Addressing disturbance risk to mountain forest ecosystem services

Mapping the natural disturbance risk to protective forests across the European Alps

Mountain forests play an essential role in protecting people and infrastructure from natural hazards. However, forests are currently experiencing an increasing rate of natural disturbances (including windthrows, bark beetle outbreaks and forest fires) that may jeopardize their capacity to provide this ecosystem service in the future. Here, we mapped the risk to forests' protective service across the European Alps by integrating the risk components of hazard (in this case, the probability of a disturbance occurring), exposure (the proportion of forests that protect people or infrastructure), and vulnerability (the probability that the forests lose their protective structure after a disturbance). We combined satellite-based data on forest disturbances from 1986 to 2020 with data on key forest structural characteristics (cover and height) from spaceborne lidar (GEDI), and used ensemble models to predict disturbance probabilities and post-disturbance forest structure based on topographic and climatic predictors. Wind and bark beetles are dominant natural disturbance agents in the Alps, with a mean annual probability of occurrence of 0.05%, while forest fires were less likely (mean annual probability <0.01%), except in the south-western Alps. After a disturbance, over 40% of forests maintained their protective structure, highlighting the important role of residual living or dead trees. Within 30 years after wind and bark beetle disturbance, 61% of forests were likely to either maintain or recover their protective structure. Vulnerability to fires was higher, with 51% of forest still lacking sufficient protective structure 30 years after fire. Fire vulnerability was especially pronounced at dry sites, which also had a high fire hazard. Combining hazard and vulnerability with the exposure of protective forests we identified 186 Alpine municipalities with a high risk to protective forests due to wind and bark beetles, and 117 with a high fire risk. Mapping the disturbance risk to ecosystem services can help identify priority areas for increasing preparedness and managing forests towards lower susceptibility under an intensifying disturbance regime.

Ecosystem services at risk from disturbance in Europe's forests

Global change impacts on disturbances can strongly compromise the capacity of forests to provide ecosystem services to society. In addition, many ecosystem services in Europe are simultaneously provided by forests, emphasizing the importance of multifunctionality in forest ecosystem assessments. To address disturbances in forest ecosystem policies and management, spatially explicit risk analyses that consider multiple disturbances and ecosystem services are needed. However, we do not yet know which ecosystem services are most at risk from disturbances in Europe, where the respective risk hotspots are, nor which of the main disturbance agents are most detrimental to the provisioning of multiple ecosystem services from Europe's forests. Here, we quantify the risk of losing important ecosystem services (timber supply, carbon storage, soil erosion control and outdoor recreation) to forest disturbances (windthrows, bark beetle outbreaks and wildfires) in Europe on a continental scale. We find that up to 12% of Europe's ecosystem service supply is at risk from current disturbances. Soil erosion control is the ecosystem service at the highest risk, and windthrow is the disturbance agent posing the highest risk. Disturbances challenge forest multifunctionality by threatening multiple ecosystem services simultaneously on 19.8 Mha (9.7%) of Europe's forests. Our results highlight priority areas for risk management aiming to safeguard the sustainable provisioning of forest ecosystem services.

A Bayesian network-GIS probabilistic model for addressing human disturbance risk to ecological conservation redline areas

Population growth and associated ecological space occupation are posing great risks to regional ecological security and social stability. In China, "Ecological Conservation Redline" (ECR) that prohibited urbanization and industrial construction has been proposed as a national policy to resolve spatial mismatches and management contradictions. However, unfriendly human disturbance activities (e.g., cultivation, mining, and infrastructure construction) still exist within the ECR, posing a great threat to ecological stability and safety. In this article, a Bayesian network (BN)-GIS probabilistic model is proposed to spatially and quantitatively address the human disturbance risk to the ECR at the regional scale. The Bayesian models integrate multiple human activities, ecological receptors of the ECR, and their exposure relationships for calculating the human disturbance risk. The case learning method geographic information systems (GIS) is then introduced to train BN models based on the spatial attribute of variables to evaluate the spatial distribution and correlation of risks. This approach was applied to the human disturbance risk assessment for the ECR that was delineated in 2018 in Jiangsu Province, China. The results indicated that most of the ECRs were at a low or medium human disturbance risk level, while some drinking water sources and forest parks in Lianyungang City possessed the highest risk. The sensitivity analysis result showed the ECR vulnerability, especially for cropland, that contributed most to the human disturbance risk. This spatially probabilistic method can not only enhance model's prediction precision, but also help decision-makers to determine how to establish priorities for policy design and conservation interventions. Overall, it presents a foundation for later ECR adjustments as well as for human disturbance risk supervision and management at the regional scale.

Balancing disturbance risk and ecosystem service provisioning in Swiss mountain forests: an increasing challenge under climate change

Climate change severely affects mountain forests and their ecosystem services, e.g., by altering disturbance regimes. Increasing timber harvest (INC) via a close-to-nature forestry may offer a mitigation strategy to reduce disturbance predisposition. However, little is known about the efficiency of this strategy at the scale of forest enterprises and potential trade-offs with biodiversity and ecosystem services (BES). We applied a decision support system which accounts for disturbance predisposition and BES indicators to evaluate the effect of different harvest intensities and climate change scenarios on windthrow and bark beetle predisposition in a mountain forest enterprise in Switzerland. Simulations were carried out from 2010 to 2100 under historic climate and climate change scenarios (RCP4.5, RCP8.5). In terms of BES, biodiversity (structural and tree species diversity, deadwood amount) as well as timber production, recreation (visual attractiveness), carbon sequestration, and protection against gravitational hazards (rockfall, avalanche and landslides) were assessed. The INC strategy reduced disturbance predisposition to windthrow and bark beetles. However, the mitigation potential for bark beetle disturbance was relatively small (- 2.4%) compared to the opposite effect of climate change (+ 14% for RCP8.5). Besides, the INC strategy increased the share of broadleaved species and resulted in a synergy with recreation and timber production, and a trade-off with carbon sequestration and protection function. Our approach emphasized the disproportionally higher disturbance predisposition under the RCP8.5 climate change scenario, which may threaten currently unaffected mountain forests. Decision support systems accounting for climate change, disturbance predisposition, and BES can help coping with such complex planning situations.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s10113-022-02015-w.

Integrating diverse model results into decision support for good environmental status and blue growth

Sustainable environmental management needs to consider multiple ecological and societal objectives simultaneously while accounting for the many uncertainties arising from natural variability, insufficient knowledge about the system's behaviour leading to diverging model projections, and changing ecosystem. In this paper we demonstrate how a Bayesian network- based decision support model can be used to summarize a large body of research and model projections about potential management alternatives and climate scenarios for the Baltic Sea. We demonstrate how this type of a model can act as an emulator and ensemble, integrating disciplines such as climatology, biogeochemistry, marine and fisheries ecology as well as economics. Further, Bayesian network models include and present the uncertainty related to the predictions, allowing evaluation of the uncertainties, precautionary management, and the explicit consideration of acceptable risk levels. The Baltic Sea example also shows that the two biogeochemical models frequently used in future projections give considerably different predictions. Further, inclusion of parameter uncertainty of the food web model increased uncertainty in the outcomes and reduced the predicted manageability of the system. The model allows simultaneous evaluation of environmental and economic goals, while illustrating the uncertainty of predictions, providing a more holistic view of the management problem.

Scenario-Based Comprehensive Assessment for Community Resilience Adapted to Fire Following an Earthquake, Implementing the Analytic Network Process and Preference Ranking Organization Method for Enriched Evaluation II Techniques

Natural hazards bring significant influences on and socioeconomic loss to cities and communities. Historic events show that fire following earthquake (FFE) is the most influential uncertain disturbance on the urban infrastructure system. Under the FFE scenario, the concept of resilience is widely implemented to make up the shortcomings derived from the traditional disaster management methodology. Resilient cities and communities are required to improve the systemic performance in responding to the FFE. To fulfill these goals, measuring community resilience is an essential work for municipal policy makers. Therefore, this study conducted a comprehensive assessment on community resilience adapted to the FFE scenario. The systematic literature review (SLR) was employed to identify the indicators, and the analytic network process (ANP) technique was implemented to determine their weights. 20 indicators were extracted, and 4 communities that encountered FFE in China were selected for the empirical analysis. Thereafter, the preference ranking organization method for enriched evaluation (PROMETHEE) II technique was selected through using the multicriteria decision analysis (MCDA) methods selection framework to fulfill the comprehensive assessment. The results were discussed and demonstrated with graphical analysis for interactive aid (GAIA) technique. The findings revealed that the G Community won the highest score and had the strongest performance. However, H Community had the lowest score and the weakest performance. The proposed comprehensive methods could benefit the decision-makers and the policy executors achieving the community resilience adapted to the FFE scenario by improving the effective indicators.