Abstract: The carbon sink capacity of tropical forests is substantially affected by tree mortality. However, the main drivers of tropical tree death remain largely unknown. Here we present a pan-Amazonian assessment of how and why trees die, analysing over 120,000 trees representing > 3800 species from 189 long-term RAINFOR forest plots. While tree mortality rates vary greatly Amazon-wide, on average trees are as likely to die standing as they are broken or uprooted—modes of death with different ecological consequences. Species-level growth rate is the single most important predictor of tree death in Amazonia, with faster-growing species being at higher risk. Within species, however, the slowest-growing trees are at greatest risk while the effect of tree size varies across the basin. In the driest Amazonian region species-level bioclimatic distributional patterns also predict the risk of death, suggesting that these forests are experiencing climatic conditions beyond their adaptative limits. These results provide not only a holistic pan-Amazonian picture of tree death but large-scale evidence for the overarching importance of the growth–survival trade-off in driving tropical tree mortality. © 2020, The Author(s).

Abstract: Abstract Aim Seasonally dry tropical forest (SDTF) of the Caribbean Islands (primarily West Indies) is floristically distinct from Neotropical SDTF in Central and South America. We evaluate whether tree species composition was associated with climatic gradients or geographical distance. Turnover (dissimilarity) in species composition of different islands or among more distant sites would suggest communities structured by speciation and dispersal limitations. A nested pattern would be consistent with a steep resource gradient. Correlation of species composition with climatic variation would suggest communities structured by broad-scale environmental filtering. Location The West Indies (The Bahamas, Cuba, Hispaniola, Jamaica, Puerto Rico, US Virgin Islands, Guadeloupe, Martinique, St. Lucia), Providencia (Colombia), south Florida (USA) and Florida Keys (USA). Taxon Seed plants?woody taxa (primarily trees). Methods We compiled 572 plots from 23 surveys conducted between 1969 and 2016. Hierarchical clustering of species in plots, and indicator species analysis for the resulting groups of sites, identified geographical patterns of turnover in species composition. Nonparametric analysis of variance, applied to principal components of bioclimatic variables, determined the degree of covariation in climate with location. Nestedness versus turnover in species composition was evaluated using beta diversity partitioning. Generalized dissimilarity modelling partitioned the effect of climate versus geographical distance on species composition. Results Despite a set of commonly occurring species, SDTF tree community composition was distinct among islands and was characterized by spatial turnover on climatic gradients that covaried with geographical gradients. Greater Antillean islands were characterized by endemic indicator species. Northern subtropical areas supported distinct, rather than nested, SDTF communities in spite of low levels of endemism. Main conclusions The SDTF species composition was correlated with climatic variation. SDTF on large Greater Antillean islands (Hispaniola, Jamaica and Cuba) was characterized by endemic species, consistent with their geological history and the biogeography of plant lineages. These results suggest that both environmental filtering and speciation shape Caribbean SDTF tree communities.

Abstract: Cyclocephala colasi beetles are facultative endotherms that spend most of their adult lives inside the inflorescences of Philodendron solimoesense, where ambient temperature (T-a) averages about 28 degrees C due to floral thermogenesis. Measurements of respiration within a range of T-a showed that active beetles became spontaneously endothermic at T-a below 28 degrees C but were rarely endothermic above it. There was no evidence of endothermy within the inflorescences, indicating that activities in the floral chamber can occur without the high energy expense of endothermy. Bouts of endothermy occurred at lower T-a in respirometer chambers mainly in the evening, when the insects normally fly from one inflorescence to another, and during the night, when they normally eat and mate within the inflorescence. Patterns of endothermy in individual episodes were studied in non-flying beetles with respirometry and infrared thermal imaging. Heat was generated in the thorax by oscillatory waves of respiration that were coupled with thoracic temperature (T-th) increases. Stationary beetles could regulate T-th at about 33 degrees C independently of T-a between 16 and 29 degrees C. At T-a=20 degrees C, this represents a 116-fold increase in metabolic rate over resting, ectothermic values. Endothermy was clearly a requirement for flight, and beetles departing inflorescences warmed to about 30 degrees C before take-off. During flight, T-th was dependent on T-a, decreasing from 37 to 28 degrees C at T-a of 37 to 20 degrees C, respectively. The lowest T-a at which flight could occur was about 20 degrees C. Thermal conductance of stationary, endothermic beetles increased at higher metabolic rates, probably because of increased ventilatory heat loss.

Abstract: Matrix population models are widely used to predict population dynamics, but when applied to species-rich ecosystems with many rare species, the small population sample sizes hinder a good fit of species-specific models. This issue can be overcome by assigning species to groups to increase the size of the calibration data sets. However, the species classification is often disconnected from the matrix modelling and from the estimation of matrix parameters, thus bringing species groups that may not be optimal with respect to the predicted community dynamics. We proposed here a method that jointly classified species into groups and fit the matrix models in an integrated way. The model was a special case of mixture with unknown number of components and was cast in a Bayesian framework. An MCMC algorithm was developed to infer the unknown parameters: the number of groups, the group of each species and the dynamics parameters. We applied the method to simulated data and showed that the algorithm efficiently recovered the model parameters. We applied the method to a data set from a tropical rain forest in French Guiana. The mixture matrix model classified tree species into well-differentiated groups with clear ecological interpretations. It also accurately predicted the forest dynamics over the 16-year observation period. Our model and algorithm can straightforwardly be adapted to any type of matrix model, using the life cycle diagram. It can be used as an unsupervised classification technique to group species with similar population dynamics. © 2012 The Authors. Methods in Ecology and Evolution © 2012 British Ecological Society.

Abstract: Reliable above-ground biomass (AGB) estimates are required for studies of carbon fluxes and stocks. However, there is a huge lack of knowledge concerning the precision of AGB estimates and the sources of this uncertainty. At the tree level, the tree height is predicted using the tree diameter at breast height (DBH) and a height sub-model. The wood-specific gravity (WSG) is predicted with taxonomic information and a WSG sub-model. The tree mass is predicted using the predicted height, the predicted WSG and the biomass sub-model. Our models were inferred with Bayesian methods and the uncertainty propagated with a Monte Carlo scheme. The uncertainties in the predictions of tree height, tree WSG and tree mass were neglected sequentially to quantify their contributions to the uncertainty in AGB. The study was conducted in French Guiana where long-term research on forest ecosystems provided an outstanding data collection on tree height, tree dynamics, tree mass and species WSG. We found that the uncertainty in the AGB estimates was found to derive primarily from the biomass sub-model. The models used to predict the tree heights and WSG contributed negligible uncertainty to the final estimate. Considering our results, a poor knowledge of WSG and the height-diameter relationship does not increase the uncertainty in AGB estimates. However, it could lead to bias. Therefore, models and databases should be used with care. This study provides a methodological framework that can be broadly used by foresters and plant ecologist. It provides the accurate confidence intervals associated with forest AGB estimates made from inventory data. When estimating region-scale AGB values (through spatial interpolation, spatial modelling or satellite signal treatment), the uncertainty of the forest AGB value in the reference forest plots has to be taken in account. We believe that in the light of the Reducing Emissions from Deforestation and Degradation debate, our method is a crucial step in monitoring carbon stocks and their spatio-temporal evolution. © 2012 The Authors. Methods in Ecology and Evolution © 2012 British Ecological Society.