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Seagrass meadows fulfil an essential ecosystem service by providing nursery habitats to various fish species. However, the habitat features driving this function remain insufficiently explored. Nevertheless, this knowledge is essential to quantify the ecosystem service provision, inform restoration efforts, and address the impact of habitat loss and fragmentation on this key coastal habitat. In this study, habitat selection experiments were conducted with 2 Mediterranean juvenile fish species (the East Atlantic peacock wrasse Symphodus tinca and the annular seabream Diplodus annularis), usually associated with Posidonia oceanica beds, to evaluate if they actively select seagrass habitats based on their structural complexity. Juvenile fish movement was recorded and tracked when visually exposed to multiple artificial habitat configurations within an experimental aquarium to determine the proportion of time spent near each habitat. Both species exhibited a clear affinity towards vegetated (seagrass mimics) habitats in comparison to unvegetated areas, demonstrating that juveniles distinguished and actively selected habitats using visual cues. However, they presented different responses in their occupancy patterns to increasing complexity of seagrass mimic habitats. While D. annularis responded positively to increases in complexity, S. tinca did not. These affinity variations between species suggest that differences in juvenile abundance between habitats found in previous field studies may be partially conditioned by active habitat choices. Understanding how different species respond to changes in seagrass structure is crucial for quantifying their nursery provision service. Moreover, this knowledge is essential to assess their potential vulnerability to seagrass loss in the context of environmental changes in the Mediterranean.

Dispersal shapes population connectivity and plays a critical role in marine metacommunities. Prominent species for coastal socioecological systems, such as jellyfish and spiny lobsters, feature long pelagic dispersal phases (LPDPs), which have long been overlooked. Here, we use a cross-scale approach combining field surveys of these species with a high-resolution hydrodynamic model to decipher the underlying mechanisms of LPDP patterns in northwestern Mediterranean shores. We identified basin-scale prevailing dispersal routes and synchronic year-to-year patterns tightly linked to prominent circulation features typical of marginal seas and semienclosed basins, with an outstanding role of a retentive source area replenishing shores and potentially acting as a pelagic nursery area. We show how the atmospheric forcing of the ocean, a marked hydrological driver of the Mediterranean Sea, modulates dispersal routes and sources of LPDP at interannual scales. These findings represent a crucial advance in our understanding of the functioning of metapopulations of species with LPDP in marginal seas and may contribute to the effective management of coastal ecosystem services in the face of climate change.

The use of Deep learning techniques in the field of Marine Science has become popular in recent years. For instance, many works propose the application of instance segmentation neural networks (in particular, Mask R-CNN) for detection and classification of fish in underwater images. The performance of these learning-based approaches depends heavily on the volume of data used for training, which, in the case of instance segmentation models for fish detection, implies that human experts must label and mark the shapes of all the fish appearing in a vast amount of underwater images. This is an enormously time-consuming task that we seek to alleviate in this paper. We propose a training strategy that combines manual and semi-automatic annotations. The latter are obtained in a weakly-supervised manner: the bounding box that contains the fish is manually selected, but its shape is automatically obtained thanks to a pretrained encoder-decoder segmentation network. Several popular architectures for this encoder-decoder network are examined. This strategy permits to reduce drastically the annotation cost for instance segmentation, at the expense of a small drop in performance with respect to the use of fully manual annotations. We show that a balance can be achieved between the segmentation performance and the time used to collect the training data by using the proposed strategy.

Early life survival is critical to successful replenishment of fish populations, and hypotheses developed under the Growth-Survival Paradigm (GSP) have guided investigations of controlling processes. The GSP postulates that recruitment depends on growth and mortality rates during early life stages, as well as their duration, after which the mortality declines substantially. The GSP predicts a shift in the frequency distribution of growth histories with age towards faster growth rates relative to the initial population because slow-growing individuals are subject to high mortality (via starvation and predation). However, mortality data compiled from 387 cases published in 153 studies (1971–2022) showed that the GSP was only supported in 56% of cases. Selection against slow growth occurred in two-thirds of field studies, leaving a non-negligible fraction of cases showing either an absence of or inverse growth-selective survival, suggesting the growth-survival relationship is more complex than currently considered within the GSP framework. Stochastic simulations allowed us to assess the influence of key intrinsic and extrinsic factors on the characteristics of surviving larvae and identify knowledge gaps on the drivers of variability in growth-selective survival. We suggest caution when interpreting patterns of growth selection because changes in variance and autocorrelation of individual growth rates among cohorts can invalidate fundamental GSP assumptions. We argue that breakthroughs in recruitment research require a comprehensive, population-specific characterization of the role of predation and intrinsic factors in driving variability in the distribution and autocorrelation of larval growth rates, and of the life stage corresponding to the endpoint of pre-recruited life.

Posidonia oceanica is a crucial seagrass species in the Mediterranean Sea, providing key ecological, physical, and economic benefits. Unfortunately, P. oceanica meadows have been in decline due to human activities and climate change, particularly in the Balearic Islands where tourism exacerbates pressures. Connectivity between patches of seagrass is an overlooked factor in conservation planning. We used a high-resolution hydrodynamic model and individual-based model to simulate the dispersal of P. oceanica fruits from semi-enclosed bays and nearshore locations in the NW Mediterranean region during the fruit-generating season from 2017 to 2021. Graph theoretic analysis of the simulations revealed significant differences in dispersal probabilities between years, with some sites being important in all years. Alcudia Bay was found to be a significant sink, and crucial links were discovered between Menorca and Mallorca and between Mallorca and Ibiza. Our study demonstrates the usefulness of dispersal modelling and network analysis in conservation planning for foundation species like P. oceanica, particularly in a regional, integrated conservation management context.

Novel insights were provided by contrasting the composition of wild and farmed fish gut microbiomes because the latter had essentially different environmental conditions from those in the wild. This was reflected in the gut microbiome of the wild Sparus aurata and Xyrichtys novacula studied here, which showed highly diverse microbial community structures, dominated by Proteobacteria, mostly related to an aerobic or microaerophilic metabolism, but with some common shared major species, such as Ralstonia sp. On the other hand, farmed non-fasted S. aurata individuals had a microbial structure that mirrored the microbial composition of their food source, which was most likely anaerobic, since several members of the genus Lactobacillus, probably revived from the feed and enriched in the gut, dominated the communities. The most striking observation was that after a short fasting period (86 h), farmed gilthead seabream almost lost their whole gut microbiome, and the resident community associated with the mucosa had a very much reduced diversity that was highly dominated by a single potentially aerobic species Micrococcus sp., closely related to M. flavus. The results pointed to the fact that, at least for the juvenile S. aurata studied, most of the microbes in the gut were transient and highly dependent on the feed source, and that only after fasting for at least 2 days could the resident microbiome in the intestinal mucosa be determined. Since an important role of this transient microbiome in relation to fish metabolism could not be discarded, the methodological approach needs to be well designed in order not to bias the results. The results have important implications for fish gut studies that could explain the diversity and occasional contradictory results published in relation to the stability of marine fish gut microbiomes, and might provide important information for feed formulation in the aquaculture industry.

Further investigation is needed to improve the identification and classification of fish in underwater images using artificial intelligence, specifically deep learning. Questions that need to be explored include the importance of using diverse backgrounds, the effect of (not) labeling small fish on precision, the number of images needed for successful classification, and whether they should be randomly selected. To address these questions, a new labeled dataset was created with over 18,400 recorded Mediterranean fish from 20 species from over 1,600 underwater images with different backgrounds. Two state-of-the-art object detectors/classifiers, YOLOv5m and Faster RCNN, were compared for the detection of the ‘fish’ category in different datasets. YOLOv5m performed better and was thus selected for classifying an increasing number of species in six combinations of labeled datasets varying in background types, balanced or unbalanced number of fishes per background, number of labeled fish, and quality of labeling. Results showed that i) it is cost-efficient to work with a reduced labeled set (a few hundred labeled objects per category) if images are carefully selected, ii) the usefulness of the trained model for classifying unseen datasets improves with the use of different backgrounds in the training dataset, and iii) avoiding training with low-quality labels (e.g., small relative size or incomplete silhouettes) yields better classification metrics. These results and dataset will help select and label images in the most effective way to improve the use of deep learning in studying underwater organisms

Marine population connectivity involves organism movement and interactions in oceanic and coastal settings. It is essential for biodiversity by aiding genetic diversity, species distribution, and recovery from disturbances. It guides resource management strategies like fisheries and conservation planning, ensuring marine ecosystem sustainability. Seagrass meadows play a pivotal role within coastal ecosystems, contributing to essential ecological, physical, and economic functions. Despite the presence of protective policies, a continuous decline of seagrass meadows has been observed in recent years due to the impact of human activities and climate change. In the Balearic Islands of the Mediterranean Sea, these pressures are further intensified by tourism, underlining the pressing requirement to prioritize areas for protection and restoration while considering the connectivity between habitat patches. In this study, the dispersal of Posidonia oceanica, the most widespread seagrass species in the Mediterranean Sea, from semi-enclosed bays and nearshore locations within the Balearic Islands archipelago, is investigated using a high-resolution hydrodynamic model and an individual-based model. The simulations were subjected to analysis through a graph-theoretic approach, revealing significant variations in dispersal probabilities across different years. Notably, Alcudia Bay emerged as a prominent sink, while critical links were identified between Menorca and Mallorca, as well as between Mallorca and Ibiza. This study underscores the value of dispersal modelling and network analysis in conservation planning for foundational species, particularly within a regional, integrated framework of conservation management.

Fish differ consistently in behavior within the same species and population, reflecting distinct behavioral types (BTs). Comparing the behavior of wild and reared individuals provides an excellent opportunity to delve into the ecological and evolutionary consequences of BTs. In this work, we evaluated the behavioral variation of wild and reared juvenile gilthead seabreams, Sparus aurata, a highly relevant species for aquaculture and fisheries. We quantified behavioral variation along the five major axes of fish behavioral traits (exploration-avoidance, aggressiveness, sociability, shyness-boldness, and activity) using standardized behavioral tests and a deep learning tracking algorithm for behavioral annotation. Results revealed significant repeatability in all five behavior traits, suggesting high consistency of individual behavioral variation across the different axes in this species. We found reared fish to be more aggressive, social and active compared to their wild conspecifics. Reared individuals also presented less variance in their aggressiveness, lacking very aggressive and very tame individuals. Phenotypic correlation decomposition between behavioral types revealed two different behavioral syndromes: exploration-sociability and exploration-activity. Our work establishes the first baseline of repeatability scores in wild and reared gilthead seabreams, providing novel insight into the behavior of this important commercial species with implications for fisheries and aquaculture.

Efforts to manage small and medium-sized pelagic fishes (SMPF) using traditional stock assessment methods are hampered by the elusive relationship between spawning stock biomass and recruitment. We propose to compute a reproductive resilience index (RRI) in three steps: (i) we selected 16 biological traits related to distinct aspects of SMPF reproductive biology and char-acterized by a continuous (e.g., growth rate) or ordinal variable (e.g., spawning site fidelity) scored from 1 to 5 (1 representing a low contribution of the trait to reproductive resilience); (ii) an expert panel assigned the traits’ scores to five exploited species in the southern Humboldt Current Large Marine Ecosystem; (iii) a Bayesian Belief Network (BBN) model was used to estimate an RRI based on the combination of the traits’ scores. The BBN was used to explore environmental effects on the species’ RRI, as some reproductive traits can show intraspecies variability under external forces (e.g., fishing pressure). Through proving the RRI application to detect variability in species’ resilience at local time series, we show how the resulting RRI can be interpreted by fishery managers to improve the current management of SMPF., Les efforts de gestion des petits et moyens poissons pélagiques (PMPP) par des méthodes traditionnelles d’évaluation des stocks sont limités par la mauvaise définition de la relation entre la biomasse du stock reproducteur et le recrutement. Nous proposons d’établir un indice de résilience reproductive (IRR) par les trois étapes suivantes : (i) nous avons choisi 16 caractères biologiques reliés à différents aspects de la biologie de reproduction des PMPP et caractérisés par une variable continue (p. ex., taux de croissance) ou ordinale (p. ex. fidélité au lieu de frai) notée de 1 à 5 (1 représentant une faible contribution du caractère à la résilience reproductive), (ii) un panel d’experts a assigné les notes pour ces caractères pour cinq espèces exploitées dans le grand écosystème marin du courant de Humboldt méridional, (iii) un modèle de réseau de croyances bayésien (RCB) a été utilisé pour estimer un IRR basé sur la combinaison des notes des caractères. Le RCB a été utilisé pour explorer les effets environnementaux sur l’IRR des espèces, certains caractères reproductifs pouvant présenter une variabilité intraspécifique sous l’effet de forçages externes (p. ex. la pression de pêche). En démontrant l’application de l’IRR pour détecter la variabilité de la résilience d’espèces pour des séries chronologiques locales, nous illustrons comment l’IRR résultant peut être interprété par les gestionnaires de pêches pour améliorer la gestion actuelle des PMPP.

With the advent of deep-learning (DL) techniques, image annotation has become a fundamental part of the research process. In the case of underwater image annotation, the human in charge of the task is faced whith the inherent quality problems of this kind of images. A large number of underwater image enhancement (UIE) methods have been developed aimed at improving the colors and contrast of these images. However, no attention has been paid to the specific problem of image annotation. In this case the global image quality of the processed image is less important than the fact that the objects to be annotated stand out and that their contours are easy to delineate. In this paper we evaluate seven state-of-the-art UIE techniques and rank them, through a subjective approach, according to their utility for the annotation process. The conclusion of our study is that, in general, the model-free Multiscale Retinex algorithm is preferred over more complex techniques that try to model the formation of the underwater images.

Generalized depletion models have proven useful to provide management information for data-limited fisheries. These models are suitable for stocks with fast depletion and recruitment inputs that reset the depletion. The Mediterranean common dolphinfish (Coryphaena hippurus) fishery shows these dynamics, being seasonally exploited mainly by fish aggregation devices in small-scale fisheries from four countries and as bycatch by longline fisheries. These fleets operate on separate fishing grounds, and with distinct effort–catch dynamics. This led to the development of a multiannual five-fleet generalized depletion model applicable to a monthly database of effort, catch, and mean weight from 2008 to 2019. Results showed a high natural mortality rate (0.25 month−1) and wide intra-season fluctuations in biomass. Annual recruitment fluctuates around 10 million individuals, and it has been stable along the analysed period. Exploitation rate only exceeded 40% during the peak month of catches, being much lower the rest of the season, indicating that this stock is fished within sustainable limits. We provide here the first estimation of reference points for the management of the shared dolphinfish Mediterranean stock and show that generalized depletion models can be extended to complex fisheries with multiple fleets with different effort–catch dynamics.

Sound production represents an integral part of social communication in many teleost fish; however, few studies have investigated the structure, organization and variability of fish sounds at the community level. Fish acoustic community structure was recorded simultaneously in three sites located along the Mediterranean basin within the endemic habitat of Posidonia oceanica seagrass beds. Acoustic diversity and species-specific sound features were expected to differ between locations. We predicted that, in communities characterized by higher acoustic richness, fish species would specialize in their use of acoustic resources (i.e. realized acoustic niche compression), while the overall allocation of resources within the community signal space would expand. The fish acoustic communities inhabiting Posidonia beds were characterized by the same main contributors (the /Kwa/, Ophidion rochei and Sciaena umbra sound types). However, their relative occurrence, abundances and use of acoustic resources were site-specific. Acoustic diversity differed between geographic locations. The range of spectral and temporal resources exploited by the fish acoustic community was wider in sites where acoustic richness was at its highest score. Ophidion rochei was highly specialized in its use of temporal resources where acoustic richness was higher, whilst S. umbra appeared less efficient in specializing the use of spectral and temporal resources. By showing that the same species can exploit different acoustic resources between locations, this study supports the concept of Acoustic Niche plasticity (i.e. plasticity of acoustic resources allocation within a species). The results suggest that the degree of acoustic niche plasticity might be determined by the species-specific degree of sound-producing system plasticity. In turn, different degrees of acoustic niche plasticity might determine different species-specific levels of acoustic adaptability to changing biotic or environmental conditi

Informed fishery management decisions require primary input data such as the fluctuations in the number of fish landed and fish length. Obtaining these data can be costly if conducted by hand, which is the case for length data in most fisheries. This cost often implies reduced sample sizes, which may introduce biases and lead to information loss at, for example, the boat level. The recent boost in artificial intelligence applied to fisheries provides a promising way to improve the assessment and management of stocks. We present an operational system using a deep convolutional network (Mask R-CNN) coupled with a statistical model that automatically estimates the number and the mean fork length of dolphinfish (Coryphaena hippurus) caught in a Mediterranean fishery with a resolution of each landed fish box from each boat. The system operates on images of fish boxes collected automatically at the centralized fish auction. The statistical model corrects for biases due to undetected fish using the convolutional network and estimates the mean fork length of the fish in a box from the number of fish and the box weight, allowing for high-resolution monitoring of fishery dynamics during the entire fishing season. The system predictions were empirically validated and showed good accuracy and precision. Our system could be readily incorporated into assessment schemes. We discuss how this type of monitoring system opens new opportunities for improving fishery management.

Worldwide climate change will influence the spatial distribution and status of exploited fish stocks, often in uncertain ways with cascading effects on the social-ecological systems depending on them. Likewise, changes in sociopolitical conditions influencing consumer demand, fuel, and fish prices may jeopardize the viability of fisheries. Predicting whether existing management systems can adapt to these changes is key and especially challenging in data-poor fisheries. In the Mediterranean Sea, the tropical and subtropical dolphinfish (Coryphaena hippurus) is at its northernmost reproductive distribution area and has sustained a seasonal age-0 based artisanal small-scale fishery since ancient times. We built a quantitative Bayesian Belief Network (BBN) model integrating a diverse set of ecological, social, and economic input data to assess the impact of plausible midterm futures (2040-2059) on the seasonal economic profit for dolphinfish fishers in Mallorca (Balearic Islands, Western Mediterranean). These future scenarios accounted for increasing sea surface temperature based on global IPCC projections, population dynamics and growth of dolphinfish, economic forecasts of future fish and fuel prices, and stakeholders' views on feasible adaptations of the local management system. Seven out of twelve scenarios point towards increased profitability for fishers. To date, the fishery is managed locally although within a wider EU regulatory framework, and options such as advancing the opening date of the fishing season in response to climate-induced changes in growth and body size of dolphinfish might be economically beneficial. This will, however, depend critically on the evolution of unknown factors such as changes in other target species, consumption habits of people, and market dynamics. We show here that, even in fisheries without information on stock status, an integrated and holistic assessment of adaptive capacities of management systems is possible

The European hake Merluccius merluccius is the third most valuable species for the North-East Atlantic and the Mediterranean fisheries. European hake has been rated as overexploited in the Mediterranean, thus careful management is advisable. Mediterranean hake is well-differentiated from Atlantic hake, but sub-population structure within the Mediterranean, and how this structure could be translated into stocks (operative management units), is still an elusive topic. Otolith shape and chemistry (concentration of trace elements) have been systematically used to distinguish fish stocks. Our aim was therefore to assess the discrimination capability (assigning fish to the correct geographical unit) of otolith shape and microchemistry at two geographical scales within the Mediterranean: (1) the official geographical subareas (GSAs), and (2) three larger units previously suggested by genetic markers (i.e., Western Mediterranean, Adriatic with Central Mediterranean, and Eastern Mediterranean). Two complementary analyses were completed because shape is more easily analyzed than chemistry. First, a large sample of juvenile hake (n = 1656) from 40 Mediterranean GSAs subunits was used for shape analysis. Second, a subsample of those fish (n = 154) from 10 GSAs was analyzed for both otolith shape and microchemistry. Irrespective of the type of data (shape and/or chemistry) and geographical scale (GSAs versus the 3-units), between-unit differences were always statistically significant. However, according to the large within-unit variability, discrimination capability was always poor but better at the GSA scale, and even better when both shape and microchemistry were combined. Moreover, unsupervised clustering methods (the number and limits of the units are data-driven and not a priori defined as above) failed to find an optimal structure. Overall, these results are fully compatible with the hypothesis of a continuous gradient, within which discrete spatial units cannot be safely

Deep learning is becoming more and more popular in marine ecology to process underwater images and videos. Implementations of several architectures are now easily applied to any field, with just the need of having a labeled dataset of the objects of interest. In the case of marine fish, most of the available underwater fish images datasets are from tropical areas. There is a lack of labeled data from temperate areas such as the Mediterranean, making it necessary to create species (and background)-specific datasets. Here we present an experiment with different underwater background images from the Mediterranean Sea. More than 1500 images have been annotated, labeling all fishes with their species names, (20 species). FASTER RCNN has been chosen to train different scenarios with the aim to understand the basics for a good (if any) generalization, how do the small fishes affect the precision, how many images are needed and if they should be randomly selected or not, questions that have not been completely solved in the literature yet. The first results indicate that it is possible to work with fewer images if they are carefully selected. The generalization of the training for using an unseen dataset improves with the use of different backgrounds in the training dataset. The color preprocessing of images plays a principal role in the annotation process but also in the training results. Finally, avoiding images with small fishes in the training set is translated in better precision and recall results

Oceans are no longer inaccessible places for data acquisition. High-throughput technological advances applied to marine sciences (from genes to global current patterns) are generating Big Data sets at unprecedented rates. How to manage, store, analyse, useand transform this data deluge into knowledge is now a fundamental challenge for ocean sciences. Artificial Intelligence and Machine Learning are the most promising and exciting approaches addressing this challenge. These technologies are directly applicable to many data analysis problems and major challenges in the study of the ocean microbiome, ocean observation and forecasting, animal biology, ecology and conservation, resource management, and marine geosciences. We are only at the beginning of an era when machines are able to solve complex tasks that, until today, have required human expertise. We envision that the combination of ocean Big Data and Artificial Intelligence will provide the means for ground-breaking advances in our understanding of ocean functioning.

Small pelagic fish (SPF) play extremely important ecological roles in marine ecosystems, form some of the most economically valuable fisheries resources, and play a vital role in global food security. Due to their short generation times and tight coupling to lower trophic levels, populations of SPF display large boom-and-bust dynamics that are closely linked to climate variability. To reveal emerging global research trends on SPF as opposed to more recently published, ecosystem-specific reviews of SPF, we reviewed the literature published in two, 6-year periods in the new millennium (2001–2006, and 2011–2016) straddling the publication of a large, global review of the dynamics of SPF in 2009. We explored intrinsic and extrinsic (bottom-up) factors influencing the dynamics of SPF such as anchovies, sardines, herrings and sprats within the sub-order Clupeidae. Published research efforts within 16 different biogeographic ocean regions were compiled (more than 900 studies) and compared to identify i) new milestones and advances in our understanding, ii) emerging research trends and iii) remaining gaps in knowledge. Studies were separated into 5 categories (field, laboratory, mesocosms, long-term statistical analyses and spatially-explicit modelling) and discussed in relation to 10 bottom-up categories including 5 abiotic factors (temperature, salinity, pH, dissolved oxygen, density), 3 physical processes (advection, turbulence, turbidity) and 2 biotic factors (prey quantity and quality). The peer-reviewed literature reflects changes in the number of studies between the two time periods including increases (Mediterranean Sea, Humboldt Current) and decreases (Australia, Benguela Current). Our review highlights i) gaps in ecological knowledge on young juveniles and, in general, on the impacts of hypoxia and heatwaves on SPF, ii) the utility of paleo studies in exploring population drivers, iii) the continued need to develop spatially-explicit, full life-cycle models, iv) t

We analyzed the influence of the stratification process in the vertical distribution of larval fish in a microtidal coastal Mediterranean zone. By applying a Self Organizing Maps (SOM) technique, we could analyze a complex dataset accounting for non-linear processes. The analysis integrated multivariate data on larval fish and environmental parameters in two depth strata through two-time components (nictimeral and fortnightly through the main spawning seasons), and considered size-based information. Although causal relationships cannot be constructed, the use of SOM analyses enabled the description of the whole system evolution overcoming the constraints of linear approaches in complex multivariate datasets with multiple dependencies in the data. We contend that this approach can help to unveil the intricate patterns of settlement/recruitment of young fish, which is often hampered by the rigidity of some formal statistical approaches.

Increasing sea temperature is a driver of change for many fish traits, particularly for fast-growing epipelagic species with short life spans. With warming, altered spawning phenology and faster growth may produce substantially larger body sizes of the new cohort, affecting fishery productivity. We present an individual-based model (IBM) that predicts the distribution of fish length at catch under observed and projected thermal scenarios, accounting for mortality, temperature-dependent spawning phenology, temperature- and photoperiod- dependent growth. This IBM was demonstrated with Coryphaena hippurus (common dolphinfish), a circumglobally-distributed and highly thermophilic species sustaining commercial and recreational fisheries where it is present. The model projected a 13.2% increase in the average length at catch under marine heatwave conditions compared to the current thermal regime (1995–2005 average). Projections under RCP scenarios 4.5 and 8.5 by the end of the century led to 5.1% and 12.8% increase in average length, respectively. Furthermore, these thermal scenarios affected spawning phenology differently, producing higher variance in body size under RCP 8.5 scenario with respect to marine heatwave conditions. This study highlights how the environmental effects of climate change can alter the distribution of species length at catch.

Climate change is anticipated to have long-term and widespread direct consequences for the European marine ecosystems and subsequently for the European fishery sector. Additionally, many socio-economic and political factors linked to climate change scenarios will impact the future development of fishing industries. Robust projection modeling of bioeconomic consequences of climate change on the European fishing sector must identify all these factors and their potential future interaction. In this study, four socio-political scenarios developed in the EU project CERES (Climate change and European aquatic RESources) were operationalized and used in model projections of marine wild capture fisheries. Four CERES scenarios (“World Markets,” “National Enterprise,” “Global Sustainability” and “Local Stewardship”) were based on the IPCC framework of Shared Socio-economic Pathways (SSPs). For each of these scenarios, a set of quantitative outputs was generated to allow projections of bio-economic impacts to mid-century (2050) on wild-capture fisheries operating in different European regions. Specifically, projections accounted for future changes in fisheries management targets, access regulations, international agreements, fish and fuel prices, technological developments and marine spatial planning. This study thoroughly describes the elements of these four fisheries scenarios and demonstrates an example of the “regionalization” of these scenarios by summarizing how they were applied to the North Sea flatfish fishery. Bioeconomic projections highlight the importance of future developments in fuel and fish price development to the viability of that and other fisheries. Adapting these scenarios for use in other models and regions outside the 10 European fisheries examined in CERES would be highly beneficial by allowing direct comparison of the bioeconomic risks and opportunities posed by climate change.

The first weeks in life are crucial for the fate of fish. During this period, fish show large dispersal rates and suffer from massive mortality due mainly to predation. Intrinsic and extrinsic processes (growth rates, advection, behavior, diseases) affect this mortality and have profound consequences on populations. For a century now, describing the distribution, physiology and dynamics of fish early life phases has been the focus of intense research, building a solid community of scientists that met at the 43rd Annual Larval Fish Conference, held in Palma, Spain, 21-24 May 2019. The present Theme Section consists of 19 papers that are a sample of the research presented at the conference. The papers are organized around 5 main topics: (1) mortality estimation and process understanding, (2) parental effects on larval fish ecology, (3) larval settlement to juvenile grounds, (4) early life stages of fish within food webs, and (5) contribution of early life stages of fish to assessment and management. Contributions to this Theme Section focus on hot topics as well as old paradigms; the latter continue to elicit much research work, which has benefited from recent advances in technology.

Chemical cues from predators induce a range of predator-induced morphological defences (PIMDs) observed across fish taxa. However, the mechanisms, consistency, direction and adaptive value of PIMDs are still poorly studied. Here, we have tested if predatory cues can induce changes in the body shape of the juvenile marine fish Sparus aurata reared under controlled conditions without the presence of predators by exposing individuals to the olfactory stimulus of a fish predator. We tested our hypothesis using a nested replicated before-after-control-impact experiment, including recovery (potential reversibility) after the cessation of the predator stimulus. Differences in the size-independent body shape were explored using landmark-based geometric morphometrics and revealed that, on average, individuals exposed to a predatory cue presented deeper bodies and longer caudal regions, according to our adaptive theoretical predictions. These average plastic responses were reversible after withdrawal of the stimulus and individuals returned to average body shapes. We, therefore, provide evidence supporting innate reversible PIMDs in marine naive fish reared under controlled conditions. The effects at the individual level, including fitness and the associated applied implications, deserve further research.

The dynamics of fish length distribution is a key input for understanding the fish population dynamics and taking informed management decisions on exploited stocks. Nevertheless, in most fisheries, the length of landed fish is still made by hand. As a result, length estimation is precise at fish level, but due to the inherent high costs of manual sampling, the sample size tends to be small. Accordingly, the precision of population-level estimates is often suboptimal and prone to bias when properly stratified sampling programmes are not affordable. Recent applications of artificial intelligence to fisheries science are opening a promising opportunity for the massive sampling of fish catches. Here, we present the results obtained using a deep convolutional network (Mask R-CNN) for unsupervised (i.e. fully automatic) European hake length estimation from images of fish boxes automatically collected at the auction centre. The estimated mean of fish lengths at the box level is accurate; for average lengths ranging 20–40 cm, the root-mean-square deviation was 1.9 cm, and maximum deviation between the estimated and the measured mean body length was 4.0 cm. We discuss the challenges and opportunities that arise with the use of this technology to improve data acquisition in fisheries.

The common dolphinfish (Coryphaena hippurus) is an epipelagic thermophilic species with a worldwide distribution in tropical and subtropical regions that is characterized by its migratory behavior and fast growth rates. This species is targeted by artisanal small-scale and recreational fisheries in most regions where it is found. This paper updates and analyzes the global scientific knowledge on the biology and ecology of this species, which was last revised at a regional level 20 years ago. This review showed an increase in knowledge about the population structure and regional differences in biological traits, in parallel with a notable lack of mechanistic and even empirical knowledge about the ecology of this species, which hampers a good understanding of the population dynamics and the potential impacts of environmental change. This paper also updates the information about the Mediterranean dolphinfish fishery, where the main four countries that exploit this species deploy 30% of fish aggregation devices (FAD) worldwide. The results suggest, among other effects, some temporal synchronicity in landings across countries, potential interannual stock movement affecting inter-country catches, diverging trends in prices and insufficient quality in the estimates of fishing effort. The authors propose a suite of specific measures to ameliorate this lack of knowledge and to better manage this complex living resource.

Despite the recognised effectiveness of networks of marine protected areas (MPAs) as a biodiversity conservation instrument, MPA network design frequently disregards the importance of connectivity patterns. In the case of sedentary marine populations, connectivity stems not only from the stochastic nature of the physical environment that affects dispersal of early life stages, but also from the spawning stock attributes that affect reproductive output (e.g. passive eggs and larvae) and survivorship. Early life stages are virtually impossible to track in the ocean. Therefore, numerical ocean current simulations coupled with egg and larval Lagrangian transport models remain the most common approach for the assessment of marine larval connectivity. Inferred larval connectivity may differ depending on the type of connectivity considered; consequently, the prioritisation of sites for the conservation of marine populations might also differ. Here, we introduce a framework for evaluating and designing MPA networks based on the identification of connectivity hotspots using graph theoretic analysis. As a case study, we used a network of open-access areas and MPAs off Mallorca Island (Spain), and tested its effectiveness for the protection of the painted comber Serranus scriba. Outputs from network analysis were used to (1) identify critical areas for improving overall larval connectivity, (2) assess the impact of species’ biological parameters in network connectivity and (3) explore alternative MPA configurations to improve average network connectivity. Results demonstrate the potential of graph theory to identify non-trivial egg/larval dispersal patterns and emerging collective properties of the MPA network, which are relevant for increasing protection efficiency

Numerical modeling is a key tool to complement the current physical and biogeochemical observational datasets. It is essential for understanding the role of oceanographic processes on marine food webs and producing climate change projections of variables affecting key ecosystem functions. In this work, we evaluate the horizontal and vertical patterns of four state-of-the-art coupled physical–biogeochemical models, three of them already published. Two of the models include data assimilation, physical and/or biological, and two do not. Simulations are compared to the most exhaustive dataset of in situ observations in the North Western Mediterranean, built ad hoc for this work, comprising gliders and conventional CTD surveys and complemented with satellite observations. The analyses are performed both in the whole domain and in four subregions (Catalan Shelf, Ebro Delta, Mallorca Channel, and Ibiza Channel), characterized by a priori divergent primary production dynamics and driving mechanisms. Overall, existing models offer a reasonable representation of physical processes including stratification, surface temperature, and surface currents, but it is shown that relatively small differences among them can lead to large differences in the response of biogeochemical variables. Our results show that all models are able to reproduce the main seasonal patterns of primary production both at the upper layer and at the deep chlorophyll maximum (DCM), as well as the differential behavior of the four subregions. However, there are significant discrepancies in the local variability of the intensity of the winter mixing, phytoplankton blooms, or the intensity and depth of the DCM. All model runs show markedly contrasting patterns of interannual phytoplankton biomass in all four subregions. This lack of robustness should dissuade end users from using them to fill gaps in time series observations without assessing their appropriateness. Finally, we discuss the usability of these mod

We studied the effect of climate change on the potential spawning habitats of two marine small pelagic fishes. We examined the projected changes in the potential spawning habitat of the summer-spawning anchovy (Engraulis encrasicolus) and round sardinella (Sardinella aurita) in the northwestern Mediterranean by combining the regionalized projections of RCP scenarios with an existing species distribution model (SDM). The SDM was based on a separate generalized additive model for the eggs and larvae of the two species computed from ichthyoplankton sampling that was conducted with simultaneous readings of surface temperature, salinity and chlorophyll-a values as predictor variables. The SDM was projected for the 2010 decade, which represented the present-day conditions, with these environmental variables obtained from the regionalized POLCOMS-ERSEM biogeochemical model forced by the RCP 4.5 and RCP 8.5 scenarios. The comparison of the present-day projection results with the projections for the middle and final decades of the twenty-first century showed that the suitability of the spawning habitat as defined by the anchovy eggs model was likely to increase over time under RCP4.5 or decrease slightly under RCP8.5, but the habitat for anchovy larvae was likely to decrease in all cases. Loss of habitat was projected to be particularly important in the south of the study area on the Ebre River delta continental shelf. Conversely, the probability of round sardinella occurrence will significantly increase under both scenarios. The potential habitat of this species, which is of subtropical origin, is likely to shift northwards. The limitations of the existing models to extrapolate the current results to future scenarios are discussed regarding (i) the uncertainty in the projections of driving environmental variables (e.g., chlorophyll-a), (ii) the simplified nature of the projection models, which did not capture the dynamics of the early life stages of the fish at a small scal

An amalgam of empirical data from laboratory and field studies is needed to build robust, theoretical models of climate impacts that can provide science-based advice for sustainable management of fish and shellfish resources. Using a semi-systematic literature review, Gap Analysis and multilevel meta-analysis, we assessed the status of empirical knowledge on the direct effects of climate change on 37 high-value species targeted by European fisheries and aquaculture sectors operating in marine and freshwater regions. Knowledge on potential climate change-related drivers (single or combined) on several responses (vital rates) across four categories (exploitation sector, region, life stage, species), was considerably unbalanced as well as biased, including a low number of studies (a) examining the interaction of abiotic factors, (b) offering opportunities to assess local adaptation, (c) targeting lower-value species. The meta-analysis revealed that projected warming would increase mean growth rates in fish and mollusks and significantly elevate metabolic rates in fish. Decreased levels of dissolved oxygen depressed rates of growth and metabolism across coherent species groups (e.g., small pelagics, etc.) while expected declines in pH reduced growth in most species groups and increased mortality in bivalves. The meta-analytical results were influenced by the study design and moderators (e.g., life stage, season). Although meta-analytic tools have become increasingly popular, when performed on the limited available data, these analyses cannot grasp relevant population effects, even in species with a long history of study. We recommend actions to overcome these shortcomings and improve mechanistic (cause-and-effect) projections of climate impacts on fish and shellfish.

The ICES Working Group on comparative analyses between European Atlantic and Mediterranean Ecosystems to move towards an Ecosystem-based Approach to Fisheries (WGCOMEDA) recently completed its second three-year cycle. WGCOMEDA was established in 2014 and works in cooperation with other groups within the ICES Integrated Ecosystem Assessments Steering Group (IEASG). The working group objectives are: i) to set the analytical and data framework to develop common Integrated Ecosystem Assessments (IEA) exercises, ii) to advance several ongoing comparative studies on ecosystems stability, and iii) to frame comparative size- and traits-based approaches in both demersal and benthic ecosystems. To fulfil these objectives, the group focused on: Providing a more complete understanding of the structural and functional role of ecological stability across different types of ecosystems. The group has made considerable progress on understanding the interplay between stability-diversity-resilience on both demersal and pelagic communities, conducting a resilience assessment to detect recent regime shifts, and examining the role of individual and species size in the stability of communities. Using functional trait information to assess the structure and functioning of demersal and benthic communities across Mediterranean and Atlantic systems and to predict their vul-nerability to fishing disturbance. Trait-based approaches (TBA) has been one of the most active topics of the group, with several Atlantic-Mediterranean comparative studies, and novel regional studies developing new approaches and methods combining trait information with methods and data beyond scientific surveys. In addition, specific trait-based approaches have been developed in the benthic realm by formulating disturbance and re-sistance indices on sensitive benthic habitats. Analyzing the link between ecological stability across different ecosystems types and eco-system properties (structure and function). Work on this

Technological advances in underwater video recording are providing novel opportunities for monitoring wild fish. Although extracting data from videos is often challenging, accurate and precise estimates of density for animals whose normal activities are restricted to a bounded area or home range can be obtained from counts averaged across a relatively low number of video frames. However, this method requires that individual detectability (PID, the probability of detecting a given animal provided that it is actually within the area surveyed by a camera) be known. Here we propose a Bayesian implementation for estimating PID after combining counts from cameras with counts from any other reference method. The proposed framework was demonstrated with a case study of Serranus scriba, a widely distributed and resident coastal fish. Density and PID were calculated after combining fish counts from unbaited remote underwater video (RUV) and underwater visual censuses (UVC) as reference methods. The relevance of the proposed framework is that after estimating PID, fish density can be estimated accurately and precisely at the UVC scale (or at the scale of the preferred reference method) using RUV only. This method is further validated using computer simulations based on empirical data. We provide a simulation tool kit for comparing the expected precision attainable for different sampling effort and for species with different levels of PID. Overall, the proposed method may contribute to substantially enlarge the spatio-temporal scope of density monitoring programmes for many resident fish.

Marine resource management is shifting from optimizing single species yield to redefining sustainable fisheries within the context of managing ocean use and ecosystem health. In this introductory article to the theme set, “Plugging spatial ecology into ecosystem-based management (EBM)” we conduct an informal horizon scan with leaders in EBM research to identify three rapidly evolving areas that will be game changers in integrating spatial ecology into EBM. These are: (1) new data streams from fishers, genomics, and technological advances in remote sensing and bio-logging; (2) increased analytical power through “Big Data” and artificial intelligence; and (3) better integration of social dimensions into management. We address each of these areas by first imagining capacity in 20 years from now, and then highlighting emerging efforts to get us there, drawing on articles in this theme set, other scientific literature, and presentations/discussions from the symposium on “Linkages between spatial ecology and sustainable fisheries” held at the ICES Annual Science Conference in September 2017.

An amalgam of empirical data from laboratory and field studies is needed to build robust, theoretical models of climate impacts that can provide science-based advice for sustainable management of fish and shellfish resources. Using a semi-systematic literature review, Gap Analysis and multilevel meta-analysis, we assessed the status of empirical knowledge on the direct effects of climate change on 37 high-value species targeted by European fisheries and aquaculture sectors operating in marine and freshwater regions. Knowledge on potential climate change-related drivers (single or combined) on several responses (vital rates) across four categories (exploitation sector, region, life stage, species), was considerably unbalanced as well as biased, including a low number of studies (a) examining the interaction of abiotic factors, (b) offering opportunities to assess local adaptation, (c) targeting lower-value species. The meta-analysis revealed that projected warming would increase mean growth rates in fish and mollusks and significantly elevate metabolic rates in fish. Decreased levels of dissolved oxygen depressed rates of growth and metabolism across coherent species groups (e.g., small pelagics, etc.) while expected declines in pH reduced growth in most species groups and increased mortality in bivalves. The meta-analytical results were influenced by the study design and moderators (e.g., life stage, season). Although meta-analytic tools have become increasingly popular, when performed on the limited available data, these analyses cannot grasp relevant population effects, even in species with a long history of study. We recommend actions to overcome these shortcomings and improve mechanistic (cause-and-effect) projections of climate impacts on fish and shellfish.

Many studies underscore the importance of incorporating the effect of environmental data within a life-history-stage–specific framework for determining the recruitment and survival of small pelagic fish. The recruitment of anchovy (Engraulis encrasicolus) in the Gulf of Cádiz (NE Atlantic) is sensitive to the effect of intense easterlies, stratification of the water column, and discharges from the Guadalquivir River on early life stages. As a proof of concept, we have developed the basis for a new Bayesian model with a dual time step resolution: monthly for juveniles and adults, and weekly for earlier life stages. This dual time step resolution resolves environmental effects on prerecruits while simulating the effect of fishing on recruits. Our estimates for juvenile abundances are validated with field data. The Bayesian framework accounts for the uncertainty, thus providing consistent length-frequency estimates and a plausible environmentally driven stock-recruitment relationship.