This dataset has been developed during the A2 Action of the SelPiBio LIFE project (www.selpibio.eu). The main aim of this project is demonstrate the effects of two thinning regimes, selective and from below, on soil biodiversity in young black pine stands. To evaluate the effect of thinning on analysed pinewoods, eighteen monitoring areas of 1 ha each were designed and realized across two study areas in Tuscany (Central Italy), the Pratomagno mountain chain and the Monte Amiata (Fig.1). In each study area, 27 circular plots of 15 metres of radius were geo-referenced (3 for each monitoring area). All the included trees were measured and data were collected between 2015 and early 2016 to characterize the horizontal and vertical structure of studied forests.

This data set provides valuable environmental information about six experimental triplets of Scots pine and Maritime pine in Spain. An array of 213 physiographic, climatic, stand, understory and edaphic (organic and mineral horizons physicochemical parameters) variables are provided.

The AIDB is the Microsoft Access database behind the CBM-CFS3 that stores default ecological information and parameters pertaining to the forest ecosystems of a country, among other functions. The EU-AIDB incorporates 1034 spatial units resulting from the intersection of 204 European administrative regions (defined at the NUTS 1 and NUTS 2 level for most of the EU countries), and 35 ecological boundaries representing climatic units. It also contains updated parameters for 192 of the main tree species reported by the National Forest Inventories of each EU country.

This data set provide valuable information about forest ecosystems in Pinus halepensis and Pinus sylvestris plantations in Spain. An array of 74 soil, climatic, physiographic and stand variables from 32 plots in P. halepensis plantations and 77 variables from 35 plots in P. sylvestris plantations are provided.

Forest-related policies, reporting obligations and decision analyses require relevant, comprehensive and reliable data. To better serve the new information demands, we present an extensive dataset of forest resources in European countries projected until 2040 based on harmonized definitions, assumptions and methodology. The results were mainly computed using the European Forestry Dynamics Model (EFDM). In the computation, each country prepared the input files required by the EFDM or corresponding projection models using their National Forest Inventory (NFI) data and harmonised definitions and standardised workflow. The initial years, areas analysed and technical details of the initial data and formatting the data for the EFDM analyses vary between countries depending on data availability and forestry practices applied. This worksheet presents the projections of the forest growing stock volume, aboveground carbon, and fellings in the area analysed and metadata of the simulations, indicating the parameterisation applied for the EFDM and any possible exceptions to the use of the standard EFDM workflow.