• Energy Research

AbstractEcological resilience is the capability of an ecosystem to maintain the same structure and function and to avoid crossing catastrophic tipping points. 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 stochasticcuspmodel derived from catastrophe theory. OurCusp Resilience Assessment(CUSPRA) has three characteristics: i) it provides estimates on how likely a system is to cross a tipping point characterized by hysteresis, ii) it assesses resilience in relation to multiple external drivers, and iii) it produces straightforward results for ecosystem-based management. We validated our approach using simulated data and demonstrated its application using empirical time-series of an Atlantic cod population and of marine ecosystems in the North and the Mediterranean Sea. We show that CUSPRA provides a powerful method to empirically estimate resilience in support of a sustainable management of our constantly adapting ecosystems under global climate change.

AbstractFisheries worldwide face uncertain futures as climate change manifests in environmental effects of hitherto unseen strengths. Developing climate-ready management strategies traditionally requires a good mechanistic understanding of stock response to climate change in order to build projection models for testing different exploitation levels. Unfortunately, model-based projections of fish stocks are severely limited by large uncertainties in the recruitment process, as the required stock-recruitment relationship is usually not well represented by data. An alternative is to shift focus to improving the decision-making process, as postulated by the decision-making under deep uncertainty (DMDU) framework. Robust Decision Making (RDM), a key DMDU concept, aims at identifying management decisions that are robust to a vast range of uncertain scenarios. Here we employ RDM to investigate the capability of North Sea cod to support a sustainable and economically viable fishery under future climate change. We projected the stock under 40,000 combinations of exploitation levels, emission scenarios and stock-recruitment parameterizations and found that model uncertainties and exploitation have similar importance for model outcomes. Our study revealed that no management strategy exists that is fully robust to the uncertainty in relation to model parameterization and future climate change. We instead propose a risk assessment that accounts for the trade-offs between stock conservation and profitability under deep uncertainty.

Understanding spatio-temporal dynamics of biotic communities containing large numbers of species is crucial to guide ecosystem management and conservation efforts. However, traditional approaches usually focus on studying community dynamics either in space or in time, often failing to fully account for interlinked spatio-temporal changes. In this study, we demonstrate and promote the use of tensor decomposition for disentangling spatio-temporal community dynamics in long-term monitoring data. Tensor decomposition builds on traditional multivariate statistics (e.g. Principal Component Analysis) but extends it to multiple dimensions. This extension allows for the synchronized study of multiple ecological variables measured repeatedly in time and space. We applied this comprehensive approach to explore the spatio-temporal dynamics of 65 demersal fish species in the North Sea, a marine ecosystem strongly altered by human activities and climate change. Our case study demonstrates how tensor decomposition can successfully (i) characterize the main spatio-temporal patterns and trends in species abundances, (ii) identify sub-communities of species that share similar spatial distribution and temporal dynamics, and (iii) reveal external drivers of change. Our results revealed a strong spatial structure in fish assemblages persistent over time and linked to differences in depth, primary production and seasonality. Furthermore, we simultaneously characterized important temporal distribution changes related to the low frequency temperature variability inherent in the Atlantic Multidecadal Oscillation. Finally, we identified six major sub-communities composed of species sharing similar spatial distribution patterns and temporal dynamics. Our case study demonstrates the application and benefits of using tensor decomposition for studying complex community data sets usually derived from large-scale monitoring programs.

Seafood trade is a global business, where catches, processing, and consumption are increasingly separated. An increasingly integrated global market creates telecouplings, i.e. connections between fish stocks that are ecologically separated. These telecouplings may spread the impact of vulnerabilities, such as climate change, between unconnected fisheries. The effect of climate change on fisheries is often analyzed on a fish stock basis, which may overlook the spread of these vulnerabilities. Atlantic cod (Gadus morhua) stocks, an iconic fish species, are no exception. Depending on the geographical location, stocks have been impacted differently by climate change, with North-East Arctic (NEA) cod, the stock in the Barents Sea, reaching record high biomass levels and other stocks being extremely depleted. Here, we investigate how these dynamics occurring in the ecological system affect global trade of cod. We find that the global export is fully dominated by NEA cod catches. Applying Structural Equation Modelling, we discover that the high biomass level of NEA cod has positive effects on catches and exports and leads to lower global market prices. However, zooming in on individual stocks and the countries exploiting them using correlation networks, we find heterogeneous responses of other countries, where catches for some stocks increase and others decrease in response to lower global prices. Our results highlight how changes on one fishery may have important repercussion on stocks in different ecosystems, as well as on societies reliant on them.

# 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). 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. 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. 

AbstractHow have North Sea skate and shark assemblages changed since the early 20th century when bottom trawling became widespread, whilst their environment became increasingly impacted by fishing, climate change, habitat degradation and other anthropogenic pressures? This article examines long‐term changes in the distribution and occurrence of the elasmobranch assemblage of the southern North Sea, based on extensive historical time series (1902–2013) of fishery‐independent survey data. In general, larger species (thornback ray, tope, spurdog) exhibited long‐term declines, and the largest (common skate complex) became locally extirpated (as did angelshark). Smaller species increased (spotted and starry ray, lesser‐spotted dogfish) as did smooth‐hound, likely benefiting from greater resilience to fishing and/or climate change. This indicates a fundamental shift from historical dominance of larger, commercially valuable species to current prevalence of smaller, more productive species often of low commercial value. In recent years, however, some trends have reversed, with the (cold‐water associated) starry ray now declining and thornback ray increasing. This shift may be attributed to (i) fishing, including mechanised beam trawling introduced in the 1960s–1970s, and historical target fisheries for elasmobranchs; (ii) climate change, currently favouring warm‐water above cold‐water species; and (iii) habitat loss, including potential degradation of coastal and outer estuarine nursery habitats. The same anthropogenic pressures, here documented to have impacted North Sea elasmobranchs over the past century, are likewise impacting shelf seas worldwide and may increase in the future; therefore, parallel changes in elasmobranch communities in other regions are to be expected.