• 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.

AbstractMarine biota is redistributing at a rapid pace in response to climate change and shifting seascapes. While changes in fish populations and community structure threaten the sustainability of fisheries, our capacity to adapt by tracking and projecting marine species remains a challenge due to data discontinuities in biological observations, lack of data availability, and mismatch between data and real species distributions. To assess the extent of this challenge, we review the global status and accessibility of ongoing scientific bottom trawl surveys. In total, we gathered metadata for 283,925 samples from 95 surveys conducted regularly from 2001 to 2019. 59% of the metadata collected are not publicly available, highlighting that the availability of data is the most important challenge to assess species redistributions under global climate change. We further found that single surveys do not cover the full range of the main commercial demersal fish species and that an average of 18 surveys is needed to cover at least 50% of species ranges, demonstrating the importance of combining multiple surveys to evaluate species range shifts. We assess the potential for combining surveys to track transboundary species redistributions and show that differences in sampling schemes and inconsistency in sampling can be overcome with vector autoregressive spatio-temporal modeling to follow species density redistributions. In light of our global assessment, we establish a framework for improving the management and conservation of transboundary and migrating marine demersal species. We provide directions to improve data availability and encourage countries to share survey data, to assess species vulnerabilities, and to support management adaptation in a time of climate-driven ocean changes.

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.

AbstractThe assessment of climate impact on marine communities dwelling deeper than the well‐studied shelf seas has been hampered by the lack of long‐term data. For a long time, the prevailing expectation has been that thermal stability in deep ocean layers will delay ecosystem responses to warming. Few observational studies have challenged this view and indicated that deep organisms can respond exceptionally fast to physical change at the sea surface. To address the depth‐specific impact of climate change, we investigated spatio‐temporal changes in fish community structure along a bathymetry gradient of 150–1500 m between 1998 and 2016 in East Greenland. Here, the Arctic East Greenland Current and the Atlantic Irminger Current meet and mix, representing a sub‐Arctic transition zone. We found the strongest signals of community reorganizations at depths between 350 and 1000 m and only weak responses in the shallowest and deepest regions. Changes were in synchrony with atmospheric warming, loss in sea ice and variability in physical sea surface conditions both within our study region and North of the Denmark Strait. These results suggest that interannual variability and long‐term climate trends of the larger ecoregion can rapidly affect fish communities down to 1000‐m depth through atmospheric ocean coupling and food web interactions.