Data from: Quantifying natural disturbances using a large-scale dendrochronological reconstruction to guide forest management
Cite this dataset
Čada, Vojtěch et al. (2020). Data from: Quantifying natural disturbances using a large-scale dendrochronological reconstruction to guide forest management [Dataset]. Dryad. https://doi.org/10.5061/dryad.08kprr507
Abstract
Methods
We used a plot network established in the remaining primary mountain spruce forests of Central and Eastern Europe (www.remoteforests.org). Data span large geographical gradient covering the Carpathian Mountains of southern and northern Romania, Ukraine, southern and northern Slovakia, the Bohemian Forest in the Czech Republic, and the Harz Mountains in Germany. We used a hierarchical sample design of 541 plots nested within 44 stands (4-63 plots·stand-1; median: 11) nested within 6 sub-regions (4-13 stands·sub-region-1; median: 6). The “West” sub-region was comprised of the Harz and Bohemian Forest mountain ranges to avoid any sub-region with a single stand.
We used a comprehensive dendrochronological method to reconstruct past disturbances based on analyses of increment cores extracted on each study plot. To quantify individual disturbance events, their severities, and stand proportions disturbed, we used tree-ring proxies (two types of radial growth patterns were assumed to indicate past disturbance: release from suppression and rapid early growth rate), which were aggregated at plot and stand levels by smoothing and detecting peaks in their distributions. The spatial aggregation of disturbance events was used to estimate patch sizes. The data collection and analyses are described in detail in the related article published in Ecological Applications. The supplementary material of the article also contains the R code which were used to obtain and process the data [besides the patch size data, which were obtained by analysis in ArcGIS 10.5 (ESRI, Redlands, United States, http://www.esri.com)].
Usage notes
The dataset contains the disturbance event data resulting from dendrochronological analyses. There are three separated tables that are related to three disturbance characteristics: disturbance severity, patch size, and stand proportion disturbed. Individual plot-level disturbance events are included in the first table (standlevel_dist_events_joined). Each plot-level event is characterized by the plot (plotid) and year of occurrence together with its severity and stand-level disturbance year (peakyear) it is related. Stand-level disturbance events are characterized in the second table (plotsprop_disturb) by stand and year (peakyear - equals to peakyear in standlevel_dist_events_joined) together with the stand proportion disturbed (plotsprop_disturb). Disturbance patch sizes are characterized in the third table (dist_patches) that contains same characteristics as second table (plotsprop_disturb) to identify the stand-level events (stand and peakyear) and adds the characteristics of patch sizes related to individual stand-level events: area of patch size before correction for a maximum size of patches represented by a single plot (patch_area_pre), number of plots within the patch (Join_Count), final patch area after mentioned correction, and stand size. Each table also contains identification of sub-region (landscape) and country, where the plots and stands are located.
Funding
Czech Science Foundation, Award: 16-23183Y
Czech Ministry of Education, Youth and Sports, Award: LTT20016; CZ.02.1.01/0.0/0.0/16_019/0000803
Czech Ministry of Education, Youth and Sports, Award: LTT20016; CZ.02.1.01/0.0/0.0/16_019/0000803