Run the required spatial analysis to create the spat_df input for
calc_vulnerability and clip the range polygon to the
appropriate scales.
analyze_spatial(
range_poly,
scale_poly,
clim_vars_lst,
non_breed_poly = NULL,
ptn_poly = NULL,
hs_rast = NULL,
hs_rcl = NULL,
gain_mod = 1,
scenario_names = "Scenario 1"
)an sf polygon object giving the species range.
an sf polygon object giving the area of the assessment
a list of climate data, the result of
get_clim_vars
Optional. An sf polygon object giving the species range in the non-breeding season.
Optional. An sf polygon object giving the locations that are considered part of the physiological thermal niche (See NatureServe Guidelines for definition).
Optional. A SpatRaster object with results from a model of the
change in the species' range caused by climate change. To supply different
results for each scenario use a raster with multiple layers and ensure that the order of
the layers matches the order of scenario_names.
a matrix used to classify hs_rast into 0: not suitable, 1:
lost, 2: maintained, 3: gained. See classify for
details on the matrix format.
a number between 0 and 1 that can be used to down-weight gains in the modeled range change under climate change
character vector with names that identify multiple future climate scenarios.
a list with elements: spat_table the results of the spatial
analysis, range_poly_assess the range polygon clipped to the
assessment area, and range_poly_clim the range polygon clipped to
the extent of the climate data.
spat_table contains the following columns:
Name identifying the scenario
The percentage of the species' range that is exposed to each class of change in mean annual temperature between the historical normal and predicted climate. Class 1 has the highest exposure and Class 6 the lowest
The percentage of the species' range that is exposed to each class of change in climate moisture deficit between the historical normal and predicted climate. Class 1 has the highest exposure and Class 6 the lowest
The percentage of the species' non-breeding range that falls into each climate change exposure index class. Class 4 indicates high exposure while Class 1 indicates low exposure
The precentage of the non-breeding range that does not overlap with the CCEI raster data
The percentage of the species' range that is exposed to each class of variation between the historical coldest and warmest monts. Class 1 has the smallest variation and Class 4 is the largest
The percentage of the species' range that falls into cool or cold environments that may be lost or reduced in the assessment area as a result of climate change
The maximum and minimum historical mean annual precipitation in the species' range
The projected decrease in range size as a percentage of current range size. Negative numbers indicate an increase in range size
The percentage of the current range that is projected to remain in the future range.
The area of the species' range in m2
The range polygon will be clipped to the area overlapping the
scale_poly and also to the area overlapping the extent of the
climate data polygon. The range within the assessment area is used to
calculate all results except the historical thermal and hydrological niches
for which the range within the extent of the climate data is used.
base_pth <- system.file("extdata", package = "ccviR")
# scenario names
scn_nms <- c("RCP 4.5", "RCP 8.5")
clim_vars <- get_clim_vars(file.path(base_pth, "clim_files/processed"),
scenario_names = scn_nms)
spat_res <- analyze_spatial(
range_poly = sf::read_sf(file.path(base_pth, "rng_poly.shp"), agr = "constant"),
scale_poly = sf::read_sf(file.path(base_pth, "assess_poly.shp"), agr = "constant"),
clim_vars_lst = clim_vars,
hs_rast = terra::rast(c(file.path(base_pth, "rng_chg_45.tif"),
file.path(base_pth, "rng_chg_85.tif"))),
hs_rcl = matrix(c(-1, 0, 1, 1, 2, 3), ncol = 2),
scenario_names = scn_nms
)
#> performing spatial analysis
#> Warning: More than 10% of the range change raster does not match the expected values.
#> Is the classification table correct?