Data README File

This README provides explicit explanation of the data used in: Beverly, D. P., Guadagno, C. R. R., & Ewers, B. E. (2020). Biophysically Informed Imaging Acquisition of Plant Water Status. Frontiers in Forests and Global Change, 3, 125.

Data Citation:

Beverly, D. P., Guadagno, C. R. R., & Ewers, B. E. (2020). Image and Physiological Data: Biophysically Informed Imaging Acquisition of Plant Water Status. University of Wyoming. DOI/////

Authors (University of Wyoming; Botany Department):

Daniel P. Beverly (corresponding author ; )
Carmela R. Guadagno
Brent E. Ewers (Principal Investigator, data correspondent )

Abstract:

Vegetation controls carbon and water fluxes because of the fundamental tradeoff between carbon dioxide uptake and water loss occurring when stomata are open. Quantifying the rates of this exchange typically requires either intensive gas exchange or destructive harvesting of tissues and mass spectrometry analyses. Recent developments in high-throughput methods have enhanced our capacity to empirically test plant–environmental interactions. The vast integration characterizing satellite remote sensing methods masks organ-level physiological mechanisms limiting the predictive capability of current process models. Hence, more ground truth studies are necessary to determine the amount of mechanistic information needed to improve our understanding of forest, crop, and land management. Imaging methodologies, such as thermal and chlorophyll a fluorescence, are currently used to collect information for relevant traits such as water use, growth, and stress response. We tested these techniques during progressive drought across species with different susceptibility in controlled greenhouse conditions. We chose two highly represented tree species in North America: the gymnosperm Pinus ponderosa and the angiosperm Populus tremuloides. To better explore the whole drought response parameter space, we also tested a crop (Brassica rapa) and desert shrub (Artemisia tridentata). Thermal and fluorescence images of the canopy were coupled with leaf-level measurements as we performed three tests to predict drought response using (1) leaf temperature, (2) chlorophyll a fluorescence, and (3) the combination of the two. At 5 days of drought, leaf temperature increased 7 and 10%, accounting for 63 and 73% of the variation in stomatal conductance for both tree species, respectively. The fluorescence signal from images decreased 0.12% and 0.83% in moderately and severely droughted leaves respectively, reaching zero at mortality. Leaf water status was then predicted using a Bayesian approach that incorporated measurements’ uncertainty and parsimony in the analysis of the parameters. Changes in canopy temperature provided confident predictions for the reductions of daily evapotranspiration at the onset of drought. Empirically combining thermal and fluorescence measurements improved predictions (R2 = 0.81) of midday leaf water potential compared to univariate models. Our results represent an important step toward quantifying plant water status during drought using first principles that do not require species-specific information.

Keywords:

aspen, chlorophyll a fluorescence, drought, phenotyping, leaf water potential, ponderosa pine, remote sensing, thermal imaging

Field of Focus:

Ecophysiology; Ecohydrology; Image-based plant phenotyping; Remote Sensing

Research Funding Sources:

Wyoming Center for Environmental Hydrology and Geophysics (WyCEHG) (NSF EPS #1208909) and NSF IOS-1547796 to BE provided funding for this research.

Data sets are available on: XXXXXXXXXXXXXXXXXXXXXX

Methods of data collection

Data collection Dates:

Severe Drought May 1 - June 19 2018

Moderate Drought May 1 - May 23 2010

Greenhouse Environment (Williams Conservatory; University of Wyoming; Botany Department)

Throughout both experiments, the greenhouse environment was continuously monitored for temperature and relative humidity, solar radiation, and soil moisture. In 2018, measurements were recorded at 15-min intervals on a CR3000 data logger (Campbell Scientific Inc., Logan, UT, United States). Long- and shortwave radiation for the greenhouse was monitored using a four-channel net radiometer (CNR4, Kipp and ZonenDelft, Netherlands) along with incoming photosynthetic photon flux densities (PPFD, LI190SB, LiCor Inc., Lincoln, NE, United States), measured at plant height in the middle of each bench. Seasonal light conditions in the greenhouse was similar between the 2 years, with an average (standard deviation) PPFD of 157 +- 225.5 mmol m2 s-1 and 135 +- 196.2 mmol m2 s-1 for severe drought (2018) and moderate drought (2019), with comparable results in life history (i.e., growth) an biomass accumulation between the 2 years. Atmospheric temperature and relative humidity measurements of the room were taken at 2 m (HC2A, Rotronic, Hauppauge, NY, United States). Continuous soil moisture was monitored using 10 Echo-10 SM (Decagon Devices Inc., Pullman, WA, United States) soil moisture probes (one per treatment) and 30 soil gypsum blocks (three per treatment) estimating soil water potential (Supplementary Figure S1). Soil moisture sensors were calibrated to each of the four soil types (POTR, PIPO, BRR5, ARTR) using gravimetric water content (Campbell et al., 2007). Soil water potentials were calibrated with water retention curves constructed from psychrometric instruments (WP4-C, Decagon Devices Inc., Pullman, WA, United States) upon completion of the experiment on each soil type.

Leaf-Level Physiological Measurements

During both moderate (2019) and severe (2018) droughts, measurements included soil volumetric water content (VWC) (Hydrosense, Campbell Scientific Inc., Logan, UT, United States, and Echo5, Decagon Devices Inc., Pullman, WA, United States) and stem diameter for the tree species (POTR and PIPO) using calipers. In 2018, repeated measurements of all pots were conducted weekly while in 2019, all plants were screened every third day before physiological and imaging intensive time points. Time point leaf-level measurements were conducted to capture gas exchange (LI-6400XT, LiCor Biosciences, Lincoln, NE, United States), leaf-water potential (9L) (PMS Instrument Company, Albany, OR, United States), and ChlF (LI-6400XT fluorimeter (6400-40), LiCor Biosciences, Lincoln, NE, United States) as dark acclimated PSII quantum yield (Fv/Fm) or light acclimated PSII efficiency (Fv’/Fm’) and fresh leaf biomass. Throughout the course of both experiments, measurements were taken every 3–7 days to cover the range of soil moisture conditions, for a total of eight to 10 time points (Table 2). Each time point consisted of measuring between 10 and 12 (five-six well-watered and five-six droughted) samples of each species randomly selected from across the blocks. Predawn measurements were taken 2 h prior to sunrise for both experiments while midday collections started at 1100 (11:00 MDT). Gas exchange parameters were set to 400 mmol mol-1 CO2 for both midday and predawn measurements. In 2018, predawn and midday TL was set to 20 and 25 C, respectively. Midday measurement PPFD was set to 500 mmol m-2 s-1. In 2019, conditions in the greenhouse were cooler than the previous year, thus, leaf temperatures were set to 18 and 20 C in the cuvette at predawn and midday time points, respectively. Additionally, midday light levels were increased to 1000 mmol m-2 s-1 for gas exchange measurements to better match the greenhouse conditions. Due to the differences in light levels, gs are either analyzed independently or normalized between zero and one (Figure 9C) to compare gas exchange measurements between moderate and severe drought scenarios. Saturation pulses for fluorescence measurements were set to approximately 4,200 photons mmol m􀀀2 s􀀀1. At both predawn and midday, leaves were excised for measurement of 9L and fluorescence imaging. On the final day of drought (i.e., last time point) after the physiological measurements, all of the excised leaves were scanned using a flatbed scanner and analyzed for total leaf area. Scans were processed using ImageJ.

Image Collection

For the severe drought experiment (2018), all thermal and ChlF images were taken at each time point corresponding with other physiological measurements between 10:00 and 14:00 mountain standard time (MST) to avoid inconsistent lighting conditions associated with sunrise (~0600 MST), sunset (~2000 MST) and grow lights in the greenhouse (~0500-0900 MST). Thermal images captured three blocks (i.e., 12 pots), from approximately 2 meters above the bench and 1.5 meters above the pots (Figure 2). Both well-watered and droughted plants were simultaneously captured in each image as blocks were randomly mixed for each treatment (Figure 2). While thermal measurements were taken in the greenhouse environment and for entire plants, ChlF imaging occurred on single leaves taken to the FluorCam directly after gas exchange and in situ ChlF measurements.

Thermal and RGB Measurements

During the severe drought experiment (2018), thermal and color (RGB) images were captured using a FLIR 420T camera (FLIR Systems Inc., Wilsonville, OR, United States) with a 35 mm lens. The senor had an accuracy of ~1% for measurements within the range of 5–120 C in ambient temperatures lower than 35 C. Thermal images were post-processed using FLIRTools, correcting for long-wave radiation attenuation effects using target distances, ambient vapor pressures, and ambient temperatures from meteorological data collected in the greenhouse (Page et al., 2018). Images were not corrected for greenhouse long-wave emissions, as corrections were less than the sensitivity of the instrument (Page et al., 2018). Corrections of leaf emissivity (+) were standardized at 0.97 because previous studies have shown no difference in droughted and watered leaves (Buitrago et al., 2016). Regions of interest (ROI) were developed based on leaf size and leaf condition determined from the RGB image. A binary mask of leaf or not-leaf was stacked on thermal images isolating leaf temperatures. In 2019, the infrared radiometers were also corrected for leaf emissivity of 0.97 (Buitrago et al., 2016). Ambient air temperature (Ta) measured on micrometeorological stations was used to correct thermal signatures for both camera and radiometers. In 2019, mean daytime TL was calculated from infrared radiometers. For both camera and radiometers, the empirical thermal index for crop water stress, ICSWI, was calculated using the greenhouse microclimate information (Equation 1).

ChlF Image Collection and Analysis

The ChlF images were collected on excised leaves using a closed FluorCam (FC 800-C, Photon Systems Instruments, Drasov, Czechia). The youngest fully developed leaves were selected for each plant. Leaves were placed flat onto the imaging plate and screenshots recorded before (Fs) and immediately after (Fm’) the application of a saturating pulse. Saturating pulses were standardized at 4000 mmol m-2 s-1. For all captured images we developed a computing pipeline for pixel analysis. First, each image was separated into individual red, green, and blue color bands. Relative Chlorophyll Fluorescence (RChlF) was then estimated using the number counts of red (Rpix), green (Gpix) pixels, high and moderate actively fluorescing regions, compared to the sum of marginally fluorescing blue (Bpix) and actively fluorescing pixel regions throughout the entire image according to equation 2.

The RChlF measurements were compared to precise handheld PAM PSII efficiency (Fv’/Fm’) measurements from the IRGA fluorometer using a similar modeling approach (equation 3) to plant water status parameters (Table 1). Normalized PSII efficiency measures, from zero to one, were used to compare across species and drought experiments.

Infrared Radiometer Data Collection

For the moderate drought experiment in 2019, micrometeorological conditions were monitored throughout the course of the drought with the addition of continuous measurement of TL using thermal radiometers (SI-111, Apogee Instruments Inc., Logan, UT, United States) 12 cm from top of both droughted and well-watered aspen and pine canopies. All measurements were measured every 5 s and averaged every minute on CR1000 data loggers (Campbell Scientific Inc., Logan, UT, United States).

Meta Data

Meta data for these experiments is essential for determining which pot numbers correspond to species and treatments

dir("//petaLibrary.arcc.uwyo.edu/Commons/TRACK1/TempData/DanielBeverly/Data for Database/BioPhysicalImaging/Data")
## [1] "BlockAssignments_2.csv" "DroughtMetaData.csv"    "FluorescenceImages"    
## [4] "GasExchange"            "LeafWaterPotential"     "MetData"               
## [7] "StemGrowthLeafArea"     "ThermalImages"
meta18 <- read.csv("//petaLibrary.arcc.uwyo.edu/Commons/TRACK1/TempData/DanielBeverly/Data for Database/BioPhysicalImaging/Data/BlockAssignments_2.csv")


names(meta18)
##  [1] "Pot_Num"         "Spp"             "Trt"             "Block"          
##  [5] "Sensor"          "Sensors.Made"    "Sensor.Type"     "Plant.notes"    
##  [9] "Germination.day" "First.true.leaf" "Repoted"         "Wet_Rootmass"
meta19 <- read.csv("//petaLibrary.arcc.uwyo.edu/Commons/TRACK1/TempData/DanielBeverly/Data for Database/BioPhysicalImaging/Data/DroughtMetaData.csv")
names(meta19)
## [1] "Plant_ID"  "Spp"       "Trt"       "Recovered"

Severe Drought 2018 Meta data

Pot_Num == Pot number
Spp == Species (ARTR, Artemisia tridentata; BRR5, Brassica rapa R500 varities; BRVT, Brassica rapa vegetable turnip varities; PIPO, Pinus ponderosa; POTR, Populus tremuloides)
Block == Block number
Sensor == Presence of sensors (yes or no)
Sensors.Made == Checklist of senosors that were prepped for experiment
Sensor.Type == Volumetric soil water or gypsum block
Plant.nots == Comments on plant health
Germination.day == day of germination (only BRR5 and BRVT)
First.true.leaf == day of first leaf (only BRR5 and BRVT)
Repoted = date that BRVT or BRR5 was repoted to larger pots prior to drought
Wet_Rootmass == wet root biomass for plants not used in experiment

Moderate Drought 2019 Meta data

Plant_ID = Pot number
Spp == Species (PIPO, Pinus ponderosa; POTR, Populus tremuloides)
Trt == Treatment (Drought, Recovered, Watered)
Recovered == Recovered plant (yes or no)

Meteorological Data

Meterological Data from both 2018 (severe drought) and 2019 (moderate drought) experiements

These data were used to track environmental conditions of radiation, temperature, soil conditions, and leaf temperature

dir("//petaLibrary.arcc.uwyo.edu/Commons/TRACK1/TempData/DanielBeverly/Data for Database/BioPhysicalImaging/Data/MetData")
## [1] "Aspen_1min.csv"   "Combined_met.csv" "Pine_1min.csv"

Severe drought (2018)

met_18 <- read.csv("//petaLibrary.arcc.uwyo.edu/Commons/TRACK1/TempData/DanielBeverly/Data for Database/BioPhysicalImaging/Data/MetData/Combined_met.csv")
names(met_18)
##  [1] "TIMESTAMP"      "RECORD"         "BattV_Avg"      "PTemp_C_Avg"   
##  [5] "SUp_Avg"        "SDn_Avg"        "LUp_Avg"        "LDn_Avg"       
##  [9] "CNR4TC_Avg"     "CNR4TK_Avg"     "RsNet_Avg"      "RlNet_Avg"     
## [13] "Albedo_Avg"     "Rn_Avg"         "LUpCo_Avg"      "LDnCo_Avg"     
## [17] "AirTC_Avg"      "PAR_Den_Avg"    "PAR_Den_2_Avg"  "PAR_Den_Max"   
## [21] "PAR_Den_2_Max"  "PAR_Tot_Tot"    "PAR_Tot_2_Tot"  "RH"            
## [25] "RH_Max"         "RH_Min"         "Pot18_VW_Avg"   "Pot22_VW_Avg"  
## [29] "Pot41_VW_Avg"   "Pot42_VW_Avg"   "Pot83_VW_Avg"   "Pot88_VW_Avg"  
## [33] "Pot102_VW_Avg"  "Pot114_VW_Avg"  "Pot126_VW_Avg"  "Pot131_VW_Avg" 
## [37] "Pot12u_WP_kPa"  "Pot12d_WP_kPa"  "Pot15u_WP_kPa"  "Pot15d_WP_kPa" 
## [41] "Pot18u_WP_kPa"  "Pot22u_WP_kPa"  "Pot41u_WP_kPa"  "Pot42u_WP_kPa" 
## [45] "Pot48u_WP_kPa"  "Pot48d_WP_kPa"  "Pot58u_WP_kPa"  "Pot58d_WP_kPa" 
## [49] "Pot62u_WP_kPa"  "Pot62d_WP_kPa"  "Pot67u_WP_kPa"  "Pot67d_WP_kPa" 
## [53] "Pot83u_WP_kPa"  "Pot88u_WP_kPa"  "Pot100u_WP_kPa" "Pot100d_WP_kPa"
## [57] "Pot102u_WP_kPa" "Pot114u_WP_kPa" "Pot120u_WP_kPa" "Pot120d_WP_kPa"
## [61] "Pot123u_WP_kPa" "Pot123d_WP_kPa" "Pot126u_WP_kPa" "Pot128u_WP_kPa"
## [65] "Pot128d_WP_kPa" "Pot131u_WP_kPa" "Pot12u_kohms"   "Pot12d_kohms"  
## [69] "Pot15u_kohms"   "Pot15d_kohms"   "Pot18u_kohms"   "Pot22u_kohms"  
## [73] "Pot41u_kohms"   "Pot42u_kohms"   "Pot48u_kohms"   "Pot48d_kohms"  
## [77] "Pot58u_kohms"   "Pot58d_kohms"   "Pot62u_kohms"   "Pot62d_kohms"  
## [81] "Pot67u_kohms"   "Pot67d_kohms"   "Pot83u_kohms"   "Pot88u_kohms"  
## [85] "Pot100u_kohms"  "Pot100d_kohms"  "Pot102u_kohms"  "Pot114u_kohms" 
## [89] "Pot120u_kohms"  "Pot120d_kohms"  "Pot123u_kohms"  "Pot123d_kohms" 
## [93] "Pot126u_kohms"  "Pot128u_kohms"  "Pot128d_kohms"  "Pot131u_kohms"

2018 Sensors, Measurements, and Units:

Datalogger (CR3000):

TIMESTAMP == Time of measurements
RECORD == Record number
BattV == Battery voltage (volts)
PTemp_C_Avg == Datalogger temperature (C)

Net Radiometer parameters (CNR 4):

SUp_Avg == incoming shortwave radiation (W m-2)
SDn_Avg == outgoing shortwave radiation (W m-2)
LUp_Avg == incoming longwave radiation (W m-2)
LDn_Avg == outgoing longwave radiation (W m-2)
CNR4TC_Avg == temperature (C)
CNR4TK_Avg == temperature (K)
RsNet_Avg == net shortwave radiation (W m-2)
RlNet_Avg == not longwave radiation (W m-2)
Albedo_Avg == albedo (unitless)
Rn_Avg == net total radiation (W m-2)
LUpCo_Avg == incoming corrected longwave radiation (W m-2)
LDnCo_Avg == outgoing corrected longwave radiation (W m-2)

Air temperaure and relative humidity parameters (HC2A):

AirTC_Avg == air temperature (C)
RH == relative humidity (%)
RH_Max == maximum RH (%)
RH_Min == minimum RH (%)

Photochemically active radaition (PAR) parameters (2, LI190SB):

PAR_Den_Avg == PAR density (umol m-2 s-1)
PAR_Den_2_Avg == PAR density (umol m-2 s-1)
PAR_Den_Max == maximum PAR density (umol m-2 s-1)
PAR_Den_2_Max == maximum PAR density (umol m-2 s-1)
PAR_Tot_Tot == total (sum) PAR density (umol m-2)
PAR_Tot_2_Tot == total (sum) PAR density (umol m-2)

Soil water moisture and potential sensors:

**Pot###_VW_Avg** == uncalibrated soil volumetric water content for stated pot #### (See block/meta data corresponding pot ID) (units = percent (%), Echo 5)

**Pot###(u/d)_WP_kPa** == uncalibrated soil water potential measurements for pot ### (See block/meta data corresponding pot ID) where (u/d) corresponds to sensor placement in the pot (upper zone or deeper zone of the pot) (units = kilopascals (kPa), gypsum block)

**Pot###(u/d)_komhs** == raw resistance measurements for pot ### (See block/meta data corresponding pot ID) where (u/d) corresponds to sensor placement in the pot (upper zone or deeper zone of the pot) (units = ohms (omega), gypsum block)

Moderate drought (2019)

met_19aspen <- read.csv("//petaLibrary.arcc.uwyo.edu/Commons/TRACK1/TempData/DanielBeverly/Data for Database/BioPhysicalImaging/Data/MetData/Aspen_1min.csv")
names(met_19aspen)
##  [1] "TIMESTAMP"             "RECORD"                "AirTC_Avg"            
##  [4] "RH"                    "PAR_Den_Avg"           "PAR_Tot_Tot"          
##  [7] "TT_C_79_drought_Avg"   "SBT_C_79_drought_Avg"  "TT_C_91_water_Avg"    
## [10] "SBT_C_91_water_Avg"    "PRI_drought_Med"       "PRI_water_Med"        
## [13] "Down_532_drought_Med"  "Down_570_drought_Med"  "Up_532_drought_Med"   
## [16] "Up_570_drought_Med"    "Down_532_water_Med"    "Down_570_water_Med"   
## [19] "Up_532_water_Med"      "Up_570_water_Med"      "Ind_down_drought_Med" 
## [22] "Ind_up_drought_Med"    "Ind_down_water_Med"    "Ind_up_water_Med"     
## [25] "delta_T_C_drought_Avg" "delta_T_C_water_Avg"

2019_Aspen Sensors, Measurements, and Units:

Datalogger (CR1000):

TIMESTAMP == Time of measurements
RECORD == Record number

Air temperaure and relative humidity parameters (HMP45):

AirTC_Avg == air temperature (C)
RH == relative humidity (%)

Photochemically active radaition (PAR) parameters (2, LI190SB):

PAR_Den_Avg == PAR density (umol m-2 s-1)
PAR_Tot_Tot == total (sum) PAR density (umol m-2)

Thermal Infrared Radiometer (2, SI111):

TT_C_79_drought_Avg == Canopy Temperature plant 79 (C)
SBT_C_79_drought_Avg == Instrument Temperature plant 79 (C)
TT_C_91_water_Avg == Canopy Temperature plant 91 (C)
SBT_C_91_water_Avg == Insturment Temperature plant 91 (C)

Photochemical Reflective Index (PRI) (2, SRS-PRI):

PRI_drought_Med == Photochemical Reflective Index (PRI) drought (unitless)
PRI_water_Med == Photochemical Reflective Index (PRI) watered (unitless)
Down_532_drought_Med == Reflected 532 nm drought (W m-2)
Down_570_drought_Med == Reflected 570 nm drought (W m-2)
Up_532_drought_Med == Incoming 532 nm drought (W m-2)
Up_570_drought_Med == Incoming 570 nm drought (W m-2)
Down_532_water_Med == Reflected 532 nm watered (W m-2)
Down_570_water_Med == Reflected 570 nm watered (W m-2)
Up_532_water_Med == Incoming 532 nm watered (W m-2)
Up_570_water_Med == Incoming 570 nm watered (W m-2)
Ind_down_drought_Med == Diagnostics reflected droguht (unitless)
Ind_up_drought_Med == Diagnostics incoming droguht (unitless)
Ind_down_water_Med == Diagnostics reflected watered (unitless)
Ind_up_water_Med == Diagnostics incoming watered (unitless)

Calculated parameters:

delta_T_C_drought_Avg == drought leaf temperature - air temperature (C)
delta_T_C_water_Avg == watered leaf temperature - air temperature (C)

met_19pine <- read.csv("//petaLibrary.arcc.uwyo.edu/Commons/TRACK1/TempData/DanielBeverly/Data for Database/BioPhysicalImaging/Data/MetData/Pine_1min.csv")
names(met_19pine)
##  [1] "TIMESTAMP"             "RECORD"                "AirTC_Avg"            
##  [4] "RH"                    "PAR_Den_Avg"           "PAR_Tot_Tot"          
##  [7] "TT_C_67_drought_Avg"   "SBT_C_67_drought_Avg"  "TT_C_92_water_Avg"    
## [10] "SBT_C_92_water_Avg"    "PRI_drought_Med"       "PRI_water_Med"        
## [13] "Down_532_drought_Med"  "Down_570_drought_Med"  "Up_532_drought_Med"   
## [16] "Up_570_drought_Med"    "Down_532_water_Med"    "Down_570_water_Med"   
## [19] "Up_532_water_Med"      "Up_570_water_Med"      "Ind_down_drought_Med" 
## [22] "Ind_up_drought_Med"    "Ind_down_water_Med"    "Ind_up_water_Med"     
## [25] "delta_T_C_drought_Avg" "delta_T_C_water_Avg"

2019_Pine Sensors, Measurements, and Units:

Datalogger (CR1000):

TIMESTAMP == Time of measurements
RECORD == Record number

Air temperaure and relative humidity parameters (HMP45):

AirTC_Avg == air temperature (C)
RH == relative humidity (%)

Photochemically active radaition (PAR) parameters (2, LI190SB):

PAR_Den_Avg == PAR density (umol m-2 s-1)
PAR_Tot_Tot == total (sum) PAR density (umol m-2)

Thermal Infrared Radiometer (2, SI111):

TT_C_67_drought_Avg == Canopy Temperature plant 67 (C)
SBT_C_67_drought_Avg == Instrument Temperature plant 67 (C)
TT_C_92_water_Avg == Canopy Temperature plant 92 (C)
SBT_C_92_water_Avg == Insturment Temperature plant 92 (C)

Photochemical Reflective Index (PRI) (2, SRS-PRI):

PRI_drought_Med == Photochemical Reflective Index (PRI) drought (unitless)
PRI_water_Med == Photochemical Reflective Index (PRI) watered (unitless)
Down_532_drought_Med == Reflected 532 nm drought (W m-2)
Down_570_drought_Med == Reflected 570 nm drought (W m-2)
Up_532_drought_Med == Incoming 532 nm drought (W m-2)
Up_570_drought_Med == Incoming 570 nm drought (W m-2)
Down_532_water_Med == Reflected 532 nm watered (W m-2)
Down_570_water_Med == Reflected 570 nm watered (W m-2)
Up_532_water_Med == Incoming 532 nm watered (W m-2)
Up_570_water_Med == Incoming 570 nm watered (W m-2)
Ind_down_drought_Med == Diagnostics reflected droguht (unitless)
Ind_up_drought_Med == Diagnostics incoming droguht (unitless)
Ind_down_water_Med == Diagnostics reflected watered (unitless)
Ind_up_water_Med == Diagnostics incoming watered (unitless)

Calculated parameters:

delta_T_C_drought_Avg == drought leaf temperature - air temperature (C)
delta_T_C_water_Avg == watered leaf temperature - air temperature (C)

Gas Exchange Data

Gasexhange data from severe drought (2018)

Severe drought (2018)

g_18 <- read.csv("//petaLibrary.arcc.uwyo.edu/Commons/TRACK1/TempData/DanielBeverly/Data for Database/BioPhysicalImaging/Data/GasExchange/IrgaCombined_working_v6.csv")
names(g_18)
##  [1] "Date"         "Time"         "DOD"          "Pot_Num"      "Needle_num"  
##  [6] "LA_corrected" "Days_Drought" "IRGA"         "Obs"          "HHMMSS"      
## [11] "FTime"        "EBal."        "Photo"        "Cond"         "Ci"          
## [16] "FCnt"         "DCnt"         "Fo"           "Fm"           "Fo."         
## [21] "Fm."          "Fs"           "Measure_Type" "Fv.Fm"        "Fv..Fm."     
## [26] "PhiPS2"       "Adark"        "RedAbs"       "BlueAbs"      "X.Blue"      
## [31] "LeafAbs"      "PhiCO2"       "qP"           "qN"           "NPQ"         
## [36] "ParIn.Fs"     "PS2.1"        "ETR"          "Trmmol"       "VpdL"        
## [41] "CTleaf"       "Area"         "BLC_1"        "StmRat"       "BLCond"      
## [46] "Tair"         "Tleaf"        "TBlk"         "CO2R"         "CO2S"        
## [51] "H2OR"         "H2OS"         "RH_R"         "RH_S"         "Flow"        
## [56] "PARi"         "PARo"         "Press"        "CsMch"        "HsMch"       
## [61] "CsMchSD"      "HsMchSD"      "CrMchSD"      "HrMchSD"      "StableF"     
## [66] "BLCslope"     "BLCoffst"     "f_parin"      "f_parout"     "alphaK"      
## [71] "Status"       "fda"          "Trans"        "Tair_K"       "Twall_K"     
## [76] "R.W.m2."      "Tl.Ta"        "SVTleaf"      "h2o_i"        "h20diff"     
## [81] "CTair"        "SVTair"       "CndTotal"     "vp_kPa"       "VpdA"        
## [86] "CndCO2"       "Ci_Pa"        "Ci.Ca"        "RHsfc"        "C2sfc"       
## [91] "AHs.Cs"       "Fv"           "PARabs"       "Fv."          "qP_Fo"       
## [96] "qN_Fo"

LiCor 6400XT

Date == Date of measurements
Time == Time (Predawn or midday)
DOD == Days of Drought
Pot_Num == Pot number
Needle_num == number of pine needles in IRGA chamber
LA_corrected == corrected leaf area for pines
Days_Drought == Days of Drought
IRGA == Insturment ID
Obs == Observation count
HHMMSS == Time
FTime == Linear Time
EBal. == Energy balance (1=yes, 0 = no)
Photo == Net assimalation (umol m-2 s-1)
Cond == stomatal conductance (mol m-2 s-1)
Ci == leaf internal CO2 concentration (umol)
FCnt == Flash count
DCnt == Dark pulse count
Fo == Minimal fluorescence (dark acclimated) (unitless)
Fm == Maximum fluorescence (dark acclimated) (unitless)
Fo. == Minimal fluorescence (light acclimated) (unitless)
Fm. == Maximum fluorescence (light acclimated) (unitless)
Fs == Steady state fluorescence (unitless)
Measure_Type == Measurement type (gas exchange or flash)
Fv.Fm == dark acclimated PSII efficiency (unitless)
Fv..Fm. == light acclimated PSII efficiency (unitless)
PhiPS2 == quantum yield of PSII efficiency (unitless)
Adark == Dark photosynthetic rate
RedAbs == Leaf absorptance at 640 nm
BlueAbs == Leaf absorptance at 460 nm
X.Blue == percent blue light
LeafAbs == Leaf absorptance
PhiCO2 == quantum yield calculated from CO2
qP == Photochemical quenching
qN == Non-photochemical quenching
NPQ == Alternative non-photochemical quenching (unitless)
ParIn.Fs == The measured value of ParIn_um when Fs was last set
PS2.1 == Photosystem distribution factor
ETR == Electron transport rate
Trmmol == Transpiration (mmol H20 m-2 s-1)
VpdL == Leaf level vapor pressure deficit (kPa)
CTleaf == Computed leaf temperature
Area == Leaf area (cm2)
BLC_1 == One-sided boundary layer conductance
StmRat == Stomatal ratio estimated
BLCond == Effective boundary layer conductance
Tair == Air temperature (C)
Tleaf == Leaf temperature (C)
TBlk == IRGA block temperature (C)
CO2R == Reference CO2 concentration (umol mol-1)
CO2S == Sample CO2 concentration (umol mol-1)
H2OR == Reference H2O concentration (mmol mol-1)
H2OS == Sample H2O concentration (mmol mol-1)
RH_R == Reference relative humidity (%)
RH_S == Sample relative humidity (%)
Flow == Gas flow rate (umol s-1)
PARi == Photochemical active radiation inside chamber (umol m-2 s-1)
PARo == Photochemical active radiation outside chamber (umol m-2 s-1)
Press == Atmospheric pressure (kPa)
CsMch == sample CO2 offest (umol mol-1)
HsMch == sample H2O offest (umol mol-1)
CsMchSD == sample CO2 offest standard deviation (umol mol-1)
HsMchSD == sample H2O offest standard deviation (umol mol-1)
CrMchSD == reference CO2 offest standard deviation (umol mol-1)
HrMchSD == reference H2O offest standard deviation (umol mol-1)
StableF == Stable / Total as fraction
BLCslope == slope as function of area
BLCoffst == offest as function of area
f_parin == Fraction of ParIN_um to use for energy balance
f_parout == Fraction of ParOut_um to use for energy balance
alphaK == Used in the conversion of umol mol-1 to W m-2
Status == Instrument Status
fda == flow / area with units conversion
Trans == Transpiration rate (mol m-2 s-1)
Tair_K == Air temperature (K)
Twall_K == temperature (K)
R.W.m2. == Incoming radiation (W m-2)
Tl.Ta == energy balance delta temperature
SVTleaf == Leaf saturated vapor pressure
h2o_i == Intercellular H2O
h20diff == difference
CTair == Chamber air temperature (C)
SVTair == Air saturated vapor pressure
CndTotal == Total conductance
vp_kPa == vapor pressure chamber air (kPa)
VpdA == atmospheric vapor pressure deficit (kPa)
CndCO2 == Conductance of CO2
Ci_Pa == Intercellular CO2 (Pa)
Ci.Ca == Ratio of inter cellular and atmospheric CO2 concentrations (unitless)
RHsfc == Surface humidity (%)
C2sfc == Surface CO2 (umol mol-1)
AHs.Cs == Ball Berry parameter
Fv == Variable fluorescence (dark acclimated) (unitless)
PARabs == Absorbed PAR (umol)
Fv. == Variable fluorescence (light acclimated) (unitless)
qP_Fo == Photochemical quenching calculated using Fo
qN_Fo == Non-photochemical quenching calculated using Fo

Gasexhange data from moderate drought (2019)

Moderate drought (2019)

g_19 <- read.csv("//petaLibrary.arcc.uwyo.edu/Commons/TRACK1/TempData/DanielBeverly/Data for Database/BioPhysicalImaging/Data/GasExchange/CombinedGasExchange.csv")
names(g_19)
##  [1] "Plant_ID"          "DOD"               "Time"             
##  [4] "Need_Num"          "CorrectedLeafArea" "MeasType"         
##  [7] "IRGA"              "Obs"               "HHMMSS"           
## [10] "FTime"             "EBal."             "Photo"            
## [13] "Cond"              "Ci"                "FCnt"             
## [16] "DCnt"              "Fo"                "Fm"               
## [19] "Fo."               "Fm."               "Fs"               
## [22] "Fv.Fm"             "Fv..Fm."           "PhiPS2"           
## [25] "Adark"             "RedAbs"            "BlueAbs"          
## [28] "X.Blue"            "LeafAbs"           "PhiCO2"           
## [31] "qP"                "qN"                "NPQ"              
## [34] "ParIn.Fs"          "PS2.1"             "ETR"              
## [37] "Trmmol"            "VpdL"              "CTleaf"           
## [40] "Area"              "BLC_1"             "StmRat"           
## [43] "BLCond"            "Tair"              "Tleaf"            
## [46] "TBlk"              "CO2R"              "CO2S"             
## [49] "H2OR"              "H2OS"              "RH_R"             
## [52] "RH_S"              "Flow"              "PARi"             
## [55] "PARo"              "Press"             "CsMch"            
## [58] "HsMch"             "StableF"           "BLCslope"         
## [61] "BLCoffst"          "f_parin"           "f_parout"         
## [64] "alphaK"            "Status"            "fda"              
## [67] "Trans"             "Tair_K"            "Twall_K"          
## [70] "R.W.m2."           "Tl.Ta"             "SVTleaf"          
## [73] "h2o_i"             "h20diff"           "CTair"            
## [76] "SVTair"            "CndTotal"          "vp_kPa"           
## [79] "VpdA"              "CndCO2"            "Ci_Pa"            
## [82] "Ci.Ca"             "RHsfc"             "C2sfc"            
## [85] "AHs.Cs"            "Fv"                "PARabs"           
## [88] "Fv."               "qP_Fo"             "qN_Fo"

LiCor 6400XT

Plant_ID == Pot number
DOD == Days of Drought
Time == Time (Predawn or midday)
Need_num == number of pine needles in IRGA chamber
CorrectedLeafArea == corrected leaf area for pines
MeasType == Measurement type (gas exchange or flash)
IRGA == Insturment ID

Other paramters are the same as 2018 data:

Leaf Water Potential / Screening Data

Leaf water potential/screening data from severe drought (2018)

Severe drought (2018)

l_18 <- read.csv("//petaLibrary.arcc.uwyo.edu/Commons/TRACK1/TempData/DanielBeverly/Data for Database/BioPhysicalImaging/Data/LeafWaterPotential/TimePoints_cleaned.csv")
names(l_18)
##  [1] "Date"       "Time"       "DoD"        "Pot_num"    "SM_Echo"   
##  [6] "FvFm"       "LWP"        "F_BM"       "D_BM"       "Leaf_Water"
## [11] "LW_FBM"     "LW_DBM"

Date == Date of measurement
Time == Time (Predawn or midday)
DoD == Days of Drought
Pot_num == Pot number
SM_Echo == Soil volumtric water content (%)
FvFm == PSII quantum yield (FluorPen) (unitless)
LWP == Leaf water potential (MPa)
F_BM == Fresh leaf biomass (g)
D_BM == Dry leaf biomass (g)
Leaf_Water == Leaf water content (F_BM - D_BM) (g)
LW_FBM == ratio of leaf water to fresh biomass
LW_DBM == ratio of leaf water to dry biomass

Leaf water potential/screening data from moderate drought (2019)

Moderate drought (2019)

l_19 <- read.csv("//petaLibrary.arcc.uwyo.edu/Commons/TRACK1/TempData/DanielBeverly/Data for Database/BioPhysicalImaging/Data/LeafWaterPotential/TimePoint_2019.csv")
names(l_19)
## [1] "Plant_ID"     "DOD"          "Time"         "PSI_Leaf"     "PSI_Stem"    
## [6] "PSI_Leaf_Mpa" "PSI_Stem_Mpa" "Diag"         "Note"

Plant_ID == Pot number
DOD == Days of Drought
Time == Time (Predawn or midday)
PSI_Leaf == Leaf water potential (bar)
PSI_Stem == Whole-plat water potential (bar)
PSI_Leaf_Mpa == Leaf water potential (MPa)
PSI_Stem_Mpa == Whole-plant water potential (MPa)
Diag == Error taking sample (0 = no error, 1 = error)
Note == Notes on plant and errors

Growth and Leaf Area Data

Stem diameter data from severe drought (2018)

Severe drought (2018)

stem_18 <- read.csv("//petaLibrary.arcc.uwyo.edu/Commons/TRACK1/TempData/DanielBeverly/Data for Database/BioPhysicalImaging/Data/StemGrowthLeafArea/StemDiam_Clean.csv")
names(stem_18)
## [1] "Date"         "Pot_Num"      "DoD"          "Stem_Dia"     "N_Leaves"    
## [6] "N_DeadLeaves" "Stem_FvFm"    "RGR"

Date == Date of measurement
Pot_Num == Pot number
DoD == Day of Drought
Stem_Dia == Stem diameter (mm)
N_Leaves == number of leaves (aspen only)
N_DeadLeaves == number of dead leaves (aspen only)
Stem_FvFm == stem PSII quantum yield (FluorPen) (unitless)
RGR == Stem relative growth rate (unitless)

Stem diameter data from moderate drought (2019)

Moderate drought (2019)

stem_19 <- read.csv("//petaLibrary.arcc.uwyo.edu/Commons/TRACK1/TempData/DanielBeverly/Data for Database/BioPhysicalImaging/Data/StemGrowthLeafArea/Screening_2019_rgr.csv")
names(stem_19)
## [1] "Plant_ID"      "DOD_Screen"    "DOD"           "Stem_diam_mm" 
## [5] "QY_FP"         "SoilMoist"     "SoilCond"      "SoilMoist_Est"
## [9] "RGR"

Plant_ID == Pot number
DOD_Screen == Screening Day of Drought of measurments (DOD - 1)
DOD == Day of Drought of measurments
Stem_diam_mm == Stem diameter (mm)
QY_PF == PSII quantum yield (FluorPen) (unitless)
SoilMoist == Soil Moisture (Campbell Sci. HydroSense) (% volumetric)
SoilCond == Soil conductivity (Campbell Sci. HydroSense) (uS)
SoilMoist_Est == Correct soil mositure (% volumetric)
RGR == Stem relative growth rate (unitless)

Leaf area data from moderate drought (2019)

Moderate drought (2019)

leaf_19 <- read.csv("//petaLibrary.arcc.uwyo.edu/Commons/TRACK1/TempData/DanielBeverly/Data for Database/BioPhysicalImaging/Data/StemGrowthLeafArea/LeafArea_2019.csv")
names(leaf_19)
## [1] "Plant_ID"        "Spp"             "Trt"             "Area_cm2"       
## [5] "Area_m2"         "LAI_pot_HalfVol" "LAI_pot"         "Diag"

Plant_ID == Pot number
Spp == Species (Pine (PIPO) and Aspen (POTR))
Trt == Treatment (Drought, Recovered, Water)
Area_cm2 == leaf area (cm2)
Area_m2 == leaf area (m2)
LAI_pot_HalfVol == projected leaf area to half pot area
LAI_pot == projected leaf area to pot area
Daig == Diagnostic Notes

Raw Scans for calculating leaf area data from moderate drought (2019)

Moderate drought (2019)

PineScan <- dir("//petaLibrary.arcc.uwyo.edu/Commons/TRACK1/TempData/DanielBeverly/Data for Database/BioPhysicalImaging/Data/StemGrowthLeafArea/RawScans/Pine")
PineScan
##  [1] "121_1.jpg" "121_2.jpg" "121_3.jpg" "122_1.jpg" "122_2.jpg" "122_3.jpg"
##  [7] "122_4.jpg" "124_1.jpg" "124_2.jpg" "124_3.jpg" "125_1.jpg" "125_2.jpg"
## [13] "127_1.jpg" "127_2.jpg" "127_3.jpg" "127_4.jpg" "127_5.jpg" "127_6.jpg"
## [19] "128_1.jpg" "129_1.jpg" "129_2.jpg" "129_3.jpg" "129_4.jpg" "130_1.jpg"
## [25] "130_2.jpg" "130_3.jpg" "131_1.jpg" "131_2.jpg" "131_3.jpg" "131_4.jpg"
## [31] "131_5.jpg" "131_6.jpg" "132_1.jpg" "132_2.jpg" "132_3.jpg" "132_4.jpg"
## [37] "132_5.jpg" "134_1.jpg" "134_2.jpg" "134_3.jpg" "134_4.jpg" "134_5.jpg"
## [43] "134_6.jpg" "134_7.jpg" "134_8.jpg" "135_1.jpg" "135_2.jpg" "135_3.jpg"

Images ID corresponds to “PlantID_Replicate.jpg”. For example, image “121_1.jpg” corresponds to the first scanned for leaves for PlantID 121.

AspenScan <- dir("//petaLibrary.arcc.uwyo.edu/Commons/TRACK1/TempData/DanielBeverly/Data for Database/BioPhysicalImaging/Data/StemGrowthLeafArea/RawScans/Aspen")
AspenScan
##  [1] "36_1.jpg"      "36_2.jpg"      "36_3.jpg"      "37_1.jpg"     
##  [5] "37_2.jpg"      "37_3.jpg"      "38_1.jpg"      "38_2.jpg"     
##  [9] "39_1.jpg"      "39_2.jpg"      "41_1.jpg"      "42_1.jpg"     
## [13] "42_2.jpg"      "42_3.jpg"      "44_1.jpg"      "44_2.jpg"     
## [17] "45_1.jpg"      "45_2.jpg"      "45_3.jpg"      "47_1.jpg"     
## [21] "47_2.jpg"      "47_3.jpg"      "47_4.jpg"      "47_5.jpg"     
## [25] "48_1.jpg"      "48_2.jpg"      "48_2_cool.jpg" "48_3.jpg"     
## [29] "48_4.jpg"      "48_5.jpg"      "49_1.jpg"      "49_2.jpg"     
## [33] "49_3.jpg"      "50_1.jpg"      "50_2.jpg"      "50_3.jpg"     
## [37] "50_4.jpg"      "50_5.jpg"

Images ID corresponds to “PlantID_Replicate.jpg”. For example, image “36_1.jpg” corresponds to the first scanned for leaves for PlantID 36.

Thermal Image Data

Extracted thermal data from severe drought (2018)

Severe drought (2018)

ThermalExtracted <- read.csv("//petaLibrary.arcc.uwyo.edu/Commons/TRACK1/TempData/DanielBeverly/Data for Database/BioPhysicalImaging/Data/ThermalImages/Therm_data_All_2018.csv")
names(ThermalExtracted)
## [1] "Date"      "DOD"       "Plant_ID"  "Shape"     "Temp_Leaf" "Temp_Air" 
## [7] "RH"

Date == Date of image
DOD == Days of Drought
Plant_ID == Pot number
Shape == Polygon shape used to extract leaf temperature
Temp_Leaf == Leaf Temperature (C)
Temp_Air == Air Temperature (C)
RH == Relative humidity (%)

Raw thermal images from severe drought (2018)

Severe drought (2018)

ThermTime <- c(dir("//petaLibrary.arcc.uwyo.edu/Commons/TRACK1/TempData/DanielBeverly/Data for Database/BioPhysicalImaging/Data/ThermalImages", pattern = "05"),dir("//petaLibrary.arcc.uwyo.edu/Commons/TRACK1/TempData/DanielBeverly/Data for Database/BioPhysicalImaging/Data/ThermalImages", pattern = "06"))

ThermTime
##  [1] "050418" "050718" "051018" "051418" "051718" "052118" "052718" "060318"
##  [9] "061218" "061618" "062018"
###//Example of thermal images in each folder
dir("//petaLibrary.arcc.uwyo.edu/Commons/TRACK1/TempData/DanielBeverly/Data for Database/BioPhysicalImaging/Data/ThermalImages/051018/DCIM/DirA")
##  [1] "DC_2633.jpg" "DC_2635.jpg" "DC_2637.jpg" "DC_2639.jpg" "DC_2641.jpg"
##  [6] "DC_2643.jpg" "DC_2645.jpg" "DC_2647.jpg" "DC_2651.jpg" "DC_2653.jpg"
## [11] "IR_2632.jpg" "IR_2634.jpg" "IR_2636.jpg" "IR_2638.jpg" "IR_2640.jpg"
## [16] "IR_2642.jpg" "IR_2644.jpg" "IR_2646.jpg" "IR_2650.jpg" "IR_2652.jpg"

ThermTime is a vector of folder names that correspond to each timepoint. Each respective folder contains both the thermal (IR_####.jpg) and rgb (DC_####.jpg) for each capture. The thermal image contains polygons of leaves that are used to extract leaf temperature after correcting for emisivity, relative humidity, temperature. These corrections and extrcations were conducted using FLIR Tools software package (Version 5.13.18031.2002).

Extracted thermal images from severe drought (2018)

Severe drought (2018)

ThermImageOut <- dir("//petaLibrary.arcc.uwyo.edu/Commons/TRACK1/TempData/DanielBeverly/Data for Database/BioPhysicalImaging/Data/ThermalImages/OutPut_Therms_2018")

ThermImageOut
##  [1] "TP_1_20180504"  "TP_10_20180616" "TP_2_20180507"  "TP_3_20180510" 
##  [5] "TP_4_20180514"  "TP_5_20180517"  "TP_6_20180521"  "TP_7_20180527" 
##  [9] "TP_8_20180603"  "TP_9_20180612"

ThermImageOut is a vector of folder names that correspond to FLIR extracts for each timepoint. Each respective folder contains .csv files that extracts thermal signature for each pixel within individaul polygons. These extrcations were conducted using FLIR Tools software package (Version 5.13.18031.2002).

Fluorescence Image Data

Extracted fluorescence data from severe drought (2018)

Severe drought (2018)

FluorExtracted_18 <- read.csv("//petaLibrary.arcc.uwyo.edu/Commons/TRACK1/TempData/DanielBeverly/Data for Database/BioPhysicalImaging/Data/FluorescenceImages/FlourOut_TimePoints.csv")
names(FluorExtracted_18)
##  [1] "TimePoint" "Image"     "Pot_ID"    "Spp"       "Trt"       "Date"     
##  [7] "DOD"       "Tissue"    "Param"     "Blue"      "Green"     "Red"      
## [13] "Total_Pix" "ID_1"      "ID_2"      "ID_3"

TimePoint == Timepoint file
Image == Image ID
Pot_ID == Pot Number
Spp == Species
Trt == Treatment
Date == Date of Measurement
DOD == Days of drought
Tissue == Tissue type
Param == Fluorescence parameter
Blue == Number of blue pixels
Green == Number of green pixels
Red == Number of red pixels
Total_Pix == count of total pixels
ID_1 == Pot ID of first plant on triplet measurements
ID_2 == Pot ID of second plant on triplet measurements
ID_3 == Pot ID of third plant on triplet measurements

Extracted fluorescence data from moderate drought (2019)

Moderate drought (2019)

FluorExtracted_19 <- read.csv("//petaLibrary.arcc.uwyo.edu/Commons/TRACK1/TempData/DanielBeverly/Data for Database/BioPhysicalImaging/Data/FluorescenceImages/FlourExtracted.csv")
names(FluorExtracted_19)
##  [1] "Image"    "Plant_ID" "DOD"      "Date"     "Time"     "Param"   
##  [7] "Blue"     "Green"    "Red"      "Flagged"  "Note"

Image == Image ID
Plant_ID == Pot Number
DOD == Days of drought
Date == Date of Measurement
Time == Time (predawn or midday)
Param == Fluorescence parameter
Blue == Number of blue pixels
Green == Number of green pixels
Red == Number of red pixels
Flagged == Diagnostics for image quality
Note == Comments regarding images

Raw fluorescence images from severe drought (2018)

Severe drought (2018)

FluorTime_18 <- dir("//petaLibrary.arcc.uwyo.edu/Commons/TRACK1/TempData/DanielBeverly/Data for Database/BioPhysicalImaging/Data/FluorescenceImages/2018", pattern = "18")

FluorTime_18
## [1] "TP1_05052018" "TP2_05092018" "TP3_05122018" "TP4_05162018" "TP5_05192018"
## [6] "TP6_05292018" "TP7_06052018" "TP8_06152018" "TP9_06222018"
###//Example of fluorescence images in each folder
dir("//petaLibrary.arcc.uwyo.edu/Commons/TRACK1/TempData/DanielBeverly/Data for Database/BioPhysicalImaging/Data/FluorescenceImages/2018/TP1_05052018")
##  [1] "115_130_108_Turnip_WW.bmp"      "115_99_Turnip_WWDroughted.bmp" 
##  [3] "13_24_Aspen_WWDroughted.bmp"    "131_147_132_R500_Droughted.bmp"
##  [5] "14_11_21_Aspen_WW.bmp"          "150_132_R500_WWDroughted.bmp"  
##  [7] "150_91_144_R500_WW.bmp"         "2_26_24_Aspen_Droughted.bmp"   
##  [9] "50_35_37_Pine_WW.bmp"           "50_38_Pine_WWDroughted.bmp"    
## [11] "55_36_38_Pine_Droughted.bmp"    "72_65_75_Sage_WW.bmp"          
## [13] "75_89_Sage_WWDroughted.bmp"     "83_69-89_Sage_Droughted.bmp"   
## [15] "93_104_99_Turnip_Droughted.bmp"

FluorTime_18 is a vector of folder names that correspond to each timepoint. Each respective folder contains fluorescence images where images are labeled as "######_Species_Treatment.bmp". The ## represent pot numbers, species correspond to Pine, sagebbrush, aspen, brassica turnips or R500 cultivars, and the treatment (droughted==DD or well-watered==WW).

Raw fluorescence images from moderate drought (2019)

Moderate drought (2019)

FluorTime_19 <- dir("//petaLibrary.arcc.uwyo.edu/Commons/TRACK1/TempData/DanielBeverly/Data for Database/BioPhysicalImaging/Data/FluorescenceImages/2019/Fluorescence_Images_raw", pattern = "19")

FluorTime_19
##  [1] "4.28.19.MD" "4.28.19.PD" "5.1.19 MD"  "5.1.19 PD"  "5.10.19 MD"
##  [6] "5.10.19 PD" "5.13.19 MD" "5.13.19 PD" "5.16.19 MD" "5.16.19 PD"
## [11] "5.22.19 MD" "5.22.19 PD" "5.30.19 MD" "5.4.19MD"   "5.4.19PD"  
## [16] "5.7.19 PD"  "5.7.19MD"   "6.2.19 MD"  "6.7.19 MD"
###//Example of fluorescence images in each folder
dir("//petaLibrary.arcc.uwyo.edu/Commons/TRACK1/TempData/DanielBeverly/Data for Database/BioPhysicalImaging/Data/FluorescenceImages/2019/Fluorescence_Images_raw/5.10.19 MD")
##  [1] "111_Fm'.bmp" "111_Fs.bmp"  "112_Fm'.bmp" "112_Fs.bmp"  "113_Fm'.bmp"
##  [6] "113_Fs.bmp"  "115_Fm'.bmp" "115_Fs.bmp"  "151_Fm'.bmp" "151_Fs.bmp" 
## [11] "153_Fm'.bmp" "153_Fs.bmp"  "154_Fm'.bmp" "154_Fs.bmp"  "155_Fm'.bmp"
## [16] "155_Fs.bmp"  "26_Fm'.bmp"  "26_Fs.bmp"   "27_Fm'.bmp"  "27_Fs.bmp"  
## [21] "28_Fm'.bmp"  "28_Fs.bmp"   "30_Fm'.bmp"  "30_Fs.bmp"   "66_Fm'.bmp" 
## [26] "66_Fs.bmp"   "67_Fm'.bmp"  "67_Fs.bmp"   "68_Fm'.bmp"  "68_Fs.bmp"  
## [31] "70_Fm'.bmp"  "70_Fs.bmp"

FluorTime_19 is a vector of folder names that correspond to each timepoint for predawn (PD) and midday (MD). Each respective folder contains fluorescence images where images are labeled as "##_Parameter.bmp". The ## represent pot numbers, Parameter corresponds to fluorescence parameters of steady state (Fs), maximal (Fm’), minimal (Fo), and maximal dark acclimated (Fm) for pine and aspen. Treatment and species indentified by matching pot numbers from meta data.