Abstract: Drought is predicted to increase in the future due to climate change, bringing with it myriad impacts on ecosystems. Plants respond to drier soils by reducing stomatal conductance in order to conserve water and avoid hydraulic damage. Despite the importance of plant drought responses for the global carbon cycle and local and regional climate feedbacks, land surface models are unable to capture observed plant responses to soil moisture stress. We assessed the impact of soil moisture stress on simulated gross primary productivity (GPP) and latent energy flux (LE) in the Joint UK Land Environment Simulator (JULES) vn4.9 on seasonal and annual timescales and evaluated 10 different representations of soil moisture stress in the model. For the default configuration, GPP was more realistic in temperate biome sites than in the tropics or high-latitude (cold-region) sites, while LE was best simulated in temperate and high-latitude (cold) sites. Errors that were not due to soil moisture stress, possibly linked to phenology, contributed to model biases for GPP in tropical savanna and deciduous forest sites. We found that three alternative approaches to calculating soil moisture stress produced more realistic results than the default parameterization for most biomes and climates. All of these involved increasing the number of soil layers from 4 to 14 and the soil depth from 3.0 to 10.8 m. In addition, we found improvements when soil matric potential replaced volumetric water content in the stress equation (the “soil14psi” experiments), when the critical threshold value for inducing soil moisture stress was reduced (“soil14p0”), and when plants were able to access soil moisture in deeper soil layers (“soil14_dr*2”). For LE, the biases were highest in the default configuration in temperate mixed forests, with overestimation occurring during most of the year. At these sites, reducing soil moisture stress (with the new parameterizations mentioned above) increased LE and increased model biases but improved the simulated seasonal cycle and brought the monthly variance closer to the measured variance of LE. Further evaluation of the reason for the high bias in LE at many of the sites would enable improvements in both carbon and energy fluxes with new parameterizations for soil moisture stress. Increasing the soil depth and plant access to deep soil moisture improved many aspects of the simulations, and we recommend these settings in future work using JULES or as a general way to improve land surface carbon and water fluxes in other models. In addition, using soil matric potential presents the opportunity to include plant functional type-specific parameters to further improve modeled fluxes.

Abstract: In the study, we developed new markers for phylogenetic relationships and intraspecies differentiation in Coffea. Nana and Divo, two novel Ty1-copia LTR-retrotransposon families, were isolated through C. canephora BAC clone sequencing. Nana- and Divo-based markers were used to test their: (1) ability to resolve recent phylogenetic relationships; (2) efficiency in detecting intra-species differentiation. Sequence-specific amplification polymorphism (SSAP), retrotransposon-microsatellite amplified polymorphism (REMAP) and retrotransposon-based insertion polymorphism (RBIP) approaches were applied to 182 accessions (31 Coffea species and one Psilanthus accession). Nana- and Divo-based markers revealed contrasted transpositional histories. At the BAC clone locus, RBIP results on C. canephora demonstrated that Nana insertion took place prior to C. canephora differentiation, while Divo insertion occurred after differentiation. Combined SSAP and REMAP data showed that Nana could resolve Coffea lineages, while Divo was efficient at a lower taxonomic level. The combined results indicated that the retrotransposon-based markers were useful in highlighting Coffea genetic diversity and the chronological pattern of speciation/differentiation events. Ongoing complete sequencing of the C. canephora genome will soon enable exhaustive identification of LTR-RTN families, as well as more precise in-depth analyses on contributions to genome size variation and Coffea evolution. © 2011 Springer-Verlag.

Abstract: Proper estimates of decomposition are essential for tropical forests, given their key role in the global carbon (C) cycle. However, the current paradigm for litter decomposition is insufficient to account for recent observations and may limit model predictions for highly diverse tropical ecosystems. In light of recent findings from a nutrient-poor Amazonian rainforest, we revisit the commonly held views that: litter traits are a mere legacy of live leaf traits; nitrogen (N) and lignin are the key litter traits controlling decomposition; and favourable climatic conditions result in rapid decomposition in tropical forests. Substantial interspecific variation in litter phosphorus (P) was found to be unrelated to variation in green leaves. Litter nutrients explained no variation in decomposition, which instead was controlled primarily by nonlignin litter C compounds at low concentrations with important soil fauna effects. Despite near-optimal climatic conditions, tropical litter decomposition proceeded more slowly than in a climatically less favourable temperate forest. We suggest that slow decomposition in the studied rainforest results from a syndrome of poor litter C quality beyond a simple lignin control, enforcing energy starvation of decomposers. We hypothesize that the litter trait syndrome in nutrient-poor tropical rainforests may have evolved to increase plant access to limiting nutrients via mycorrhizal associations.

Abstract: Analyses of tree diversity and community composition in tropical rain forests are usually based either on general herbarium data or on a restricted number of botanical plots. Despite their high taxonomic accuracy, both types of data are difficult to extrapolate to landscape scales. Meanwhile, forestry surveys provide quantitative occurrence data on large areas, and are thus increasingly used for landscape-scale analyses of tree diversity. However, the reliability of these approaches has been challenged because of the ambiguity of the common (vernacular) names used by foresters and the complexity of tree taxonomy in those hyper-diverse communities.We developed and tested a novel approach to evaluate taxonomic reliability of forestry surveys and to propagate the resulting uncertainty in the estimates of several diversity indicators (alpha and beta entropy, Fisher-alpha and Sørensen similarity). Our approach is based on Monte-Carlo processes that simulate communities by taking into account the expected accuracy and reliability of common names. We tested this method in French Guiana, on 9 one-hectare plots (4279 trees – DBH. ≥. 10. cm) for which both common names and standardized taxonomic determinations were available. We then applied our method of community simulation on large forestry inventories (560. ha) at the landscape scale and compared the diversity indices obtained for 10 sites with those computed from precise botanical determination situated at the same localities.We found that taxonomic reliability of forestry inventories varied from 22% (species level) to 83% (family level) in this Amazonian region. Indices computed directly with raw forestry data resulted in incorrect values, except for Gini-Simpson beta-diversity. On the contrary, our correction method provides more accurate diversity estimates, highly correlated with botanical measurements, for almost all diversity indices at both regional and local scales. We obtained a robust ranking of sites consistent with those shown by botanical inventories.These results show that (i) forestry inventories represent a significant part of taxonomic information, (ii) the relative diversity of regional sites can be successfully ranked using forestry inventory data using our method and (iii) forestry inventories can valuably contribute to the detection of large-scale diversity patterns when biases are well-controlled and corrected.The tools we developed as R-functions are available in supplementary material and can be adapted with local parameters to be used for forest management and conservation issues in other regional contexts. © 2014 Elsevier B.V.

Abstract: Geomorphic landscape features have been suggested as indicators of forest diversity. However, their explanatory power has not yet been explicitly tested at a regional scale in tropical rainforest. We used forest inventories conducted according to a stratified sampling design (3,132 plots in 111 transects at 33 sites) and holistic multi-scale geomorphological mapping derived from a Shuttle Radar Topography Mission digital elevation model to describe and explain spatial patterns in floristic composition across French Guiana (80,000 km2). We measured and identified 123,906 trees with DBH ≥20 cm and used constrained and unconstrained ordinations to analyze variations in the abundance of 221 taxa and 51 families. Variance partitioning and variograms were used to detect spatial patterns in species composition, compare the explanatory power of spatial and environmental factors, and select the variables that best explain forest composition. Strong floristic patterns corresponded to a major latitudinal gradient and significant sub-regional floristic structure. Geomorphological landscapes shaped by historic climate fluctuations and major geological events successfully captured these patterns and explained the variation in abundance of 80 taxa, corresponding to 65 % of the inventoried trees. Our findings suggest that long-term forest dynamics are under substantial “geomorphographic control”. A geomorphological perspective on landscapes that incorporates current and past environmental filters and historical biogeographical processes could thus be used more systematically in tropical regions for regional planning and forest conservation. © 2014, Springer Science+Business Media Dordrecht.