Package 'dendRoAnalyst'

Calculating relative growth change during no-rain periods.

Description

This function calculates the number and the location of climatically adverse periods within a climate time series. The user can define a duration and threshold of these conditions. The function also provides the relative radial/circumferencial change during each adverse period for the original or normalized data. See Raffelsbauer et al., (2019) for more details.

Usage

clim.twd(
  df,
  Clim,
  dailyValue = "max",
  thresholdClim = "<10",
  thresholdDays = ">5",
  showPlot = TRUE
)

Arguments

df	dataframe with first column containing date and time in the format yyyy-mm-dd HH:MM:SS and the dendrometer data in following columns.
Clim	dataframe with the first column containing Date in yyyy-mm-dd and second column containing corresponding climate data.
dailyValue	either 'max', 'min', 'mean', or 'sum' for selecting the daily resampled value. Default is 'max'. See dendro.resample for details.
thresholdClim	string, the theshold for the respective climatic parameter. E.g. if climatic data is precipitation then days, where precipitation is below or equal to this value, are considered as adverse climate. Dafault is '<10'.
thresholdDays	string, the minimum number of consecutive adverse days to be considered for analysis. For example, thresholdDays=2 means the relative radial/circumferential change is calculated for adverse periods lasting for more than 2 days. Default is '>5'.
showPlot	logical, if TRUE, generates plots.

Value

A dataframe containing the respective periods, relative radial/circumference change for each tree, the ID for each period and their beginning and end.

References

Raffelsbauer V, Spannl S, Peña K, Pucha-Cofrep D, Steppe K, Bräuning A (2019) Tree Circumference Changes and Species-Specific Growth Recovery After Extreme Dry Events in a Montane Rainforest in Southern Ecuador. Front Plant Sci 10:342. doi:10.3389/fpls.2019.00342

Examples

library(dendRoAnalyst)
data(gf_nepa17)
data(ktm_rain17)
relative_dry_growth<-clim.twd(df=gf_nepa17, Clim=ktm_rain17, dailyValue='max', showPlot=TRUE)
1

head(relative_dry_growth,10)

---

Calculation of daily statistics for dendrometer data

Description

This function calculates various statistics of dendrometer data on a daily basis. The daily statistics includes the daily maximum and minimum with their corresponding times and daily amplitude (difference between daily maximum and minimum). See King et al. (2013) for details.

Usage

daily.data(df, TreeNum)

Arguments

df	dataframe with first column containing date and time in the format yyyy-mm-dd HH:MM:SS and the dendrometer data in following columns.
TreeNum	numerical value indicating the tree to be analysed. E.g. '1' refers to the first dendrometer data column in df.

Value

A dataframe with the daily statistics of the dendrometer data that contains:

Columns		Description
DATE		The day of year in "yyyy-mm-dd".
Min		The minimum value record for the corresponding day.
Time_min		The time when minimum value recorded for the corresponding day.
Max		The maximum value record for the corresponding day.
Time_max		The time when maximum value recorded for the corresponding day.
mean		The daily average value of the dendrometer reading.
median		The daily median value of the dendrometer reading.
amplitude		The difference between daily maximum and daily minimum.
Remarks		"*" if Time_max > Time_min otherwise "".

References

King G, Fonti P, Nievergelt D, Büntgen U, Frank D (2013) Climatic drivers of hourly to yearly tree radius variations along a 6°C natural warming gradient. Agricultural and Forest Meteorology 168:36–46. doi:10.1016/j.agrformet.2012.08.002

Examples

library(dendRoAnalyst)
data(nepa17)
daily_stats<-daily.data(df=nepa17, TreeNum=1)
head(daily_stats,10)

---

Resampling temporal resolution of dendrometer and climate data

Description

This function is designed to change the temporal resolution of data. Depending on the objective, the user can define either maximum, minimum, or mean values to resample data in hourly, daily, weekly or monthly frequency.

Usage

dendro.resample(df, by, value)

Arguments

df	dataframe with first column containing date and time in the format yyyy-mm-dd HH:MM:SS.
by	either H, D, W or M to resample data into hourly, daily, weekly or monthly resolution.
value	either max, min, mean or sum for the resampling value.

Value

Dataframe with resampled data.

Examples

library(dendRoAnalyst)
data(nepa17)
# To resample monthly with maximum value
resample_M<-dendro.resample(df=gf_nepa17, by='M', value='max')
head(resample_M,10)

---

Truncation of the dendrometer data

Description

This function is helpful to truncate dendrometer data for a user-defined period.

Usage

dendro.truncate(df, CalYear, DOY)

Arguments

df	dataframe with the first column named date and time in the format yyyy-mm-dd HH:MM:SS.
CalYear	numerical value or array of two elements for the desired year of calculation.
DOY	numerical value or array of two elements representing the day of year. If we provide an array instead of a single value for CalYear and a single value for DOY, it truncates data from the DOY of the first CalYear to the same DOY of the second CalYear. Conversely, if we provide one value for CalYear and an array of two elements for DOY truncates the data form first DOY to second DOY within the same CalYear. Finally, if we provide an array with two values for both DOY and CalYear, it truncates data from the first DOY of the first CalYear to the second DOY of second CalYear.

Value

A dataframe with the truncated data for the defined periods.

Examples

library(dendRoAnalyst)
data(nepa)
#Extracting data from doy 20 to 50 in 2017.
trunc1<-dendro.truncate(df=nepa, CalYear=2017, DOY=c(20,50))
head(trunc1,10)

---

Detrend the dendrometer data

Description

This function detrends the dendrometer data either using first difference or using gam function from mgcv package or the Gompertz function.

Usage

dm.detrend(df, method = "gam", CalYear)

Arguments

df	dataframe with first column containing date and time in the format yyyy-mm-dd HH:MM:SS and the dendrometer data in following columns.
method	either 'f_diff', 'gam' or 'gomp' indicating the method to detrend the dendrometer data column in df.
CalYear	numeric for year of calculation. If df has more than one year, assigning CalYear truncates the data of only that year.

Value

A dataframe with the detrended dendrometer series.

Examples

library(dendRoAnalyst)
data(gf_nepa17)
detrended<-dm.detrend(df=nepa17, method='f_diff', CalYear=2017)
head(detrended,10)

---

Fitting gompertz function on annual dendrometer data

Description

This function modells the annual growth of dendrometer data using gompertz function.

Usage

dm.fit.gompertz(df, CalYear, TreeNum, f_derivative = F)

Arguments

df	dataframe with first column containing date and time in the format yyyy-mm-dd HH:MM:SS and the dendrometer data in following columns.
CalYear	numeric for year of calculation. If df has more than one year, assigning CalYear truncates the data of only that year.
TreeNum	numerical value indicating the tree to be analysed. E.g. '1' refers to the first dendrometer data column in df.
f_derivative	logical if yes returns first derivative of gompertz curve.

Value

A dataframe with the modelled dendrometer series.

Examples

library(dendRoAnalyst)
data(gf_nepa17)
gomp_fitted<-dm.fit.gompertz(df=gf_nepa17, TreeNum = 1, CalYear=2017)
head(gomp_fitted,10)

---

Detection and interpolation of missing values in dendrometer data.

Description

This function detects gap(s) in time series, inserts the missing rows based on the provided temporal resolution and assings NA values to the corresponding value. If required the NA values can be replaced by spline interpolation using na.spline of the package zoo or seasonal interpolation considering the seasonality of the daily pattern using na.interp of the package forecast.

Usage

dm.na.interpolation(df, resolution, fill = FALSE, method = "spline")

Arguments

df	dataframe with first column containing date and time in the format yyyy-mm-dd HH:MM:SS and following columns with dendrometer data for the same temporal resolution and time period.
resolution	integer, indicating the resolution of dendrometer data in minutes.
fill	logical, if TRUE it fills the NA values using spline interpolation. Default is FALSE.
method	string, 'spline' for the spline interpolation or 'seasonal' for the seasonal interpolation.

Value

A dataframe containing the dendrometer data including gaps filled with either NA or interpolated values.

Examples

library(dendRoAnalyst)
data(nepa17)
gf_nepa17<-dm.na.interpolation(df=nepa17, resolution=60)
head(gf_nepa17,10)

---

Dendrometer data of Kathmandu for 2017 with gap filled

Description

The dendrometer data from three Chir pine tree collected in hourly resolution for 2017.

Usage

gf_nepa17

Format

A data frame with 8760 rows and 3 variables:

Time

datetime time of data recording

T2

double reading for first tree

T3

double reading for second tree

---

Removing artefacts due to manual adjustments of dendrometers interactively

Description

Dendrometers generally have limited memory capacity beyond which it stops recording. To keep the measurement ongoing, they should be adjusted periodically, which can cause positive or negative jumps in the data. This function locates these artefacts and interactively adjusts them one by one.

Usage

i.jump.locator(df, TreeNum, v)

Arguments

df	dataframe with first column containing date and time in the format yyyy-mm-dd HH:MM:SS and the dendrometer data in following columns.
TreeNum	numerical value indicating the tree to be analysed. E.g. '1' refers to the first dendrometer data column in df.
v	numerical value which is considered as artefact. E.g. v=1 implies that if the difference to the consecutive data point is more than 1 or less than -1, it will be considered as an artefact.

Value

A dataframe containing jump-free dendrometer data.

---

Removing artefacts due to manual adjustments of dendrometers automatically for more than one dendrometers

Description

Dendrometers generally have limited memory capacity beyond which it stops recording. To keep the measurement ongoing, they should be adjusted periodically, which can cause positive or negative jumps in the data. This function locates these artefacts and adjusts them.Unlike in i.jump.locator, it can handle dataset with more than one dendrometers.

Usage

jump.locator(df, v)

Arguments

df	dataframe with first column containing date and time in the format yyyy-mm-dd HH:MM:SS and the dendrometer data in following columns.
v	numerical value which is considered as artefact. E.g. v=1 implies that if the difference to the consecutive data point is more than 1 or less than -1, it will be considered as an artefact.

Value

A dataframe containing jump-free dendrometer data.

Examples

library(dendRoAnalyst)
data(nepa)
jump_free_nepa<-jump.locator(df=nepa ,v=1)
head(jump_free_nepa,10)

---

Daily rainfall data of Kathmandu for 2017.

Description

This file contains daily rainfall data of Kathmandu. The source of this data is 'Government of Nepal, Department of Hydrology and Meteorology'.

Usage

ktm_rain17

Format

A data frame with 365 rows and 2 variables:

TIME

Date in YYYY-MM-DD format.

rainfall

double rainfall in millimeters

Source

http://www.mfd.gov.np/city?id=31/

---

Calculate mean of detrended dendrometer data.

Description

This function calculate the mean detrended series of dendrometer data. It is usefull to make a single averaged detrended dendrometer series of a species in a site. Further, it provides option for removing first order autocorrelation from dendrometer series using auto.arima function of "forecast" package.

Usage

mean_detrended.dm(detrended.dm, ac1.remove = T, robust.mean = T)

Arguments

detrended.dm	dataframe output data frame of dm.detrend function.
ac1.remove	logical if TRUE removes first order autocorrelation.
robust.mean	logical if TRUE calculates robust mean.

Value

A data frame with the mean of detrended dendrometer series.

Examples

library(dendRoAnalyst)
data(gf_nepa17)
detrended<-dm.detrend(df=nepa17, method='f_diff', CalYear=2017)
m_detre <- mean_detrended.dm(detrended)
head(m_detre,10)

---

Calculating running correlation between dendrometer data and daily climate.

Description

This function calculates running correlation between dendrometer series and provided climate variables. Users can choose methods such as pearson, kendall and spearman, see cor.test for further information.

Usage

mov.cor.dm(df, Clim, TreeNum, win_size, cor_method = "pearson")

Arguments

df	dataframe with first column containing date and time in the format yyyy-mm-dd HH:MM:SS and the dendrometer data in following columns.
Clim	dataframe with the first column containing Date in yyyy-mm-dd and second column containing corresponding climate data.
TreeNum	numerical value indicating the tree to be analysed. E.g. '1' refers to the first dendrometer data column in df.
win_size	numerical, the running days windows of which the correlation is to be calculated. The minimum value is 18.
cor_method	string, method to be applied during correlation calculation. One of the following: pearson, kendall and spearman

Value

A list of dataframes containing the correlation and significant value between dendrometer series and provided climates.

Examples

library(dendRoAnalyst)
data(gf_nepa17)
data(ktm_rain17)
out_corr<-mov.cor.dm(df=gf_nepa17, Clim=ktm_rain17, TreeNum=1, win_size=21)
head(out_corr[1],10)

---

Dendrometer data from Kathmandu

Description

Dendrometer data from three Chir pine trees collected in hourly resolution for 2 years.

Usage

nepa

Format

A data frame with 14534 rows and 3 variables:

Time

datetime time of data recording

T2

double reading for first tree

T3

double reading for second tree

---

Dendrometer data of Kathmandu for 2017

Description

Dendrometer data from three Chir pine tree collected in hourly resolution for 2017.

Usage

nepa17

Format

A data frame with 8753 rows and 3 variables:

Time

datetime time of data recording

T2

double reading for first tree

T3

double reading for second tree

---

Dendrometer data from Kathmandu version 2

Description

Dendrometer data from three Chir pine trees collected in hourly resolution for 2 years with separated time.

Usage

nepa2

Format

A data frame with 14534 rows and 8 variables:

year

numeric year of data recording

month

numeric months of data recording

day

numeric days of data recording

hours

numeric hours of data recording

minutes

numeric minutes of data recording

seconds

numeric seconds of data recording

T2

double reading for first tree

T3

double reading for second tree

---

Interpolation of NA values using the dendrometer network

Description

A function to interpolate the missing data of a dendrometer with the help of other dendrometers from the same site, provided they have the same measurement period and temporal resolution.

Usage

network.interpolation(df, referenceDF, niMethod)

Arguments

df	dataframe with first column containing date and time in the format yyyy-mm-dd HH:MM:SS and dendrometer data in the second column and onward. The data gaps must be filled with NA using the gap.interpolation function.
referenceDF	dataframe with other dendrometers to be used as reference for the interpolation. The more dendrometers are included, the more robust will be the interpolation.
niMethod	string, either 'linear' or 'proportional' for interpolation method.

Value

A dataframe with NA values replaced by interpolated data.

Examples

library(dendRoAnalyst)
data("gf_nepa17")
df1<-gf_nepa17
# Creating an artificial reference dataset.
df2<-cbind(gf_nepa17,gf_nepa17[,2:3],gf_nepa17[,2:3])
colnames(df2) <- c('Time','T1','T2','T3','T4','T5','T6')
# Creating gaps in dataset by replacing some of the reading with NA in dataset.
df1[40:50,3]<-NA
# Using proportional interpolation method.
df1_NI<-network.interpolation(df=df1, referenceDF=df2, niMethod='proportional')
head(df1_NI,10)

---

Application of the stem-cycle approach to calculate different phases, their duration and to plot them.

Description

This function analyses the dendrometer data using Stem-cycle approach (Downs et al. 1999; Deslauriers et al. 2011). A function that defines three phases: 1) Shrinkage, when the dendrometer reading is less than previous reading, 2) Expansion, when current reading is more than previous reading and 3) Increment, when current reading is higher than the previous maximum. Additionally, it calculates various statistics for each phase.

Usage

phase.sc(df, TreeNum, smoothing = NULL)

Arguments

df	dataframe with first column containing date and time in the format yyyy-mm-dd HH:MM:SS. It should contain data with constant temporal resolution for best results.
TreeNum	numerical value indicating the tree to be analysed. E.g. '1' refers to the first dendrometer data column in df.
smoothing	numerical value from 1 to 12 which indicates the length of the smoothing spline, i.e. 1 = 1 hour and 12 = 12 hours. Default is NULL for no smoothing.The function smooth.Pspline is used for smoothing the data.

Value

A list of two dataframes. The first dataframe SC_cycle with cyclic phases along with various statistics and the second dataframe SC_phase with assigned phases for each data point.The dataframe SC_cycle contains the beginning, end, duration, magnitude and rate of each phase. The dataframe SC_phase contains time and corresponding phases during that time. The contents of SC_cycle are:

Columns		Description
Phase		Cyclic phases. 1, 2, and 3 for Shrinkage, Expansion, and Increment respectively.
start		Time when the corresponding phase starts.
end		Time when the corresponding phase ends.
Duration_h		Duration of the corresponding phase in hours.
Duration_m		Duration of the corresponding phase in minutes.
Magnitude		Radial/circumferential change during the corresponding phase in millimeters.
rate		Rate of Radial/circumferential change in micrometers per hour.
DOY		Day of year for the corresponding phase.

References

Deslauriers A, Rossi S, Turcotte A, Morin H, Krause C (2011) A three-step procedure in SAS to analyze the time series from automatic dendrometers. Dendrochronologia 29:151–161. doi:10.1016/j.dendro.2011.01.008

Downes G, Beadle C, Worledge D (1999) Daily stem growth patterns in irrigated Eucalyptus globulus and E. nitens in relation to climate. Trees 14:102–111. doi:10.1007/PL00009752

Examples

library(dendRoAnalyst)
data(gf_nepa17)
sc.phase<-phase.sc(df=gf_nepa17, TreeNum=1, smoothing=12)
head(sc.phase[[1]],10)
head(sc.phase[[2]],10)

---

Application of the zero-growth approach to calculate different phases, their duration and to plot them.

Description

This function analyses data using the zero-growth approach. Initially, it divides the data in two categories: 1) Tree water deficiency (TWD), i.e. the reversible shrinkage and expansion of the tree stem when the current reading is below the previous maximum and, 2) Increment (GRO), the irreversible expansion of the stem when the current reading is above the previous maximum. Then it calculates the TWD for each data point as the difference between the modelled "growth line" and the observed measurement. See Zweifel et. al.,(2016) for details.

The severity value of each TWD was introduced in version 0.1.4 of the package.

Usage

phase.zg(df, TreeNum)

Arguments

df	dataframe with first column containing date and time in the format yyyy-mm-dd HH:MM:SS. It should contain data with constant temporal resolution for best results.
TreeNum	numerical value indicating the tree to be analysed. E.g. '1' refers to the first dendrometer data column in df.

Value

A list of two dataframes. The first dataframe ZG_cycle contains the cyclic phases along with various statistics and the second dataframe ZG_phase with assigned phases for each data point.The contents of ZG_cycle are:

Columns		Description
DOY		Day of year for the corresponding phase.
Phase		TWD for tree water deficit and GRO for irreversible expansion.
start		Time when the corresponding phase starts.
end		Time when the corresponding phase ends.
Duration_h		Duration of the corresponding phase in hours.
Magnitude		Radial/circumferential change in millimeters.
rate		Rate of Radial/circumferential change in micrometers per hour.
Max.twd		Maximum TWD recorded for the corresponding TWD phase.
twd.severity		The severity of the individual TWD period (see description below).
Max.twd.time		Time of occurrence of maximum TWD value for each TWD phase.
Avg.twd		Average of TWD values for each TWD phase.
STD.twd		Standard deviation of TWD values for each TWD phase.

References

Zweifel R, Haeni M, Buchmann N, Eugster W (2016) Are trees able to grow in periods of stem shrinkage? New Phytol 211:839–849. doi:10.1111/nph.13995

Examples

library(dendRoAnalyst)
data(gf_nepa17)
zg.phase<-phase.zg(df=gf_nepa17[1:600,], TreeNum=1)
head(zg.phase[[1]],10)
head(zg.phase[[2]],10)

---

Plotting moving correlation with climate.

Description

This function plots the moving correlation i.e., output of mov.cor.dm.

Usage

plot_mov.cor(mov.cor.output, sig.only = T, ci = 0.95, clim_vars = "all")

Arguments

mov.cor.output	list the output of mov.cor.dm function.
sig.only	logical TRUE to plot only significant correlation.
ci	numeric confidence interval
clim_vars	array of climate variables or "all" for all climate variables.

Value

A plot with correlation.

Examples

library(dendRoAnalyst)
data(gf_nepa17)
data(ktm_rain17)
out_corr<-mov.cor.dm(df=gf_nepa17, Clim=ktm_rain17, TreeNum=1, win_size=21)
plot_mov.cor(mov.cor.output=out_corr, sig.only=TRUE, ci=0.95)

---

Plotting output of stem cycle approach (i.e., output of phase.sc function).

Description

This function plots the stem cycle of dendrometer data.

Usage

plot_SC_output(
  SC_output,
  DOY,
  Year,
  cols = c("#fee8c8", "#fdbb84", "#e34a33"),
  phNames = c("Shrinkage", "Expansion", "Increment")
)

Arguments

SC_output	list the output of phase.sc function.
DOY	array with initial and final day for plotting. E.g. c(a,b), where a = initial date and b = final date.
Year	array for indicating year for plotting.
cols	array of three elements: colour for each phases.
phNames	array with three elements for three different phases. Default is "Shrinkage", "Expansion" and "Increment".

Value

A plot with different phases.

Examples

library(dendRoAnalyst)
data(gf_nepa17)
sc.phase<-phase.sc(df=gf_nepa17, TreeNum=1, smoothing=12)
plot_SC_output(SC_output=sc.phase,DOY=c(50,60), Year=2017)

---

Plotting output of zero-growth approach (i.e., output of phase.zg function).

Description

This function plots the GRO and TWD of dendrometer data for a defined time period.

Usage

plot_ZG_output(ZG_output, DOY, Year)

Arguments

ZG_output	list the output of phase.zg function.
DOY	array with initial and final day for plotting. E.g. c(a,b), where a = initial date and b = final date.
Year	array for indicating year for plotting.

Value

A plot with GRO and TWD in two different subplots.

Examples

library(dendRoAnalyst)
data(gf_nepa17)
zg.phase<-phase.zg(df=gf_nepa17, TreeNum=1)
plot_ZG_output(ZG_output=zg.phase,DOY=c(50,51), Year=2017)

---

Reading dendrometer data.

Description

This function reads dendrometer data from .csv or .txt or .xlsx files. This function automatically recognizes the date time format of the first column of the data frame and changes it to "yyyy-mm-dd HH:MM:SS format".

Usage

read.dendrometer(file, sep = NULL, dec = NULL)

Arguments

file	string file name or path of the file.
sep	string the separator of the files. Only if they are different than the standard separators such as tab for .txt file and comma for .csv file.
dec	the character used in the file for decimal points.

Value

A dataframe with the dendrometer data:

---

Locating the maxima of TWD periods

Description

This function detects the TWD phases, including their beginning (TWDb), using the phase.zg function. Then it calculates the number, time of occurance (Tm) and value of every local maximum within each TWD phase. In addition it calculates the time difference between 'TWDb' and each 'Tm' within each TWD phase.

Usage

twd.maxima(df, TreeNum, smoothing = 5)

Arguments

df	data frame with first column containing date and time in the format yyyy-mm-dd HH:MM:SS. It should contain data with constant temporal resolution for best results.
TreeNum	numerical value indicating the tree to be analysed. E.g. '1' refers to the first dendrometer data column in df.
smoothing	numerical value from 1 to 12 which indicates the length of the smoothing spline, i.e. 1 = 1 hour and 12 = 12 hours. Default is 5.

Value

A data frame with statistics of maxima in each TWD phase.

Examples

library(dendRoAnalyst)
data(gf_nepa17)
df1=gf_nepa17[2500:3500,]
twd_max<-twd.maxima(df=df1, TreeNum=2)
head(twd_max,10)

---