Modelling salmon lice,Lepeophtheirus salmonis,reproduction on farmed Atlantic salmon,Salmo salar L

The aim of this study was to model sea lice levels and the effect on reproduction by a stochastic simulation model and to evaluate the uncertainty of lice estimates based upon counts. Two empirical data sets were examined to parameterize the models. An overall fit of the data to the Poisson distribution was found and thus was used as the base of the stochastic models. In the model, salmon lice reproduction is not linear with the number of adult females and at low lice loads a smaller proportion of the adult female lice will reproduce. Depending on the variance structure, it was estimated that between 40% and 60% of the adult female lice will reproduce at an abundance of 0.5 adult females per fish. Lice counts, especially when examining few fish at low lice loads, are uncertain and at a true abundance of 0.1 one may count between 0 and 5 lice when examining 10 fish. Understanding the dynamics of sea lice reproduction is a key factor in the development of sustainable control strategies.     

Applying distribution model projections for an uncertain future: the case of the Pacific oyster in UK waters

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Modelling the habitat requirement of riverine fish species at the European scale: sensitivity to temperature and precipitation and associated uncertainty

Abstract – Species distribution models (SDMs) have been widely used for different purposes such as studying species niche or forecasting the effects of global change on species distribution. Nevertheless, these models are often calibrated on datasets that only cover a fraction of the species’ realised niches, which could lead to unrealistic results. The aim of this study was to model the habitat requirements of 21 freshwater fish species that are native to Europe, using a dataset that accurately reflects their realised niches. Both temperature and precipitation were used as climatic factors to model the habitat requirements of the species, and the uncertainty associated with the fitted environment–occurrence relationships was examined. The results demonstrated the importance of accounting for these two climatic components when estimating the habitat requirements of riverine fish species and whether the uncertainty associated with model expectations varies with the species and the environmental factor considered. These results are discussed with regard to the known ecology of the 21 riverine fish species and within the perspective of climate change.     

Measuring uncertainty in fisheries stock assessment: the delta method,bootstrap, and MCMC

Fisheries management depends on reliable quantification of uncertainty for decision‐making. We evaluate which uncertainty method can be expected to perform best for fisheries stock assessment. The method should generate confidence intervals that are neither too narrow nor too wide, in order to cover the true value of estimated quantities with a probability matching the claimed confidence level. This simulation study compares the performance of the delta method, the bootstrap, and Markov chain Monte Carlo (MCMC). A statistical catch‐at‐age model is fitted to 1000 simulated datasets, with varying recruitment and observation noise. Six reference points are estimated, and confidence intervals are constructed across a range of significance levels. Overall, the delta method and MCMC performed considerably better than the bootstrap, and MCMC was the most reliable method in terms of worst‐case performance, for our relatively data‐rich scenario and catch‐at‐age model, which was not subject to substantial model misspecification. All three methods generated too narrow confidence intervals, underestimating the true uncertainty. Bias correction improved the bootstrap performance, but not enough to match the performance of the delta method and MCMC. We recommend using MCMC as the default method for quantifying uncertainty in fisheries stock assessment, although the delta method is the fastest to apply, and the bootstrap is useful to diagnose estimator bias.     

种群模拟在渔业资源评估中的研究现状及展望

随着渔业资源评估理论、数理统计方法和计算机技术的进步, 资源评估模型朝着多样化和复杂化不断发展, 其中种群模拟技术是检测模型适用性和局限性的重要手段。该技术由种群仿真理念发展而来, 通过模拟“真实”种群的方式, 对资源评估结果和管理策略进行有效的评价和预测, 并凭借可结合海洋环境因子、鱼类洄游空间分布以及多鱼种渔业进行资源评估的特性, 已成为开发新资源的重要评估方法之一。为此, 本文对种群模拟的结构和发展过程进行了回顾, 对该技术的核心组成部分操作模型和常见的四类误差(过程误差、观测误差、模型结构误差和管理误差)展开分类讨论。此外, 本文还结合近年来迅速发展的数据缺乏和数据适中模型的特点, 根据实际应用案例对种群模拟的作用和使用前景进行梳理, 并就种群模拟技术发展中存在的主要问题和潜在解决办法提出分析和建议。     

适用于数据缺乏渔业的资源评估方法研究进展

开展渔业资源评估研究是制定渔业可持续发展策略的重要前提,而数据有限是全球渔业资源评估面临的普遍挑战.传统资源评估方法具有数据需求量大、要求高等特点,无法应用于数据缺乏渔业的资源评估中.数据缺乏方法(data-limited method)可结合少量易获得数据和相关历史生物学信息对渔业资源状况、生物学参考点以及资源量等进...     

Reproductive strategies in small populations: using Atlantic salmon as a case study

Abstract –  Wild salmonid populations with only a few breeding adults may not exhibit a significant reduction in genetic variability compared with larger populations. Such an observation suggests that effective population sizes are larger than population size estimates based on direct adult counts and/or the mating strategy maximises outbreeding, contributing to increased heterozygosity. In the case of wild Atlantic salmon Salmo salar populations, stratification by age classes and sexes on the spawning grounds avoids inbreeding and increases genetic variability. We studied the breeding composition of four Spanish salmon populations. Over a 7-year period we concluded that the probability of within-cohort mating is very low: females generally reproduce after two sea-winters whereas males reproduce mostly as one sea-winter ( grilse ) and/or mature parr. Considering different levels of contribution of mature parr to spawning derived from field surveys, we developed a simple model for estimating effective population sizes and found that they doubled with 65% parr contribution expected for rivers at this latitude (43°N), and ranged from 100–800 individuals. The effect of between-cohort mating was modelled considering different ranges of differences in allele frequencies between cohorts and resulted in 28–50% increases in heterozygosity when considering a 65% parr contribution. The complex mating strategy of Atlantic salmon contributes to explain the high levels of genetic variability found for small populations of this species. This model can probably be extended to other animal species with mating strategies involving different cohorts.     

Human behaviour: the key source of uncertainty in fisheries management

There is broad consensus that the main problem facing fisheries globally is too many boats chasing too few fish. Unfortunately it is also possible to argue that there are too many proposed solutions and not enough practical answers to improving fisheries management. There is a deepening divide between those who propose alternative regulatory controls on fishers, including establishing large areas permanently closed to fishing, and those who argue for better alignment of incentives combined with broad participation of resource users in fishery management decisions (in simple terms, between top down and bottom up systems of governance). However despite the choice of policy instruments used, a consistent outcome is that resource users behave in a manner that is often unintended by the designers of the management system. Hence whilst uncertainty is broadly recognized as a pervasive feature of fisheries management, to date most of the attention has focussed on only part of that uncertainty – scientific uncertainty about the status of exploited resources. The effect of uncertainty generated on the human side of fisheries science and management has received much less attention. However, the uncertainty generated by unexpected resource user behaviour is critical as it has unplanned consequences and leads to unintended management outcomes. Using empirical evidence of unexpected resource user behaviour and reviewing current responses to unexpected management outcomes, we identify different approaches that both improve prediction of human behaviour in fisheries systems and identify management measures that are more robust to these sources of uncertainty. However, unless the micro scale drivers of human behaviour that contribute to macro scale implementation uncertainty are communicated effectively to managers and considered more regularly and in greater depth, unanticipated responses to management actions will continue to undermine management systems and threaten the sustainability of fisheries.     

Toward sustainable management of small cetacean fisheries around Japan

Abstract:   The management of Japanese domestic fisheries for small cetaceans has been based on a traditional approach that involves only the best set of assumptions and data. However, uncertainty, which is pervasive and inevitable for most fisheries resources, often leads the traditional approach to serious failure. Here is presented the basic framework of a simulation-based approach with a simple Bayesian method that is applicable to Japanese fisheries for small cetaceans. For illustration, the simulation model was applied to Dall's porpoise data in order to investigate the robustness of several management procedures against uncertainty. The simulation showed that the current management procedure, based on only the best values, could fail to manage the stocks at a considerably high probability. However, the use of the conservative management procedure, potential biological removal, permits the sustainable harvesting of Dall's porpoise for at least 100 years into the future, even under uncertainty. In conjunction with a firm structure for implementation of management procedures, the spread of simulation-based approaches will quickly enable successful sustainable management of small cetaceans.     

Modeling the coastal ecosystem complex: present situation and challenges

To enhance numerical modeling of the coastal ecosystem complex (CEC), we reviewed the CEC and related concepts along with the current coastal ecosystem model framework in this study. We identified two model implementation paths from the initial objectives to numerical models: specific model building, and the use of existing model frameworks. As the CEC is still at the conceptual stage, both paths are possible. Four important ecological features of CEC modeling (population connectivity, habitat heterogeneity, ontogeny of organisms, and trophic interactions) were also identified. Models for population connectivity, species distributions, life histories, and food webs were categorized using these features. We found that some previously established concepts (between–habitat interactions, coastal ecosystem mosaic, and seascape nursery) overlap with the CEC concept. Several existing integrated model frameworks were reviewed, focusing on their potential to simulate CEC processes. Building specific models for the CEC at the current conceptual stage will be challenging, and modification of existing models will be needed if they are to be used for CEC modeling. Habitat function, ontogenetic development in early life stages, and recruitment variability are important factors when modifying existing models for the development of CEC models. Although model complexity should become high to reproduce observed ecoclogical processes, an intermediate level of model ccomplexity is feasible to decrease parameter uncertainty in models for fisheries management.     

Model uncertainty in the ecosystem approach to fisheries

Fisheries scientists habitually consider uncertainty in parameter values, but often neglect uncertainty about model structure, an issue of increasing importance as ecosystem models are devised to support the move to an ecosystem approach to fisheries (EAF). This paper sets out pragmatic approaches with which to account for uncertainties in model structure and we review current ways of dealing with this issue in fisheries and other disciplines. All involve considering a set of alternative models representing different structural assumptions, but differ in how those models are used. The models can be asked to identify bounds on possible outcomes, find management actions that will perform adequately irrespective of the true model, find management actions that best achieve one or more objectives given weights assigned to each model, or formalize hypotheses for evaluation through experimentation. Data availability is likely to limit the use of approaches that involve weighting alternative models in an ecosystem setting, and the cost of experimentation is likely to limit its use. Practical implementation of an EAF should therefore be based on management approaches that acknowledge the uncertainty inherent in model predictions and are robust to it. Model results must be presented in ways that represent the risks and trade‐offs associated with alternative actions and the degree of uncertainty in predictions. This presentation should not disguise the fact that, in many cases, estimates of model uncertainty may be based on subjective criteria. The problem of model uncertainty is far from unique to fisheries, and a dialogue among fisheries modellers and modellers from other scientific communities will therefore be helpful.     

大渡河安谷水电站施工期春、秋季浮游动物群落特征分析

研究安谷水电站施工期浮游动物对水文情势改变、栖息地条件变化等工程影响的响应,为径流式水电工程建设对浮游动物的影响预测评价提供参考,为安谷水电站水生态环境发展或生态修复提供科学依据。2014年春季、秋季对大渡河安谷水电站施工期浮游动物及水环境因子进行调查采样,利用SPSS和Canoco软件进行数据分析。结果表明:大渡河安谷水电站施工期春、秋季共检出浮游动物69属116种,密度平均为619.65个/L、生物量平均0.093 mg/L。左侧河网浮游动物种类、密度和生物量高于干流。浮游动物种类、密度、生物量和多样性指数在季节分布上差异显著,春季明显高于秋季。浮游动物种类、密度、生物量与环境因子硝氮、总氮、磷酸盐、总磷呈显著正相关,与流速呈负相关;RDA结果进一步说明硝氮、磷酸盐、TN、TP是影响浮游动物群落结构的重要环境因子。     

Balanced exploitation and coexistence of interacting,size‐structured,fish species

This paper examines some effects of exploitation on a simple ecosystem containing two interacting fish species, with life histories similar to mackerel (Scomber scombrus) and cod (Gadus morhua), using a dynamic, size‐spectrum model. Such models internalize body growth and mortality from predation, allowing bookkeeping of biomass at a detailed level of individual predation and growth and enabling scaling up to the mass balance of the ecosystem. Exploitation set independently for each species with knife‐edge, size‐at‐entry fishing can lead to collapse of cod. Exploitation to achieve a fixed ratio of yield to productivity across species can also lead to collapse of cod. However, harvesting balanced to the overall productivity of species in the exploited ecosystem exerts a strong force countering such collapse. If balancing across species is applied to a fishery with knife‐edge selection, size distributions are truncated, changing the structure of the system and reducing its resilience to perturbations. If balancing is applied on the basis of productivity at each body size as well as across species, there is less disruption to size‐structure, resilience is increased, and substantially greater biomass yields are possible. We note an identity between the body size at which productivity is maximized and the age at which cohort biomass is maximized. In our numerical results based on detailed bookkeeping of biomass, cohort biomass reaches its maximum at body masses <1 g, unlike standard yield‐per‐recruit models, where body growth and mortality are independent externalities, and cohort biomass is maximized at larger body sizes.     

Estimating uncertainty in fish stock assessment and forecasting

A variety of tools are available to quantify uncertainty in age‐structured fish stock assessments and in management forecasts. These tools are based on particular choices for the underlying population dynamics model, the aspects of the assessment considered uncertain, and the approach for assessing uncertainty (Bayes, frequentist or likelihood). The current state of the art is advancing rapidly as a consequence of the availability of increased computational power, but there remains little consistency in the choices made for assessments and forecasts. This can be explained by several factors including the specifics of the species under consideration, the purpose for which the analysis is conducted and the institutional framework within which the methods are developed and used, including the availability and customary usage of software tools. Little testing of either the methods or their assumptions has yet been done. Thus, it is not possible to argue either that the methods perform well or perform poorly or that any particular conditioning choices are more appropriate in general terms than others. Despite much recent progress, fisheries science has yet to identify a means for identifying appropriate conditioning choices such that the probability distributions which are calculated for management purposes do adequately represent the probabilities of eventual real outcomes. Therefore, we conclude that increased focus should be placed on testing and carefully examining the choices made when conducting these analyses, and that more attention must be given to examining the sensitivity to alternative assumptions and model structures. Provision of advice concerning uncertainty in stock assessments should include consideration of such sensitivities, and should use model‐averaging methods, decision tables or management procedure simulations in cases where advice is strongly sensitive to model assumptions.     

Ecosystem models for fisheries management: finding the sweet spot

The advent of an ecosystem‐based approach dramatically expanded the scope of fisheries management, creating a critical need for new kinds of data and quantitative approaches that could be integrated into the management system. Ecosystem models are needed to codify the relationships among drivers, pressures and resulting states, and to quantify the trade‐offs between conflicting objectives. Incorporating ecosystem considerations requires moving from the single‐species models used in stock assessments, to more complex models that include species interactions, environmental drivers and human consequences. With this increasing model complexity, model fit can improve, but parameter uncertainty increases. At intermediate levels of complexity, there is a ‘sweet spot’ at which the uncertainty in policy indicators is at a minimum. Finding the sweet spot in models requires compromises: for example, to include additional component species, the models of each species have in some cases been simplified from age‐structured to logistic or bioenergetic models. In this paper, we illuminate the characteristics, capabilities and short‐comings of the various modelling approaches being proposed for ecosystem‐based fisheries management. We identify key ecosystem needs in fisheries management and indicate which types of models can meet these needs. Ecosystem models have been playing strategic roles by providing an ecosystem context for single‐species management decisions. However, conventional stock assessments are being increasingly challenged by changing natural mortality rates and environmentally driven changes in productivity that are observed in many fish stocks. Thus, there is a need for more tactical ecosystem models that can respond dynamically to changing ecological and environmental conditions.     

Minimizing the impact of fishing

Minimizing the impact of fishing is an explicit goal in international agreements as well as in regional directives and national laws. To assist in practical implementation, three simple rules for fisheries management are proposed in this study: 1) take less than nature by ensuring that mortality caused by fishing is less than the natural rate of mortality; 2) maintain population sizes above half of natural abundance, at levels where populations are still likely to be able to fulfil their ecosystem functions as prey or predator; and 3) let fish grow and reproduce, by adjusting the size at first capture such that the mean length in the catch equals the length where the biomass of an unexploited cohort would be maximum (L_opt). For rule 3), the basic equations describing growth in age‐structured populations are re‐examined and a new optimum length for first capture (L_{c_opt}) is established. For a given rate of fishing mortality, L_{c_opt} keeps catch and profit near their theoretical optima while maintaining large population sizes. Application of the three rules would not only minimize the impact of fishing on commercial species, it may also achieve several goals of ecosystem‐based fisheries management, such as rebuilding the biomass of prey and predator species in the system and reducing collateral impact of fishing, because with more fish in the water, shorter duration of gear deployment is needed for a given catch. The study also addresses typical criticisms of these common sense rules for fisheries management.     

Stock assessment on fishery-dependent data: Effect of data quality and parametrisation for a red snapper fishery

Data availability, and unreported and unregulated fishing are significant obstacles to evaluating stock status, especially in tropical areas. Limitations in data quantity and quality can lead to model misspecification and erroneous data treatments, potentially causing important changes in model outputs and subsequent management implications. Red snapper Lutjanus purpureus (Poey) in French Guiana provides an example of a stock with a long-time series of fishery-dependent data subject to large uncertainty. A flexible catch-at-age model (Stock Synthesis) was applied to the available data and compared to an historically applied assessment approach. Inter-model variability based on different model specifications and data treatments were compared to identify better the status of the resource. Results showed that a major source of uncertainty in the model was the inclusion of a catch-per-unit-effort abundance index with questionable ability to track abundance. The Stock Synthesis model provided a more flexible and viable method than the virtual population analysis approach. Despite large uncertainty, models depicted a similar trend with a notable stock depletion in the late 1990s but with two distinct biomass trends in more recent years depending on the treatment. To reduce uncertainty and preserve this important economic resource, new data collection programmes and management policies are needed.     

Establishing survey and monitoring protocols for the assessment of conservation status of fish populations in river Special Areas of Conservation in the UK

• 1. Under the EC Habitats Directive (92/43/EEC), several rivers in the UK have been designated Special Areas of Conservation (SACs) because they support important populations of fish species. Several factors need to be considered when establishing survey protocols to assess the conservation status of fish species in these rivers.
• 2. It is important to understand the biology and habitat requirements of fish species and to design appropriate monitoring surveys that adequately assess the status of the fish populations. This information can, in turn, provide the basis for establishing sustainable conservation targets for specific stocks, a process known as condition assessment.
• 3. Favourable condition of fish species in SAC rivers is assessed using three criteria. The first relates to density of fish in various life stages against pre‐set targets, assuming that an appropriate number of sites is surveyed to account for natural spatial and temporal variations in fish populations. The second is evaluation of the demographic structure to demonstrate continued recruitment success. Third, by mapping the distribution of the target species in individual rivers and where favourable condition is only achieved where there has been no reduction in the distribution range river between surveys.
• 4. The framework described was developed from limited survey data. As more monitoring and assessment data become available, the threshold criteria for favourable condition for each species should be improved, and uncertainty in the procedures reduced.

Copyright © 2008 John Wiley & Sons, Ltd.     

A conditional simulation of acoustic survey data: advantages and potential pitfalls

Standard geostatistical techniques provide effective methods for estimating the global abundance and precision of a variable of interest, for mapping its spatial distribution and for describing its spatial structure. In the case of acoustic survey data, however, obtaining a measure of precision of the global abundance estimate is confounded by the combination of variances from the interpolation of both the acoustic data and the concomitant fish length data. Even if the global estimation variance could be calculated, the distribution of the estimation error is not known and so confidence intervals cannot be determined. Furthermore, kriged distribution maps, in minimising the estimation variance, tend to smooth out local details of the attribute’s spatial variation: small values can be overestimated and larger ones underestimated, such that the kriged map is smoother than reality. This can lead to serious shortcomings when trying to detect patterns of extreme attribute values, such as the high densities encountered in some fish schools. Stochastic geostatistical simulations, conditional on sampled locations, provide a solution to many of these problems. They can deliver a measure of uncertainty for local (density) estimates, a confidence interval estimation for the global mean density, and finally, reproduce global statistics, such as the sample histogram and variogram. In so doing, they also provide maps of the attribute, which are spatially realistic because the variogram is reproduced; these are generated as a number of equiprobable realisations. In the present paper, we apply these techniques to acoustic data from an acoustic survey of North Sea herring. Sequential gaussian simulations are used to generate realisations for fish length and values of the nautical area scattering coefficient. These are then combined to produce realisations of herring density. The combined set of multiple realisations is then used to provide confidence intervals for the global abundance estimate: 95% of the herring abundance estimates are between 5677 and 6271 millions of individuals. Although the method presented in this paper contributes to the assessment of total uncertainty for acoustic surveys, the approach may have suffered from bias due to the use of off-the-shelf data transformation algorithms on fisheries acoustic data, which are often very positively skewed. We discuss this limitation and propose corrections for future work.     

Application of the Tweedie distribution to zero-catch data in CPUE analysis

We focus on the zero-catch problem of CPUE (catch per unit effort) standardization. Because the traditional CPUE model with a log-normal error structure cannot be applied in this case, three methods have often been utilized as follows:

(1) Ad hoc method adds a small constant value to all response variables.

(2) Catch model with a Poisson or negative-binomial (NB) error structure.

(3) Delta-type two-step method such as the delta-normal model (after estimating the ratio of zero-catch using a logit or probit model, a model such as CPUE log-normal or Catch-Poisson is applied to CPUE without zero-data).

However, there are some statistical problems with each of these methods.In this paper, we carried out the CPUE standardization mainly using the Tweedie distribution model based on the actual by-catch data (silky shark, Carcharhimus falciformis, in the North Pacific Ocean caught by Japanese training vessels) including many observations with zero-catch (>2/3rd) and tuna fishery data as a target (yellowfin tuna, Thunnus albacares, in the Indian Ocean caught by Japanese commercial vessels) where the ratio of zero-catch is not so high (<1/3rd). The Tweedie model is an extension of compound Poisson model derived from the stochastic process where the weight of the counted objects (i.e., number of fish) has a gamma distribution and has an advantage of handling the zero-catch data in a unified way.We also compared four candidate models, the Catch-NB model, ad hoc method, Delta-lognormal model (delta-type two-step method) and Tweedie distribution, through CPUE analyses of actual fishery data in terms of the statistical performance. Square error and Pearson's correlation coefficient were calculated based on the observed CPUE and the corresponding predicted CPUE using the n-fold cross-validation.As a result, the differences in the trend of CPUE between years and model performance between the ad hoc method and Tweedie model were found to be not so large in the example of yellowfin tuna (target species). However, the statistical performance of Tweedie distribution is rather better than Delta-lognormal model, the Catch-NB distribution and ad hoc method in the example of silky shark (by-catch species). Standardized CPUE year trend of ad hoc method was found to be quite different from that of the Tweedie distribution and other two models. Model performance of the Tweedie distribution is good judging from the 5-fold cross-validation using the fishery data if including many zero-catch data such as by-catch species.