Book proposal

Title: New Developments in Ecosystem Modeling

1. Models and modeling studies: Introduction and history

Part I. Basic skills in model development

2. Definition of a good modeling question and model conceptualization (components, interactions, mechanisms, boundary, and qualitative assessment of dynamic nature of a system).

3. Quantitative representation of relationships, programming (brief description of some programming languages such as MATLAB), and basic structure of a model (stocks, flows, feedback loops, input, and output).

4. Parameterization of models: Use of observed data to determine model parameters and fitting of observed data to mathematical functions.

5. Verification and validation of models: Use of observed data to test model performance and build confidence in model predictions.

6. Sensitivity and error analysis: Use of mathematical techniques to derive confidence intervals on model predictions and provide information about the sensitivity of models to driving variables and parameters.

7. Interpretation and use of model results for environmental change assessment.

8. Scaling of model results at different temporal and spatial scales: Extrapolation of point model results at regional and global scales and linkage to atmospheric circulation models.

Part II. Samples of commonly used models in ecosystem sciences

9. Process oriented plant growth models: Description of plant growth models that include the prediction of physiological plant processes such as photosynthesis, plant respiration, carbon allocation, and plant growth.

10. Global plant production models: Description of plant production sub-models used in large scale ecosystem models that operate at regional and global scales.

11. Process oriented biogeochemistry models: Description of process oriented biogeochemistry models that simulate carbon and nutrient cycling and trace gas fluxes (N₂, N₂O, NO_x, CH₄).

12. Global biogeochemistry models: Description of simplified biogeochemistry models typically used in large scale ecosystem models.

13. Biophysical models of energy and water flux: Description of models that simulate energy and water fluxes in the plant canopy and soil. These models include the effects of atmospheric variables and CO₂ levels on energy and water fluxes.

14. Dynamic global vegetation models: Functional group models that predict changes in plant composition at the global scale as a function of environmental variables, soil texture and land use management.

15. Dynamic vegetation models: FORET style models that predict plant competition at the plot level as a function of environmental variables soil texture and land management.

Part III. New developments in ecosystem modeling

16. New developments and challenges in ecosystem modeling: Discussion of the problems associated with using ecosystem models to predict future environmental changes, new mathematical techniques for developing and testing models, and the future direction for the science of ecosystem modeling.

17. Data-model assimilation in community ecology using the Bayesian Approach: Use of the Bayesian Statistics to parameterize and test community ecology models.

18. Inverse analysis of ecological observation at regional scales: Use of observed patterns of ecological variables to constrain and test ecological models,

19. What role do experiments have in models? Discussion of the use of field and laboratory experimental data to develop and test ecological models.

20. Model comparisons: Use of new statistical (Akaike information criteria-AIC) for ranking model performance and standard approaches for comparing results from different models.