Calculate the NatureServe Climate Change Vulnerability Index from the completed inputs.
Arguments
- spat_df
data.frame of spatial inputs produced by
analyze_spatial- vuln_df
data.frame of answers to vulnerability questions. The format must match
make_vuln_df.- tax_grp
taxonomic group of the species. Must match one of the options below
- n_rnds
number of Monte Carlo repetitions for determining variability when multiple values are given for a factor
Value
A data.frame with 1 row per scenario and columns:
- scenario_name
Name identifying the scenario
- index
The index value as a string. EV: Extremely Vulnerable, HV: Highly Vulnerable, MV: Moderately Vulnerable, LV: Less Vulnerable, IE: Insufficient Evidence
- conf_index
An index of the confidence in the index based on the monte carlo. Options are Low, Moderate, High, Very High or Insufficient Evidence.
- mc_results
A data.frame with the b_c_score, d_score, and index for each monte carlo repitition
- mig_exp
An index of the migratory exposure. Options are Low, Moderate, High, or "N/A"
- d_score
Total score from section D the modelled response to climate change section
- b_c_score
Total score from sections B and C the indirect exposure, sensitivity and adaptive capacity sections
- vuln_df
Answers to each question plus any comments. Numerical scores correspond to: -1) Unknown, 0) Neutral, 1) Somewhat increase, 2) Increase, 3) Greatly increase. If there are 2 numbers then two boxes were checked and the average of the scores was used in the total score
- n_b_factors
Number of factors scored in section B
- n_c_factors
Number of factors scored in section C
- n_d_factors
Number of factors scored in section D
- slr_vuln
Whether the species' vulnerability to sea level rise and dispersal limitations led to an index of EV
Details
Options for tax_grp are Vascular Plant, Nonvascular Plant, Lichen,
Invert-Insect, Invert-Mollusk, Invert-Other, Fish, Amphibian, Reptile,
Mammal, Bird
Examples
# Setup
library(sf)
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 = read_sf(file.path(base_pth, "rng_poly.shp"), agr = "constant"),
scale_poly = 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
)
#> 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
#> Warning: More than 10% of the range change raster does not match the expected values.
#> Is the classification table correct?
#> Finalizing outputs
# vulnerability factor table with score 1 (somewhat increase vulnerability)
# for all factors
vuln <- make_vuln_df("test_species", val1 = 1, mig = 1)
v <- calc_vulnerability(spat_res$spat_table, vuln, "Bird")
#> calculating vulnerability index RCP 4.5
#> finished vulnerability
#> calculating vulnerability index RCP 8.5
#> finished vulnerability
# With protected areas
spat_res <- analyze_spatial(
range_poly = read_sf(file.path(base_pth, "rng_poly.shp"), agr = "constant"),
scale_poly = read_sf(file.path(base_pth, "assess_poly.shp"), agr = "constant"),
protected_poly = read_sf(file.path(base_pth, "protected_areas.shp")),
hs_rast = terra::rast(c(file.path(base_pth, "rng_chg_45.tif"),
file.path(base_pth, "rng_chg_85.tif"))),
clim_vars_lst = clim_vars,
hs_rcl = matrix(c(-1, 0, 1, 1, 2, 3), ncol = 2),
scenario_names = scn_nms
)
#> Checking files
#> Preparing polygon 'Climate Data Extext'
#> Preparing polygon 'Assessment Area'
#> Preparing polygon 'Range'
#> Clipping 'Range' to 'Climate Data Extent'
#> Clipping 'Range' to 'Assessment Area'
#> Assessing local climate exposure
#> Assessing thermal & hydrological niches
#> Assessing modelled range response to climate change
#> Warning: More than 10% of the range change raster does not match the expected values.
#> Is the classification table correct?
#> Preparing polygon 'Protected Areas'
#> Clipping 'Protected Areas' to 'Assessment Area'
#> Transforming polygon 'Protected Areas'
#> Finalizing outputs
# Fill in vulnerability Questions
vuln <- make_vuln_df("test_species")
vuln$Value1[3:19] <- c(0, 0, 1, 0, -1, -1, -1, -1, 0, 0, 1, 0, 0, 1, 0, 0, 0)
# include a second value to reflect uncertainty and trigger a monte carlo to
# determine confidence
vuln$Value2[3:5] <- c(2, 0, 0)
v <- calc_vulnerability(spat_res$spat_table, vuln, "Bird")
#> calculating vulnerability index RCP 4.5
#> performing monte carlo
#> finished vulnerability
#> calculating vulnerability index RCP 8.5
#> performing monte carlo
#> finished vulnerability
v$vuln_df[[1]] %>% print(n = Inf)
#> # A tibble: 29 × 14
#> Species Code Question Max_Value is_spatial Value1 Value2 Value3 Value4
#> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <lgl> <lgl>
#> 1 test_species Z2 "Is the … NA NA -1 NA NA NA
#> 2 test_species Z3 "Is the … NA NA -1 NA NA NA
#> 3 test_species B1 "Exposur… 3 0 0 2 NA NA
#> 4 test_species B2a "Natural… 3 0 0 0 NA NA
#> 5 test_species B2b "Anthrop… 3 0 1 0 NA NA
#> 6 test_species B3 "Predict… 2 0 0 NA NA NA
#> 7 test_species C1 "Dispers… 3 0 -1 NA NA NA
#> 8 test_species C2aii "Physiol… 3 1 -1 NA NA NA
#> 9 test_species C2bii "Physiol… 3 0 0 NA NA NA
#> 10 test_species C2c "Depende… 2 0 0 NA NA NA
#> 11 test_species C2d "Depende… 3 0 1 NA NA NA
#> 12 test_species C3 "Restric… 2 0 0 NA NA NA
#> 13 test_species C4a "Depende… 2 0 0 NA NA NA
#> 14 test_species C4b "Dietary… 2 0 1 NA NA NA
#> 15 test_species C4c "Pollina… 2 0 -1 NA NA NA
#> 16 test_species C4d "Depende… 2 0 0 NA NA NA
#> 17 test_species C4e "Sensiti… 2 0 0 NA NA NA
#> 18 test_species C4f "Sensiti… 2 0 -1 NA NA NA
#> 19 test_species C4g "Forms p… 2 0 -1 NA NA NA
#> 20 test_species C5a "Measure… 2 0 -1 NA NA NA
#> 21 test_species C5b "Occurre… 2 0 -1 NA NA NA
#> 22 test_species C5c "Reprodu… 2 0 -1 NA NA NA
#> 23 test_species C6 "Phenolo… 2 0 -1 NA NA NA
#> 24 test_species D1 "Documen… 3 0 -1 NA NA NA
#> 25 NA C2ai NA NA NA 1 NA NA NA
#> 26 NA C2bi NA NA NA 2 NA NA NA
#> 27 NA D2 NA NA NA 2 NA NA NA
#> 28 NA D3 NA NA NA 2 NA NA NA
#> 29 NA D4 NA NA NA 1 NA NA NA
#> # ℹ 5 more variables: comment <chr>, evidence <chr>, N <int>, exp <dbl>,
#> # score <dbl>