Response of tree biomass and wood litter to disturbance in a Central Amazon forest.

Jeffrey Q Chambers,Susan G Laurance,Niro Higuchi,Liliane M Teixeira,Susan E Trumbore,Joaquim Dos Santos

doi:10.1007/s00442-004-1676-2

Jeffrey Q Chambers, Susan G Laurance + Show 4 more

Open Access

https://doi.org/10.1007/s00442-004-1676-2

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Abstract

We developed an individual-based stochastic-empirical model to simulate the carbon dynamics of live and dead trees in a Central Amazon forest near Manaus, Brazil. The model is based on analyses of extensive field studies carried out on permanent forest inventory plots, and syntheses of published studies. New analyses included: (1) growth suppression of small trees, (2) maximum size (trunk base diameter) for 220 tree species, (3) the relationship between growth rate and wood density, and (4) the growth response of surviving trees to catastrophic mortality (from logging). The model simulates a forest inventory plot, and tracks recruitment, growth, and mortality of live trees, decomposition of dead trees (coarse litter), and how these processes vary with changing environmental conditions. Model predictions were tested against aggregated field data, and also compared with independent measurements including maximum tree age and coarse litter standing stocks. Spatial analyses demonstrated that a plot size of approximately 10 ha was required to accurately measure wood (live and dead) carbon balance. With the model accurately predicting relevant pools and fluxes, a number of model experiments were performed to predict forest carbon balance response to perturbations including: (1) increased productivity due to CO2 fertilization, (2) a single semi-catastrophic (10%) mortality event, (3) increased recruitment and mortality (turnover) rates, and (4) the combined effects of increased turnover, increased tree growth rates, and decreased mean wood density of new recruits. Results demonstrated that carbon accumulation over the past few decades observed on tropical forest inventory plots (approximately 0.5 Mg C ha(-1) year(-1)) is not likely caused by CO2 fertilization. A maximum 25% increase in woody tissue productivity with a doubling of atmospheric CO2 only resulted in an accumulation rate of 0.05 Mg C ha(-1) year(-1) for the period 1980-2020 for a Central Amazon forest, or an order of magnitude less than observed on the inventory plots. In contrast, model parameterization based on extensive data from a logging experiment demonstrated a rapid increase in tree growth following disturbance, which could be misinterpreted as carbon sequestration if changes in coarse litter stocks were not considered. Combined results demonstrated that predictions of changes in forest carbon balance during the twenty-first century are highly dependent on assumptions of tree response to various perturbations, and underscores the importance of a close coupling of model and field investigations.

Highlights

We explored how carbon balance responds to various values of β, and constrained an upper limit for an above-ground CO2 fertilization response in Central Amazon forests
The model predicted a number of fluxes, pools, and structural features of Central Amazon terra firme forest that compared well with field data
Chambers et al (2001a), employing essentially the same model presented here, demonstrated that in response to an arbitrary 25% increase in tree growth rates, there was a lag-time of more than a century before large wood carbon balance reach a new dynamic equilibrium

Summary

Introduction

After accounting for Db dependent variation in growth rates, the model still over-predicted the number of large trees (>50 cm Db), resulting in substantial overestimates of live wood mass (Fig. 3a) and annual biomass increment. For the purposes of developing our carbon cycling model, the most important results from the BIONTE growth data were that (1) growth rates for surviving trees increased rapidly in response to the death of neighboring trees (Eq 11), (2) growth rates decrease back to predisturbance levels rapidly (−20.7% year−1, Eq 10), and (3) a relatively small disturbance (e.g., before the plot was established) may cause a large increase in growth rates (Fig. 4, T0).

Results

Conclusion