DOI: 10.5061/dryad.pc866t1qd
title: High-resolution tropical rain-forest canopy climate data

description: 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.
description: 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 <em>sensu</em> 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: <em>i</em>) 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 kWm<sup>2</sup> to
 W/m<sup>2</sup>, 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).