WSS: Well sampled sites model

In BiologicalRecordsCentre/sparta: Trend Analysis for Unstructured Data

Well sampled sites model

Description

This function is a wrapper for siteSelection and reportingRateModel that allows users the run a well sampled sites analysis as in Roy et al (2012).

Usage

WSS(
  taxa,
  site,
  time_period,
  minL = 2,
  minTP = 3,
  species_to_include = unique(taxa),
  overdispersion = FALSE,
  family = "Binomial",
  verbose = FALSE,
  print_progress = 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.
time_period	A numeric vector of user defined time periods, or a date vector, as long as the number of observations.
minL	numeric, The minimum number of taxa recorded at a site at a given time period (list-length) for the visit to be considered well sampled.
minTP	numeric, The minimum number of time periods, or if time_period is a date the minimum number of years, a site must be sampled in for it be be considered well sampled.
species_to_include	A character vector giving the name of species to model. By default all species will be modelled
overdispersion	This option allows modelling overdispersion (TRUE) in models. Default is FALSE.
family	The type of model to be use. Can be "Binomial" or "Bernoulli".
verbose	This option, if TRUE, sets models to verbose, allowing the interations of each model to be viewed.
print_progress	Logical, if TRUE progress is printed to console when running models. Default is TRUE

Value

A dataframe of results are returned to R. Each row gives the results for a single species, with the species name given in the first column, species_name. For each of the following columns the prefix (before ".") gives the covariate and the sufix (after the ".") gives the parameter of that covariate. number_observations gives the number of visits where the species of interest was observed. If any of the models encountered an error this will be given in the column error_message.

The data.frame has a number of attributes:

• intercept_year - The year used for the intercept (i.e. the year whose value is set to 0). Setting the intercept to the median year helps to increase model stability

• min_year and max_year - The earliest and latest year in the dataset (after years have been centered on intercept_year

• nVisits - The total number of visits that were in the dataset

• model_formula - The model used, this will vary depending on the combination of arguements used

• minL - The setting of minL used in site selection

• minTP - The setting of minTP used in site selection

References

Roy, H.E., Adriaens, T., Isaac, N.J.B. et al. (2012) Invasive alien predator causes rapid declines of native European ladybirds. Diversity & Distributions, 18, 717-725.

Examples

# Create data
n <- 1500 #size of dataset
nyr <- 8 # number of years in data
nSamples <- 20 # set number of dates

# Create somes dates
first <- as.POSIXct(strptime("2003/01/01", "%Y/%m/%d"))
last <- as.POSIXct(strptime(paste(2003+(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(c('one', 'two', 'three'), size = n, TRUE)

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

# combine this to a dataframe
df <- data.frame(taxa, site, time_period)

results <- WSS(df$taxa,
                df$site,
                df$time_period,
                minL = 4,
                minTP = 3,
                species_to_include = c('a', 'b', 'c'))

# Look at the results for the first few species
head(results)
# Look at the attributes of the object returned
attributes(results)

BiologicalRecordsCentre/sparta documentation built on April 22, 2024, 2:34 p.m.