Old-growth forests are subject to substantial changes in structure and species composition due to the intensification of human activities, gradual climate change and extreme weather events. Trees store ca. 90 % of the total aboveground biomass (AGB) in tropical forests and precise tree biomass estimation models are crucial for management and conservation. In the central Amazon, predicting AGB at large spatial scales is a challenging task due to the heterogeneity of successional stages, high tree species diversity and inherent variations in tree allometry and architecture. We parameterized generic AGB estimation models applicable across species and a wide range of structural and compositional variation related to species sorting into height layers as well as frequent natural disturbances. We used 727 trees (diameter at breast height ≥ 5 cm) from 101 genera and at least 135 species harvested in a contiguous forest near Manaus, Brazil. Sampling from this data set we assembled six scenarios designed to span existing gradients in floristic composition and size distribution in order to select models that best predict AGB at the landscape level across successional gradients. We found that good individual tree model fits do not necessarily translate into reliable predictions of AGB at the landscape level. When predicting AGB (dry mass) over scenarios using our different models and an available pantropical model, we observed systematic biases ranging from -31 % (pantropical) to +39 %, with root-mean-square error (RMSE) values of up to 130 Mg ha-1 (pantropical). Our first and second best models had both low mean biases (0.8 and 3.9 %, respectively) and RMSE (9.4 and 18.6 Mg ha-1) when applied over scenarios. Predicting biomass correctly at the landscape level in hyperdiverse and structurally complex tropical forests, especially allowing good performance at the margins of data availability for model construction/calibration, requires the inclusion of predictors that express inherent variations in species architecture. The model of interest should comprise the floristic composition and size-distribution variability of the target forest, implying that even generic global or pantropical biomass estimation models can lead to strong biases. Reliable biomass assessments for the Amazon basin (i.e., secondary forests) still depend on the collection of allometric data at the local/regional scale and forest inventories including species-specific attributes, which are often unavailable or estimated imprecisely in most regions.

Isoprene (Is) emissions by plants represent a loss of carbon and energy resources leading to the initial hypothesis that fast growing pioneer species in secondary tropical forests allocate carbon primarily to growth at the expense of isoprenoid defenses. In this study, we quantified leaf isoprene and methanol emissions from the abundant pantropical pioneer tree species Vismia guianensis and ambient isoprene concentrations above a diverse secondary forest in the central Amazon. As photosynthetically active radiation (PAR) was varied (0 to 3000μ2s-1) under standard leaf temperature (30°C), isoprene emissions from V. guianensis increased without saturation up to 80n2s-1. A nonlinear increase in isoprene emissions with respect to net photosynthesis (Pn) resulted in the fraction of Pn dedicated to isoprene emissions increasing with light intensity (up to 2% of Pn). Emission responses to temperature under standard light conditions (PAR of 1000μ2s-1) resulted in the classic uncoupling of isoprene emissions (Topt, iso>40°C) from net photosynthesis (Topt, Pn Combining double low line 30.0-32.5°C) with up to 7% of Pn emitted as isoprene at 40°C. Under standard environmental conditions of PAR and leaf temperature, young V. guianensis leaves showed high methanol emissions, low Pn, and low isoprene emissions. In contrast, mature leaves showed high Pn, high isoprene emissions, and low methanol emissions, highlighting the differential control of leaf phenology over methanol and isoprene emissions. High daytime ambient isoprene concentrations (11ppbv) were observed above a secondary Amazon rainforest, suggesting that isoprene emissions are common among neotropical pioneer species. The results are not consistent with the initial hypothesis and support a functional role of methanol during leaf expansion and the establishment of photosynthetic machinery and a protective role of isoprene for photosynthesis during high temperature extremes regularly experienced in secondary rainforest ecosystems.

Reducing uncertainties in the response of tropical forests to global change requires understanding how intra- and interannual climatic variability selects for different species, community functional composition and ecosystem functioning, so that the response to climatic events of differing frequency and severity can be predicted. Here we present an extensive dataset of hydraulic traits of dominant species in two tropical Amazon forests with contrasting precipitation regimes - low seasonality forest (LSF) and high seasonality forest (HSF) - and relate them to community and ecosystem response to the El Niño-Southern Oscillation (ENSO) of 2015. Hydraulic traits indicated higher drought tolerance in the HSF than in the LSF. Despite more intense drought and lower plant water potentials in HSF during the 2015-ENSO, greater xylem embolism resistance maintained similar hydraulic safety margin as in LSF. This likely explains how ecosystem-scale whole-forest canopy conductance at HSF maintained a similar response to atmospheric drought as at LSF, despite their water transport systems operating at different water potentials. Our results indicate that contrasting precipitation regimes (at seasonal and interannual time scales) select for assemblies of hydraulic traits and taxa at the community level, which may have a significant role in modulating forest drought response at ecosystem scales.