DOI: 10.5061/dryad.44j0zpcnb
title: Resilience assessment in complex natural systems

description: Ecological resilience is the capability of an ecosystem to maintain the
 same structure and function and to avoid crossing catastrophic tipping
 points (i.e. irreversible regime shifts). While fundamental for
 management, concrete ways to estimate and interpret resilience in real
 ecosystems are still lacking. Here, we develop an empirical approach to
 estimate resilience based on the stochastic cusp model derived from
 catastrophe theory. The cusp model models tipping points derived from a
 cusp bifurcation. We extend cusp in order to identify the presence of
 stable and unstable states in complex natural systems. Our Cusp Resilience
 Assessment (CUSPRA) has three characteristics: i) it provides estimates on
 how likely a system is to cross a tipping point (in the form of a cusp
 bifurcation) characterized by hysteresis, ii) it assesses resilience in
 relation to multiple external drivers, and iii) it produces
 straightforward results for ecosystem-based management. We validate our
 approach using simulated data and demonstrate its application using
 empirical time-series of an Atlantic cod population and of marine
 ecosystems in the North Sea and the Mediterranean Sea. We show that CUSPRA
 is a powerful method to empirically estimate resilience in support of a
 sustainable management of our adapting ecosystems under global climate
 change.
description: The data were collected three different publications. The first
 dataset used to test the method was the stock assessment of North-East
 Arctic cod collected from Sguotti et al., 2019. These data can be found in
 the ICES Stock Assessment data. The second dataset was the North Sea
 community. These data were collected from Sguotti et al., 2022. Finally,
 the last dataset was a trait dataset and was collected from Tsimara et
 al., 2021. 
description: # Resilience assessment in complex natural systems.
 [https://doi.org/10.5061/dryad.44j0zpcnb](https://doi.org/10.5061/dryad.44j0zpcnb) The dataset used in the papers are available. The dataset are three: 1\) a dataset of biomass of North-East Arctic cod and the relative stressors (Sguotti et al., 2019) 2\) a dataset of the community (represented by PC1) of the North Sea and the stressors (Sguotti et al., 2022) 3\) a dataset of the community of traits of the Mediterranean Sea (also represented by PC1) and the stressors (Tsimara et al., 2021). ## Description of the data and file structure The data have all the same structure: a state variable for which resilience needs to be measured, and two drivers, fishing as the asymmetry variable and temperature as the bifurcation variable to be fitted in the cuspra model. The data of North-East Arctic cod (Sguotti et al., 2019) contain: 1\) SSB = biomass of the North-East Arctic cod derived from stock assessment data. 2\) F= fishing mortality derived from the stock assessment data. 3\) SST = Sea Surface Temperature as a yearly average collected from the NOAA ErSST v4. The data on the North Sea community (Sguotti et al., 2022) contain: 1\) PC1 as a proxy of the community state. The data are derived from Sguotti et al., 2022 were the community of fish, crustaceans and mollusks, collected from ICES Datras database and the community of plankton from the Continuous Plankton Recorder (CPR) were assembled. A Principal Component Analysis was then performed on this data to obtain a proxy of community state (PC1 and PC2). Details about the analysis can be found in Sguotti et al., 2022. 2\) F= yearly averaged Fishing effort, collected from Couce et al., 2019. 3\) sst = as an yearly averaged temperature over the entire North Sea collected from NOAA ERSST v5. The data on the Mediterranean Traits Community (Tsimara et al., 2021) contain: 1\) PC1 = the main mode of variability of the trait space of the fish community as assembled in Tsimara et al. 2021. Landings data were collected from FAO and traits from available databases. 2\) GT= Gross Tonnage as a proxy of fishing effort. 3\) T = Sea Surface Temperature The data are in RData file, ready to be used in R. Additionally to the data there are three scripts: 1\) Cuspra: contains the function to run a cuspra analysis 2\) plotRS: contains the function to plot the results of cuspra 3\) evalcusp: contains the functions to evaluate the cuspra model The scripts are commented and described in the File. Additionally, a ShinyApp is also available to allow users to test the data and the model at: [https://rfrelat.shinyapps.io/CUSPRA/](https://rfrelat.shinyapps.io/CUSPRA/) ## Sharing/Access information The data and the code are stored also at: * [https://github.com/rfrelat/Cuspra](https://github.com/rfrelat/Cuspra) The Data were derived from the following sources: Sguotti, C. *et al.* Catastrophic dynamics limit Atlantic cod recovery. *Proceedings of the Royal Society B* **286**, (2019). Sguotti, C. *et al.* Irreversibility of regime shifts in the North Sea. *Front Mar Sci* **9**, 1830 (2022). Tsimara, E. *et al.* An Integrated Traits Resilience Assessment of Mediterranean fisheries landings. *Journal of Animal Ecology***90**, 2122–2134 (2021). ## Code/Software All data and codes are additionally stored in a package “cuspra” at the GitHub repository ([https://github.com/rfrelat/Cuspra](https://github.com/rfrelat/Cuspra) and are freely accessible. The package to perform the model can be downloaded directly in R by typing: devtools::install_github("rfrelat/cuspra").*** ***A Shiny App was also developed to allow other researchers or stakeholder to easily try the method with their data or simulated data ([https://rfrelat.shinyapps.io/CUSPRA](https://rfrelat.shinyapps.io/CUSPRA).