Cumulative effects assessments

Assessment of cumulative effects using the Halpern et al. 2008 method.

Usage

cea(drivers, vc, sensitivity, exportAs = "list")

get_cekm_cea(dat, vc)

Arguments

drivers: distribution and intensity of environmental drivers as stars object
vc: distribution of valued components as stars object
sensitivity: matrix of environmental drivers and valued component, with same name as those used in drivers and vc
exportAs: string, the type of object that should be created, either a "list" or a "stars" object.
dat: TODO

Functions

get_cekm_cea(): get effects per km2

Examples

# Data
drivers <- rcea:::drivers
vc <- rcea:::vc
sensitivity <- rcea:::sensitivity

# Species-scale effects
(halpern <- cea(drivers, vc, sensitivity, "stars"))
#> stars object with 3 dimensions and 20 attributes
#> attribute(s):
#>             Min.     1st Qu.     Median      Mean   3rd Qu.      Max.  NA's
#> vc1   0.00000000 0.197844533 0.34959492 0.3374289 0.4932443 0.9801095 19256
#> vc2   0.00000000 0.000000000 0.11506835 0.1974660 0.3511900 0.9098666 19357
#> vc3   0.00000000 0.028556899 0.05427342 0.2055286 0.3892334 0.9999287 18655
#> vc4   0.00000000 0.152847296 0.39069861 0.3570992 0.5125498 0.9547808 18527
#> vc5   0.00000000 0.000000000 0.27112643 0.2837859 0.4618074 0.9093915 18953
#> vc6   0.00000000 0.000000000 0.39358294 0.3081420 0.5254207 0.8704866 18680
#> vc7   0.01297907 0.125280091 0.25660111 0.2799213 0.4276007 0.8765720 18948
#> vc8   0.00000000 0.076629590 0.28697593 0.3023585 0.5139304 0.9302585 19468
#> vc9   0.00000000 0.108950922 0.22388530 0.2683110 0.4175093 0.8018385 18613
#> vc10  0.00000000 0.131755392 0.25594767 0.3054904 0.4745609 0.9928748 18404
#> vc11  0.00000000 0.136290943 0.26145624 0.2902475 0.4435404 0.9609001 17697
#> vc12  0.00000000 0.024573000 0.35095529 0.2977374 0.4931013 0.8410465 18929
#> vc13  0.00000000 0.000000000 0.06812157 0.1318422 0.2460166 0.4855436 18840
#> vc14  0.00000000 0.001754965 0.14231378 0.2371141 0.4687777 0.8972964 18938
#> vc15  0.00000000 0.103734955 0.22250580 0.2872501 0.4796972 0.9036774 19300
#> vc16  0.02732438 0.119494253 0.28250320 0.3018985 0.4581889 0.8433375 18759
#> vc17  0.00000000 0.000000000 0.13293700 0.1900736 0.2629435 0.9295225 18601
#> vc18  0.00000000 0.059879067 0.10645798 0.1628473 0.1921856 0.8944353 18169
#> vc19  0.00000000 0.015881054 0.29676611 0.2664306 0.4466082 0.8374260 17974
#> vc20  0.00000000 0.000000000 0.11460514 0.1906253 0.2842038 0.8975917 18437
#> dimension(s):
#>         from to offset delta              values x/y
#> x          1 45   -100     5                NULL [x]
#> y          1 45    125    -5                NULL [y]
#> drivers    1 10     NA    NA driver1,...,driver9    
plot(halpern)

halpern <- merge(halpern, name = "vc") |>
  split("drivers")
plot(halpern)

# do not work
# get_cekm_cea(halpern, vc)