# Alternative Stability Measures

#### Duncan O’Brien

#### 2024-08-27

Source:`vignettes/articles/resilience_measures.Rmd`

`resilience_measures.Rmd`

## About this tutorial

This tutorial introduces stability metrics distinct from critical slowing down based/Early Warning Signal indicators. The primary benefit of this collection of techniques is that they are both model and equilibrium free; they don’t assume a system is a equilibrium.

`EWSmethods`

provides three distinct approaches to
quantifying system stability. The tutorial will go through each in turn
and give examples of their usage.

Greater detail on each function can be found at the Reference page.

## Getting started

```
set.seed(123) #to ensure reproducible data
library(EWSmethods)
library(ggplot2) #for plotting
```

We will use the same multi-species community described in the Performing Early Warning Signal Assessments tutorial.

Briefly, the data object `"simTransComms"`

contains three
replicate datasets of a simulated five species community that has been
driven to transition by the introduction of an invasive species
(following Dakos 2018). The strength of impact by the invasive species
gradually increases through time between *t100* and *t200*
at which point it is held constant.

Lets load the data in:

`data("simTransComms")`

and plot one of the communities. The vertical dashed line represents a sudden transition due to the invasive species.

```
matplot(simTransComms$community1[,3:7], type = "l", xlab = "Time", ylab = "Density", main = "Transitioning five species community")
abline(v=simTransComms$community1$inflection_pt,col = "grey60", lty = "dashed")
```

Now we have a system/community that is experiencing some form of stress and we can apply univariate or multivariate stability measures to give us an idea of how the system is responding to that stress.

## 1) Index of Variability

The simplest measure provided by `EWSmethods`

is the
multivariate index of variability (MVI - Brock and Carpenter, 2006). The
MVI is represented by the square root of the dominant eigenvalue of the
covariance matrix of all species.

####
*Usage*

Provide the community data (first column being time, the remainder
being species values) to the `mvi()`

function, and specify a
`winsize`

to calculate the index over. The default
`winsize`

is 50% the total length of the time series, but we
have set `winsize = 25`

to ensure that the periods before and
after the sudden change in are distinct.

`egMVI <- mvi(data = simTransComms$community1[,2:7], winsize = 25)`

If we then plot the index, we can see that before and after the ‘inflection point’ (where the community goes through a transition) there are clear differences in stability.

What is also apparent is that the MVI begins to increase prior to the sudden change, albeit only slightly.

```
mvi_plot_data <- merge(simTransComms$community1,as.data.frame(egMVI),by="time") # combine the mvi data with the raw time series
ggplot(mvi_plot_data,aes(x = time, y = mvi)) + geom_line() + geom_vline(xintercept = mvi_plot_data$inflection_pt,col = "grey60", linetype = "dashed") + theme_bw() + xlab("Time") + ylab("MVI value")
```

## 2) S-map Jacobians

The cutting edge of stability metrics belong to approaches which
exploit empirical dynamic modelling (EDM - Sugihara *et al.*
2012). EDM exploits Takens’ embedding theorem (Takens 1981) which
suggests that an underlying latent system/attractor manifold can be
reconstructed from one or more related time series. By lag embedding two
or more time series, their interaction strength (or causal strength) can
be estimated.

This approach consequently requires no knowledge of the underlying equations nor numerical estimates of the system’s equilibrium state as the local Jacobians are directly estimated from the time series. The local Lyapunov stability is calculated as the dominant eigenvalue of the estimated Jacobian matrix.

Jacobians consist of the first-order partial derivatives for a multivariate function and are critical to resilience quantification. They are so important because they allow us to differentiate two or more functions with respect to two or more variables, something not achievable using standard derivation. Jacobian matrices therefore provide insight in to how each variable (or species) in a system’s master equation will change in the successive time step; if we know the true Jacobian, we know the future.

`EWSmethods`

provides access to two forms of s-map
estimated Jacobians, one for univariate data (following Grziwotz *et
al.* 2023) and another for multivariate data (following Ushio *et
al.* 2018).

####
*Interpretation*

A local Lyapunov stability value (the returned index value) of less
than `1`

indicates that the community tends to recover faster
from perturbations, assuming the interspecific interaction strengths and
self-regulation effects are constant. Therefore, `1`

represents a threshold above which the system is unstable and below
which the system is stable. However, any persistent increase in Jacobian
index is of concern for stability loss.

####
*Usage - Univariate*

The univariate form of Jacobian estimated stability is performed
using the `uniJI()`

function. Following Grziwotz *et
al.* (2023), `uniJI()`

estimates the strength of time
embedded relationship between a univariate time series and lagged forms
of itself.

In `uniJI()`

, we again provide community data but for a
single species (first column being time, focal species the second). We
must also provide a `winsize`

, embedding dimension
(`E`

) and `tau`

. `E`

and
`tau`

are complicated to parameterise and requires a degree
of exploration. Initially, we suggest `E`

to be in the range
1 to 6, and `tau`

in the range 3 - 12, but refer readers to
the excellent introduction by Chang *et al.* (2017) for further
details.

`eg_uniJI <- uniJI(data = simTransComms$community1[,2:3], winsize = 25, E = 3)`

Plotting the index for this species, we observe a loss of stability prior to the transition and dramatic overcompensation following it.

```
uniJI_plot_data <- merge(simTransComms$community1,eg_uniJI,by="time") # combine the mvi data with the raw time series
ggplot(uniJI_plot_data,aes(x = time, y = smap_J)) + geom_line() + geom_vline(xintercept = uniJI_plot_data$inflection_pt,col = "grey60", linetype = "dashed") + theme_bw() + xlab("Time") + ylab("Univariate stability index value")
```

Lets repeat this for all species to give us an overall idea of system resilience.

```
all_spp_uniJI <- sapply(grep("spp_",colnames(simTransComms$community1)),FUN = function(x){
if(x == 3){
uniJI(data = simTransComms$community1[,c(2,x)], winsize = 25, E = 3)
}else{
uniJI(data = simTransComms$community1[,c(2,x)], winsize = 25, E = 3)[,2]
}
}) # for each species, calculate the univariate stability index
all_spp_uniJI <- do.call("cbind",all_spp_uniJI) # merge the list in to a data.frame
names(all_spp_uniJI) <- c("time",paste("spp",1:5,sep="_")) # and rename missing columns
all_spp_plot_data <- merge(stats::reshape(data = all_spp_uniJI,
direction = "long",
varying = colnames(all_spp_uniJI)[-1],
v.names = "smap_J",
times = colnames(all_spp_uniJI)[-1],
timevar = "species"),
simTransComms$community1[,c("time","inflection_pt")], by = "time") #pivot_longer for easier plotting and merge with the inflection point data
ggplot(all_spp_plot_data,aes(x = time, y = smap_J)) + geom_line(aes(col=species)) + geom_vline(xintercept = all_spp_plot_data$inflection_pt,col = "grey60", linetype = "dashed") + theme_bw() + xlab("Time") + ylab("Univariate stability index value") + scale_colour_manual(values = c("black","#6886c4","#bfbd3d","#69c756","#e281fe")) + facet_wrap(~species)
```

This approach highlights how each species responds uniquely and can help identify the most sensitive species. I.e which are possible indicator species.

####
*Usage - Multivariate*

Conversely, to compute the multivariate form of Jacobian estimation,
we can use the function `multiJI()`

. `multiJI()`

fits a local linear model between all species which predicts the
system’s future value from the reconstructed state-space. The resulting
coefficients of this model are therefore a proxy for the interaction
strength between species.

Note `E`

and `tau`

are not required for the
`multiJI()`

form of index. For `E`

, the number of
species themselves are used for embedding, though this does limit the
minimum length of time series to being the number of species. Instead,
`theta`

is impactful, and represents the tuning parameter for
forecasts (if not provided, `multiJI()`

identifies the
optimal `theta`

using root mean square error). Conversely,
`tau`

is held constant at `-1`

as there is no self
embedding due to the index being a multivariate measure.

⚠️

Warning!-.`multiJI()`

is significantly slower than any of the other resilience indicators provided by`EWSMethods`

due to the multiple cross referencing required between species. Therefore increasing the number of species and length of time series (or decreasing`winsize`

) can dramatically increase computation time

`eg_multiJI <- multiJI(data = simTransComms$community1[,2:7], winsize = 25)`

Plotting the index for this species, we observe a loss of stability
significantly prior to the transition and much earlier than the
`uniJI()`

estimated index.

```
multiJI_plot_data <- merge(simTransComms$community1,eg_multiJI,by="time") # combine the mvi data with the raw time series
ggplot(multiJI_plot_data,aes(x = time, y = smap_J)) + geom_line() + geom_vline(xintercept = multiJI_plot_data$inflection_pt,col = "grey60", linetype = "dashed") + theme_bw() + xlab("Time") + ylab("Mulivariate stability index value")
```

####
*Caveats*

Both s-map methods are sensitive to the choice of `E`

and
`tau`

. Additionally, in seasonal/cyclical data, the shared
cycles or seasons across species can artificially inflate estimates of
causality and relatedness. In these circumstances, it is recommended to
detrend data prior to estimating these stability metrics (Ushio *et
al.* 2018). `EWSmethods`

provides the capability to
detrend and deaseason data via the functions `detrend_ts()`

and `deseason_ts()`

respectively. Similarly, it is sensible
to scale/normalise all time series to be of equivalent magnitude to aid
model fitting. A `scale = TRUE`

argument is present in both
`uniJI()`

and `multiJI()`

to achieve this.

Additionally, the two approaches require time series with no extended
period of unchanging values (specifically a period equal to or longer
than the window size). If `uniJI()`

or `multiJI()`

detect such a circumstance, the current iteration returns
`NA`

and the time series is removed respectively. For
`multiJI()`

if there are more species than data points in the
window, then the index will fail. This is due to a multivariate index
calculated across `n`

species time delays up to lag
`n-1`

, which may not be possible if the time series are
short. In this circumstance, we recommend sampling a subset of species,
either randomly or the most abundance species (those less likely to have
a period of unchanging values).

## 3) Autocorrelation Jacobians

An alternative method of estimating the Jacobian has been suggested by Williamson and Lenton (2015) which uses multivariate lag-1 autoregressive models rather than S-map reconstruction. In this approach, multivariate models estimate an autocorrelation matrix between all time series, the real eigenvalues of which are related to the real eigenvalues of the Jacobian:

$\begin{equation} \lambda_{j}= \frac{1}{\Delta t}log \left| a_{j} \right| \end{equation}$

where $\lambda$ is the Jacobian eigenvalues, $a_{j}$ is the eigenvalues of the autocorrelation matrix and $\Delta t$ is the time step of the data generating process. As $\Delta t$ is typically unknown for empirical data, we have assumed it is equal to 1, but this is an argument that can be altered.

####
*Interpretation*

As with `multiJI`

, `multiAR`

estimates local
Lyapunov stability but with a threshold of `0`

rather than
`1`

. Consequently, we expect instability when
`multiAR > 0`

, stability when `multiAR < 0`

,
and for the value to trend positively with stress.

####
*Usage*

Lets estimate the index:

`eg_multiAR <- multiAR(data = simTransComms$community1[,2:7], winsize = 25)`

Plotting the index for this species, we observe very similar
behaviour to `multiJI()`

. This is encouraging as both indices
estimate equivalent Jacobian matrices.

```
multiAR_plot_data <- merge(simTransComms$community1,eg_multiAR,by="time") # combine the mvi data with the raw time series
ggplot(multiAR_plot_data,aes(x = time, y = multiAR)) + geom_line() + geom_vline(xintercept = multiAR_plot_data$inflection_pt,col = "grey60", linetype = "dashed") + theme_bw() + xlab("Time") + ylab("Mulivariate stability index value")
```

####
*Caveats*

`multiAR`

suffers similar caveats to the S-map indices
with a sensitivity to time series length and choice of time series
contributing to the estimate. Anecdotally, the multivariate
autoregressive models also require a minimum of 16 time points to
function though the index estimation is ~25x faster than
`multiJI`

.

## 4) Fisher Information

Fisher Information (FI) attempts to estimate the amount of
information data can provide on an unmeasured parameter (Fisher and
Russell 1922). It consequently has been applied as an indicator of
system dynamics and stability (Ahmad et al. 2016).
`EWSmethods`

provides a simplified discrete time form of
Fisher and Russel (1922)’s mathematic proof following Karunanithi *et
al.* (2008):

$\begin{equation} FI\approx 4 \sum_{i=1}^{m}[q_i-q_{(i+1)}]^2 \end{equation}$

where $q_{i}^2$ is the amplitude of the probability of observing states of the system at time window $i$ and $m$ is the number of possible ‘states’. Possible states are defining by comparing the difference between temporally adjacent data windows to a reference ‘uncertainty’. If the absolute difference in density is less than the reference deviation for all variables (i.e. species densities), then the windows are binned in to the same ‘state’.

####
*Usage*

Provide the community data (first column being time, the remainder
being species values) to the `FI()`

function, specify a
`winsize`

to calculate the index over, and a size-of-states
vector (`sost`

). The default `winsize`

is 50% the
total length of the time series, and `sost`

is typically
suggested to be the standard deviation of each species across the entire
time series multiplied by 2 (Karunanithi et al. 2008).
`winspace`

indicates the number of data points to slide the
rolling window along (`1`

is standard) and `TL`

is
the ‘tightening level’. `TL`

is the percentage of points
shared between states and allows the algorithm to classify data points
to/from the same state.

```
eg.sost <- t(apply(simTransComms$community1[,3:7], MARGIN = 2, FUN = sd)) # define size-of-states using the standard deviation of each species separately. Must be wide format hence t()
egFI <- FI(data = simTransComms$community1[,2:7], sost = eg.sost, winsize = 25,winspace = 1, TL = 90)$FI
```

If we again plot the index, we can see that before and after the ‘inflection point’ (where the community goes through a transition) there are clear differences in stability.

What is also apparent, is that the FI begins to decrease prior to the
sudden change, and stability only returns after an extended period post
constant stress (stress is constant from *t200* onward).

```
fi_plot_data <- merge(simTransComms$community1,egFI,by="time") # combine the mvi data with the raw time series
ggplot(fi_plot_data,aes(x = time, y = FI)) + geom_line() + geom_vline(xintercept = fi_plot_data$inflection_pt,col = "grey60", linetype = "dashed") + theme_bw() + xlab("Time") + ylab("Fisher Information value")
```

## References

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