A tale of two seas: a meta‐analysis of crustacean stocks in the NE Atlantic and the Mediterranean Sea

Meta‐analysis of marine biological resources can elucidate general trends and patterns to inform scientists and improve management. Crustacean stocks are indispensable for European and global fisheries; however, studies of their aggregate development have been rare and confined to smaller spatial and temporal scales compared to fish stocks. Here, we study the aggregate development of 63 NE Atlantic and Mediterranean crustacean stocks of six species (Nephrops norvegicus, Pandalus borealis, Parapenaeus longirostris, Aristeus antennatus, Aristaeomorpha foliacea and Squilla mantis) in 1990–2013 using biomass index data from official stock assessments. We implemented a dynamic factor analysis (DFA) to identify common underlying trends in biomass indices and investigate the correlation with the North Atlantic Oscillation (NAO) index. The analysis revealed increasing and decreasing trends in the northern and southern NE Atlantic, respectively, and stable or slowly increasing trends in the Mediterranean, which were not related to NAO. A separate meta‐analysis of the fishing mortality (F) and biomass (B) of 39 analytically assessed crustacean stocks was also carried out to explore their development relative to MSY. NE Atlantic crustacean stocks have been exploited on average close to F_MSY and remained well above B_MSY in 1995–2013, while Mediterranean stocks have been exploited 2–4 times above F_MSY in 2002–2012. Aggregate trends of European crustacean stocks are somewhat opposite to trends of fish stocks, suggesting possible cascading effects. This study highlights the two‐speed fisheries management performance in the northern and southern European seas, despite most stocks being managed in the context of the European Union's Common Fisheries Policy.     

Revisiting safe biological limits in fisheries

The appropriateness of three official fisheries management reference points used in the north‐east Atlantic was investigated: (i) the smallest stock size that is still within safe biological limits (SSB_pa), (ii) the maximum sustainable rate of exploitation (F_msy) and (iii) the age at first capture. As for (i), in 45% of the examined stocks, the official value for SSB_pa was below the consensus estimates determined from three different methods. With respect to (ii), the official estimates of F_msy exceeded natural mortality M in 76% of the stocks, although M is widely regarded as natural upper limit for F_msy. And regarding (iii), the age at first capture was below the age at maturity in 74% of the stocks. No official estimates of the stock size (SSB_msy) that can produce the maximum sustainable yield (MSY) are available for the north‐east Atlantic. An analysis of stocks from other areas confirmed that twice SSB_pa provides a reasonable preliminary estimate. Comparing stock sizes in 2013 against this proxy showed that 88% were below the level that can produce MSY. Also, 52% of the stocks were outside of safe biological limits, and 12% were severely depleted. Fishing mortality in 2013 exceeded natural mortality in 73% of the stocks, including those that were severely depleted. These results point to the urgent need to re‐assess fisheries reference points in the north‐east Atlantic and to implement the regulations of the new European Common Fisheries Policy regarding sustainable fishing pressure, healthy stock sizes and adult age/size at first capture.     

Identifying spawner biomass per‐recruit reference points from life‐history parameters

Analysis of spawning biomass per‐recruit has been widely adopted in fisheries management. Fishing mortality expressed as spawning potential ratio (SPR) often requires a reference point as an appropriate proxy for the fishing mortality that supports a maximum sustainable yield—F_MSY. To date, a single generic level between F_30% and F_40% is routinely used. Using records from stock assessments in the RAM Legacy Database (RAMLD), we confirm that SPR at MSY (SPR_MSY) is a declining function of stock productivity quantified by F_MSY. We then use general linear models (GLM) and Bayesian errors‐in‐variables models (BEIVM) to show that SPR_MSY can be predicted from life‐history parameters (LHPs, including maximum lifespan, age‐ and length‐at‐maturation, growth parameters, natural mortality, and taxonomic Class) as well as gear selectivity. The calculated SPR_MSY ranges from about 13% to 95% with a mean of 47%. About 64% of the stocks in the RAMLD require SPR_MSY > 40%. Modelling SPR_MSY reveals that LHPs plus Class explain 61% of the deviance in SPR_MSY. Faster‐growing, low‐survival, and short‐lived species generally require a high SPR. With equal LHPs, elasmobranchs require about 20% higher SPR_MSY than teleosts. When F_MSY is estimated from fisheries that harvest older fish, increasing the vulnerable age by one year leads to about an 8% increase in SPR_MSY. The BEIVM yields smaller variance and bias than the GLM. The models developed in this study could be used to predict SPR_MSY reference points for new stocks using the same LHPs for calculating F_x%, but without knowledge of the stock‐recruitment parameters.     

Selectivity metrics for fisheries management and advice

Fisheries management typically aims at controlling exploitation rate (e.g., Fbar) to ensure sustainable levels of stock size in accordance with established reference points (e.g., F_MSY, B_MSY). Population selectivity (“selectivity” hereafter), that is the distribution of fishing mortality over the different demographic components of an exploited fish stock, is also important because it affects both Maximum Sustainable Yield (MSY) and F_MSY, as well as stock resilience to overfishing. The development of an appropriate metric could make selectivity operational as an additional lever for fisheries managers to achieve desirable outcomes. Additionally, such a selectivity metric could inform managers on the uptake by fleets and effects on stocks of various technical measures. Here, we introduce three criteria for selectivity metrics: (a) sensitivity to selectivity changes, (b) robustness to recruitment variability and (c) robustness to changes in Fbar. Subsequently, we test a range of different selectivity metrics against these three criteria to identify the optimal metric. First, we simulate changes in selectivity, recruitment and Fbar on a virtual fish stock to study the metrics under controlled conditions. We then apply two shortlisted selectivity metrics to six European fish stocks with a known history of technical measures to explore the metrics’ response in real‐world situations. This process identified the ratio of F of the first recruited age–class to Fbar (Frec/Fbar) as an informative selectivity metric for fisheries management and advice.     

A study on relationships between large‐scale climate indices and estimates of North Pacific albacore tuna productivity

A logistic production model was used to examine potential relationships between three climate indices, the North Pacific Gyre Oscillation (NPGO), the Pacific Decadal Oscillation (PDO), and the Multivariate El Niño‐Southern Oscillation Index (MEI), and productivity estimates of the North Pacific albacore tuna (Thunnus alalunga) population. Catch and standardized catch‐per‐unit‐effort data from three longline fisheries (Japan, US, and Taiwan) were used in the model. The climate indices were incorporated into the model by correlating time‐varying intrinsic population growth rate (r_y) of the production model with the annual mean value for each index. The estimated probability that the NPGO is positively correlated with stock productivity, as measured by r_y, was 0.99, and the calculated probability that MEI is negatively correlated with the productivity was 0.95. The time lag for these correlations is 4 yr, which is consistent with the timing of recruitment to the Japan longline fishery. The PDO did not seem to have any detectable relationship with stock productivity. However, it remains uncertain if there is a conclusive linkage between the albacore productivity and the NPGO or the MEI index, because model fit to the data is about the same as that of a base model which does not use any climate index and assumes a time‐invariant r.     

Quantifying uncertainty in the wild‐caught fisheries goal of the Ocean Health Index

Sustainability indices are proliferating, both to help synthesize scientific understanding and inform policy. However, it remains poorly understood how such indices are affected by underlying assumptions of the data and modelling approaches used to compute indicator values. Here, we focus on one such indicator, the fisheries goal within the Ocean Health Index (OHI), which evaluates the sustainable provision of food from wild fisheries. We quantify uncertainty in the fisheries goal status arising from the (a) approach for estimating missing data (i.e., fish stocks with no status) and (b) reliance on a data‐limited method (catch‐MSY) to estimate stock status (i.e., B/B_MSY). We also compare several other models to estimate B/B_MSY, including an ensemble approach, to determine whether alternative models might reduce uncertainty and bias. We find that the current OHI fisheries goal model results in overly optimistic fisheries goal statuses. Uncertainty and bias can be reduced by (a) using a mean (vs. median) gap‐filling approach to estimate missing stock scores and (b) estimating fisheries status using the central tendency from a simulated distribution of status scores generated by a bootstrap approach that incorporates error in B/B_MSY. This multitiered approach to measure and describe uncertainty improves the transparency and interpretation of the indicator and allows us to better understand uncertainty around our OHI fisheries model and outputs for country‐level interpretation and use.     

Estimating fisheries reference points from catch and resilience

This study presents a Monte Carlo method (CMSY) for estimating fisheries reference points from catch, resilience and qualitative stock status information on data‐limited stocks. It also presents a Bayesian state‐space implementation of the Schaefer production model (BSM), fitted to catch and biomass or catch‐per‐unit‐of‐effort (CPUE) data. Special emphasis was given to derive informative priors for productivity, unexploited stock size, catchability and biomass from population dynamics theory. Both models gave good predictions of the maximum intrinsic rate of population increase r, unexploited stock size k and maximum sustainable yield MSY when validated against simulated data with known parameter values. CMSY provided, in addition, reasonable predictions of relative biomass and exploitation rate. Both models were evaluated against 128 real stocks, where estimates of biomass were available from full stock assessments. BSM estimates of r, k and MSY were used as benchmarks for the respective CMSY estimates and were not significantly different in 76% of the stocks. A similar test against 28 data‐limited stocks, where CPUE instead of biomass was available, showed that BSM and CMSY estimates of r, k and MSY were not significantly different in 89% of the stocks. Both CMSY and BSM combine the production model with a simple stock–recruitment model, accounting for reduced recruitment at severely depleted stock sizes.     

Stock–environment–recruitment models for North Atlantic albacore (Thunnus alalunga)

Different stock–recruitment models were fitted to North Atlantic albacore (Thunnus alalunga) recruitment and spawning stock biomass data. A classical density dependence hypothesis, a recent environmental‐dependence hypothesis and a combination of both were considered. For the latter case, four stock–environment–recruitment models were used: Ricker, Beverton‐Holt, Deriso's General Model (modified to take into account environmental effects) and conditioned Neural Networks. Cross‐validation analysis showed that the modified Deriso model had the best predictive capability. It detected an inverse effect of the North Atlantic Oscillation (NAO) on recruitment, a Ricker‐type behaviour with density dependent overcompensation when environmental conditions are unfavourable and a Beverton–Holt‐type behaviour towards an asymptotic recruitment carrying capacity with favourable environmental conditions. The Neural Network model also detected that under favourable environmental conditions high spawning stock biomass does not necessarily have a depensatory effect on recruitment. Moreover, they suggest that under extremely favourable environmental conditions, albacore recruitment could increase well above the asymptotic carrying capacity predicted by Beverton–Holt‐type models. However, the general decrease in spawning stock biomass in recent years and increasing NAO trends suggest that there is low probability of exceptionally large recruitment in the future and instead there is a danger of recruitment overfishing.     

Coping with information gaps in stock productivity for rebuilding and achieving maximum sustainable yield for grouper–snapper fisheries

Maintaining fish stocks at optimal levels is a goal of fisheries management worldwide; yet, this goal remains somewhat elusive, even in countries with well‐established fishery data collection, assessment and management systems. Achieving this goal often requires knowledge of stock productivity, which can be challenging to obtain due to both data limitations and the complexities of marine populations. Thus, scientific information can lag behind fishery policy expectations in this regard. Steepness of the stock–recruitment relationship affects delineation of target biomass level reference points, a problem which is often circumvented by using a proxy fishing mortality rate (F) in place of the rate associated with maximum sustainable yield (F_MSY). Because MSY is achieved in the long term only if an F proxy is happenstance with F_MSY, characterizing productivity information probabilistically can support reference point delineation. For demersal stocks of equatorial and tropical regions, we demonstrate how the use of a prior probability distribution for steepness can help identify suitable F proxies. F proxies that reduce spawning biomass per recruit to a target percentage of the unfished quantity (i.e., SPR) of 40% to 50% SPR had the highest probabilities of achieving long‐term MSY. Rebuilding was addressed through closed‐loop simulation of broken‐stick harvest control rules. Similar biomass recovery times were demonstrated for these rules in comparison with more information‐intensive rebuilding plans. Our approach stresses science‐led advancement of policy through a lens of information limitations, which can make the assumptions behind rebuilding plans more transparent and align management expectations with biological outcomes.     

Stock assessment and management implications of anchovy kilka (Clupeonella engrauliformis) in Iranian waters of the Caspian Sea

Recent changes in sea level of the Caspian Sea and ecological impacts caused by the invasive ctenophore (Mnemiopsis leidyi) have altered the ecosystem. A consequence is the changes in the absolute and relative abundance of the commercially important anchovy kilka (Clupeonella engrauliformis) in Iranian waters. To adjust to this change more rigorous management of this fishery is required. This paper examines the maximum sustainable yield (MSY) and fishing intensity at MSY. The paper presents estimates of f_MSY, yield-per-recruit and spawning biomass-per-recruit under various harvest strategies of F_max, F_0.1 and F_40%. We propose a method for estimating acceptable biological catch (ABC) that accounts for large differences in the quality and quantity of information and available data. The MSY and f_MSY were estimated 44,652 mt (metric tons) and 18,609 vessel × nights (a unit of effort). The ABC was estimated at 2190 mt in 2004. In 2005, however, the catch of anchovy kilka was about 4300, over twice the estimated ABC. In 2008 (from January to October) the catch declined to 220 mt. The analyses indicate that overfishing, especially between 2005 and 2008, is the main reason of the collapse of anchovy kilka in the Caspian Sea.     

Rebuilding Mediterranean fisheries: a new paradigm for ecological sustainability

In Mediterranean European countries, 85% of the assessed stocks are currently overfished compared to a maximum sustainable yield reference value (MSY) while populations of many commercial species are characterized by truncated size‐ and age‐structures. Rebuilding the size‐ and age‐structure of exploited populations is a management objective that combines single species targets such as MSY with specific goals of the ecosystem approach to fisheries management (EAF), preserving community size‐structure and the ecological role of different species. Here, we show that under the current fishing regime, stock productivity and fleet profitability are generally impaired by a combination of high fishing mortality and inadequate selectivity patterns. For most of the stocks analysed, a simple reduction in the current fishing mortality (F_cur) towards an MSY reference value (F_MSY), without any change in the fishing selectivity, will allow neither stock biomass nor fisheries yield and revenue to be maximized. On the contrary, management targets can be achieved only through a radical change in fisheries selectivity. Shifting the size of first capture towards the size at which fish cohorts achieve their maximum biomass, the so‐called optimal length, would produce on average between two and three times higher economic yields and much higher biomass at sea for the exploited stocks. Moreover, it would contribute to restore marine ecosystem structure and resilience to enhance ecosystem services such as reservoirs of biodiversity and functioning food webs.     

Improving estimates of population status and trend with superensemble models

Fishery managers must often reconcile conflicting estimates of population status and trend. Superensemble models, commonly used in climate and weather forecasting, may provide an effective solution. This approach uses predictions from multiple models as covariates in an additional “superensemble” model fitted to known data. We evaluated the potential for ensemble averages and superensemble models (ensemble methods) to improve estimates of population status and trend for fisheries. We fit four widely applicable data‐limited models that estimate stock biomass relative to equilibrium biomass at maximum sustainable yield (B/B_MSY). We combined these estimates of recent fishery status and trends in B/B_MSY with four ensemble methods: an ensemble average and three superensembles (a linear model, a random forest and a boosted regression tree). We trained our superensembles on 5,760 simulated stocks and tested them with cross‐validation and against a global database of 249 stock assessments. Ensemble methods substantially improved estimates of population status and trend. Random forest and boosted regression trees performed the best at estimating population status: inaccuracy (median absolute proportional error) decreased from 0.42 – 0.56 to 0.32 – 0.33, rank‐order correlation between predicted and true status improved from 0.02 – 0.32 to 0.44 – 0.48 and bias (median proportional error) declined from ?0.22 – 0.31 to ?0.12 – 0.03. We found similar improvements when predicting trend and when applying the simulation‐trained superensembles to catch data for global fish stocks. Superensembles can optimally leverage multiple model predictions; however, they must be tested, formed from a diverse set of accurate models and built on a data set representative of the populations to which they are applied.     

Spatio‐temporal distribution of albacore (Thunnus alalunga) catches in the northeastern Atlantic: relationship with the thermal environment

When the spring seasonal warming starts, North Atlantic albacore (Thunnus alalunga) juveniles and pre‐adults perform a trophic migration to the northeastern Atlantic, to the Bay of Biscay and to the southeast of Ireland. During this migration, they are exploited by Spanish trolling and baitboat fleets. The present study analyzes the relationship between the albacore spatio‐temporal distribution and the thermal environment. For this approach, several analyses have been performed on a database including fishing logbooks and sea surface temperature (SST) images, covering the period between 1987 and 2003. SST values and the SST gradients at the catch locations have been statistically compared to broader surrounding areas to test whether the thermal environment determines the spatial distribution of albacore. General additive models (GAM) have been used also to evaluate the relative importance of environmental variables and fleet behaviour. The results obtained show that, although juvenile albacore catch locations are affected by fleet dynamics, there is a close spatial and temporal relationship with the seasonal evolution of a statistically significant preferential SST window (16–18°C). However, differences have been identified between the relationship of albacore with SST within the Bay of Biscay in July and August (higher temperature). Such differences are found also in the spatial distribution of the catch locations; these reflect clearly the presence of two groups, differentiated after the third week of the fishing campaign at the end of June. The analysis undertaken relating the distribution of North Atlantic albacore juveniles with thermal gradients did not provide any evidence of a relationship between these catch locations and the nearby occurrence of thermal gradients.     

Biomass management targets and the conservation and economic benefits of marine reserves

The establishment of no‐take marine reserves has been increasingly promoted as a key measure to achieve conservation and sustainability goals in fisheries. Regardless of the wide range of benefits cited, the effectiveness of reserve establishment depends critically on fisheries management outside the reserves. We construct a bioeconomic model of a fishery that allows for the establishment of a no‐take marine reserve and evaluate how the choice of the off‐reserve management target influences the effectiveness of reserve establishment. We evaluate two biomass targets: (i) B_MSY or the biomass that produces the maximum sustainable yield (MSY) and (ii) B_MEY or the biomass that maximizes the net present value of the returns to fishing. The parameterized model shows that, for a wide range of scenarios, the fishery will be better off in terms of both conservation and economic objectives when the no‐take reserve is established in conjunction with the B_MEY target rather than with the B_MSY target. Model results further show that the opportunity cost of securing additional fish biomass, in both deterministic and stochastic environments, is lower when the reserve size is increased under the B_MEY target. This finding is important because marine reserves have been established as a key measure to restore depleted fish stocks, and the results suggest that this objective can be achieved with lower economic costs in a B_MEY managed fishery.     

A new role for MSY in single-species and ecosystem approaches to fisheries stock assessment and management

In 1977, Peter Larkin published his now‐famous paper, ‘An epitaph for the concept of maximum sustained yield’. Larkin criticized the concept of single‐species maximum sustained yield (MSY) for many reasons, including the possibility that it may not guard against recruitment failure, and the impossibility of maximising sustainable yields for all species simultaneously. However, in recent years, there has been a fundamental change in the perception of the fishing mortality associated with MSY (F_MSY) as a limit to be avoided rather than a target that can routinely be exceeded. The concept of F_MSY as a limit is embodied in several United Nations Food and Agriculture Organization (FAO) agreements and guidelines, and has now been incorporated into the US Magnuson–Stevens Fishery Conservation and Management Act. As a result, the United States now requires the development of overfishing definitions based on biological reference points that treat the F_MSY as a limit reference point and must also define a lower limit on biomass below which rebuilding plans with strict time horizons must be developed. This represents a major paradigm shift from the previously mandated (but often unachieved) objective to simply maintain fishing mortalities at levels below those associated with recruitment overfishing. In many cases, it requires substantial reductions in current fishing mortality levels. Therefore, the necessity of the new paradigm is continually questioned. This paper draws on examples from several fisheries, but specifically focuses on the recent US experience illustrating the practical difficulties of reducing fishing mortality to levels below those corresponding to MSY. However, several studies suggest that even more substantial reductions in fishing mortality may be necessary if ecosystem considerations, such as multispecies interactions, maintenance of biodiversity and genetic diversity, and reduction of bycatch and waste, are taken into account. The pros and cons of moving beyond single‐species assessment and management are discussed. A US plan for improving stock assessments indicates that even a ‘basic’ objective such as ‘adequate baseline monitoring of all managed species’ may be extremely costly. Thus, the suggestion of Larkin (1983, 1997) that the costs of research and management should not exceed 10–20% of the landed value of the catch may preclude comprehensive ecosystem management. More importantly, neither single‐species nor ecosystem‐based fisheries management is likely to improve appreciably unless levels of fishing capacity are aligned with resource productivity, as is currently being promoted by FAO and several individual nations.     

Variable variability: difficulties in estimation and consequences for fisheries management

Recent analyses propose that the key regulatory processes in fisheries are stochastic, characterized by increased recruitment variance at low stock sizes (heteroscedasticity). Here, we investigate the consequences of this idea, with the aim of testing its practical relevance to fisheries management. We argue that stock‐recruitment time series are at least one order of magnitude too short to reliably fit heteroscedastic models; indeed, they are typically insufficient even to establish in which direction recruitment variance changes with stock size. Unreliable estimates of heteroscedasticity can have important management implications, depending on the sign of the coefficient of heteroscedasticity. Maximum sustainable yield (MSY) estimates from simple models, which include heteroscedasticity can be volatile, unrealistically high and sometimes non‐existent, as illustrated by an analysis of North Sea cod (Gadus morhua) data. In contrast, for North Sea herring (clupea harengus) data, heteroscedasticity has a negligible effect on MSY estimates. Statistical models are useful to elucidate broad‐scale regulatory processes, but will need to combined with the mechanistic understanding offered by models of population dynamics before being applied in a management setting.     

A simple method for estimating MSY from catch and resilience

The Law of the Sea requires that fish stocks are maintained at levels that can produce the maximum sustainable yield (MSY). However, for most fish stocks, no estimates of MSY are currently available. Here, we present a new method for estimating MSY from catch data, resilience of the respective species, and simple assumptions about relative stock sizes at the first and final year of the catch data time series. We compare our results with 146 MSY estimates derived from full stock assessments and find excellent agreement. We present principles for fisheries management of data‐poor stocks, based only on information about catches and MSY.     

Examining the knowledge base and status of commercially exploited marine species with the RAM Legacy Stock Assessment Database

Meta‐analyses of stock assessments can provide novel insight into marine population dynamics and the status of fished species, but the world’s main stock assessment database (the Myers Stock‐Recruitment Database) is now outdated. To facilitate new analyses, we developed a new database, the RAM Legacy Stock Assessment Database, for commercially exploited marine fishes and invertebrates. Time series of total biomass, spawner biomass, recruits, fishing mortality and catch/landings form the core of the database. Assessments were assembled from 21 national and international management agencies for a total of 331 stocks (295 fish stocks representing 46 families and 36 invertebrate stocks representing 12 families), including nine of the world’s 10 largest fisheries. Stock assessments were available from 27 large marine ecosystems, the Caspian Sea and four High Seas regions, and include the Atlantic, Pacific, Indian, Arctic and Antarctic Oceans. Most assessments came from the USA, Europe, Canada, New Zealand and Australia. Assessed marine stocks represent a small proportion of harvested fish taxa (16%), and an even smaller proportion of marine fish biodiversity (1%), but provide high‐quality data for intensively studied stocks. The database provides new insight into the status of exploited populations: 58% of stocks with reference points (n = 214) were estimated to be below the biomass resulting in maximum sustainable yield (B_MSY) and 30% had exploitation levels above the exploitation rate resulting in maximum sustainable yield (U_MSY). We anticipate that the database will facilitate new research in population dynamics and fishery management, and we encourage further data contributions from stock assessment scientists.     

The obvious heterosis and genetic characters of intergeneric cross and backcross juveniles between blunt snout bream (Megalobrama amblycephala) and topmouth culter (Culter alburnus)

In this study, we used artificial insemination to generate hybrid groups of fish [MC‐F₁(MA♀×CA♂) and MC‐F₂(MC‐F₁♀×♂)] by intergeneric crosses of Megalobrama amblycephala (MA) and Culter alburnus (CA); sequential backcrosses [CAM‐B₁ (CA♀×MC‐F₁♂) and MCC‐B₁ (MC‐F₁♀×CA♂)] were also performed. All these hybrids showed high rates of fertilization, hatching and survival (p > 0.05). For genetic traits, compared with those of the M. amblycephala and C. alburnus parental lines (Table 1), the fertilization rate, hatching rate and 7‐day survival rate of MC‐F1(MA♀×CA♂), MC‐F2(MC‐F1♀×♂), CAM‐B1 (CA♀×MC‐F1♂) and MCC‐B1 (MC‐F1♀×CA♂) by artificial insemination exhibited similar high rates (p > 0.05). The morphology of the four hybrids MC‐F₁/F_2, CAM‐B₁ and MCC‐B₁ were intermediate between those of their parents. Compared with their parents of MA and CA, weight gain rate (WG), specific growth rate (SGR) and protein efficiency ratio (PER) of hybrids MC‐F₁/F₂, CAM‐B₁ and MCC‐B₁ were significantly (p < 0.05) increased and feed conversion ratio (FCR) was significantly (p < 0.05) decreased after 3 months feeding. Moreover, protein content of muscle for MC‐F₁/F₂, CAM‐B₁ and MCC‐B₁ was significantly (p < 0.05) higher and carbohydrate content of muscle was significantly (p < 0.05) lower than their parents. The females and males of the four hybrids had normal gonadal development. In this study, we successfully generated intergeneric and backcross hybridization lines with fertile potential among fish of the Cultrinae subfamily and these hybrids had obvious heterosis in terms of growth performance, feed utilization and muscle quality.     

Effect of short‐term fasting and re‐feeding on growth,digestive enzyme activities and antioxidant defence in yellowfin seabream,Acanthopagrus latus (Houttuyn, 1782)

An 80‐day feeding trial was conducted to evaluate the influence of different short‐term fasting and re‐feeding strategies on growth and physiological responses in yellowfin seabream, Acanthopagrus latus (2.4 ± 0.2 g) fingerlings. The fish were subjected to four different feeding regimes, and the control group fed four times daily to apparent satiation throughout the whole feeding period, while the other three groups were deprived for 2, 4 and 8 days followed by 8, 16 or 32 days of re‐feeding (F₂R₈, F₄R₁₆ and F₈R₃₂, respectively) in repeated cycles for 80 days. The fish in the control and F₂R₈ groups had the highest and the lowest total length, respectively (p < .05). Moreover, fish exposed to F₄R₁₆ had the highest hepatosomatic indices, while control fish had the lowest hepatosomatic indices (p < .05). Fish in the F₂R₈ group relatively had higher catalase and glutathione‐S‐transferase activities than other groups (p < .05). Furthermore, total protease, α‐amylase and alkaline phosphatase activities in the F₄R₁₆ and F₈R₃₂ were higher than the F₂R₄ and control groups (p < .05). Overall, this study showed that compensatory growth in weight and length and digestive enzyme activities were observed in the F₄R₁₆ and F₈R₃₂; however, the increase in the activity of antioxidant enzymes in the F₈R₃₂ group indicated that oxidative stress remained after 80 days of re‐feeding in the liver.