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In commercial fish farming, growth performance is the most influential factor in economic profitability; so, biomass optimization has become a growing concern. We analyzed the influence of 3 harvest densities (15, 20, and 22 kg·m–3) on the growth of spotted rose snappers reared in floating net cages during a production cycle. To assess the impact of stocking density on growth performance, we used 2 indicators: final total length-at-age (12 months) and the growth rate estimated from growth models (von Bertalanffy, logistic, and Gompertz). For the first indicator, we tested for normality. We did the Kruskal–Wallis and the post hoc Kruskal–Wallis tests to compare the mean total final length from each density. Accordingly, the means of densities D15 and D20 were the same (P value = 0.22). For the second indicator, we fitted the models with the subroutine optim of the R statistical package using the L-BFGS-B algorithm. Model selection was made with the Akaike and the Bayesian information criteria. Both criteria suggested that the logistic model fitted the data best. With the best model (logistic), we did 1,000 bootstrap simulations for each density scenario to determine the distribution of the maximum likelihood estimation for the instantaneous growth rate. Because the estimates were normally distributed, we used ANOVA to test the equality of the instantaneous growth. The Tukey HSD test suggested that all means were statistically different from each other. The fastest growth rate (K = 0.275) corresponded to the cage with a density of 20 kg·m–3. These findings demonstrate that the logistic model can predict the growth of spotted rose snappers under culture conditions using floating net cages. These results strengthen the productive potential and economic profitability of snapper aquaculture using floating cage and may help the start of commercial scale aquaculture.

Background: Results indicate the fishery might be catching small California sardine in some fishing areas that could produce growth overfishing. Objectives: To estimate the percentage of organisms caught below the minimum size and determining the potential variables affecting the organisms size caught by the fishery. Methods: we did data exploratory analysis, correlation analysis and explored the size´s seasonal and interannual variability. We used ANCOVA and ANOVA to find out the main driver determining sardine´s size. The response variable was the standard length, the ANCOVA model included six covariates, six factors and the first level interactions. Results: the standard length ranged from 64 mm to 230 mm, the mean was 158.6 ± 19.8 mm. Results suggest 32.9% of the organisms caught are below the minimum size, 12.9% above the tolerance limit (20%). Regarding the seasonal variation, results suggest the lower standard length value occurred during June and July, while the highest occurred in March. The ANCOVA model included 60 significant predictors; it explained 82.2% of the observed variability. ANOVA results suggested that the predictor variable explaining the majority of the variability was the factor Month (31.5%); the second was the factor year (9.5%) and the third was Boat (7.2%). Conclusions: Compliance with regulations would require reducing operations during June and July in zones IV-G and VI-G; this would help to avoid growth overfishing. However, it could face fishermen opposition that already face a close season. Regulations compliance would require that CONAPESCA and fishermen reach a consensus. Our results contribute to provide information to avoid growth overfishing and yield overfishing. They support the efforts that managers and fishermen are making to assure the sustainable exploitation of this fishing resource.

Background. Most growth analyses of Yellowtail Snapper neglect consideration of model and parameter uncertainty. Goals. In this paper, we explore model uncertainty using three models (von Bertalanffy, logistic, and Gompertz) as well as the Akaike criterion for model selection. We also estimate growth parameters and its uncertainty using the maximum likelihood estimation approach (under different assumptions of error variance) and Bayesian methods. Methods. Models were fitted to length-at-age data from organisms caught in Anton Lizardo, Veracruz. Regarding the Bayesian methods, a prior distribution for the asymptotic length was built based on data gathered from literature. We used Monte Carlo Markov Chains (MCMC) methods to fit the logistic model. Results. The Akaike criterion results suggest that the logistic model provided the best fit for the observed data (lowest AIC = 31.4). Parameter estimates included asymptotic length (L? = 64.9 ± 5.43), growth rate (K = 0.49 ± 0.07), and age at the curve inflection point (I = 3.28 ± 0.42). Regarding the Bayesian analysis, MCMC simulations suggest that the most probable value for the asymptotic length was 64.3 cm with an interval of 95% probability (58.7,70.1). The most probable value for the growth rate was 0.48 with an interval of 95% probability (0.42, 0.55). Last, the most probable value for the age at the curve inflection point was 1.7 years with a range of 95% probability (1.31, 2.16). Conclusions. The maximum likelihood estimation (MLE) and the Bayesian framework should be considered basic statistical techniques in the evaluation of individual growth of the species of interest, as they provide a robust analysis of available information of the species and the opportunity to incorporate such analysis to sustainable management practices.

A multispecies virtual population analysis (MSVPA) model for the southern Chilean demersal fishery was developed. Species included the Southern Hake Merluccius australis, Pink Cusk-eel Genypterus blacodes (hereafter, “Kingklip”), Southern Blue Whiting (SBW) Micromesistius australis, and Patagonian Grenadier (also known as Argentine Straptail) Macruronus magellanicus (hereafter, “Hoki”). Due to a lack of stomach content data, we constructed suitability coefficients based on predator–prey size ratios. Terminal fishing mortality (Fterm) was estimated by fitting a cohort analysis model to estimates of abundance from single-species models developed by the Chilean Instituto de Fomento Pesquero (IFOP; Institute for Fishing Development). Values of Fterm were used as input data in the MSVPA, driving the dynamics of the species and producing adult abundance estimates that were similar to IFOP estimates. Comparison of MSVPA and IFOP estimates suggested consistency in adult abundance and total abundance estim...

The red sea urchin (Strongylocentrotus franciscanus) fishery is of importance to the economy of Baja California (Mexico). The commercial fishery started in the early 1970s as a result of expanding export markets, but has experienced substantial decline in landings and abundance since 1986. Fishery-independent surveys have not been conducted for all fishing areas, thus CPUE and catch data were used to conduct a stock assessment and decision analysis for the red sea urchin stock. The red sea urchin population dynamics was described with the Schaefer biomass dynamic model. Bayesian approaches were used for the estimation of the model parameters and for projecting the population dynamics of the species under different management scenarios, including constant harvest rate and constant catch strategies. This study suggests that the current stock is only 17% of the virgin stock biomass and that, for a constant catch policy, a 10% increase in the current catch rate could potentially cause the collapse of the fishery in 20 years. Simulation results suggested that a constant harvest rate between 15% and 25% would cause the population to recover and maximize the catch in 2024. Higher harvest rate levels would increase the probability of the biomass being less than 40% of the population carrying capacity. La pesqueria del erizo rojo (Strongylocentrotus franciscanus) es importante para la economia de Baja California (Mexico). Su pesca comercial se inicio a principios de los anos 70 como resultado de la expansion de su mercado de exportacion; sin embargo, se ha detectado una baja sustancial en sus capturas y abundancia desde 1986. Dada la falta de un indice de abundancia relativa independiente de la pesqueria para todas las areas de captura, se utilizaron datos de CPUE y captura para llevar a cabo una valoracion del stock del erizo rojo y realizar un analisis de decision con relacion a su pesqueria. La dinamica poblacional fue descrita mediante el modelo de biomasa excedente de Schaefer usando metodos Bayesianos para estimar los parametros del modelo y para proyectar la dinamica poblacional de la especie bajo diferentes escenarios de manejo, los cuales incluyeron estrategias de tasas de explotacion constantes y capturas constantes. Los resultados sugieren que el stock actual es solo 17% de la bioma virgen y que, para una estrategia de captura constante, un incremento del 10% en el nivel de captura actual podria causar el colapso de la pesqueria en 20 anos. Los resultados de las simulaciones sugieren que una estrategia de tasa de explotacion constante entre 15% y 25% causaria que la poblacion se recuperara y la captura se maximizara en 2024. Mayores niveles de explotacion incrementarian la probabilidad de que la biomasa sea menor al 40% de la capacidad de carga de la poblacion.

We incorporated predation equations from the multispecies virtual population analysis model MSVPA into an age-structured model for the Chilean hake (Merluccius gayi gayi) to estimate cannibalism. Two models, model I with constant natural mortality and the MSM, were fitted to the total annual catch, spawning biomass from acoustic surveys and length composition data from fishery and acoustic surveys. Model I fitted the data better than MSM. The majority of the MSM estimates of adult population and spawning biomass were larger than the model I estimates; probably due to the choice of residual mortality M1. High estimates of predation mortality were observed for age-0 hake. In spite of a decreasing fishing mortality, the spawning biomass decreased in the last years. Preliminary MSM results suggest that this might be due to an increase in cannibalism. A sensitivity analysis suggested all response variables were not sensible to the “other food” parameter but sensible to M1 and the predator annual ration. MSM is a promising approach that introduces the predation mortality equations into a statistical framework, allowing the incorporation of the uncertainty in the estimation of the parameters and the use of standard statistical tools in a multispecies context. This approach will contribute to provide useful information on the indirect effects of fishing on non-target species to fisheries managers.

Virtual population analysis and the statistical catch-at-age methods are common stock assessment models used for management advice. The difference between them is the statistical assumptions allowing the fitting of parameters by considering how errors enter into the models and the data sources for the estimation. Fishery managers are being asked to consider multispecies interactions in their decisions. One option to achieve this goal is the multispecies virtual population analysis (MSVPA); however, its lack of statistical assumptions does not allow the use of tools used in single-species stock assessment. We chose to use a two-species system, walleye pollock (Theragra chalcogramma) and Pacific cod (Gadus macrocephalus), to incorporate the predation equations from MSVPA into an age-structured multispecies statistical model (MSM). Results suggest that both models produced similar estimates of suitability coefficients and predation mortalities. The adult population estimates from the single-species stock assessment and MSM were also comparable. MSM provides a measure of parameter uncertainty, which is not available with the MSVPA technologies. MSM is an important advancement in providing advice to fisheries managers because it incorporates the standard tools such as Bayesian methods and decision analysis into a multispecies context, helping to establish useful scenarios for management in the Bering Sea.

Assessment of the historical, present, and future states of marine ecosystems and the effects that humans and climate have on the state of an ecosystem are crucial to the scientific advice required to implement an ecosystem-based fishery management system. Management of federal groundfish fisheries in Alaska considers not just the target fishery, but also the possible impact those fisheries might have on other species and the ecosystem. Management actions have ranged from providing protection of endangered species in the region to preventing new fisheries from starting on key foodweb components such as forage fish. A scientific framework for providing ecosystem-based advice that puts the ecosystem first has been evolving over the past few years. This framework provides a way of assessing ecosystem factors that influence target species, the impact the target fishery may have on associated species, and ecosystem-level impacts of fishing. An indicator approach that describes ecosystem status, and trends and measures of human and climate influence has been developed to provide advice to fishery managers. This approach is now being expanded to utilize a variety of models to predict possible future trends in various ecosystem indicators. Future implementation challenges include the refinement of these predictive models, and the inclusion of climate into the models. Identification of sensitive and meaningful ecosystem indicators is also required before a more formalized decision-making process, one that includes ecosystem considerations, can be developed. Most important, the culture of fishery management and research organizations needs to change to embrace the ecosystem-based protections already mandated by various laws.

Suitability coefficients are important for the estimation of predation mortality in a multispecies virtual population analysis (MSVPA) and subsequent use in the multispecies forecasting model (MSFOR). Testing the assumption of the stability of the suitability coefficients is important in assessing the robustness of the predictions made with MSFOR. We used different statistical methods to partially test this assumption for the eastern Bering Sea MSVPA model with eight species, using stomach content data for the years 1985–1989. Comparison of the estimates from two different sets of stomach content data (set one with all data and set two mainly with data from 1985) suggested that the differences between the two types of estimates were much reduced when the number of predator stomachs sampled increased. In a second approach, we contrasted the residual variances of partial data sets with the results from the fit of the total data set. Results suggested a small increase (∼10.8%) in the variation of the suitability coefficients. Comparison of the means of the suitability coefficients associated with each predator species suggests that only 13 of the 50 possible pairwise contrasts were significantly different (α = 0.05). In general, results suggested that the predator preferences and prey vulnerabilities remained stable over the time period studied. Therefore, MSFOR could be considered as a tool to advise fisheries managers within a multispecies context.

Commercially important groundfish populations in the Bering Sea are connected through the food web as predators and prey. In addition to having different trophic roles, the recruitment of these species varies on interdecadal time scales and may be related to climate forcing. We simulate the effects of fishing mortality on eight trophically linked species under two scenarios of climate regimes using the multispecies virtual population analysis (MSVPA) model and the multispecies forecasting model (MSFOR). Species respond differently to climate change assumptions and fishing mortality depending on their position in the food web. Results suggest that the assumptions regarding climate regime shifts on mean recruitment may produce effects comparable to the ones produced by fishing and predation interactions. Therefore, accurate models for fisheries management would require considering these factors and their potential interactions. Because responses are complex and difficult to predict, it is necessary to take a risk-averse approach in managing the species with the largest potential variation. The incorporation of climate regime shifts in fisheries management will require a better understanding of recruitment during a particular regime and a reliable way to identify regime shifts based on biological and (or) physical indices.

The need to understand the multispecies implications of various harvesting regimes is becoming more important as fishery managers move toward ecosystem-based management. We explore the possible effects that different exploitation rates may have on eastern Bering Sea groundfish using a multispecies simulation context that incorporates predator–prey relationships. At present, some groundfish species in the eastern Bering Sea are exploited up to the recommended levels of allowable biological catch, whereas others, for economic or bycatch limitation reasons, are only lightly exploited. We explore the possible long-term multispecies implications of different exploitation patterns on the biomass and yield of several groundfish species using predator−prey suitability estimates derived from multispecies virtual population analysis (MSVPA) in a multispecies simulation modeling context and compare those predictions with those from single-species forecasting models. Three different fishing scenarios that in...

The eastern Bering Sea shelf supports a large biomass of several groundfish populations, particularly walleye pollock (Theragra chalcogramma). The main trophic interactions between dominant groundfish populations in this region involve walleye pollock as prey. A multispecies virtual population analysis model (MSVPA) covering the period 1979–1995 has been parameterized for the eastern Bering Sea in order to move closer to providing multispecies management advice for this region. The MSVPA model is based on extensive diet data from 70 predator/prey/year/quarter combinations and currently includes the following species as predators: walleye pollock (Theragra chalcogramma), Pacific cod (Gadus macrocephalus), Greenland turbot (Reinhardtius hippoglossoides), yellowfin sole (Pleuronectes asper), arrowtooth flounder (Atheresthes stomias), and northern fur seal (Callorhinus ursinus). Arrowtooth flounder and northern fur seals are entered as ‘‘other predators’’, which means that population and mortality estimates are not directly made for these species. Estimates of their consumption rates, diet, and population abundance are input, however, so that their predation on prey species in the model can be calculated. Prey species are walleye pollock, Pacific cod, Greenland turbot, yellowfin sole, rock sole (Lepidopsetta bilineatus), and Pacific herring (Clupea pallasi). Results show that large numbers of walleye pollock, particularly age-0 and age-1 fish, are consumed and cannibalism by adult pollock constitutes the largest source of predation mortality for age-0 fish. Predation plays an important role in explaining the recruitment dynamics of pollock. Further advances may be made when these estimates are linked to models that examine the effect of climate-related factors influencing larval survival. 2000 International Council for the Exploration of the Sea

The Western and Central Pacific Ocean sustains the highest tuna production in the world. This province is also characterized by many islands and a complex bathymetry that induces specific current circulation patterns with the potential to create a high degree of interaction between coastal and oceanic ecosystems. Based on a large dataset of oceanic predator stomach contents, our study used generalized linear models to explore the coastal-oceanic system interaction by analyzing predator-prey relationship. We show that reef organisms are a frequent prey of oceanic predators. Predator species such as albacore (Thunnus alalunga) and yellowfin tuna (Thunnus albacares) frequently consume reef prey with higher probability of consumption closer to land and in the western part of the Pacific Ocean. For surface-caught-predators consuming reef prey, this prey type represents about one third of the diet of predators smaller than 50 cm. The proportion decreases with increasing fish size. For predators caught at depth and consuming reef prey, the proportion varies with predator species but generally represents less than 10%. The annual consumption of reef prey by the yellowfin tuna population was estimated at 0.8 +/- 0.40CV million tonnes or 2.17x10(12)+/- 0.40CV individuals. This represents 6.1%+/- 60.17CV in weight of their diet. Our analyses identify some of the patterns of coastal-oceanic ecosystem interactions at a large scale and provides an estimate of annual consumption of reef prey by oceanic predators.

The red octopus (Octopus maya) is an endemic species of the Yucatan Peninsula and its fishery is one of the most important along the Atlantic coast of Mexico. Commercial exploitation started in 1949. Since 2002 an index of abundance has been estimated, and this index was used to perform a stock assessment and decision analysis using the Schaefer model. A Bayesian approach was applied to estimate the model parameters and to project the species population under two management scenarios with a constant harvest rate and a positive implementation error. Results suggest that in 1995 the biomass corresponded to 23% of the population carrying capacity (K) and that the current stock is only 14% of K. The population may be depleted and a rebuilding plan might be necessary. In the decision analysis, when the implementation error was included, the Markov Chain Monte Carlo simulations suggested that the current level of exploitation (50% harvest rate) could produce a decreasing trend with the most probable biomass of 9679 t and an expected catch of 7920 t in 2018, and an expected probability of 0.82 of the population being less than 40% of K. On the contrary, a 30% harvest rate would raise the expected catch in 2018 (12,058 t), also reducing the probability of the population being smaller than 40% of K. The inclusion of the implementation error provides a more realistic scenario and represents a more conservative option; therefore, using this type of auxiliary data within a Bayesian framework is recommended for the decision making process. If adopted by Mexican fisheries managers, the approach used in this study could help improve the management of this resource and keep exploitation at sustainable levels. El pulpo rojo (Octopus maya) es una especie endemica de la Peninsula de Yucatan. La suya es una de las pesquerias mas importantes de la costa Atlantica de Mexico; su explotacion comercial inicio en 1949, y desde 2002 se ha estimado su indice de abundancia. Este indice se uso para llevar a cabo una valoracion del stock y un analisis de decision usando el modelo de Schaefer. Se uso un enfoque Bayesiano para la estimacion de los parametros del modelo y para proyectar la poblacion de esta especie bajo dos escenarios de manejo con una tasa de explotacion constante y un error de implementacion positivo. Los resultados sugieren que en 1995 la biomasa correspondia al 23% de la capacidad de carga de la poblacion K y que el stock actual es solo 14% de K. Los resultados sugieren que la poblacion podria estar sobreexplotada y que podria ser necesario un plan de reconstruccion del recurso. Cuando se uso error de implementacion en el analisis de decision, las simulaciones de cadenas de Markov-Monte Carlo sugieren que el nivel actual de explotacion (tasa de explotacion del 50%) podria producir una tendencia decreciente en la biomasa, con un valor de biomasa mas probable de 9679 t, una esperanza de captura de 7920 t en 2018 y una probabilidad de 0.82 de que la poblacion sea menor que 40% de K. Por el contrario, una tasa de explotacion del 30% incrementaria la esperanza de la captura en 2018 (12,058 t), tambien reduciendo la probabilidad de que la poblacion sea menor al 40% de K. La inclusion del error de implementacion proporciona un escenario mas realista y representa una opcion mas conservadora; por consiguiente se recomienda usar este tipo de datos auxiliares en un marco Bayesiano para la toma de decision. Si las autoridades pesqueras mexicanas adoptan el metodo usado en este trabajo se puede mejorar el manejo de este recurso, manteniendo su explotacion en niveles sustentables.