File name: ReadMe-ForKarst microclimate This (ReadMe) file was created on 2026-04-07 by Janez Kermavnar ------------------- GENERAL INFORMATION ------------------- Name of the dataset: Dataset on summer microclimate buffering in two beech forest types on carbonate rocks (Dinaric karst, Slovenia) Authors Name and surname: Janez Kermavnar ORCID: https://orcid.org/0000-0001-8052-4653 Institution/Affiliation: Slovenian Forestry Institute, Vecna pot 2, 1000 Ljubljana, Slovenia ROR organization identifier: https://ror.org/0232eqz57 Email: janez.kermavnar@gozdis.si Name and surname: Mitja Ferlan ORCID: Institution/Affiliation: Slovenian Environment Agency, Vojkova 1b, 1000 Ljubljana, Slovenia ROR organization identifier: https://ror.org/05e75yx66 Email: mitja.ferlan@gov.si Name and surname: Lado Kutnar ORCID: https://orcid.org/0000-0001-9785-1263 Institution/Affiliation: Slovenian Forestry Institute, Vecna pot 2, 1000 Ljubljana, Slovenia ROR organization identifier: https://ror.org/0232eqz57 Email: lado.kutnar@gozdis.si Name and surname: Aleksander Marinšek ORCID: https://orcid.org/0000-0002-6190-9096 Institution/Affiliation: Slovenian Forestry Institute, Vecna pot 2, 1000 Ljubljana, Slovenia ROR organization identifier: https://ror.org/0232eqz57 Email: aleksander.marinsek@gozdis.si Name and surname: Nataša Ravbar ORCID: https://orcid.org/0000-0002-0160-1460 Institution/Affiliation: Karst Research Institute ZRC SAZU, Titov trg 2, 6230 Postojna, Slovenia ROR organization identifier: https://ror.org/01ffqaw45 Email: natasa.ravbar@zrc-sazu.si Name and surname: Urša Vilhar ORCID: https://orcid.org/0000-0001-6765-0452 Institution/Affiliation: Slovenian Forestry Institute, Vecna pot 2, 1000 Ljubljana ROR organization identifier: https://ror.org/0232eqz57 Email: ursa.vilhar@gozdis.si Date of data collection: from 2021-06-01 to 2023-08-31 Type of data: c-data set / series Type of research data: j-observational data Geographical location of data collection: near Planina (Slovenia) 45.818° N 14.248° E; near Postojna (Slovenia) 45.787° N, 14.209° E Information on the funders/programmes/projects that made the data collection possible: the Republic of Slovenia, Ministry of Education, Science and Sport and the European Union from the European Regional Development Fund: the eLTER Preparatory Phase Project (eLTER PPP), eLTER Advanced Community Project (eLTER PLUS), "Development of research infrastructure for the international competitiveness of the Slovenian RRI space – RI-SI- LifeWatch"; Slovenian Research and Innovation Agency (ARIS): Infiltration processes in forested karst aquifers under changing environment (No. 852 J2-1743), Natural regeneration processes in beech forests after disturbance (No. J4-4542 ), postdoctoral project Long-term changes of forest vegetation caused by global and local environmental change drivers (No. Z4-4543), Research Programme “Forest biology, ecology and technology” (No. P4–0107); the Ministry of Agriculture, Forestry and Food of the Republic of Slovenia: the Public Forest Service Task 1/2.3 “Hydrological and protective forest functions” License: CC BY 4.0 DOI: 10.20315/data1000 ----------------------------- SHARING/ACCESSING INFORMATION ----------------------------- Data licences/restrictions: The data will be made available once the related paper has been published. Links to publications that cite or use the data: Not yet available. Links to other publicly available data sites: / Links to ancillary databases: / Was the data obtained from another source? NO ---------------------- VIEWING DATA AND FILES ---------------------- File list: a) Plot_metadata.csv: General information of the plots, including mean values of max. temperature (Tmax), max. vapor pressure deficit (VPDmax), soil water content (SWC) and leaf area index (LAI) b) Tmax_daily_data.csv: Measured daily Tmax for 276 days in total (summer seasons 2021, 2022 and 2023). c) Tmax_gaps_data.csv: Measured daily Tmax in two canopy gaps, i.e. open-field reference conditions. d) Tmax_offset_data.csv: Calculated daily Tmax offset (Tmax in the plot/forest minus Tmax in the canopy gap) for 276 days in total (summer seasons 2021, 2022 and 2023). e) VPDmax_daily_data.csv: Calculated daily VPDmax (based on temperature and relative humidity) for 276 days in total (summer seasons 2021, 2022 and 2023). f) SWC_daily_data.csv: Measured daily SWC for 276 days in total (summer seasons 2021, 2022 and 2023). g) Vegetation_data.csv: Species-by-plot matrix for herb-layer abundances of recorded plant taxa, including Ellenberg indicator values ("x" means indifferent). File ratio, if relevant: / Additional related data collected that were not included in this dataset: NO Are there multiple versions of the dataset? NO -------------------------- METHODOLOGICAL INFORMATION -------------------------- Description of the methods used to collect/obtain the data: The study area comprises two forest vegetation types: (1) mesophilous type of beech forest, typically occurring on more fertile soils with higher soil water availability, and (2) thermophilous type of beech forests, occurring on drier soils with higher solar exposure. Within each forest type, we established sampling plots in two forest developmental stages (mature forest and regeneration phase) and two contrasting karst topographic settings (bottom of karst doline vs. adjacent interdoline area with flatter terrain). One plot per forest type was established in the nearby large canopy gap representing open-field conditions without overstory vegetation cover. In total, 10 plots were established, i.e. eight in forests and two in canopy gaps. At each plot, air temperature and relative humidity were measured using a Sensirion SHT21 digital sensor housed in solar radiation shield. The sensors were installed at a height of 2 m above the ground surface, positioned within the understory to capture near-surface conditions relevant for vegetation and soil processes. Volumetric soil water content (SWC, %) was monitored continuously at the depth of 30 cm using METER Group ECH2O EC-5 sensors. Due to the rocky terrain, sensors with a small detectable volume were required. All sensors were connected to the IoTmini-mForest data logger manufactured by the Laboratory of Electronic Devices, Slovenian Forestry Institute. Loggers recorded air temperature and humidity at 30-minute intervals throughout the study period from 2021 to 2023. The eEMIS platform managed by the Slovenian Forestry Institute, collected the data and entered it into an SQL database. At each plot, the leaf area index (LAI; m2/m2) was measured from April to November in 2021, 2022 and 2023 using the LAI2200 Plant Canopy Analyzer. These measurements were done weekly or bi-weekly, following a standardized protocol for tall canopies and forests (LAI-2200 2012). Canopy gaps nearby were used as reference points. All woody and herbaceous vascular plant species within a sample area of 10 × 10 m were recorded as part of a comprehensive vegetation inventory (Canullo et al., 2020) in August 2022. The abundance of the individual species was estimated using the standard phytosociological method according to Braun-Blanquet (1964). In addition, the percentage of cover of the different vegetation layers was visually estimated: the tree layer, which includes trees and shrubs with a height of more than 5 m, the shrub layer, which includes all woody species with a height between 0.5 m and 5 m, and the herb layer (ground vegetation), which consists of all herbaceous plants and tree and shrub seedlings and saplings < 0.5 m in height. Soil samples were taken from each plot according to the methodology described in Kobal et al. (2007). The organic horizons (subhorizons Ol, Of and Oh) and the mineral part of the soil were sampled in 10 cm depth increments down to bedrock. The organic part was sampled inside a wooden frame (25 × 25 cm), while the mineral part was sampled with a metal probe with an inner diameter of 6.7 cm. In each plot, five samples were systematically taken from each sub-horizon or depth and then combined into composite samples to reduce the influence of soil variability. In addition, the bulk density in the mineral part of the soil was measured at the same depths using Kopecky cylinders. The soil texture was analyzed in the Forest Ecology Laboratory at the Slovenian Forestry Institute and texture classes were determined by the proportion of sand, silt and clay particles in the mineral part of the soil according to ISO 11277. Data processing methods: Measurements for summer months (June-August) were included in the analysis. Continuous 30-min measurements were first screened for data quality checking (e.g. sensor malfunctions, obvious outliers) and then aggregated to daily values (Man et al., 2023). We extracted data for daily maximum air temperature for each day across three summer periods (2021-2023, 276 days in total). To quantify temperature buffering, we calculated daily maximum air temperature offset (ΔTmax) as the absolute difference between recorded maximum air temperature beneath forest canopies and that measured outside the forest at a plot in the nearby canopy gap without tree cover, representing macroclimate reference conditions (De Frenne et al., 2021; Wei et al., 2025). Negative offset values indicate cooling while positive values indicate warmer conditions inside the forest relative to open conditions (De Lombaerde et al., 2022). Air temperature and relative humidity data were used to calculate vapor pressure deficit (VPD), using the RH to VPD function included in the “plantecophys” package (Duursma, 2015). Daily maximum VPD (VPDmax) was included in the analysis as response variable. 30-min measurements of SWC were first aggregated to daily mean values and the averaged to obtain mean-plot level values. For further analyses, LAI values from individual measurement campaigns during the summer months (seven in 2021, six in 2022 and seven in 2023) were averaged to obtain mean-plot level values. Based on plant species recorded in the herb layer, we calculated community-weighted means for Ellenberg indicator values (Ellenberg et al., 1992) to compare the studied beech forest types in terms of vegetation-derived estimates of ecological conditions (i.e. phytoindication). For soil samples, we used results from laboratory analyses regarding two parameters: mineral soil depth (in cm) and stoniness (% vol). Software information: R software version 4.3.0 (R Core Team, 2023). Standards and calibration data: ISO 11277:2009 Soil quality — determination of particle size distribution in mineral soil material — method by sieving and sedimentation. Experimental conditions: natural conditions ------------------------------------- DATA-SPECIFIC INFORMATION: [Plot_metadata, Tmax_daily_data, Tmax_gaps_data, Tmax_offset_data, VPDmax_daily_data, SWC_daily_data, Vegetation_data] ------------------------------------- List of variables: Plot_metadata: Plot_name, Plot_ID, Beech_forest_type, Stand_maturity, Topographic_position, Mean_Tmax, Mean_Tmax_offset, Mean_VPDmax, Mean_SWC, Mean_LAI, Location, Latitude, Longitude, Elevation_m Tmax_daily_data: Date, Year, Month, Day, Mesophilous_mature_doline, Mesophilous_regeneration_doline, Mesophilous_mature_interdoline, Mesophilous_regeneration_interdoline, Thermophilous_mature_doline, Thermophilous_regeneration_doline, Thermophilous_mature_interdoline, Thermophilous_regeneration_interdoline Tmax_gaps_data: Date, Year, Month, Day, Mesophilous_gap, Thermophilous_gap Tmax_offset_data: Date, Year, Month, Day, Mesophilous_mature_doline, Mesophilous_regeneration_doline, Mesophilous_mature_interdoline, Mesophilous_regeneration_interdoline, Thermophilous_mature_doline, Thermophilous_regeneration_doline, Thermophilous_mature_interdoline, Thermophilous_regeneration_interdoline VPDmax_daily_data: Date, Year, Month, Day, Mesophilous_mature_doline, Mesophilous_regeneration_doline, Mesophilous_mature_interdoline, Mesophilous_regeneration_interdoline, Thermophilous_mature_doline, Thermophilous_regeneration_doline, Thermophilous_mature_interdoline, Thermophilous_regeneration_interdoline SWC_daily_data: Date, Year, Month, Day, Mesophilous_mature_doline, Mesophilous_regeneration_doline, Mesophilous_mature_interdoline, Mesophilous_regeneration_interdoline, Thermophilous_mature_doline, Thermophilous_regeneration_doline, Thermophilous_mature_interdoline, Thermophilous_regeneration_interdoline Vegetation_data: Date, Year, Month, Day, Mesophilous_mature_doline, Mesophilous_regeneration_doline, Mesophilous_mature_interdoline, Mesophilous_regeneration_interdoline, Thermophilous_mature_doline, Thermophilous_regeneration_doline, Thermophilous_mature_interdoline, Thermophilous_regeneration_interdoline. The herb layer contains 112 herbaceous (forbs, graminoids, ferns) and woody plant species (in rows), with nomenclature following national flora of Slovenia (Martinčič et al., 2007). The abundance of encountered species in herb layer was visually estimated using a Braun-Blanquet scale (1964) and transformed to mid-class % cover.