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International audienceThe 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)

International audienceIn tropical areas, Dynamic Global Vegetation Models (DGVMs) still have deficiencies in simulating the timing of vegetation phenology. To start addressing this problem, standard Fourier-based methods are applied to aerosol screened monthly remotely sensed phenology time series (Enhanced Vegetation Index, EVI) and two major driving factors of phenology: solar radiation and precipitation (for March 2000 through December 2006 over northern South America). At 1 × 1 km scale using, power (or variance) spectra on good quality aerosol screened time series, annual cycles in EVI are detected across 58.24% of the study area, the strongest (largest amplitude) occurring in the savanna. Terra Firme forest have weak but significant annual cycles in comparison with savannas because of the heterogeneity of vegetation and nonsynchronous phenological events within 1 × 1 km scale pixels. Significant annual cycles for radiation and precipitation account for 86% and 90% of the region, respectively, with different spatial patterns to phenology. Cross-spectral analysis was used to compare separately radiation with phenology/EVI, precipitation with phenology/EVI and radiation with precipitation. Overall the majority of the Terra Firme forest appears to have radiation as the driver of phenology (either radiation is in phase or leading phenology/EVI at the annual scale). These results are in agreement with previous research, although in Acre, central and eastern Peru and northern Bolivia there is a coexistence of 'in phase' precipitation over Terra Firme forest. In contrast in most areas of savanna precipitation appears to be a driver and savanna areas experiencing an inverse (antiphase) relationship between radiation and phenology is consistent with inhibited grassland growth due to soil moisture limitation. The resulting maps provide a better spatial understanding of phenology-driver relationships offering a bench mark to parameterize ecological models

The strong El Niño Southern Oscillation (ENSO) event that occurred in 2015-2016 caused extreme drought in the northern Brazilian Amazon, especially in the state of Roraima, increasing fire occurrence. Here we map the extent of precipitation and fire anomalies and quantify the effects of climatic and anthropogenic drivers on fire occurrence during the 2015-2016 dry season (from December 2015 to March 2016) in the state of Roraima. To achieve these objectives we first estimated the spatial pattern of precipitation anomalies, based on long-term data from the TRMM (Tropical Rainfall Measuring Mission), and the fire anomaly, based on MODIS (Moderate Resolution Imaging Spectroradiometer) active fire detections during the referred period. Then, we integrated climatic and anthropogenic drivers in a Maximum Entropy (MaxEnt) model to quantify fire probability, assessing (1) the model accuracy during the 2015-2016 and the 2016-2017 dry seasons; (2) the relative importance of each predictor variable on the model predictive performance; and (3) the response curves, showing how each environmental variable affects the fire probability. Approximately 59% (132,900 km ) of the study area was exposed to precipitation anomalies ≤-1 standard deviation (SD) in January and ~48% (~106,800 km ) in March. About 38% (86,200 km ) of the study area experienced fire anomalies ≥1 SD in at least one month between December 2015 and March 2016. The distance to roads and the direct ENSO effect on fire occurrence were the two most influential variables on model predictive performance. Despite the improvement of governmental actions of fire prevention and firefighting in Roraima since the last intense ENSO event (1997-1998), we show that fire still gets out of control in the state during extreme drought events. Our results indicate that if no prevention actions are undertaken, future road network expansion and a climate-induced increase in water stress will amplify fire occurrence in the northern Amazon, even in its humid dense forests. As an additional outcome of our analysis, we conclude that the model and the data we used may help to guide on-the-ground fire-prevention actions and firefighting planning and therefore minimize fire-related ecosystems degradation, economic losses and carbon emissions in Roraima.