ecoregime 
ecoregime
ecoregime implements the EDR framework to characterize and compare groups of ecological trajectories in multidimensional spaces defined by ecosystem state variables. The EDR framework was introduced in:
ecoregime can be used to assess ecological resilience using ecological dynamic regimes as the system’s reference. This approach was introduced in:
You can install ecoregime via CRAN:
You can also install the development version of ecoregime with:
You can get an overview about its functionality and the workflow of the EDR framework in the package documentation and vignettes.
# Force the inclusion of the vignette in the installation
devtools::install_github("MSPinillos/ecoregime",
build_opts = c("--no-resave-data", "--no-manual"),
build_vignettes = TRUE)
# Load the package after the installation
library(ecoregime)
# Access the documentation and vignette
?ecoregime
vignette("EDR_framework", package = "ecoregime")
vignette("Resilience", package = "ecoregime")Identify and plot representative trajectories in ecological dynamic regimes.
library(ecoregime)
# Calculate state dissimilarities from a matrix of state variables (e.g., species abundances)
variables <- data.frame(EDR_data$EDR3$abundance)
d <- vegan::vegdist(variables[, -c(1:3)])
# Identify the trajectory (or site) and states in d
trajectories <- variables$traj
states <- as.integer(variables$state)
# Compute RETRA-EDR
RT <- retra_edr(d = d, trajectories = trajectories, states = states,
minSegs = 5)
# Plot representative trajectories of the EDR
plot(x = RT, d = d, trajectories = trajectories, states = states, select_RT = "T4",
traj.colors = "lightblue", RT.colors = "orange", sel.color = "darkgreen",
link.lty = 1, asp = 1, main = "Representative trajectories - EDR")
Characterize the internal structure of ecological dynamic regimes calculating the dispersion (dDis), beta diversity (dBD), and evenness (dEve) of the individual trajectories.
# Dynamic dispersion considering trajectory "1" as a reference
dDis(d = d, d.type = "dStates", trajectories = trajectories, states = states, reference = "1")
#> dDis (ref. 1)
#> 0.4083905
# Dynamic beta diversity
dBD(d = d, d.type = "dStates", trajectories = trajectories, states = states)
#> dBD
#> 0.07946371
# Dynamic evenness
dEve(d = d, d.type = "dStates", trajectories = trajectories, states = states)
#> dEve
#> 0.842375Compare ecological dynamic regimes.
# Load species abundances and compile in a data frame
variables1 <- EDR_data$EDR1$abundance
variables2 <- EDR_data$EDR2$abundance
variables3 <- EDR_data$EDR3$abundance
all_variables <- data.frame(rbind(variables1, variables2, variables3))
# Calculate dissimilarities between every pair of states
d <- vegan::vegdist(all_variables[, -c(1:3)])
# Compute dissimilarities between EDRs:
dist_edr(d = d, d.type = "dStates",
trajectories = all_variables$traj, states = all_variables$state,
edr = all_variables$EDR, metric = "dDR", symmetrize = NULL)
#> 1 2 3
#> 1 0.0000000 0.5895458 0.6467200
#> 2 0.5700499 0.0000000 0.2768759
#> 3 0.6317846 0.5050273 0.0000000Assess ecological resilience to pulse disturbances.
# Species abundances for disturbed communities
disturbed <- EDR_data$EDR3_disturbed$abundance[disturbed_states %in% c(0, 1, 14)]
# Species abundances for disturbed and reference communities
variables$disturbed_states <- 0
disturbed_ref <- rbind(variables, disturbed)
# Calculate dissimilarities between every pair of states
d <- vegan::vegdist(disturbed_ref[, -c(1:3, 16)])
# Use one or more representative trajectories as the reference
RT_ref <- define_retra(RT$T4$Segments)
# Resistance
resistance(d = d, trajectories = disturbed_ref$traj, states = disturbed_ref$state,
disturbed_trajectories = unique(disturbed$traj),
disturbed_states = disturbed[disturbed_states == 1]$state)
#> disturbed_trajectories Rt
#> 1 31 0.9578947
#> 2 32 0.8117647
#> 3 33 0.6928105
# Amplitude
amplitude(d = d, trajectories = disturbed_ref$traj, states = disturbed_ref$state,
disturbed_trajectories = unique(disturbed$traj),
disturbed_states = disturbed[disturbed_states == 1]$state,
reference = RT_ref)
#> disturbed_trajectories reference A_abs A_rel
#> 1 31 newT 0.0275000 0.6531250
#> 2 32 newT 0.1187553 0.6308877
#> 3 33 newT 0.2675325 0.8709036
# Recovery
recovery(d = d, trajectories = disturbed_ref$traj, states = disturbed_ref$state,
disturbed_trajectories = unique(disturbed$traj),
disturbed_states = disturbed[disturbed_states == 1]$state,
reference = RT_ref)
#> disturbed_trajectories states reference Rc_abs Rc_rel
#> 1 31 16 newT -0.46424129 -0.6288659
#> 2 32 17 newT 0.07232084 0.1065781
#> 3 33 19 newT 0.24753247 0.6885903
# Net change
net_change(d = d, trajectories = disturbed_ref$traj, states = disturbed_ref$state,
disturbed_trajectories = unique(disturbed$traj),
disturbed_states = disturbed[disturbed_states == 1]$state,
reference = RT_ref)
#> disturbed_trajectories states reference NC_abs NC_rel
#> 1 31 16 newT 0.49174129 0.7357633
#> 2 32 17 newT 0.04643449 0.1132549
#> 3 33 19 newT 0.02000000 0.5000000To cite ecoregime in publications use:
Sánchez-Pinillos M, Kéfi S, De Cáceres M, Dakos V (2023). “Ecological dynamic regimes: Identification, characterization, and comparison.” Ecological Monographs, e1589. https://doi.org/10.1002/ecm.1589.
Sánchez-Pinillos M, Dakos V, Kéfi S (2024). “Ecological dynamic regimes: A key concept for assessing ecological resilience.” Biological Conservation, 110409. https://doi.org/10.1016/j.biocon.2023.110409.
Sánchez-Pinillos M (2023). ecoregime: Analysis of Ecological Dynamic Regimes. https://doi.org/10.5281/zenodo.7584943.
This project has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 891477 (RESET project).