DOI: 10.5061/dryad.s4mw6m9g6
title: Wayqecha Amazon cloud curtain ecosystem experiment: Climate data and R processing code

description: Fog makes a significant contribution to the hydrology of a wide range of
 important terrestrial ecosystems. The amount and frequency of fog
 immersion are likely to be affected by rapid ongoing anthropogenic changes
 but the effects of these changes remain relatively poorly understood
 compared to changes in rainfall. Here, we present the design and
 performance of a novel experiment to actively manipulate low lying fog
 abundance in an old-growth tropical montane cloud forest (TMCF) in Peru -
 the Wayqecha Amazon Cloud Curtain Ecosystem Experiment (WACCEE). The
 treatment consists of a 30m high, 40m wide mesh curtain suspended between
 two towers and extending down to the ground, and two supplementary
 curtains orientated diagonally inwards from the top of each tower and
 secured to the ground upslope. The curtains divert and intercept airborne
 water droplets in fog moving upslope, thereby depriving a ~ 420 m2 patch
 of forest immediately behind the curtains of this water source. The
 treatment caused a strong reduction in both air humidity and leaf wetness,
 and an increase in vapour pressure deficit, above the canopy compared to a
 nearby unmodified control plot. This effect was most pronounced during the
 nighttime (20:00 - 05:00). Below-canopy shifts within the treatment were
 more subtle: relative humidity at 2 m height above the ground was
 significantly suppressed during the daytime, while soil moisture was
 apparently elevated. The treatment caused a small but significant increase
 in air temperature above the canopy but a decrease in temperature in and
 near the soil, while mixed effects were observed at 2 m height above the
 ground. Above-canopy radiation was slightly elevated due to the treatment,
 which was mainly caused by a notable increase relative to the control
 during the dry season. We employ the WACCEE infrastructure to understand
 in situ impacts of fog reduction within a pristine TCMF, but the basic
 principle of the method is extremely versatile. Further application of the
 method in other systems where fog plays a major role in ecosystem
 processes could improve our understanding of the ecological impacts of
 this important but understudied climate driver.
description: Above-canopy meteorology
 Three weather stations were installed at the WACCEE experimental
 site in May 2022, two stations were installed inside the FE plot at
 approximately 20 m height above the canopy and one station was installed
 at approximately 20 m height adjacent to the experiment on a third
 aluminum tower 40 m from the FE plot. The weather stations included a
 CS320 digital thermopile pyranometer, a HygroVUE5 temperature and relative
 humidity (RH) sensor enclosed in a RAD06 radiation shield (Campbell
 Scientific Ltd, Loughborough, UK). Data was recorded on a CR1000X
 measurement and control datalogger and the stations were powered by a SP30
 30W solar panel (Campbell Scientific Ltd, Loughborough, UK). Temperature,
 RH and radiation sensors were calibrated with each other prior to
 installation. On one station installed inside the WACCEE plot and the
 station installed adjacent to the experiment, two PHYTOS 31 leaf wetness
 sensors (METER Group, Munich, Germany) were installed perpendicular to the
 prevailing wind at a 45° angle. One leaf wetness sensor was installed
 facing upwards to estimate real leaf wetness conditions capturing both fog
 and precipitation water. The other leaf wetness sensor was installed
 facing downwards to mostly capture fog water, although there may be
 indirect inputs from rainfall via droplet drip and splash.
 Within-canopy meteorology
 Hourly air temperature and humidity were sampled with 10 sensors
 per plot (Hygrochron, Maxim Integrated, San Jose, CA, USA), suspended at 2
 m height above the ground. Loggers were shielded to remove any confounding
 influences from direct radiation and precipitation.
 Soil-level meteorology
 Hourly temperature and soil moisture were sampled with 5 sensors
 per plot (TMS-4 datalogger, Tomst s.r.o., Michelská, Czech Republic).
 Temperature was recorded at 3 levels: 15 and 2 cm above the soil surface
 and 6 cm beneath the soil surface. Moisture was recorded with the time
 domain transmission method and recorded in raw form as electromagnetic
 pulses, then converted to volumetric soil moisture with a calibration
 curve derived for “Loamy sand A” (Wild et al. 2019) in line with previous
 work from the study site (Halbritter et al. 2024).
 Vapour pressure deficit was calculated from temperature and
 relative humidity by subtracting actual vapour pressure from saturation
 vapour pressure using Teten’s equation (Teten 1930). Since fog only forms
 once relative air humidity reaches saturation point (100%), we estimated
 fog formation from relative humidity data by categorizing relative
 humidity as either equal to 100% or &lt;100%. To
 understand differences in climatic conditions between the CON and FE
 plots, we fitted linear mixed effect models using the lme4 package in R
 statistical software (Bates et al. 2015). Each climatic variable was
 considered as the response variable, with the treatment effect considered
 as a fixed effect. Date and hour were included as random intercept
 variables to control for variation in climatic conditions across time,
 whilst the treatment effect was also included as a random slope effect
 with hour to test for differences in response with time of day. The
 maximal model was compared with both a null model and a model without the
 treatment random slope effect based on the Akaike Information Criterion
 scores, with the model with the lowest AIC selected following Zuur et al.
 (2007). To compare between treatments, we fitted equivalent models to all
 climatic variables with the exception of the categorized relative humidity
 data where we used a generalised linear mixed model with a binomial error
 distribution with logit link function to account for the binary nature of
 the data. Model selection was undertaken in the same manner as the linear
 mixed effect models described above. To understand
 variation in diurnal variation in climatic variables, a generalised
 additive model was fitted using the mgcv R package (Wood 2017), with
 treatment and season included as fixed effects. To
 understand how leaf wetness varies with relative humidity, temperature and
 VPD, we fitted a general linear model with binomial family distribution
 with treatment and season included as fixed effect variables. Models were
 fitted using the stats R package (R Core Team 2023). All analyses were
 done in R version 4.2.3 (R Core Team 2023).
description: # Wayqecha Amazon Cloud Curtain Ecosystem Experiment: Climate Data and R
 processing Code
 [https://doi.org/10.5061/dryad.s4mw6m9g6](https://doi.org/10.5061/dryad.s4mw6m9g6) ## Description of the data and file structure ## **Files and variables** **File: Weather_station_data.zip** **Description:** Three excel files from 3 weather stations recording air temperature, relative humidity, solar radiation and leaf wetness (rain and fog & fog only). “WAYSTAT2”: weather station installed at approximately 20 m height above the canopy on an aluminum tower ~40 m from the FE plot, “WAYSTAT3” & “WAYSTAT4”: two stations installed inside the FE plot ~5 m behind the mesh curtain at approximately 20 m height above the canopy. "WAYSTAT3" station lacked a solar radiation sensor, "WAYSTAT4" station lacked leaf wetness sensors - missing values for these variables in the respective datasheets are denoted with "NAN". **File structure:** Column A: timestamp (year, month, day, hour, minute) – time of logged measurement. Column B: temperature (degrees Celsius) – air temperature measured at timestamp specified. Column C: relative humidity (percent) – relative humidity measured at timestamp specified. Column D: radiation (watts per m2) – solar radiation measured at timestamp specified. Column E: LeafWetCount_rain&fog (counts) – number of minutes over the preceding 30 minutes that the sensor was wet measured at timestamp specified. Column F: LeafWetCount_fog only (counts) – number of minutes over the preceding 30 minutes that the sensor was wet measured at timestamp specified. ### **File:** **iButton\_data.zip** **Description:** Eight excel files with data from 10 Ibutton sensors in each of the FE and CON plot, suspended at 2 m above the ground recording air temperature and relative humidity. Files “RH_2017”, “RH_2018”, “RH_2019”, “RH_2020” contain air relative humidity records from all Ibutton sensors in both plots from 2017, 2018, 2019 and 2020 respectively. Files “T_2017”, “T_2018”, “T_2019”, “T_2020” contain air temperature records from all Ibutton sensors in both plots from 2017, 2018, 2019 and 2020 respectively. | **Plot** | **Ibutton sensor identity number** | | :------- | :--------------------------------------------------- | | FE plot | 1-Wire/iButton Registration Number: 810000005133EA41 | | FE plot | 1-Wire/iButton Registration Number: E1000000512C9F41 | | FE plot | 1-Wire/iButton Registration Number: 4F00000051336541 | | FE plot | 1-Wire/iButton Registration Number: 3C000000512E2C41 | | FE plot | 1-Wire/iButton Registration Number: 1A000000512E3041 | | FE plot | 1-Wire/iButton Registration Number: 6400000051277B41 | | FE plot | 1-Wire/iButton Registration Number: 020000005132C141 | | FE plot | 1-Wire/iButton Registration Number: 5600000051322D41 | | FE plot | 1-Wire/iButton Registration Number: BE000000512F8241 | | FE plot | 1-Wire/iButton Registration Number: 260000005131E541 | | CON plot | 1-Wire/iButton Registration Number: 9C000000512D8A41 | | CON plot | 1-Wire/iButton Registration Number: 7400000051297D41 | | CON plot | 1-Wire/iButton Registration Number: 8400000051248F41 | | CON plot | 1-Wire/iButton Registration Number: B4000000512BB941 | | CON plot | 1-Wire/iButton Registration Number: 6900000051290A41 | | CON plot | 1-Wire/iButton Registration Number: C7000000512C8341 | | CON plot | 1-Wire/iButton Registration Number: 4C0000005014EF41 | | CON plot | 1-Wire/iButton Registration Number: 4C0000005130D841 | | CON plot | 1-Wire/iButton Registration Number: 00000000512EF741 | | CON plot | 1-Wire/iButton Registration Number: 4D000000512B3741 |  **File structure:** Column A: ID – Ibutton sensor identity number Column B: MM – month of measurement (numerical). Column C: DD – day of measurement (numerical). Column D: YY – year of measurement (numerical). Column E: HH – hour of measurement, 12 hour format (numerical). Column F: MI – minute of measurement (numerical). Column G: SS – second of measurement (numerical). Column H: AM/PM – denoting whether hour of measurement is ante meridiem (AM) or post meridium (PM). Column I: RH or T – denoting either air relative humidity (%) or temperature (degrees Celsius) measured at timestamp specified.   ### **File:** **Tomst\_data.zip** **Description:** Two excel files with data from five Tomst loggers in each plot recording temperature (15 and 2 cm above the soil surface and 6 cm beneath the soil surface) and soil moisture. File “tomst CON” contains data from five Tomst loggers in the CON plot. File “tomst_FE” contains data from five Tomst loggers in the CON plot. **File structure:** Column A: Date – date and time of measurement (year.month.day hour:minute). Column B: T1 – soil temperature at 6 cm soil depth (degrees Celsius) measured at timestamp specified. Column C: T2 – air temperature at 2 cm above the soil surface (degrees Celsius) measured at timestamp specified. Column D: T3 – soil temperature at 15 cm above the soil surface (degrees Celsius) measured at timestamp specified. Column E: VMC – volumetric soil moisture in the upper 30 cm soil layer (%) measured at timestamp specified. ## Code/software R code (version 4.2.3) for processing the attached weather data and perform following statistical analyses/packages: linear mixed effect models using the lme4 package, generalised additive model using the mgcv R package. Package "MEE.R" for analyzing data in file "Weather_station_data.zip", package "soil_moisture_-MEE.R" for analyzing data in file "Tomst_data.zip", package "ibutton_analysis-_MEE.R" for analyzing data in file " iButton_data.zip".