1. Introduction.- 2. Tree Form and Stem Taper.- 3. Tree-stem Volume Equations.- 4. Tree Weight and Biomass Estimation.- 5. Quantifying Tree Crowns.- 6. Growth Functions.- 7. Evaluating Site Quality.- 8. Quantifying Stand Density.- 9. Indices of Individual-tree Competition.- 10. Modeling Forest Stand Development.- 11. Whole-stand Models for Even-aged Stands.- 12. Diameter-distribution Models for Even-aged Stands.- 13. Size-class Models for Even-aged Stands.- 14. Individual-tree Models for Even-aged Stands.- 15. Growth and Yield Models for Uneven-aged Stands.- 16. Modeling Response to Silvicultural Treatments.- 17. Modeling Wood Characteristics.- 18. Model Implementation and Evaluation.-

Modeling Forest Trees and Stands

Biodiversity experiments have shown that species loss reduces ecosystem functioning in grassland. To test whether this result can be extrapolated to forests, the main contributors to terrestrial primary productivity, requires large-scale experiments. We manipulated tree species richness by planting more than 150,000 trees in plots with 1 to 16 species. Simulating multiple extinction scenarios, we found that richness strongly increased stand-level productivity. After 8 years, 16-species mixtures had accumulated over twice the amount of carbon found in average monocultures and similar amounts as those of two commercial monocultures. Species richness effects were strongly associated with functional and phylogenetic diversity. A shrub addition treatment reduced tree productivity, but this reduction was smaller at high shrub species richness. Our results encourage multispecies afforestation strategies to restore biodiversity and mitigate climate change.

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Impacts of species richness on productivity in a large-scale subtropical forest experiment.

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.

Forest growth and yield modeling

Summary Plant interactions can be defi ned as the ways plants act upon the growth, fi tness, survival and reproduction of other plants, largely by modifying their environment. These interactions can be positive (facilitation) or negative (competition or exploitation). During plantation establishment or natural forest regeneration after a disturbance, high light levels and, sometimes, increased availability of water and nutrients favour the development of opportunistic, fast-growing herbaceous and woody species which capture resources at the expense of crop trees. As a consequence, the growth and survival of crop trees can be dramatically reduced. Although the effects of this competition are well documented, the physical and physiological mechanisms of competition are not. Moreover, the competition process is never constant in time or space. We present a conceptual competition model based on plant growth forms common in global forests, i.e. graminoids, forbs, small shrubs, large shrubs and mid-storey trees, and main-storey trees. Their competitive attributes and successional dynamics are examined. An overview is presented on the way forest vegetation management (FVM) treatments infl uence these components and outcomes regarding crop tree performance and diversity conservation. Finally, a synthesis of literature yields FVM guidelines for effi ciently optimizing crop tree performance and safeguarding diversity. Future research needs to further sustainable FVM are presented.

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Designing forest vegetation management strategies based on the mechanisms and dynamics of crop tree competition by neighbouring vegetation

1 Plantation Forests.- 2 Biology of Plantation Growth.- 3 Growth Rates and Wood Quality.- 4 Choosing the Species and Site.- 5 Establishment.- 6 Nutrient Management.-7 Stand Density and Initial Spacing.- 8 Thinning.- 9 Pruning.- 10 Pests.- 11 Diseases.- 12 Tree Breeding.- 13 Mixed-species Plantations.- 14 Conclusion.- References.

Growing Plantation Forests

The management of competing vegetation has evolved with forest management over the past half century and is now an integral part of modern forestry practice in many parts of the world. Vegetation management, primarily using herbicides, has proven especially important in the establishment of high-yield forest plantations. There has been a substantial amount of research quantifying the wood yield gains from the management of competing vegetation over the past few decades. We reviewed results from 60 of the longest-term studies in North America (Canada and US), South Africa, South America (Brazil) and New Zealand/Australia. About three-quarters of the studies reported 30-500 per cent increases in wood volume from the most effective vegetation treatments. In North America, where the longest-term studies for a variety of tree species were between 10 and 35 years old (or from 20-100 per cent of rotation age), gains in wood volume ranged from 4-11 800 per cent in Pacific north-western forests, 14-5840 per cent in the south-eastern forests, and 49-5478 per cent in northern forests. In South Africa and South America (Brazil), several full-rotation (6-8 years) studies with eucalyptus indicate 29-122 per cent and 10-179 per cent increases in wood volume yield, respectively, from effective vegetation management. In New Zealand, time gains of 1 to 4 years from early vegetation control in radiata pine plantations translated into 7-27 per cent increases in wood volume yield over a 25- to 30-year rotation.

Ken Mcnabb

Papers

The role of vegetation management for enhancing productivity of the world's forests