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In the framework of global human-induced change, marine communities’ often respond to changing conditions abruptly reorganizing into new equilibria. These shifts are difficult to predict and often imply irreversible adjustments due to hysteresis. Unraveling the role of the forces leading regime shifts is a major challenge. We explored the temporal evolution of 63 fish species representing the Cantabrian bentho-demersal community in response to environmental changes and fishing pressure in the period 1983–2018, using survey data. Via multivariate analysis and non-additive modeling of a community index and the system's main stressors, two decadal-scale regimes were revealed, suggesting a non-linear response of the community to its environment. The Integrated Resilience Assessment framework elucidated the response mechanism to the candidate stressors and allowed quantifying resilience dynamics. The decline in fishing pressure in the 1990s was associated with a gradual transition of the system, while further decline during the 2000s eroded the resilience of the system towards changes in its stressors, leading to a discontinuous response expressed as an abrupt, possibly irreversible shift in the 2010s. Given the teleconnected character of marine ecosystems, this regional study endorses the scientific effort for actions facing the dynamic impacts of climate change on exploited marine ecosystems., Efecto del cambio climático en las comunidades y en las pesquerías del mar Cantábrico, CICLICA, SI

An increasing number of studies have been examining the functional configuration of biological communities or ecosystems using biological traits. Here, we investigated the temporal dynamics and resilience of the traits composition in Mediterranean fisheries landings over 31 years (1985–2015). We transcribed the FAO Mediterranean landings dataset for 101 marine species into a dataset of 23 traits related to the life cycle, distribution, ecology and behaviour. Mediterranean mean Sea Surface Temperature (SST) was evaluated as a potential driver of the traits composition. Trait dynamics were evaluated both individually and holistically by developing an Integrated Traits Resilience Assessment (ITRA). ITRA is a variation of the Integrated Resilience Assessment (IRA), a method to infer resilience dynamics and build stability landscapes of complex natural systems. Changes in landings trait dynamics were documented both for individual traits and for the entire traits ‘system’, and a relevant regime shift was detected in the second half of the 1990s. The traits system switched to higher optimal temperature, more summer spawning, shorter life span, smaller maximum size, shallower optimal depth and planktivorous diet. This shift was found to be a lagged discontinuous response to sea warming, which gradually eroded the resilience of the original state of the traits system, leading it into a new basin of attraction. The inclusion of ecological/response traits (related to environmental preferences) in our analyses indicates potential mechanisms that explain the observed shift, while changes in functional/effect traits indicate potential impacts on ecosystem functioning. Our findings suggest that changes in the Mediterranean ecosystems are evidently larger than previously thought, with profound implications for the management of this highly impacted sea. ​. © 2021 British Ecological Society

Commission Decision of 25 February 2016 setting up a Scientific, Technical and Economic Committee for Fisheries, C(2016) 1084, OJ C 74, 26.2.2016, p. 4–10. The Commission may consult the group on any matter relating to marine and fisheries biology, fishing gear technology, fisheries economics, fisheries governance, ecosystem effects of fisheries, aquaculture or similar disciplines. This report evaluates the performance of technical measures to conserve fishery resources and protect marine ecosystems in accordance with Article 31 of Regulation (EU) 1241/201.

There is a growing number of methods to assess data-limited stocks. However, most of these methods require at least some basic data, such as commercial catches and life history information. Meanwhile, there are many commercial stocks with an even higher level of data limitation, for which the inference of stock status and the formulation of advice remain challenging. Here, we present a stepwise approach to achieve the best possible understanding of extremely data-limited stocks and facilitate their management. As a case study we use a stock of the shrimp Plesionika edwardsii (Decapoda, Pandalidae) from the eastern Mediterranean Sea, where the only available data was a sub-optimal sample of length frequencies coming from a small-scale trap fishery. We use a suite of different methods to explore and process the data, estimate the growth parameters, estimate the natural and fishing mortalities, and approximate the reference points, in order to provide a preliminary evaluation of stock status. We implement multiple methods for each step of this process, highlighting the strong and weak points of each one of them. Our approach illustrates the better insights that can be gained by applying ensembles of models, rather than a single ‘best’ model when working with limited data of poor quality. The stepwise approach we propose here is transferable to other extremely data-limited stocks to elucidate their status and inform their management. © Copyright © 2020 Pantazi, Mannini, Vasilakopoulos, Kapiris, Megalofonou and Kalogirou.

In this GreenMAR project we look into the historical development of the marine ecosystems that surround the Nordic countries in an effort to forecast their future evolution. We pay particular attention to the way their food webs responded to similar stressors (warming) and fishing regimes in the past. We have compiled historical information on environmental and biological components, from plankton to fish, over the last 25 to 45 years, depending on the system. On these four ecosystems we have: (i) carried out multivariate analyses to describe their main trends and (ii) constructed stability landscapes to quantify their resilience. We will show these results and discuss their implications.