Deroba J.J.,National Oceanic and Atmospheric Administration |
Butterworth D.S.,University of South Africa |
Methot R.D.,National Oceanic and Atmospheric Administration |
DeOliveira J.A.A.,CEFAS - Center for Environment, Fisheries and Aquaculture Science |
And 32 more authors.
ICES Journal of Marine Science | Year: 2014
The World Conference on Stock Assessment Methods (July 2013) included a workshop on testing assessment methods through simulations. The exercise was made up of two steps applied to datasets from 14 representative fish stocks from around the world. Step 1 involved applying stock assessments to datasets with varying degrees of effort dedicated to optimizing fit. Step 2 was applied to a subset of the stocks and involved characteristics of given model fits being used to generate pseudo-data with error. These pseudo-data were then provided to assessment modellers and fits to the pseudo-data provided consistency checks within (self-tests) and among (cross-tests) assessment models. Although trends in biomass were often similar across models, the scaling of absolute biomass was not consistent across models. Similar types of models tended to perform similarly (e.g. age based or production models). Self-testing and cross-testing of models are a useful diagnostic approach, and suggested that estimates in the most recent years of time-series were the least robust. Results from the simulation exercise provide a basis for guidance on future large-scale simulation experiments and demonstrate the need for strategic investments in the evaluation and development of stock assessment methods. © 2014 Published by Oxford University Press 2014. This work is written by US Government employees and is in the public domain in the US.
Summer distribution and abundance of the giant devil ray (Mobula mobular) in the Adriatic Sea: Baseline data for an iterative management framework [Distribución estival y abundancia de la gran manta raya (Mobula mobular) en el mar Adriático: Datos de base para un marco de gestión iterativo]
Fortuna C.M.,European Commission - Joint Research Center Ispra |
Kell L.,ICCAT Secretariat |
Holcer D.,Marine Conservation Institute |
Canese S.,European Commission - Joint Research Center Ispra |
And 3 more authors.
Scientia Marina | Year: 2014
The giant devil ray (Mobula mobular) is a poorly understood protected endemic species of the eastern Atlantic-Mediterranean region. However, to date there are no range-wide management actions in place. This paper provides the first overview of the summer distribution and abundance of this species and other Myliobatiformes within the Adriatic Sea based on an aerial survey. Although the survey's primary targets were cetaceans and sea turtles, the study showed that it was possible to use the survey to monitor other species. Abundance estimates are derived using conventional distance sampling analysis. Giant devil rays were observed mainly in the central-southern Adriatic (88% of total sightings). A total of 1595 giant devil rays were estimated in the central-southern Adriatic Sea [coefficient of variation(CV)=25%, uncorrected estimate for perception and availability bias]. When corrected for availability bias the number of specimens was estimated at 3255 (CV=56%). Population growth rate was estimated using life history traits and a sensitivity analysis was conducted to evaluate the benefit of improving biological knowledge on this data-poor species. A power analysis showed that a long-term commitment to an aerial survey would be necessary to monitor population trends. Conservation implications and future work, including how the study could be used to conduct an ecological risk assessment are discussed. © 2014 CSIC.
Hillary R.M.,CSIRO |
Levontin P.,Imperial College London |
Kuikka S.,University of Helsinki |
Manteniemi S.,University of Helsinki |
And 2 more authors.
Ecological Modelling | Year: 2012
Meta-analytic and multi-level stock-recruit analyses have traditionally focussed on the similar stock approach, but for a specific stock-recruit model. For six European herring stocks we embed both the stock and model levels within a fully Bayesian hierarchical framework, thus permitting the consideration of a wider class of models, specifically those that do not admit parameterisation via the steepness and unfished spawning potential. Model and parametric uncertainty is jointly characterised and the challenge of addressing model selection when the model itself is part of the hierarchy is addressed using the deviance information criterion (DIC) and posterior predictive analysis. For the six herring stocks the across-stock posterior evidence in favour of over-compensatory dynamics is fairly strong, with the Ricker and Shepherd models performing the best across the model-selection criteria. For a specific model form we perform a 20 year retrospective analysis (hierarchical and non-hierarchical) to see how temporal information flow occurs in a hierarchical framework, how this can improve our estimates of key parameters, and how this might influence management paradigms (such as Maximum Sustainable Yield) that are based on such estimates. © 2012 Elsevier B.V.
Arrizabalaga H.,Tecnalia |
Dufour F.,NALDEO |
Kell L.,ICCAT Secretariat |
Merino G.,Tecnalia |
And 14 more authors.
Deep-Sea Research Part II: Topical Studies in Oceanography | Year: 2015
In spite of its pivotal role in future implementations of the Ecosystem Approach to Fisheries Management, current knowledge about tuna habitat preferences remains fragmented and heterogeneous, because it relies mainly on regional or local studies that have used a variety of approaches making them difficult to combine. Therefore in this study we analyse data from six tuna species in the Pacific, Atlantic and Indian Oceans in order to provide a global, comparative perspective of habitat preferences. These data are longline catch per unit effort from 1958 to 2007 for albacore, Atlantic bluefin, southern bluefin, bigeye, yellowfin and skipjack tunas. Both quotient analysis and Generalised Additive Models were used to determine habitat preference with respect to eight biotic and abiotic variables. Results confirmed that, compared to temperate tunas, tropical tunas prefer warm, anoxic, stratified waters. Atlantic and southern bluefin tuna prefer higher concentrations of chlorophyll than the rest. The two species also tolerate most extreme sea surface height anomalies and highest mixed layer depths. In general, Atlantic bluefin tuna tolerates the widest range of environmental conditions. An assessment of the most important variables determining fish habitat is also provided. © 2014 Elsevier Ltd.
Tidd A.N.,Cefas |
Hutton T.,CSIRO |
Kell L.T.,ICCAT Secretariat |
ICES Journal of Marine Science | Year: 2011
A profitable fishery attracts additional effort (vessels enter), eventually leading to overcapacity and less profit. Similarly, fishing vessels exit depending on their economic viability (or reduced expectations of future benefits) or encouraged by schemes such as decommissioning grants and/or when there is consolidation of fishing effort within a tradable rights-based quota system (e.g. individual transferable quotas). The strategic decision-making behaviour of fishers in entering or exiting the English North Sea beam trawl fishery is analysed using a discrete choice model by integrating data on vessel characteristics with available cost data, decommissioning grant information, and other factors that potentially influence anticipated benefits or future risks. It is then possible to predict whether operators choose to enter, stay, exit, or decommission. Important factors affecting investment include vessel age and size, future revenues, operating costs (e.g. fuel), stock status of the main target species, and the impact of management measures (e.g. total allowable catches) and total fleet size (a proxy for congestion). Based on the results, the predicted marginal effects of each factor are presented and the impact of each is discussed in the context of policies developed to align fleet capacity with fishing opportunities. ©  Crown copyright.
Butterworth D.S.,University of Cape Town |
Bentley N.,Trophia Ltd. |
De Oliveira J.A.A.,Center for Environment |
Donovan G.P.,International Whaling Commission |
And 7 more authors.
ICES Journal of Marine Science | Year: 2010
Rochet and Rice, while recognizing management strategy evaluation (MSE) as an important step forward in fisheries management, level a number of criticisms at its implementation. Some of their points are sound, such as the need for care in representing uncertainties and for thorough documentation of the process. However, others evidence important misunderstandings. Although the difficulties in estimating tail probabilities and risks, as discussed by Rochet and Rice, are well known, their arguments that Efron's nonparametric bootstrap re-sampling method underestimates the probabilities of low values are flawed. In any case, though, the focus of MSEs is primarily on comparing performance and robustness across alternative management procedures (MPs), rather than on estimating absolute levels of risk. Qualitative methods can augment MSE, but their limitations also need to be recognized. Intelligence certainly needs to play a role in fisheries management, but not at the level of tinkering in the provision of annual advice, which Rochet and Rice apparently advocate, inter alia because this runs the risk of advice following noise rather than signal. Instead, intelligence should come into play in the exercise of oversight through the process of multiannual reviews of MSE and associated MPs. A number of examples are given of the process of interaction with stakeholders which should characterize MSE. © 2010 UK and Australian Crown Copyright.
Kell L.T.,ICCAT Secretariat |
Kimoto A.,Japan National Research Institute of Fisheries And Environment of Inland Sea |
Kitakado T.,Tokyo University of Marine Science and Technology
Fisheries Research | Year: 2016
A major uncertainty in stock assessment is the difference between models and reality. The validation of model prediction is difficult, however, as fish stocks can rarely be observed and counted. We therefore show how hindcasting and model-free validation can be used to evaluate multiple measures of prediction skill. In a hindcast a model is fitted to the first part of a time series and then projected over the period omitted in the original fit. Prediction skill can then be evaluated by comparing the predictions from the projection with the observations. We show that uncertainty increased when different datasets and hypotheses were considered, especially as time-series of model-derived parameters were sensitive to model assumptions. Using hindcasting and model-free validation to evaluate prediction skill is an objective way to evaluate risk, i.e., to identify the uncertainties that matter. A hindcast is also a pragmatic alternative to hindsight, without the associated risks. While the use of multiple measures helps in evaluating prediction skill and to focus research onto the data and the processes that generated them. © 2016.
Tidd A.N.,Cefas |
Hutton T.,CSIRO |
Kell L.T.,ICCAT Secretariat |
Blanchard J.L.,University of Sheffield |
Blanchard J.L.,Imperial College London
Fisheries Research | Year: 2012
A discrete choice model is applied to determine how fishing effort is allocated spatially and temporally by the English and Welsh North Sea beam trawl fleet. Individual vessels can fish in five distinct areas, and the utility of fishing in an area depends on expected revenue measured as previous success (value per unit effort) and experience (past fishing effort allocation), as well as perceived costs (measured as distance to landing port weighted by fuel price). The model predicts fisher location choice, and the predictions are evaluated using iterative partial cross validation by fitting the model over a series of separate time-periods (nine separate time-periods). Results show the relative importance of the different drivers that change over time. They indicate that there are three main drivers throughout the study, past annual effort, past monthly effort in the year of fishing, and fuel price, largely reflecting the fact that previous practices where success was gained are learned (i.e. experience) and become habitual, and that seasonal variations also dominate behaviour in terms of the strong monthly trends and variable costs. In order to provide an indication of the model's predictive capabilities, a simulated closure of one of the study areas was undertaken (an area that mapped reasonably well with the North Sea cod 2001 partial closure of the North Sea for 10 weeks of that year). The predicted reallocation of effort was compared against realized/observed reallocation of effort, and there was good correlation at the trip level, with a maximum 10% misallocation of predicted effort for that year. © 2012.