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Completely updated and expanded new edition of this widely cited book, Modelling Forest Growth and Yield, 2nd Edition synthesizes current scientific literature, provides insights in how models are constructed, gives suggestions for future developments, and outlines keys for successful implementation of models. The book describes current modeling approaches for predicting forest growth and yield and explores the components that comprise the various modeling approaches. It provides the reader with the tools for evaluating and calibrating growth and yield models and outlines the steps necessary…mehr
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Completely updated and expanded new edition of this widely cited book, Modelling Forest Growth and Yield, 2nd Edition synthesizes current scientific literature, provides insights in how models are constructed, gives suggestions for future developments, and outlines keys for successful implementation of models. The book describes current modeling approaches for predicting forest growth and yield and explores the components that comprise the various modeling approaches. It provides the reader with the tools for evaluating and calibrating growth and yield models and outlines the steps necessary for developing a forest growth and yield model.
Forest Growth and Yield Modeling synthesizes current scientific literature and provides insights in how models are constructed. Giving suggestions for future developments, and outlining keys for successful implementation of models the book provides a thorough and up-to-date, single source reference for students, researchers and practitioners requiring a current digest of research and methods in the field. The book describes current modelling approaches for predicting forest growth and yield and explores the components that comprise the various modelling approaches. It provides the reader with the tools for evaluating and calibrating growth and yield models and outlines the steps necessary for developing a forest growth and yield model. * Single source reference providing an evaluation and synthesis of current scientific literature * Detailed descriptions of example models * Covers statistical techniques used in forest model construction * Accessible, reader-friendly style
Forest Growth and Yield Modeling synthesizes current scientific literature and provides insights in how models are constructed. Giving suggestions for future developments, and outlining keys for successful implementation of models the book provides a thorough and up-to-date, single source reference for students, researchers and practitioners requiring a current digest of research and methods in the field. The book describes current modelling approaches for predicting forest growth and yield and explores the components that comprise the various modelling approaches. It provides the reader with the tools for evaluating and calibrating growth and yield models and outlines the steps necessary for developing a forest growth and yield model. * Single source reference providing an evaluation and synthesis of current scientific literature * Detailed descriptions of example models * Covers statistical techniques used in forest model construction * Accessible, reader-friendly style
Produktdetails
- Produktdetails
- Verlag: John Wiley & Sons / Wiley
- Seitenzahl: 432
- Erscheinungstermin: 22. August 2011
- Englisch
- Abmessung: 250mm x 175mm x 28mm
- Gewicht: 919g
- ISBN-13: 9780470665008
- ISBN-10: 0470665009
- Artikelnr.: 33684892
- Verlag: John Wiley & Sons / Wiley
- Seitenzahl: 432
- Erscheinungstermin: 22. August 2011
- Englisch
- Abmessung: 250mm x 175mm x 28mm
- Gewicht: 919g
- ISBN-13: 9780470665008
- ISBN-10: 0470665009
- Artikelnr.: 33684892
Jerry Vanclay, Professor for Sustainable Forestry and Head, School of Environmental Science and Management, Southern Cross University, Australia Aaron Weiskittel, Assistant Professor of Forest Biometrics and Modelling, School of Forest Resources, University of Maine, Orono, USA John A. Kershaw, Jr., Professor of Forest Mensuration/Biometrics, Faculty of Forestry and Environmental Management, University of New Brunswick, Fredericton, Canada
Preface. Acknowledgements. 1 Introduction. 1.1 Model development and
validation. 1.2 Important uses. 1.3 Overview of the book. 2 Indices of
competition. 2.1 Introduction. 2.2 Two-sided competition. 2.2.1
Distance-independent. 2.2.2 Distance-dependent. 2.3 One-sided competition.
2.3.1 Distance-independent. 2.3.2 Distance-dependent. 2.4 Limitations.
2.4.1 Low predictive power. 2.4.2 Distance-independent vs.
distance-dependent. 2.4.3 Influence of sampling design. 2.5 Summary. 3
Forest site evaluation. 3.1 Introduction. 3.2 Phytocentric measures of site
quality. 3.2.1 Site index. 3.2.2 Plant indicators. 3.2.3 Other phytocentric
measures. 3.3 Geocentric measures of site productivity. 3.3.1 Physiographic
measures. 3.3.2 Climatic measures. 3.3.3 Soil measures. 3.4 Summary. 4
Whole-stand and size-class models. 4.1 Introduction. 4.2 Whole-stand
models. 4.2.1 Yield tables and equations. 4.2.2 Compatible growth and yield
equations. 4.2.3 Systems of equations. 4.2.4 State-space models. 4.2.5
Transition matrix models. 4.3 Size-class models. 4.3.1 Stand table
projection. 4.3.2 Matrix models. 4.3.3 Diameter-class models. 4.3.4 Cohort
models. 4.4 Summary. 5 Tree-level models. 5.1 Introduction. 5.2 Single-tree
distance-dependent models. 5.2.1 Example models. 5.3 Tree-list
distance-independent models. 5.3.1 Example models. 5.4 Summary. 6
Components of tree-list models. 6.1 Introduction. 6.2 Diameter increment.
6.2.1 Potential diameter increment equations with multiplicative modifiers.
6.2.2 Realized diameter increment equations. 6.3 Height increment. 6.3.1
Potential height increment equations with multiplicative modifiers. 6.3.2
Realized height increment equations. 6.4 Crown recession. 6.4.1
Individual-tree crown recession models. 6.4.2 Branch-level crown recession
models. 6.5 Summary. 7 Individual-tree static equations. 7.1 Introduction.
7.2 Total height. 7.3 Crown length. 7.4 Crown width and profile. 7.5 Stem
volume and taper. 7.6 Biomass. 7.7 Use of static equations to predict
missing values. 7.8 Summary. 8 Mortality. 8.1 Introduction. 8.2 Stand-level
mortality. 8.3 Individual-tree-level mortality. 8.4 Mechanistic models of
mortality. 8.5 Development and application of mortality equations. 8.6
Summary. 9 Seeding, regeneration, and recruitment. 9.1 Introduction. 9.2
Seeding. 9.2.1 Flowering and pollination. 9.2.2 Seed production. 9.2.3 Seed
dispersal. 9.2.4 Seed germination. 9.3 Regeneration. 9.4 Recruitment. 9.4.1
Static. 9.4.2 Dynamic. 9.5 Summary. 10 Linking growth models of different
resolutions. 10.1 Introduction. 10.2 Linked stand- and size-class models.
10.2.1 Parameter recovery. 10.2.2 Modified stand table projection. 10.3
Linked stand- and tree-models. 10.3.1 Disaggregation. 10.3.2 Constrained.
10.3.3 Combined. 10.4 Summary. 11 Modeling silvicultural treatments. 11.1
Introduction. 11.2 Genetic improvements. 11.2.1 Stand-level. 11.2.2
Tree-level. 11.3 Early stand treatments. 11.3.1 Stand-level. 11.3.2
Tree-level. 11.4 Thinning. 11.4.1 Stand-level. 11.4.2 Tree-level. 11.5
Fertilization. 11.5.1 Stand-level. 11.5.2 Tree-level. 11.6 Combined
thinning and fertilization. 11.6.1 Stand-level. 11.6.2 Tree-level. 11.7
Harvesting. 11.7.1 Stand-level. 11.7.2 Tree-level. 11.8 Summary. 12
Process-based models. 12.1 Introduction. 12.2 Key physiological processes.
12.2.1 Light interception. 12.2.2 Photosynthesis. 12.2.3 Stomatal
conductance. 12.2.4 Respiration. 12.2.5 Carbon allocation. 12.2.6 Soil
water and nutrients. 12.3 Example models. 12.3.1 Forest-BGC. 12.3.2 CenW.
12.3.3 BALANCE. 12.4 Limitations. 12.4.1 Initialization. 12.4.2
Parameterization. 12.4.3 Scale. 12.4.4 Sensitivity. 12.5 Summary. 13 Hybrid
models of forest growth and yield. 13.1 Introduction. 13.2 Types of hybrid
models. 13.2.1 Statistical growth equations with physiologically derived
covariate. 13.2.2 Statistical growth equations with physiologically derived
external modifier. 13.2.3 Allometric models. 13.3 Comparison to statistical
models. 13.4 Summary. 14 Model construction. 14.1 Introduction. 14.2 Data
requirements. 14.2.1 Stem analysis. 14.2.2 Temporary plots. 14.2.3
Permanent plots. 14.3 Model form. 14.4 Parameter estimation. 14.4.1
Regression. 14.4.2 Quantile regression. 14.4.3 Generalized linear
regression models. 14.4.4 Mixed models. 14.4.5 Generalized algebraic
difference approach. 14.4.6 System of equations. 14.4.7 Bayesian. 14.4.8
Nonparametric. 14.4.9 Annualization. 14.5 Summary. 15 Model evaluation and
calibration. 15.1 Introduction. 15.2 Model criticism. 15.2.1 Model form and
parameterization. 15.2.2 Variable selection and model simplicity. 15.2.3
Biological realism. 15.2.4 Compatibility. 15.2.5 Reliability. 15.2.6
Adaptability. 15.3 Model benchmarking. 15.3.1 Statistical tests. 15.3.2
Model error characterization. 15.4 Model calibration. 15.5 Summary. 16
Implementation and use. 16.1 Introduction. 16.2 Collection of appropriate
data. 16.3 Generation of appropriate data. 16.4 Temporal scale. 16.5
Spatial scale. 16.6 Computer interface. 16.7 Visualization. 16.8 Output.
16.9 Summary. 17 Future directions. 17.1 Improving predictions. 17.2
Improving input data. 17.3 Improving software. 17.4 Summary. Bibliography.
Appendix 1: List of species used in the text. Appendix 2: Expanded outline
for ORGANON growth and yield model. Index.
validation. 1.2 Important uses. 1.3 Overview of the book. 2 Indices of
competition. 2.1 Introduction. 2.2 Two-sided competition. 2.2.1
Distance-independent. 2.2.2 Distance-dependent. 2.3 One-sided competition.
2.3.1 Distance-independent. 2.3.2 Distance-dependent. 2.4 Limitations.
2.4.1 Low predictive power. 2.4.2 Distance-independent vs.
distance-dependent. 2.4.3 Influence of sampling design. 2.5 Summary. 3
Forest site evaluation. 3.1 Introduction. 3.2 Phytocentric measures of site
quality. 3.2.1 Site index. 3.2.2 Plant indicators. 3.2.3 Other phytocentric
measures. 3.3 Geocentric measures of site productivity. 3.3.1 Physiographic
measures. 3.3.2 Climatic measures. 3.3.3 Soil measures. 3.4 Summary. 4
Whole-stand and size-class models. 4.1 Introduction. 4.2 Whole-stand
models. 4.2.1 Yield tables and equations. 4.2.2 Compatible growth and yield
equations. 4.2.3 Systems of equations. 4.2.4 State-space models. 4.2.5
Transition matrix models. 4.3 Size-class models. 4.3.1 Stand table
projection. 4.3.2 Matrix models. 4.3.3 Diameter-class models. 4.3.4 Cohort
models. 4.4 Summary. 5 Tree-level models. 5.1 Introduction. 5.2 Single-tree
distance-dependent models. 5.2.1 Example models. 5.3 Tree-list
distance-independent models. 5.3.1 Example models. 5.4 Summary. 6
Components of tree-list models. 6.1 Introduction. 6.2 Diameter increment.
6.2.1 Potential diameter increment equations with multiplicative modifiers.
6.2.2 Realized diameter increment equations. 6.3 Height increment. 6.3.1
Potential height increment equations with multiplicative modifiers. 6.3.2
Realized height increment equations. 6.4 Crown recession. 6.4.1
Individual-tree crown recession models. 6.4.2 Branch-level crown recession
models. 6.5 Summary. 7 Individual-tree static equations. 7.1 Introduction.
7.2 Total height. 7.3 Crown length. 7.4 Crown width and profile. 7.5 Stem
volume and taper. 7.6 Biomass. 7.7 Use of static equations to predict
missing values. 7.8 Summary. 8 Mortality. 8.1 Introduction. 8.2 Stand-level
mortality. 8.3 Individual-tree-level mortality. 8.4 Mechanistic models of
mortality. 8.5 Development and application of mortality equations. 8.6
Summary. 9 Seeding, regeneration, and recruitment. 9.1 Introduction. 9.2
Seeding. 9.2.1 Flowering and pollination. 9.2.2 Seed production. 9.2.3 Seed
dispersal. 9.2.4 Seed germination. 9.3 Regeneration. 9.4 Recruitment. 9.4.1
Static. 9.4.2 Dynamic. 9.5 Summary. 10 Linking growth models of different
resolutions. 10.1 Introduction. 10.2 Linked stand- and size-class models.
10.2.1 Parameter recovery. 10.2.2 Modified stand table projection. 10.3
Linked stand- and tree-models. 10.3.1 Disaggregation. 10.3.2 Constrained.
10.3.3 Combined. 10.4 Summary. 11 Modeling silvicultural treatments. 11.1
Introduction. 11.2 Genetic improvements. 11.2.1 Stand-level. 11.2.2
Tree-level. 11.3 Early stand treatments. 11.3.1 Stand-level. 11.3.2
Tree-level. 11.4 Thinning. 11.4.1 Stand-level. 11.4.2 Tree-level. 11.5
Fertilization. 11.5.1 Stand-level. 11.5.2 Tree-level. 11.6 Combined
thinning and fertilization. 11.6.1 Stand-level. 11.6.2 Tree-level. 11.7
Harvesting. 11.7.1 Stand-level. 11.7.2 Tree-level. 11.8 Summary. 12
Process-based models. 12.1 Introduction. 12.2 Key physiological processes.
12.2.1 Light interception. 12.2.2 Photosynthesis. 12.2.3 Stomatal
conductance. 12.2.4 Respiration. 12.2.5 Carbon allocation. 12.2.6 Soil
water and nutrients. 12.3 Example models. 12.3.1 Forest-BGC. 12.3.2 CenW.
12.3.3 BALANCE. 12.4 Limitations. 12.4.1 Initialization. 12.4.2
Parameterization. 12.4.3 Scale. 12.4.4 Sensitivity. 12.5 Summary. 13 Hybrid
models of forest growth and yield. 13.1 Introduction. 13.2 Types of hybrid
models. 13.2.1 Statistical growth equations with physiologically derived
covariate. 13.2.2 Statistical growth equations with physiologically derived
external modifier. 13.2.3 Allometric models. 13.3 Comparison to statistical
models. 13.4 Summary. 14 Model construction. 14.1 Introduction. 14.2 Data
requirements. 14.2.1 Stem analysis. 14.2.2 Temporary plots. 14.2.3
Permanent plots. 14.3 Model form. 14.4 Parameter estimation. 14.4.1
Regression. 14.4.2 Quantile regression. 14.4.3 Generalized linear
regression models. 14.4.4 Mixed models. 14.4.5 Generalized algebraic
difference approach. 14.4.6 System of equations. 14.4.7 Bayesian. 14.4.8
Nonparametric. 14.4.9 Annualization. 14.5 Summary. 15 Model evaluation and
calibration. 15.1 Introduction. 15.2 Model criticism. 15.2.1 Model form and
parameterization. 15.2.2 Variable selection and model simplicity. 15.2.3
Biological realism. 15.2.4 Compatibility. 15.2.5 Reliability. 15.2.6
Adaptability. 15.3 Model benchmarking. 15.3.1 Statistical tests. 15.3.2
Model error characterization. 15.4 Model calibration. 15.5 Summary. 16
Implementation and use. 16.1 Introduction. 16.2 Collection of appropriate
data. 16.3 Generation of appropriate data. 16.4 Temporal scale. 16.5
Spatial scale. 16.6 Computer interface. 16.7 Visualization. 16.8 Output.
16.9 Summary. 17 Future directions. 17.1 Improving predictions. 17.2
Improving input data. 17.3 Improving software. 17.4 Summary. Bibliography.
Appendix 1: List of species used in the text. Appendix 2: Expanded outline
for ORGANON growth and yield model. Index.
Preface. Acknowledgements. 1 Introduction. 1.1 Model development and
validation. 1.2 Important uses. 1.3 Overview of the book. 2 Indices of
competition. 2.1 Introduction. 2.2 Two-sided competition. 2.2.1
Distance-independent. 2.2.2 Distance-dependent. 2.3 One-sided competition.
2.3.1 Distance-independent. 2.3.2 Distance-dependent. 2.4 Limitations.
2.4.1 Low predictive power. 2.4.2 Distance-independent vs.
distance-dependent. 2.4.3 Influence of sampling design. 2.5 Summary. 3
Forest site evaluation. 3.1 Introduction. 3.2 Phytocentric measures of site
quality. 3.2.1 Site index. 3.2.2 Plant indicators. 3.2.3 Other phytocentric
measures. 3.3 Geocentric measures of site productivity. 3.3.1 Physiographic
measures. 3.3.2 Climatic measures. 3.3.3 Soil measures. 3.4 Summary. 4
Whole-stand and size-class models. 4.1 Introduction. 4.2 Whole-stand
models. 4.2.1 Yield tables and equations. 4.2.2 Compatible growth and yield
equations. 4.2.3 Systems of equations. 4.2.4 State-space models. 4.2.5
Transition matrix models. 4.3 Size-class models. 4.3.1 Stand table
projection. 4.3.2 Matrix models. 4.3.3 Diameter-class models. 4.3.4 Cohort
models. 4.4 Summary. 5 Tree-level models. 5.1 Introduction. 5.2 Single-tree
distance-dependent models. 5.2.1 Example models. 5.3 Tree-list
distance-independent models. 5.3.1 Example models. 5.4 Summary. 6
Components of tree-list models. 6.1 Introduction. 6.2 Diameter increment.
6.2.1 Potential diameter increment equations with multiplicative modifiers.
6.2.2 Realized diameter increment equations. 6.3 Height increment. 6.3.1
Potential height increment equations with multiplicative modifiers. 6.3.2
Realized height increment equations. 6.4 Crown recession. 6.4.1
Individual-tree crown recession models. 6.4.2 Branch-level crown recession
models. 6.5 Summary. 7 Individual-tree static equations. 7.1 Introduction.
7.2 Total height. 7.3 Crown length. 7.4 Crown width and profile. 7.5 Stem
volume and taper. 7.6 Biomass. 7.7 Use of static equations to predict
missing values. 7.8 Summary. 8 Mortality. 8.1 Introduction. 8.2 Stand-level
mortality. 8.3 Individual-tree-level mortality. 8.4 Mechanistic models of
mortality. 8.5 Development and application of mortality equations. 8.6
Summary. 9 Seeding, regeneration, and recruitment. 9.1 Introduction. 9.2
Seeding. 9.2.1 Flowering and pollination. 9.2.2 Seed production. 9.2.3 Seed
dispersal. 9.2.4 Seed germination. 9.3 Regeneration. 9.4 Recruitment. 9.4.1
Static. 9.4.2 Dynamic. 9.5 Summary. 10 Linking growth models of different
resolutions. 10.1 Introduction. 10.2 Linked stand- and size-class models.
10.2.1 Parameter recovery. 10.2.2 Modified stand table projection. 10.3
Linked stand- and tree-models. 10.3.1 Disaggregation. 10.3.2 Constrained.
10.3.3 Combined. 10.4 Summary. 11 Modeling silvicultural treatments. 11.1
Introduction. 11.2 Genetic improvements. 11.2.1 Stand-level. 11.2.2
Tree-level. 11.3 Early stand treatments. 11.3.1 Stand-level. 11.3.2
Tree-level. 11.4 Thinning. 11.4.1 Stand-level. 11.4.2 Tree-level. 11.5
Fertilization. 11.5.1 Stand-level. 11.5.2 Tree-level. 11.6 Combined
thinning and fertilization. 11.6.1 Stand-level. 11.6.2 Tree-level. 11.7
Harvesting. 11.7.1 Stand-level. 11.7.2 Tree-level. 11.8 Summary. 12
Process-based models. 12.1 Introduction. 12.2 Key physiological processes.
12.2.1 Light interception. 12.2.2 Photosynthesis. 12.2.3 Stomatal
conductance. 12.2.4 Respiration. 12.2.5 Carbon allocation. 12.2.6 Soil
water and nutrients. 12.3 Example models. 12.3.1 Forest-BGC. 12.3.2 CenW.
12.3.3 BALANCE. 12.4 Limitations. 12.4.1 Initialization. 12.4.2
Parameterization. 12.4.3 Scale. 12.4.4 Sensitivity. 12.5 Summary. 13 Hybrid
models of forest growth and yield. 13.1 Introduction. 13.2 Types of hybrid
models. 13.2.1 Statistical growth equations with physiologically derived
covariate. 13.2.2 Statistical growth equations with physiologically derived
external modifier. 13.2.3 Allometric models. 13.3 Comparison to statistical
models. 13.4 Summary. 14 Model construction. 14.1 Introduction. 14.2 Data
requirements. 14.2.1 Stem analysis. 14.2.2 Temporary plots. 14.2.3
Permanent plots. 14.3 Model form. 14.4 Parameter estimation. 14.4.1
Regression. 14.4.2 Quantile regression. 14.4.3 Generalized linear
regression models. 14.4.4 Mixed models. 14.4.5 Generalized algebraic
difference approach. 14.4.6 System of equations. 14.4.7 Bayesian. 14.4.8
Nonparametric. 14.4.9 Annualization. 14.5 Summary. 15 Model evaluation and
calibration. 15.1 Introduction. 15.2 Model criticism. 15.2.1 Model form and
parameterization. 15.2.2 Variable selection and model simplicity. 15.2.3
Biological realism. 15.2.4 Compatibility. 15.2.5 Reliability. 15.2.6
Adaptability. 15.3 Model benchmarking. 15.3.1 Statistical tests. 15.3.2
Model error characterization. 15.4 Model calibration. 15.5 Summary. 16
Implementation and use. 16.1 Introduction. 16.2 Collection of appropriate
data. 16.3 Generation of appropriate data. 16.4 Temporal scale. 16.5
Spatial scale. 16.6 Computer interface. 16.7 Visualization. 16.8 Output.
16.9 Summary. 17 Future directions. 17.1 Improving predictions. 17.2
Improving input data. 17.3 Improving software. 17.4 Summary. Bibliography.
Appendix 1: List of species used in the text. Appendix 2: Expanded outline
for ORGANON growth and yield model. Index.
validation. 1.2 Important uses. 1.3 Overview of the book. 2 Indices of
competition. 2.1 Introduction. 2.2 Two-sided competition. 2.2.1
Distance-independent. 2.2.2 Distance-dependent. 2.3 One-sided competition.
2.3.1 Distance-independent. 2.3.2 Distance-dependent. 2.4 Limitations.
2.4.1 Low predictive power. 2.4.2 Distance-independent vs.
distance-dependent. 2.4.3 Influence of sampling design. 2.5 Summary. 3
Forest site evaluation. 3.1 Introduction. 3.2 Phytocentric measures of site
quality. 3.2.1 Site index. 3.2.2 Plant indicators. 3.2.3 Other phytocentric
measures. 3.3 Geocentric measures of site productivity. 3.3.1 Physiographic
measures. 3.3.2 Climatic measures. 3.3.3 Soil measures. 3.4 Summary. 4
Whole-stand and size-class models. 4.1 Introduction. 4.2 Whole-stand
models. 4.2.1 Yield tables and equations. 4.2.2 Compatible growth and yield
equations. 4.2.3 Systems of equations. 4.2.4 State-space models. 4.2.5
Transition matrix models. 4.3 Size-class models. 4.3.1 Stand table
projection. 4.3.2 Matrix models. 4.3.3 Diameter-class models. 4.3.4 Cohort
models. 4.4 Summary. 5 Tree-level models. 5.1 Introduction. 5.2 Single-tree
distance-dependent models. 5.2.1 Example models. 5.3 Tree-list
distance-independent models. 5.3.1 Example models. 5.4 Summary. 6
Components of tree-list models. 6.1 Introduction. 6.2 Diameter increment.
6.2.1 Potential diameter increment equations with multiplicative modifiers.
6.2.2 Realized diameter increment equations. 6.3 Height increment. 6.3.1
Potential height increment equations with multiplicative modifiers. 6.3.2
Realized height increment equations. 6.4 Crown recession. 6.4.1
Individual-tree crown recession models. 6.4.2 Branch-level crown recession
models. 6.5 Summary. 7 Individual-tree static equations. 7.1 Introduction.
7.2 Total height. 7.3 Crown length. 7.4 Crown width and profile. 7.5 Stem
volume and taper. 7.6 Biomass. 7.7 Use of static equations to predict
missing values. 7.8 Summary. 8 Mortality. 8.1 Introduction. 8.2 Stand-level
mortality. 8.3 Individual-tree-level mortality. 8.4 Mechanistic models of
mortality. 8.5 Development and application of mortality equations. 8.6
Summary. 9 Seeding, regeneration, and recruitment. 9.1 Introduction. 9.2
Seeding. 9.2.1 Flowering and pollination. 9.2.2 Seed production. 9.2.3 Seed
dispersal. 9.2.4 Seed germination. 9.3 Regeneration. 9.4 Recruitment. 9.4.1
Static. 9.4.2 Dynamic. 9.5 Summary. 10 Linking growth models of different
resolutions. 10.1 Introduction. 10.2 Linked stand- and size-class models.
10.2.1 Parameter recovery. 10.2.2 Modified stand table projection. 10.3
Linked stand- and tree-models. 10.3.1 Disaggregation. 10.3.2 Constrained.
10.3.3 Combined. 10.4 Summary. 11 Modeling silvicultural treatments. 11.1
Introduction. 11.2 Genetic improvements. 11.2.1 Stand-level. 11.2.2
Tree-level. 11.3 Early stand treatments. 11.3.1 Stand-level. 11.3.2
Tree-level. 11.4 Thinning. 11.4.1 Stand-level. 11.4.2 Tree-level. 11.5
Fertilization. 11.5.1 Stand-level. 11.5.2 Tree-level. 11.6 Combined
thinning and fertilization. 11.6.1 Stand-level. 11.6.2 Tree-level. 11.7
Harvesting. 11.7.1 Stand-level. 11.7.2 Tree-level. 11.8 Summary. 12
Process-based models. 12.1 Introduction. 12.2 Key physiological processes.
12.2.1 Light interception. 12.2.2 Photosynthesis. 12.2.3 Stomatal
conductance. 12.2.4 Respiration. 12.2.5 Carbon allocation. 12.2.6 Soil
water and nutrients. 12.3 Example models. 12.3.1 Forest-BGC. 12.3.2 CenW.
12.3.3 BALANCE. 12.4 Limitations. 12.4.1 Initialization. 12.4.2
Parameterization. 12.4.3 Scale. 12.4.4 Sensitivity. 12.5 Summary. 13 Hybrid
models of forest growth and yield. 13.1 Introduction. 13.2 Types of hybrid
models. 13.2.1 Statistical growth equations with physiologically derived
covariate. 13.2.2 Statistical growth equations with physiologically derived
external modifier. 13.2.3 Allometric models. 13.3 Comparison to statistical
models. 13.4 Summary. 14 Model construction. 14.1 Introduction. 14.2 Data
requirements. 14.2.1 Stem analysis. 14.2.2 Temporary plots. 14.2.3
Permanent plots. 14.3 Model form. 14.4 Parameter estimation. 14.4.1
Regression. 14.4.2 Quantile regression. 14.4.3 Generalized linear
regression models. 14.4.4 Mixed models. 14.4.5 Generalized algebraic
difference approach. 14.4.6 System of equations. 14.4.7 Bayesian. 14.4.8
Nonparametric. 14.4.9 Annualization. 14.5 Summary. 15 Model evaluation and
calibration. 15.1 Introduction. 15.2 Model criticism. 15.2.1 Model form and
parameterization. 15.2.2 Variable selection and model simplicity. 15.2.3
Biological realism. 15.2.4 Compatibility. 15.2.5 Reliability. 15.2.6
Adaptability. 15.3 Model benchmarking. 15.3.1 Statistical tests. 15.3.2
Model error characterization. 15.4 Model calibration. 15.5 Summary. 16
Implementation and use. 16.1 Introduction. 16.2 Collection of appropriate
data. 16.3 Generation of appropriate data. 16.4 Temporal scale. 16.5
Spatial scale. 16.6 Computer interface. 16.7 Visualization. 16.8 Output.
16.9 Summary. 17 Future directions. 17.1 Improving predictions. 17.2
Improving input data. 17.3 Improving software. 17.4 Summary. Bibliography.
Appendix 1: List of species used in the text. Appendix 2: Expanded outline
for ORGANON growth and yield model. Index.