Individual-based Methods in Forest Ecology and Management

 
 
Springer (Verlag)
  • erscheint ca. am 11. Oktober 2020
 
  • Buch
  • |
  • Softcover
  • |
  • XV, 411 Seiten
978-3-030-24530-6 (ISBN)
 
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.
1st ed. 2019
  • Englisch
  • Cham
  • |
  • Schweiz
Springer International Publishing
126 s/w Abbildungen, 60 farbige Abbildungen
  • Höhe: 23.5 cm
  • |
  • Breite: 15.5 cm
978-3-030-24530-6 (9783030245306)
10.1007/978-3-030-24528-3
weitere Ausgaben werden ermittelt

Arne Pommerening works as Professor in Mathematical Statistics Applied to Forest Sciences at the Swedish University of Agricultural Sciences (SLU) in Umeå. He is a theoretical forest scientist and biometrician specialised in quantitative forest ecology and management. Arne Pommerening is particularly known for his research in woodland structure analysis and modelling, spatio-temporal dynamics of plant point patterns, individual-based modelling, plant growth analysis and methods for quantifying and monitoring biodiversity. More recently he has also developed an interest in the analysis of human behaviour in selecting trees. Much of his research involves combinations of field trials and computer-based simulation experiments.

Pavel Grabarnik is a Professor of Mathematical Modelling in Ecology at Pushchino University in Russia. Currently he serves as the Director of the Pushchino Scientific Centre of Biological Research of the Russian Academy of Sciences and heads the Laboratory of Ecosystems Modelling. His research work includes ecological modelling focussed on the spatial structure of forest ecosystems. His main achievements in statistics are in parameter estimation and hypothesis testing for spatial point processes. Pavel Grabarnik has also developed computational tools for different aspects of applications in spatial statistics.

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. Human disturbances and tree selection behavior.- 7.1. Impacts and disturbances.- 7.2. Human tree selection and marking behavior.- 7.2.1. Problem and origins.- 7.2.2. Marteloscope experiments.- 7.2.3. Reference marking.- 7.2.4. Active and passive rating behavior.- 7.2.5. Agreement.- 7.2.6. Impact intensity and type of impact.- 7.2.7. Tree selection probabilities.- 7.2.8. Growth rates and growth projection.- A. Qualitative forest description.- B. Survey protocol for the establishment of permanent forest research plots.- B.1. Site selection.- B.2. Plot establishment.- B.2.1. Marking plot boundaries.- B.2.2. Plot identification number.- B.3. Individual-tree measurements.- B.3.1. Tree numbers.- B.3.2 Tree species.- B.3.3. Stem diameter at breast height.- B.3.4. Total tree height and height to base of crown.- B.3.5. Tree locations.- B.3.6. Crown measures.- B.4. Additional measurements and observations.- B.4.1. Growth rates, volume and age.- B.4.2. Diameter at stump height/root collar diameter.- B.4.3. Particularities.- B.4.4. Additional marteloscope requirements.- B.4.5. Open-grown trees.- B.4.6. Upper stem diameters.- B.4.7. Trees of special scientific interest.- B.4.8. Maintenance.- B.4.9. Soil survey.- C. Brief Introduction to the R language.- C.1. Basics.- C.2. Data frames.- C.3. Input and output.- C.4. Graphs and regressions.- C.5. Functions and flow control.- C.6. Extending R with C++.- References.- Index.


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