Abstract
The benefits and risks of forests to human health are widely recognized. Yet, variation across forest types and their ecological characteristics driving health effects remain underexplored. Based on a continental-scale, interdisciplinary empirical database from 164 European forest stands, we constructed a Bayesian Belief Network to quantify seven causal pathways relating distinct forest types to physical and mental health. These forest–health pathways included mental well-being via visual or auditory stimuli, thermal comfort, polyphenol content of medicinal plants, mushrooms and nutrition, air quality, and ticks and Lyme disease. Results show that forests consistently provide net health benefits regardless of their ecological characteristics. Forest canopy density and tree species diversity emerge as key drivers, but their effect size and directionality are strongly pathway-dependent. Changes in forest canopy density can generate trade-offs. For example, forests optimized for heat buffering and air pollution mitigation may compromise medicinal plant yield and enhance Lyme disease prevalence. Tree diversity effects were weaker but more uniformly positive. Therefore, forest management should account for such trade-offs to tailor forest biodiversity and functioning to local public health priorities.
The benefits and risks of nature to human health have been studied, however, robust empirical research on forest biodiversity and health outcomes is still lacking. Here the authors use a unique dataset from 164 European forest stands to explore the associations between forest types and well-being.