Data from: Drivers of snag fall rates in Fennoscandian boreal forests ... - Aakala, Tuomas; Storaunet, Ken Olaf; Jonsson, Bengt Gunnar; Korhonen, Kari T. (Dryad, 2025)
Persistence of standing dead trees (snags) is an important determinant for their role for biodiversity and dead wood associated carbon fluxes. How fast snags fall varies widely among species and regions and is further influenced by a variety of stand- and tree-level factors. However, our understanding of this variation is fragmentary at best, partly due to lack of empirical data. Here, we took advantage of the accruing time series of snag observations in the Finnish, Norwegian, and Swedish National Forest Inventories that have been followed in these programs since the mid-1990s. We first harmonized observations from slightly different inventory protocols and then, using this harmonized dataset of ca. 43 000 observations that had a consistent 5-year census interval, we modeled the probability of snags of the main boreal tree species Pinus sylvestris, Picea abies, and Betula spp. falling, as a function of tree- and stand-level variables, using Bayesian logistic regression modeling. The models were moderately ... : # Data from: Drivers of snag fall rates in Fennoscandian boreal forests [https://doi.org/10.5061/dryad.37pvmcvtt](https://doi.org/10.5061/dryad.37pvmcvtt) Data used for fitting the models in Aakala et al. (2024). Drivers of snag fall rates in Fennoscandian boreal forests to be published in Journal of Applied Ecology. ## Description of the data and file structure The tab-separated text file contains variables used in modeling the probability of standing dead trees to remain standing over the 5-year remeasurement interval. The variables (with full descriptions in the main article): survival = binary response variable, whether the standing dead tree survived as standing (1), or fell (0). dbh = Diameter at 1.3 m height, in cm. gdd0 = Growing degree days, with a threshold value of 0 °C. Originally obtained from the Envirem data set (Title, P.O. and Bemmels, J.B., 2018. ENVIREM: an expanded set of bioclimatic and topographic variables increases flexibility and improves performance of ecological niche modeling. ...