Conclusion
The ability to use accessible and easy to interpret tools are key for
ecological monitoring. In this note we present EWSmethods , an R
package consolidating the simplest methods of early warning signal
assessments into a coherent suite of metrics and visualisations. Each
function is consistent in its parameterisations, terminology, and output
to allow any user to interpret the assessment confidently, regardless of
the data dimensionality or EWS approach.
It would however be remiss to overlook the pivotal earlywarningspackage and work of Dakos et al. (2012). EWSmethodsinnovates on earlywarnings by providing alternative calculations
(rolling vs expanding windows) and data types (univariate vs
multivariate), but does not provide the additional modelling techniquesearlywarnings supports (diffusion-drift-jump models, BDS tests
etc). We direct readers to that package on github (as it is no longer
maintained on CRAN at the time of writing -
https://github.com/earlywarningtoolbox/earlywarnings-R) for the typical
rolling window EWS approach due to the additional modelling capabilities
it provides. EWSmethods better supports multivariate analyses and
standardises across univariate EWSs, multivariate EWSs and machine
learning models to allow comparability. It also provides access to
purpose-built machine learning models not otherwise available to R
users. Consequently, users are able to explore an ensemble of generic
forecasting methods to identify oncoming transitions and tipping points
in their system.
Generic approaches also facilitate wider research interest into the
universal challenge of identifying oncoming tipping points.
Resilience-based approaches are critical for the management of globally
imperilled systems (Folke et al. 2010, Oliver et al. 2015, Capdevila et
al. 2022) but are applicable in other disciplines. Remotely sensed data
could allow global level tipping point assessments for example (Forzieri
et al. 2022), individual mortality risk may be detectable (Cailleret et
al. 2019) or positive thresholds can be encouraged (Lenton et al. 2022).
The low barrier to entry that EWSmethods provides for R users can
aid the development of these developing research avenues.
To cite EWSmethods or acknowledge its use, cite this Software
note as follows, substituting the version of the application that you
used for ‘version 1.0’: