https://doi.org/10.5061/dryad.rr4xgxdhh

Description of the data and file structure

These datasets present original data on post-fire regeneration dynamics of Pinus halepensis Mill. (Aleppo pine), experimentally generated through a field-based climate change simulation with Open Top Chambers (OTCs).
The experiment was installed between March 2021 and November 2022 in a forest area in SW Catalonia that was affected by a stand-replacing wildfire in June 2019 but showed ongoing natural regeneration of Aleppo pine. We selected 15 plots, at least 5 m apart from each other, where we installed a paired-subplot design consisting of one Open Top Chamber and a control subplot. OTCs are glass-house like devices primarily designed to locally increase temperature in field experiments. To monitor their effects, we installed 10 air temperature and relative humidity sensors (HOBO Pro v2 U23-002A, Onset Computer Corporation, Bourne, MA, USA) along with 10 soil temperature and volumetric water content sensors (RT-1 and ECH2O EC-5, respectively, METER Group, Pullman, WA, USA) in five paired subplots (in both OTC and CON). A rain gauge (ECRN-100, METER Group, Pullman, WA, USA) was also installed to monitor precipitation.

In this experimental setup, we performed:

1. a simulation of seasonal variation in post-fire seed dispersal through artificial seeding, in order to understand the effects of OTC microclimatic variation on seedlings emergence pattern and early survival;
2. a 15-months monitoring of the effects of Open Top Chambers and seedling size on the growth and survival of naturally regenerating pines that were included within subplots.

1. We set up a germination experiment within 10 plots. In both OTC and control subplots, we installed a seeding unit consisting in a metal grid of 10 x 40 cm, divided into 8 seeding subunits. Each seeding subunit was sown with 50 P. halepensis seeds at eight different times between May 2021 and April 2022, totalling 7900 seeds. The eight different seeding times were numbered according to the chronological order of sowing (from 1 to 8) and categorized into seasons according to the equinoxes and solstices (seeding season).Seedling emergence was monitored approximately monthly until the experiment was completed in November 2022, totaling 16 assessments for the initial seed batch (sown on 17 May 2021) and 6 assessments for the last one (sown on 29 April 2022). At each visit, the emergence of new seedlings was recorded in each subunit, with mortality determined as the difference in seed count between consecutive visits.

2. All pines individuals enclosed within the subplots were tagged, resulting in 98 pine saplings in OTC and 81 in control subplots. Each tagged seedling was tracked for height growth and mortality (alive or dead) through 25 monitoring sessions between March 2021 and November 2022, with an average frequency of 22 days between visits. Heights were measured from the root collar to the tip of the needles surrounding the apical bud with a meter (cm, with a precision of ± 0.5 cm). When recording the status of the plants, trees showing complete desiccation of the crown were considered dead. Saplings presented a marked variability in initial height, and were classified into three size classes based on the 33rd and 66th percentiles of the initial height distribution, into small (< 11.5 cm), medium (between 11.5 and 16 cm) and big (< 16 cm).

Files and variables

Two zipped folders had been added. They contain the exact same files, Paneghel_et_al_2024_csv.zip in .csv format, while Paneghel_et_al_2024_xlsx.zip in .xlsx.

File: Paneghel_et_al_2024_csv.zip

Description

The folder contains 6 numbered dataset files in .csv format. Files 1 and 2 contain information about the seedling emergence experiment, files 3 and 4 about pine saplings growth and mortality, and files 5 and 6 contain information about climatic variables collected by sensors at the experimental site.\
In all files where mentioned, PLOT or plot_id is a single identifier of each subplot; plot_type identifies subplot type (OTC – Open Top Chamber or CON – control); pplot_n or Plot_N identifies the number of the plot (from 1 to 15). Dates are presented in the year/month/day format. The experiment lasted for more than one year, and when dates from the second monitoring year were transformed into DOY for analytical purposes, we added DOY+365.

1_emergence.csv\
The dataset presents information about seedling emergence from the germination experiment. The dataset is prepared for running a survival analysis (time-to-event analysis), hence each line represents a single seed.

• seed_n: identifier of the seed planted (1-50 per PLOT).
• seed_event: identifier of the seed batch according to the time of seeding (from 1 to 8).
• seeding_date: date of seeding of each seed batch.
• seeding_day: seeding date transformed in DOY (day of the year).
• seeding_season: season of seeding of each seed batch.
• interm_date: date in which seed emergence was recorded.
• interm_insp_date: DOY (day of the year) in which seed emergence was recorded.
• status: status of the seed (1: non emerged, 2: emerged).

2_emerged_mortality.csv: records of mortality of emerged seedlings from the germination experiment.

• interm_insp_doy: DOY (day of the year) in which the data was recorded.
• interm_date.x: date in which the data was recorded.
• time_fact2: Season of the date in which the data was recorded by year.
• germs: emerged seedling recorded at the time of the visit.
• germs_tot_mom: total amount of seedlings presents in the seeding until dead events were recorded (dead seedlings are discounted at the following visit).
• deaths: number of death seedlings recorded at the visit.
• cum_germ: cumulative count of emerged seedlings (deaths are not discounted).
• cum_death: cumulative count of dead seedlings.
• death_ratio: ratio of dead seedlings at each visit, calculated as deaths/germ_tot_mom (from 0 to 1).
• cum_death_ratio: cumulative ratio of dead seedlings, calculated as cum_death/cum_germ (from 0 to 1).

3_pine_growth.csv: dataset of the monitoring of the growth and survival of pine saplings naturally regenerating at the experimental site, included within the subplots.

• measurements: identifier of the monitoring visit (from 1 to 25).
• Date: date in which the visit was performed.
• Pinus_N: identifier of each tagged pine.
• Height_cm: height measurement (cm).
• Status: status of the plant at the moment of the visit (A: alive, or D: dead).
• height_class: classification into a height class according to the 33rd and 66th percentile of initial height distribution (small, medium, big).
• h_incr_n: height growth increment calculated as the difference between the height measured as subsequent visits (cm).
• cum_incr_n: cumulative increment of height growth, calculated as the sum of h_incr_n (cm).

4_pine_mortality.csv: dataset of pine saplings mortality, as prepared for survival analysis (time-to-event analysis).

• start_date: date of beginning of pine monitoring.
• Date: date in which an individual has been recorded as dead.
• Pinus_N: identifier of each tagged pine individual included in the growth and survival study.
• time: time difference in days between the beginning of the monitoring and the date in which mortality was recorded.
• Status: status of the plant (1: alive, or 2: dead).
• height_class3366: classification of each pine individual to a height class according to the 33rd and 66th percentile of initial height distribution (small, medium, big).

5_climate_air_soil.csv: dataset of microclimatic conditions measurement at the subplot level, obtained from the air and soil sensors installed at the experiment.

• Date_time: Date and time of measurement recording.
• air_temp: air temperature recorded at Date_time (°C).
• air_RH: air relative humidity recorded at Date_time (%).
• soil_temp: soil temperature recorded at Date_time (°C).
• soil_VWC: soil volumetric water content recorded at Date_time (m3/m3).

6_precipitation.csv: : recordings of daily precipitation recorded by the rain gauge at the experimental site. The rain gauge recorded data with an hourly frequency and further calculated as daily precipitation by summing all the records of a single day.

• Date: date of precipitation recording.
• ppt: daily cumulative precipitation (mm).

Due to logging malfunctioning, precipitation data of 3 periods (between the 2021/03/12 and the 2021/04/07; between the 2021/04/20 and the 2021/07/25; and between the 2022/10/20 and the 2022/11/30) were missing and were complemented with daily precipitation data obtained from the closest meteorological station (in Maials, Servei Meteorològic de Catalunya), which can be requested at their website. Data are freely accessible for research and study purpose by filling in the form at the website.

File: Paneghel_et_al_2024_xlsx.zip

Description: The folder contains 6 numbered dataset files in .xlsx format that contain the exact same information as those in Paneghel_et_al_2024_csv.zip. You can relate to the previous section for the detailed description of each single file and the information therein.

Code/software

All datasets were viewed and analysed using R software, version 4.2.3. (R Core Team, 2023).

All the packages used for data visualization, management and analyses are described in the methodology section of the related publication (Paneghel et al., 2024).

Access information

Data was derived from the following sources: \
Data for complementing missing precipitation were obtained from the Catalan meteorological service (Servei Metereològic de Catalunya) and retrieved under request from their website.