amt (version 0.1.7)

extract_covariates: Extract covariate values

Description

Extract the covariate values at relocations, or at the beginning or end of steps.

Usage

extract_covariates(x, ...)
# S3 method for track_xy
extract_covariates(x, covariates, ...)
# S3 method for random_points
extract_covariates(x, covariates, ...)
# S3 method for steps_xy
extract_covariates(x, covariates, where = "end", ...)
extract_covariates_along(x, ...)
# S3 method for steps_xy
extract_covariates_along(x, covariates, ...)
extract_covariates_var_time(x, ...)
# S3 method for track_xyt
extract_covariates_var_time(
  x,
  covariates,
  when = "any",
  max_time,
  name_covar = "time_var_covar",
  ...
)
# S3 method for steps_xyt
extract_covariates_var_time(
  x,
  covariates,
  when = "any",
  max_time,
  name_covar = "time_var_covar",
  where = "end",
  ...
)

Value

A tibble with additional columns for covariate values.

Arguments

x

[track_xy, track_xyt, steps]
Either a track created with mk_track or track, or steps.

...

Additional arguments passed to raster::extract().

covariates

[RasterLayer,RasterStack,RasterBrick]
The (environmental) covariates. For extract_covariates_var_time the argument covariates need to have a z-column (i.e. the time stamp).

where

[character(1)="end"]{"start", "end", "both"}
For steps this determines if the covariate values should be extracted at the beginning or the end of a step. or end.

when

[character(1)="any"]{"any", "before", "after"}
Specifies for for extract_covariates_var_time whether to look before, after or in both direction (any) for the temporally closest environmental raster.

max_time

[Period(1)]
The maximum time difference between a relocation and the corresponding raster. If no rasters are within the specified max. distance NA is returned.

name_covar

[character(1)="time_var_covar"]
The name of the new column.

Details

extract_covariates_along extracts the covariates along a straight line between the start and the end point of a (random) step. It returns a list, which in most cases will have to be processed further.

Examples

data(deer)
data(sh_forest)
mini_deer <- deer[1:20, ]
mini_deer %>% extract_covariates(sh_forest)
mini_deer %>% steps %>% extract_covariates(sh_forest)
mini_deer %>% steps %>% extract_covariates(sh_forest, where = "start")

# Buffer
mini_deer %>% extract_covariates(sh_forest) # no buffer
# The command buffer can be used, to buffer each point together with a
# function to summarize the results.
mini_deer %>% extract_covariates(sh_forest, buffer = 10, fun = mean)
# This can also be a use-specified function.
mini_deer %>% extract_covariates(sh_forest, buffer = 100, fun = function(x) length(x))
# \donttest{
# Illustration of extracting covariates along the a step
mini_deer %>% steps() %>% random_steps() %>%
  extract_covariates(sh_forest) %>% # extract at the endpoint
  mutate(for_path = extract_covariates_along(., sh_forest))  %>%
  # 1 = forest, lets calc the fraction of forest along the path
  mutate(for_per = purrr::map_dbl(for_path, ~ mean(. == 1)))
# }