Future Developments

The impetus ofthe need to predict future forest condition in the face of change that has driven the development of model forestry models should be an even stronger force in the future. Concerns over the responses of forests to climate change have increased the world interest in predicting future forests. Our rather poor appreciation of the potential direct effects of CO2 in the atmosphere (effects involving the rates of photosynthesis and other plant functions) in addition to the concern over greenhouse-gas-generated climate change will continue to create a

Observed basal area

Observed basal area

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740 890 1190 1680

Elevation (m)

Simulated basal area

Simulated basal area

740 890 1190 1680

Elevation (m)

Figure 3 A comparison of observed and simulated basal area (m2ha~1) at four elevations (740, 890, 1190, and 1680 m) along the North Slope of Changbai Mountain (42.2° N, 128.0° E), Northeastern China using the gap model, FAREAST. In this simulation the basal area (m2 of the sum of stem cross-sections of living trees per ha of forest) measure on the mountain at different elevations is compared to the forest vegetation simulated by the FAREAST gap model for these elevations. Such a model test provides insight in the capability of the model to simulate forest composition expected for different climate conditions. Modified from Yan X and Shugart HH (2005) A forest gap model to simulate dynamics and patterns of Eastern Eurasian forests. Journal of Biogeography 32: 1641-1648.

740 890 1190 1680

Elevation (m)

Figure 3 A comparison of observed and simulated basal area (m2ha~1) at four elevations (740, 890, 1190, and 1680 m) along the North Slope of Changbai Mountain (42.2° N, 128.0° E), Northeastern China using the gap model, FAREAST. In this simulation the basal area (m2 of the sum of stem cross-sections of living trees per ha of forest) measure on the mountain at different elevations is compared to the forest vegetation simulated by the FAREAST gap model for these elevations. Such a model test provides insight in the capability of the model to simulate forest composition expected for different climate conditions. Modified from Yan X and Shugart HH (2005) A forest gap model to simulate dynamics and patterns of Eastern Eurasian forests. Journal of Biogeography 32: 1641-1648.

need to scale up basic plant physiology to longer time-scale and larger spatial-scale consequences. Today's research challenges involve the incorporation of these novel effects and the rich problem of designing ways to test our model predictions. An important application of gap models in this direction was developed by Moorcroft and several of his colleagues. They formulated a gap model for Amazonian rainforest using a postulated relationship between growth rate, photosynthesis, and wood density. This gap model was used to obtain the parameters for a statistical model of the size distribution of trees across the Amazon Basin (based on an extension of the Japanese approaches to forest dynamics modeling pioneered by Kohyama in 1993). This was then driven by a photosynthesis/production model to incorporate the effects of temperature and moisture. The resultant model called the ecosystem demography (ED) model represents a synthesis of the Japanese and individual-based approaches to forestry modeling with process models of the sort initially championed in the IBP.

See also: Boreal Forest; Forest Models; Individual-Based Models; Succession; Temperate Forest; Trace Elements; Tree Growth.

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