criterion_A: Assess IUCN Criterion A

In gdauby/ConR: Computation of Parameters Used in Preliminary Assessment of Species Conservation Status

Assess IUCN Criterion A

Description

Preliminary assessment of species conservation status following IUCN Criterion A, which is based on population size reductions (Criteria A1, A2, A3, and A4)

Usage

criterion_A(
  x,
  years = NULL,
  assess.year = NULL,
  project.years = NULL,
  generation.time = NULL,
  models = c("linear", "quadratic", "exponential", "logistic", "general_logistic",
    "piecewise"),
  subcriteria = c("A1", "A2", "A3", "A4"),
  exploitation = NULL,
  correction = NULL,
  data.type = NULL,
  nature.evidence = NULL,
  A1.threshold = c(50, 70, 90),
  A234.threshold = c(30, 50, 80),
  all.cats = TRUE,
  parallel = FALSE,
  NbeCores = 2,
  show_progress = TRUE,
  ...
)

Arguments

x	a vector (one species) or a data frame (multiple species/ subpopulations) containing the population size per year, from the oldest to the most recent population estimate.
years	a vector containing the years for which the population sizes are available (i.e. time series). It can be NULL if x contains the years as names.
assess.year	numeric. The year for which the assessment should be performed.
project.years	a vector containing the years for which population sizes were or should be projected.
generation.time	a value or vector of generation lengths, i.e. the average age of parents of the current cohort (IUCN 2019).
models	a vector containing the names of the models to be fitted to species population size to perform projections.
subcriteria	a vector containing the sub-criteria that should be included in the assessment (i.e. A1, A2, A3 and/or A4).
exploitation	a value or vector of levels of exploitation, that should be provided as the reduction in population size caused by the exploitation (in %).
correction	a value or vector of correction values, that should applyed to the reduction in population size estimated from 'x'.
data.type	a character corresponding to the type of data (IUCN 2019): "observation", "index" or "AOO_EOO" (only these types are currently implemented)
nature.evidence	a character corresponding to the nature of evidence (IUCN 2019): "observed", "estimated", "projected", "inferred" or "suspected"
A1.threshold	numeric vector with the A1 thresholds to convert decline estimates into categories. Default values are the thresholds recommended by the IUCN.
A234.threshold	numeric vector with the A2, A3, and A4 thresholds to convert decline estimate into categories. Default values are the thresholds recommended by the IUCN.
all.cats	logical. Should the categories from all criteria be returned and not just the consensus categories?
parallel	logical. Should calculations be parallelized? Default to FALSE.
NbeCores	integer. Number of cores for parallel computing. Default to 2.
show_progress	logical. Should the progress bar be displayed? Default to TRUE.
...	other parameters to be passed as arguments for function pop.decline.fit

Details

As described in IUCN (2019), the choice between criteria A1 or A2 depends on three conditions: the reduction must be reversible, the causes of the reduction must be understood, and the threats must have ceased. "If any of the three conditions (reversible and understood and ceased) are not met (...), then A2 should be used instead of A1" (IUCN, 2019).

Some important notes. The function can return the predictions of population estimates for years not in the observed data, based on the fit of a set of different statistical models. As stated in IUCN (2019), the model used to make the predictions can result in very different estimates. So, it is preferable that the user choose one or two of the models based on the best available information on types of threat (i.e. patterns of exploitation or habitat loss), life history and ecology of the taxon being evaluated or any other processes that may contribute to population decline. See IUCN (2019) for more details on the assumptions of each model. The selection of models based solely on their fit to population size should only be used for larger time series (Number of observations > 10).

Some more technical notes. If years is a subset of all the years contained in x, then x is filtered based on years. So, make sure you have selected the right years. If the year of assessment is not given, the most recent year is taken instead. The function accepts a single generation length for all species or species-specific generation lengths. In the latter case, it is necessary to provide exactly one value for each species analyzed. Currently, only one assessment year can be assigned for all taxa. Similarly, only one vector of years with population size available. Thus, it is advised not to mix taxa with great differences in generation length.

As for generation lengths, the function accepts single and species-specific reductions of population size due to actual or potential exploitation of the species (see IUCN subcriterion A2d), via the argument exploitation. Here, this reduction is applied on top of the reduction obtained from the vector of population sizes per year provided in x, and only for the subcriterion A2. Thus, this argument should be used when users want to account for an extra reduction due to other causes, such as harvesting-related or habitat quality-related reductions on top of the ones obtained from habitat loss-population size relationships. If exploitation is not empty, a new column is added to the output ('basis_d') in which a short report of the impact of the added reduction is provided. If one or more species have no evidence of exploitation just enter the value zero. Values can range between 0 and 100%.

Differently than the argument exploitation explained above, the argument correction applies any correction desired to the reduction obtained from the vector of population sizes per year provided in x and this correction is applied for the subcriteria A1 and A2. Here, values should be positive (and generally below 100) and if one or more species do not need for correction just enter the value one. Values between zero and one will reduced the value of population size reduction and values above one will increase them.

Population size reduction can be negative (i.e. population size increase). But final reduction values above 100% will be reduced to 100% for any subcriterion (A1, A2, A3 and A4) with a warning.

Value

A data frame containing, for each taxon, the year of assessment, the time interval of the assessment (include past and future estimates, if any), the population size in the interval of assessment, the reduction of the population size using the chosen sub-criteria (A1, A2, A3, and A4), the model used to obtain the projections of population size (if used), the IUCN categories associated with these sub-criteria and the consensus category for criterion A.

Author(s)

Renato A. Ferreira de Lima & Gilles Dauby

References

IUCN 2019. Guidelines for Using the IUCN Red List Categories and Criteria. Version 14. Standards and Petitions Committee. Downloadable from: http://www.iucnredlist.org/documents/RedListGuidelines.pdf.

Examples

## Simplest example: one species, two observations in time, one subcriterion
 pop = c("1970" = 10000, "2000" = 6000)
 criterion_A(x = pop,
  years = c(1970, 2000), 
  assess.year = 2000,
  project.years = NULL,
  subcriteria = c("A2"),
  generation.time = 10)
  
## Another example: one species, more observations and subcriteria
pop = c("1970" = 10000, "1980" = 8900, "1990" = 7000, "2000" = 6000, "2030" = 4000)
criterion_A(x = pop,
  years = c(1970, 1980, 1990, 2000, 2030), 
  assess.year = 2000,
  project.years = c(2010, 2020, 2030),
  subcriteria = c("A1", "A2", "A3", "A4"),
  generation.time = 10)
  
## Another example: subcriterion A2 and exploitation (A2d)
 pop = c("1980" = 9000, "1985" = 7500, "1990" = 6000)
 criterion_A(x = pop,
  years = c(1980, 1985, 1990), 
  assess.year = 2000,
  project.years = NULL,
  subcriteria = c("A2"),
  generation.time = 10,
  exploitation = 5)

## The data and criterion A assessment as described in IUCN (2019)
#available at: https://www.iucnredlist.org/resources/criterion-a
data(example_criterionA)
criterion_A(example_criterionA,
  years = seq(1970, 2000, by = 2), 
  assess.year = 2000,
  project.years = seq(2002, 2030, by = 2),
  subcriteria = c("A1", "A2", "A3", "A4"),
  generation.time = 10)

## Same data and options but assuming different generation length for each taxon
criterion_A(example_criterionA,
  years = seq(1970, 2000, by = 2), 
  assess.year = 2000,
  project.years = seq(2002, 2030, by = 2),
  subcriteria = c("A1", "A2", "A3", "A4"),
  generation.time = c(2,5,10,15,30,50))
  

gdauby/ConR documentation built on Jan. 30, 2024, 11:10 p.m.