Tropical carbon emissions are largely derived from direct forest clearing processes. Yet, emissions from drought-induced forest fires are, usually, not included in national-level carbon emission inventories. Here we examine Brazilian Amazon drought impacts on fire incidence and associated forest fire carbon emissions over the period 2003–2015. We show that despite a 76% decline in deforestation rates over the past 13 years, fire incidence increased by 36% during the 2015 drought compared to the preceding 12 years. The 2015 drought had the largest ever ratio of active fire counts to deforestation, with active fires occurring over an area of 799,293 km². Gross emissions from forest fires (989 ± 504 Tg CO₂ year⁻¹) alone are more than half as great as those from old-growth forest deforestation during drought years. We conclude that carbon emission inventories intended for accounting and developing policies need to take account of substantial forest fire emissions not associated to the deforestation process.

Summary: 1. Functional traits are posited to explain interspecific differences in performance, but these relationships are difficult to describe for long‐lived organisms such as trees, which exhibit strong ontogenetic changes in demographic rates. Here, we use a size‐dependent model of tree growth to test the extent to which of 17 functional traits related to leaf and stem economics, adult stature and seed size predict the ontogenetic trajectory of tree growth.2. We used a Bayesian modelling framework to parameterize and contrast three size‐dependent diameter growth models using 16 years of census data from 5524 individuals of 50 rain forest tree species: a size‐dependent model, a size‐dependent model with species‐specific parameters and a size‐dependent model based on functional traits.3. Most species showed clear hump‐shaped ontogenetic growth trajectories and, across species, maximum growth rate varied nearly tenfold, from 0.58 to 5.51 mm year⁻¹. Most species attained their maximum growth at 60% of their maximum size, whereas the magnitude of ontogenetic changes in growth rate varied widely among species.4. The Trait‐Model provided the best compromise between explained variance and model parsimony and needed considerably fewer parameters than the model with species terms.5. Stem economics and adult stature largely explained interspecific differences in growth strategy. Maximum absolute diameter growth rates increased with increasing adult stature and leaf δ¹³C and decreased with increasing wood density. Species with light wood had the greatest potential to modulate their growth, resulting in hump‐shaped ontogenetic growth curves. Seed size and leaf economics, generally thought to be of paramount importance for plant performance, had no significant relationships with the growth parameters.6. Synthesis. Our modelling approach offers a promising way to link demographic parameters to their functional determinants and hence to predict growth trajectories in species‐rich communities with little parameter inflation, bridging the gap between functional ecology and population demography.

Mapping forest types and tree species at regional scales to provide information for ecologists and forest managers is a new challenge for the remote sensing community. Here, we assess the potential of a U‐net convolutional network, a recent deep learning algorithm, to identify and segment (1) natural forests and eucalyptus plantations, and (2) an indicator of forest disturbance, the tree species Cecropia hololeuca, in very high resolution images (0.3 m) from the WorldView‐3 satellite in the Brazilian Atlantic rainforest region. The networks for forest types and Cecropia trees were trained with 7611 and 1568 red‐green‐blue (RGB) images, respectively, and their dense labeled masks. Eighty per cent of the images were used for training and 20% for validation. The U‐net network segmented forest types with an overall accuracy >95% and an intersection over union (IoU) of 0.96. For C. hololeuca, the overall accuracy was 97% and the IoU was 0.86. The predictions were produced over a 1600 km² region using WorldView‐3 RGB bands pan‐sharpened at 0.3 m. Natural and eucalyptus forests compose 79 and 21% of the region's total forest cover (82 250 ha). Cecropia crowns covered 1% of the natural forest canopy. An index to describe the level of disturbance of the natural forest fragments based on the spatial distribution of Cecropia trees was developed. Our work demonstrates how a deep learning algorithm can support applications such as vegetation, tree species distributions and disturbance mapping on a regional scale.

Key message: We review the recent findings on the influence of drought on tree mortality, growth or ecosystem functioning in tropical rainforests. Drought plays a major role in shaping tropical rainforests and the response mechanisms are highly diverse and complex. The numerous gaps identified here require the international scientific community to combine efforts in order to conduct comprehensive studies in tropical rainforests on the three continents. These results are essential to simulate the future of these ecosystems under diverse climate scenarios and to predict the future of the global earth carbon balance.Context: Tropical rainforest ecosystems are characterized by high annual rainfall. Nevertheless, rainfall regularly fluctuates during the year and seasonal soil droughts do occur. Over the past decades, a number of extreme droughts have hit tropical rainforests, not only in Amazonia but also in Asia and Africa. The influence of drought events on tree mortality and growth or on ecosystem functioning (carbon and water fluxes) in tropical rainforest ecosystems has been studied intensively, but the response mechanisms are complex.Aims: Herein, we review the recent findings related to the response of tropical forest ecosystems to seasonal and extreme droughts and the current knowledge about the future of these ecosystems.Results: This review emphasizes the progress made over recent years and the importance of the studies conducted under extreme drought conditions or in through-fall exclusion experiments in understanding the response of these ecosystems. It also points to the great diversity and complexity of the response of tropical rainforest ecosystems to drought.Conclusion: The numerous gaps identified here require the international scientific community to combine efforts in order to conduct comprehensive studies in tropical forest regions. These results are essential to simulate the future of these ecosystems under diverse climate scenarios and to predict the future of the global earth carbon balance.

The seasonal climate drivers of the carbon cycle in tropical forests remain poorly known, although these forests account for more carbon assimilation and storage than any other terrestrial ecosystem. Based on a unique combination of seasonal pan-tropical data sets from 89 experimental sites (68 include aboveground wood productivity measurements and 35 litter productivity measurements), their associated canopy photosynthetic capacity (enhanced vegetation index, EVI) and climate, we ask how carbon assimilation and aboveground allocation are related to climate seasonality in tropical forests and how they interact in the seasonal carbon cycle. We found that canopy photosynthetic capacity seasonality responds positively to precipitation when rainfall is < 2000 mm yr⁻¹ (water-limited forests) and to radiation otherwise (light-limited forests). On the other hand, independent of climate limitations, wood productivity and litterfall are driven by seasonal variation in precipitation and evapotranspiration, respectively. Consequently, light-limited forests present an asynchronism between canopy photosynthetic capacity and wood productivity. First-order control by precipitation likely indicates a decrease in tropical forest productivity in a drier climate in water-limited forest, and in current light-limited forest with future rainfall < 2000 mm yr⁻¹.