Map of average sea surface temperature in January 1990–2011 showing the relatively warm high-saline eastern Atlantic water flowing north-eastwards on and along the continental shelf edge, flanked by cooler water masses. Temperature measurement measured by satellite and mapped with permission from Bundesamt fu  ̈ r Seeschifffahrt und Hydrographie, Germany (www.bsh.de). doi:10.1371/journal.pone.0051541.g002 

Map of average sea surface temperature in January 1990–2011 showing the relatively warm high-saline eastern Atlantic water flowing north-eastwards on and along the continental shelf edge, flanked by cooler water masses. Temperature measurement measured by satellite and mapped with permission from Bundesamt fu ̈ r Seeschifffahrt und Hydrographie, Germany (www.bsh.de). doi:10.1371/journal.pone.0051541.g002 

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It has been suggested that observed spatial variation in mackerel fisheries, extending over several hundreds of kilometers, is reflective of climate-driven changes in mackerel migration patterns. Previous studies have been unable to clearly demonstrate this link. In this paper we demonstrate correlation between temperature and mackerel migration/di...

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... was calculated by year and quarter as the weighted average of distances. The weighting factor was the mass (in kg) of each projected landing record. Po50%CL was calculated as the position along the CSE where the cumulative landings represented 50% of the total landings by year and quarter. A literature survey and an interview with the skipper of a vessel that fished throughout the study period were carried out in order to identify periods where changes in the behavior of the commercial fishery were driven by factors other than mackerel behavior. Data from international bottom trawl surveys (IBTS) carried out in quarter 1 (January–March) between 1985 and 2011 on the shelf out to 500 m were downloaded from the ICES repository (http:// datras.ices.dk). The study area was limited to the area described for the commercial landings. Relatively few mackerel were caught outside the study area, e.g. in Kattegat/Skagerrak [12] and over 90% were from surveys in March. Further south, in the Bay of Biscay, mackerel arrive at the spawning grounds around the time of this survey [13]: the present dataset therefore covers the northern part of the NEA mackerel population. Catch per Unit Effort (CPUE) of adult mackerel was calculated as catch in numbers per trawl hour, where adult mackerel were defined as being longer than 27 cm (most mackerel first spawn at the age of 2 (58%) and the mean length at age 2 in Q1 west of Scotland is 27 cm [4]). For ease of comparison with the commercial landings dataset, first quarter surveys were treated as being a ‘5 th ’ quarter of the previous year. Hauls were projected onto the CSE axis as described for commercial landings and the CoG and Po50%CL of CPUEs calculated. In the present study, we investigate links between water temperature and mackerel distribution that could support the hypothesis of a temperature-driven migration. The continental shelf edge current which flows along the shelf edge to the northwest of Scotland, north and then east of the Shetland Islands, along the western edge of the Norwegian trench and into the northern North Sea, is warmer than both the surrounding coastal waters and the oceanic waters off the shelf during winter (Figure 2) [8,9]. It is the temperature of this water mass that is of interest in this study. Unfortunately, relevant observations are not available for the entire study period. A relevant temperature record was therefore obtained by embedding the available hydrographic observations within a statistical model. The modelled area is shown in Figure 1 and was selected because it is the coldest area of the warm core of the current (Figure 2) and therefore the area where cold avoidance by mackerel would be most pronounced. Also, there are a significant number of observations available for this area. It is within this core of relatively warm water in the northern North Sea that acoustic surveys found mackerel to aggregate in 50–220 m depth in early winter [8–10,14]. Due to the fact that water is cooled throughout the winter, both downstream (along) and away from the CSE, temperature was modeled with year, day of year, distance parallel (CSE) and perpendicular (dCSE) to the CSE axis as explanatory variables ...
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... was calculated by year and quarter as the weighted average of distances. The weighting factor was the mass (in kg) of each projected landing record. Po50%CL was calculated as the position along the CSE where the cumulative landings represented 50% of the total landings by year and quarter. A literature survey and an interview with the skipper of a vessel that fished throughout the study period were carried out in order to identify periods where changes in the behavior of the commercial fishery were driven by factors other than mackerel behavior. Data from international bottom trawl surveys (IBTS) carried out in quarter 1 (January–March) between 1985 and 2011 on the shelf out to 500 m were downloaded from the ICES repository (http:// datras.ices.dk). The study area was limited to the area described for the commercial landings. Relatively few mackerel were caught outside the study area, e.g. in Kattegat/Skagerrak [12] and over 90% were from surveys in March. Further south, in the Bay of Biscay, mackerel arrive at the spawning grounds around the time of this survey [13]: the present dataset therefore covers the northern part of the NEA mackerel population. Catch per Unit Effort (CPUE) of adult mackerel was calculated as catch in numbers per trawl hour, where adult mackerel were defined as being longer than 27 cm (most mackerel first spawn at the age of 2 (58%) and the mean length at age 2 in Q1 west of Scotland is 27 cm [4]). For ease of comparison with the commercial landings dataset, first quarter surveys were treated as being a ‘5 th ’ quarter of the previous year. Hauls were projected onto the CSE axis as described for commercial landings and the CoG and Po50%CL of CPUEs calculated. In the present study, we investigate links between water temperature and mackerel distribution that could support the hypothesis of a temperature-driven migration. The continental shelf edge current which flows along the shelf edge to the northwest of Scotland, north and then east of the Shetland Islands, along the western edge of the Norwegian trench and into the northern North Sea, is warmer than both the surrounding coastal waters and the oceanic waters off the shelf during winter (Figure 2) [8,9]. It is the temperature of this water mass that is of interest in this study. Unfortunately, relevant observations are not available for the entire study period. A relevant temperature record was therefore obtained by embedding the available hydrographic observations within a statistical model. The modelled area is shown in Figure 1 and was selected because it is the coldest area of the warm core of the current (Figure 2) and therefore the area where cold avoidance by mackerel would be most pronounced. Also, there are a significant number of observations available for this area. It is within this core of relatively warm water in the northern North Sea that acoustic surveys found mackerel to aggregate in 50–220 m depth in early winter [8–10,14]. Due to the fact that water is cooled throughout the winter, both downstream (along) and away from the CSE, temperature was modeled with year, day of year, distance parallel (CSE) and perpendicular (dCSE) to the CSE axis as explanatory variables ...

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... Animal distributions exhibit deliberate spatial patterns and fish movements are no exception (Levin, 1992). The knowledge of fish occurrence is essential to determinate the appropriate management when fish are the object of commercial, biomanipulation, or conservation interest (Jansen et al., 2012;Kanno et al., 2012). Such knowledge also provides information about using appropriate methods in different habitat and diel periods. ...
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Day and nighttime autumn fish abundance and biomass were studied in the pelagic and littoral zones of Lake Sauka in Latvia. Both pelagic methods (hydroacoustics and trawling) revealed significantly higher fish abundance and biomass during the day than at night, especially in deeper zones (below 3 m). Roach (Rutilus rutilus) and Eurasian perch (Perca fluviatilis) dominated the trawl catches during the day, while roach and ruffe dominated at night. Fish smaller than 14 cm strongly dominated in both the trawl catches and hydroacoustic observations. Our hydroacoustic sampling found inhomogeneous pelagic distributions of fish aggregated in big shoals during the day. In the littoral zone, which was sampled by beach seining, both the abundance and biomass were significantly higher at night than during the day. Roach, bleak, and European perch usually dominated in beach seine catches during the day and at night. The daytime pelagic biomass found by hydroacoustics was 62 kg/ha and it decreased to approximately 11 kg/ha at night. The littoral biomass found by beach seining was diurnally opposite, 4 kg/ha during the day and 37 kg/ha at night. It is obvious that diurnal horizontal migrations between pelagic and littoral zones, and shoaling behavior during the daytime are common patterns in the shallow Lake Sauka during the autumn. The study of the spatial distribution of fish is extremely important for the establishment of an appropriate monitoring plan for the purposes of the Water Framework Directive with regard to the morphometry of the lake, the geographical location, and the sampling period of the year. This study also shows that the combination of completely non‐invasive hydroacoustic and other methods that are invasive (trawls, beach seines) but not as destructive as gillnets, which are normally used for scientific fish monitoring in Europe, could be a future way forward for fish monitoring.
... The frequency and timing of calibration will depend on the specific properties of the fishery, such as the geographical location, and thus possible bycatch species, and seasonal spread of the fisheries, as many economically important pelagic fishes are fished during at least two seasons (i.e. Baltic Sea and North Sea sprat fisheries, North Sea herring fisheries 44 , and mackerel fisheries 45 ). Accordingly, we recommend model selection and calibration at the start and at regular intervals during each fishing season. ...
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Pelagic fish like herring, sardines, and mackerel constitute an essential and nutritious human food source globally. Their sustainable harvest is promoted by the application of precise, accurate, and cost-effective methods for estimating bycatch. Here, we experimentally test the new concept of using eDNA for quantitative bycatch assessment on the illustrative example of the Baltic Sea sprat fisheries with herring bycatch. We investigate the full pipeline from sampling of production water on vessels and in processing factories to the estimation of species weight fractions. Using a series of controlled mixture experiments, we demonstrate that the eDNA signal from production water shows a strong, seasonally consistent linear relationship with herring weight fractions, however, the relationship is influenced by the molecular method used (qPCR or metabarcoding). In four large sprat landings analyzed, despite examples of remarkable consistency between eDNA and visual reporting, estimates of herring bycatch biomass varied between the methods applied, with the eDNA-based estimates having the highest precision for all landings analyzed. The eDNA-based bycatch assessment method has the potential to improve the quality and cost effectiveness of bycatch assessment in large pelagic fisheries catches and in the long run lead to more sustainable management of pelagic fish as a precious marine resource.
... Afterwards, mackerel migrates farther into the Nordic Seas for feeding during the productive high-latitude summer (Bachiller et al., 2016;Nøttestad et al., 2016). In autumn, mackerel returns to shelf areas off the British Isles and thereafter continues either southwards or to places near the Norwegian Trench to overwinter (Jansen et al., 2012). The following lengthy spawning dynamics of each individual are supported by active feeding 2 T.C. dos Santos Schmidt et al. Figure S2), (B) age-specific abundance of mackerel (ages 2 to 12+ years) from 1980 to 2021 (ICES, 2022a), (C) mean and coefficient interval (±95% CI) of body condition (Fulton K = 100 × W/TL 3 ) for sexually mature mackerel (ages 4 to 12+ years), and (D) mean (±95% CI) total length-at-age for 6 years old (TL age6 ) mackerel, based on data from the Norwegian purse seine (unselective gear, Slotte et al., 2007) sampled in September and October from 1980 to 2021 (IMR database, see also Olafsdottir et al., 2016;Jansen et al., 2021). ...
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... The percentage of landings in the Newfoundland regions relative to those elsewhere have also been linked to SST, differential food availability between regions, and stock characteristics . Although the lack of georeferenced fishery data for northern contingent mackerel has so far precluded a detailed study on species distribution and associated potential drivers, an effect of temperature as well as food availability and stock size and structure should be expected as they are key drivers of the distribution of the northeast Atlantic mackerel stock and the northwest Atlantic southern contingent (Jansen and Gislason, 2011;Overholtz et al., 2011;Jansen et al., 2012;Utne et al., 2012;Radlinski et al., 2013;Van Der Kooij et al., 2016;Nikolioudakis et al., 2019). Therefore, although variations in fishing effort inevitably affect the amounts caught, largeamplitude fluctuations in annual landings are likely highly associated with substantial changes in mackerel biomass in the (at least near-shore) waters surrounding Newfoundland. ...
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... The fish of all three components remain in these feeding areas until autumn, thus constituting the so-called 'autumn mackerel' that are particularly targeted in the present study. After extensive feeding, each component migrates back to its respective spawning area (Belikov et al., 1998;Iversen, 2002;Jansen et al., 2012;Nøttestad et al., 2016;Uriarte and Lucio, 2001;Villamor et al., 2004), where new infections with K. thyrsites may occur. Hence, fish of the southern and western components may represent the mackerel stock fraction that carries K. thyrsites-infections andmay develop 'soft flesh' (Levsen et al., 2008). ...
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Northeast Atlantic (NEA) mackerel (Scomber scombrus, Scombridae) represents an economically important target for the Norwegian pelagic fishing industry. Despite the commercial significance of NEA mackerel, little is known about the infections with the myxosporean parasite Kudoa thyrsites (Kudoidae). The parasite may cause post-mortem myoliquefaction of the fish skeletal muscle and therefore reduce the quality of the fish product. In this study, we examined 'soft flesh' occurrence in commercial size groups of NEA 'autumn mackerel' caught between 2007 and 2020, and investigated the prevalence and density of K. thyrsites (qPCR) and how they related to the occurrence of 'soft flesh'. The present study is the first long-term investigation of the occurrence of K. thyrsites-induced 'soft flesh' in NEA mackerel. After appearing stable for over a decade, the 'soft flesh' occurrence increased three-to six-fold in 2019 and 2020. This increase, together with the findings that 'soft flesh' seems primarily to affect the commercially most valuable mackerel size group (>400 g), may have important implications for the fishing industry and the fishery management. Molecular analysis (qPCR) suggests that the prevalence of K. thyrsites is substantially higher than 'soft flesh' occurrence. The majority (87.4%, n = 76/87) of infected mackerel did not develop 'soft flesh' and only individuals with high parasite density in the musculature (12.6%, n = 11/87) showed the condition. Therefore, qPCR analyses should be used for estimating the prevalence of K. thyrsites in fish. The method may also be used to assess the risk of NEA mackerel to develop post-mortem 'soft flesh'.
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... Vertical dynamics have been shown (Fernandes et al., 2016), but the extent and bioenergetics implications are unknown and are, for the purpose of this study, assumed neglectable in relation to horizontal dynamics. The mackerel generally migrate against the current from the Northern North Sea to the spawning areas (Jansen et al., 2012). The core of the current varies substantially over short distances in space and time. ...
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... Since the nutrients brought by the North Atlantic warm current will decrease from the central area to both sides, we naturally fit its distribution law with a normal distribution(ND). According to the 3σ principle in ND, and the historical fishing area of the mackerel we found [8], the standard deviation( = 2√2) was determined, and considering the nutrition-rich position will come with greater pressure of homospecies competition and natural enemy predation, so A competitionpredator correction factor C = 0.1 was added to the ND function. Based on the work we have done, we use these variables and data to establish DLP models for herring and mackerel positions, respectively. ...
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With the pace of global warming getting faster and faster, the temperature of the sea is gradually rising. Meanwhile, Scotland, located on the east coast of the North Atlantic, is facing a serious problem: how to develop their fishing industry while fish are migrating. In this article, we will use a series of models to analyze the current situation and recommend some ways for the development of small Scottish fishing companies. In Task 1, based on the sufficient global ocean temperature data recorded by the Met Office Hadley Centre, we firstly used the convolutional neural network (CNN) model to learn the ocean temperature change in the waters around Scotland in the past 50 years, to find out the changing trend and make a reasonable prediction on the sea temperature (SST) change in the next 50 years. Then, by using the obtained data in ocean temperature, continental shelves, and ocean currents, the behavior of herring and mackerel was excavated and simulated. Besides, we build a double-objective Linear Programming (DLP) model to predict the location of these two fish species in the next 50 years, based on the distribution patterns of the Scottish fisheries over the past 50 years. What’s more, we also performed the Process of Gridding on the map of Scotland, making all the models easier to calculate. In Task 2, we used the Logistic Growth Model (LGM) to establish a freshness model for captured fish, combined with the speed of a Scottish fishery boat to calculate the company's fishing range. Then we obtained the best and worst situations of the two fish habitats for the company's fishing business in the next 50 years through the fine-tuning of the parameters and sensitivity analysis. Also, we predicted the most likely future time for the company to be unable to continue fishing if maintaining a former strategy. In Task 3, we simulated the model in Task 2 to obtain a fish freshness model with refrigeration equipment, which provides basic elements needed for relocating a fishing port or updating fishing boats. Lastly, through the establishment of the Abstract Value-Estimated Model (AVEM), we roughly calculated the value of the two schemes to finally assure that the relocation of the fishing port was more economical. Although we did a lot of research to improve the accuracy of our work, the political decision of the government will change indefinitely, which means that our research still needs some innovational work before the actual adjustments of Scottish fishery.
... De la même manière, en raison de variations des paramètres abiotiques, de nombreuses espèces effectuent des migrations saisonnières causant des variations de structure et donc de comportement trophique (Brönmark et al., 2010). C'est le cas par exemple du maquereau (Scomber scombrus), dont les importantes migrations s'avèrent être fortement liées à la température, permettant à cette espèce de trouver des zones favorables pour son alimentation et sa reproduction (Jansen et al., 2012). La température est également un facteur important influençant la production primaire puisqu'elle détermine dans une large mesure l'initiation des blooms phytoplanctoniques (Guallar et al., 2017;Leterme et al., 2008). ...
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Identifying and quantifying trophic interactions between organisms is crucial to understand the structure and functioning of food webs. However, many sources of variation are poorly known, such as seasonal and ontogenetic variations. These variations have been studied at several levels of organization, assemblage, species and individual in the Eastern English Channel (EEC) and the Southern North Sea (SNS) ecosystems. These variations were studied by analyzing the stable isotopes of carbon (d13C) and nitrogen (d15N), which provides information on resource and habitat use in a two-dimensional space. These analyses were performed on several tissues because isotopic values of tissues reflect the diet over the period during which the tissue was synthesized providing dietary information over different temporal periods. These analyses were coupled with stomach contents analyses providing a short-term picture of feeding patterns. At the assemblage scale, coupling between benthic and pelagic habitats has emerged as an important feature of the EEC ecosystem, due to its shallow depth, as well as to the combination of two ecological processes. First, trophic interaction revealed trophic plasticity and resource partitioning. Second, changes in the composition of fish assemblage did not impact benthic-pelagic couplings as most dominant species were generalist within a period, allowing a complete use of all available resources. Further analyses were carried out on whiting, the dominant species in the EEC-SNS ecosystems in winter, and revealed seasonal and ontogenetic trophic changes for this species. Finally, at the individual scale, species tend to be generalist but composed of specialist individuals along the d13C and d15N axes. The species increase their niche mainly through an increase of the niche variation between individuals. This behavior may be a strategy to avoid competition and favored by the important benthic-pelagic coupling offering a wide diversity of resources for species. This study revealed the importance of considering ontogenetic and seasonal changes of trophic interactions. Informing these changes in ecosystem models would increase their ability to capture the complexity of marine ecosystems and inform fisheries management. Modeling these effects at several levels of organization is necessary to predict the effects of global change on ecosystem structure and functioning.