Forecasting Forest Futures

Modelling is an important tool for understanding the complexity of forest ecosystems and the variety of interactions of ecosystem components, processes and values. This book describes the hybrid approach to modelling forest ecosystems and their possible response to natural and management-induced disturbance. The book describes the FORECAST family of ecosystem management models at three different spatial scales (tree, stand and landscape), and compares them with alternative models at these three spatial scales. The book will help forest managers to understand what to expect from ecosystem-based forest models; serve as a tool for use in teaching about sustainability, scenario analysis and value trade-offs in natural resources management; and assist policy makers, managers and researches working in assessment of sustainable forest management and ecosystem management. Several real-life examples of using the FORECAST family of models in forest management and other applications are presented from countries including Canada, China, Spain and the USA, to illustrate the concepts described in the text. The book also demonstrates how these models can be extended for scenario and value trade-off analysis through visualization and educational or management games.

Hamish Kimmins is Professor Emeritus of Forest Ecology at the University of British Columbia, Canada. Juan A. Blanco is a Postdoctoral Fellow in the Department of Forest Sciences, University of British Columbia. Brad Seely is a Research Associate in the Department of Forest Sciences at UBC. Clive Welham is also a Research Associate in the Department of Forest Sciences at UBC. Kim Scoullar is a professional programmer with more than 35 years of experience developing code for ecosystem models.

Preface	p. ix
Acknowledgements	p. xi
List of abbreviations	p. xiii
Introduction: Why do we Need Ecosystem-Level Models as Decision-Support Tools in Forestry?	p. 1
Introduction	p. 1
Human population growth: the ultimate environmental threat to the world's forests	p. 2
The failure of science to provide the necessary analytical and forecasting tools for resource management: the question of complexity and prediction	p. 5
Complexity and prediction in forestry: levels of biological organization and integration	p. 9
The role of process-based, ecosystem-level hybrid simulation models as a component of the solution to problems posed by complexity in forestry	p. 11
Take-home message	p. 14
Additional material	p. 14
Notes	p. 14
Ecological and Environmental Concepts that should be Addressed in Forestry Decision-Support Tools	p. 17
Introduction	p. 17
The problem of uncritical use of, or failure to define, terms commonly used in environmental debates about forestry: the dangers posed by the anthropomorphizing of ecosystems	p. 18
Ecosystem stability	p. 22
Ecosystem resilience	p. 33
Ecosystem health	p. 35
Forest ecosystem integrity	p. 37
Are ecosystems 'complex adaptive systems'?	p. 39
Old growth	p. 40
Dangers of the misuse of terminology: feeding inappropriate belief systems	p. 42
Take-home message	p. 43
Additional material	p. 43
Hybrid Simulation in the Context of Other Classes of Forest Models, and the Development of the FORECAST Family of Hybrid Simulation Models	p. 45
Introduction	p. 45
A three-level classification of models	p. 45
Brief history of the development of these three categories	p. 48
The FORECAST-HORIZON family of hybrid simulation models	p. 52
Major categories of hybrid simulation models we have developed	p. 53
Take-home message	p. 57
Additional material	p. 57
Forestry in Transition: The Need for Individual Tree Models	p. 59
Introduction	p. 59
The ideal individual tree model	p. 60
Individual tree models as management tools	p. 65
FORCEE: a comprehensive, spatially explicit, individual tree management and agroforestry model	p. 69
Take-home message	p. 76
Additional material	p. 77
Stand-Level Hybrid Models as Tools to Support Ecosystem-Based Management	p. 79
Introduction	p. 79
Classification of stand-level hybrid models	p. 81
Description of the hybrid modelling approach employed in FORECAST	p. 82
Overview of FORECAST evaluation studies	p. 88
Applications of FORECAST	p. 101
Development of FORECAST Climate	p. 115
Take-home message	p. 117
Additional material	p. 118
Landscape-Level Models in Forest Management	p. 121
Introduction	p. 121
Development of landscape-level models for forest management	p. 122
The LLEMS model: a multi-value, local landscape extension of FORECAST for variable retention harvesting	p. 126
Example application of LLEMS to evaluate dispersed retention alternatives	p. 132
Ongoing development	p. 134
Take-home message	p. 135
Additional material	p. 135
Educational Models in Forest Management	p. 137
Introduction	p. 137
The use of ecological models as educational tools	p. 143
FORTOON: a high-school-level, introductory, multiple-value forest management game	p. 147
PFF: Possible Forest Futures	p. 151
Multiple run manager for FORECAST	p. 152
Take-home message	p. 153
Additional material	p. 154
How to Develop a Model for Forest Management	p. 155
Introduction	p. 155
Basic steps to develop a model of forest management	p. 162
Uncertainty and complexity in model development	p. 183
Take-home message	p. 191
Additional material	p. 191
Note	p. 191
The Role of Ecosystem Management Models in Adaptive Management, Certification and Land Reclamation	p. 193
Introduction	p. 193
The role of forest ecosystem management models in adaptive management	p. 194
The need for credible forecasts of possible forecasts of possible forest futures as an essential component of forest certification	p. 200
Ecosystem management models in reclamation planning	p. 207
Take-home message	p. 210
Additional material	p. 211
Note	p. 211
Future Perspectives in Hybrid Modelling	p. 213
Introduction	p. 213
Linking forest management models that work at different scales	p. 214
Using hybrid models and visualizations to communicate with the general public	p. 216
Take-home message	p. 235
Additional material	p. 236
Notes	p. 236
References	p. 237
Index	p. 273
Table of Contents provided by Ingram. All Rights Reserved.

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Forecasting Forest Futures

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