Type:	Package
Title:	Forecasting Tipping Points at the Community Level
Version:	1.3.1
Maintainer:	Duncan O'Brien <duncan.a.obrien@gmail.com>
Description:	Rolling and expanding window approaches to assessing abundance based early warning signals, non-equilibrium resilience measures, and machine learning. See Dakos et al. (2012) <doi:10.1371/journal.pone.0041010>, Deb et al. (2022) <doi:10.1098/rsos.211475>, Drake and Griffen (2010) <doi:10.1038/nature09389>, Ushio et al. (2018) <doi:10.1038/nature25504> and Weinans et al. (2021) <doi:10.1038/s41598-021-87839-y> for methodological details. Graphical presentation of the outputs are also provided for clear and publishable figures. Visit the 'EWSmethods' website for more information, and tutorials.
License:	MIT + file LICENSE
Encoding:	UTF-8
LazyData:	true
Imports:	curl, egg, ggplot2, gtools, forecast, foreach, infotheo, mAr, moments, rEDM (≥ 1.15.0), reticulate, scales, zoo
RoxygenNote:	7.3.1
URL:	https://github.com/duncanobrien/EWSmethods, https://duncanobrien.github.io/EWSmethods/
BugReports:	https://github.com/duncanobrien/EWSmethods/issues
Suggests:	devtools, doParallel, knitr, fs, parallel, rmarkdown, testthat (≥ 3.0.0)
VignetteBuilder:	knitr
Depends:	R (≥ 4.4)
Config/testthat/edition:	3
NeedsCompilation:	no
Packaged:	2024-05-15 16:00:25 UTC; ul20791
Author:	Duncan O'Brien [aut, cre, cph], Smita Deb [aut], Sahil Sidheekh [aut], Narayanan Krishnan [aut], Partha Dutta [aut], Christopher Clements [aut]
Repository:	CRAN
Date/Publication:	2024-05-15 16:20:02 UTC

Three Recovering Cod Populations

Description

A dataset containing three simulated cod populations. Community 1 does not recovery whereas Community 100 and 200 do.

Usage

CODrecovery

Format

A list of three dataframes with 191 rows and 6 variables each:

community_id

the identity of the simulated community

time

time index

biomass

population total biomass

mean.size

average length of cod individuals

sd.size

variation in length of cod individuals

inflection_pt

the time index where transition occurs

Source

Clements C., McCarthy M., Blanchard J. (2019) Early warning signals of recovery in complex systems. Nature Communications, 10:1681.

Examples

data("CODrecovery", package = "EWSmethods")

cod_data <- CODrecovery$scenario2

Calculate Fisher Information

Description

Uses a multivariate array of time series to estimate Fisher information following the approach of Karunanithi et al. (2010).

Usage

FI(data, sost, winsize = 50, winspace = 1, TL = 90)

Arguments

Value

A list containing three objects:

Examples

#Load the multivariate simulated
#dataset `simTransComms`

data(simTransComms)

#Estimate the size-of-states for each
#time series in the first community.
#This is typically suggested
#to be the standard deviation of a
#reference period or the entire time
#series

eg.sost <- t(apply(simTransComms$community1[,3:7], MARGIN = 2, FUN = sd))
 #transpose required to ensure a 1 x n matrix

egFI <- FI(data = simTransComms$community1[1:50,2:7],
sost =  eg.sost,
winsize = 10,
winspace = 1,
TL = 90)

Information Imbalance

Description

Estimates the information imbalance of two hypothesised linked system measurements for a given scalar ('alpha').

Usage

II(X, Y, tau = 1, alpha = 1, k = 1, method = "euclidean")

Arguments

Value

Information imbalance

Source

Del Tatto, V., Bueti, D. & Laio, A. (2024) Robust inference of causality in high-dimensional dynamical processes from the Information Imbalance of distance ranks. PNAS 121 (19) e2317256121.

Examples

#Load the multivariate simulated
#dataset `simTransComms`

data(simTransComms)

#Embed the spp_2 and spp_5 of the third community

embedX <- embed_ts(X = simTransComms$community3[,c("time","spp_2")],
E = 5, tau = 1)

embedY <- embed_ts(X = simTransComms$community3[,c("time","spp_5")],
E = 5, tau = 1)

#Estimate the forward information imbalance
#between spp_2 and spp_5

egII_for <- II(X = embedX[,-1], Y = embedY[,-1],
tau = 1, alpha = 1, k = 5)

#Estimate the reverse information imbalance
#between spp_2 and spp_5

egII_rev <- II(X = embedY[,-1], Y = embedX[,-1],
tau = 1, alpha = 1, k = 5)

Python Removal

Description

Removes ewsnet_init() downloaded Anaconda binaries and environments.

Usage

conda_clean(envname, conda_path = reticulate::miniconda_path(), auto = FALSE)

Arguments

Value

Does not return an object as is cleaning Python and its environments.

Examples

#Prior to running `conda_clean()`, you must restart
#your R session to detach any activated environments

conda_clean("EWSNET_env", auto = TRUE)

Path to Model Weights

Description

The default path for EWSNet model weights to use. Is the location of the EWSmethods package. If you'd like to instead set your own path, ewsnet_reset() contains the argument weights_path to do so.

Usage

default_weights_path()

Value

No return value, called for reference.

Deseason Seasonal Time Series

Description

Removes seasonal signals from time series using either averaging or time series decomposition methods. Three decomposition methods are available: traditional decompostion, loess decomposition and X11 decompostion.

Usage

deseason_ts(
  data,
  increment = c("month", "year", "week", "day"),
  method = c("average", "decompose", "stl"),
  order = NULL
)

Arguments

Value

Dataframe of deseasoned time series.

Examples

#Generate five random monthly time series
#of 5 years length.

spp_data <- matrix(nrow = 5*12, ncol = 5)
spp_data <- sapply(1:dim(spp_data)[2], function(x){
spp_data[,x] <- rnorm(5*12,mean=20,sd=5)})
multi_spp_data <- cbind("time" =
 seq(as.Date('2000/01/01'), as.Date('2004/12/01'), by="month"),
   as.data.frame(spp_data))

#Deseason using time series
#decomposition.

decomp_dat <- deseason_ts(data = multi_spp_data,
increment = "month",
method = "decompose",
order = "ymd")

#Deseason using loess

decomp_dat <- deseason_ts(data = multi_spp_data,
increment = "month",
method = "stl",
order = "ymd")

Detrend Time Series

Description

Removes directional signals from time series using loess, linear regression or gaussian detrending.

Usage

detrend_ts(data, method = "linear", bandwidth = NULL, span = 0.25, degree = 2)

Arguments

Value

Dataframe of deseasoned time series.

Examples

#Generate five random monthly time series
#of 5 years length.

spp_data <- matrix(nrow = 5*12, ncol = 5)
spp_data <- sapply(1:dim(spp_data)[2], function(x){
spp_data[,x] <- rnorm(5*12,mean=20,sd=5)})
multi_spp_data <- cbind("time" =
 seq(as.Date('2000/01/01'), as.Date('2004/12/01'), by="month"),
   as.data.frame(spp_data))

detrend_dat <- detrend_ts(data = multi_spp_data,
method = "gaussian",
bandwidth = 2)

Construct an Embedded Timeseries

Description

Embeds timeseries given an embedding dimension ('E') and a time lag ('tau').

Usage

embed_ts(X, E, tau = 1, sample_times = NULL)

Arguments

Value

A matrix where the first column is last time index of the window and the second column is the estimated index value.

Examples

#Load the multivariate simulated
#dataset `simTransComms`

data(simTransComms)

#Embed one timeseries by 5 time points

eg_embed <- embed_ts(X = simTransComms$community2[,2:3],
E = 5, tau = 1)

EWSNet Finetune

Description

Communicates with EWSNet (https://ewsnet.github.io), a deep learning framework for modelling and anticipating regime shifts in dynamical systems, and finetunes the model to match the inputted training data. This overwrites the Pretrained weights bundled with EWSmethods. See reset_ewsnet() on how to reset these trained weights.

Usage

ewsnet_finetune(
  x,
  y,
  scaling = TRUE,
  envname,
  weights_path = default_weights_path()
)

Arguments

Value

No return value, called for side effects.

Examples

#Activate python environment (only necessary
#on first opening of R session).

## Not run: 
ewsnet_init(envname = "EWSNET_env")

## End(Not run)

#A dummy dataset of a hedgerow bird population
#monitored over 50 years that needs to be tuned.

abundance_data <- data.frame(time = seq(1:50),
 abundance = rnorm(50,mean = 20))

#Generate training data (this is random data as
#an example).

x <- matrix(nrow = 50, ncol = 10)
x <- sapply(1:dim(x)[2], function(i){
 x[,i] <- rnorm(50,mean=20,sd=10)})

#Label each time series.

y <- sample(0:2,10,replace = TRUE)

#Finetune EWSNet.

## Not run: 
ewsnet_finetune(
 x = x,
 y = y,
 scaling = TRUE,
 envname = "EWSNET_env")
 
## End(Not run)

#Generate new EWSNet predictions.

## Not run: 
pred <- ewsnet_predict(
 abundance_data$abundance,
 scaling = TRUE,
 ensemble = 15,
 envname = "EWSNET_env")
 
## End(Not run)

EWSNet Initialisation

Description

Prepares your R session for communicating with Python. This function first searches your computer for an appropriate Python environment and activates it, importing the vital Python packages required. If no appropriate Python install or environment is found, after asking permission, miniconda is downloaded and an environment created.

Usage

ewsnet_init(
  envname,
  conda_path = reticulate::miniconda_path(),
  pip_ignore_installed = FALSE,
  auto = FALSE
)

Arguments

Value

Does not return an object as is simply preparing your R session.

Examples

## Not run: 
ewsnet_init(envname = "EWSNET_env", auto = FALSE)

## End(Not run)
#Common errors at this stage result from 'reticulate's
#behaviour. For example, conflicts between 'ewsnet_init'
#and RETICULATE_PYTHON may occur if run inside a
#RStudio R project. To fix this, navigate to
#Preferences -> Python, untick 'Automatically
#activate project-local Python environments'
#and restart R.

#if this fails due to timeout, you may need to
#increase the timeout length using something
#like below:

options(timeout = max(300, getOption("timeout")))


## Not run: 
reticulate::py_config()

## End(Not run)
#If successful, 'EWSNET_env forced by use_python
#function' will be printed.

EWSNet Predict

Description

Communicates with EWSNet (https://ewsnet.github.io), a deep learning framework for modelling and anticipating regime shifts in dynamical systems, and returns the model's prediction for the inputted univariate time series.

Usage

ewsnet_predict(
  x,
  scaling = TRUE,
  ensemble = 25,
  envname,
  weights_path = default_weights_path()
)

Arguments

Value

A dataframe of EWSNet predictions. Values represent the estimated probability that the quoted event will occur.

Examples

#A dummy dataset of a hedgerow bird population
#monitored over 50 years.

abundance_data <- data.frame(time = seq(1:50),
 abundance = rnorm(50,mean = 20))

#Activate python environment (only necessary
#on first opening of R session).

## Not run: 
ewsnet_init(envname = "EWSNET_env")

## End(Not run)

#Generate EWSNet predictions.

## Not run: 
pred <- ewsnet_predict(
 abundance_data$abundance,
 scaling = TRUE,
 ensemble = 15,
 envname = "EWSNET_env")
 
## End(Not run)

Reset EWSNet Model Weights

Description

Restores EWSNet model weights to the pretrained defaults published at https://ewsnet.github.io. This is vital on first use of EWSNet as no model weights are provided with 'EWSmethods'. The use of this function may also be necessary after finetuning to reset the model.

Usage

ewsnet_reset(
  weights_path = default_weights_path(),
  remove_weights = FALSE,
  auto = FALSE
)

Arguments

Value

No return value, called for side effects.

Examples

# on first use of EWSNet via `EWSmethods`
ewsnet_reset(remove_weights = FALSE, auto = TRUE,
weights_path = tempdir())


# if this fails due to timeout, you may need to
# increase the timeout length using something
# like below:

options(timeout = max(300, getOption("timeout")))



# to remove all downloaded weights
ewsnet_reset(remove_weights = TRUE, auto = TRUE,
weights_path = tempdir())

Information Gain

Description

Estimates the information imbalance of two hypothesised linked system measurements using distance ranks.

Usage

imbalance_gain(info_imbalance)

Arguments

Value

A dataframe of the optimal alpha and the estimated information gain.

Source

Del Tatto, V., Bueti, D. & Laio, A. (2024) Robust inference of causality in high-dimensional dynamical processes from the Information Imbalance of distance ranks. PNAS 121 (19) e2317256121.

Examples

#Load the multivariate simulated
#dataset `simTransComms`

data(simTransComms)

#Embed the spp_4 and spp_3 of the third community

embedX <- embed_ts(X = simTransComms$community3[,c("time","spp_4")],
E = 5, tau = 1)

embedY <- embed_ts(X = simTransComms$community3[,c("time","spp_3")],
E = 5, tau = 1)

alphas <- seq(from = 0, to = 1, by = 0.1)

#Estimate the forward information imbalance
#between spp_4 and spp_3

egII_for <- tuneII(target = embedX[,-1], columns = embedY[,-1],
tau = 1, alphas = alphas, k = 5)

#Estimate the reverse information imbalance
#between spp_4 and spp_3

egII_rev <- tuneII(target = embedY[,-1], columns = embedX[,-1],
tau = 1, alphas = alphas, k = 5)

#Calculate the information gain
igain_for <- imbalance_gain(egII_for)
igain_rev <- imbalance_gain(egII_rev)

Multivariate Jacobian Index Estimated From Multivariate Autocorrelation Matrix

Description

Estimate the dominant Jacobian eigenvalue of a multivariate time series using autocorrelated stochastic differential equations

Usage

multiAR(data, scale = TRUE, winsize = 50, p = 1, dt = 1)

Arguments

Value

A dataframe where the first column is last time index of the window and the second column is the estimated index value. A value <1.0 indicates stability, a value >1.0 indicates instability.

Source

Williamson and Lenton (2015). Detection of bifurcations in noisy coupled systems from multiple time series. Chaos, 25, 036407

Examples

#Load the multivariate simulated
#dataset `simTransComms`

data(simTransComms)

#Subset the second community prior to the transition

pre_simTransComms <- subset(simTransComms$community2,time < inflection_pt)

#Estimate the univariate stability index for the first species in
#the second community

egarJ <- multiAR(data = pre_simTransComms[,2:7],
winsize = 25, dt = 1)

Multivariate Early Warning Signal Assessment

Description

A single function for performing early warning signal (EWS) assessment on multivariate systems where multiple time series have been measured. Both methods of EWS assessment can be performed (rolling or expanding windows) with the assessments returned as a dataframe. The two methods of dimension reduction used to perform these assessments are Principal Component Analysis and Maximum/Minimum Autocorrelation Factors.

Usage

multiEWS(
  data,
  metrics = c("meanAR", "maxAR", "meanSD", "maxSD", "eigenMAF", "mafAR", "mafSD",
    "pcaAR", "pcaSD", "eigenCOV", "maxCOV", "mutINFO"),
  method = c("expanding", "rolling"),
  winsize = 50,
  burn_in = 5,
  threshold = 2,
  tail.direction = "one.tailed"
)

Arguments

Value

A list containing up to two objects: EWS outputs through time (EWS), and an identifier string (method).

Examples

#Generate a random five species, non-transitioning
#ecosystem with 50 years of monitoring data.

spp_data <- matrix(nrow = 50, ncol = 5)
spp_data <- sapply(1:dim(spp_data)[2], function(x){
 spp_data[,x] <- rnorm(50,mean=20,sd=5)})
 multi_spp_data <- as.data.frame(cbind("time" =
 seq(1:50), spp_data))

#Rolling window early warning signal assessment of
#the ecosystem.

roll_ews <- multiEWS(
 data = multi_spp_data,
 method = "rolling",
 winsize = 50)

#Expanding window early warning signal assessment of
#the ecosystem.

exp_ews <- multiEWS(
 data = multi_spp_data,
 method = "expanding",
 burn_in = 10)

Multivariate S-map Jacobian index function

Description

Calculate a stability metric from the multivariate s-map estimated Jacobian

Usage

multiJI(data, winsize = 50, theta_seq = NULL, scale = TRUE)

Arguments

Value

A dataframe where the first column is last time index of the window and the second column is the estimated index value. A value <1.0 indicates stability, a value >1.0 indicates instability.

Source

Ushio, M., Hsieh, Ch., Masuda, R. et al. (2018) Fluctuating interaction network and time-varying stability of a natural fish community. Nature 554, 360–363.

Examples

#Load the multivariate simulated
#dataset `simTransComms`

data(simTransComms)

#Subset the third community prior to the transition

pre_simTransComms <- subset(simTransComms$community3,time < inflection_pt)

#Estimate the stability index for the third community
#(trimmed for speed)

egJI <- multiJI(data = pre_simTransComms[1:10,2:5],
winsize = 75)

Multivariate S-map Inferred Jacobian

Description

Performs the S-map on a multivariate time series to infer the Jacobian matrix at different points in time across thetas.

Usage

multi_smap_jacobian(data, theta_seq = NULL, scale = TRUE)

Arguments

Value

A list containing three objects:

Source

Medeiros, L.P., Allesina, S., Dakos, V., Sugihara, G. & Saavedra, S. (2022) Ranking species based on sensitivity to perturbations under non-equilibrium community dynamics. Ecology Letters, 00, 1– 14.

Examples

#Load the multivariate simulated
#dataset `simTransComms`

data("simTransComms")

#Subset the third community prior to the transition

pre_simTransComms <- subset(simTransComms$community3,time < inflection_pt)


#Estimate the Jacobian using s-map (typically only
#the final estimate is informative)
est_jac <- multi_smap_jacobian(pre_simTransComms[1:10,2:7])

Multivariate Variance Index function

Description

Calculate a multivariate variance following Brock, W. A., and S. R. Carpenter. 2006. Variance as a leading indicator of regime shift in ecosystem services. Ecology and Society 11(2): 9.

Usage

mvi(data, winsize = 50)

Arguments

Value

A matrix where the first column is last time index of the window and the second column is the estimated index value.

Source

Brock, W.A. & Carpenter, S.R. (2006) Variance as a leading indicator of regime shift in ecosystem services. Ecology and Society 11(2): 9.

Examples

#Load the multivariate simulated
#dataset `simTransComms`

data(simTransComms)

#Estimate the MVI for the second community

egMVI <- mvi(data = simTransComms$community2[,2:7],
winsize = 10)

Significance Testing of Rolling Window Early Warning Signals

Description

A function for identifying whether a warning has been generated from rolling early warning signal data using permutation tests. If a parallel connection is setup via parallel or future prior to usage of perm_rollEWS(), then the function is parallelised.

Usage

perm_rollEWS(
  data,
  metrics,
  winsize = 50,
  variate = c("uni", "multi"),
  perm.meth = "arima",
  iter = 500
)

Arguments

Value

A list containing up to two objects: EWS outputs through time (EWS), and an identifier string (method).

Examples

data(simTransComms)

#Permute p value for a univariate
#time series using resampling

#(data trimmed for speed)
perm_uni <- perm_rollEWS(
data = simTransComms$community1[1:10,2:3],
 winsize = 75,
 variate = "uni",
 metrics = c("ar1", "SD", "skew"),
 perm.meth = "sample",
 iter = 25)

 #Permute p value for a multivariate
#community using a red.noise process


#if parallelisation desired,
#this can be achieved using the
#below code
cl <- parallel::makeCluster(2)



doParallel::registerDoParallel(cl)


perm_multi <- perm_rollEWS(
data = simTransComms$community1[1:10,2:7],
 winsize = 75,
 variate = "multi",
 metrics = c("meanAR", "maxAR", "meanSD"),
 perm.meth = "red.noise",
 iter = 25)


parallel::stopCluster(cl)

Plot an EWSmethods object

Description

A function for visualising the output of uniEWS or multiEWS using ggplot2 inspired figures.

Usage

## S3 method for class 'EWSmethods'
plot(
  x,
  ...,
  y_lab = "Generic indicator name",
  trait_lab = "Generic trait name",
  trait_scale = 1000
)

Arguments

Value

A ggplot2 object.

Examples

data(simTransComms)

#Subset the third community prior to the transition

pre_simTransComms <- subset(simTransComms$community3,time < inflection_pt)

#Perform multivariate EWS assessments
roll_ews <- multiEWS(
 data = pre_simTransComms[,2:7],
 method = "rolling",
 winsize = 50)

 #Plot outcome
## Not run: 
 plot(roll_ews)

## End(Not run)

#Perform univariate EWS assessments on
#simulated data with traits

abundance_data <- data.frame(time = seq(1:50),
 abundance = rnorm(50,mean = 20),
 trait = rnorm(50,mean=1,sd=0.25))

trait_ews <- uniEWS(
 data = abundance_data[,1:2],
 metrics = c("ar1","SD","trait"),
 method = "expanding",
 trait = abundance_data[,3],
 burn_in = 10)

#Plot outcome
## Not run: 
 plot(trait_ews, y_lab = "Abundance",
 trait_lab = "Trait value",
 trait_scale = 10)

## End(Not run)

Three Simulated Transitioning Communities.

Description

A dataset containing three simulated five species communities stressed through a critical transition.

Usage

simTransComms

Format

A list of three dataframes with 301 rows and 7 variables each:

community_id

the identity of the simulated community

time

time index

spp_1

density of species 1

spp_2

density of species 1

spp_3

density of species 1

spp_4

density of species 1

spp_5

density of species 1

inflection_pt

the time index where transition occurs

Examples

data("simTransComms", package = "EWSmethods")

community_data <- simTransComms$community1

Information Imbalance Across Alphas

Description

Estimates the information imbalance of two hypothesised linked system measurements using distance ranks.

Usage

tuneII(columns, target, tau, alphas, k = 1, method = "euclidean")

Arguments

Value

A dataframe of alphas and the estimate information imbalance

Source

Del Tatto, V., Bueti, D. & Laio, A. (2024) Robust inference of causality in high-dimensional dynamical processes from the Information Imbalance of distance ranks. PNAS 121 (19) e2317256121.

Examples

#Load the multivariate simulated
#dataset `simTransComms`

data(simTransComms)

#Embed the spp_2 and spp_5 of the third community

embedX <- embed_ts(X = simTransComms$community3[,c("time","spp_2")],
E = 5, tau = 1)

embedY <- embed_ts(X = simTransComms$community3[,c("time","spp_5")],
E = 5, tau = 1)

alphas <- seq(from = 0, to = 1, by = 0.1)


#if parallelisation desired,
#this can be achieved using the
#below code
cl <- parallel::makeCluster(2)



doParallel::registerDoParallel(cl)


#Estimate the forward information imbalance
#between spp_2 and spp_5

egII_for <- tuneII(target = embedX[,-1], columns = embedY[,-1],
tau = 1, alphas = alphas, k = 5)

#Estimate the reverse information imbalance
#between spp_2 and spp_5

egII_rev <- tuneII(target = embedY[,-1], columns = embedX[,-1],
tau = 1, alphas = alphas, k = 5)


parallel::stopCluster(cl)

Univariate Jacobian Index Estimated From Univariate Autocorrelation Matrix

Description

Estimate the dominant Jacobian eigenvalue of a univariate time series using autocorrelated stochastic differential equations

Usage

uniAR(data, scale = TRUE, winsize = 50, p = 1, dt = 1)

Arguments

Value

A dataframe where the first column is last time index of the window and the second column is the estimated index value. A value <1.0 indicates stability, a value >1.0 indicates instability.

Examples

#Load the multivariate simulated
#dataset `simTransComms`

data(simTransComms)

#Subset the second community prior to the transition

pre_simTransComms <- subset(simTransComms$community2,time < inflection_pt)

#Estimate the univariate stability index for the first species in
#the second community

egarJ <- uniAR(data = pre_simTransComms[,2:3],
winsize = 25, dt = 1)

Univariate Early Warning Signal Assessment

Description

A function for performing early warning signal (EWS) assessment on univariate time series. Both rolling and expanding window methods of EWS assessment can be performed with the assessments returned as a dataframe.

Usage

uniEWS(
  data,
  metrics,
  method = c("expanding", "rolling"),
  winsize = 50,
  burn_in = 5,
  threshold = 2,
  tail.direction = "one.tailed",
  trait = NULL
)

Arguments

Value

A list containing up to two objects: EWS outputs through time (EWS), and an identifier string (method).

Examples

#A dummy dataset of a hedgerow bird population over
#25 years where both the number of individuals and
#the average bill length has been measured.

abundance_data <- data.frame(time = seq(1:25),
 abundance = rnorm(25,mean = 20),
 trait = rnorm(25,mean=1,sd=0.5))

#The early warning signal metrics to compute.

ews_metrics <- c("SD","ar1","skew")

#Rolling window early warning signal assessment of
#the bird abundance.

roll_ews <- uniEWS(
 data = abundance_data[,1:2],
 metrics =  ews_metrics,
 method = "rolling",
 winsize = 50)

#Expanding window early warning signal assessment of
#the bird abundance (with plotting).

exp_ews <- uniEWS(
 data = abundance_data[,1:2],
 metrics = ews_metrics,
 method = "expanding",
 burn_in = 10)

#Expanding window early warning signal assessment of
#the bird abundance incorporating the trait
#information.

ews_metrics_trait <- c("SD","ar1","trait")

trait_exp_ews <- uniEWS(
 data = abundance_data[,1:2],
 metrics = ews_metrics_trait,
 method = "expanding",
 burn_in = 10,
 trait = abundance_data$trait)

Univariate S-map Jacobian index function

Description

Calculate a stability metric from the s-map estimated Jacobian of a univariate time series

Usage

uniJI(data, winsize = 50, theta_seq = NULL, E = 1, tau = NULL, scale = TRUE)

Arguments

Value

A dataframe where the first column is last time index of the window and the second column is the estimated index value. A value <1.0 indicates stability, a value >1.0 indicates instability.

Source

Grziwotz, F., Chang, C.-W., Dakos, V., van Nes, E.H., Schwarzländer, M., Kamps, O., et al. (2023). Anticipating the occurrence and type of critical transitions. Science Advances, 9.

Examples

#Load the multivariate simulated
#dataset `simTransComms`

data(simTransComms)

#Subset the second community prior to the transition

pre_simTransComms <- subset(simTransComms$community2,time < inflection_pt)

#Estimate the univariate stability index for the first species in
#the second community

egJI <- uniJI(data = pre_simTransComms[1:25,2:3],
winsize = 75, E = 3)

Univariate S-map Inferred Jacobian

Description

Performs the S-map on a univariate time series to infer the Jacobian matrix at different points in time across thetas.

Usage

uni_smap_jacobian(data, theta_seq = NULL, E = 1, tau = NULL, scale = TRUE)

Arguments

Value

A list containing three objects:

Source

Grziwotz, F., Chang, C.-W., Dakos, V., van Nes, E.H., Schwarzländer, M., Kamps, O., et al. (2023). Anticipating the occurrence and type of critical transitions. Science Advances, 9.

Examples

#Load the multivariate simulated
#dataset `simTransComms`

data("simTransComms")

#Subset the second community prior to the transition

pre_simTransComms <- subset(simTransComms$community2,time < inflection_pt)
winsize <- round(dim(pre_simTransComms)[1] * 50/100)

#Estimate the Jacobian for the first 50 timepoints of the
#second species using s-map
est_jac <- uni_smap_jacobian(pre_simTransComms[1:50,2:3])