| Title: | Calculate Bark Beetle Phenology Using Different Models |
|---|---|
| Description: | Calculate the bark beetle phenology based on raster data or point-related data. There are multiple models implemented for two bark beetle species. The models can be customized and their submodels (onset of infestation, beetle development, diapause initiation, mortality) can be combined. The following models are available in the package: PHENIPS-Clim (first-time release in this package), PHENIPS (Baier et al. 2007) <doi:10.1016/j.foreco.2007.05.020>, RITY (Ogris et al. 2019) <doi:10.1016/j.ecolmodel.2019.108775>, CHAPY (Ogris et al. 2020) <doi:10.1016/j.ecolmodel.2020.109137>, BSO (Jakoby et al. 2019) <doi:10.1111/gcb.14766>, Lange et al. (2008) <doi:10.1007/978-3-540-85081-6_32>, Jönsson et al. (2011) <doi:10.1007/s10584-011-0038-4>. The package may be expanded by models for other bark beetle species in the future. |
| Authors: | Jakob Jentschke [aut, cre], FVA BW, Abt. Waldschutz [cph, fnd] |
| Maintainer: | Jakob Jentschke <[email protected]> |
| License: | GPL (>= 3) |
| Version: | 1.0.1 |
| Built: | 2024-11-04 16:18:18 UTC |
| Source: | https://github.com/jjentschke/barrks |
Here, all functions are listed that are available to analyse the results of
a phenology()-call.
Get phenology properties:
Get phenology results (raster-based):
Get phenology results (station-based):
Plot phenology results (station-based):
Here, all functions are listed that are available to analyse the results of
a bso_phenology() call.
Get BSO phenology properties:
Get BSO phenology results (raster-based):
Get BSO phenology results (station-based):
Plot BSO phenology results (station-based):
barrks default color palettesGet barrks default color palettes.
barrks_colors(type = "raster")barrks_colors(type = "raster")
type |
Select the desired color palette. There are different variants
for particular purposes. Allowed values are |
A character vector of hex colors.
colors <- barrks_colors() # use the colors of 'barrks' for your individual plot...colors <- barrks_colors() # use the colors of 'barrks' for your individual plot...
The package comes with sample data that allow the application of all models available. The following data sets are available:
barrks_data(dataset = "raster")barrks_data(dataset = "raster")
dataset |
Choose the data set that should be returned. |
raster Contains a list of raster weather datasets for a sample area.
The data was taken from Deutscher Wetterdienst (DWD).
stations Contains sample station weather data for some cities in Germany.
The data was taken from Deutscher Wetterdienst (DWD).
Missing global radiation values were replaced by the mean value of the other stations.
station_coords Contains the coordinates (longitude/latitude) of the
stations that are included in the stations data set.
The data was taken from Deutscher Wetterdienst (DWD).
The respective data set. Can be a list of SpatRasters (for
dataset = 'raster') or a data frame.
https://opendata.dwd.de/climate_environment/CDC/grids_germany/daily/hyras_de/
https://opendata.dwd.de/climate_environment/CDC/observations_germany/climate/daily/kl/historical/
https://opendata.dwd.de/climate_environment/CDC/observations_germany/climate/daily/solar/
# plot first layer of the minimum temperature of the sample raster data terra::plot(barrks_data()$tmin[[1]]) # print the first lines of the sample station data head(barrks_data('stations'), 10) # print the coordinates of the sample stations barrks_data('station_coords')# plot first layer of the minimum temperature of the sample raster data terra::plot(barrks_data()$tmin[[1]]) # print the first lines of the sample station data head(barrks_data('stations'), 10) # print the coordinates of the sample stations barrks_data('station_coords')
barrks default legend labelsGet barrks default legend labels.
barrks_labels(type = "raster")barrks_labels(type = "raster")
type |
Select the desired legend labels. There are different variants
for particular purposes. Allowed values are |
A character vector of labels.
labels <- barrks_labels() # use the labels of 'barrks' for your individual plot...labels <- barrks_labels() # use the labels of 'barrks' for your individual plot...
Get the number of individuals that are flying.
bso_get_flight_rst(pheno, generation, flight = 1, dates = prop_dates(pheno)) bso_get_flight_df( pheno, generation, stations = prop_stations(pheno), flight = 1, dates = prop_dates(pheno) )bso_get_flight_rst(pheno, generation, flight = 1, dates = prop_dates(pheno)) bso_get_flight_df( pheno, generation, stations = prop_stations(pheno), flight = 1, dates = prop_dates(pheno) )
pheno |
A BSO phenology (see |
generation |
Generation of interest. For sister broods, 0.5 should be added. |
flight |
Specifies which flight of the respective generation should be
returned. Can be |
dates |
Select dates that should be present in the output. |
stations |
Pass a character vector to choose stations assigned to |
bso_get_flight_rst: a multi-layer SpatRaster.
bso_get_flight_df: a data frame.
# This may take a few minutes... # calculate phenology p <- bso_phenology('bso', barrks_data('stations'), .quiet = TRUE) # get the number of individuals of the hibernating generation on their first flight bso_get_flight_df(p, 0, 'Freiburg', flight = 1)# This may take a few minutes... # calculate phenology p <- bso_phenology('bso', barrks_data('stations'), .quiet = TRUE) # get the number of individuals of the hibernating generation on their first flight bso_get_flight_df(p, 0, 'Freiburg', flight = 1)
Get the number of individuals of a generation that are in a specific development stage.
bso_get_individuals_rst( pheno, generation, stage = "all", dates = prop_dates(pheno) ) bso_get_individuals_df( pheno, generation, stations = prop_stations(pheno), stage = "all", dates = prop_dates(pheno) )bso_get_individuals_rst( pheno, generation, stage = "all", dates = prop_dates(pheno) ) bso_get_individuals_df( pheno, generation, stations = prop_stations(pheno), stage = "all", dates = prop_dates(pheno) )
pheno |
A BSO phenology (see |
generation |
Generation of interest. For sister broods, 0.5 should be added. |
stage |
If it is a numeric, the individuals of the
slots specified will be retrieved. Otherwise it could be one of the following
values: |
dates |
Select dates that should be present in the output. |
stations |
Pass a character vector to choose stations assigned to |
bso_get_individuals_rst(): a multi-layer SpatRaster.
bso_get_individuals(): a data frame.
# This may take a few minutes... # calculate phenology p <- bso_phenology('bso', barrks_data('stations'), .quiet = TRUE) # get the number of individuals of the hibernating generation that are waiting to fly bso_get_individuals_df(p, 0, 'Freiburg', stage = 'preflight')# This may take a few minutes... # calculate phenology p <- bso_phenology('bso', barrks_data('stations'), .quiet = TRUE) # get the number of individuals of the hibernating generation that are waiting to fly bso_get_individuals_df(p, 0, 'Freiburg', stage = 'preflight')
A flight diagram illustrates the daily share of flying individuals over time.
bso_plot_flight_diagram( .pheno, .station = prop_stations(.pheno)[1], .colors = barrks_colors("bso_flight"), .labels = barrks_labels("bso_flight"), .xlim = NULL, .legend = "topright", ... )bso_plot_flight_diagram( .pheno, .station = prop_stations(.pheno)[1], .colors = barrks_colors("bso_flight"), .labels = barrks_labels("bso_flight"), .xlim = NULL, .legend = "topright", ... )
.pheno |
A BSO phenology (see |
.station |
Pass a character vector to choose a station assigned to |
.colors, .labels
|
Vectors of colors/labels starting from the first and the second flght of the hibernating generation followed consecutively by elements for the filial generations (first and second flight). |
.xlim |
Date vector of length to that limits the dates plotted. |
.legend |
Pass |
... |
arguments passed to |
None
# This may take a few minutes... # calculate phenology p <- bso_phenology('bso', barrks_data('stations'), .quiet = TRUE) bso_plot_flight_diagram(p)# This may take a few minutes... # calculate phenology p <- bso_phenology('bso', barrks_data('stations'), .quiet = TRUE) bso_plot_flight_diagram(p)
A stage diagram illustrates the share of individuals that are in a specific developmental stage of a particular generation over time.
bso_plot_stage_diagram( .pheno, .station = prop_stations(.pheno)[1], .stages = list("white", "brown"), .lty = c("dashed", "solid"), .lwd = 2, .colors = barrks_colors("bso_stages"), .labels = barrks_labels("bso_stages"), .legend_col = TRUE, .legend_lty = TRUE, ... )bso_plot_stage_diagram( .pheno, .station = prop_stations(.pheno)[1], .stages = list("white", "brown"), .lty = c("dashed", "solid"), .lwd = 2, .colors = barrks_colors("bso_stages"), .labels = barrks_labels("bso_stages"), .legend_col = TRUE, .legend_lty = TRUE, ... )
.pheno |
A BSO phenology (see |
.station |
Pass a character vector to choose a station assigned to |
.stages |
List of stages to plot. Elements will be passed to
|
.lty, .lwd
|
Vectors of line types or line widths that are used to plot
the different stages. Should have the same length as |
.colors, .labels
|
Vectors of colors/labels starting from the hibernating generation followed consecutively by elements for the filial generations (not including sisterbroods). |
.legend_col, .legend_lty
|
Manipulate the appearance of the legends for
colors and line types. Pass |
... |
arguments passed to |
None
# This may take a few minutes... # calculate phenology p <- bso_phenology('bso', barrks_data('stations'), .quiet = TRUE) bso_plot_stage_diagram(p)# This may take a few minutes... # calculate phenology p <- bso_phenology('bso', barrks_data('stations'), .quiet = TRUE) bso_plot_stage_diagram(p)
A BSO generated phenology cannot be analysed in the same way as other phenology objects.
To be able to use the functions that are available for phenology objects
returned by phenology(), the BSO generated phenology should be translated.
bso_translate_phenology(pheno, threshold = 0.1, .quiet = FALSE)bso_translate_phenology(pheno, threshold = 0.1, .quiet = FALSE)
pheno |
A BSO phenology (see |
threshold |
Share of individuals that must have reached a specific development in the BSO phenology to account for them in the corresponding standard phenology. |
.quiet |
If |
Returns a standard phenology as a list. Look here to find out how a phenology can be analysed. It is not recommended to access the list elements directly.
# This may take a few minutes... # calculate and translate BSO phenology p <- bso_phenology('bso', barrks_data('stations'), .quiet = TRUE) pt <- bso_translate_phenology(p, .quiet = TRUE) # print the generations data frame of station 'Freiburg' df <- get_generations_df(pt, 'Freiburg') df# This may take a few minutes... # calculate and translate BSO phenology p <- bso_phenology('bso', barrks_data('stations'), .quiet = TRUE) pt <- bso_translate_phenology(p, .quiet = TRUE) # print the generations data frame of station 'Freiburg' df <- get_generations_df(pt, 'Freiburg') df
Make a numeric generations raster categorical. Useful when mathematical
operations were performed with generations rasters (use
get_generations_rst(..., categorical = FALSE) to get numeric generations
rasters).
categorize_generations_rst( rst, colors = barrks_colors(), labels = barrks_labels() )categorize_generations_rst( rst, colors = barrks_colors(), labels = barrks_labels() )
rst |
A numeric SpatRaster that represents bark beetle generations.
Sister broods are defined by adding |
colors, labels
|
Vectors of colors/labels starting from zero generations followed consecutively by elements for the respective generations (including sister broods). |
A categorical SpatRaster.
# calculate phenology with different models p1 <- phenology('phenips-clim', barrks_data(), .quiet = TRUE) p2 <- phenology('phenips', barrks_data(), .quiet = TRUE) # get the generation as numerical rasters to allow mathematical operations gens1 <- get_generations_rst(p1, categorical = FALSE) gens2 <- get_generations_rst(p2, categorical = FALSE) # calculate the maximum generations from the 2 models gens_max <- max(gens1, gens2) # categorize the results gens_max_cat <- categorize_generations_rst(gens_max) # plot the categorized raster terra::plot(gens_max_cat) # plot the uncategorized raster terra::plot(gens_max)# calculate phenology with different models p1 <- phenology('phenips-clim', barrks_data(), .quiet = TRUE) p2 <- phenology('phenips', barrks_data(), .quiet = TRUE) # get the generation as numerical rasters to allow mathematical operations gens1 <- get_generations_rst(p1, categorical = FALSE) gens2 <- get_generations_rst(p2, categorical = FALSE) # calculate the maximum generations from the 2 models gens_max <- max(gens1, gens2) # categorize the results gens_max_cat <- categorize_generations_rst(gens_max) # plot the categorized raster terra::plot(gens_max_cat) # plot the uncategorized raster terra::plot(gens_max)
Generate a data frame of day lengths for given latitudes.
The package geosphere is required to use this function.
create_daylength_df(lat, dates, .quiet = FALSE)create_daylength_df(lat, dates, .quiet = FALSE)
lat |
Data frame with the fields |
dates |
Dates that should be processed. |
.quiet |
If |
A data frame with the columns date, station and daylength.
# dates of interest date_start <- as.Date('2020-01-01') date_end <- as.Date('2020-12-31') # calculate day length dl <- create_daylength_df(barrks_data('station_coords'), seq(date_start, date_end, by = 'day'), .quiet = TRUE) # print day lengths of station 'Freiburg' dl[dl$station == 'Freiburg',]# dates of interest date_start <- as.Date('2020-01-01') date_end <- as.Date('2020-12-31') # calculate day length dl <- create_daylength_df(barrks_data('station_coords'), seq(date_start, date_end, by = 'day'), .quiet = TRUE) # print day lengths of station 'Freiburg' dl[dl$station == 'Freiburg',]
Generate a multi-layer SpatRaster of day lengths for a given template.
The package geosphere is required to use this function.
create_daylength_rst( template, dates = terra::time(template), crs = "EPSG:4258", .quiet = FALSE )create_daylength_rst( template, dates = terra::time(template), crs = "EPSG:4258", .quiet = FALSE )
template |
(Multi-layer) SpatRaster that determines the spatial extent of the result. |
dates |
Dates that should be processed. If not specified, the dates of
|
crs |
Coordinate reference system with longitude/latitude metrics. It is used to project the raster coordinates to be able to retrieve the latitude. |
.quiet |
If |
A multi-layer SpatRaster. Each layer represents one date.
# calculate day length, use barrks_data()$tmin as template dl <- create_daylength_rst(barrks_data()$tmin, .quiet = TRUE) # plot day length on May 1st, 2015 terra::plot(dl[[terra::time(dl) == '2015-05-01']])# calculate day length, use barrks_data()$tmin as template dl <- create_daylength_rst(barrks_data()$tmin, .quiet = TRUE) # plot day length on May 1st, 2015 terra::plot(dl[[terra::time(dl) == '2015-05-01']])
Generate onset, diapause or mortality manually to be able to run phenology()
with observed or arbitrary inputs.
create_onset( template, doys = NULL, stations = NULL, dates = NULL, .quiet = FALSE ) create_diapause( template, doys = NULL, stations = NULL, dates = NULL, .quiet = FALSE ) create_mortality( template, doys = NULL, stations = NULL, dates = NULL, .quiet = FALSE )create_onset( template, doys = NULL, stations = NULL, dates = NULL, .quiet = FALSE ) create_diapause( template, doys = NULL, stations = NULL, dates = NULL, .quiet = FALSE ) create_mortality( template, doys = NULL, stations = NULL, dates = NULL, .quiet = FALSE )
template |
SpatRaster or data frame that determines the
spatial and temporal extent of the result. If a single-layer SpatRaster was
passed, the temporal extent should be defined by using |
doys |
Numeric vector, (multi-layer) SpatRaster or data frame that
specifies the days of year when the event is triggered. Vectors will define
the events globally whereas SpatRasters allow spatially explict definitions.
For the creation of events based on stations, data frames are used.
In that case, the field |
stations |
If |
dates |
Dates to define the temporal extent of the output if |
.quiet |
If |
A logical multi-layer SpatRaster. Each layer represents one date.
create_onset(): Create a onset.
create_diapause(): Create a diapause.
create_mortality(): Create mortality events.
# load sample data d <- barrks_data('stations') # create onset, diapause, mortality on <- create_onset(d, lubridate::yday('2015-04-15')) dia <- create_diapause(d, lubridate::yday('2015-08-15')) mort <- create_mortality(d, lubridate::yday('2015-11-15')) # claculate phenologe p <- phenology('phenips-clim', d, .quiet = TRUE, .onset = on, .diapause = dia, .mortality = mort) # plot development plot_development_diagram(p, .lwd = 4)# load sample data d <- barrks_data('stations') # create onset, diapause, mortality on <- create_onset(d, lubridate::yday('2015-04-15')) dia <- create_diapause(d, lubridate::yday('2015-08-15')) mort <- create_mortality(d, lubridate::yday('2015-11-15')) # claculate phenologe p <- phenology('phenips-clim', d, .quiet = TRUE, .onset = on, .diapause = dia, .mortality = mort) # plot development plot_development_diagram(p, .lwd = 4)
Generate a data frame that specifies sunrises and sunsets for different
coordinates and dates.
The package suncalc is required to use this function.
create_suntimes_df(coords, dates, tz = Sys.timezone(), .quiet = FALSE)create_suntimes_df(coords, dates, tz = Sys.timezone(), .quiet = FALSE)
coords |
Data frame with the fields |
dates |
Dates that should be processed. |
tz |
Timezone of the results. |
.quiet |
If |
A data frame with the columns date, station and sunrise and
sunset. The values of sunrise and sunset indicate the respective time in
minutes.
date_start <- as.Date('2020-01-01') date_end <- as.Date('2020-12-31') st <- create_suntimes_df(barrks_data('station_coords'), seq(date_start, date_end, by = 'day'), .quiet = TRUE) # print results of station 'Freiburg' st[st$station == 'Freiburg',]date_start <- as.Date('2020-01-01') date_end <- as.Date('2020-12-31') st <- create_suntimes_df(barrks_data('station_coords'), seq(date_start, date_end, by = 'day'), .quiet = TRUE) # print results of station 'Freiburg' st[st$station == 'Freiburg',]
Generate a list of two multi-layer SpatRasters for a given template that
indicate sunrise and sunset for the respective cells.
The package suncalc is required to use this function.
create_suntimes_rsts( template, dates = terra::time(template), crs = "EPSG:4258", tz = Sys.timezone(), .quiet = FALSE )create_suntimes_rsts( template, dates = terra::time(template), crs = "EPSG:4258", tz = Sys.timezone(), .quiet = FALSE )
template |
(Multi-layer) SpatRaster that determines the spatial extent of the result. |
dates |
Dates that should be processed. If not specified, the dates of
|
crs |
Coordinate reference system with longitude/latitude metrics. It is used to project the raster coordinates to be able to retrieve longitude and latitude. |
tz |
Timezone of the results. |
.quiet |
If |
A list with the elements sunrise and sunset which are
both multi-layer SpatRasters. The values indicate the respective time in
minutes. Each layer represents one date.
# calculate suntimes, use barrks_data()$tmin as template st <- create_suntimes_rsts(barrks_data()[[1]], .quiet = TRUE) # plot results on May 1st, 2015 terra::plot(st$sunrise[[terra::time(st$sunrise) == '2015-05-01']]) terra::plot(st$sunset[[terra::time(st$sunset) == '2015-05-01']])# calculate suntimes, use barrks_data()$tmin as template st <- create_suntimes_rsts(barrks_data()[[1]], .quiet = TRUE) # plot results on May 1st, 2015 terra::plot(st$sunrise[[terra::time(st$sunrise) == '2015-05-01']]) terra::plot(st$sunset[[terra::time(st$sunset) == '2015-05-01']])
Get the beetles development of specific generations. A value of -1 implies that the generation is not present yet.
get_development_rst(pheno, generation, dates = prop_dates(pheno)) get_development_df( pheno, stations = prop_stations(pheno), generation = prop_hatched_generations(pheno), dates = prop_dates(pheno) )get_development_rst(pheno, generation, dates = prop_dates(pheno)) get_development_df( pheno, stations = prop_stations(pheno), generation = prop_hatched_generations(pheno), dates = prop_dates(pheno) )
pheno |
A phenology (see |
generation |
Generation of interest. For sister broods, 0.5 should be added. |
dates |
Select dates that should be present in the output. |
stations |
Pass a character vector to choose stations assigned to |
get_development_rst(): A multi-layer SpatRaster.
get_development_df(): A data frame which contains a field for
each generation (gen_1, gen_1.5, gen_2, gen_2.5, ...) requested.
# calculate station-based phenology p <- phenology('phenips-clim', barrks_data('stations'), .quiet = TRUE) # print the development data frame of station 'Freiburg' df <- get_development_df(p, 'Freiburg') df[,4:ncol(df)] <- round(df[,4:ncol(df)], 3) # round results df# calculate station-based phenology p <- phenology('phenips-clim', barrks_data('stations'), .quiet = TRUE) # print the development data frame of station 'Freiburg' df <- get_development_df(p, 'Freiburg') df[,4:ncol(df)] <- round(df[,4:ncol(df)], 3) # round results df
Find out how many generations are present (or have reached a development threshold).
get_generations_rst( pheno, dates = prop_last_date(pheno), threshold = 0, generations = prop_hatched_generations(pheno), categorical = TRUE, colors = barrks_colors("raster"), labels = barrks_labels("raster") ) get_generations_df( pheno, stations = prop_stations(pheno), dates = prop_dates(pheno), threshold = 0, generations = prop_hatched_generations(pheno) ) get_hibernating_generations_rst( pheno, categorical = TRUE, colors = barrks_colors("raster"), labels = barrks_labels("raster") ) get_hibernating_generations_df(pheno, stations = prop_stations(pheno))get_generations_rst( pheno, dates = prop_last_date(pheno), threshold = 0, generations = prop_hatched_generations(pheno), categorical = TRUE, colors = barrks_colors("raster"), labels = barrks_labels("raster") ) get_generations_df( pheno, stations = prop_stations(pheno), dates = prop_dates(pheno), threshold = 0, generations = prop_hatched_generations(pheno) ) get_hibernating_generations_rst( pheno, categorical = TRUE, colors = barrks_colors("raster"), labels = barrks_labels("raster") ) get_hibernating_generations_df(pheno, stations = prop_stations(pheno))
pheno |
A phenology (see |
dates |
Select dates that should be present in the output. |
threshold |
Threshold of the beetle development to account for a generation. |
generations |
Numeric vector that determines which generations should be included in the result. |
categorical |
Set |
colors, labels
|
Vectors of colors/labels starting from zero generations followed consecutively by elements for the respective generations (including sister broods). |
stations |
Pass a character vector to choose stations assigned to |
get_generations_rst(): A multi-layer SpatRaster.
get_hibernating_generations_rst(): A SpatRaster. Only available
if the model's end date has been reached. Otherwise all values will be NA.
get_generations_df(): A data frame.
get_hibernating_generations_df(): A data frame. Only available if the
model's end date has been reached. Otherwise all values will be NA.
# calculate phenology p <- phenology('phenips-clim', barrks_data(), .quiet = TRUE) # get the generations raster gens <- get_generations_rst(p) # plot the generations raster terra::plot(gens)# calculate phenology p <- phenology('phenips-clim', barrks_data(), .quiet = TRUE) # get the generations raster gens <- get_generations_rst(p) # plot the generations raster terra::plot(gens)
The function returns a list that contains the input data of the phenology as
well as some intermediate results that are needed as preprocessed inputs for the
model. The result can be used as input for phenology() to avoid redundant
calculations.
get_input_data(pheno)get_input_data(pheno)
pheno |
A phenology (see |
A list of SpatRasters.
# setup phenology p <- phenology('phenips-clim', barrks_data(), .setup_only = TRUE, .quiet = TRUE) # get the (preprocessed) input data inputs <- get_input_data(p) # print the names to show which input data is available names(inputs)# setup phenology p <- phenology('phenips-clim', barrks_data(), .setup_only = TRUE, .quiet = TRUE) # get the (preprocessed) input data inputs <- get_input_data(p) # print the names to show which input data is available names(inputs)
Get onset, diapause or mortality as day of year or raw output. Note that multiple mortality events are possible over the season.
get_onset_rst(pheno, as_doy = TRUE, dates = prop_dates(pheno)) get_onset_df( pheno, stations = prop_stations(pheno), as_doy = TRUE, dates = prop_dates(pheno) ) get_diapause_rst(pheno, as_doy = TRUE, dates = prop_dates(pheno)) get_diapause_df( pheno, stations = prop_stations(pheno), as_doy = TRUE, dates = prop_dates(pheno) ) get_mortality_rst(pheno, as_doy = TRUE, dates = prop_dates(pheno)) get_mortality_df( pheno, stations = prop_stations(pheno), as_doy = TRUE, dates = prop_dates(pheno) )get_onset_rst(pheno, as_doy = TRUE, dates = prop_dates(pheno)) get_onset_df( pheno, stations = prop_stations(pheno), as_doy = TRUE, dates = prop_dates(pheno) ) get_diapause_rst(pheno, as_doy = TRUE, dates = prop_dates(pheno)) get_diapause_df( pheno, stations = prop_stations(pheno), as_doy = TRUE, dates = prop_dates(pheno) ) get_mortality_rst(pheno, as_doy = TRUE, dates = prop_dates(pheno)) get_mortality_df( pheno, stations = prop_stations(pheno), as_doy = TRUE, dates = prop_dates(pheno) )
pheno |
A phenology (see |
as_doy |
If |
dates |
Select dates that should be present in the output. |
stations |
Pass a character vector to choose stations assigned to |
get_onset_rst(), get_diapause_rst(), get_mortality_rst(): A
(multi-layer) SpatRaster.
get_onset_df(), get_diapause_df(), get_mortality_df(): A data frame.
get_onset_rst(): Returns a (multi-layer) SpatRaster of the onset.
get_onset_df(): Returns a data frame of the onset.
get_diapause_rst(): Returns a (multi-layer) SpatRaster of the diapause.
get_diapause_df(): Returns a data frame of the diapause.
get_mortality_rst(): Returns a (multi-layer) SpatRaster of the mortality.
get_mortality_df(): Returns a data frame of the mortality.
# calculate phenology p <- phenology('phenips-clim', barrks_data(), .quiet = TRUE) # plot onset, diapause, mortality get_onset_rst(p) |> terra::plot() get_diapause_rst(p) |> terra::plot() get_mortality_rst(p)[[1]] |> terra::plot()# calculate phenology p <- phenology('phenips-clim', barrks_data(), .quiet = TRUE) # plot onset, diapause, mortality get_onset_rst(p) |> terra::plot() get_diapause_rst(p) |> terra::plot() get_mortality_rst(p)[[1]] |> terra::plot()
Get the names of all available models.
list_models()list_models()
A character vector.
# print all available models list_models()# print all available models list_models()
Returns a (customized) phenology model.
model(m, ...)model(m, ...)
m |
Name of the model or the return value of another |
... |
List of parameters to customize the model. |
A phenology model. Can be passed to phenology().
Look at the customization manuals, to find out which parameters
can be customized for a specific model: model.bso.customize, model.phenips.customize, model.rity.customize, model.chapy.customize, model.joensson.customize, model.lange.customize, model.phenips_clim.customize.
# customize the temperature beetles need to fly for PHENIPS-Clim m <- model('phenips-clim', tfly = 16) # calculate phenology p <- phenology(m, barrks_data(), .quiet = TRUE) # plot generations gens <- get_generations_rst(p) terra::plot(gens)# customize the temperature beetles need to fly for PHENIPS-Clim m <- model('phenips-clim', tfly = 16) # calculate phenology p <- phenology(m, barrks_data(), .quiet = TRUE) # plot generations gens <- get_generations_rst(p) terra::plot(gens)
Combine different (sub-)models.
model_combine(...)model_combine(...)
... |
Phenology models, model names or lists with the keys |
A phenology model. Can be passed to phenology().
Customize (sub-)models: model.bso.customize, model.phenips.customize, model.rity.customize, model.chapy.customize, model.joensson.customize, model.lange.customize, model.phenips_clim.customize
Use (sub-)models: model.bso.apply, model.phenips.apply, model.rity.apply, model.chapy.apply, model.joensson.apply, model.lange.apply, model.phenips_clim.apply
# combine PHENIPS with the diapause submodel of PHENIPS-Clim m <- model_combine('phenips', list(model = 'phenips-clim', submodels = 'diapause')) # calculate phenology p <- phenology(m, barrks_data(), .quiet = TRUE) # plot calculated generations gens <- get_generations_rst(p) terra::plot(gens)# combine PHENIPS with the diapause submodel of PHENIPS-Clim m <- model_combine('phenips', list(model = 'phenips-clim', submodels = 'diapause')) # calculate phenology p <- phenology(m, barrks_data(), .quiet = TRUE) # plot calculated generations gens <- get_generations_rst(p) terra::plot(gens)
This page describes the usage of BSO with phenology().
The model-specific inputs are listed and its basic functionality is explained.
BSO was published by Jakoby et al. (2019) and
parametrized for Ips typographus in Switzerland. Note that the onset and
the development submodel do not support the usage of a storage (except for
some precalculations).
... |
See |
tmin, tmax
|
Daily minimum/maximum air temperatures in °C. |
sunrise, sunset
|
Time of sunrise/sunset in minutes from midnight. Can
be created with |
n |
number of individuals to simulate. |
max_generations |
maximum number of generations to calculate. |
In barrks, phenology() is used to apply a model. The following code
illustrates which inputs are required to apply BSO and which additional
parameters are available.
bso_phenology("bso", ..., tmin, tmax, sunrise, sunset,
n = 1e+09, max_generations = 4)
# calculate submodels separately
bso_phenology("bso", ..., .submodels = 'onset',
tmin, tmax, sunrise, sunset, n = 1e+09)
bso_phenology("bso", ..., .submodels = 'diapause', tmin, tmax)
bso_phenology("bso", ..., .submodels = 'development',
.onset, .diapause = NULL, .mortality = NULL,
tmin, tmax, sunrise, sunset,
max_generations = 4)
The function returns a BSO phenology. Look here to find out how it can be analysed.
In the following, the basic functioning of BSO is explained.
Onset: The onset of swarming will start when the degree days of the mean temperature reach a specific threshold and regeneration feeding of the individuals has finished (Look at development for details).
Development: The development of single individuals is simulated. The simulation of each individual is realized by passing a multitude of slots that are grouped in stages. The hourly probability for an individual to enter the next slot depends on the current stage and the phloem temperature. The hourly temperature is derived from the minimum and maximum temperatures using a sine interpolition. The hourly phloem temperature is calculated using Newton's Law of Cooling (see Trân et al. 2007).
Diapause: Specific photoperiod-related dates define when the diapause is initiated at the earliest and at the latest. In between these dates, the diapause is initiated when the mean temperature falls below a specific threshold.
Mortality: BSO does not have a mortality submodel implemented.
Look here to find out how the model parameters affect the actual calculations and which values are used by default.
Jakoby O, Lischke H, Wermelinger B (2019).
“Climate change alters elevational phenology patterns of the European spruce bark beetle (Ips typographus).”
Global Change Biology, 25(12), 4048-4063.
doi:10.1111/gcb.14766.
Trân JK, Ylioja T, Billings RF, Régnière J, Ayres MP (2007).
“Impact of minimum winter temperatures on the population dynamics of Dendroctonus frontalis.”
Ecological Applications, 17(3), 882-899.
doi:10.1890/06-0512.
model(), bso_phenology(), model.bso.customize
Other phenology applications:
model.chapy.apply,
model.joensson.apply,
model.lange.apply,
model.phenips.apply,
model.phenips_clim.apply,
model.rity.apply
This page describes the parameters that can be used to customize BSO. The model was developed by Jakoby et al. (2019). Look here to find out how to apply the model.
dd_onset_start_date |
The date, when the degree days start to sum up ('MM-DD'). |
dd_onset_base |
Base temperature to calculate degree days to trigger the onset. |
dd_onset_threshold |
Degree days that are required before the individuals
start regeneration feeding in |
slot_dia |
Maturation feeding slot where the individuals start regeneration feeding after diapause. |
k |
Factor for the calculation of the phloem temperature. |
alpha, tlo, tup
|
Parameters used to calculate the transition probabilities for each stage (except preflight) in the following order: development, maturation feeding, reproduction. |
tfly_min, tfly_max, pfly_max, beta
|
Parameters used to calculate the transition probabilities for the preflight stage. |
num_slots |
Named vector that defines the number of slots for each stage.
The development stage is subdivided into the stages |
psis |
Probability that a sister brood will be established. |
slot_sis |
Maturation feeding slot where the individuals start regeneration feeding before they establish a sister brood. |
model_end_date |
Date when the model ends (no further development will be modeled). |
diapause_first |
The day of year when the diapause could start at the earliest. |
diapause_last |
The day of year when the diapause could start at the latest. |
tdia_min |
The diapause will be initiated when the average daily temperature falls below that value. |
In barrks, model() is used to customize a model. The following code
illustrates which parameters are available for BSO and specifies their
default values.
model("bso",
# ==== onset ====
dd_onset_start_date = '01-01',
dd_onset_base = 5.124198,
dd_onset_threshold = 100,
slot_dia = 6,
# ==== onset + development ====
k = 2.853738e-02,
alpha = c(2.549060e-05, 0.0000789, 1.009450e-05),
tlo = c(-1.297644e+01, 4.760089e+00, -4.424628e+00),
tup = c(3.600070e+01, 4.002483e+01, 3.999390e+01),
tfly_min = 16.1064,
tfly_max = 31.2901,
pfly_max = 9.863263e-03,
beta = 1.363763,
num_slots = c(
'reproduction' = 11,
'egg' = 18,
'larva' = 45,
'pupa' = 8,
'maturation' = 8,
'preflight' = 1
),
# ==== development ====
psis = 2.994450e-01,
slot_sis = 4,
model_end_date = '12-30',
# ==== diapause ====
diapause_first = 210,
diapause_last = 232,
tdia_min = 1.645209e+01
)
Jakoby O, Lischke H, Wermelinger B (2019). “Climate change alters elevational phenology patterns of the European spruce bark beetle (Ips typographus).” Global Change Biology, 25(12), 4048-4063. doi:10.1111/gcb.14766.
model(), phenology(), model.bso.apply
Other model customizations:
model.chapy.customize,
model.joensson.customize,
model.lange.customize,
model.phenips.customize,
model.phenips_clim.customize,
model.rity.customize
This page describes the usage of CHAPY with phenology().
The model specific inputs are listed and its basic functionality is explained.
CHAPY was published by Ogris et al. (2020) and
parametrized for Pityogenes chalcographus in Slovenia.
tmin, tmean, tmax
|
Daily minimum/mean/maximum air temperatures in °C.
For the |
daylength |
Length of the day in hours. Can be created with
|
mode |
Specifies which temperature should be used to calculate the
development. Can be |
.submodels, .onset, .diapause, .mortality, ...
|
See |
In barrks, phenology() is used to apply a model. The following code
illustrates which inputs are required to apply CHAPY and which additional
parameters are available.
phenology("chapy", ..., tmin = NULL, tmean = NULL, tmax, daylength, mode = 'max')
# calculate submodels separately
phenology("chapy", ..., .submodels = 'onset', tmax)
phenology("chapy", ..., .submodels = 'diapause', daylength)
phenology("chapy", ..., .submodels = 'mortality', tmax)
phenology("chapy", ..., .submodels = 'development',
.onset, .diapause = NULL, .mortality = NULL,
tmin = NULL, tmean = NULL, tmax = NULL, mode = 'max')
The functioning of CHAPY is identical to RITY but it is has a different parametrization.
Ogris N, Ferlan M, Hauptman T, Pavlin R, Kavčič A, Jurc M, de Groot M (2020). “Sensitivity analysis, calibration and validation of a phenology model for Pityogenes chalcographus (CHAPY).” Ecological Modelling, 430, 109137. ISSN 0304-3800, doi:10.1016/j.ecolmodel.2020.109137.
model(), phenology(), model.chapy.customize
Other phenology applications:
model.bso.apply,
model.joensson.apply,
model.lange.apply,
model.phenips.apply,
model.phenips_clim.apply,
model.rity.apply
This page describes the parameters that can be used to customize CHAPY. The model was developed by Ogris et al. (2020). Look here to find out how to apply the model.
dd_onset_start_date |
The date, when the degree days start to sum up ('MM-DD'). |
dd_onset_base |
Base temperature to calculate degree days to trigger the onset. |
dd_onset_threshold |
Degree days that are required to trigger the onset of
infestation. Additionally, the maximum temperature must exceed |
tfly |
Minimum temperature that beetles need to fly. |
dd_development_base |
Base temperature to calculate degree days for development. |
dd_total_dev |
Degree days that are required for a generation to fully develop |
dev_start, dev_end
|
Share in total development when the egg development starts and the juvenile beetle's development ends respectively. Usable if the development below/above these thresholds should account for mating, oviposition etc. |
dev_sister_brood |
Share in the total development, when a sister brood will be established. |
dev_mortal_min, dev_mortal_max
|
The beetles are considered to be in
white stages (egg, larva, pupa) if their development exceeds |
func_ftmin, func_ftmean, func_ftmax
|
Functions to caclulate the air temperature in forest stands (see Ogris et al. 2019, equations 1 - 3). Each parameter will be passed as SpatRaster:
|
func_btmin, func_btmean, func_btmax
|
Functions to caclulate the bark temperature (see Ogris et al. 2019, equations 4 - 6). Each parameter will be passed as SpatRaster:
|
dt_low, dt_up, topt, tmax, alpha, beta, gamma
|
Parameters to calculate the effective bark temperature (see Ogris et al. 2020, equations A.7 - A.9). |
model_end_date |
Date when the model ends (no further development will be modeled). |
daylength_dia |
When the daylength falls below this threshold, diapause will be initiated. |
mortality_date |
Date when all white stages (egg, larva, pupa) die. |
In barrks, model() is used to customize a model. The following code
illustrates which parameters are available for CHAPY and specifies their
default values.
model("chapy",
# ==== onset ====
dd_onset_start_date = '03-09',
dd_onset_base = 7.4,
dd_onset_threshold = 216.5,
# ==== onset + development ====
tfly = 15.6,
# ==== development ====
dd_development_base = 7.4,
dd_total_dev = 635.4,
dev_start = 0,
dev_end = 1,
dev_sister_brood = 0.5,
dev_mortal_min = NULL,
dev_mortal_max = 0.8,
func_ftmin = function(tmin) { 1.44 + 0.82 * tmin },
func_ftmean = function(tmean) { 0.50 + 0.81 * tmean },
func_ftmax = function(tmax) { 1.03 + 0.86 * tmax },
func_btmin = function(atmin) { 0.56 + 0.99 * atmin },
func_btmean = function(atmean) { -0.48 + 1.03 * atmean },
func_btmax = function(atmax) { 0.03 + 0.99 * atmax },
dt_low = 7.4,
dt_up = 39.4,
topt = 30,
tmax = 41.97,
alpha = 0.031,
beta = 5.3,
gamma = 1.25,
model_end_date = '12-31',
# ==== diapause ====
daylength_dia = 13.6,
# ==== mortality ====
mortality_date = '12-31'
)
Ogris N, Ferlan M, Hauptman T, Pavlin R, Kavčič A, Jurc M, De Groot M (2019).
“RITY–A phenology model of Ips typographus as a tool for optimization of its monitoring.”
Ecological Modelling, 410, 108775.
doi:10.1016/j.ecolmodel.2019.108775.
Ogris N, Ferlan M, Hauptman T, Pavlin R, Kavčič A, Jurc M, de Groot M (2020).
“Sensitivity analysis, calibration and validation of a phenology model for Pityogenes chalcographus (CHAPY).”
Ecological Modelling, 430, 109137.
ISSN 0304-3800, doi:10.1016/j.ecolmodel.2020.109137.
model(), phenology(), model.chapy.apply
Other model customizations:
model.bso.customize,
model.joensson.customize,
model.lange.customize,
model.phenips.customize,
model.phenips_clim.customize,
model.rity.customize
This page describes the usage of the the Jönsson model with phenology().
The model specific inputs are listed and its basic functionality is explained.
The Jönsson model was published by Jönsson et al. (2011) and
parametrized for Ips typographus in southern Sweden.
... |
See |
tmean, tmax
|
Daily mean/maximum temperatures in °C. |
daylength |
Length of the day in hours. Can be created with
|
mode |
Can be |
In barrks, phenology() is used to apply a model. The following code
illustrates which inputs are required to apply the Jönsson model and which additional
parameters are available.
phenology("joensson", ..., tmean, tmax, daylength, mode = 'fast')
# calculate submodels separately
phenology("joensson", ..., .submodels = 'onset', tmax)
phenology("joensson", ..., .submodels = 'diapause', tmax, daylength)
phenology("joensson", ..., .submodels = 'development',
.onset, .diapause = NULL, .mortality = NULL,
tmean, mode = 'fast')
The function returns a phenology. Look here to find out how it can be analysed.
In the following, the basic functioning of the Jönsson model is explained.
Onset: The onset of swarming is triggerd when the degree days of the maximum temperature reach a specific threshold and the maximum temperature exceeds the minimum flight temperature. The onset of infestation is triggered seven days later to account for a pre-oviposition period.
Development: The development progresses proportional to the degree days of the mean temperature. To account for varying sun exposures, two different thermal thresholds are defined that reflect the lower and the upper limit of development. A generations starts swarming when it has finished its development and the maximum temperature exceeds the minimum flight temperature. Seven days later, the development of a new generation starts.
Diapause: The diapause is initiated when the daylength falls below a threshold. It is recommended to adjust the daylength threshold when applying elsewhere (e.g. values from literature). Jönsson et al. (2011) proposes a model to calculate the daylength threshold based on long-term climate data.
Mortality: The Jönsson model does not have a mortality submodel implemented.
Look here to find out how the model parameters affect the actual calculations and which values are used by default.
Jönsson AM, Harding S, Krokene P, Lange H, Åke Lindelöw A, Økland B, Ravn HP, Schroeder LM (2011). “Modelling the potential impact of global warming on Ips typographus voltinism and reproductive diapause.” Climatic Change, 109, 695-718. doi:10.1007/s10584-011-0038-4.
model(), phenology(), model.joensson.customize
Other phenology applications:
model.bso.apply,
model.chapy.apply,
model.lange.apply,
model.phenips.apply,
model.phenips_clim.apply,
model.rity.apply
This page describes the parameters that can be used to customize the Jönsson model. The model was developed by Jönsson et al. (2011). Look here to find out how to apply the model.
dd_onset_start_date |
The date, when the degree days start to sum up ('MM-DD'). |
dd_onset_base |
Base temperature to calculate degree days to trigger the onset. |
dd_onset_threshold |
Degree days that are required to trigger the onset of
infestation. Additionally, the maximum temperature must exceed |
tfly |
Minimum temperature that beetles need to fly. |
dd_development_base |
Base temperature to calculate degree days for development. |
dd_total_dev_lower, dd_total_dev_upper
|
Lower/upper limit of degree days that are required for a generation to fully develop |
dev_start, dev_end
|
Share in total development when the egg development starts and the juvenile beetle's development ends respectively. Usable if the development below/above these thresholds should account for mating, oviposition etc. |
dev_mortal_min, dev_mortal_max
|
The beetles are considered to be in
white stages (egg, larva, pupa) if their development exceeds |
model_end_date |
Date when the model ends (no further development will be modeled). |
daylength_dia, tdia_min
|
When the daylength falls below |
In barrks, model() is used to customize a model. The following code
illustrates which parameters are available for the Jönsson model and specifies their
default values.
model("joensson",
# ==== onset ====
dd_onset_start_date = '01-01',
dd_onset_base = 5,
dd_onset_threshold = 120,
# ==== onset + development ====
tfly = 20,
# ==== development ====
dd_development_base = 5,
dd_total_dev_lower = 625,
dd_total_dev_upper = 750,
dev_start = 0,
dev_end = 1,
dev_mortal_min = NULL,
dev_mortal_max = NULL,
model_end_date = '12-31',
# ==== diapause ====
daylength_dia = 19.3,
tdia_min = 15
)
Jönsson AM, Harding S, Krokene P, Lange H, Åke Lindelöw A, Økland B, Ravn HP, Schroeder LM (2011).
“Modelling the potential impact of global warming on Ips typographus voltinism and reproductive diapause.”
Climatic Change, 109, 695-718.
doi:10.1007/s10584-011-0038-4.
Schroeder M, Dalin P (2017).
“Differences in photoperiod-induced diapause plasticity among different populations of the bark beetle Ips typographus and its predator Thanasimus formicarius.”
Agricultural and Forest Entomology, 19(2), 146-153.
doi:10.1111/afe.12189.
model(), phenology(), model.joensson.apply
Other model customizations:
model.bso.customize,
model.chapy.customize,
model.lange.customize,
model.phenips.customize,
model.phenips_clim.customize,
model.rity.customize
This page describes the usage of the the Lange model with phenology().
The model specific inputs are listed and its basic functionality is explained.
The model was published by Lange et al. (2008) for
Ips typographus.
tmin, tmean, tmax
|
Daily minimum/mean/maximum temperatures in °C. |
.submodels, .onset, .diapause, .mortality, ...
|
See |
In barrks, phenology() is used to apply a model. The following code
illustrates which inputs are required to apply the Lange model and which additional
parameters are available.
phenology("lange", ..., tmin, tmean, tmax)
# calculate submodels separately
phenology("lange", ..., .submodels = 'onset', tmean, tmax)
phenology("lange", ..., .submodels = 'mortality', tmin)
phenology("lange", ..., .submodels = 'development',
.onset, .diapause = NULL, .mortality = NULL,
tmean, tmax)
The function returns a phenology. Look here to find out how it can be analysed.
In the following, the basic functioning of the Lange model is explained.
Onset: The onset of swarming is triggerd when the degree days of the maximum temperature reach a specific threshold and the maximum temperature exceeds the minimum flight temperature according to Annila (1969).
Development: The development is calculated using stage-specific temperature sums and thresholds (Wermelinger and Seifert 1998). A new generation starts its development when the last generation finished its development and the maximum temperature exceeds the minimum flight temperature.
Diapause: The Lange model does not have a diapause submodel implemented.
Mortality: White stages (egg to pupa) die when the minimum temperature falls below a specific threshold.
Look here to find out how the model parameters affect the actual calculations and which values are used by default.
Annila E (1969).
“Influence of temperature upon the development and voltinism of Ips typographus L. (Coleoptera, Scolytidae).”
Annales Zoologici Fennici, 6(2), 161–208.
http://www.jstor.org/stable/23731366.
Lange H, Økland B, Krokene P (2008).
“To be or twice to be? The life cycle development of the spruce bark beetle under climate change.”
In Unifying Themes in Complex Systems: Proceedings of the Sixth International Conference on Complex Systems, 251–258.
Springer.
doi:10.1007/978-3-540-85081-6_32.
Wermelinger B, Seifert M (1998).
“Analysis of the temperature dependent development of the spruce bark beetle Ips typographus (L) (Col., Scolytidae).”
Journal of Applied Entomology, 122(1-5), 185-191.
doi:10.1111/j.1439-0418.1998.tb01482.x.
model(), phenology(), model.lange.customize
Other phenology applications:
model.bso.apply,
model.chapy.apply,
model.joensson.apply,
model.phenips.apply,
model.phenips_clim.apply,
model.rity.apply
This page describes the parameters that can be used to customize Lange. The model was developed by Lange et al. (2008). Look here to find out how to apply the model.
dd_onset_start_date |
The date, when the degree days start to sum up ('MM-DD'). |
dd_onset_base |
Base temperature to calculate degree days to trigger the onset. |
dd_onset_threshold |
Degree days that are required to trigger the onset of
infestation. Additionally, the maximum temperature must exceed |
tfly |
Minimum temperature that beetles need to fly. |
dd_base_stages |
Base temperatures to calculate degree days for the different stages in the following order: egg, larva, pupa, juvenile adult. |
dd_threshold_stages |
Thermal thresholds for the different stages in the following order: egg, larva, pupa, juvenile adult. |
model_end_date |
Date when the model ends (no further development will be modeled). |
first_lethal_date |
Date before which no mortality will be modeled. |
tlethal |
Temperature threshold below which white stages (egg, larva, pupa) will die. |
In barrks, model() is used to customize a model. The following code
illustrates which parameters are available for the Lange model and specifies their
default values.
model("lange",
# ==== onset ==== #
dd_onset_start_date = '01-01',
dd_onset_base = 5,
dd_onset_threshold = 110,
# ==== onset + development ====
tfly = 19.5,
# ==== development ==== #
dd_base_stages = c(10.6, 8.2, 9.9, 3.2),
dd_threshold_stages = c(51.8, 204.4, 57.7, 238.5),
model_end_date = '12-31',
# ==== mortality ==== #
first_lethal_date = '09-01',
tlethal = 0
)
Lange H, Økland B, Krokene P (2008). “To be or twice to be? The life cycle development of the spruce bark beetle under climate change.” In Unifying Themes in Complex Systems: Proceedings of the Sixth International Conference on Complex Systems, 251–258. Springer. doi:10.1007/978-3-540-85081-6_32.
model(), phenology(), model.lange.apply
Other model customizations:
model.bso.customize,
model.chapy.customize,
model.joensson.customize,
model.phenips.customize,
model.phenips_clim.customize,
model.rity.customize
This page describes the usage of PHENIPS-Clim with phenology().
The model specific inputs are listed and its basic functionality is explained.
PHENIPS-Clim is not published yet. This manual will be updated when a
publication is available. It was parametrized for Ips typographus in southern Germany.
... |
See |
tmin, tmean, tmax
|
Daily minimum/mean/maximum temperatures in °C. |
rad |
Daily radiation in W * h / m^2. |
daylength |
Length of the day in hours. Can be created with
|
sister_broods |
Set |
scenario |
Choose a scenario to use a suitable combination of parameters for specific situations. The scenario defines a default value for each value that can be overwritten by specifying a value for the respective parameter. The following scenarios are available:
|
exposure |
Specifies the sun exposure. Can be |
onset_mode |
Share of beetles that are already infesting trees necessary to
trigger the onset. Must be |
oviposition_mode |
Share of beetles that should have finished oviposition
to trigger the beginning of the development. Must be |
diapause_mode |
Determines how the diapause is initiated. Can be one of the following options:
Share of beetles that already stopped reproducing necessary to
trigger the diapause. Must be |
In barrks, phenology() is used to apply a model. The following code
illustrates which inputs are required to apply PHENIPS-Clim and which additional
parameters are available.
phenology("phenips-clim", ..., tmin, tmean, tmax, rad, daylength,
sister_broods = TRUE, scenario = 'max', exposure = NULL,
onset_mode = NULL, oviposition_mode = NULL, diapause_mode = NULL)
# calculate submodels separately
phenology("phenips-clim", ..., .submodels = 'onset', tmax, scenario = 'max', onset_mode = NULL)
phenology("phenips-clim", ..., .submodels = 'diapause', tmax, daylength, scenario = 'max', diapause_mode = NULL)
phenology("phenips-clim", ..., .submodels = 'mortality', tmin)
phenology("phenips-clim", ..., .submodels = 'development',
.onset, .diapause = NULL, .mortality = NULL,
tmin, tmean, tmax, rad, sister_broods = TRUE,
scenario = 'max', exposure = NULL, oviposition_mode = NULL)
In the following, the basic functioning of PHENIPS-Clim is explained.
Onset: A base onset is triggered by a logistic model that relates to the maximum temperature and the respective degree days. Beginning from the base onset, a specific level of degree days (depending on the share of individuals that should be accounted for) and maximum air temperature must be reached to trigger the actual onset.
Development: While the bark temperature and the emergence of new generations are determined according to PHENIPS, the calculation of the beetles' development rates is refined. Rather than implying a constant development within a day, temperature fluctuations are incorporated by taking the daily temperature amplitude into account. Additionally, the first part of development represents the pre-oviposition period and will not appear in the resulting output.
Diapause: The diapause can be initiated due to the photoperiod according to PHENIPS or by a logistic model that depends on the daylength and the maximum temperature and accounts for beetles that reproduce even on shorter days if the temperatures are favorable. In the second case, PHENIPS-Clim detects a reproductive arrest, due to adverse abiotic parameters, and not an actual diapause as the output can be adjusted, if conditions improve and allow for further reproduction later in the season.
Mortality: White stages (egg to pupa) die when the minimum temperature falls below a specific threshold.
Look here to find out how the model parameters affect the actual calculations and which values are used by default.
model(), phenology(), model.phenips_clim.customize
Other phenology applications:
model.bso.apply,
model.chapy.apply,
model.joensson.apply,
model.lange.apply,
model.phenips.apply,
model.rity.apply
In barrks, model() is used to customize a model. Here, the parameters are
described that can be used to customize PHENIPS-Clim. The model
is currently unpublished. This manual will be updated as soon as a
publication is available. Look here to find
out how to apply the model.
dd_onset_start_date |
The date, when the degree days start to sum up ('MM-DD'). |
dd_onset_base |
Base temperature to calculate degree days to trigger the onset. |
onset_func |
Function with the SpatRasters |
onset_add_dd |
Vector of options to calculate the actual onset of
infestation. The vector should be named after the share of beetles that
already started breeding when the onset is triggered (choose an option via
|
tfly |
Minimum temperature that beetles need to fly. |
dd_total_dev |
Degree days that are required for a generation to fully develop |
dev_oviposition |
Named numeric vector of shares in the total development
when the oviposition is finished. The vector should be named after the share
of beetles that should be taken into account (choose an option via
|
dev_end |
Share in total development when the juvenile beetle's development ends. Usable if the development above this threshold should account for mating, oviposition etc. |
dev_sister_brood |
Share in the total development, when a sister brood will be established. |
dev_mortal_min, dev_mortal_max
|
The beetles are considered to be in
white stages (egg, larva, pupa) if their development exceeds |
topt |
Temperature for optimal development. |
func_btmean, func_btmax, func_btdiff
|
Functions to calculate the bark temperatures (see Baier et al. 2007, equations A.3 to A.5). Each parameter will be passed as SpatRaster:
|
dev_rates |
Data frame that specifies the development rates per day depending
on the mean temperature and the temperature amplitude. Column names are the
mean temperatures and row names the temperature amplitudes both with one
decimal place.
base onset (see |
model_end_date |
Date when the model ends (no further development will be modeled). |
first_diapause_date |
Date before which an initiation of the diapause is impossible ('MM-DD'). |
diapause_thermal_func |
Function to calculate the initiation
of the diapause if the model was applied using |
daylength_dia |
When the daylength falls below this threshold, diapause
will be initiated if the model was applied using
|
tlethal |
Temperature threshold below which white stages will die. |
In barrks, model() is used to customize a model. The following code
illustrates which parameters are available for PHENIPS-Clim and specifies their
default values.
model("phenips-clim",
# ==== onset ====
dd_onset_start_date = '03-01',
dd_onset_base = 12,
onset_func = function(tmax, dd_tmax) {
0.564071 * tmax + 0.006434 * dd_tmax - 12.37046 > 0
},
onset_add_dd = c('0.1' = 0, '0.5' = 90, '0.9' = 190),
# ==== development ====
tfly = 16.5,
dd_total_dev = 557,
dev_oviposition = c('0.1' = 0.1,
'0.5' = 0.15,
'0.9' = 0.26),
dev_end = 1,
dev_sister_brood = 0.3,
dev_mortal_min = NULL,
dev_mortal_max = 0.6,
topt = 30.4,
func_btmean = function(tmean, rad) {
-0.173 + 0.0008518 * rad + 1.054 * tmean
},
func_btmax = function(tmax, rad) {
1.656 + 0.002955 * rad + 0.534 * tmax + 0.01884 * tmax ^ 2
},
func_btdiff = function(tmax) {
(-310.667 + 9.603 * tmax) / 24
},
# dev_rates too large to show here, type `params('phenips-clim')$dev_rates`
# to get the dev_rates that are used by default
# dev_rates = matrix(...),
model_end_date = '12-31',
# ==== diapause ====
first_diapause_date = '08-12',
diapause_thermal_func = function(daylength, tmax) {
0.8619156 * daylength + 0.5081128 * tmax - 23.63691 > 0
},
daylength_dia = 14.5,
# ==== mortality ====
tlethal = -5
)
Baier P, Pennerstorfer J, Schopf A (2007). “PHENIPS—A comprehensive phenology model of Ips typographus (L.)(Col., Scolytinae) as a tool for hazard rating of bark beetle infestation.” Forest Ecology and Management, 249(3), 171–186. doi:10.1016/j.foreco.2007.05.020.
model(), phenology(), model.phenips_clim.apply
Other model customizations:
model.bso.customize,
model.chapy.customize,
model.joensson.customize,
model.lange.customize,
model.phenips.customize,
model.rity.customize
This page describes the usage of PHENIPS with phenology().
The model specific inputs are listed and its basic functionality is explained.
PHENIPS was published by Baier et al. (2007) and
parametrized at the Kalkalpen National Park in Austria for Ips typographus.
... |
See |
tmean, tmax
|
Daily mean/maximum temperatures in °C. |
rad |
Daily radiation in W * h / m^2. |
daylength |
Length of the day in hours. Can be created with
|
exposure |
Specifies the sun exposure. Can be |
sister_broods |
Set |
In barrks, phenology() is used to apply a model. The following code
illustrates which inputs are required to apply PHENIPS and which additional
parameters are available.
phenology("phenips", ..., tmean, tmax, rad, daylength,
exposure = 'sunny', sister_broods = TRUE)
# calculate submodels separately
phenology("phenips", ..., .submodels = 'onset', tmax)
phenology("phenips", ..., .submodels = 'diapause', daylength)
phenology("phenips", ..., .submodels = 'mortality', tmax)
phenology("phenips", ..., .submodels = 'development',
.onset, .diapause = NULL, .mortality = NULL,
tmean, tmax, rad,
exposure = 'sunny', sister_broods = TRUE)
The function returns a phenology. Look here to find out how it can be analysed.
In the following, the basic functioning of PHENIPS is explained.
Onset: The onset is triggered when the degree days of the maximum temperature reach a specific threshold and the maximum temperature exceeds the minimum flight temperature.
Development: The beetles develop according to a slightly modified version of the optimum curve described by Wermelinger and Seifert (1998) depending on the bark temperature. The bark temperature is modeled based on mean and maximum temperature, global radiation and sun exposure. A new generation will emerge when the last generation is fully developed and the maximum temperature exceeds the minimum flight temperature.
Diapause: The diapause is initiated when the daylength falls below a threshold.
Mortality: White stages (egg to pupa) die on a fixed date.
Look here to find out how the model parameters affect the actual calculations and which values are used by default.
Baier P, Pennerstorfer J, Schopf A (2007).
“PHENIPS—A comprehensive phenology model of Ips typographus (L.)(Col., Scolytinae) as a tool for hazard rating of bark beetle infestation.”
Forest Ecology and Management, 249(3), 171–186.
doi:10.1016/j.foreco.2007.05.020.
Wermelinger B, Seifert M (1998).
“Analysis of the temperature dependent development of the spruce bark beetle Ips typographus (L) (Col., Scolytidae).”
Journal of Applied Entomology, 122(1-5), 185-191.
doi:10.1111/j.1439-0418.1998.tb01482.x.
model(), phenology(), model.phenips.customize
Other phenology applications:
model.bso.apply,
model.chapy.apply,
model.joensson.apply,
model.lange.apply,
model.phenips_clim.apply,
model.rity.apply
This page describes the parameters that can be used to customize PHENIPS. The model was developed by Baier et al. (2007). Look here to find out how to apply the model.
dd_onset_start_date |
The date, when the degree days start to sum up ('MM-DD'). |
dd_onset_base |
Base temperature to calculate degree days to trigger the onset. |
dd_onset_threshold |
Degree days that are required to trigger the onset of
infestation. Additionally, the maximum temperature must exceed |
tfly |
Minimum temperature that beetles need to fly. |
dd_development_base |
Base temperature to calculate degree days for calculating the beetles development. |
dd_total_dev |
Degree days that are required for a generation to fully develop |
dev_start, dev_end
|
Share in total development when the egg development starts and the juvenile beetle's development ends respectively. Usable if the development below/above these thresholds should account for mating, oviposition etc. |
dev_sister_brood |
Share in the total development when a sister brood will be established. |
dev_mortal_min, dev_mortal_max
|
The beetles are considered to be in
white stages (egg, larva, pupa) if their development exceeds |
topt |
Temperature for optimal development. |
tlow, tup
|
Temperature below/above which no development happens. |
func_btmean, func_btmax, func_btdiff
|
Functions to calculate the effective bark temperature (see Baier et al. 2007, equations A.3 to A5). Each parameter will be passed as SpatRaster:
|
model_end_date |
Date when the model ends (no further development will be modeled). |
daylength_dia |
When the daylength falls below this threshold, diapause will be initiated. |
mortality_date |
Date when all white stages (egg, larva, pupa) die. |
In barrks, model() is used to customize a model. The following code
illustrates which parameters are available for PHENIPS and specifies their
default values.
model("phenips",
# ==== onset ====
dd_onset_start_date = '04-01',
dd_onset_base = 8.3,
dd_onset_threshold = 140,
# ==== onset + development ====
tfly = 16.5,
# ==== development ====
dd_development_base = 8.3,
dd_total_dev = 557,
dev_start = 0,
dev_end = 1,
dev_sister_brood = 0.5,
dev_mortal_min = NULL,
dev_mortal_max = 0.6,
topt = 30.4,
tlow = 8.3,
tup = 38.9,
func_btmean = function(tmean, rad) {
-0.173 + 0.0008518 * rad + 1.054 * tmean
},
func_btmax = function(tmax, rad) {
1.656 + 0.002955 * rad + 0.534 * tmax + 0.01884 * tmax ^ 2
},
func_btdiff = function(btmax) {
(-310.667 + 9.603 * btmax) / 24
},
model_end_date = '10-31',
# ==== diapause ====
daylength_dia = 14.5,
# ==== mortality ====
mortality_date = '10-31'
)
Baier P, Pennerstorfer J, Schopf A (2007). “PHENIPS—A comprehensive phenology model of Ips typographus (L.)(Col., Scolytinae) as a tool for hazard rating of bark beetle infestation.” Forest Ecology and Management, 249(3), 171–186. doi:10.1016/j.foreco.2007.05.020.
model(), phenology(), model.phenips.apply
Other model customizations:
model.bso.customize,
model.chapy.customize,
model.joensson.customize,
model.lange.customize,
model.phenips_clim.customize,
model.rity.customize
This page describes the usage of RITY with phenology().
The model specific inputs are listed and its basic functionality is explained.
RITY (also called RITY-2) was published by Ogris et al. (2019) and
parametrized for Ips typographus in Slovenia.
tmin, tmean, tmax
|
Daily minimum/mean/maximum temperatures in °C.
For the |
daylength |
Length of the day in hours. Can be created with
|
mode |
Specifies which temperature should be used to calculate the
development. Can be |
.submodels, .onset, .diapause, .mortality, ...
|
See |
In barrks, phenology() is used to apply a model. The following code
illustrates which inputs are required to apply RITY and which additional
parameters are available.
phenology("rity", ..., tmin = NULL, tmean = NULL, tmax, daylength, mode = 'max')
# calculate submodels separately
phenology("rity", ..., .submodels = 'onset', tmax)
phenology("rity", ..., .submodels = 'diapause', daylength)
phenology("rity", ..., .submodels = 'mortality', tmax)
phenology("rity", ..., .submodels = 'development',
.onset, .diapause = NULL, .mortality = NULL,
tmin = NULL, tmean = NULL, tmax = NULL, mode = 'max')
The function returns a phenology. Look here to find out how it can be analysed.
In the following, the basic functioning of RITY is explained.
Onset: See PHENIPS.
Development: Based on PHENIPS with a few modifications:
The optimum curve is calculated according to Wermelinger and Seifert (1998) without simplification.
The minimum, mean or maximum bark temperature can be used to calculate the development. These temperatures depend only on the respective air temperatures.
Diapause: See PHENIPS.
Mortality: See PHENIPS.
Look here to find out how the model parameters affect the actual calculations and which values are used by default.
Ogris N, Ferlan M, Hauptman T, Pavlin R, Kavčič A, Jurc M, De Groot M (2019).
“RITY–A phenology model of Ips typographus as a tool for optimization of its monitoring.”
Ecological Modelling, 410, 108775.
doi:10.1016/j.ecolmodel.2019.108775.
Wermelinger B, Seifert M (1998).
“Analysis of the temperature dependent development of the spruce bark beetle Ips typographus (L) (Col., Scolytidae).”
Journal of Applied Entomology, 122(1-5), 185-191.
doi:10.1111/j.1439-0418.1998.tb01482.x.
model(), phenology(), model.rity.customize
Other phenology applications:
model.bso.apply,
model.chapy.apply,
model.joensson.apply,
model.lange.apply,
model.phenips.apply,
model.phenips_clim.apply
This page describes the parameters that can be used to customize RITY (also called RITY-2). The model was developed by Ogris et al. (2019). Look here to find out how to apply the model.
dd_onset_start_date |
The date, when the degree days start to sum up ('MM-DD'). |
dd_onset_threshold |
Degree days that are required to trigger the onset of
infestation. Additionally, the maximum temperature must exceed |
tfly |
Minimum temperature that beetles need to fly. |
dd_onset_base |
Base temperature to calculate degree days for development. |
dd_total_dev |
Degree days that are required for a generation to fully develop |
dev_start, dev_end
|
Share in total development when the egg development starts and the juvenile beetle's development ends respectively. Usable if the development below/above these thresholds should account for mating, oviposition etc. |
dev_sister_brood |
Share in the total development, when a sister brood will be established. |
dev_mortal_min, dev_mortal_max
|
The beetles are considered to be in
white stages (egg, larva, pupa) if their development exceeds |
func_ftmean, func_ftmax, func_atdiff
|
Functions to caclulate the air temperature in forest stands (see Ogris et al. 2019, equations 1 - 3). Each parameter will be passed as SpatRaster:
|
func_btmean, func_btmax, func_btdiff
|
Functions to caclulate the bark temperature (see Ogris et al. 2019, equations 4 - 6). Each parameter will be passed as SpatRaster:
|
dt_low, dt_up, topt, tmax, alpha, beta, gamma
|
Parameters to calculate the effective bark temperature (see Ogris et al. 2019, equations 7 - 9). |
model_end_date |
Date when the model ends (no further development will be modeled). |
daylength_dia |
When the daylength falls below this threshold, diapause will be initiated. |
mortality_date |
Date when all white stages (egg, larva, pupa) die. |
In barrks, model() is used to customize a model. The following code
illustrates which parameters are available for RITY and specifies their
default values.
model("rity",
# ==== onset ====
dd_onset_start_date = '03-07',
dd_onset_base = 8.3,
dd_onset_threshold = 155.6,
# ==== onset + development ====
tfly = 14.5,
# ==== development ====
dd_development_base = 8.3,
dd_total_dev = 557,
dev_start = 0,
dev_end = 1,
dev_sister_brood = 0.5,
dev_mortal_min = NULL,
dev_mortal_max = 0.6,
func_ftmin = function(tmin) { 1.44 + 0.82 * tmin },
func_ftmean = function(tmean) { 0.50 + 0.81 * tmean },
func_ftmax = function(tmax) { 1.03 + 0.86 * tmax },
func_btmin = function(ftmin) { 0.56 + 0.99 * ftmin },
func_btmean = function(ftmean) { -0.48 + 1.03 * ftmean },
func_btmax = function(ftmax) { 0.03 + 0.99 * ftmax },
dt_low = 8.3,
dt_up = 38.9,
topt = 30.4,
tmax = 40.9958913,
alpha = 0.02876507,
beta = 3.5922336,
gamma = 1.24657367,
model_end_date = '10-31',
# ==== diapause ====
daylength_dia = 14.5,
# ==== mortality ====
mortality_date = '10-31'
)
Ogris N, Ferlan M, Hauptman T, Pavlin R, Kavčič A, Jurc M, De Groot M (2019). “RITY–A phenology model of Ips typographus as a tool for optimization of its monitoring.” Ecological Modelling, 410, 108775. doi:10.1016/j.ecolmodel.2019.108775.
model(), phenology(), model.rity.apply
Other model customizations:
model.bso.customize,
model.chapy.customize,
model.joensson.customize,
model.lange.customize,
model.phenips.customize,
model.phenips_clim.customize
Get the parameters of a model.
params(m, ...)params(m, ...)
m |
Name of the model or the return value of another |
... |
List of parameters to customize the model. |
A list.
# print the first parameters of `phenips-clim` head(params('phenips-clim'))# print the first parameters of `phenips-clim` head(params('phenips-clim'))
Calculate a phenology (or its subparts) with a specific model.
phenology( .model, .data = NULL, .dates = NULL, .win = NULL, .ext = "tif", .onset = NULL, .diapause = NULL, .mortality = NULL, .submodels = c("onset", "diapause", "mortality", "development"), .setup_only = FALSE, .stations = NULL, .storage = NULL, .quiet = FALSE, ... ) bso_phenology( .model = "bso", .data = NULL, .dates = NULL, .win = NULL, .ext = "tif", .onset = NULL, .diapause = NULL, .mortality = NULL, .submodels = c("onset", "diapause", "mortality", "development"), .setup_only = FALSE, .stations = NULL, .storage = NULL, .quiet = FALSE, ... )phenology( .model, .data = NULL, .dates = NULL, .win = NULL, .ext = "tif", .onset = NULL, .diapause = NULL, .mortality = NULL, .submodels = c("onset", "diapause", "mortality", "development"), .setup_only = FALSE, .stations = NULL, .storage = NULL, .quiet = FALSE, ... ) bso_phenology( .model = "bso", .data = NULL, .dates = NULL, .win = NULL, .ext = "tif", .onset = NULL, .diapause = NULL, .mortality = NULL, .submodels = c("onset", "diapause", "mortality", "development"), .setup_only = FALSE, .stations = NULL, .storage = NULL, .quiet = FALSE, ... )
.model |
A phenology model or a model name (see |
.data |
Data that will be passed to the model. It can be one of the following:
Look at the model application manuals to find out which inputs are required
by a specific model: |
.dates |
Vector of dates that the data should be restricted to. |
.win |
SpatExtent to set a window (area of interest) if |
.ext |
Extension of the files that should be used if |
.onset, .diapause, .mortality
|
Pass custom or precalculated phenological
events to the model. See |
.submodels |
Character vector. Specifies which submodels should be
calculated. Can be a subset of
|
.setup_only |
If |
.stations |
Assign stations to the phenology. See |
.storage |
If set, the path specified here will be used to save the
(intermediate) results. If |
.quiet |
If |
... |
Parameters that will be passed to the model. Must be named according
to the model inputs. See |
A phenology as a list. Look here to find out how a phenology can be analysed. It is not recommended to access the list elements directly.
bso_phenology(): As BSO works a bit different than the other models, a seperate
phenology function is implemented for this model. Note that while the
onset and the development submodels are needed to be taken from BSO,
the diapause and the mortality submodels are compatible with other models.
The function returns a BSO phenology as a list. Look here to find out how
a BSO phenology can be analysed. It is not recommended to access the list elements directly.
To be able to use the functions that are
available for phenology objects returned by phenology(), call bso_translate_phenology().
model.bso.apply, model.phenips.apply, model.rity.apply, model.chapy.apply, model.joensson.apply, model.lange.apply, model.phenips_clim.apply
# calculate phenology p <- phenology('phenips-clim', barrks_data()) # plot calculated generations gens <- get_generations_rst(p) terra::plot(gens)# calculate phenology p <- phenology('phenips-clim', barrks_data()) # plot calculated generations gens <- get_generations_rst(p) terra::plot(gens)
A development diagram illustrates the beetles' development of all appearing generations within a year.
plot_development_diagram( .phenos, .station = prop_stations(.phenos[[1]])[1], .generations = NULL, .colors = barrks_colors("diagram_lines"), .fill = barrks_colors("diagram_fill"), .labels = barrks_labels("diagram"), .legend_col = TRUE, .legend_lty = TRUE, .group = TRUE, .minmax_only = FALSE, .fun_bg = NULL, .lty = "solid", .lwd = 2, .date_split = NULL, .date_stop = NULL, .lty2 = "dotted", .lwd2 = 2, .fill2 = NA, ... )plot_development_diagram( .phenos, .station = prop_stations(.phenos[[1]])[1], .generations = NULL, .colors = barrks_colors("diagram_lines"), .fill = barrks_colors("diagram_fill"), .labels = barrks_labels("diagram"), .legend_col = TRUE, .legend_lty = TRUE, .group = TRUE, .minmax_only = FALSE, .fun_bg = NULL, .lty = "solid", .lwd = 2, .date_split = NULL, .date_stop = NULL, .lty2 = "dotted", .lwd2 = 2, .fill2 = NA, ... )
.phenos |
List of (named) phenology objects or a single phenology that will be
plotted (see |
.station |
Pass a character vector to choose a station assigned to |
.generations |
Generations that will be shown. |
.colors, .fill, .labels
|
Character vectors. Change the line colors, fill or labels of the generations starting from the first generation followed consecutively by elements for the other generations (including sister broods). |
.legend_col, .legend_lty
|
Manipulate the appearance of the legends for
colors and line types. Pass |
.group |
Select the phenology objects that will be used to draw the filling. It can be
a character vector of the phenology names, an integer vector of the phenology
numbers, or |
.minmax_only |
If |
.fun_bg |
Function to draw a background. |
.lty, .lwd
|
Use specific line types and line widths.
Vectors of the same length as |
.date_split, .lty2, .lwd2, .fill2
|
When |
.date_stop |
If specified, no data will be plotted after the respective date. |
... |
Parameters passed to |
None
# calculate phenology p <- phenology('phenips-clim', barrks_data('stations'), .quiet = TRUE) # plot development diagram of the station 'Mannheim' plot_development_diagram(p, 'Mannheim', .lwd = 4, .legend_lty = FALSE)# calculate phenology p <- phenology('phenips-clim', barrks_data('stations'), .quiet = TRUE) # plot development diagram of the station 'Mannheim' plot_development_diagram(p, 'Mannheim', .lwd = 4, .legend_lty = FALSE)
To examine a phenology, there are different functions to query its properties.
## get the year the phenology was calculated for prop_year(pheno) ## get all dates that are covered by the phenology prop_dates(pheno) ## get the first date that is covered by the phenology prop_first_date(pheno) ## get the last date that is covered by the phenology prop_last_date(pheno) ## get all hatched generations as numeric vector prop_hatched_generations(pheno) ## get all hatched filial generations as numeric vector prop_filial_generations(pheno) ## get all hatched sister broods as numeric vector prop_sister_broods(pheno) ## get the stations assigned to the phenology prop_stations(pheno)## get the year the phenology was calculated for prop_year(pheno) ## get all dates that are covered by the phenology prop_dates(pheno) ## get the first date that is covered by the phenology prop_first_date(pheno) ## get the last date that is covered by the phenology prop_last_date(pheno) ## get all hatched generations as numeric vector prop_hatched_generations(pheno) ## get all hatched filial generations as numeric vector prop_filial_generations(pheno) ## get all hatched sister broods as numeric vector prop_sister_broods(pheno) ## get the stations assigned to the phenology prop_stations(pheno)
pheno |
A phenology (see |
The requested property.
# calculate phenology p <- phenology('phenips-clim', barrks_data(), .quiet = TRUE) # print all generations that were hatched prop_hatched_generations(p)# calculate phenology p <- phenology('phenips-clim', barrks_data(), .quiet = TRUE) # print all generations that were hatched prop_hatched_generations(p)
Saves/loads a phenology to/from a path.
save_phenology( pheno, .storage, .submodels = c("onset", "diapause", "mortality", "development"), .overwrite = FALSE, .ext = ".tif", .quiet = FALSE ) load_phenology( .storage, .submodels = c("onset", "diapause", "mortality", "development"), .ext = ".tif", .quiet = FALSE )save_phenology( pheno, .storage, .submodels = c("onset", "diapause", "mortality", "development"), .overwrite = FALSE, .ext = ".tif", .quiet = FALSE ) load_phenology( .storage, .submodels = c("onset", "diapause", "mortality", "development"), .ext = ".tif", .quiet = FALSE )
pheno |
A phenology, calculated with |
.storage |
Path to save/load the phenology. |
.submodels |
Which submodels should be saved/loaded. |
.overwrite |
Should an existing storage be overwritten? |
.ext |
Extension for raster files. |
.quiet |
If |
save_phenology(): None
load_phenology(): A phenology as a list. Look here to find out how
a phenology can be analysed. It is not recommended to access the list elements directly.
save_phenology(): Saves a phenology to a path.
load_phenology(): Loads a phenology from a path.
# calculate phenology p <- phenology('phenips-clim', barrks_data(), .quiet = TRUE) # choose path to save the phenology path <- file.path(tempdir(), 'pheno') # save phenology save_phenology(p, path, .overwrite = TRUE, .quiet = TRUE) ### # load phenology from path p2 <- load_phenology(path, .quiet = TRUE) # plot generations gens <- get_generations_rst(p2) terra::plot(gens)# calculate phenology p <- phenology('phenips-clim', barrks_data(), .quiet = TRUE) # choose path to save the phenology path <- file.path(tempdir(), 'pheno') # save phenology save_phenology(p, path, .overwrite = TRUE, .quiet = TRUE) ### # load phenology from path p2 <- load_phenology(path, .quiet = TRUE) # plot generations gens <- get_generations_rst(p2) terra::plot(gens)
In barrks, stations are references to specific raster cells. Thus, they
can be used to extract point-related data from a phenology. Look
here to find out which station-based functions are
available to analyse a phenology.
stations_create(station_names, cells) stations_assign(pheno, stations) stations_names(stations) stations_cells(stations)stations_create(station_names, cells) stations_assign(pheno, stations) stations_names(stations) stations_cells(stations)
station_names |
Character vector that specifies the names of the stations. |
cells |
Numbers of the cells that should be represented by the stations. |
pheno |
A phenology (see |
stations |
Stations created with |
stations_create(): A named numeric vector.
stations_assign(): A phenology object (see phenology()).
stations_names(): A character vector.
stations_cells(): A numeric vector.
stations_create(): Create stations.
stations_assign(): Assign stations to a phenology. Returns the phenology
that was passed with respective stations assigned.
stations_names(): Get the names of stations.
stations_cells(): Get the raster cells of stations.
# calculate phenology p <- phenology('phenips-clim', barrks_data(), .quiet = TRUE) # create stations and assign them to the phenology object stations <- stations_create(c('station a', 'station b'), c(234, 345)) p <- stations_assign(p, stations) # plot the development of 'station b' plot_development_diagram(p, 'station b', .lwd = 4, .legend_lty = FALSE)# calculate phenology p <- phenology('phenips-clim', barrks_data(), .quiet = TRUE) # create stations and assign them to the phenology object stations <- stations_create(c('station a', 'station b'), c(234, 345)) p <- stations_assign(p, stations) # plot the development of 'station b' plot_development_diagram(p, 'station b', .lwd = 4, .legend_lty = FALSE)