Stand-level modelling and mapping of a pine-dependent indicator fungus across Sweden: effects of stand structure and climate

Keystone wood-decaying fungi affect structure and conservation value of forests. Using Bayesian regression, we modelled the stand-level occurrence of the Scots pine ( Pinus sylvestris )-dependent Porodaedalea pini across Sweden using a presence–absence dataset from 1889 permanent plots of the Swedish National Forest Inventory (NFI). Using the model, we then predicted the spatial distribution of P. pini across the country represented by 33,474 NFI plots for which data on the explanatory variables were available. We found that P. pini occurrence increased with stand age, living pine volume, and mean annual temperature, and decreased with growing-season precipitation. The model showed good predictive performance and identified areas of high suitability in southeastern Sweden. Our results underscore the importance of mature pine stands and warm, relatively dry climatic conditions for P. pini , and demonstrate the potential of combining national forest inventory data with species distribution modelling to inform conservation about a keystone wood-decaying fungus.