Package 'phenModel' reference manual

Title:	Insect Phenology Model Evaluation Based on Daily Temperatures
Description:	Generates predicted stage change days for an insect, based on daily temperatures and development rate parameters, as developed by Pollard (2014) <http://mural.maynoothuniversity.ie/view/ethesisauthor/Pollard=3ACiaran_P=2E=3A=3A.html>. A few example datasets are included and implemented for P. vulgatissima, the blue willow beetle, but the approach can be readily applied to other species that display similar behaviour.
Authors:	Rafael de Andrade Moral [aut, cre], Rowan Fealy [aut]
Maintainer:	Rafael de Andrade Moral <[email protected]>
License:	GPL (>= 2)
Version:	1.0
Built:	2024-12-01 08:00:12 UTC
Source:	CRAN

Insect Phenology Model Evaluation Based on Daily Temperatures

Description

Generates predicted stage change days for an insect, based on daily temperatures and development rate parameters, as developed by Pollard (2014) <http://mural.maynoothuniversity.ie/view/ethesisauthor/Pollard=3ACiaran_P=2E=3A=3A.html>. A few example datasets are included and implemented for P. vulgatissima, the blue willow beetle, but the approach can be readily applied to other species that display similar behaviour.

Details

The DESCRIPTION file:

Package:	phenModel
Type:	Package
Title:	Insect Phenology Model Evaluation Based on Daily Temperatures
Version:	1.0
Date:	2019-08-01
Authors@R:	c(person("Rafael", "de Andrade Moral", role = c("aut", "cre"), email = "[email protected]"), person("Rowan", "Fealy", role = "aut"))
Author:	Rafael de Andrade Moral [aut, cre], Rowan Fealy [aut]
Maintainer:	Rafael de Andrade Moral <[email protected]>
Depends:	R (>= 3.0.0), ggplot2, dplyr, reshape, grid
Description:	Generates predicted stage change days for an insect, based on daily temperatures and development rate parameters, as developed by Pollard (2014) <http://mural.maynoothuniversity.ie/view/ethesisauthor/Pollard=3ACiaran_P=2E=3A=3A.html>. A few example datasets are included and implemented for P. vulgatissima, the blue willow beetle, but the approach can be readily applied to other species that display similar behaviour.
License:	GPL (>= 2)
NeedsCompilation:	no
Packaged:	2019-08-01 12:35:04 UTC; rafael
Repository:	CRAN
Date/Publication:	2019-08-02 10:40:02 UTC

Index of help topics:

assign_observed_data    Observed Data Preparation
bp_maxmin               Internal functions to used to evaluate a
                        phenology model and plot the results
casement9596            Casement 1995-1996 Data
cdl_check               Plots for Checking Sensitivity to CDL
cum_dev_rate            Cumulative Development Rates
get_cycle               Obtain Yearly Cycles
get_total_info          Compute Day Length, Mean Temperature, Thermal
                        Units, and Cumulative Chill Days
phenModel-package       Insect Phenology Model Evaluation Based on
                        Daily Temperatures
phen_model              Phenology Model Evaluation
phen_model_forecast     Multi-Year Forecast Based on the Phenology
                        Model
phen_model_gen2         Phenology Model Evaluation for Further
                        Generations
plot_phen_forecast      Plot Phenology Model Multi-Year Forecast
plot_phen_model         Plot Phenology Model Evaluations
pvulg_budburst_parameters
                        Estimated Parameters for the Exponential Decay
                        Model for Budburst
pvulg_lactin_parameters
                        Estimated Parameters for the Lactin Model for
                        P. Vulgatissima
pvulg_weibull_parameters
                        Estimated Parameters for the Weibull Model for
                        P. Vulgatissima
sensitivity             Sensitivity Analysis for Evaluated Phenology
                        Model
valentia                Valentia Data

Author(s)

Rafael de Andrade Moral [aut, cre], Rowan Fealy [aut]

Maintainer: Rafael de Andrade Moral <[email protected]>

References

Pollard C.P., Griffin C.T., Moral R.A., Duffy C., Chuche J., Gaffney M.T., Fealy R. (submitted) phenModel: a temperature-dependent phenology model for the blue willow beetle, Phratora vulgatissima.

Observed Data Preparation

Description

Prepares observed data for plotting with model evaluation.

Usage

assign_observed_data(obj, eggs = NA, larvae = NA, pupae = NA, adults)
assign_observed_data(obj, eggs = NA, larvae = NA, pupae = NA, adults)

Arguments

`obj`	an object generated from `phen_model`
`eggs`	vector of days in which eggs were observed
`larvae`	vector of days in which larvae were observed
`pupae`	vector of days in which pupae were observed
`adults`	vector of days in which adults were observed

Details

Reads in the data and returns an object ready to be passed to plot_phen_model for plotting. Uses a simple probability checking to determine whether the observations for the adults category belong to the post-diapause, ovipositing or adult emergence stage, based on normal distributions.

Value

Returns a data.frame with five columns, one for each stage (post-diapause, eggs, larvae, pupae and adults).

Author(s)

Rafael de Andrade Moral ([email protected]) and Rowan Fealy

Examples

data(casement9596)
casement1995 <- data.frame(casement9596, 
                           with(casement9596, 
                                get_total_info(Tmax, Tmin, Tbase = 7, Lat, day)))

## loading fitted parameters
data(pvulg_budburst_parameters)
data(pvulg_weibull_parameters)
data(pvulg_lactin_parameters)

## evaluating model
quantiles_seq <- seq(.001, .95, .001)

phen95 <- phen_model(Tmean = "Tmean",
                     thermal_units = "thermal_units",
                     chill_days = "chill_days",
                     day_length = "day_length",
                     bud_pars = pvulg_budburst_parameters,
                     weib_pars = pvulg_weibull_parameters,
                     lactin_pars = pvulg_lactin_parameters,
                     max_day_length = 14.92,
                     pop_quantiles = quantiles_seq,
                     data = casement1995)

phen95_gen2 <- phen_model_gen2(Tmean = "Tmean",
                               day_length = "day_length",
                               bud_pars = pvulg_budburst_parameters,
                               weib_pars = pvulg_weibull_parameters,
                               lactin_pars = pvulg_lactin_parameters,
                               sex_mat_day = phen95["0.05","sexual_maturation_day"],
                               pop_quantiles = .05,
                               data = casement1995)

## adding observed points
made_up_data <- assign_observed_data(obj = phen95,
                                     eggs = c(182,184,204,227,233,242),
                                     larvae = c(204,227,233,242,249),
                                     adults = c(204,212,220,221,222,224,227,233,242,269,283,298,
                                                305,310,311,312,313,314,315,316,317,318,
                                                319,320,321,322,323,334,325,326,327,328,
                                                329,330,331,332))

plot_phen_model(obj = phen95, obj_gen2 = phen95_gen2,
                observed_data = made_up_data)
data(casement9596)
casement1995 <- data.frame(casement9596, 
                           with(casement9596, 
                                get_total_info(Tmax, Tmin, Tbase = 7, Lat, day)))

## loading fitted parameters
data(pvulg_budburst_parameters)
data(pvulg_weibull_parameters)
data(pvulg_lactin_parameters)

## evaluating model
quantiles_seq <- seq(.001, .95, .001)

phen95 <- phen_model(Tmean = "Tmean",
                     thermal_units = "thermal_units",
                     chill_days = "chill_days",
                     day_length = "day_length",
                     bud_pars = pvulg_budburst_parameters,
                     weib_pars = pvulg_weibull_parameters,
                     lactin_pars = pvulg_lactin_parameters,
                     max_day_length = 14.92,
                     pop_quantiles = quantiles_seq,
                     data = casement1995)

phen95_gen2 <- phen_model_gen2(Tmean = "Tmean",
                               day_length = "day_length",
                               bud_pars = pvulg_budburst_parameters,
                               weib_pars = pvulg_weibull_parameters,
                               lactin_pars = pvulg_lactin_parameters,
                               sex_mat_day = phen95["0.05","sexual_maturation_day"],
                               pop_quantiles = .05,
                               data = casement1995)

## adding observed points
made_up_data <- assign_observed_data(obj = phen95,
                                     eggs = c(182,184,204,227,233,242),
                                     larvae = c(204,227,233,242,249),
                                     adults = c(204,212,220,221,222,224,227,233,242,269,283,298,
                                                305,310,311,312,313,314,315,316,317,318,
                                                319,320,321,322,323,334,325,326,327,328,
                                                329,330,331,332))

plot_phen_model(obj = phen95, obj_gen2 = phen95_gen2,
                observed_data = made_up_data)

Internal functions to used to evaluate a phenology model and plot the results

Description

Internal functions used to evaluate a phenology model and plot the results

Author(s)

Rafael A. Moral <[email protected]> and Rowan Fealy

Casement 1995-1996 Data

Description

Daily minimum and maximum temperature data obtained at the Casement Aerodrome weather station for the period of 1st November 1995 to 31st October 1996.

Usage

data("casement9596")data("casement9596")

Format

A data frame with 366 observations on the following 7 variables.

Year: a numeric vector
Month: a numeric vector
Day: a numeric vector
Lat: a numeric vector
Tmin: a numeric vector
Tmax: a numeric vector
day: a numeric vector

Details

The data was obtained from the Irish National Meteorological Agency (Met Éireann).

Source

https://www.met.ie/climate/available-data/historical-data

Examples

data(casement9596)

casement1995 <- data.frame(casement9596, 
                           with(casement9596, 
                                get_total_info(Tmax, Tmin, Tbase = 7, Lat, day)))

## evaluating model
quantiles_seq <- seq(.001, .95, .001)

## loading fitted parameters
data(pvulg_budburst_parameters)
data(pvulg_weibull_parameters)
data(pvulg_lactin_parameters)

phen95 <- phen_model(Tmean = "Tmean",
                     thermal_units = "thermal_units",
                     chill_days = "chill_days",
                     day_length = "day_length",
                     bud_pars = pvulg_budburst_parameters,
                     weib_pars = pvulg_weibull_parameters,
                     lactin_pars = pvulg_lactin_parameters,
                     max_day_length = 14.92,
                     pop_quantiles = quantiles_seq,
                     data = casement1995)

phen95_gen2 <- phen_model_gen2(Tmean = "Tmean",
                               day_length = "day_length",
                               bud_pars = pvulg_budburst_parameters,
                               weib_pars = pvulg_weibull_parameters,
                               lactin_pars = pvulg_lactin_parameters,
                               sex_mat_day = phen95["0.05","sexual_maturation_day"],
                               pop_quantiles = .05,
                               data = casement1995)

plot_phen_model(obj = phen95, obj_gen2 = phen95_gen2)
data(casement9596)

casement1995 <- data.frame(casement9596, 
                           with(casement9596, 
                                get_total_info(Tmax, Tmin, Tbase = 7, Lat, day)))

## evaluating model
quantiles_seq <- seq(.001, .95, .001)

## loading fitted parameters
data(pvulg_budburst_parameters)
data(pvulg_weibull_parameters)
data(pvulg_lactin_parameters)

phen95 <- phen_model(Tmean = "Tmean",
                     thermal_units = "thermal_units",
                     chill_days = "chill_days",
                     day_length = "day_length",
                     bud_pars = pvulg_budburst_parameters,
                     weib_pars = pvulg_weibull_parameters,
                     lactin_pars = pvulg_lactin_parameters,
                     max_day_length = 14.92,
                     pop_quantiles = quantiles_seq,
                     data = casement1995)

phen95_gen2 <- phen_model_gen2(Tmean = "Tmean",
                               day_length = "day_length",
                               bud_pars = pvulg_budburst_parameters,
                               weib_pars = pvulg_weibull_parameters,
                               lactin_pars = pvulg_lactin_parameters,
                               sex_mat_day = phen95["0.05","sexual_maturation_day"],
                               pop_quantiles = .05,
                               data = casement1995)

plot_phen_model(obj = phen95, obj_gen2 = phen95_gen2)

Plots for Checking Sensitivity to CDL

Description

Produces plots based on sensitivity analysis for the critical day length threshold.

Usage

cdl_check(cdl, quantiles, max_day_length)
cdl_check(cdl, quantiles, max_day_length)

Arguments

`cdl`	a vector with the CDL thersholds calculated by `sensitivity`
`quantiles`	the quantiles corresponding to each value from `cdl`
`max_day_length`	the maximum day length set when running `sensitivity`

Value

A ggplot object.

Author(s)

Rafael de Andrade Moral ([email protected]) and Rowan Fealy

Examples

data(casement9596)

casement1995 <- data.frame(casement9596, 
                           with(casement9596, 
                                get_total_info(Tmax, Tmin, Tbase = 7, Lat, day)))

data(pvulg_budburst_parameters)
data(pvulg_weibull_parameters)
data(pvulg_lactin_parameters)

test_quantiles <- c(.01,.05,.1,.25,.5,.75,.9,.95,.99)
cdl_thresholds <- NULL
for(q in test_quantiles) {
  sens_casement_cdl <- sensitivity(Tmax = casement9596$Tmax,
                                   Tmin = casement9596$Tmin,
                                   Tbase = 7,
                                   lat = casement9596$Lat,
                                   day = casement9596$day,
                                   bud_pars = pvulg_budburst_parameters,
                                   weib_pars = pvulg_weibull_parameters,
                                   lactin_pars = pvulg_lactin_parameters,
                                   max_day_length = 14.92, 
                                   pop_quantile = q,
                                   data = casement9596,
                                   study_type = "cdl",
                                   percent_variation = 1,
                                   resolution = 20) # increase resolution for better visualisation
  cdl_thresholds <- c(cdl_thresholds, summary_sensitivity(sens_casement_cdl)[1])
}

names(cdl_thresholds) <- paste(test_quantiles)
cdl_thresholds
                                 
cdl_check(cdl = cdl_thresholds,
          quantiles = test_quantiles,
          max_day_length = 14.92) +
  ggtitle("CDL sensitivity")
data(casement9596)

casement1995 <- data.frame(casement9596, 
                           with(casement9596, 
                                get_total_info(Tmax, Tmin, Tbase = 7, Lat, day)))

data(pvulg_budburst_parameters)
data(pvulg_weibull_parameters)
data(pvulg_lactin_parameters)

test_quantiles <- c(.01,.05,.1,.25,.5,.75,.9,.95,.99)
cdl_thresholds <- NULL
for(q in test_quantiles) {
  sens_casement_cdl <- sensitivity(Tmax = casement9596$Tmax,
                                   Tmin = casement9596$Tmin,
                                   Tbase = 7,
                                   lat = casement9596$Lat,
                                   day = casement9596$day,
                                   bud_pars = pvulg_budburst_parameters,
                                   weib_pars = pvulg_weibull_parameters,
                                   lactin_pars = pvulg_lactin_parameters,
                                   max_day_length = 14.92, 
                                   pop_quantile = q,
                                   data = casement9596,
                                   study_type = "cdl",
                                   percent_variation = 1,
                                   resolution = 20) # increase resolution for better visualisation
  cdl_thresholds <- c(cdl_thresholds, summary_sensitivity(sens_casement_cdl)[1])
}

names(cdl_thresholds) <- paste(test_quantiles)
cdl_thresholds
                                 
cdl_check(cdl = cdl_thresholds,
          quantiles = test_quantiles,
          max_day_length = 14.92) +
  ggtitle("CDL sensitivity")

Cumulative Development Rates

Description

Returns the cumulative development rates calculated when the phenology model is being evaluated.

Usage

cum_dev_rate(Tmean, thermal_units, chill_days, day_length,
             bud_pars, weib_pars, lactin_pars, max_day_length,
             pop_quantiles = 0.5, data)
cum_dev_rate(Tmean, thermal_units, chill_days, day_length,
             bud_pars, weib_pars, lactin_pars, max_day_length,
             pop_quantiles = 0.5, data)

Arguments

`Tmean`	name of the column in the data representing the mean temperature
`thermal_units`	name of the column in the data representing the thermal units
`chill_days`	name of the column in the data representing the number of chill days
`day_length`	name of the column in the data representing the day length
`bud_pars`	vector of parameters for the budburst model
`weib_pars`	matrix of parameters for the Weibull model for each developmental stage
`lactin_pars`	matrix of parameters for the lactin model for each developmental stage
`max_day_length`	critical day length threshold
`pop_quantiles`	quantile of the population to be evaluated by the phenology model
`data`	a `data.frame`

Value

Returns the data with an appended column representing the cumulative development rates evaluated by the model.

Author(s)

Rafael de Andrade Moral ([email protected]) and Rowan Fealy

Examples

data(casement9596)

casement1995 <- data.frame(casement9596, 
                           with(casement9596, 
                                get_total_info(Tmax, Tmin, Tbase = 7, Lat, day)))

## loading fitted parameters
data(pvulg_budburst_parameters)
data(pvulg_weibull_parameters)
data(pvulg_lactin_parameters)

casement1995 <- cum_dev_rate(Tmean = "Tmean",
                             thermal_units = "thermal_units",
                             chill_days = "chill_days",
                             day_length = "day_length",
                             bud_pars = pvulg_budburst_parameters,
                             weib_pars = pvulg_weibull_parameters,
                             lactin_pars = pvulg_lactin_parameters,
                             max_day_length = 14.92,
                             data = casement1995)
data(casement9596)

casement1995 <- data.frame(casement9596, 
                           with(casement9596, 
                                get_total_info(Tmax, Tmin, Tbase = 7, Lat, day)))

## loading fitted parameters
data(pvulg_budburst_parameters)
data(pvulg_weibull_parameters)
data(pvulg_lactin_parameters)

casement1995 <- cum_dev_rate(Tmean = "Tmean",
                             thermal_units = "thermal_units",
                             chill_days = "chill_days",
                             day_length = "day_length",
                             bud_pars = pvulg_budburst_parameters,
                             weib_pars = pvulg_weibull_parameters,
                             lactin_pars = pvulg_lactin_parameters,
                             max_day_length = 14.92,
                             data = casement1995)

Obtain Yearly Cycles

Description

Given a data.frame with columns representing days and months, returns a vector of indices representing which cycle each row corresponds to.

Usage

get_cycle(data, day, month, day.end, month.end)
get_cycle(data, day, month, day.end, month.end)

Arguments

`data`	a `data.frame`
`day`	name of the column representing the days
`month`	name of the column representing the months
`day.end`	which day determines the end of a cycle?
`month.end`	which month determines the end of a cycle?

Value

A vector of integers.

Author(s)

Rafael de Andrade Moral ([email protected]) and Rowan Fealy

Examples

data(valentia)

# cycle ends on 31st of October
valentia$cycle <- get_cycle(data = valentia, day = "day", month = "month",
                            day.end = 31, month.end = 10)
data(valentia)

# cycle ends on 31st of October
valentia$cycle <- get_cycle(data = valentia, day = "day", month = "month",
                            day.end = 31, month.end = 10)

Compute Day Length, Mean Temperature, Thermal Units, and Cumulative Chill Days

Description

Computes daily day length, mean temperature, thermal units and cumulative number of chill days for a dataset.

Usage

get_total_info(Tmax, Tmin, Tbase, lat, day)
get_total_info(Tmax, Tmin, Tbase, lat, day)

Arguments

`Tmax`	a vector of daily maximum temperatures
`Tmin`	a vector of daily minimum temperatures
`Tbase`	the base temperature used to define a chill day
`lat`	the latitude
`day`	day of the year (vector)

Details

Implements the method by Forsythe et al. (1995) to calculate the day length, and the single-sine degree-day method to compute the thermal units.

Value

Returns a data.frame with the day lengths, mean temperatures, thermal units and cumulative number of chill days.

Author(s)

Rafael de Andrade Moral ([email protected]) and Rowan Fealy

References

Cannell, M.G.R., Smith, R.I. (1983) Thermal time, chill days and prediction of budburst in Picea sitchensis. Journal of Applied Ecology, 20(3), 951–963.

Forsythe et al. (1995) A model comparison for daylength as a function of latitude and day of the year. Ecological Modeling 80:87-95.

Examples

data(casement9596)

with(casement9596, get_total_info(Tmax, Tmin, Tbase = 7, Lat, day))
data(casement9596)

with(casement9596, get_total_info(Tmax, Tmin, Tbase = 7, Lat, day))

Phenology Model Evaluation

Description

Evaluates the phenology model for specified population quantiles, based on daily temperatures.

Usage

phen_model(Tmean, thermal_units, chill_days, day_length,
           bud_pars, weib_pars, lactin_pars, max_day_length,
           pop_quantiles = c(0.05, 0.5, 0.95),
           print.l = FALSE, save.l = FALSE, data)
phen_model(Tmean, thermal_units, chill_days, day_length,
           bud_pars, weib_pars, lactin_pars, max_day_length,
           pop_quantiles = c(0.05, 0.5, 0.95),
           print.l = FALSE, save.l = FALSE, data)

Arguments

`Tmean`	name of the column in the data representing the mean temperature
`thermal_units`	name of the column in the data representing the thermal units
`chill_days`	name of the column in the data representing the number of chill days
`day_length`	name of the column in the data representing the day length
`bud_pars`	vector of parameters for the budburst model
`weib_pars`	matrix of parameters for the Weibull model for each developmental stage
`lactin_pars`	matrix of parameters for the lactin model for each developmental stage
`max_day_length`	critical day length threshold
`pop_quantiles`	vector of quantiles of the population to be evaluated by the phenology model
`print.l`	logical. If `TRUE`, prints the cumulative development rates as the model iterates.
`save.l`	logical. If `TRUE`, returns the cumulative development rates instead of model evaluations.
`data`	a `data.frame`

Details

As inputs, the model requires site latitude and daily minimum and maximum temperatures from the 1st November of the preceding year. Development, quantified by a temperature dependent development rate model defined for each life cycle stage, is initialised in the model following the simulated date of budburst, a biofix dependent on the accumulation of required thermal and chilling days from the 1st November of the preceding year, described below. Once budburst is simulated to have occurred, post-diapause pre-oviposition development commences. Following completion of the post-diapause pre-oviposition stage, the first day of egg-lay is returned for adult females emerging from overwintering. Oviposition period stage advancement occurs when the required development for this life cycle stage has been completed. Egg, larval and pupal development stages are then initiated sequentially. Following completion of the pupal development stage, the emerging adults enter a sexual maturation or post-eclosion pre-oviposition period. This life-cycle stage is hypothesised to be sensitive to a diapause-inducing stimulus - photoperiod (Tauber et al., 1986; Danks, 1987; Dalin, 2011; Hodek 2012; Pollard, 2014) - defined in the model as a critical day length (CDL) threshold. New generation adults that finish sexual maturation after the critical day length (CDL) enter into a state of reproductive diapause. P. vulgatissima adults that complete development prior to the CDL threshold can begin ovipositing to initiate subsequent generations.

Value

A data.frame containing model evaluations for each life cycle (columns) and population quantile (rows).

Author(s)

Rafael de Andrade Moral ([email protected]) and Rowan Fealy

References

Dalin P. (2011) Diapause induction and termination in a commonly univoltine leaf beetle (Phratora vulgatissima). Insect Science, 18, 443-450. doi:10.1111/j.1744-7917.2011.01417.x

Danks, H.V. (1987) Insect dormancy: an ecological perspective 1st ed. H. V. Danks, ed., Biological Survey of Canada (Terrestrial Artropods) (Ottawa).

Hodek, I. (2012) Adult diapause in Coleoptera. Psyche, 2012, 1–10.

Tauber M.J., Tauber C.A. and Masaki S. (1986) Seasonal adaptations of insects. Oxford University Press, New York, USA

Pollard, C. (2014) A Temperature-Dependent Development Model for Willow Beetle Species (Coleoptera: Chrysomelidae) in Ireland: Simulation of Phenology/Voltinism in Response to Climate Change. Unpublished PhD thesis, submitted to the National University of Ireland Maynooth, Maynooth, 1-373.

Pollard C.P., Griffin C.T., Moral R.A., Duffy C., Chuche J., Gaffney M.T., Fealy R. (submitted) phenModel: a temperature-dependent phenology model for the blue willow beetle, Phratora vulgatissima.

Examples

data(casement9596)

casement1995 <- data.frame(casement9596, 
                           with(casement9596, 
                                get_total_info(Tmax, Tmin, Tbase = 7, Lat, day)))

data(pvulg_budburst_parameters)
data(pvulg_weibull_parameters)
data(pvulg_lactin_parameters)

phen_model(Tmean = "Tmean",
           thermal_units = "thermal_units",
           chill_days = "chill_days",
           day_length = "day_length",
           bud_pars = pvulg_budburst_parameters,
           weib_pars = pvulg_weibull_parameters,
           lactin_pars = pvulg_lactin_parameters,
           max_day_length = 14.92,
           data = casement1995)
data(casement9596)

casement1995 <- data.frame(casement9596, 
                           with(casement9596, 
                                get_total_info(Tmax, Tmin, Tbase = 7, Lat, day)))

data(pvulg_budburst_parameters)
data(pvulg_weibull_parameters)
data(pvulg_lactin_parameters)

phen_model(Tmean = "Tmean",
           thermal_units = "thermal_units",
           chill_days = "chill_days",
           day_length = "day_length",
           bud_pars = pvulg_budburst_parameters,
           weib_pars = pvulg_weibull_parameters,
           lactin_pars = pvulg_lactin_parameters,
           max_day_length = 14.92,
           data = casement1995)

Multi-Year Forecast Based on the Phenology Model

Description

A forecast based on the phenology model for multiple years, based on daily temperatures.

Usage

phen_model_forecast(Tmean, thermal_units, chill_days, day_length,
                    bud_pars, weib_pars, lactin_pars,
                    max_day_length = 14.92, pop_quantile, year, data)
phen_model_forecast(Tmean, thermal_units, chill_days, day_length,
                    bud_pars, weib_pars, lactin_pars,
                    max_day_length = 14.92, pop_quantile, year, data)

Arguments

`Tmean`	name of the column in the data representing the mean temperature
`thermal_units`	name of the column in the data representing the thermal units
`chill_days`	name of the column in the data representing the number of chill days
`day_length`	name of the column in the data representing the day length
`bud_pars`	vector of parameters for the budburst model
`weib_pars`	matrix of parameters for the Weibull model for each developmental stage
`lactin_pars`	matrix of parameters for the lactin model for each developmental stage
`max_day_length`	critical day length threshold
`pop_quantile`	quantile of the population to be evaluated by the phenology model
`year`	a vector of indices representing each yearly cycle (usually the output from `get_cycle`)
`data`	a `data.frame`

Details

See phen_model

Value

A data.frame with the number of rows equal to the total number of yearly cycles, and each column representing the developmental stage of the insect.

Author(s)

Rafael de Andrade Moral ([email protected]) and Rowan Fealy

Examples

data(valentia)

valentia$cycle <- get_cycle(data = valentia, day = "day", month = "month",
                            day.end = 31, month.end = 10)
valentia_split <- split(valentia, valentia$cycle)
valentia_split <- lapply(valentia_split,
                         function(x) data.frame(x,
                                                with(x, get_total_info(Tmax = x$max_temp,
                                                                       Tmin = x$min_temp,
                                                                       Tbase = 7,
                                                                       lat = x$Lat,
                                                                       x$day_of_year))))
valentia <- do.call("rbind", valentia_split)

data(pvulg_budburst_parameters)
data(pvulg_weibull_parameters)
data(pvulg_lactin_parameters)

phen_forecast <- phen_model_forecast(Tmean = "Tmean",
                                     thermal_units = "thermal_units",
                                     chill_days = "chill_days",
                                     day_length = "day_length",
                                     bud_pars = pvulg_budburst_parameters,
                                     weib_pars = pvulg_weibull_parameters,
                                     lactin_pars = pvulg_lactin_parameters,
                                     pop_quantile = .5,
                                     year = "cycle",
                                     data = valentia)
data(valentia)

valentia$cycle <- get_cycle(data = valentia, day = "day", month = "month",
                            day.end = 31, month.end = 10)
valentia_split <- split(valentia, valentia$cycle)
valentia_split <- lapply(valentia_split,
                         function(x) data.frame(x,
                                                with(x, get_total_info(Tmax = x$max_temp,
                                                                       Tmin = x$min_temp,
                                                                       Tbase = 7,
                                                                       lat = x$Lat,
                                                                       x$day_of_year))))
valentia <- do.call("rbind", valentia_split)

data(pvulg_budburst_parameters)
data(pvulg_weibull_parameters)
data(pvulg_lactin_parameters)

phen_forecast <- phen_model_forecast(Tmean = "Tmean",
                                     thermal_units = "thermal_units",
                                     chill_days = "chill_days",
                                     day_length = "day_length",
                                     bud_pars = pvulg_budburst_parameters,
                                     weib_pars = pvulg_weibull_parameters,
                                     lactin_pars = pvulg_lactin_parameters,
                                     pop_quantile = .5,
                                     year = "cycle",
                                     data = valentia)

Phenology Model Evaluation for Further Generations

Description

Evaluates the phenology model for specified population quantiles, based on daily temperatures for further generations.

Usage

phen_model_gen2(Tmean, day_length,
                bud_pars, weib_pars, lactin_pars,
                sex_mat_day, pop_quantiles = 0.5,
                max_day_length = 14.92, data)
phen_model_gen2(Tmean, day_length,
                bud_pars, weib_pars, lactin_pars,
                sex_mat_day, pop_quantiles = 0.5,
                max_day_length = 14.92, data)

Arguments

`Tmean`	name of the column in the data representing the mean temperature
`day_length`	name of the column in the data representing the day length
`bud_pars`	vector of parameters for the budburst model
`weib_pars`	matrix of parameters for the Weibull model for each developmental stage
`lactin_pars`	matrix of parameters for the lactin model for each developmental stage
`sex_mat_day`	the day individuals from the first generation reached the sexual maturation stage
`pop_quantiles`	vector of quantiles of the population to be evaluated by the phenology model
`max_day_length`	critical day length threshold
`data`	a `data.frame`

Details

See phen_model

Value

A data.frame containing model evaluations for each life cycle (columns) and population quantile (rows).

Author(s)

Rafael de Andrade Moral ([email protected]) and Rowan Fealy

Examples

data(casement9596)

casement1995 <- data.frame(casement9596, 
                           with(casement9596, 
                                get_total_info(Tmax, Tmin, Tbase = 7, Lat, day)))

data(pvulg_budburst_parameters)
data(pvulg_weibull_parameters)
data(pvulg_lactin_parameters)

phen95 <- phen_model(Tmean = "Tmean",
                     thermal_units = "thermal_units",
                     chill_days = "chill_days",
                     day_length = "day_length",
                     bud_pars = pvulg_budburst_parameters,
                     weib_pars = pvulg_weibull_parameters,
                     lactin_pars = pvulg_lactin_parameters,
                     max_day_length = 14.92,
                     data = casement1995)
           
phen95_gen2 <- phen_model_gen2(Tmean = "Tmean",
                               day_length = "day_length",
                               bud_pars = pvulg_budburst_parameters,
                               weib_pars = pvulg_weibull_parameters,
                               lactin_pars = pvulg_lactin_parameters,
                               sex_mat_day = phen95["0.05","sexual_maturation_day"],
                               pop_quantiles = .05,
                               data = casement1995)
data(casement9596)

casement1995 <- data.frame(casement9596, 
                           with(casement9596, 
                                get_total_info(Tmax, Tmin, Tbase = 7, Lat, day)))

data(pvulg_budburst_parameters)
data(pvulg_weibull_parameters)
data(pvulg_lactin_parameters)

phen95 <- phen_model(Tmean = "Tmean",
                     thermal_units = "thermal_units",
                     chill_days = "chill_days",
                     day_length = "day_length",
                     bud_pars = pvulg_budburst_parameters,
                     weib_pars = pvulg_weibull_parameters,
                     lactin_pars = pvulg_lactin_parameters,
                     max_day_length = 14.92,
                     data = casement1995)
           
phen95_gen2 <- phen_model_gen2(Tmean = "Tmean",
                               day_length = "day_length",
                               bud_pars = pvulg_budburst_parameters,
                               weib_pars = pvulg_weibull_parameters,
                               lactin_pars = pvulg_lactin_parameters,
                               sex_mat_day = phen95["0.05","sexual_maturation_day"],
                               pop_quantiles = .05,
                               data = casement1995)

Plot Phenology Model Multi-Year Forecast

Description

Produces a plot of the phenology model multi-year forecasting evaluations.

Usage

plot_phen_forecast(obj, year_from = 1, xlab = "Year",
                   ylab = "Days from 1st November")
plot_phen_forecast(obj, year_from = 1, xlab = "Year",
                   ylab = "Days from 1st November")

Arguments

`obj`	an output from the `phen_model_forecast` function
`year_from`	the first year we are forecasting for (e.g. "2015")
`xlab`	the x-axis label
`ylab`	the y-axis label

Value

A ggplot object.

Author(s)

Rafael de Andrade Moral ([email protected]) and Rowan Fealy

Plot Phenology Model Evaluations

Description

Produces a plot of the phenology model evaluations.

Usage

plot_phen_model(obj, obj_gen2, binwidth = 8, forecast = FALSE,
                observed_data = NULL, ylab = "Days from 1st November",
                xlab = "Modelled Life-Cycle Stages", 
                labels = c("Budburst",
                           "Post-Diapause",
                           "Ovipositing Period (Eggs)",
                           "Larvae Emergence",
                           "Pupae Emergence",
                           "Adult Emergence",
                           "Sexual Maturation"))
plot_phen_model(obj, obj_gen2, binwidth = 8, forecast = FALSE,
                observed_data = NULL, ylab = "Days from 1st November",
                xlab = "Modelled Life-Cycle Stages", 
                labels = c("Budburst",
                           "Post-Diapause",
                           "Ovipositing Period (Eggs)",
                           "Larvae Emergence",
                           "Pupae Emergence",
                           "Adult Emergence",
                           "Sexual Maturation"))

Arguments

`obj`	an output of the `phen_model` or `phen_model_forecast` functions
`obj_gen2`	an output of the `phen_model_gen2` or `phen_model_forecast` functions
`binwidth`	binwidth of the density estimator
`forecast`	logical. Set to `TRUE` if you are plotting a multi-year forecast (i.e. if `obj` and `obj_gen2` are outputs of `phen_model_forecast`)
`observed_data`	an output from the `assign_observed_data` function
`ylab`	the y-axis label
`xlab`	the x-axis label
`labels`	a vector of strings representing the names of the life-cycle stages

Details

Visualisation of days until stage completion for different life cycle stages and generations. Plot can incorporate observed data, where available. All dates are counted from the 1st of November of the previous year. The estimated density is based on a Gaussian kernel whose binwidth can be easily modified using argument binwidth. To add observed data points, see assign_observed_data.

Since this is a ggplot object, layers can be easily added by using standard ggplot functions.

Value

A ggplot object.

Author(s)

Rafael de Andrade Moral ([email protected]) and Rowan Fealy

References

Pollard C.P., Griffin C.T., Moral R.A., Duffy C., Chuche J., Gaffney M.T., Fealy R. (submitted) phenModel: a temperature-dependent phenology model for the blue willow beetle, Phratora vulgatissima.

Estimated Parameters for the Exponential Decay Model for Budburst

Description

Vector of estimated parameters.

Usage

data("pvulg_budburst_parameters")data("pvulg_budburst_parameters")

Format

The format is: Named num [1:2] 540.196 -0.016 - attr(*, "names")= chr [1:2] "b" "m"

Details

The method implied is based on the Alternating Model, which is a degree-day model that accumulates thermal/chilling units above/below a base threshold from a specified date (Cannell and Smith, 1983). The b and m values correspond to the estimates for budburst for Salix viminalis (Pollard et al., submitted).

Source

Chmielewski, F.M., Heider, S., Moryson, S. and Bruns, E. (2013) International phenological observation networks: concept of IPG and GPM. In M. D. Schwartz, ed. Phenology: An integrative environmental science. New York London: Springer, p. 610.

References

Cannell, M.G.R. and Smith, R.I. (1983) Thermal time, chill days and prediction of budburst in Picea sitchensis. Journal of Applied Ecology, 20(3), 951–963.

Pollard C.P., Griffin C.T., Moral R.A., Duffy C., Chuche J., Gaffney M.T., Fealy R. (submitted) phenModel: a temperature-dependent phenology model for the blue willow beetle, Phratora vulgatissima.

Examples

data(pvulg_budburst_parameters)
data(pvulg_budburst_parameters)

Estimated Parameters for the Lactin Model for P. Vulgatissima

Description

Matrix of estimated parameters for each stage of the life-cycle of P. vulgatissima

Usage

data("pvulg_lactin_parameters")data("pvulg_lactin_parameters")

Format

The format is: num [1:4, 1:6] 0.00864 28.94449 0.52103 -1.06402 0.002 ... - attr(*, "dimnames")=List of 2 ..$ : chr [1:4] "rho" "Tmax" "deltaT" "lambda" ..$ : chr [1:6] "post_diapause" "ovipositing" "larva_emergence" "pupa_emergence" ...

Details

The parameters were derived based on constant temperature laboratory experiments (see Pollard, 2014) for different life-cycle stages of P. vulgatissima. Development times were converted to rates and subsequently used as input for the Lactin model (Lactin et al., 1995). The model was fitted to the entire temperature range, including where there was no development rate (at the upper temperature threshold).

Source

References

Lactin D.J., Holliday N.J., Johnson D.L. and Craigen R. (1995) Improved rate model of temperature-dependent development by arthropods. Environmental Entomology, 24, 68–75. doi:10.1093/ee/24.1.68

Pollard C.P., Griffin C.T., Moral R.A., Duffy C., Chuche J., Gaffney M.T., Fealy R. (submitted) phenModel: a temperature-dependent phenology model for the blue willow beetle, Phratora vulgatissima.

Examples

data(pvulg_lactin_parameters)
data(pvulg_lactin_parameters)

Estimated Parameters for the Weibull Model for P. Vulgatissima

Description

Matrix of estimated parameters for each stage of the life-cycle of P. vulgatissima

Usage

data("pvulg_weibull_parameters")data("pvulg_weibull_parameters")

Format

The format is: num [1:3, 1:6] 0.7207 0.3517 1.7918 -0.0716 0.5117 ... - attr(*, "dimnames")=List of 2 ..$ : chr [1:3] "gamma" "eta" "beta" ..$ : chr [1:6] "post_diapause" "ovipositing" "larva_emergence" "pupa_emergence" ...

Details

The parameters were derived based on constant temperature laboratory experiments (see Pollard, 2014) for different life-cycle stages of P. vulgatissima. Development times (or rates) were derived as cumulative frequency distributions at each temperature; development times for each percentile were then divided by the respective median value of the distribution to produce a normalised ("same shape") distribution of development times at each temperature (Wagner et al., 1984; 1991). Based on the "same shape" assumption, a single cumulative distribution representing the normalised distributions for all temperature treatments was produced by averaging all the distributions, weighted by the total frequency of each distribution (Wagner et al., 1984; 1991).

Source

References

Wagner T.L., Olson R.L. and Willers J.L. (1991) Modeling arthropod development time. Journal of Agricultural Entomology, 8, 251-270.

Wagner T.L., Wu H.I., Sharpe P.J.H. and Coulson R.N. (1984) Modeling distributions of insect development time: a literature review and application of the Weibull function. Annals of the Entomological Society of America, 77, 475-487. doi:10.1093/aesa/77.2.208

Pollard C.P., Griffin C.T., Moral R.A., Duffy C., Chuche J., Gaffney M.T., Fealy R. (submitted) phenModel: a temperature-dependent phenology model for the blue willow beetle, Phratora vulgatissima.

Examples

data(pvulg_weibull_parameters)
data(pvulg_weibull_parameters)

Sensitivity Analysis for Evaluated Phenology Model

Description

Runs a sensitivity analysis on the evaluated phenology model, by simulating random changes to fitted parameters and/or specifying a range of base temperatures to determine a chill day.

Usage

sensitivity(Tmax, Tmin, Tbase, lat, day,
            bud_pars, weib_pars, lactin_pars, max_day_length, 
            pop_quantile = .5, data,
            study_type = c("Tbase","lactin","weibull","bud_days","bud_parms","cdl"),
            percent_variation = .1, resolution = 1000)

summary_sensitivity(obj)

boxplot_sensitivity(obj)
sensitivity(Tmax, Tmin, Tbase, lat, day,
            bud_pars, weib_pars, lactin_pars, max_day_length, 
            pop_quantile = .5, data,
            study_type = c("Tbase","lactin","weibull","bud_days","bud_parms","cdl"),
            percent_variation = .1, resolution = 1000)

summary_sensitivity(obj)

boxplot_sensitivity(obj)

Arguments

`obj`	an output from a call to the `sensitivity` function
`Tmax`	a vector of daily maximum temperatures
`Tmin`	a vector of daily minimum temperatures
`Tbase`	the base temperature
`lat`	the latitude
`day`	day of the year (vector)
`bud_pars`	vector of parameters for the budburst model
`weib_pars`	matrix of parameters for the Weibull model for each developmental stage
`lactin_pars`	matrix of parameters for the lactin model for each developmental stage
`max_day_length`	critical day length threshold
`pop_quantile`	quantile of the population to be evaluated by the phenology model
`data`	a `data.frame`
`study_type`	a character string indicating what type of sensitivity study to be performed. `"Tbase"` runs a sensitivity study for the temperature threshold that classifies a chill day; `"lactin"` runs a sensitivity study on variations of the values for the parameters estimated by the lactin model; `"weibull"` does the same as the latter, but for the Weibull model; `"bud_days"` changes the budburst day while `"bud_parms"` randomly changes the parameter estimates for the exponential decay function associated with budburst prediction; `"cdl"` changes the critical day length threshold
`percent_variation`	percentage of variation of the values to be changed for the sensitivity analysis
`resolution`	number of model evaluations to be performed

Value

A list containing the sensitivity analysis results as the first component, and the original model evaluation results as the second component.

Author(s)

Rafael de Andrade Moral ([email protected]) and Rowan Fealy

Examples

data(casement9596)

casement1995 <- data.frame(casement9596, 
                           with(casement9596, 
                                get_total_info(Tmax, Tmin, Tbase = 7, Lat, day)))

data(pvulg_budburst_parameters)
data(pvulg_weibull_parameters)
data(pvulg_lactin_parameters)

sens_casement1995 <- sensitivity(Tmax = casement9596$Tmax,
                                 Tmin = casement9596$Tmin,
                                 Tbase = 7,
                                 lat = casement9596$Lat,
                                 day = casement9596$day,
                                 bud_pars = pvulg_budburst_parameters,
                                 weib_pars = pvulg_weibull_parameters,
                                 lactin_pars = pvulg_lactin_parameters,
                                 max_day_length = 14.92, 
                                 pop_quantile = .5,
                                 data = casement9596,
                                 study_type = "lactin",
                                 percent_variation = .1,
                                 resolution = 100)
                                 
summary_sensitivity(sens_casement1995)

boxplot_sensitivity(sens_casement1995)
data(casement9596)

casement1995 <- data.frame(casement9596, 
                           with(casement9596, 
                                get_total_info(Tmax, Tmin, Tbase = 7, Lat, day)))

data(pvulg_budburst_parameters)
data(pvulg_weibull_parameters)
data(pvulg_lactin_parameters)

sens_casement1995 <- sensitivity(Tmax = casement9596$Tmax,
                                 Tmin = casement9596$Tmin,
                                 Tbase = 7,
                                 lat = casement9596$Lat,
                                 day = casement9596$day,
                                 bud_pars = pvulg_budburst_parameters,
                                 weib_pars = pvulg_weibull_parameters,
                                 lactin_pars = pvulg_lactin_parameters,
                                 max_day_length = 14.92, 
                                 pop_quantile = .5,
                                 data = casement9596,
                                 study_type = "lactin",
                                 percent_variation = .1,
                                 resolution = 100)
                                 
summary_sensitivity(sens_casement1995)

boxplot_sensitivity(sens_casement1995)

Valentia Data

Description

Daily minimum and maximum temperature data obtained at the Valentia synoptic weather station for the period of 1st November 1939 to 31st October 2000.

Usage

data("valentia")data("valentia")

Format

A data frame with 22281 observations on the following 7 variables.

year: a numeric vector
month: a numeric vector
day: a numeric vector
Lat: a numeric vector
max_temp: a numeric vector
min_temp: a numeric vector
day_of_year: a numeric vector

Details

The data was obtained from the Irish National Meteorological Agency (Met Éireann).

Source

https://www.met.ie/climate/available-data/historical-data

Examples

data(valentia)

valentia$cycle <- get_cycle(data = valentia, day = "day", month = "month",
                            day.end = 31, month.end = 10)
valentia_split <- split(valentia, valentia$cycle)
valentia_split <- lapply(valentia_split,
                         function(x) data.frame(x,
                                                with(x, get_total_info(Tmax = x$max_temp,
                                                                       Tmin = x$min_temp,
                                                                       Tbase = 7,
                                                                       lat = x$Lat,
                                                                       x$day_of_year))))
valentia <- do.call("rbind", valentia_split)

data(pvulg_budburst_parameters)
data(pvulg_weibull_parameters)
data(pvulg_lactin_parameters)

phen_forecast <- phen_model_forecast(Tmean = "Tmean",
                                     thermal_units = "thermal_units",
                                     chill_days = "chill_days",
                                     day_length = "day_length",
                                     bud_pars = pvulg_budburst_parameters,
                                     weib_pars = pvulg_weibull_parameters,
                                     lactin_pars = pvulg_lactin_parameters,
                                     pop_quantile = .5,
                                     year = "cycle",
                                     data = valentia)

plot_phen_model(obj = phen_forecast[,1:7], obj_gen2 = phen_forecast[,8:12],
                forecast = TRUE)

plot_phen_forecast(obj = phen_forecast, year_from = 1939)
data(valentia)

valentia$cycle <- get_cycle(data = valentia, day = "day", month = "month",
                            day.end = 31, month.end = 10)
valentia_split <- split(valentia, valentia$cycle)
valentia_split <- lapply(valentia_split,
                         function(x) data.frame(x,
                                                with(x, get_total_info(Tmax = x$max_temp,
                                                                       Tmin = x$min_temp,
                                                                       Tbase = 7,
                                                                       lat = x$Lat,
                                                                       x$day_of_year))))
valentia <- do.call("rbind", valentia_split)

data(pvulg_budburst_parameters)
data(pvulg_weibull_parameters)
data(pvulg_lactin_parameters)

phen_forecast <- phen_model_forecast(Tmean = "Tmean",
                                     thermal_units = "thermal_units",
                                     chill_days = "chill_days",
                                     day_length = "day_length",
                                     bud_pars = pvulg_budburst_parameters,
                                     weib_pars = pvulg_weibull_parameters,
                                     lactin_pars = pvulg_lactin_parameters,
                                     pop_quantile = .5,
                                     year = "cycle",
                                     data = valentia)

plot_phen_model(obj = phen_forecast[,1:7], obj_gen2 = phen_forecast[,8:12],
                forecast = TRUE)

plot_phen_forecast(obj = phen_forecast, year_from = 1939)

Package 'phenModel'

Help Index

Insect Phenology Model Evaluation Based on Daily Temperatures

Description

Details

Author(s)

References

Observed Data Preparation

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

Internal functions to used to evaluate a phenology model and plot the results

Description

Author(s)

Casement 1995-1996 Data

Description

Usage

Format

Details

Source

Examples

Plots for Checking Sensitivity to CDL

Description

Usage

Arguments

Value

Author(s)

See Also

Examples

Cumulative Development Rates

Description

Usage

Arguments

Value

Author(s)

See Also

Examples

Obtain Yearly Cycles

Description

Usage

Arguments

Value

Author(s)

Examples

Compute Day Length, Mean Temperature, Thermal Units, and Cumulative Chill Days

Description

Usage

Arguments

Details

Value

Author(s)

References

Examples

Phenology Model Evaluation

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Multi-Year Forecast Based on the Phenology Model

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

Phenology Model Evaluation for Further Generations

Description

Usage