran simulations for the next century using predictions of the IPCC 5AR, building three different climate scenarios (optimistic RCP2.6, intermediate, pessimistic RCP8.5) and a control (current climate). The basal area, above-ground fresh biomass, quadratic diameter, tree growth and mortality rates were then computed as summary statistics to characterize the resulting forest ecosystem. Whatever the scenario, all ecosystem process and structure variables exhibited decreasing values as compared to the control. A sensitivity analysis identified the temperature as the strongest climate driver of this behavior, highlighting a possible temperature-driven drop of 40% in average forest growth. This conclusion is alarming, as temperature rises have been consensually predicted by all climate scenarios of the IPCC 5AR. Our study highlights the potential slow-down danger that tropical forests will face in the Guiana Shield during the next century. © 2019, The Author(s).

Abstract: Tropical rainforests harbor a particularly high plant diversity. We hypothesize that potential causes underlying this high diversity should be linked to distinct overall functionality (defense and growth allocation, anti-stress mechanisms, reproduction) among the different sympatric taxa. In this study we tested the hypothesis of the existence of a metabolomic niche related to a species-specific differential use and allocation of metabolites. We tested this hypothesis by comparing leaf metabolomic profiles of 54 species in two rainforests of French Guiana. Species identity explained most of the variation in the metabolome, with a species-specific metabolomic profile across dry and wet seasons. In addition to this “homeostatic” species-specific metabolomic profile significantly linked to phylogenetic distances, also part of the variance (flexibility) of the metabolomic profile was explained by season within a single species. Our results support the hypothesis of the high diversity in tropical forest being related to a species-specific metabolomic niche and highlight ecometabolomics as a tool to identify this species functional diversity related and consistent with the ecological niche theory. © 2020, The Author(s).

Abstract: We observed strong positive relationships between soil properties and forest dynamics of growth and mortality across twelve primary lowland tropical forests in a phosphorus-poor region of the Guiana Shield. Average tree growth (diameter at breast height) increased from 0.81 to 2.1 mm yr-1 along a soil texture gradient from 0 to 67% clay, and increasing metal-oxide content. Soil organic carbon stocks in the top 30 cm ranged from 30 to 118 tons C ha-1, phosphorus content ranged from 7 to 600 mg kg-1 soil, and the relative abundance of arbuscular mycorrhizal fungi ranged from 0 to 50%, all positively correlating with soil clay, and iron and aluminum oxide and hydroxide content. In contrast, already low extractable phosphorus (Bray P) content decreased from 4.4 to <0.02 mg kg-1 in soil with increasing clay content. A greater prevalence of arbuscular mycorrhizal fungi in more clayey forests that had higher tree growth and mortality, but not biomass, indicates that despite the greater investment in nutrient uptake required, soils with higher clay content may actually serve to sustain high tree growth in tropical forests by avoiding phosphorus losses from the ecosystem. Our study demonstrates how variation in soil properties that retain carbon and nutrients can help to explain variation in tropical forest growth and mortality, but not biomass, by requiring niche specialization and contributing to biogeochemical diversification across this region.

Abstract: The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.

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).