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Occupancy detection models

Source: R/occDetModel.r
occDetModel.Rd

Run occupancy detection models as described in Isaac et al, 2014

Usage

occDetModel(
  taxa,
  site,
  survey,
  replicate = NULL,
  closure_period = NULL,
  criterion = 1,
  provenance = NULL,
  rem_aggs_with_missing_regions = TRUE,
  allowSitesMultiRegions = FALSE,
  species_list = unique(taxa),
  write_results = TRUE,
  output_dir = getwd(),
  nyr = 2,
  n_iterations = 5000,
  burnin = 1500,
  thinning = 3,
  n_chains = 3,
  modeltype = "sparta",
  regional_codes = NULL,
  region_aggs = NULL,
  model.function = NULL,
  max_year = NULL,
  seed = NULL,
  additional.parameters = NULL,
  additional.BUGS.elements = NULL,
  additional.init.values = NULL,
  return_data = FALSE
)

Arguments

taxa

A character vector of taxon names, as long as the number of observations.

site

A character vector of site names, as long as the number of observations.

survey

A vector as long as the number of observations. This must be a Date if includeJDay = TRUE

replicate

An optional vector to identify replicate samples (visits) per survey. Need not be globally unique (e.g can be 1, 2, .. n within surveys)

closure_period

An optional vector of integers specifying the closure period. If FALSE then closure_period will be extracted as the year from the survey.

criterion

Determines whether the model should be run. If an integer then this defines the threshold number of records (50 in Outhwaite et al 2019). Other options are `EqualWt` or `HighSpec`, which define the application of "rules of thumb" defined in Pocock et al 2019. Defaults to 1, in which case the model is applied for so long there is a single record of the focal species.

provenance

An optional text string allowing the user to identify the dataset.

rem_aggs_with_missing_regions

An option which if TRUE will remove all aggregates which contain at least one region with no data.

allowSitesMultiRegions

An option that permits sites to be included in more than one region. If `FALSE` then these sites are dropped.

species_list

A character vector of taxa names for which models should be run. This is optional and by default models will be run for all taxa

write_results

logical, should results be saved to output_dir. This is recommended since these models can take a long time to run. If TRUE (default) the results from each species will be saved as an .rdata file once the species has run. This prevents loss of data should anything go wrong.

output_dir

character, the output directory were the output for each taxa will be saved as .rdata files. This will defualt to the working directory

nyr

numeric, the minimum number of years on which a site must have records for it to be included in the models

n_iterations

numeric, An MCMC parameter - The number of interations

burnin

numeric, An MCMC parameter - The length of the burn in

thinning

numeric, An MCMC parameter - The thinning factor

n_chains

numeric, an MCMC parameter - The number of chains to be run

modeltype

A character string or vector that specifies the model to use. See details. If used then model.function is ignored.

regional_codes

A data.frame object detailing which site is associated with which region. each row desginates a site and each column represents a region. The first column represents the site name (as in site). Subsequent columns are named for each regions with 1 representing the site is in that region and 0 that it is not. NOTE a site should only be in one region

region_aggs

A named list giving aggregations of regions that you want trend estimates for. For example region_aggs = list(GB = c('england', 'scotland', 'wales')) will produced a trend for GB (Great Britain) as well as its constituent nations. Note that 'england', scotland' and 'wales' must appear as names of columns in regional_codes. More than one aggregate can be given, eg region_aggs = list(GB = c('england', 'scotland', 'wales'), UK = c('england', 'scotland', 'wales', 'northern_ireland')).

model.function

optionally a user defined BUGS model coded as a function (see ?jags, including the example there, for how this is done)

max_year

numeric, final year to which analysis will be run, this can be set if it is beyond the limit of the dataset. Defaults to final year of the dataset.

seed

numeric, uses set.seed to set the randon number seed. Setting this number ensures repeatabl analyses

additional.parameters

A character vector of additional parameters to monitor

additional.BUGS.elements

A named list giving additioanl bugs elements passed to R2jags::jags 'data' argument

additional.init.values

A named list giving user specified initial values to be added to the defaults.

return_data

Logical, if TRUE (default) the bugs data object is returned with the data

Value

A list of occDet objects (see occDetFunc), as an occDetList class of object

Details

This function requires both the R package R2jags and the program JAGS. These are not installed by default when sparta is loaded and so should be installed by the user. More details can be found in the vignette.

modeltype is used to choose the model as well as the associated initial values, and parameters to monitor. Elements to choose from can be separated into the following components:

A. Prior type: this has 3 options, each of which was tested in Outhwaite et al (in review): 1. sparta - This uses the same model as in Isaac et al (2014). 2. indran - This is the adaptive stationary model. 3. ranwalk - This is the random walk model.

B. Hyperprior type: This has 3 options, each of these are discussed in Outhwaite et al (in review): 1. halfuniform - the original formulation in Isaac et al (2014). 2. halfcauchy - preferred form, tested in Outhwaite et al (in review). 3. inversegamma - alternative form presented in the literature.

C. List length specification: This has 3 options: 1. catlistlength - list length as a categorical variable. 2. contlistlength - list length as a continuous variable. 3. nolistlength - no list length variable.

D. Julian date: this is an additional option for including Julian date within the detection model: 1. jul_date.

Not all combinations are available in sparta. You will get an error if you try and use a combination that is not supported. There is usually a good reason why that combination is not a good idea. Here are the model elements available:

  • "sparta" - This uses the same model as in Isaac et al (2014).

  • "indran" - Here the prior for the year effect of the state model is modelled as a random effect. This allows the model to adapt to interannual variability.

  • "ranwalk" - Here the prior for the year effect of the state model is modelled as a random walk. Each estimate for the year effect is dependent on that of the previous year.

  • "halfcauchy" - Includes half-Cauchy hyperpriors for all random effects within the model. The half-Cauchy is a special case of the Student’s t distribution with 1 degree of freedom.

  • "inversegamma" - Includes inverse-gamma hyperpriors for random effects within the model.

  • "catlistlength" - This specifies that list length should be considered as a categorical variable. There are 3 classes: lists of length 1, 2-3, and 4 and over. If none of the list length options are specified, 'contlistlength' is used.

  • "contlistlength" - This specifies that list length should be considered as a continuous variable. If none of the list length options are specified, 'contlistlength' is used.

  • "nolistlength" - This specifies that no list length should be used. If none of the list length options are specified, 'contlistlength' is used.

  • "jul_date" - This adds Julian date to the model as a normal distribution with its mean and standard deviation as monitored parameters.

  • "intercept" - No longer available. Includes an intercept term in the state and observation model. By including intercept terms, the occupancy and detection probabilities in each year are centred on an overall mean level.

  • "centering" - No longer available. Includes hierarchical centering of the model parameters. Centering does not change the model explicitly but writes it in a way that allows parameter estimates to be updated simultaneously.

These options are provided as a vector of characters, e.g. modeltype = c('indran', 'halfcauchy', 'catlistlength')

References

Isaac, N.J.B., van Strien, A.J., August, T.A., de Zeeuw, M.P. and Roy, D.B. (2014). Statistics for citizen science: extracting signals of change from noisy ecological data. Methods in Ecology and Evolution, 5: 1052-1060.

Outhwaite, C.L., Chandler, R.E., Powney, G.D., Collen, B., Gregory, R.D. & Isaac, N.J.B. (2018). Prior specification in Bayesian occupancy modelling improves analysis of species occurrence data. Ecological Indicators, 93: 333-343.

Isaac, N.J.B., van Strien, A.J., August, T.A., de Zeeuw, M.P. and Roy, D.B. (2014). Statistics for citizen science: extracting signals of change from noisy ecological data. Methods in Ecology and Evolution, 5: 1052-1060.

Outhwaite, C.L., Chandler, R.E., Powney, G.D., Collen, B., Gregory, R.D. & Isaac, N.J.B. (2018). Prior specification in Bayesian occupancy modelling improves analysis of species occurrence data. Ecological Indicators, 93: 333-343.

Examples

if (FALSE) {

# Create data
set.seed(125)
n <- 15000 #size of dataset
nyr <- 20 # number of years in data
nSamples <- 100 # set number of dates
nSites <- 50 # set number of sites

# Create somes dates
first <- as.Date(strptime("1980/01/01", "%Y/%m/%d")) 
last <- as.Date(strptime(paste(1980+(nyr-1),"/12/31", sep=''), "%Y/%m/%d")) 
dt <- last-first 
rDates <- first + (runif(nSamples)*dt)

# taxa are set as random letters
taxa <- sample(letters, size = n, TRUE)

# three sites are visited randomly
site <- sample(paste('A', 1:nSites, sep=''), size = n, TRUE)

# the date of visit is selected at random from those created earlier
survey <- sample(rDates, size = n, TRUE)

# run the model with these data for one species
# using defaults
results <- occDetModel(taxa = taxa,
                       site = site,
                       survey = survey,
                       species_list = 'a',
                       write_results = FALSE,
                       n_iterations = 1000,
                       burnin = 10,
                       thinning = 2)
                       
# run with a different model type
results <- occDetModel(taxa = taxa,
                       site = site,
                       survey = survey,
                       species_list = 'a',
                       write_results = FALSE,
                       n_iterations = 1000,
                       burnin = 10,
                       thinning = 2,
                       seed = 125, 
                       modeltype = c("indran", "intercept"))
                       
# run with regions

# Create region definitions
regions <- data.frame(site = unique(site),
                      region1 = c(rep(1, 20), rep(0, 30)),
                      region2 = c(rep(0, 20), rep(1, 15), rep(0, 15)),
                      region3 = c(rep(0, 20), rep(0, 15), rep(1, 15)))
 
results <- occDetModel(taxa = taxa,
                       site = site,
                       survey = survey,
                       species_list = 'a',
                       write_results = FALSE,
                       n_iterations = 1000,
                       burnin = 10,
                       thinning = 2,
                       seed = 125, 
                       modeltype = c("indran", "intercept"),
                       regional_codes = regions,
                       region_aggs = list(agg1 = c('region1', 'region2')))       
}