Canopy habitats challenge researchers with their intrinsically difficult access. The current scarcity of climatic data from forest canopies limits our understanding of the conditions and environmental variability of these diverse and dynamic habitats. We present 307 days of climate records collected between 2019 and 2020 in the tropical rainforest canopy of the Yasuní National Park, Ecuador. We monitored climate with a 10-minute temporal resolution in the middle crowns of eight canopy trees. The distance between canopy climate stations ranged from 700 m to 10 km. Apart from air temperature, relative humidity, leaf wetness, and photosynthetically active radiation (PAR), measured in each canopy climate station, global radiation, rainfall, and wind speed were measured in different subsets of them. We processed the eight data series to omit erroneous records resulting from sensor failures or lack of the solar-based power supply. In addition to the eight original data series, we present three derived data series, two aggregating canopy climate for valleys or for ridges (from four stations each), and one overall average (from the eight stations). This last derived data series contains 306 days, while the shortest of the original data series covers 22 days and the longest 296 days. In addition to the data, two open-source tools, developed in RStudio, are presented that facilitate data visualization (a dashboard) and data exploration (a filtering app) of the original and aggregated records.

To monitor canopy climate eight canopy climate stations were established in a stratified sampling design targeting ridges (four stations) and valleys (four stations). A nearby valley and a ridge correspond to a site. Air temperature, relative humidity, photosynthetic active radiation, and leaf wetness were recorded by all canopy climate stations, while precipitation, wind, and global radiation were recorded by selected canopy climate stations.

All canopy climate stations collected data every 30 seconds and recorded data every 10 minutes between April 2019 and February 2020.

To monitor canopy climate with a high temporal resolution, we installed eight climate stations, each in the crown of a tree, using adapted climbing techniques (Perry 1978) in the second half of April 2019. These trees belonged to seven species distributed in 6 botanical families and averaged 64 + 0.6 cm (mean + SE) for DBH, 26 + 0.2 m of height, and 7.2 + 0.2 m of crown radius (Table 1); we targeted canopy trees, i.e., those immersed in canopy strata of the forest, therefore we excluded emergent trees. In each tree, the sensor set (Fig. 2) was established in the medium section and upper side of crown branches (i.e., in the middle canopy sensu Johansson 1974). We verified the performance of the canopy climate stations before installation in the lab, and after installation from the ground via WIFI, using the interphase provided by the data logger maker (Device Configuration Utility, 2.21.16 by Campbell scientific). Verifications from the ground were performed one week, four months, and ten months after installation. After the last verification, we removed the climate stations.

Data verification

Data were checked in by the authors. In each data series, a variable of each climate parameter was plotted to identify record gaps and suspicious data, suggesting partial or definitive sensor failure (Fig. 3), this data exploration was enriched with fieldnotes taken at the time of removing stations. To ease comparisons among stations and sites, data series were aligned, and suspicious data were labeled as 999 (R script Data_Manipulation.R).

There is no restriction for using data from this data paper, as long as the data paper is cited as the source of the information used.

The original data series went through a data verification process while the derived data series resulted from a data compilation process. Data verification consisted of four steps: i) visualization and diagnosis of suspicious data, i.e., those occurring the possible values for the climate parameter in the locality or those with cumulative error (for instance, PAR that was above cero during night hours), resulting from malfunctioning of specific sensors; ii) replacement of suspicious data with NA; iii) calculation of derived climate variables, average relative humidity as the mean value of the recorded relative humidity variables (maximum and minimum), vapor pressure deficit (VPD) derived from average relative humidity and average air temperature following Frego and Fenton (2005), calculated dew point (DewPtC) derived from average air temperature and average relative humidity following Lawrence (2005), and transformation of solar radiation data, recorded in kWm² to W/m², because this is the most commonly used unit), and; and iv) creation of fields used to filter data, i.e., Date, Hour, and DateHour, by using functions of R base (trunc; R Core Team 2020), and “lubridate”(hour; Grolemund & Wickham 2011) and “chron”(as.chron; James & Hornik 2020) R packages. Data compilation consisted of three steps: i) aligning series for each climate parameter; ii) assigning a value for each climate parameter to each timestep, by either copying the value recorded by a single station or calculating a mean value when two or more stations registered that parameter; absent data were filled with NA; and iii) writing the aggregated data series as an output (in format csv file).