Run the required spatial analysis to create the spat_df input for
calc_vulnerability and clip the range polygon to the
appropriate scales.
Usage
analyze_spatial(
range_poly,
scale_poly,
clim_vars_lst,
non_breed_poly = NULL,
ptn_poly = NULL,
hs_rast = NULL,
hs_rcl = NULL,
protected_poly = NULL,
gain_mod = 1,
scenario_names = "Scenario 1",
quiet = FALSE
)Arguments
- range_poly
an sf polygon object giving the species range.
- scale_poly
an sf polygon object giving the area of the assessment
- clim_vars_lst
a list of climate data, the result of
get_clim_vars- non_breed_poly
Optional. An sf polygon object giving the species range in the non-breeding season.
- ptn_poly
Optional. An sf polygon object giving the locations that are considered part of the physiological thermal niche (See NatureServe Guidelines for definition).
- hs_rast
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.- hs_rcl
a matrix used to classify
hs_rastinto 0: not suitable, 1: lost, 2: maintained, 3: gained. Seerng_chg_mat()for a helper function. Seeclassifyfor details on the matrix format.- protected_poly
Optional. A sf polygon object with protected areas.
- gain_mod
a number between 0 and 1 that can be used to down-weight gains in the modeled range change under climate change
- scenario_names
character vector with names that identify multiple future climate scenarios.
- quiet
Logical. Whether to suppress progress messages.
Value
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:
- scenario_name
Name identifying the scenario
- MAT_#
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
- CMD_#
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
- CCEI_#
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
- perc_non_breed_not_over_ccei
The precentage of the non-breeding range that does not overlap with the CCEI raster data
- HTN_#
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
- PTN
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
- MAP_max/min
The maximum and minimum historical mean annual precipitation in the species' range
- range_change
The projected decrease in range size as a percentage of current range size. Negative numbers indicate an increase in range size
- range_overlap
The percentage of the current range that is projected to remain in the future range.
- range_size
The area of the species' range in m2
Details
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.
The projections used will be that of the Mean Annual Temperature (in
clim_vars_lst), the CCEI (in clim_vars_lst, if used), and the
predicted changes in breeding range (hs_rasts; if used). Therefore these
spatial data files will ideally be projected with a projection appropriate
for preserving area in calculations (Equal Area projections, for example).
Examples
# Setup
library(sf)
#> Linking to GEOS 3.12.1, GDAL 3.8.4, PROJ 9.4.0; sf_use_s2() is TRUE
base_pth <- system.file("extdata", package = "ccviR")
scn_nms <- c("RCP 4.5", "RCP 8.5") # scenario names
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 = rng_chg_mat(-1, 0, 1),
scenario_names = scn_nms
)
#> Checking files
#> Preparing polygon 'Climate Data Extext'
#> Preparing polygon 'Assessment Area'
#> Preparing polygon 'Range'
#> Clipping 'Range' to 'Climate Data Extent'
#> Clipping 'Range' to 'Assessment Area'
#> Assessing local climate exposure
#> Assessing thermal & hydrological niches
#> Assessing modelled range response to climate change
#> Finalizing outputs
# With only one range change scenario (suboptimal)
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(file.path(base_pth, "rng_chg_45.tif")),
hs_rcl = rng_chg_mat(-1, 0, 1),
scenario_names = scn_nms
)
#> Checking files
#> Preparing polygon 'Climate Data Extext'
#> Preparing polygon 'Assessment Area'
#> Preparing polygon 'Range'
#> Clipping 'Range' to 'Climate Data Extent'
#> Clipping 'Range' to 'Assessment Area'
#> Assessing local climate exposure
#> Assessing thermal & hydrological niches
#> Assessing modelled range response to climate change
#> Finalizing outputs
# With protected areas
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"),
protected_poly = sf::read_sf(file.path(base_pth, "protected_areas.shp")),
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 = rng_chg_mat(-1, 0, 1),
scenario_names = scn_nms
)
#> Checking files
#> Preparing polygon 'Climate Data Extext'
#> Preparing polygon 'Assessment Area'
#> Preparing polygon 'Range'
#> Clipping 'Range' to 'Climate Data Extent'
#> Clipping 'Range' to 'Assessment Area'
#> Assessing local climate exposure
#> Assessing thermal & hydrological niches
#> Assessing modelled range response to climate change
#> Preparing polygon 'Protected Areas'
#> Clipping 'Protected Areas' to 'Assessment Area'
#> Transforming polygon 'Protected Areas'
#> Finalizing outputs
# With CCEI (by supplying `non_breed.shp` spatial example)
spat_res <- analyze_spatial(
range_poly = read_sf(file.path(base_pth, "rng_poly.shp"), agr = "constant"),
scale_poly = read_sf(file.path(base_pth, "assess_poly.shp"), agr = "constant"),
protected_poly = read_sf(file.path(base_pth, "protected_areas.shp")),
non_breed_poly = read_sf(file.path(base_pth, "non_breed.shp")),
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 = rng_chg_mat(-1, 0, 1),
scenario_names = scn_nms
)
#> Checking files
#> Preparing polygon 'Climate Data Extext'
#> Preparing polygon 'Assessment Area'
#> Preparing polygon 'Range'
#> Preparing polygon 'non-breeding range'
#> Transforming polygon 'non-breeding range'
#> Clipping 'Range' to 'Climate Data Extent'
#> Clipping 'Range' to 'Assessment Area'
#> Assessing local climate exposure
#> Assessing thermal & hydrological niches
#> Assessing modelled range response to climate change
#> Preparing polygon 'Protected Areas'
#> Clipping 'Protected Areas' to 'Assessment Area'
#> Transforming polygon 'Protected Areas'
#> Finalizing outputs