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Climate-Smart Forestry in Mountain Regions.

Bibliographic Details
Main Author: Tognetti, Roberto.
Other Authors: Smith, Melanie., Panzacchi, Pietro.
Format: eBook
Language:English
Published: Cham : Springer International Publishing AG, 2021.
Edition:1st ed.
Series:Managing Forest Ecosystems Series
Subjects:
Electronic books.
Online Access:Click to View
Table of Contents:
  • Intro
  • Preface
  • Acknowledgements
  • Contents
  • About the Editors
  • Contributors
  • Chapter 1: An Introduction to Climate-Smart Forestry in Mountain Regions
  • 1.1 Forests and Climate Change
  • 1.2 A Climate-Smart Perspective: Becoming Climate Smart
  • 1.3 Referencing True Long-Term Ecological Data for CSF
  • 1.4 Integrating Forest Disturbance and Ecological Stability
  • 1.5 The Climate-Smart Forestry Framework
  • 1.6 A European Way to Climate-Smart Forestry
  • 1.7 Pilot Forests
  • 1.8 Putting Climate-Smart Forestry into Practice
  • References
  • Chapter 2: Defining Climate-Smart Forestry
  • 2.1 Introduction
  • 2.1.1 Why Do we Need Climate Smart Forestry?
  • 2.1.2 Definition and Approaches to Climate Smart Forestry
  • 2.2 A Brief History of Climate Smart Forestry
  • 2.3 A Definition from the EU COST Action Climate Smart Forestry in Mountain Regions
  • 2.4 Criteria and Indicators for the Assessment of Climate-Smart Forestry
  • 2.4.1 Assessing Climate Smart Forestry
  • 2.4.2 Criteria and Indicators for Sustainable Forest Management
  • 2.4.3 From Sustainable Forest Management to Climate-Smart Forestry Indicators
  • 2.5 A Critical Analysis of the Definition, Gaps, and Uncertainties
  • 2.5.1 Gaps and Uncertainties
  • 2.6 Developing a Forest Manager Vision of CSF
  • 2.6.1 Forest manager's Response
  • 2.6.2 Refinement of Definition and Indicators
  • 2.7 Future Perspectives for CSF
  • References
  • Chapter 3: Assessment of Indicators for Climate Smart Management in Mountain Forests
  • 3.1 Introduction
  • 3.2 Concepts for Assessing Climate-Smart Forestry at Stand and Forest Management Unit Level
  • 3.2.1 Indicator Selection
  • 3.2.2 Indicator Normalization
  • 3.2.3 Weighting and Aggregating
  • 3.2.4 Framework for CSF Assessment at Stand and Management Unit Level.
  • 3.3 Assessment of CSF in Mountain Forest Stands: Exemplified by Norway Spruce-Silver Fir-European Beech Mixed Stands
  • 3.3.1 Development of C&amp
  • I Framework for Assessing Indicators of CSF at Stand Level
  • 3.3.1.1 Selection of Indicators
  • 3.3.1.2 Normalization
  • 3.3.1.3 Description of Indicators
  • 3.3.2 Indicator Assessment in Spruce-Fir-Beech Mixed Forest Stands
  • 3.3.3 Redundancy and Trade-offs Among Indicators
  • 3.3.4 Assessing CSF in Spruce-Fir-Beech Mixed Stands
  • 3.3.5 Sensitivity of CSF Indicators
  • 3.4 Importance of C&amp
  • I of CSF in Forest Management Planning
  • 3.4.1 Forest Planning and Climate-Smart Forestry
  • 3.4.2 Involvement of CSF Indicators in the Forest Planning Process
  • 3.4.3 Estimation of Importance of CSF Indicators in Forest Planning at the Forest Management Unit Level
  • 3.5 Challenges and Perspectives
  • 3.5.1 Refining the Selection of Indicators/Sub-indicators at Stand Level
  • 3.5.2 Strengthening CSF Assessment at Stand Level
  • 3.5.3 Use of Indicators of Climate Smartness for Development of Silvicultural Prescriptions
  • 3.5.4 Prospects for Adapting the Set of Indicators for Climate Smart Forest Planning
  • Appendices
  • Appendix 3.1. Overview of Growth and Yield Characteristics of the 10 Long-Term Experimental Plots Used in the Evaluation of CSF Indicators Development (Sect. 3.3.5). B, E. beech
  • S, N. spruce
  • F, s. fir
  • Appendix 3.2. List of Indicators Assessed for Their Importance for Climate-Smart Forestry Planning
  • References
  • Chapter 4: National Forest Inventory Data to Evaluate Climate-Smart Forestry
  • 4.1 Introduction
  • 4.2 Indicators to Quantify Adaptation and Mitigation, a Review
  • 4.3 National Forest Inventories: Harmonization of Mitigation and Adaptation Indicators
  • 4.4 Methods To Assess Forest Development Using NFI-Data and CSF Indicators
  • 4.4.1 Case Study 1: Switzerland.
  • 4.4.2 Case Study 2: Selected EU Countries
  • 4.5 Results of the Swiss Case Study
  • 4.6 Results of the European Case Study
  • 4.7 Critical Evaluation of Indicators and Potential for Improvement
  • 4.8 Inventory-Based Assessments of CSF in a Broader Context
  • 4.9 Conclusions and Outlook
  • References
  • Chapter 5: Efficacy of Trans-geographic Observational Network Design for Revelation of Growth Pattern in Mountain Forests Across Europe
  • 5.1 Assessing the Climate Sensitivity of the Growth of European Mountain Forests
  • 5.2 State of the Art of Monitoring and Observational Approaches
  • 5.3 The CLIMO Design of Transnational Observational Network
  • 5.3.1 Study Design and Data Used
  • 5.3.2 Site Selection Criteria
  • 5.3.3 Plot Metadata
  • 5.3.4 Tree Inventory and Dendrochronology
  • 5.4 Network, Locations, Site Characteristics
  • 5.5 Stand Growth
  • 5.6 Tree Growth
  • 5.7 Growth Characteristics Analysed Along Elevation Gradients
  • 5.8 Concept of Statistical Evaluation of Drought Events
  • 5.9 Climate Smartness
  • 5.9.1 Assessing Climate-Smart Indicators
  • 5.9.2 European Dataset of Climate-Smart Indicators
  • 5.9.3 Linking Yield and Climate-Smart Indicators: Research Objectives
  • 5.10 Soils
  • 5.11 Genetic Resources
  • 5.12 Trans-Geographic Database of Long-Term Forest Plots in Mountainous Areas
  • 5.13 Discussion and Conclusion
  • 5.13.1 Exploiting Scattered Long-Term Experiments for Assessing Stand Growth, Resistance, and Climate Smartness by Pooling and Overarching Evaluation of Data
  • 5.13.2 The Information Potential of Long-Term Versus Inventory Plots
  • 5.13.3 Need for Further Coordination and Standardization of Experimental Design and Set-ups
  • 5.13.4 Maintenance of Both Unmanaged and Managed Observation Plots
  • 5.13.5 The Relevance and Perspectives of Common Platforms for Forest Research
  • References.
  • Chapter 6: Changes of Tree and Stand Growth: Review and Implications
  • 6.1 Introduction: The Information Potential of Tree and Stand Growth Trajectories
  • 6.2 Theoretical Considerations on Growth Changes: Effects of Site Conditions and Species Identity
  • 6.2.1 Standard of Comparison
  • 6.2.2 Long- and Short-Term Deviations from Normality
  • 6.3 Empirical Evidence of Growth Trends and Events
  • 6.3.1 Overarching Growth Trends in the Lowlands of Europe
  • 6.3.2 Growth Trends in High-Elevation Forest Ecosystems
  • 6.3.3 Stress Events and Low-Growth Years
  • 6.3.4 Vulnerability Related to High Productivity Level
  • 6.4 Acclimation, Adaptation and Recovery
  • 6.4.1 Acclimation
  • 6.4.2 Adaptation
  • 6.4.3 Recovery
  • 6.5 Discussion: Implications for Environmental Monitoring, Forest Ecology and Management
  • 6.5.1 Environmental Monitoring
  • 6.5.2 Forest Ecology
  • 6.5.3 Forest Management
  • 6.6 The Importance of Long-Term Experiments for Fact-Finding
  • References
  • Chapter 7: Modelling Future Growth of Mountain Forests Under Changing Environments
  • 7.1 Introduction
  • 7.2 Prediction of Future Climate Conditions
  • 7.2.1 Climate Models
  • 7.2.2 Climate Change Scenarios
  • 7.3 Simulating Future Forest Growth in the Context of CSF
  • 7.3.1 Empirical Growth Models
  • 7.3.1.1 Yield Models
  • 7.3.1.2 Empirical Growth Simulators
  • 7.3.1.3 Dendroecological Models
  • 7.3.2 Process-Based Growth Models
  • 7.3.3 Considering Environmental Conditions in Growth Models
  • 7.3.4 Integrating the Effects of Species Mixture into Growth Models
  • 7.3.5 Integrating Silvicultural Prescriptions and the Induced Treatment Responses into Growth Models
  • 7.3.6 Effects of Genetic Structure on Forest Growth
  • 7.4 Source of Data to Parameterise, Calibrate and Validate Growth Models
  • 7.4.1 National Forest Inventory
  • 7.4.2 Stand-Wise Forest Inventory.
  • 7.4.3 Long-Term Research and Monitoring Plots
  • 7.4.4 Eddy Covariance Measurements
  • 7.4.5 Remote and Proximal Sensing
  • 7.4.6 Tree-Ring Time Series
  • 7.5 Conclusions and Perspectives
  • Appendix
  • References
  • Chapter 8: Climate-Smart Silviculture in Mountain Regions
  • 8.1 Introduction
  • 8.2 Risks to Forests Induced by Climate Change
  • 8.3 Indicators that Could Be Modified by Silvicultural Measures at Stand Level (Silvicultural Indicators)
  • 8.4 Silvicultural Treatments Improving Stand Adaptation
  • 8.4.1 Forest Area (Afforestation)
  • 8.4.2 Structure of Forest Stands (Age and Diameter Distribution, Vertical and Horizontal Distribution of Tree Crowns)
  • 8.4.3 Soil Condition
  • 8.4.4 Forest Damages
  • 8.4.5 Increment and Felling
  • 8.4.6 Tree Species Composition
  • 8.4.7 Regeneration
  • 8.4.8 Naturalness
  • 8.4.9 Introduced Tree Species
  • 8.4.10 Deadwood
  • 8.4.11 Genetic Resources
  • 8.4.12 Threatened Forest Species
  • 8.4.13 Protective Forests (Soil, Water, and Other Ecosystem Functions)
  • 8.4.14 Slenderness Coefficient
  • 8.5 Silvicultural Treatments Improving Stand Mitigation
  • 8.5.1 Growing Stock
  • 8.5.2 Carbon Stock (Soil)
  • 8.5.3 Roundwood (Timber Products)
  • 8.6 Application of Simulation Models for Development, Testing, and Improving Silvicultural Prescriptions
  • 8.6.1 The Role of Models in Forest Science and Practice
  • 8.6.2 Models as a Substitute for Missing Experiments
  • 8.6.3 Models as Decision Support in the Case of an Unclear Future Development
  • 8.6.4 Model Scenarios to Fathom Out the Potential of Adapting Forest Stands to Climate Change by Silvicultural Measures
  • 8.6.5 Example of the Application of Models for the Development of Silvicultural Guidelines
  • 8.6.6 From Models for Regulation and Optimization to Guidelines for Silvicultural Steering
  • References.
  • Chapter 9: Smart Harvest Operations and Timber Processing for Improved Forest Management.

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