Resilience and regime shifts in a marine biodiversity hotspot

Complex natural systems, spanning from individuals and populations to ecosystems and socialecological systems, often exhibit abrupt reorganizations in response to changing stressors, known as regime shifts or critical transitions. Theory suggests that such systems feature folded stability landscapes with fluctuating resilience, fold-bifurcations, and alternate basins of attraction. However, the implementation of such features to elucidate response mechanisms in an empirical context is scarce, due to the lack of generic approaches to quantify resilience dynamics in individual natural systems. Here, we introduce an Integrated Resilience Assessment (IRA) framework: a three-step analytical process to assess resilience and construct stability landscapes of empirical systems. The proposed framework involves a multivariate analysis to estimate holistic system indicator variables, non-additive modelling to estimate alternate attractors, and a quantitative resilience assessment to scale stability landscapes. We implement this framework to investigate the temporal development of the Mediterranean marine communities in response to sea warming during 1985–2013, using fisheries landings data. Our analysis revealed a nonlinear tropicalisation of the Mediterranean Sea, expressed as abrupt shifts to regimes dominated by thermophilic species. The approach exemplified here for the Mediterranean Sea, revealing previously unknown resilience dynamics driven by climate forcing, can elucidate resilience and shifts in other complex systems

Habitat Selection and Temporal Abundance Fluctuations of Demersal Cartilaginous Species in the Aegean Sea (Eastern Mediterranean)

Predicting the occurrence of keystone top predators in a multispecies marine environment, such as the Mediterranean Sea, can be of considerable value to the long-term sustainable development of the fishing industry and to the protection of biodiversity. We analysed fisheries independent scientific bottom trawl survey data of two of the most abundant cartilaginous fish species (Scyliorhinus canicula, Raja clavata) in the Aegean Sea covering an 11-year sampling period. The current findings revealed a declining trend in R. clavata and S. canicula abundance from the late ′90 s until 2004. Habitats with the higher probability of finding cartilaginous fish present were those located in intermediate waters (depth: 200–400 m). The present results also indicated a preferential species' clustering in specific geographic and bathymetric regions of the Aegean Sea. Depth appeared to be one of the key determining factors for the selection of habitats for all species examined. With cartilaginous fish species being among the more biologically sensitive fish species taken in European marine fisheries, our findings, which are based on a standardized scientific survey, can contribute to the rational exploitation and management of their stocks by providing important information on temporal abundance trends and habitat preferences

Predictive habitat suitability models to aid conservation of elasmobranch diversity in the central Mediterranean Sea

Commercial fisheries have dramatically impacted elasmobranch populations worldwide. With high capture and bycatch rates, the abundance of many species is rapidly declining and around a quarter of the world’s sharks and rays are threatened with extinction. At a regional scale this negative trend has also been evidenced in the central Mediterranean Sea, where bottom-trawl fisheries have affected the biomass of certain rays (e.g. Raja clavata) and sharks (e.g. Mustelus spp.). Detailed knowledge of elasmobranch habitat requirements is essential for biodiversity conservation and fisheries management, but this is often hampered by a poor understanding of their spatial ecology. Habitat suitability models were used to investigate the habitat preference of nine elasmobranch species and their overall diversity (number of species) in relation to five environmental predictors (i.e. depth, sea surface temperature, surface salinity, slope and rugosity) in the central Mediterranean Sea. Results showed that depth, seafloor morphology and sea surface temperature were the main drivers for elasmobranch habitat suitability. Predictive distribution maps revealed different species-specific patterns of suitable habitat while high assemblage diversity was predicted in deeper offshore waters (400–800 m depth). This study helps to identify priority conservation areas and diversity hot-spots for rare and endangered elasmobranchs in the Mediterranean Sea

Macroscale factors affecting diatom abundance: a synergistic use of Continuous Plankton Recorder and satellite remote sensing data

Diatoms exist in almost every aquatic regime; they are responsible for 20% of global carbon fixation and 25% of global primary production, and are regarded as a key food for copepods, which are subsequently consumed by larger predators such as fish and marine mammals. A decreasing abundance and a vulnerability to climatic change in the North Atlantic Ocean have been reported in the literature. In the present work, a data matrix composed of concurrent satellite remote sensing and Continuous Plankton Recorder (CPR) in situ measurements was collated for the same spatial and temporal coverage in the Northeast Atlantic. Artificial neural networks (ANNs) were applied to recognize and learn the complex non-monotonic and non-linear relationships between diatom abundance and spatiotemporal environmental factors. Because of their ability to mimic non-linear systems, ANNs proved far more effective in modelling the diatom distribution in the marine ecosystem. The results of this study reveal that diatoms have a regular seasonal cycle, with their abundance most strongly influenced by sea surface temperature (SST) and light intensity. The models indicate that extreme positive SSTs decrease diatom abundances regardless of other climatic conditions. These results provide information on the ecology of diatoms that may advance our understanding of the potential response of diatoms to climatic change

Cascaded neural networks improving fish species prediction accuracy: the role of the biotic information

Abstract Species distribution is the result of complex interactions that involve environmental parameters as well as biotic factors. However, methodological approaches that consider the use of biotic variables during the prediction process are still largely lacking. Here, a cascaded Artificial Neural Networks (ANN) approach is proposed in order to increase the accuracy of fish species occurrence estimates and a case study for Leucos aula in NE Italy is presented as a demonstration case. Potentially useful biotic information (i.e. occurrence of other species) was selected by means of tetrachoric correlation analysis and on the basis of the improvements it allowed to obtain relative to models based on environmental variables only. The prediction accuracy of the L. aula model based on environmental variables only was improved by the addition of occurrence data for A. arborella and S. erythrophthalmus. While biotic information was needed to train the ANNs, the final cascaded ANN model was able to predict L. aula better than a conventional ANN using environmental variables only as inputs. Results highlighted that biotic information provided by occurrence estimates for non-target species whose distribution can be more easily and accurately modeled may play a very useful role, providing additional predictive variables to target species distribution models