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Methods to correct overprediction of species distribution models based on occurrences and suitability patterns.

Source: R/msdm_posteriori.R
msdm_posteriori.Rd

These methods reduce overprediction of species distribution models based on a posteriori methods (see Mendes et al 2020), i.e., the combination of the patterns of species occurrences and predicted suitability

Usage

msdm_posteriori(
  records,
  x,
  y,
  pr_ab,
  cont_suit,
  method = c("obr", "pres", "lq", "mcp", "bmcp"),
  thr = "equal_sens_spec",
  buffer = NULL,
  crs = NULL
)

Arguments

records

tibble or data.frame. A database with spatial coordinates of species presences and absences (or pseudo-absence) used to create species distribution models.

x

character. Column name with spatial x coordinates.

y

character. Column name with spatial y coordinates.

pr_ab

character. Column name with presence and absence data (i.e. 1 and 0)

cont_suit

SpatRaster. Raster with continuous suitability predictions "species_specific" type calculates the minimum pairwise-distances between all occurrences and then selects the maximum distance, i.e., the value of the buffer will be the maximum distance from the minimum distance. This procedure depends on the spatial pattern of each species' occurrences; thus, for each species, a value of buffer width will be calculated (usage buffer="species_specific").

method

character. A character string indicating which constraint method will be used.

thr

character or numeric. Threshold used to get binary suitability values (i.e. 0,1), needed for threshold-dependent performance metrics. Only one threshold type can be specified. It is necessary to provide a vector for this argument. The following threshold criteria are available:

  • lpt: The highest threshold at which there is no omission.

  • equal_sens_spec: Threshold at which the sensitivity and specificity are equal.

  • max_sens_spec: Threshold at which the sum of the sensitivity and specificity is the highest (aka threshold that maximizes the TSS).

  • max_jaccard: The threshold at which the Jaccard index is the highest.

  • max_sorensen: The threshold at which the Sorensen index is highest.

  • max_fpb: The threshold at which FPB is highest.

  • sensitivity: Threshold based on a specified sensitivity value. Usage thr = c('sensitivity', sens='0.6') or thr = c('sensitivity'). 'sens' refers to sensitivity value. If it is not specified a sensitivity values, function will use by default 0.9

Also, it is possible specifying the threshold value using a numeric values (thr = 0.623). Default "equal_sens_spec".

buffer

numeric. Buffer width use in 'bmcp' approach. The buffer width will be interpreted in m if Coordinate reference system used in "crs" argument has a longitude/latitude, or map units in other cases. Usage buffer=50000. Default NULL

crs

character. Coordinate reference system used for calculating buffer in "bmcp" method.

Value

This function return a SpatRaster with continuous and binary prediction.

Details

These function help reduce overprediction of species distribution models based on the combination of the patterns of species occurrences and predicted suitability. It is recommended to use these approaches only for current distribution not for models projected for different time periods (past or future).

Five methods are implemented:

Abbreviation list

  • SDM: species distribution model

  • l: suitability patches that intercept species occurrences

  • k: suitability patches that do not intercept species occurrences

  • T: threshold distances used to select suitability patches

These methods reduce overprediction of species distribution models already fitted based on the occurrences and suitability patterns of species (see 'thr' arguments)

Method 'obr' (Occurrences Based Restriction). This method assumes that suitable patches intercepting species occurrences (l) are more likely to be part of species distributions than suitable patches that do not intercept any occurrence (k). Distance from all k patches to the closest l patch is calculated, then k patches are removed that exceed a species-specific distance threshold from SDMs models. This threshold (T) is calculated as the maximum distance in a vector of minimum pairwise distances between occurrences. Whenever a suitable pixel is within a k patch that is more than distance T from the closest l patch, the suitability of the pixel is reduced to zero. It is assumed that this simple threshold is a surrogate for the species-specific dispersal ability. If T is low, either the species has been sampled throughout its distribution, or the species is geographically restricted, justifying a narrow inclusion of k patches (Mendes et al., 2020).

Method 'pres' (only occurrences based restriction). This is a more restrictive variant of the 'obr' method. It only retains those pixels in suitability patches intercepting occurrences (k) (Mendes et al., 2020).

Method 'lq' (Lower Quantile). This method is similar to the 'obr' method, except by the procedure to define a distance threshold to withdrawn k patches, which is the lower quartile distance between k patches to the closest l patch. Whenever a suitable pixel is within a k patch, i.e., not within this lower quartile, the suitability of the pixel is reduced to zero. This means that 75% of k patches were withdrawn from the model (Mendes et al., 2020).

Method 'mcp' (Minimum Convex Polygon). Compiled and adapted from Kremen et al. (2008), this method excludes from SDM predictions suitable pixels that do not intercept a minimum convex polygon, with interior angles smaller than 180, enclosing all occurrences of a species.

Method 'bmcp' (Buffered Minimum Convex Polygon). Compiled and adapted from Kremen et al. (2008), it is similar to the 'mcp' method except for the inclusion of a buffer zone surrounding minimum convex polygons. For this method a buffer width value must be provided in "buffer" argument and CRS in "crs" argument.

For further methodological and performance information of these methods see Mendes et al. (2020).

If using one these constraining methods, cite Mendes et al (2020).

References

  • Mendes, P.; Velazco S.J.E.; Andrade, A.F.A.; De Marco, P. (2020) Dealing with overprediction in species distribution models: how adding distance constraints can improve model accuracy, Ecological Modelling, in press. https://doi.org/10.1016/j.ecolmodel.2020.109180

  • Kremen, C., Cameron, A., Moilanen, A., Phillips, S. J., Thomas, C. D., Beentje, H., . Zjhra, M. L. (2008). Aligning Conservation Priorities Across Taxa in Madagascar with High-Resolution Planning Tools. Science, 320(5873), 222-226. doi:10.1126/science.1155193

See also

msdm_priori

Examples

if (FALSE) { # \dontrun{
require(dplyr)
require(terra)

data("spp")
somevar <- system.file("external/somevar.tif", package = "flexsdm")
somevar <- terra::rast(somevar)


# Preparing data for modeling a species
set.seed(10)
occ <- spp %>%
  dplyr::filter(species == "sp2") %>% # filter a species
  sdm_extract(
    data = ., x = "x", y = "y",
    env_layer = somevar, filter_na = TRUE
  ) %>% # extrac variables values
  part_random(.,
    pr_ab = "pr_ab",
    method = c(method = "kfold", folds = 10)
  ) # add columns with partition

# Fit a model
m_glm <- fit_glm(
  data = occ,
  response = "pr_ab",
  predictors = names(somevar),
  partition = ".part",
  thr = "equal_sens_spec",
)


# Lets predict this model
m_pred <- sdm_predict(models = m_glm, pred = somevar, thr = NULL, con_thr = FALSE)
plot(m_pred[[1]])
m_pred[[1]] %>% plot()

# Lets extract the raster from this list
m_pred <- m_pred[[1]]

### bmcp method
m_bmcp <- msdm_posteriori(
  records = occ,
  x = "x",
  y = "y",
  pr_ab = "pr_ab",
  method = "bmcp",
  cont_suit = m_pred,
  thr = "equal_sens_spec",
  buffer = 30000,
  crs = crs(m_pred)
)

plot(m_bmcp)


### mcp method
m_mcp <- msdm_posteriori(
  records = occ,
  x = "x",
  y = "y",
  pr_ab = "pr_ab",
  method = "mcp",
  cont_suit = m_pred,
  thr = "equal_sens_spec",
  buffer = NULL
)

plot(m_mcp)


### pres method
m_pres <- msdm_posteriori(
  records = occ,
  x = "x",
  y = "y",
  pr_ab = "pr_ab",
  method = "pres",
  cont_suit = m_pred,
  thr = "equal_sens_spec",
  buffer = NULL
)

plot(m_pres)


### lq method
m_lq <- msdm_posteriori(
  records = occ,
  x = "x",
  y = "y",
  pr_ab = "pr_ab",
  method = "lq",
  cont_suit = m_pred,
  thr = "equal_sens_spec",
  buffer = NULL
)

plot(m_lq)


### obr method
m_obr <- msdm_posteriori(
  records = occ,
  x = "x",
  y = "y",
  pr_ab = "pr_ab",
  method = "obr",
  cont_suit = m_pred,
  thr = "equal_sens_spec",
  buffer = NULL
)

plot(m_obr)
} # }