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Cham : Springer International Publishing AG, 2021

1 online resource (587 pages)

ISBN 9783030807672 (electronic bk.)

ISBN 9783030807665

Managing Forest Ecosystems Ser. ; v.40

Print version: Tognetti, Roberto Climate-Smart Forestry in Mountain Regions Cham : Springer International Publishing AG,c2021 ISBN 9783030807665

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.

3.5.1 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 ---

5.13.5 The Relevance and Perspectives of Common Platforms for Forest Research -- References.

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 ---

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 ---

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& -- 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& -- 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 ---

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 ---

Chapter 9: Smart Harvest Operations and Timber Processing for Improved Forest Management.

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.

5.13.5 The Relevance and Perspectives of Common Platforms for Forest Research -- References.

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 ---

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.

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.

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& -- 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& -- 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 ---

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 ---

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 ---

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 ---

Chapter 9: Smart Harvest Operations and Timber Processing for Improved Forest Management.

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