Search

Models to predict the effects of different silvicultural treatments on future forest development are the best available tools to demonstrate and test possible climate-smart pathways of mountain forestry. This chapter reviews the state of the art in modelling approaches to predict the future growth of European mountain forests under changing environmental and management conditions. Growth models, both mechanistic and empirical, which are currently available to predict forest growth are reviewed. The chapter also discusses the potential of integrating the effects of genetic origin, species mixture and new silvicultural prescriptions on biomass production into the growth models. The potential of growth simulations to quantify indicators of climate-smart forestry (CSF) is evaluated as well. We conclude that available forest growth models largely differ from each other in many ways, and so they provide a large range of future growth estimates. However, the fast development of computing capacity allows and will allow a wide range of growth simulations and multi-model averaging to produce robust estimates. Still, great attention is required to evaluate the performance of the models. Remote sensing measurements will allow the use of growth models across ecological gradients.

Centro de Investigación Forestal (CIFOR), The increasing demand for innovative forest management strategies to adapt to and mitigate climate change and benefit forest production, the so-called Climate-Smart Forestry, calls for a tool to monitor and evaluate their implementation and their effects on forest development over time. The pan-European set of criteria and indicators for sustainable forest management is considered one of the most important tools for assessing many aspects of forest management and sustainability. This study offers an analytical approach to selecting a subset of indicators to support the implementation of Climate-Smart Forestry. Based on a literature review and the analytical hierarchical approach, 10 indicators were selected to assess, in particular, mitigation and adaptation. These indicators were used to assess the state of the Climate-Smart Forestry trend in Europe from 1990 to 2015 using data from the reports on the State of Europe’s Forests. Forest damage, tree species composition, and carbon stock were the most important indicators. Though the trend was overall positive with regard to adaptation and mitigation, its evaluation was partly hindered by the lack of data. We advocate for increased efforts to harmonize international reporting and for further integrating the goals of Climate-Smart Forestry into national-and European-level forest policy making., This study generated from the COST (European Cooperation in Science and Technology) Action CLIMO (Climate-Smart Forestry in Mountain Regions — CA15226) was financially supported by the EU Framework Programme for Research and Innovation HORIZON 2020. Most of the work was carried out during the Short Term Scientific Mission that Giovanni Santopuoli undertook at the WSL in Birmensdorf (Switzerland) hosted by Christian Temperli, Alessandra Bottero, Paolo Cherubini, and Marco Ferretti., 10 Pág.

Background Scenario analyses that evaluate management effects on the long-term provision and sustainability of forest ecosystem services and biodiversity (ESB) also need to account for disturbances. The objectives of this study were to reveal potential trade-offs and synergies between ESB provision and disturbance predisposition at the scale of a whole country. Methods The empirical scenario model MASSIMO was used to simulate forest development and management from years 2016 to 2106 on 5086 sample plots of the Swiss National Forest Inventory (NFI). We included a business-as-usual (BAU) scenario and four scenarios of increased timber harvesting. Model output was evaluated with indicators for 1) ESB provision including a) timber production, b) old-growth forest characteristics as biodiversity proxies and c) protection against rockfall and avalanches and 2) for a) storm and b) bark beetle predisposition. Results The predisposition indicators corresponded well (AUC: 0.71–0.86) to storm and insect (mostly bark beetle) damage observations in logistic regression models. Increased timber production was generally accompanied with decreased predisposition (storm: >−11%, beetle: >−37%, depending on region and scenario), except for a scenario that promoted conifers where beetle predisposition increased (e.g. + 61% in the Southern Alps). Decreased disturbance predisposition and decreases in old-growth forest indicators in scenarios of increased timber production revealed a trade-off situation. In contrast, growing stock increased under BAU management along with a reduction in conifer proportions, resulting in a reduction of beetle predisposition that in turn was accompanied by increasing old-growth forest indicators. Disturbance predisposition was elevated in NFI plots with high avalanche and rockfall protection value. Conclusions By evaluating ESB and disturbance predisposition based on single-tree data at a national scale we bridged a gap between detailed, stand-scale assessments and broader inventory-based approaches at the national scale. We discuss the limitations of the indicator framework and advocate for future amendments that include climate-sensitive forest development and disturbance modelling to strengthen decision making in national forest policy making.

Timber use in central Europe is expected to increase in the future, in line with forest policy goals to strengthen local wood supply for CO2-neutral energy production, construction and other uses. Growing stocks in low-elevation forests in Switzerland are currently high as exemplified by the Swiss canton of Aargau, for which an average volume of 346 ± 16 m3 ha−1 was measured in the 3rd Swiss National forest inventory (NFI) in 2004–2006. While this may justify a reduction of growing stocks through increased timber harvesting, we asked whether such a strategy may conflict with the sustainability of timber production and conservation goals. We evaluated a range of operationally relevant forest management scenarios that varied with respect to rotation length, growing stock targets and the promotion of conifers in the regeneration. The scenarios aimed at increased production of softwood, energy wood, the retention of potential habitat trees (PHTs) and the conversion to a continuous cover management system. They were used to drive the inventory-based forest simulator MASSIMO for 100 years starting in 2007 using the NFI sampling plots in Aargau. We analyzed model outputs with respect to projected future growing stock, growth, timber and energy yield and harvesting costs. We found growing stock to drop to 192 m3 ha−1 in 2106 if business-as-usual (BAU as observed between the 2nd and 3rd NFI) timber volumes were set as harvesting targets for the whole simulation period. The promotion of conifers and a reduction of rotation lengths in a softwood scenario yielded 25% more timber over the whole simulation period than BAU. An energy wood scenario that reduced growing stock to 200 m3 ha−1 by 2056 and promoted the natural broadleaved regeneration yielded 9% more timber than BAU before 2056 and 30% less thereafter due to decreasing increments. The softwood scenario resulted in higher energy yield than the energy wood scenario despite the lower energy content of softwood. Retaining PHT resulted in a reduction of timber harvest (0.055 m3 ha−1 yr−1 per habitat tree) and higher harvesting costs. Continuous cover management yielded moderate timber amounts throughout the simulation period, yet sustainably. Considering climate change, we discuss the risks associated with favoring drought- and disturbance-susceptible conifers at low elevations and emphasize that continuous cover management must allow for the regeneration of drought-adapted tree species. In conclusion, our simulations show potential for short-term increases in timber mobilization but also that such increases need to be carefully balanced with future forest productivity and other forest ecosystem services.

The demand for wood as construction material, renewable source for energy and feedstock for chemicals is expected to increase. However, timber increments are currently only partly harvested in many European mountain regions, which may lead to supply shortages for local timber industries, decreases in forest resistance to disturbances and functioning as protection from gravitational hazards. Using an inventory-based forest simulator, we evaluated scenarios to increase wood mobilization in the 7105-km2 Swiss canton of Grisons for the period 2007–2106. Scenarios varied with respect to landscape-scale harvesting amounts and silvicultural strategies (low vs. high stand-scale treatment intensity) and accounted for regulations and incentives for protection forest management. With 50 and 100% increases of harvests, the current average growing stock of 319 m3 ha−1 was simulated to be reduced by 12 and 33%, respectively, until 2106 in protection forests of Northern Grisons, where management is prioritized due to subsidies. Outside protection forests and in Southern Grisons, growing stock was simulated to continually increase, which led to divergent developments in forest structure in- and outside protection forests and in the Northern and Southern Grisons. The effect of silvicultural strategies on simulated forest structure was small compared to the effect of future harvesting levels. We discuss opportunities and threats of decreasing management activities outside protection forests and advocate for incentives to promote natural regeneration also outside protection forests to safeguard long-term forest stability.

Forest development models have been used to predict future harvesting potentials and forest management reference levels under the Kyoto guidelines. This contribution aims at presenting the individual-tree simulator MASSIMO (MAnagement Scenario SImulation Model) and demonstrating its scope of applications with simulations of two possible forest management reference levels (base or business as usual) in an example application. MASSIMO is a suitable tool to predict timber harvesting potentials and forest management reference levels to assess future carbon budgets of Swiss forests. While the current version of MASSIMO accurately accounts for legacy effects and management scenarios, effects of climate and nitrogen deposition on growth, mortality, and regeneration are not yet included. In addition to including climate sensitivity, the software may be further improved by including effects of species mixture on tree growth and assessing ecosystem service provision based on indicators.

Past and future forest composition and distribution in temperate mountain ranges is strongly influenced by temperature and snowpack. We used LANDCLIM, a spatially explicit, dynamic vegetation model, to simulate forest dynamics for the last 16,000 years and compared the simulation results to pollen and macrofossil records at five sites on the Olympic Peninsula (Washington, USA). To address the hydrological effects of climate-driven variations in snowpack on simulated forest dynamics, we added a simple snow accumulation-and-melt module to the vegetation model and compared simulations with and without the module. LANDCLIM produced realistic present-day species composition with respect to elevation and precipitation gradients. Over the last 16,000 years, simulations driven by transient climate data from an atmosphere-ocean general circulation model (AOGCM) and by a chironomid-based temperature reconstruction captured Late-glacial to Late Holocene transitions in forest communities. Overall, the reconstruction-driven vegetation simulations matched observed vegetation changes better than the AOGCM-driven simulations. This study also indicates that forest composition is very sensitive to snowpack-mediated changes in soil moisture. Simulations without the snow module showed a strong effect of snowpack on key bioclimatic variables and species composition at higher elevations. A projected upward shift of the snow line and a decrease in snowpack might lead to drastic changes in mountain forests composition and even a shift to dry meadows due to insufficient moisture availability in shallow alpine soils.

Recent studies projecting future climate change impacts on forests mainly consider either the effects of climate change on productivity or on disturbances. However, productivity and disturbances are intrinsically linked because 1) disturbances directly affect forest productivity (e.g. via a reduction in leaf area, growing stock or resource-use efficiency), and 2) disturbance susceptibility is often coupled to a certain development phase of the forest with productivity determining the time a forest is in this specific phase of susceptibility. The objective of this paper is to provide an overview of forest productivity changes in different forest regions in Europe under climate change, and partition these changes into effects induced by climate change alone and by climate change and disturbances. We present projections of climate change impacts on forest productivity from state-of-the-art forest models that dynamically simulate forest productivity and the effects of the main European disturbance agents (fire, storm, insects), driven by the same climate scenario in seven forest case studies along a large climatic gradient throughout Europe. Our study shows that, in most cases, including disturbances in the simulations exaggerate ongoing productivity declines or cancel out productivity gains in response to climate change. In fewer cases, disturbances also increase productivity or buffer climate-change induced productivity losses, e.g. because low severity fires can alleviate resource competition and increase fertilization. Even though our results cannot simply be extrapolated to other types of forests and disturbances, we argue that it is necessary to interpret climate change-induced productivity and disturbance changes jointly to capture the full range of climate change impacts on forests and to plan adaptation measures., published version, peerReviewed

Bark beetles are a key forest disturbance agent worldwide, with their impact shaped by climate, forest susceptibility, and interactions with other disturbances such as windthrow and fire. There is ample evidence of the interactions among these factors at small spatial and temporal scales, but projecting their long-term and landscape-scale impacts remains a challenge. We developed a spatially explicit model of European spruce bark beetle (Ips typographus) dynamics that incorporates beetle phenology and forest susceptibility and integrated it in a climate-sensitive landscape model (LandClim). We first corroborated model outputs at various spatial and temporal scales and then applied the model in three case studies (in the Black Forest, Germany, and Davos, Switzerland) that cover an extended climatic gradient. We used this model and case study framework to examine the mechanisms and feedbacks that are driving short-term and long-term interactions among beetle disturbance, climate change, and windthrow, and h...

Projections of indicators of forest ecosystem goods and services (EGS) based on process-based landscape models are critical for adapting forest management to climate change. However, the scarcity of fine-grained, spatially explicit forest data means that initializing these models is both a challenge and a source of uncertainty. To test how different initialization approaches influence the simulation of forest dynamics and EGS indicators we initialized the forest landscape model LandClim with fine resolution empirical data, coarse empirical data, and simulation-derived data, and evaluated the results at three spatial scales (stand, management area and landscape). Simulations were performed for a spruce (Picea abies) dominated landscape in the Black Forest, Germany, under current climate and a climate change scenario. We found that long-term (>150 years) projections are robust to initialization uncertainty. In contrast, shorter-term projections are sensitive to initialization uncertainty, with sensitivity increasing when EGS are assessed at smaller spatial scales, and when the EGS indicators depend on the spatial distribution of individual species. EGS dynamics are strongly influenced by interactions between the density, species composition, and age structure of initialized forests and simulated forest management. If EGS dynamics are strongly influenced by climate change, such as when climate change induces mortality in drought-sensitive species, some of the initialization uncertainty can be masked. We advocate for initializing landscape models with fine-grained data in applications that focus on spatial management problems in heterogeneous landscapes, and stress that the scale of analysis must be in accordance with the accuracy that is warranted by the initialization data.

Developing adaptive forest management strategies is essential to maintain the provisioning of forest goods and services (FGS) under future climate change. We assessed how climate change and forest management affect forest development and FGS for a diverse case-study landscape in Central Europe. Using a process-based forest model (LandClim) we simulated forest dynamics and FGS under a range of climate change and management scenarios in the Black Forest, Germany, which is shaped by various management practices. We focused on the interdependencies between timber production and forest diversity, the most valued FGS in this region. We found that the conversion to more drought-adapted forest types is required to prevent climate change-induced forest dieback and that this conversion must be the target of any adaptive management, especially in areas where monocultures of drought-sensitive Norway spruce (Picea abies) were promoted in the past. Forest conversion takes up to 120 years, however, with past and future adaptive management being the key drivers of timber and forest diversity provision. The conversion of drought-sensitive conifer monocultures maintains timber production in the short-term and enhances a range of forest diversity indices. Using uneven-aged forest management that targets a drought-adapted, diverse, and resilient species mixture, high species diversity can be combined with timber production in the long term. Yet, the promotion of mature-stand attributes requires management restrictions. Selecting future adaptive management options thus implies the consideration of trade-offs between forest resource use and environmental objectives, but also the exploitation of synergies between FGS that occur during forest conversion. Lastly, the large impact of past management practices on the spatial heterogeneity of forest dynamics underpins the need to assess FGS provisioning at the landscape scale.

We study climate uncertainty and how managers' beliefs about climate change develop and influence their decisions. We develop an approach for updating knowledge and beliefs based on the observation of forest and climate variables and illustrate its application for the adaptive management of an even-aged Norway spruce (Picea abies L. Karst) forest in the Black Forest, Germany. We simulated forest development under a range of climate change scenarios and forest management alternatives. Our analysis used Bayesian updating and Dempster's rule of combination to simulate how observations of climate and forest variables may influence a decision maker's beliefs about climate development and thereby management decisions. While forest managers may be inclined to rely on observed forest variables to infer climate change and impacts, we found that observation of climate state, e.g. temperature or precipitation is superior for updating beliefs and supporting decision-making. However, with little conflict among information sources, the strongest evidence would be offered by a combination of at least two informative variables, e.g., temperature and precipitation. The success of adaptive forest management depends on when managers switch to forward-looking management schemes. Thus, robust climate adaptation policies may depend crucially on a better understanding of what factors influence managers' belief in climate change. We study climate uncertainty and how managers' beliefs about climate change develop and influence their decisions. We develop an approach for updating knowledge and beliefs based on the observation of forest and climate variables and illustrate its application for the adaptive management of an even-aged Norway spruce (Picea abies L. Karst) forest in the Black Forest, Germany. We simulated forest development under a range of climate change scenarios and forest management alternatives. Our analysis used Bayesian updating and Dempster's rule of combination to simulate how observations of climate and forest variables may influence a decision maker's beliefs about climate development and thereby management decisions. While forest managers may be inclined to rely on observed forest variables to infer climate change and impacts, we found that observation of climate state, e.g. temperature or precipitation is superior for updating beliefs and supporting decision-making. However, with little conflict among information sources, the strongest evidence would be offered by a combination of at least two informative variables, e.g., temperature and precipitation. The success of adaptive forest management depends on when managers switch to forward-looking management schemes. Thus, robust climate adaptation policies may depend crucially on a better understanding of what factors influence managers' belief in climate change.

Adapting the management of forest resources to climate change involves addressing several crucial aspects to provide a valid basis for decision making. These include the knowledge and belief of decision makers, the mapping of management options for the current as well as anticipated future bioclimatic and socioeconomic conditions, and the ways decisions are evaluated and made. We investigate the adaptive management process and develop a framework including these three aspects, thus providing a structured way to analyze the challenges and opportunities of managing forests in the face of climate change. We apply the framework for a range of case studies that differ in the way climate and its impacts are projected to change, the available management options, and how decision makers develop, update, and use their beliefs about climate change scenarios to select among adaptation options, each being optimal for a certain climate change scenario. We describe four stylized types of decision-making processes that differ in how they (1) take into account uncertainty and new information on the state and development of the climate and (2) evaluate alternative management decisions: the “no-change,” the “reactive,” the “trend-adaptive,” and the “forward-looking adaptive” decision-making types. Accordingly, we evaluate the experiences with alternative management strategies and recent publications on using Bayesian optimization methods that account for different simulated learning schemes based on varying knowledge, belief, and information. Finally, our proposed framework for identifying adaptation strategies provides solutions for enhancing forest structure and diversity, biomass and timber production, and reducing climate change-induced damages. They are spatially heterogeneous, reflecting the diversity in growing conditions and socioeconomic settings within Europe. Adapting the management of forest resources to climate change involves addressing several crucial aspects to provide a valid basis for decision making. These include the knowledge and belief of decision makers, the mapping of management options for the current as well as anticipated future bioclimatic and socioeconomic conditions, and the ways decisions are evaluated and made. We investigate the adaptive management process and develop a framework including these three aspects, thus providing a structured way to analyze the challenges and opportunities of managing forests in the face of climate change. We apply the framework for a range of case studies that differ in the way climate and its impacts are projected to change, the available management options, and how decision makers develop, update, and use their beliefs about climate change scenarios to select among adaptation options, each being optimal for a certain climate change scenario. We describe four stylized types of decision-making processes that differ in how they (1) take into account uncertainty and new information on the state and development of the climate and (2) evaluate alternative management decisions: the “no-change,” the “reactive,” the “trend-adaptive,” and the “forward-looking adaptive” decision-making types. Accordingly, we evaluate the experiences with alternative management strategies and recent publications on using Bayesian optimization methods that account for different simulated learning schemes based on varying knowledge, belief, and information. Finally, our proposed framework for identifying adaptation strategies provides solutions for enhancing forest structure and diversity, biomass and timber production, and reducing climate change-induced damages. They are spatially heterogeneous, reflecting the diversity in growing conditions and socioeconomic settings within Europe.