Modelling the oceanic habitats of two pelagic species using recreational fisheries data

Defining the oceanic habitats of migratory marine species is important for both single species and ecosystem‐based fisheries management, particularly when the distribution of these habitats vary temporally. This can be achieved using species distribution models that include physical environmental predictors. In the present study, species distribution models that describe the seasonal habitats of two pelagic fish (dolphinfish, Coryphaena hippurus and yellowtail kingfish, Seriola lalandi), are developed using 19 yr of presence‐only data from a recreational angler‐based catch‐and‐release fishing programme. A Poisson point process model within a generalized additive modelling framework was used to determine the species distributions off the east coast of Australia as a function of several oceanographic covariates. This modelling framework uses presence‐only data to determine the intensity of fish (fish km^?2), rather than a probability of fish presence. Sea surface temperature (SST), sea level anomaly, SST frontal index and eddy kinetic energy were significant environmental predictors for both dolphinfish and kingfish distributions. Models for both species indicate a greater fish intensity off the east Australian coast during summer and autumn in response to the regional oceanography, namely shelf incursions by the East Australian Current. This study provides a framework for using presence‐only recreational fisheries data to create species distribution models that can contribute to the future dynamic spatial management of pelagic fisheries.     

Decreasing uncertainty in catch rate analyses using Delta-AdaBoost: An alternative approach in catch and bycatch analyses with high percentage of zeros

The gillnet data of walleye (Sander vitreus), yellow perch (Perca flavescens), and white perch (Morone americana), collected by a fishery-independent survey (Lake Eire Partnership Index Fishing Survey, PIS) from 1989 to 2008, contained 75–83% of zero observations. AdaBoost algorithm was applied to the model analyses with such fishery data for each species. The 3- and 5-fold cross-validations were conducted to evaluate the performance of each candidate model. The performance of the delta model consisting of one generalized additive model and one AdaBoost model (Delta-AdaBoost) was compared with five candidate models. The five candidate models included: the delta model comprising two generalized linear models (Delta-GLM), the delta model comprising two generalized linear models with polynomial terms up to degree 3 (Delta-GLM-Poly), the delta model comprising two generalized additive models (Delta-GAM), the generalized linear model with Tweedie distribution (GLM-Tweedie), and the generalized additive model with Tweedie distribution (GAM-Tweedie). To predict the presence/absence of fish species, the performance of AdaBoost model was compared in terms of error rate with conventional generalized linear and additive models assuming a binomial distribution. Results from 3- and 5-fold cross-validation indicated that Delta-AdaBoost model yielded the smallest training error (0.431–0.433 for walleye, 0.528–0.519 for yellow perch and 0.251 for white perch) and test error (0.435–0.436 for walleye, 0.524 for yellow perch and 0.254–0.255 for white perch) on average, followed by Delta-GLM-Poly model for yellow perch and white perch, and Delta-GAM model for walleye. In the prediction of the presence/absence of fish species, AdaBoost model had the lowest error rate, compared with generalized linear and additive models. We suggested AdaBoost algorithm to be an alternative to deal with the high percentage of zero observations in the catch and bycatch analyses in fisheries studies.     

Contributions of FOCI research to forecasts of year-class strength of walleye pollock in Shelikof Strait, Alaska

NOAA's Fisheries Oceanography Coordinated Investigations (FOCI) contributes information to help forecast year-class strength of walleye pollock (Theragra chalcogramma ) in the Gulf of Alaska. Quantitative estimates of recruitment are obtained from models of stock assessment and stock projection employing information supplied by FOCI. To generate its information, FOCI convenes specialists in marine biology, physical and fisheries oceanography, meteorology, and statistics to assemble and analyse relevant biological and physical time series with respect to recruitment and processes hypothesized to influence fish survival. Statistical methods encompass linear and nonlinear regression, stochastic simulation modelling, transfer function time series modelling, and tree-modelling regression. The current database consists of 31 years of data, and analyses have identified factors that affect ocean stratification and circulation during spring and summer of the fish's birth year as being important to recruitment. A conceptual model of the recruitment process serves as the framework for a recruitment forecast scheme. A stochastic mathematical simulation model of the conceptual model produces similarities between simulated and observed recruitment time series. FOCI has successfully forecast recruitment observed over the past several years.     

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.     

Guidelines for incorporating fish distribution shifts into a fisheries management context

The impacts of climate change have been demonstrated to influence fisheries resources. One way climate has affected fish stocks is via persistent shifts in spatio‐temporal distribution. Although examples of climate‐forced distribution shifts abound, it is unclear how these shifts are practically accounted for in the management of fish stocks. In particular, how can we take into account shifting stock distribution in the context of stock assessments and their management outputs? Here, we discuss examples of the types of fish stock distribution shifts that can occur. We then propose a decision tree framework of how shifting stock distributions can be addressed. Generally, the approaches for addressing such shifts fall into one of three main alternatives: re‐evaluate stock identification, re‐evaluate a stock unit area, or implement spatially explicit modelling. We conclude by asserting that the approach recommended here is feasible with existing information and as such fisheries managers should be able to begin addressing the role of changes in stock distribution in these fish stocks. The implications of not doing so could be notably undesirable.     

Predicting the age of fish using general and generalized linear models of biometric data: A case study of two estuarine finfish from New South Wales, Australia

Direct ageing of fish can be a laborious and expensive task when age estimates from a large population are required, and often involves a degree of subjectivity. This study examined the application of general and generalized linear models that predict the age of fish from a range of efficiently and objectively measured covariates. The data sampled were from yellowfin bream (Acanthopagrus australis (Sparidae) (Owen, 1853)) and sand whiting (Sillago ciliata (Sillaginidae) Cuvier, 1829) populations from New South Wales, Australia. The covariates evaluated in the models were fish length, otolith weight, sex and location (the estuary from which the fish were sampled). Akaike Information Criteria were used for model selection and residual plots of the final models revealed a satisfactory fit to the observations. The best fitting model for each species included all covariates. An additional investigation considered whether general and generalized linear models that predict age from two different categories of biometric information outperform age-length keys with respect to subsequent estimates of total mortality from catch-curve analysis. The two categories of biometric information differed in the ease and cost with which the information could be collected. The first category only included fish length and location as covariates, whilst the second category also included otolith weight and sex. It was found that traditional age-length keys outperformed the predictive models that estimated age from only fish length and location, because the results from the models were prone to significant bias. However, when otolith weight and sex were added as covariates to the predictive models, some of them, including a generalized linear model with a Poisson-distributed response variable, performed similarly to the age-length key. Given that otolith weight and the sex of fish are cheaper to quantify than age from a sectioned otolith in many situations, general or generalized linear models may represent a cheaper and faster method of estimating mortality compared to age-length keys. Such models can also easily incorporate the influence of spatial, temporal and demographic variation.     

GLMs, GAMs and GLMMs: an overview of theory for applications in fisheries research

This paper provides an overview of the modelling process using generalized linear models (GLMs), generalized additive models (GAMs) and generalized linear mixed models (GLMMs), especially as they are applied within fisheries research. We describe the essential aspect of model interpretation and construction so as to achieve its correct application. We start with the simplest models and show the progression from GLMs to either GAMs or GLMMs. Although this is not a comprehensive review, we emphasise topics relevant to fisheries science such as transformation options, link functions, adding model flexibility through splines, and using random and fixed effects. We finish by discussing the various aspects of these models and their variants, and provide a view on their relative benefits to fisheries research.     

An application of generalized linear models in production model and sequential population analysis

In fitting production models and age-structured models to an index of the relative abundance of a fish population, errors are usually assumed to follow a log-normal or normal distribution, without any diagnostic analyses. A generalized linear model can readily deal with many types of error structures. In this paper, a generalized linear model is coupled with a production model and a sequential population model to assess the stock of the Atlantic cod (Gadus morhua) 2J3KL. This study suggests that the parameter estimates in these models can be greatly influenced by the assumption about the error structures in the estimation and that log-normal and gamma distributions are appropriate for the production model in assessing the Atlantic cod 2J3KL stock, whereas gamma distribution is appropriate for the sequential population model. We recommend that generalized linear models should be used to identify the appropriate error structure in modeling fish population dynamics.     

Transform high seas management to build climate resilience in marine seafood supply

Climate change is projected to redistribute fisheries resources, resulting in tropical regions suffering decreases in seafood production. While sustainably managing marine ecosystems contributes to building climate resilience, these solutions require transformation of ocean governance. Recent studies and international initiatives suggest that conserving high seas biodiversity and fish stocks will have ecological and economic benefits; however, implications for seafood security under climate change have not been examined. Here, we apply global‐scale mechanistic species distribution models to 30 major straddling fish stocks to show that transforming high seas fisheries governance could increase resilience to climate change impacts. By closing the high seas to fishing or cooperatively managing its fisheries, we project that catches in exclusive economic zones (EEZs) would likely increase by around 10% by 2050 relative to 2000 under climate change (representative concentration pathway 4.5 and 8.5), compensating for the expected losses (around ?6%) from ‘business‐as‐usual’. Specifically, high seas closure increases the resilience of fish stocks, as indicated by a mean species abundance index, by 30% in EEZs. We suggest that improving high seas fisheries governance would increase the resilience of coastal countries to climate change.     

A review of ecological models for brown trout: towards a new demogenetic model

Abstract – Ecological models for stream fish range in scale from individual fish to entire populations. They have been used to assess habitat quality and to predict the demographic and genetic responses to management or disturbance. In this paper, we conduct the first comprehensive review and synthesis of the vast body of modelling literature on the brown trout, Salmo trutta L., with the aim of developing the framework for a demogenetic model, i.e., a model integrating both population dynamics and genetics. We use a bibliometric literature review to identify two main categories of models: population ecology (including population dynamics and population genetics) and population distribution (including habitat–hydraulic and spatial distribution). We assess how these models have previously been applied to stream fish, particularly brown trout, and how recent models have begun to integrate them to address two key management and conservation questions: (i) How can we predict fish population responses to management intervention? and (ii) How is the genetic structure of fish populations influenced by landscape characteristics? Because salmonid populations tend to show watershed scale variation in both demographic and genetic traits, we propose that models combining demographic, genetic and spatial data are promising tools for improving their management and conservation. We conclude with a framework for an individual‐based, spatially explicit demogenetic model that we will apply to stream‐dwelling brown trout populations in the near future.     

Developing robust frequentist and Bayesian fish stock assessment methods

Errors in fitting models to data are usually assumed to follow a normal (or log normal) distribution in fisheries. This assumption is usually used in formulating likelihood functions often required in frequentist and Bayesian stock assessment modelling. Fisheries data are commonly subject to atypical errors, resulting in outliers in stock assessment modelling. Because most stock assessment models are nonlinear and contain multiple variables, it is difficult, if not impossible, to identify outliers by plotting fisheries data alone. Commonly used normal distribution‐based frequentist and Bayesian stock assessment methods are sensitive to outliers, resulting in biased estimates of model parameters that are vital in defining the dynamics of fish stocks and evaluating alternative strategies for fisheries management. Because of the high likelihood of having outliers in fisheries data, frequentist or Bayesian methods robust to outliers are more desirable in fisheries stock assessment. This study reviews three approaches that can be used to develop robust frequentist or Bayesian stock assessment methods. Using simulated fisheries as examples, we demonstrate how these approaches can be used to develop the frequentist and Bayesian stock assessment approaches that are robust to outliers in fisheries data and compare the robust approaches with the commonly used normal distribution‐based approach. The proposed robust approaches provide alternative ways to developing frequentist or Bayesian stock assessment methods.     

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.     

Yield predictive models for Sri Lankan reservoir fisheries

Tropical reservoirs are primarily constructed for irrigation, generation of hydroelectricity and water supply schemes. Development of inland fisheries is a secondary use of most reservoirs. In Sri Lanka, most reservoirs are scattered in the rural areas of the country so that investigation of the fisheries of individual reservoirs with a view to developing management plans is prohibitive. The present study was instigated to explore the possibilities of developing suitable yield predictive models, which can be used in developing management strategies for the Sri Lankan reservoirs. The study was carried out in 11 perennial reservoirs of Sri Lanka. Basic limnological parameters (conductivity, dissolved phosphorus, total phosphorus, chlorophyll a [chl a] content and alkalinity) were determined in each of these reservoirs. Daily data on fish catch and fishing effort were collected in each reservoir. Data on catchment areas (CA), reservoir area (RA) and reservoir capacity (RC) were obtained from the irrigation and survey departments. It is evident that chl a is positively influenced by nutrients (dissolved phosphorus and total phosphorus), morphoedaphic indices derived as alkalinity to mean depth (MEIa) and conductivity to mean depth (MEIc) ratios and CA/RC ratios. MEIa and MEIc are also positively influenced by CA/RC ratios. All these morphological and edaphic parameters were found to positively influence fish yield in reservoirs. As fishing intensity (FI) is also a major determinant of fish yields, fish yield was better accounted by multiple regression models in which FI and individual morphological and edaphic parameters were used as independent variables. Of these multiple regression relationships, the best predictive power for fish yield (Y in kg ha?1 yr?1) was found by Y=18.9+6.78 FI+0.0073 CA/RC where FI is expressed as boat‐days ha?1 yr?1 and CA and RC are in km2 and km3, respectively. In this relationship, FI and CA/RC account for about 68% of the variation in fish yield.     

Rethinking length‐based fisheries regulations: the value of protecting old and large fish with harvest slots

Managing fisheries using length‐based harvest regulations is common, but such policies often create trade‐offs among conservation (e.g. maintaining natural age‐structure or spawning stock biomass) and fishery objectives (e.g. maximizing yield or harvest numbers). By focusing harvest on the larger (older) fish, minimum‐length limits are thought to maximize biomass yield, but at the potential cost of severe age and size truncation at high fishing mortality. Harvest‐slot‐length limits (harvest slots) restrict harvest to intermediate lengths (ages), which may contribute to maintaining high harvest numbers and a more natural age‐structure. However, an evaluation of minimum‐length limits vs. harvest slots for jointly meeting fisheries and conservation objectives across a range of fish life‐history strategies is currently lacking. We present a general age‐ and size‐structured population model calibrated to several recreationally important fish species. Harvest slots and minimum‐length limits were both effective at compromising between yield, numbers harvested and catch of trophy fish while conserving reproductive biomass. However, harvest slots consistently produced greater numbers of fish harvested and greater catches of trophy fish while conserving reproductive biomass and a more natural population age‐structure. Additionally, harvest slots resulted in less waste in the presence of hooking mortality. Our results held across a range of exploitation rates, life‐history strategies and fisheries objectives. Overall, we found harvest slots to represent a valuable option to meet both conservation and recreational fisheries objectives. Given the ubiquitous benefits of harvest slots across all life histories modelled, rethinking the widespread use of minimum‐length limits is warranted.     

Environmental associations of Pacific bluefin tuna (Thunnus orientalis) catch in the California Current system

We investigate the impact of oceanographic variability on Pacific bluefin tuna (Thunnus orientalis: PBF) distributions in the California Current system using remotely sensed environmental data, and fishery‐dependent data from multiple fisheries in a habitat‐modeling framework. We examined the effects of local oceanic conditions (sea surface temperature, surface chlorophyll, sea surface height, eddy kinetic energy), as well as large‐scale oceanographic phenomena, such as El Niño, on PBF availability to commercial and recreational fishing fleets. Results from generalized additive models showed that warmer temperatures of around 17–21°C with low surface chlorophyll concentrations (<0.5 mg/m³) increased probability of occurrence of PBF in the Commercial Passenger Fishing Vessel and purse seine fisheries. These associations were particularly evident during a recent marine heatwave (the “Blob”). In contrast, PBF were most likely to be encountered on drift gillnet gear in somewhat cooler waters (13–18°C), with moderate chlorophyll concentrations (0.5–1.0 mg/m³). This discrepancy was likely a result of differing spatiotemporal distribution of fishing effort among fleets, as well as the different vertical depths fished by each gear, demonstrating the importance of understanding selectivity when building correlative habitat models. In the future, monitoring and understanding environmentally driven changes in the availability of PBF to commercial and recreational fisheries can contribute to the implementation of ecosystem approaches to fishery management.     

DEMAND FOR AQUACULTURE PRODUCTS IN TAIWAN: AN APPLICATION OF THE GENERALIZED MODEL

ABSTRACT

Policy decisions on what aquaculture products to develop require information on consumer demand for cultured species. However, information on the structure of demand for aquaculture products is limited and what few studies there are in Taiwan, where aquaculture is a major industry, suffer from methodological problems. To clear up some of these problems, we used modified nonnested testing techniques and performance forecasting to determine which generalized models could best estimate the demand for Taiwanese aquaculture products. The results of modified nonnested testing of the aquaculture demand system showed that prices predetermined and quantities predetermined could be used to estimate demand. The generalized ordinary demand model was able to better forecast performance than the generalized inverse demand model. We used the likelihood ratio test to discriminate among the four competing models for the generalized ordinary model; the AIDS model could be more suitably applied to the data. A more general model that is able to incorporate different dynamic structures (partial adjustment, first autoregressive, and static). This general framework is applied to the AIDS model. The first autoregressive AIDS model we used to calculate the own and cross-price elasticities for milkfish, tilapia, shrimp, shellfish, and carps found that price elasticities varied across fish type, that some products had high long-run own price elasticities, and that the demand for aquaculture products was largely determined by inertia.     

Structure and stability in exploited marine fish communities: quantifying critical transitions

Correlations between time series of the abundance of predator and prey fish species in heavily exploited western North Atlantic marine fisheries vary temporally but are generally positive in southern, warmer waters and negative in northern, colder ones. The correlations provide an index of trophic structure and dynamics. We construct a framework to quantify critical thresholds between states in which the predator–prey correlations are positive or negative. We do so by developing a quantitative model of the distribution of the correlations between predator (15 species) and prey (8 species) functional groups based on the annual predator depletion rates and bottom temperatures (or alternatively species richness). The model accounts for 58% of the variance of the correlations with a root mean square error of 0.3. This index of trophic structure indicates that warmer, species-rich, southern fish populations resist transformation from positive to negative predator–prey correlations at exploitation rates that can be double those in the colder, relatively species-poor, northern areas. The model can be used to set limits for exploitation rates that preserve the functional relationships between predator–prey groups in emerging fisheries, and to assess the potential for and measures required to achieve recovery of degraded fish communities.     

基于分期的实际种群分析法研究

实际种群分析法(virtual population analysis,VPA)是开展渔业资源评估最有效的技术之一,一般以世代为基础开展评估.基于实际渔业存在渔汛期、休渔期等特点,本研究运用分期评估的概念对传统实际种群分析进行了扩展,即分期种群分析法,并根据不同时期的捕捞死亡特征,评估与分析了4种不同分期情景对评估结果的影响.模拟研究表明,由于分期不当造成评估结果的误差为6％～33％.文中一并给出了开展分期实际种群分析法对资料收集和参数评估的要求.该方法克服了传统实际种群分析法中没有全面分期产生的误差,使其扩展至适合于评估全年捕捞死亡率不稳定或非连续性渔业种群,评估结果也更接近于评估种群的真实值.     

Towards integrated research in fisheries science

In this paper we present a call for a re-directing fisheries science towards an integrated scientific activity which includes the trajectory of fish from eggs and their life underwater to consumption on the table. We propose that integrated research in fisheries science be defined as “interdisciplinary research for society and human well-being which deals with the sustainability of dynamic fisheries systems, taking various knowledge, values, and needs into account.” As an integrative discipline, fisheries science offers many advantages to overcoming the limitations of the traditional sciences. The ability to conduct traditional research activities based on each academic discipline is a prerequisite to participating in integrated research. However, “additional processes” are needed, such as the introduction of new integrated thinking, joint determination of the research framework, mutual learning by participating researchers, interaction with stakeholders, among others. Integrated research in fisheries science would allow overall influences to be analyzed, including exogenous factors such as environmental changes and other marine industries. Walleye pollock Theragra chalcogramma provides a good model species for analyzing future scenarios of the structure of the industry as well as potential strategies for addressing and considering the effects of other highly fluctuating resources. One of the most challenging topics is identifying possible paths from a depleted stock to its future recovery. Scientific analyses on “How fast?” “Who should bear the costs?” “When it will occur”, etc., should be conducted by close interactions with stakeholders.