Abstract

<p>For centuries, mountain forests in the Alps have provided essential ecosystem services such as wood production and protection from natural hazards (e.g. avalanches and landslides), which enable mountain societies to thrive in these marginal environments. These ecosystem services are affected by climate and land use change, as well as changes in societal demand and management regimes. In recent years, the management of mountain forests has been increasingly driven by forest disturbances, such as windthrow, bark beetle outbreaks, and forest fires. The increasing rate of disturbances has the potential to convert forests from carbon sinks to carbon sources, and may also affect the provision of other ecosystem services, such as avalanche protection. The capacity of forests to provide services, their vulnerability to disturbance, and their resilience depend on their structure, composition and management regime. Forests with a heterogeneous structure and species composition are expected to better maintain their protection function after disturbances.</p><p>Information on forest structure and its link to functions and services is available from a variety of sources, from Earth Observation and in-situ data, existing process-based models, to local expert knowledge. We use Bayesian Networks to integrate these different types of information and model ecosystem services (carbon sequestration, wood production, and avalanche protection) in the Swiss Alps. This probabilistic modelling approach allows us to identify knowledge gaps and explore uncertainties in the future provision of ecosystem services. Since disturbances are a major source of uncertainty, we combine remote sensing and forest management data to investigate how disturbance severity and post-disturbance recovery are influenced by stand characteristics, such as structural heterogeneity. Based on this analysis, we discuss how forest management can help ensure the provision of mountain forest ecosystem services under changing disturbance regimes. </p>

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