Predicting biomass of hyperdiverse and structurally complex Central Amazon forests – a virtual forest approach using extensive field data

Abstract

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 AGB above-ground biomass in tropical forests and AGB estimation models are crucial for forest 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 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 from 101 genera and at least 135 species harvested in a contiguous forest near Manaus, Brazil. Sampling from this dataset 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 good 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 RMSE root-mean-square error 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 complex tropical forests, especially allowing good performance at the margins of data availability for model parametrization, requires the inclusion of predictors related to 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 still depend on the collection of destructive allometry data at the local/regional scale and forest inventories including species-specific attributes, which are often unavailable or estimated imprecisely in most regions.