Network modularity analysis reveals strategies for strengthening forest resistance, recovery and resilience to disturbances

Resilience is crucial to maintaining the structure and functions of forest ecosystems to cope with increasing disturbances, yet its complexity often hinders the development of effective management strategies to confront rapid environmental changes. Specifically, there is limited knowledge about the factors affecting resilience and related processes, such as resistance and recovery, and their synergies or trade‐offs.

Using network modularity analysis on data from 316 studies on forests around the world, we built networks to explore how manageable predictors and unmanageable contextual factors influence resistance, recovery and resilience of forest attributes (i.e. system variables).

Conservation of biodiversity and implementation of forest management strategies emerged as key drivers associated with increased resistance, recovery and resilience, despite inherent trade‐offs. This pattern was observed across all biomes. However, most predictors were not consistently associated with all resilience components. For example, specific management practices, promoting mature forests and higher functional diversity were associated with greater forest resistance and resilience but showed no consistent association with higher recovery. When disaggregating forest management into specific practices, most management types were predominantly associated with higher resistance, recovery and resilience, although cutting and water supply regulation showed prevalent negative associations with recovery.

Environmental factors, such as climate, microhabitat characteristics and disturbance regimes, were found to be crucial in shaping forest responses to disturbance. Specifically, drought and fire interacted to modulate the effect of key predictors.

Synthesis . Our findings highlight the need to jointly consider resistance, recovery and resilience when conceptualizing or implementing forest management strategies. Our network approach identifies the bundles of manageable predictors most consistently associated with forest resistance and resilience, providing a basis for developing management strategies, while effective strategies to promote post‐disturbance recovery remain more elusive in the available literature.