• Energy Research

AbstractAbout 160 000 km2 of forests in the border zone between Brazil and Peru are dominated by semi-scandent bamboos (Guadua spp.). We argue that both predicted decreased precipitation during the dry season and widespread anthropogenic disturbances will significantly increase the distribution and biomass of bamboos in the area. Seasonal dryness favours the growth of evergreen bamboos in relation to trees that shed their leaves during the dry season. Disturbance can be beneficial for the bamboo because, as a clonal plant, it is often able to recover more rapidly than trees. It also withstands dry season better than many trees. The bamboo life cycle ends in a mass mortality event every 28 years, producing potential fuel for a forest fire. Presently, natural forest fires hardly exist in the area. However, in the projected future climate with more pronounced dry season and with increased fuel load after bamboo die-off events the forests may start to catch fire that has escaped from inhabited areas or even started naturally. Fires can kill trees, thus further increasing the fuel load of the forest. As a result, the landscape may start to convert to a savanna ecosystem.

Abstract Wood density is a critical control on tree biomass, so poor understanding of its spatial variation can lead to large and systematic errors in forest biomass estimates and carbon maps. The need to understand how and why wood density varies is especially critical in tropical America where forests have exceptional species diversity and spatial turnover in composition. As tree identity and forest composition are challenging to estimate remotely, ground surveys are essential to know the wood density of trees, whether measured directly or inferred from their identity. Here, we assemble an extensive dataset of variation in wood density across the most forested and tree-diverse continent, examine how it relates to spatial and environmental variables, and use these relationships to predict spatial variation in wood density over tropical and sub-tropical South America. Our analysis refines previously identified east-west Amazon gradients in wood density, improves them by revealing fine-scale variation, and extends predictions into Andean, dry, and Atlantic forests. The results halve biomass prediction errors compared to a naïve scenario with no knowledge of spatial variation in wood density. Our findings will help improve remote sensing-based estimates of aboveground biomass carbon stocks across tropical South America.