Assessing tropical rainforest growth traits: Data – Model fusion in the Congo basin and beyond

Pietsch S (2017). Assessing tropical rainforest growth traits: Data – Model fusion in the Congo basin and beyond. In: European Geosciences Union (EGU) General Assembly 2017, 23–28 April 2017, Vienna, Austria.

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Abstract

Virgin forest ecosystems resemble the key reference level for natural tree growth dynamics. The mosaic cycle
concept describes such dynamics as local disequilibria driven by patch level succession cycles of breakdown,
regeneration, juvenescence and old growth. These cycles, however, may involve different traits of light demanding
and shade tolerant species assemblies.

In this work a data model fusion concept will be introduced to assess the differences in growth dynamics
of the mosaic cycle of the Western Congolian Lowland Rainforest ecosystem. Field data from 34 forest patches
located in an ice age forest refuge, recently pinpointed to the ground and still devoid of direct human impact
up to today - resemble the data base. A 3D error assessment procedure versus BGC model simulations for the
34 patches revealed two different growth dynamics, consistent with observed growth traits of pioneer and late
succession species assemblies of the Western Congolian Lowland rainforest.

An application of the same procedure to Central American Pacific rainforests confirms the strength of the
3D error field data model fusion concept to Central American Pacific rainforests confirms the strength of the
3D error field data model fusion concept to assess different growth traits of the mosaic cycle of natural forest
dynamics.

Item Type: Conference or Workshop Item (Poster)
Research Programs: Ecosystems Services and Management (ESM)
Depositing User: Luke Kirwan
Date Deposited: 08 May 2017 11:48
Last Modified: 08 May 2017 11:48
URI: http://pure.iiasa.ac.at/14575

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