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Update waterbird predictors: pwater & pfld

Source: R/update_pwater.R
update_pwater.Rd

Helper function for updating pwater and pfld predictors for the waterbird distribution models. Generates file pwater.tif at locations pathout/pwater/landscape_name and pathout/SDM/landscape_name.

Usage

update_pwater(
  waterdat,
  mask = NULL,
  pathout,
  SDM,
  landscape_name,
  overwrite = FALSE,
  baseline_landscape = NULL,
  scenario_landscape = NULL,
  floor = FALSE
)

Arguments

waterdat

SpatRaster or character string giving the filepath to a raster representing the probability of open water (pwater) in each cell, specific to the time frames appropriate to each scenario_landscape and waterbird SDM (i.e., fall vs. winter)

mask

Optional SpatRaster or character string giving the filepath to a raster that should be used to mask the output, e.g. a study area boundary

pathout, SDM, landscape_name

Character strings defining the filepath (pathout/SDM/landscape_name) where output rasters should be written; landscape_name should either correspond to the landscape represented by waterdat or the scenario_landscape, if given; see Details

overwrite

Logical; passed to terra::writeRaster(); default FALSE

baseline_landscape, scenario_landscape

Optional SpatRasters created by terra::rast() to compare with each other for estimating pwater for the changed portions of the scenario_landscape; see Details

floor

Logical; if TRUE, don't allow new values of pwater to be lower than baseline values

Value

Nothing; all files written to pathout

Details

The waterbird distribution models incorporate information about surface water data in two ways: as pwater, the expected probability of open surface water in each cell of the landscape raster, specific to the waterbird season being modeled and perhaps averaged over multiple years, and as pfld focal statistics which represent the proportion of each land cover class within a given distance of each cell that is flooded (see python_focal_prep() and python_focal_run()). Therefore, pwater data must be available for every landscape under analysis before the pfld focal statistics can be generated and distribution models fit.

Due to the dual needs for generating pwater and pfld predictors, this function writes results in two places within pathout. The first will be written to pathout/pwater/landscape_name, intended for later use with python_focal_prep() and generating pfld predictors. The second will be written to pathout/SDM/landscape_name, which is expected to be a directory containing all final predictors for later use with fit_SDM() in fitting waterbird models.

In addition, this function has two modes of operation. If scenario_landscape is not provided, the waterdat is assumed to to represent pwater data for the landscape_name, and is simply renamed and copied to both pathout locations for use in later steps of analysis, optionally masking before pathout/SDM/landscape_name is written. The mask is never applied to the pathout/pwater/landscape_name output intended for later focal statistics to avoid errors in processing near the boundaries of the study area.

Alternatively, in the second mode, if both baseline_landscape and scenario_landscape rasters are provided, this function will estimate new pwater values for cells in the scenario_landscape that have changed cover class, based on the mean probability of open surface water for that land cover class in the baseline_landscape. Optionally, if floor = TRUE, new probabilities of open water will be assigned only if they are higher than the baseline values. In this mode, the result represents pwater for the scenario_landscape, and landscape_name should reflect the name of the scenario.

The original pwater baseline data used in the development of these models was derived from Point Blue's Water Tracker and may be downloaded from doi:10.5281/zenodo.7672193.

See also

update_covertype(), update_roosts()

Examples

# See vignette