Network-scale cumulative effects assessments

Assessment of cumulative effects and related metrics using the Beauchesne et al. 2021 method.

Usage

ncea(
  drivers,
  vc,
  sensitivity,
  metaweb,
  trophic_sensitivity,
  w_d = 0.5,
  w_i = 0.25,
  exportAs = "stars"
)

cea_binary(effect)

triads(metaweb, trophic_sensitivity)

cea_pathways(effect, vc)

ncea_pathways(vc_id, motifs)

ncea_pathways_(direct_pathways, motifs)

ncea_motifs(direct_effect, indirect_pathways)

ncea_effects(motif_summary, w_d = 0.5, w_i = 0.25)

get_species_contribution(motif_effects)

get_direct_indirect(motif_effects)

get_net(motif_effects)

get_cekm_ncea(motif_effects, vc)

ncea_split(
  drivers,
  vc,
  sensitivity,
  metaweb,
  trophic_sensitivity,
  w_d = 0.5,
  w_i = 0.25,
  output = "output/ncea",
  niter = NULL,
  run = NULL
)

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
metaweb: matrix of valued component by valued component describing the binary interations structuring the network of valued components
trophic_sensitivity: data.frame of trophic sensitivities, default from Beauchesne. Available as data package with data(trophic_sensitivity)
w_d, w_i: weight for the direct (w_d) and indirect (w_i) modules when calculating network-scale cea scores; w_d + 2*w_i should be equal to 1.
exportAs: string, the type of object that should be created, either multiple "data.frame" or "stars" objects
effect: TODO
vc_id: TODO
motifs: TODO
direct_pathways: TODO
direct_effect: TODO
indirect_pathways: TODO
motif_summary: TODO
motif_effects: TODO
output: TODO
niter: TODO
run: TODO

Functions

cea_binary(): transform effects assessment into binary 2D matrix to assess the presence of an effect to a valued component in a specific grid cell
triads(): assess all triads of interest from metaweb and attach trophic sensitivities
cea_pathways(): pathways of direct effect
ncea_pathways(): assess all triads of interest from metaweb
ncea_pathways_(): apply ncea_pathways to get pathways of indirect effect and trophic sensitivity for all cells
ncea_motifs(): get effects of drivers for species in all motifs in each cell
ncea_effects(): evaluate effects for all motifs using trophic sensitivity and effect weights
get_species_contribution(): get contribution of species to indirect effects
get_direct_indirect(): get direct and indirect effects of drivers
get_net(): get net effects of drivers
get_cekm_ncea(): get effects per km2
ncea_split(): split the assessment in smaller parts for larger analyses that run into memory issues and need to be run in parallel

Examples

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

if (FALSE) {
# Network-scale effects
beauchesne <- ncea(drivers, vc, sensitivity, metaweb, trophic_sensitivity)
plot(beauchesne$net)
plot(beauchesne$direct)
plot(beauchesne$indirect)
}