summarizeCosts {invacost}

Summarize costs of invasions over periods of time

Description

This function summarizes the cumulative costs and average annual costs of invasive alien species and breaks it down into regular periods of time, on the basis of cost estimates as they appeared in the provided in the source references collected in the 'InvaCost' database

Usage

summarizeCosts(
  costdb,
  cost.column = "Cost_estimate_per_year_2017_USD_exchange_rate",
  year.column = "Impact_year",
  in.millions = TRUE,
  minimum.year = 1960,
  maximum.year = max(costdb[, year.column]),
  year.breaks = seq(minimum.year, maximum.year, by = 10),
  include.last.year = TRUE
)

Arguments

costdb

The expanded 'InvaCost' database output from expandYearlyCosts, where costs occurring over several years are expanded over each year of impact.

cost.column

Name of the cost column to use in costdb (usually, choose between the exchange rate (default) or Purchase Power parity cost per year)

year.column

Name of the year column to use in costdb( usually, "Impact_year" from expandYearlyCosts

in.millions

If TRUE, cost values are transformed in millions (to make graphs easier to read), otherwise if FALSE, cost values are not transformed.

minimum.year

The starting year of the analysis. By default, 1960 was chosen because it marks the period from which world bank data is available for exchange rates and inflation values.

maximum.year

The ending year for the analysis. By default, the last year of costdb is chosen.

year.breaks

A vector of breaks for the year intervals over which you want to summarize cost values

include.last.year

TRUE or FALSE. Defines if the last year of the dataset is included in the last interval (TRUE) or is considered as an interval of its own (FALSE). Generally only useful if the last year is at the limit of an interval.

Details

Missing data are ignored. However, note that the average for each interval is always calculated on the basis of the full temporal range. For example, if there is only data for 1968 for the 1960-1969 interval, then the total cost for the interval will be equal to the cost of 1968, and the average annual cost for 1960-1969 will be the cost of 1968 / 10.

Value

A list with 6 elements:

• cost.data: the input data

• parameters: parameters used to run the function. The minimum.year and maximum.year are based on the input data (i.e., the user may specify minimum.year = 1960 but the input data may only have data starting from 1970, hence the minimum.year will be 1970)

• year.breaks: the years used to define year intervals over which costs were calculated.

• cost.per.year: the costs of invasions expressed per year, as sums of all costs for each year

• average.total.cost: the average annual cost of invasive Alien species calculated over the entire time period

• average.cost.per.period: a data.frame containing the average annual cost of invasive alien species calculated over each time interval

The structure of this object can be seen using str().

Author(s)

Boris Leroy [e-mail], Andrew Kramer, Anne-Charlotte Vaissière, Christophe Diagne

References

https://github.com/Farewe/invacost

Leroy Boris, Kramer Andrew M, Vaissière Anne-Charlotte, Kourantidou Melina, Courchamp Franck & Diagne Christophe (2022). Analysing economic costs of invasive alien species with the invacost R package. Methods in Ecology and Evolution. doi:10.1111/2041-210X.13929

See Also

expandYearlyCosts to get the database in appropriate format.

Examples

data(invacost)

### Cleaning steps
# Eliminating data with no information on starting and ending years
invacost <- invacost[-which(is.na(invacost$Probable_starting_year_adjusted)), ]
invacost <- invacost[-which(is.na(invacost$Probable_ending_year_adjusted)), ]
# Keeping only observed and reliable costs
invacost <- invacost[invacost$Implementation == "Observed", ]
invacost <- invacost[which(invacost$Method_reliability == "High"), ]
# Eliminating data with no usable cost value
invacost <- invacost[-which(is.na(invacost$Cost_estimate_per_year_2017_USD_exchange_rate)), ]

### Expansion

db.over.time <- expandYearlyCosts(invacost,
                                  startcolumn = "Probable_starting_year_adjusted",
                                  endcolumn = "Probable_ending_year_adjusted")
                                  
### Analysis
res <- summarizeCosts(db.over.time,
                      maximum.year = 2020) # Excluding data after 2020 (e.g. planned budgets)
res