sampleOccurrences: Sample occurrences in a virtual species distribution

In virtualspecies: Generation of Virtual Species Distributions

Sample occurrences in a virtual species distribution

Description

This function samples occurrences/records (presence only or presence-absence) within a species distribution, either randomly or with a sampling bias. The sampling bias can be defined manually or with a set of predefined biases.

Usage

sampleOccurrences(
  x,
  n,
  type = "presence only",
  extract.probability = FALSE,
  sampling.area = NULL,
  detection.probability = 1,
  correct.by.suitability = FALSE,
  error.probability = 0,
  bias = "no.bias",
  bias.strength = 50,
  bias.area = NULL,
  weights = NULL,
  sample.prevalence = NULL,
  replacement = FALSE,
  plot = TRUE
)

Arguments

x	a SpatRaster object or the output list from generateSpFromFun, generateSpFromPCA, generateRandomSp, convertToPA or limitDistribution The raster must contain values of 0 or 1 (or NA).
n	an integer. The number of occurrence points / records to sample.
type	"presence only" or "presence-absence". The type of occurrence points to sample.
extract.probability	TRUE or FALSE. If TRUE, then true probability at sampled locations will also be extracted
sampling.area	a character string, a polygon or an extent object. The area in which the sampling will take place. See details.
detection.probability	a numeric value between 0 and 1, corresponding to the probability of detection of the species. See details.
correct.by.suitability	TRUE or FALSE. If TRUE, then the probability of detection will be weighted by the suitability, such that cells with lower suitabilities will further decrease the chance that the species is detected when sampled. NOTE: this will NOT increase likelihood of samplings in areas of high suitability. In this case look for argument weights.
error.probability	TRUE or FALSE. Probability to attribute an erroneous presence (False Positive) in cells where the species is actually absent.
bias	"no.bias", "country", "region", "extent", "polygon" or "manual". The method used to generate a sampling bias: see details.
bias.strength	a positive numeric value. The strength of the bias to be applied in area (as a multiplier). Above 1, area will be oversampled. Below 1, area will be undersampled.
bias.area	NULL, a character string, a polygon or an extent object. The area in which the sampling will be biased: see details. If NULL and bias = "extent", then you will be asked to draw an extent on the map.
weights	NULL or a raster layer. Only used if bias = "manual". The raster of bias weights to be applied to the sampling of occurrences. Higher weights mean a higher probability of sampling. For example, species suitability raster can be entered here to increase likelihood of sampling occurrences in areas with high suitability.
sample.prevalence	NULL or a numeric value between 0 and 1. Only useful if type = "presence-absence". Defines the sample prevalence, i.e. the proportion of presences sampled. Note that the probabilities of detection and error are applied AFTER this parameter, so the final sample prevalence may not different if you apply probabilities of detection and/or error
replacement	TRUE or FALSE. If TRUE, multiple samples can occur in the same cell. Can be useful to mimic real datasets where samplings can be duplicated or repeated in time.
plot	TRUE or FALSE. If TRUE, the sampled occurrence points will be plotted.

Details

Online tutorial for this function

How the function works:

The function randomly selects n cells in which samples occur. If a bias is chosen, then the selection of these cells will be biased according to the type and strength of bias chosen. If the sampling is of type "presence only", then only cells where the species is present will be chosen. If the sampling is of type "presence-absence", then all non-NA cells can be chosen.

The function then samples the species inside the chosen cells. In cells where the species is present the species will always be sampled unless the parameter detection.probability is lower than 1. In that case the species will be sampled with the associated probability of detection.

In cells where the species is absent (in case of a "presence-absence" sampling), the function will always assign absence unless error.probability is greater than 1. In that case, the species can be found present with the associated probability of error. Note that this step happens AFTER the detection step. Hence, in cells where the species is present but not detected, it can still be sampled due to a sampling error.

How to restrict the sampling area:

Use the argument sampling.area:

• Provide the name (s) (or a combination of names) of country(ies), region(s) or continent(s). Examples:

  • sampling.area = "Africa"

  • sampling.area = c("Africa", "North America", "France")

• Provide a polygon (SpatialPolygons or SpatialPolygonsDataFrame of package sp)

• Provide an extent object

How the sampling bias works:

The argument bias.strength indicates the strength of the bias. For example, a value of 50 will result in 50 times more samples within the bias.area than outside. Conversely, a value of 0.5 will result in half less samples within the bias.area than outside.

How to choose where the sampling is biased:

You can choose to bias the sampling in:

1. a particular country, region or continent (assuming your raster has the WGS84 projection):

   Set the argument bias to "country", "region" or "continent", and provide the name(s) of the associated countries, regions or continents to bias.area (see examples).

   List of possible bias.area names:

   • Countries: type unique(rnaturalearth::ne_countries(returnclass ='sf')$sovereignt) in the console

   • Regions: "Africa", "Antarctica", "Asia", "Oceania", "Europe", "Americas"

   • Continents: "Africa", "Antarctica", "Asia", "Europe", "North America", "Oceania", "South America"

2. a polygon:

   Set bias to "polygon", and provide your polygon to area.

3. an extent object:

   Set bias to "extent", and either provide your extent object to bias.area, or leave it NULL to draw an extent on the map.

Otherwise you can enter a raster of sampling probability. It can be useful if you want to increase likelihood of samplings in areas of high suitability (simply enter the suitability raster in weights; see examples below), or if you want to define sampling biases manually, e.g. to to create biases along roads. In that case you have to provide to weights a raster layer in which each cell contains the probability to be sampled.

The .Random.seed and RNGkind are stored as attributes when the function is called, and can be used to reproduce the results as shown in the examples (though it is preferable to set the seed with set.seed before calling sampleOccurrences() and to then use the same value in set.seed to reproduce results later. Note that reproducing the sampling will only work if the same original distribution map is used.

Value

a list with 8 elements:

• type: type of occurrence sampled (presence-absences or presence-only)

• sample.points: data.frame containing the coordinates of samples, true and sampled observations (i.e, 1, 0 or NA), and, if asked, the true environmental suitability in sampled locations

• detection.probability: the chosen probability of detection of the virtual species

• error.probability: the chosen probability to assign presence in cells where the species is absent

• bias: if a bias was chosen, then the type of bias and the associated area will be included.

• replacement: indicates whether multiple samples could occur in the same cells

• original.distribution.raster: the distribution raster from which samples were drawn

• sample.plot: a recorded plot showing the sampled points overlaying the original distribution.

Note

Setting sample.prevalence may at least partly override bias, e.g. if bias is specified with extent to an area that contains no presences, but sample prevalence is set to > 0, then cells outside of the biased sampling extent will be sampled until the number of presences required by sample.prevalence are obtained, after which the sampling of absences will proceed according to the specified bias.

Author(s)

Boris Leroy [e-mail] Willson Gaul [e-mail]

with help from C. N. Meynard, C. Bellard & F. Courchamp

Examples

# Create an example stack with six environmental variables
a <- matrix(rep(dnorm(1:100, 50, sd = 25)), 
            nrow = 100, ncol = 100, byrow = TRUE)
env <- c(rast(a * dnorm(1:100, 50, sd = 25)),
         rast(a * 1:100),
         rast(a * logisticFun(1:100, alpha = 10, beta = 70)),
         rast(t(a)),
         rast(exp(a)),
         rast(log(a)))
names(env) <- paste("Var", 1:6, sep = "")   

# More than 6 variables: by default a PCA approach will be used
sp <- generateRandomSp(env, niche.breadth = "wide")

# Sampling of 25 presences
sampleOccurrences(sp, n = 25)

# Sampling of 30 presences and absences
sampleOccurrences(sp, n = 30, type = "presence-absence")


# Reducing of the probability of detection
sampleOccurrences(sp, n = 30, type = "presence-absence", 
                  detection.probability = 0.5)
                  
# Further reducing in relation to environmental suitability
sampleOccurrences(sp, n = 30, type = "presence-absence", 
                  detection.probability = 0.5,
                  correct.by.suitability = TRUE)
                  
                  
# Creating sampling errors (far too much)
sampleOccurrences(sp, n = 30, type = "presence-absence", 
                  error.probability = 0.5)
                  
# Introducing a sampling bias (oversampling)
biased.area <- ext(1, 50, 1, 50)
sampleOccurrences(sp, n = 50, type = "presence-absence", 
                  bias = "extent",
                  bias.area = biased.area)
# Showing the area in which the sampling is biased
plot(biased.area, add = TRUE)     

# Introducing a sampling bias (no sampling at all in the chosen area)
biased.area <- ext(1, 50, 1, 50)
sampleOccurrences(sp, n = 50, type = "presence-absence", 
                  bias = "extent",
                  bias.strength = 0,
                  bias.area = biased.area)
# Showing the area in which the sampling is biased
plot(biased.area, add = TRUE)    
samps <- sampleOccurrences(sp, n = 50, 
                           bias = "manual",
                           weights = sp$suitab.raster)
plot(sp$suitab.raster)
points(samps$sample.points[, c("x", "y")])

# Create a sampling bias so that more presences are sampled in areas with 
# higher suitability

  
    

# Reproduce sampling based on the saved .Random.seed from a previous result
samps <- sampleOccurrences(sp, n = 100, 
                           type = "presence-absence", 
                           detection.probability = 0.7, 
                           bias = "extent", 
                           bias.strength = 50, 
                           bias.area = biased.area)
# Reset the random seed using the value saved in the attributes               
.Random.seed <- attr(samps, "seed") 
reproduced_samps <- sampleOccurrences(sp, n = 100, 
                                      type = "presence-absence",
                                      detection.probability = 0.7,
                                      bias = "extent",
                                      bias.strength = 50,
                                      bias.area = biased.area)
identical(samps$sample.points, reproduced_samps$sample.points)          

virtualspecies documentation built on Sept. 27, 2023, 1:06 a.m.