Individual-based Methods in Forest Ecology and Management - Arne Pommerening Pavel Grabarnik

Individual-based Methods in Forest Ecology and Management

; Pavel Grabarnik

Model-driven individual-based forest ecology and individual-based methods in forest management are of increasing importance in many parts of the world. For the first time this book integrates three main fields of forest ecology and management, i. Les mer
Vår pris
928,-

(Paperback) Fri frakt!
Leveringstid: Sendes innen 21 dager
På grunn av Brexit-tilpasninger og tiltak for å begrense covid-19 kan det dessverre oppstå forsinket levering.

Paperback
Legg i
Paperback
Legg i
Vår pris: 928,-

(Paperback) Fri frakt!
Leveringstid: Sendes innen 21 dager
På grunn av Brexit-tilpasninger og tiltak for å begrense covid-19 kan det dessverre oppstå forsinket levering.

Om boka

Model-driven individual-based forest ecology and individual-based methods in forest management are of increasing importance in many parts of the world. For the first time this book integrates three main fields of forest ecology and management, i.e. tree/plant interactions, biometry of plant growth and human behaviour in forests. Individual-based forest ecology and management is an interdisciplinary research field with a focus on how the individual behaviour of plants contributes to the formation of spatial patterns that evolve through time. Key to this research is a strict bottom-up approach where the shaping and characteristics of plant communities are mostly the result of interactions between plants and between plants and humans. This book unites important methods of individual-based forest ecology and management from point process statistics, individual-based modelling, plant growth science and behavioural statistics. For ease of access, better understanding and transparency the methods are accompanied by R code and worked examples.

Fakta

Innholdsfortegnelse

Foreword; Dan Binkley.- Preface.- Acknowledgements.- 1. Introduction.- 1.1. Individual-based forest ecology.- 1.2. Individual-based forest management.- 1.3. Fundamental importance of tree and forest structure.- 1.4. Sampling and quantitative forest description.- 1.5. Individual-based forest ecology and management.- 2. Theories and concepts in individual-based forest ecology.- 2.1. Forest ecology.- 2.1.1. Basic terms and definitions related to individual-based ecology.- 2.1.2. Theories related to individual-based ecology.- 2.2. Tree mechanics and interaction effects on stem growth.- 3. Theories and concepts in individual-based forest management.- 3.1. Introduction to forest management.- 3.2. Sustainability.- 3.3. Silvicultural regimes and types of forest management.- 3.4. Continuous cover forestry and individual-based forest management.- 3.5. Silvicultural planning.- 3.6. Thinning interventions.- 3.6.1. Thinning types.- 3.6.2. Thinning intensity.- 3.6.3. Thinning cycle.- 3.7. Regenerating forest stands and silvicultural systems.- 3.7.1. Uniform shelterwood system.- 3.7.2. Seed tree system.- 3.7.3. Strip shelterwood.- 3.7.4. Group shelterwood.- 3.7.5. Irregular shelterwood.- 3.7.6. Shelterwood combinations.- 3.7.7. Single-tree and group selection.- 3.7.8. Managing regeneration and juvenile trees.- 4. Spatial methods of tree interaction analysis.- 4.1. Spatial statistics for plant pattern analysis.- 4.2. Geostatistics.- 4.3. Random set statistics.- 4.4. Point process statistics.- 4.4.1. Point pattern components.- 4.4.2. Point pattern and marked point pattern types.- 4.4.3. Stationarity and isotropy.- 4.4.4. Test-location and point-related summary characteristics.- 4.4.5. Defining local neighbourhood.- 4.4.6. Principles for constructing marks and test functions.- 4.4.7. Summary characteristics.- 4.4.8. Edge effects.- 4.4.9. Hypothesis testing.- 5. Spatial and individual-based modelling.- 5.1. Modelling of (marked) point patterns.- 5.1.1. Parametric point process models.- 5.1.2. Non-parametric modelling methods.- 5.1.3. Reconstruction algorithm.- 5.1.4. Applications of reconstruction.- 5.2. Individual-based modelling.- 5.2.1. Interaction-kernel models.- 5.2.2. Kernel types and components.- 5.2.3. Species representation.- 5.2.4. Seed and offspring dispersal.- 5.2.5. Growth processes.- 5.2.6. Death processes.- 5.2.7. Parameter estimation.- 5.2.8. Sensitivity analysis.- 5.2.9. Model implementation.- 5.2.10. Example model.- 6. Principles of relative growth analysis.- 6.1. Importance of growth and growth metrics.- 6.2. Concept of relative growth.- 6.2.1. Definition of growth processes.- 6.2.2. Absolute growth rate.- 6.2.3. Relative growth rate.- 6.2.4. Multiple RGR and the concept of allometry.- 6.2.5. Functions of relative growth rate.- 6.2.6. Sampling and growth rate combinations.- 6.3. Size-dependent relative growth rates.- 6.4. Growth rates as marks in point process statistics.- 7.

Om forfatteren