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Madrid, Spain

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.

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.

Tidd A.N.,CEFAS | Hutton T.,CSIRO | Kell L.T.,ICCAT Secretariat | Padda G.,Defra
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. © [2011] Crown copyright.

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.

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.

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