An individual-based spatially explicit simulation model for strategic forest management planning in the eastern Amazon

A model to simulate the ecological processes of tree growth, mortality and recruitment, and the processes of forest management, in the terra firme forests of the eastern Amazon is described. It is implemented within the SYMFOR (http://www.symfor.org) framework. It is based on measurements of all trees that have a diameter greater than 5cm from experimental plots in the Jarí Cellulose and Tapajós National Forest areas over a 16-year period. Ten species groups are used to describe the natural processes affecting tree behaviour. Growth rates are calculated for each species group using the tree diameter and a competition index. Mortality and recruitment are simulated as stochastic processes. Recruitment probability is based on the predicted growth rate of a hypothetical tree. Options exist to vary the human interaction with the forest reflecting forest management decisions, as for other SYMFOR models. Model evaluation compares the performance of the model with data describing forest recovery for 16 years following logging. The model was applied to simulate current forest management practice in the Brazilian Amazon, with 40mha of timber extracted with a cutting cycle of 30 years. Results show that yields are sustained for three harvests following the first logging of primary forest, but that the composition of timber moves towards lightwooded species rather than hardwooded. The predicted size of extracted trees decreases and the number of trees extracted increases with successive harvests, leading to a prediction of increased costs and lower profits for the logging company despite constant yields. The standing volume of all trees just before harvest is reduced by 15% over 150 years, with pioneer species becoming increasingly prevalent in the stand. The model, in the SYMFOR framework, can be used to help understand the differences between alternative forest management strategies in the Brazilian Amazon. Such knowledge is required to improve forest management, regulation and certification, and help to conserve the worlds largest remaining tropical forest. © 2003 Elsevier B.V. All rights reserved.