Data and Code from: Sheltered or suppressed? Tree regeneration in unmanaged European forests

Tree regeneration is a key demographic process influencing long-term forest dynamics. It is driven by climate, disturbances, biotic factors, and their interactions. Thus, predictions of tree regeneration are challenging due to complex feedbacks along the wide climatic gradients covered by most tree species. We developed statistical models based on >50,000 recruitment events observed for 24 tree species in an extensive permanent plot network (6,540 plots from 299 unmanaged European temperate, boreal, and subalpine forests) covering a wide climatic gradient. 

This repository stores csv files with response and explanatory variables and R-Scripts that are needed to run the analysis and create the figures that are presented in the accompanying article titled "Sheltered or suppressed? Tree regeneration in unmanaged European forests" which was published in Journal of Ecology in 2023.

Our study shows that competition between trees towards climatic stress decreases systematically but depends on species stress tolerance to climate and shade. These findings explain within- and between-species differences in tree recruitment patterns in European temperate forests. Moreover, our findings imply that projections of forest dynamics along wide climatic gradients and under climate change must accommodate both competition and positive interactions, as they strongly affect rates of community turnover.

Methods
The files with the endings dbh7 and dbh10 show the variables for all plots with a size measurement threshold of a diameter at breast height (dbh) of 7 and 10 cm respectively.

Environmental Variables
data/processed_data/site_data_unique_plot_id2.csv

unique_plot_id2: ID of the forest sampling plot.

dds: the seasonal degree-day sum (DDS) is the integral under the temperature curve when ignoring temperatures be-low 5.5 °C (Allen 1976; Fischlin et al. 1995).

swb: the site water balance (SWB; cf. Speich 2019). The calculation of the SWB is explained in detail in Appendix A1 of the main article. The unit of the SWB is mm and refers to the amount of water which can be storred in the soil.

Allen, J. C. (1976). A modified sine wave method for calculating degree days. Environmental Entomology, 5(6), 388–396. https://doi.org/10.1093/ee/5.3.388

Fischlin, A., Bugmann, H., & Gyalistras, D. (1995). Sensitivity of a forest ecosystem model to climate parametrization schemes. Environmental Pollution, 87(3), 267–282. https://doi.org/10.1016/0269-7491(94)P4158-K 

Speich, M. J. R. (2019). Quantifying and modeling water availability in temperate forests: A review of drought and aridity indices. IForest, 12(1), 1–16. https://doi.org/10.3832/ifor2934-011

Stand Variables
data/processed_data/stand_variables_dbh7.csv
data/processed_data/stand_variables_dbh10.csv

The files with the endings dbh7 and dbh10 show the variables for all plots with a size measurement threshold of a diameter at breast height (dbh) of 7 and 10 cm respectively.

species: tree species name in latin

r.trees: number of trees that surpass the measurement threshold of diameter at breast height (dbh) of 7 or 10 cm.

plot_area2: plot area in ha. This variable is used as an offset in the models.

period_length: number of years between two consecutive forest inventories. This variable is used as an offset in the models.

LAI_tot: sum of gFolA over all species.

gFolA: species’ one sided foliage area of all leafs per ha. These variables are derived as described in Bugmann (1994)

year: ID of the forest inventory period.

unique_plot_id2: ID of the forest sampling plot.

site_id: ID of site within institute

institute_id: ID of institute

Bugmann, H. (1994). On the ecology of mountainous forests in a changing climate: A simulation study [PhD Thesis].