Methane emissions from Amazonian Rivers and their contribution to the global methane budget.

Methane (CH4 ) fluxes from world rivers are still poorly constrained, with measurements restricted mainly to temperate climates. Additional river flux measurements, including spatio-temporal studies, are important to refine extrapolations. Here we assess the spatio-temporal variability of CH4 fluxes from the Amazon and its main tributaries, the Negro, Solimões, Madeira, Tapajós, Xingu, and Pará Rivers, based on direct measurements using floating chambers. Sixteen of 34 sites were measured during low and high water seasons. Significant differences were observed within sites in the same river and among different rivers, types of rivers, and seasons. Ebullition contributed to more than 50% of total emissions for some rivers. Considering only river channels, our data indicate that large rivers in the Amazon Basin release between 0.40 and 0.58 Tg CH4  yr(-1) . Thus, our estimates of CH4 flux from all tropical rivers and rivers globally were, respectively, 19-51% to 31-84% higher than previous estimates, with large rivers of the Amazon accounting for 22-28% of global river CH4 emissions.

Strategies to optimize modeling habitat suitability of Bertholletia excelsa in the Pan-Amazonia.

AIM: Amazon-nut (Bertholletia excelsa) is a hyperdominant and protected tree species, playing a keystone role in nutrient cycling and ecosystem service provision in Amazonia. Our main goal was to develop a robust habitat suitability model of Amazon-nut and to identify the most important predictor variables to support conservation and tree planting decisions. LOCALIZATION: Amazon region, South America. METHODS: We collected 3,325 unique Amazon-nut records and assembled >100 spatial predictor variables organized across climatic, edaphic, and geophysical categories. We compared suitability models using variables (a) selected through statistical techniques; (b) recommended by experts; and (c) integrating both approaches (a and b). We applied different spatial filtering scenarios to reduce overfitting. We additionally fine-tuned MAXENT settings to our data. The best model was selected through quantitative and qualitative assessments. RESULTS: Principal component analysis based on expert recommendations was the most appropriate method for predictor selection. Elevation, coarse soil fragments, clay, slope, and annual potential evapotranspiration were the most important predictors. Their relative contribution to the best model amounted to 75%. Filtering of the presences within a radius of 10 km displayed lowest overfitting, a satisfactory omission rate and the most symmetric distribution curve. Our findings suggest that under current environmental conditions, suitable habitat for Amazon-nut is found across 2.3 million km2, that is, 32% of the Amazon Biome. MAIN CONCLUSION: The combination of statistical techniques with expert knowledge improved the quality of our suitability model. Topographic and soil variables were the most important predictors. The combination of predictor variable selection, fine-tuning of model parameters and spatial filtering was critical for the construction of a reliable habitat suitability model.