DOI: 10.5061/dryad.ncjsxkt0z
title: Recovery of European temperate forests after large and severe disturbances

description: As climate change progresses, there is increasing concern that large and
 severe disturbances may diminish the resilience of forest ecosystems and
 alter their recovery dynamics. We investigated recovery of temperate
 forests in Europe following large and severe disturbance events (more than
 70 % canopy cover reduction in patches larger than 1 ha) that span a time
 since disturbance range of one to five decades. Across a ground-based plot
 network at 143 sites, featuring various forest types and management
 practices, subjected to 28 disturbance events, including windthrow, fire,
 and bark beetle, we studied post-disturbance tree density and composition,
 which are key indicators of forest resilience. We used this dataset to
 qualitatively assess forest recovery in structure and composition by
 comparing plot-level post-disturbance height-weighted densities with
 site-specific pre-disturbance densities. Additionally, we analyzed
 ecological drivers of post-windthrow tree density, including forest
 management, topography, and bioclimate,  using a series of
 generalized additive models. The present dataset includes the plot-level
 data necessary to carry out the aforementioned analyses.
description: ## # Recovery of European temperate forests after large and severe
 disturbances The csv file "Post_disturbance_recovery_dataset"
 include all the data necessary to carry out the analyses performed in the
 manuscript that we are going to submit to the journal "Global Change
 Biology". Analyses include the statistical modeling of tree densities
 (i.e.response variables) after wind disturbances and their climatic,
 topographic and management drivers (i.e.explanatory variables); and the
 assessment of forest recovery in structure and composition, by comparing
 forest density and composition before and after the disturbance. ##
 Description of the data and file structure The csv file
 "Post_disturbance_recovery_dataset" include plot-level data to
 carry out the aforementioned analyses: * "site" is the site
 identifier * "plot" is the plot alphanumeric identifier *
 "Pre-disturbance management" indicates the type of forest
 management before the disturbance happened, it's a categorical
 variable with 3 levels: "Y" if managed at the time of the
 disturbance, "L" if the forest was managed until less than 50
 years before the disturbance, "N" if the forest was unmanaged *
 "Post-disturbance management" indicates the type of forest
 management after the disturbance happened, it's a categorical
 variable with 3 levels: "None" if there was no post-disturbance
 management, "Logging only" if only salvage logging (i.e.removal
 of biomass damaged by a disturbance) was carried out,
 "Intensive" if salvage logging was carried out together with
 tree planting * "Years since disturbance" is a discrete variable
 indicating the number of years passed between when the disturbance
 happened and when the plot sampling was carried out * "Disturbance
 agent" is a categorical variable with 3 levels:
 "BarkBeetle", "Fire", "Wind" *
 "Elevation" is a continuous variable indicating the elevation of
 the plot center, unit measure is m a.s.l. * "Aridity Change" is
 a continuous variable. Yearly aridity index (AI) was calculated as the
 average of monthly June, July, August aridity indices, which were computed
 as 1- P/pev, with P for Total precipitation, and pev for Potential
 evaporation. Aridity change was calculated as the difference between the
 mean AI of the 7 years post-disturbance and the aridity baseline,
 calculated as average of the index for years between 1950 and 1980.
 It's a proxy for post-disturbance summer drought anomalies, which
 could have a negative impact on seedlings and small trees *
 "HLI" is a continuous variable standing for Heat Load Index,
 calculated after McCune & Keon (2002). It's a proxy for
 topographically driven potential heat load * Eight columns featuring the
 response variables, related to the density per hectare, used in
 statistical modeling: combinations of raw densities
 ("RawDensity", i.e.number of individual trees) and weighted
 densities ("WDensity", i.e.individual trees were weighted
 according to their respective heights, then summed up, following a scheme
 similar to Vickers et al.(2019), with the difference that we gave the
 weight of 1 to any trees larger than 7 cm in DBH or taller than 7.54 m),
 with tree species groups ("Total" and according to their role in
 forest succession, i.e."Pioneer",
 "Early-successional", and "Late-successional") *
 "Pre_Density" indicates the tree density per hectare of the
 forest before the disturbance happened * "Pre_Sp1" and
 "Pre_Sp2" are categorical variables that indicate the dominant
 and co-dominant tree species present before the disturbance happened,
 respectively. In case that no co-dominant species was present Pre_Sp2
 equals "N" * "WDensity_Sp1" and
 "WDensity_Sp2" indicate the post-disturbance\ weighted densities
 per hectare of the pre-disturbance dominant \ and co-dominant tree
 species, respectively. References McCune, B., & Keon, D. (2002).
 Equations for potential annual direct incident radiation and heat load.
 Journal of Vegetation Science, 13, 603--606. Vickers, L. A., McWilliams,
 W. H., Knapp, B. O., D'Amato, A. W., Dey, D. C., Dickinson, Y. L.,
 Kabrick, J. M., Kenefic, L. S., Kern, C. C., Larsen, D. R., Royo, A. A.,
 Saunders, M. R., Shifley, S. R., & Westfall, J. A. (2019). Are Current
 Seedling Demographics Poised to Regenerate Northern US Forests? Journal of
 Forestry, 117(6), 592--612.