Tagging and Tracking of Marine Animals with Electronic Devices

Tagging and Tracking of Marine Animals with Electronic Devices Reviews: Methods and Technologies in Fish Biology and ...

Author: Jennifer L. Nielsen | Haritz Arrizabalaga | Nuno Fragoso | Alistair Hobday | Molly Lutcavage | John Sibert

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Tagging and Tracking of Marine Animals with Electronic Devices

Reviews: Methods and Technologies in Fish Biology and Fisheries VOLUME 9 Series editor:

Jennifer L. Nielsen U.S. Geological Survey, Alaska Science Center Anchorage, Alaska

For further volumes: http://www.springer.com/series/6481

Jennifer L. Nielsen · Haritz Arrizabalaga · Nuno Fragoso · Alistair Hobday · Molly Lutcavage · John Sibert Editors

Tagging and Tracking of Marine Animals with Electronic Devices

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Editors Jennifer L. Nielsen US Geological Survey Alaska Science Center 4210 University Drive Anchorage AK 99508 USA [email protected] [email protected] Nuno Fragoso Large Pelagic Research Center University of New Hampshire Durham NH 03824 USA Molly Lutcavage Large Pelagic Research Center University of New Hampshire Durham NH 03824 USA

Haritz Arrizabalaga Marine Research Division AZTI Tecnalia Herrera Kaia Portualdea, z/g 20110 Passaia Spain Alistair Hobday CSIRO Marine & Atmospheric Research Castray Esplanade Hobart TAS 7001 Australia John Sibert Pelagic Fisheries Research Program University of Hawaii 1000 Pope Road Honolulu HI 96822 USA

c UK Crown Chapters 7, 15 c Canadian Crown Chapter 9 Chapter 19 was created within the capacity of an US governmental employment and therefore is in the public domain.

ISSN 1571-3075 ISBN 978-1-4020-9639-6 e-ISBN 978-1-4020-9640-2 DOI 10.1007/978-1-4020-9640-2 Springer Dordrecht Heidelberg London New York Library of Congress Control Number: 2008942367 c Springer Science+Business Media B.V. 2009 No part of this work may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, microfilming, recording or otherwise, without written permission from the Publisher, with the exception of any material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)

Preface: Applications of Electronic Tagging to Understanding Marine Animals

Overview The 2nd international tagging and tracking symposium was held in San Sebastian, Spain, in October 2007, seven years after the first symposium was held in Hawaii in 2000 (Sibert and Nielsen, 2001). In the intervening seven years, there have been major advances in both the capability and reliability of electronic tags and analytical approaches for geolocation. Advances such as increased data storage capacity, sensor development, and tag miniaturization have allowed researchers to track a much wider array of marine animals, not just large and charismatic species. Importantly, data returned by these tags are now being used in population analyses and movement simulations that can be directly utilized in stock assessments and other management applications. Since the first symposium, the increased use of electronic tags on fish in particular is also evident in the growth of peer reviewed literature on this subject (Fig. 1). The number of papers increased more rapidly after the first symposium, and we anticipate a similar positive effect following publication of these proceedings. Given the number of papers published each year, the relevance of these proceedings is also clear, as it is likely to contain a significant proportion of the expected peer reviewed literature produced in 2008.

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San Sebastian

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30 20 10

2006

2004

2002

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1996

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0 1990

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Fig. 1 Number of publications each year related to electronic tagging of fish identified from ASFA using the keyword combinations: (electronic or sonic or archival or pop-up or (pop up) or PSAT) and (tag∗ and fish and PY = 1990–2007)

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The 2nd symposium attracted over 150 participants from 19 countries, including 29 students. A total of 70 oral presentations and 29 posters were presented over five days.1 While studies related to commercially important tuna and billfish dominated, presentations spanned a wider range of taxa than in 2001, including the behavior of escaped cod, migrating salmon, and shark feeding to conservation and fisheries management. Twenty five of these contributions are included in this volume. These papers are divided into three sections, the first describing insights in behavior achieved using acoustic, archival, and novel tags, the second reporting on advances in methods of geolocation, while the final section includes contributions where tag data have been used in management of marine species.

Behavioral Insights Based on Use of Electronic Tags Behavior Using Acoustic Tags Acoustic transmitters have a long history of use in the ocean, with individual fish followed for as long as the ocean conditions and vessel or researcher stamina allowed (e.g., Brill et al., 1999; Holland et al., 1999; Lutcavage et al., 2000; Arnold and Dewar, 2001). More recently, acoustic monitoring has been used to monitor the movements of fish using pre-deployed acoustic receivers (e.g. Klimley and Butler, 1988; Heupel et al., 2006). The advantage of both approaches is that there is no need to recapture animals, and data is gathered in real-time (tracking) or near real-time (monitoring). Since the first symposium, there has been an increase of what might be termed experimental studies, where comparisons between treatment groups are made. Enhancement programs are one approach to bolster population sizes following overexploitation, yet the behavioral differences between wild and cultured animals may result in complex interactions leading to failure of these programs, and potentially negative effects on wild stocks (Ruggerone et al., 2003; Kostow, 2008). Fairchild et al. (this volume) compared the movements and habitat use of cultured and wild juvenile winter flounder (Pseudopleuronectes americanus) with acoustic tags and three tracking systems in New Hampshire, USA. They found similar home range sizes between groups, but cultured fish initially dispersed more widely while wild fish maintained high release site fidelity. A second example is provided by Lino et al. (this volume) using wild and hatchery reared white seabream (Diplodus sargus) released on artificial reefs in Southern Portugal. Individuals had different behavior at release, habitat association and daily movements, showing that habitats used by wild bream may not be optimal release locations for hatchery-produced fish. Understanding movement patterns and habitat associations of cultured and wild fish is significant for developing techniques for enhancement programs and for defining

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essential fish habitats (Palumbi, 2004). As both these studies showed, electronic tags allow particular insight into these behaviors. Survival of Atlantic salmon (Salmo salar) following the first of several spawning years was investigated in Norway using acoustic tags and receivers by Halttunen et al. (this volume). Despite their weakened condition after spawning and overwintering in the river, survival of fish returning to the ocean was very high. Estimates of mortality will be useful for improved understanding of the relatively poorly defined contribution of iteroparity (more than one spawning event by individual fish) to the population dynamics of this species. While many of the electronic tagging studies have a single species focus, Goñi et al. (this volume) report on vertical behavior of juvenile albacore in the Bay of Biscay and simultaneously collected prey distribution data from echosounders. Although tuna depth distribution did not relate to prey distribution, the combination of these technologies is suitable for developing fine-scale understanding of tuna behavior. Ultimately, simultaneous monitoring of predators and their prey could be used to correct catchability estimates from surface trolling fleets fishing in the area. The last two contributions in this section call for the need for increased coordination between scientists and equipment manufacturers in design of acoustic tracking networks and development of data sharing protocols as part of an international tagging and monitoring program. Grothues (this volume) notes that a range of automated telemetry systems for tracking acoustically tagged marine fauna are deployed in coastal regions around the world. These systems use various coding, signal reception, data handling and storage, and deployment architecture. Problems arise because tags of one code scheme are not detected by listening devices from another scheme due to technical constraints and proprietary interests. Solutions for cross-equipment communication exist at both the transmitter and receiver ends of the technology, and Grothues outlines why scientists and manufacturers need to discuss and mitigate negative effects of diversification without the loss of its benefits. Similarly, as part of the development of the Ocean Tracking Network, O’Dor and Stokesbury (this volume) report that researchers are pressing for standards and protocols to allow universal storage and sharing of a broad spectrum of biological and physical oceanographic information. Such data sharing will be crucial if this ambitious international network is to maximize contributions to ocean management.

Behavior Using Internal or Pop-Up Archival Tags Despite advances since the first symposium, there is still much to be learned about the vertical behaviors of marine fishes. Quayle et al. (this volume) report on the behavioral modes of European sea bass (Dicentrarchus labrax) as they migrate between seasonal feeding and spawning grounds using miniature electronic data storage tags (DSTs). Schaefer et al. (this volume) describe the vertical movements and habitat utilization of skipjack (Katsuwonus pelamis), yellowfin (Thunnus albacares), and bigeye (Thunnus obesus) tunas in the equatorial eastern Pacific Ocean, ascertained through archival tag data. Significant portions of time for

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all species were spent above the thermocline at night, but much less at day. Differences in species-specific physiological abilities and tolerances to environmental characteristics of their vertical habitat seem to be responsible. Neilson et al. (this volume) investigated horizontal movements of Atlantic swordfish (Xiphias gladius) using pop-up satellite archival tags on fish in Canadian waters. Reported tag attachments of up to 411 days are among the longest periods of attachment reported for any fish species. They reported no movement from the western North Atlantic to the eastern North Atlantic from the tags recovered to date. Their results challenge the assumption of current stock assessments that swordfish move freely between these regions. Ultimately, results from these types of study on the comparative vertical and horizontal movements, behavior, and habitat utilization, should improve understanding of species-specific vulnerability to purse-seine and longline fisheries, estimates of stock mixing rates, and how to avoid unwanted bycatch. Industrialized purse-seine fishing on anchored fish attraction devices (FADs) has existed in the Papua New Guinea EEZ for more than a decade, yet little is known about the effect of the FADs on fish behavior and distribution. Leroy et al. (this volume) describe vertical behavior and FAD effects on three species of tropical tuna via archival and acoustic tagging carried out in this region. Distinct vertical behavior modes for bigeye and yellowfin tuna were identified that may influence potential vulnerability to industrial purse-seine capture. Unfortunately, the considerable vertical overlap in distribution limits the potential for targeting particular species or size classes of tuna through fishing depth selection. It is widely assumed that the impact of tagging on the subsequent behavior of the fish is negligible, however, this is rarely tested (see however Wilson and McMahon, 2006). Jolivet et al. (this volume) examined the effects of T-bar and DST tagging on survival and growth of European hake (Merluccius merluccius). While conventional tagging affects fish survival rates, electronic tagging is feasible on small individuals of this species with expected survival rate and recapture probability close to that of conventional tagging. Electronic tagging can also shed light on the physiology of marine species. Addis et al. (this volume) report on body temperature in the Atlantic bluefin tuna (Thunnus thynnus) in the Mediterranean Sea and temperature variability associated with the stress of capture. The investigation was carried out in traditional traps (tonnara) of the Mediterranean Sea. Fish body temperature increased by about 2◦ C during capture (“mattanza”). High body temperature and stress-related physiological disturbance can speed up the onset and progression of bacterial growth and biochemical deterioration, reducing the quality and marketability of harvested meat.

Behavior Using Novel Tags While information on location, depth, and temperature are now routinely provided from both archival and acoustic tags, additional insight can be gained when additional sensors are employed. Gleiss et al. (this volume) deployed a multi-channel


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data logger on two lemon sharks (Negaprion brevirostris) as a first step determination of behavior and metabolic rate in free-swimming sharks. Individuals were equipped with archival tags measuring 13 parameters, and four behaviors were characterized. Tail-beat frequency was correlated with overall dynamic body acceleration, which can in turn be correlated with activity-specific metabolic rate, potentially allowing estimates of energy expenditure in sharks. Very high resolution data collection often requires short deployment periods, secure tag attachment and rapid release. Houghton et al. (this volume) describe a novel tag attachment method applied to ocean sunfish (Mola mola) for short-term deployments of high-resolution data loggers. They tested a low-impact harness system with an automated release mechanism that allowed retrieval of the detached devices at sea. This study revealed interesting high resolution movement data, which suggested that molas employ a unique mode of swimming. Feeding is a key factor affecting the growth, survival and reproductive success of marine fish. Understanding feeding in relation to spatial movements and habitat use is therefore critical to realistic parameterization of models of fish populations and ecosystem dynamics. Nonetheless, obtaining information about feeding from fish in the wild over extended time scales is problematic. Metcalfe et al. (this volume) describe an archival tag for monitoring feeding and spawning based on a modification of the archival tag (Cefas G5, Lowestoft, UK) previously used to monitor penguin feeding via jaw movements. In typical usage, the new tag can store ∼96 h of data when logged at 30 Hz, or months of data collected if pre-programmed to log data for short, separate occasions. In laboratory studies with Atlantic cod different types of jaw movement could be discriminated. These authors also reported preliminary attempts to monitor movements of the cloacae of female cod as a possible means of identifying spawning activity. The next step is to translate these tagging approaches to wild fish, which is sure to yield novel insights and allow greater understanding of movement, distribution patterns and associated behavior.

Geolocation Methods Archival tags (implanted and pop-up) provide ambient light records from which estimates of dawn and dusk can be used to calculate longitude (from local noon) and latitude (from local day length) (Gunn et al., 1994; Hill, 1994; Musyl et al., 2001). Unfiltered and uncorrected geolocation data are often noisy leading to errors in estimated location, the so-called “geolocation problem” (Welch and Eveson, 1999). Light-based estimates of latitude are especially difficult around the two equinox periods since day length is approximately equal at all latitudes. A range of statistical approaches have recently been developed to improve light-based position estimation (Sibert et al., 2003), and in particular, light has been supplemented with additional information, such as sea surface temperature, or depth, to refine the position estimation (e.g., Teo et al., 2004; Lam et al., 2008). Estimates of uncertainty with location estimates were routinely provided by presenters at this symposium, which

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is to be commended. This should become standard practice throughout the field, and while perfect location estimates may be unattainable, this field of study has matured and will continue to add increased value to electronic tagging studies. Thygesen and Nielsen (this volume) provide some simple and general expressions for the accuracy of geolocation, which may be obtained by optimal filtering of measurements from archival tags. Using an idealized geolocation problem where the animal performs a random walk, they derive simple closed-form expressions for the steady-state variance and for the characteristic time scale of the filter. This leads to temporal and spatial scales defining the limit of resolution and explains the difference between what can be obtained for fast-moving and slow-moving animals. These results are particularly useful to the planning of a tagging study, because estimates of accuracy can be computed ahead of time using only three parameters: the swimming speed of the animal, the sample interval, and the variance on the measurement error. Thygesen et al. (this volume) present a novel statistical analysis, based on Hidden Markov Models to reduce the effect of measurement errors that impact the accuracy of estimates of fish position based on data from archival tags. The method is illustrated using a simulated data set where geolocation relies on depth data exclusively. Nielsen et al. (this volume) have developed a state space model for light-based geolocation of electronically tagged marine animals for improving the precision of the reconstructed geographical tracks. Unlike earlier models reliant on previously calculated raw geolocation estimates from outside sources, their model uses light data to produce the most probable track. This paper illustrates the application of the model to mako sharks and blue marlin and compares the results to satellite-based tracks from the same animals. This new model was vastly superior to previous light-based methods in all cases. Sibert et al. (this volume) employ error propagation analysis to the fundamental equation relating latitude to solar elevation and time of day, and find that large latitude errors are caused by mathematical “amplification” of small errors in relating solar irradiance to solar elevation. Their analysis leads directly to a method of removing bias from latitude estimates in state-space track reconstruction models, and test this using archival tags deployed on moorings and on freely swimming tuna. Another geolocation alternative, particularly suited for pop-up archival satellite tags, proposed by Royer and Lutcavage (this volume) is for the tag sensor to detect a sunrise or sunset event and then transmit only this information. They show that this geolocation technique retains the essential information to correct for known problems such as errors at the equinoxes and conclude that sunrise/sunset-based geolocation is a viable technique to surmount tag design or engineering limitations when tracking pelagic fish. It is apparent that significant advances in geolocation are being made, and it is timely to consider if this issue is now close to optimal resolution. Evans and Arnold (this volume) report on a workshop on geolocation methods held prior to the 2nd electronic tagging symposium in San Sebastian. The geolocation workshop was convened by the Scientific Committee on Ocean Research Panel on New Technologies for Observing Marine Life. In addition to summarizing current research, technologies and the limitations of principal methods as discussed at the workshop,


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they present recommendations including a working group to promote and consider analytical techniques and software needs. They also advocated for increased dialogue between tag manufacturers and tag users regarding sensor performance, additional sensors, improved algorithms for light attenuation, and the compression and processing of data by tags prior to storage or transmission to satellite. If these recommendations are followed, consolidation of approaches may occur in the coming years, such that scientific effort currently devoted to geolocation can be redeployed to address the management and conservation challenges to which electronic tagging is claimed to address.

Applications of Electronic Tagging to Fisheries Management Problems Electronic tags have been deployed on a wide range of species for over a decade. In the case of exploited species, research proposals often stated that the ultimate goal of the research was “to contribute to the sustainable management of the species”. Similarly, conservation goals are also claimed to be advanced by knowledge of migration paths derived from electronic tags. Despite these claims, the transfer to management has not happened often enough. This is due in part to the large volume of raw data from the tags which is non-trivial to manage, and often not well understood. For some tag types, issues with geolocation and the subsequent effort directed at determining positions may have slowed uptake. However, many questions can be answered with the current level of position accuracy. A first application has been “correcting longline effort for habitat preferences” (e.g., Bigelow et al., 2002), but after a decade, there are limited examples of application of electronic tagging data to stock assessment, fisheries management, and conservation (see however Block et al., 2001, 2005; Graves et al., 2002; Kerstetter et al., 2003; Luo et al., 2004; Sibert et al., 2006; Seitz et al., 2007). One challenge is processing and handling of tag data, such that it can be easily visualized and analyzed by a range of users, including assessment scientists and conservation planners. Before data can be efficiently used, this step must be solved, and many of the larger tagging laboratories around the world have developed systems for data management. In this section, Hartog et al. (this volume) describe a data handling system developed at CSIRO, Australia. Data from a variety of tags from different manufacturers, including archival tags, satellite tags and acoustic tags, are downloaded, processed and stored, in some cases without human intervention. The relational database in turn interfaces with (i) software programs to allow data access from local servers or via the internet, and (ii) a suite of environmental data to aid initial analyses and development and refinement of scientific hypotheses. This database is used to manage tag data that can be used in real-time spatial management (Hobday and Hartmann, 2006). The contribution by Hobday et al. (this volume) describes one of the few examples where electronic tag data have been used in real-time fisheries management.

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Pop-up satellite archival tagging data are incorporated into a habitat prediction model to support spatial management in an Australian longline fishery, specifically through reduction of unwanted bycatch of a quota-managed species, southern bluefin tuna (Thunnus maccoyii). Spatial zoning in the fishery is updated every two weeks, in part based on results from this model. The authors describe the development of the management application, in use since 2003, and emphasize that cooperation between scientists, fishery managers and stakeholders has led to successful uptake. This work clearly demonstrates that real-time, fine-scale spatial management is possible. A second example of the potential use of electronic tagging data, in this case acoustic tag data, is provided by Hobday et al. (second paper in this volume). These authors derive correction factors for a juvenile southern bluefin tuna abundance index based on five years of acoustic tag data in southern Western Australia, and show that electronic tagging data can be used to improve understanding of abundance patterns necessary for sustainable management of an exploited species. In addition to fisheries management application, electronic tag data can be used to support conservation, such as in the design of marine protected areas. Afonso et al. (this volume) used a combination of active tracking with passive, multi-scale monitoring and standard tag-release approaches to determine the spatial behaviors used throughout the life history of red porgy (Pagrus pagrus). They clearly illustrate how the observed movements of this exploited species might influence the size and spacing of a marine protected area network in the Azores.

The Future Around the time of the first symposium (late 1990s) the focus of many studies was on the behavior of the fish, particularly in the vertical dimension, in part due to problems with geolocation. Bluefin tuna tagging studies were among the first to utilize implanted data loggers and fishery independent pop-up tags. These early studies aimed to address ocean basin scale migrations and identification of spawning areas (Gunn et al., 1994; Lutcavage et al., 1999; Block et al., 2001), but lacked the ability to finely geolocate. It was apparent at the 2nd symposium that the improvement in geolocation methods has allowed a shift to the horizontal dimension, to utilization of specific habitats, and behaviors in these locations. Most studies were now considering uncertainty in position estimation, and this should become standard practice in future. In the near future we are likely to see an increased focus on the integrated ecology of fish movements: why are fish behaving in the way we observe, how do they interact with each other and other species, and what is the outcome for the population and ecosystem? There were some excellent examples showing that electronic tag data are being utilized in management and assessment, but it is apparent that we can do more with the data that have already been collected. One challenge for the tagging community is to encourage and facilitate data sharing to provide more comprehensive information and longer time-series.


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In the next seven years, until a possible 3rd symposium, electronic tags are likely to become increasingly sophisticated with tags monitoring aspects ranging from physiology to behavior as well as environmental parameters (Ropert-Coudert and Wilson, 2005). We can expect continued development of technology in several areas. The concept of tags that download data at fixed nodes has evolved from the simple bottom mounted transceivers discussed in the first symposium to the ecosystembased Ocean Tracking Network, and we can look forward to further development of this concept to even larger systems that can be included in global-scale integrated ocean observing systems. We can expect interactive tags that will exchange data with tags deployed on other animals not necessarily of the same species. New data processing algorithms and software packages will become available that are seamlessly integrated with data structures that include both oceanographic variables and data from other fish. We can expect new developments in non-light based location methodologies such as geomagnetic compasses as first proposed in investigations by Hunter et al. (1986) and Carey and Scharold (1999). Physiological, ecological, and ecosystem studies will all be improved by clever use of electronic tags and development of new sensors. Reduction in tag sizes, and extended battery times for tags will allow more complete descriptions of species’ life cycles and movements. This will support development of energetic models that include the costs of migrations or vertical behaviors, and testing of alternative models. In addition to the improved primary understanding of physiology, ecology and behavior, increased uptake by fisheries management should be a focus. Electronic tagging studies could support improved management by providing information on how changes in fishing regulations, such as spatial or temporal closures might modify sustainability. Increased collaboration with stock assessment scientists will ensure that analysis produces the desired parameters for spatially-explicit population dynamics models. Management uptake of electronic tag science will be maximized by partnerships with management that are established at the start of the research (Hobday et al., this volume), and by the use of data repositories that share the data on species from multiple sources (e.g., open access portals and websites). A second management need is in marine conservation. Tagging data may provide input for reserve design, in particular, to allow estimates of appropriate reserve size. Developments in reserve theory continue to evolve (Palumbi, 2004; Hughes et al., 2005; Baskett et al., 2007). Partnership with scientists involved in theoretical design of marine reserves will ensure appropriate data are gathered that facilitate fisheries management goals (Sale et al., 2005) and conservation. Sentinel animals with realtime reporting tags may also be used in real-time management of other ocean uses, such as drilling, and seismic surveys. Finally, the impact of climate change on the ocean is now apparent. As large scale research programs such as CLIOTOP2 aimed at addressing the question of how climate change affects the oceans, tagging studies will be crucial for monitoring impacts on marine species, informing the development of adaptation strategies, and

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underpinning approaches to enhance the resilience of marine ecosystems. Data from electronic tracking and tagging already play an important role in shaping the models used for predicting the effects of climate change on the future of tuna populations and changes in fish distribution (Lehodey et al., 2008).

Acknowledgements The symposium organizers thank the science committee, and the local hosts of this symposium for contributions to a successful event. Invaluable editorial efforts by the associate editors have made this a quality volume with strong scientific contributions. We especially thank individual reviewers for their significant contributions to the quality of papers in this volume. Mention of trade names does not imply governmental endorsement. Alistair Hobday, Haritz Arrizabalaga, Nuno Fragoso, John Sibert, Molly Lutcavage, Jennifer Nielsen,

Tasmania, Australia Pasaia, Spain Durham, USA Hawaii, USA Durham, USA Anchorage, USA

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Contents

Part I Behavioural Insights Based on the Use of Electronic Tags Behaviour Using Acoustic Tags Using Telemetry to Monitor Movements and Habitat Use of Cultured and Wild Juvenile Winter Flounder in a Shallow Estuary . . . . . . . . . . . . . . . Elizabeth A. Fairchild, Nathan Rennels and Hunt Howell

5

Comparative Behavior of Wild and Hatchery Reared White Sea Bream (Diplodus sargus) Released on Artificial Reefs Off the Algarve (Southern Portugal) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 Pedro G. Lino, Lu´ıs Bentes, David Abecasis, Miguel Neves dos Santos and Karim Erzini Survival, Migration Speed and Swimming Depth of Atlantic Salmon Kelts During Sea Entry and Fjord Migration . . . . . . . . . . . . . . . . . . . . . . . . . . 35 Elina Halttunen, Audun H. Rikardsen, Jan G. Davidsen, Eva B. Thorstad and J. Brian Dempson Small Scale Vertical Behaviour of Juvenile Albacore in Relation to Their Biotic Environment in the Bay of Biscay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 Nicolas Goñi, Igor Arregui, Ainhoa Lezama, Haritz Arrizabalaga and Gala Moreno Acoustic Tag Networks A Review of Acoustic Telemetry Technology and a Perspective on its Diversification Relative to Coastal Tracking Arrays . . . . . . . . . . . . . . . . . . . . 77 Thomas M. Grothues The Ocean Tracking Network – Adding Marine Animal Movements to the Global Ocean Observing System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 Ronald K. O’Dor and Michael J.W. Stokesbury xvii

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Contents

Behaviour Using Archival Tags Observations of the Behaviour of European Sea Bass (Dicentrarchus labrax) in the North Sea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 V.A. Quayle, D. Righton, S. Hetherington and G. Pickett Vertical Movements and Habitat Utilization of Skipjack (Katsuwonus pelamis), Yellowfin (Thunnus albacares), and Bigeye (Thunnus obesus) Tunas in the Equatorial Eastern Pacific Ocean, Ascertained Through Archival Tag Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 Kurt M. Schaefer, Daniel W. Fuller and Barbara A. Block Investigations of Horizontal Movements of Atlantic Swordfish Using Pop-up Satellite Archival Tags . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 John D. Neilson, Sean Smith, François Royer, Stacey D. Paul, Julie M. Porter and Molly Lutcavage Vertical Behavior and the Observation of FAD Effects on Tropical Tuna in the Warm-Pool of the Western Pacific Ocean . . . . . . . . . . . . . . . . . . . . . . . . 161 Bruno Leroy, David G. Itano, Thomas Usu, Simon J. Nicol, Kim N. Holland and John Hampton Effects of T-bar and DST Tagging on Survival and Growth of European Hake . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181 Aurélie Jolivet, Hélène de Pontual, François Garren and Marie-Laure Bégout Body Temperature of the Atlantic Bluefin Tuna (Thunnus thynnus L.) in the Western Mediterranean . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195 Piero Addis, Ivan Locci, Aldo Corriero and Angelo Cau Behaviour Using Novel Tags Multi-Channel Data-Logging: Towards Determination of Behaviour and Metabolic Rate in Free-Swimming Sharks . . . . . . . . . . . . . . . . . . . . . . . . . 211 Adrian C. Gleiss, Samuel H. Gruber and Rory P. Wilson Harnessing the Sun: Testing a Novel Attachment Method to Record Fine Scale Movements in Ocean Sunfish (Mola mola) . . . . . . . . . . . . . . . . . . . . . . . . 229 Jonathon D.R. Houghton, Nikolai Liebsch, Thomas K. Doyle, Adrian C. Gleiss, Martin K.S. Lilley, Rory P. Wilson and Graeme C. Hays An Archival Tag for Monitoring Key Behaviours (Feeding and Spawning) in Fish . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243 J.D. Metcalfe, M.C. Fulcher, S.R. Clarke, M.J. Challiss and S. Hetherington

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Part II Geolocation Methods Lessons from a Prototype Geolocation Problem . . . . . . . . . . . . . . . . . . . . . . . . 257 Uffe Høgsbro Thygesen and Anders Nielsen Geolocating Fish Using Hidden Markov Models and Data Storage Tags . . 277 Uffe Høgsbro Thygesen, Martin Wæver Pedersen and Henrik Madsen State Space Model for Light Based Tracking of Marine Animals: Validation on Swimming and Diving Creatures . . . . . . . . . . . . . . . . . . . . . . . . 295 Anders Nielsen, John R. Sibert, Suzanne Kohin and Michael K. Musyl Removing Bias in Latitude Estimated from Solar Irradiance Time Series . 311 John R. Sibert, Anders Nielsen, Michael K. Musyl, Bruno Leroy and Karen Evans Positioning Pelagic Fish from Sunrise and Sunset Times: Complex Observation Errors Call for Constrained, Robust Modeling . . . . . . . . . . . . . 323 François Royer and Molly Lutcavage Summary Report of a Workshop on Geolocation Methods for Marine Animals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343 Karen Evans and Geoff Arnold Part III Applications of Electronic Tags to Fisheries Management Developing Integrated Database Systems for the Management of Electronic Tagging Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 367 Jason R. Hartog, Toby A. Patterson, Klaas Hartmann, Paavo Jumppanen, Scott Cooper and Russell Bradford Electronic Tagging Data Supporting Flexible Spatial Management in an Australian Longline Fishery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 381 Alistair J. Hobday, Nicole Flint, Trysh Stone and John S. Gunn Correction Factors Derived from Acoustic Tag Data for a Juvenile Southern Bluefin Tuna Abundance Index in Southern Western Australia . 405 Alistair J. Hobday, Ryo Kawabe, Yoshimi Takao, Kazushi Miyashita and Tomoyuki Itoh A Multi-Scale Study of Red Porgy Movements and Habitat Use, and Its Application to the Design of Marine Reserve Networks . . . . . . . . . . . . . . . . . 423 Pedro Afonso, Jorge Fontes, Rui Guedes, Fernando Tempera, Kim N. Holland and Ricardo S. Santos Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 445

Contributors

David Abecasis Centro de Ciências do Mar (CCMAR), Universidade do Algarve, Faculdade de Ciências do Mar e Ambiente, Campus de Gambelas, 8005-139 Faro, Portugal Piero Addis University of Cagliari, Department of Animal Biology and Ecology, Viale Poetto 1, 09126 Cagliari, Italy Pedro Afonso IMAR/DOP – University of the Azores; Cais de Santa Cruz, 9901-862 Horta, Portugal; Hawai’i Institute of Marine Biology, University of Hawai’i at Manoa; 46-007, Lilipuna Road, Kane’ohe, HI 96744, USA Haritz Arrizabalaga AZTI-Tecnalia, Marine Research Division, Herrera kaia portualdea z/g, 20110 Pasaia, Gipuzkoa, Spain Igor Arregui AZTI-Tecnalia, Marine Research Division, Herrera kaia portualdea z/g, 20110 Pasaia, Gipuzkoa, Spain Geoff Arnold SCOR Panel on New Technologies for Observing Marine Life, Weybredd House, Hulver Road, Mutford, Beccles, Suffolk, NR34 7UL, UK Lu´ıs Bentes Centro de Ciências do Mar (CCMAR), Universidade do Algarve, Faculdade de Ciências do Mar e Ambiente, Campus de Gambelas, 8005-139 Faro, Portugal Marie-Laure Bégout Ifremer, UMR 6217 CRELA CNRS-IFREMER-University De La Rochelle, place du séminaire, BP 5, F-17137 L’Houmeau, France Barbara A. Block Tuna Research and Conservation Center, Hopkins Marine Station, Stanford University, 120 Oceanview Boulevard, Pacific Grove, CA 93950, USA Russell Bradford CSIRO Marine and Atmospheric Research, GPO Box 1538, Hobart, Tasmania, 7001, Australia Angelo Cau Department of Animal Biology and Ecology, University of Cagliari, Viale Poetto 1, 09126 Cagliari, Italy xxi

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Contributors

Michael J. Challiss Centre for Environment, Fisheries and Aquaculture Science, Lowestoft Laboratory, Lowestoft, Suffolk, England, UK Stephen R. Clarke Centre for Environment, Fisheries and Aquaculture Science, Lowestoft Laboratory, Lowestoft, Suffolk, England, UK Scott Cooper CSIRO Marine and Atmospheric Research, GPO Box 1538, Hobart, Tasmania, 7001, Australia Aldo Corriero University of Bari, Department of Animal Health and Well-Being, Valenzano, Bari, Italy Jan G. Davidsen Institute of Aquatic Biology, Norwegian College of Fishery Science, University of Tromsø, NO-9037 Tromsø, Norway J. Brian Dempson Fisheries and Oceans Canada, Science Branch, St John’s, Newfoundland and Labrador, A1C 5X1 Canada Thomas K. Doyle Coastal Marine Resources Centre, University College Cork, Lewis Glucksman Marine Facility, Naval Base, Haulbowline, Cork, Ireland Karim Erzini Centro de Ciências do Mar (CCMAR), Universidade do Algarve, Faculdade de Ciências do Mar e Ambiente, Campus de Gambelas, 8005-139 Faro, Portugal Karen Evans CSIRO Marine and Atmospheric Research, GPO Box 1538, Hobart, Tasmania 7001, Australia Elizabeth A. Fairchild Department of Zoology, University of New Hampshire, Spaulding Hall, Durham, New Hampshire 03824 USA Nicole Flint Australian Fisheries Management Authority, Canberra Jorge Fontes IMAR/DOP – University of the Azores; Cais de Santa Cruz, 9901-862 Horta, Portugal. M.C. Fulcher Centre for Environment, Fisheries and Aquaculture Science, Lowestoft Laboratory, Lowestoft, Suffolk, England UK Daniel W. Fuller Inter-American Tropical Tuna Commission, 8604 La Jolla Shores Drive, La Jolla, California 92037-1508, USA François Garren Ifremer, Laboratoire de Sclérochronologie des Animaux Aquatiques STH/LASAA, Z.I. Pointe du diable, BP 70, F-29280 Plouzané, France Adrian C. Gleiss Institute of Environmental Sustainability, Biological Sciences, Swansea University, Singleton Park, SA1 8PP, UK ˜ AZTI-Tecnalia, Marine Research Division, Herrera kaia portualdea Nicolas Goni z/g, 20110 Pasaia, Gipuzkoa, Spain Thomas M. Grothues Institute of Marine and Coastal Sciences, Rutgers University Marine Field Station, 800 c/o 132 Great Bay Blvd, Tuckerton, NJ 08087, USA

Contributors

xxiii

Samuel H. Gruber Bimini Biological Field Station and University of Miami, Rosenstiel School of Marine and Atmospheric Research, Miami, FL 33149-1098, USA Rui Guedes IMAR/DOP – University of the Azores; Cais de Santa Cruz, 9901-862 Horta, Portugal John S. Gunn Wealth from Oceans National Research Flagship, CSIRO Marine and Atmospheric Research, Hobart, Tasmania 7000, Australia Elina Halttunen Institute of Aquatic Biology, Norwegian College of Fishery Science, University of Tromsø, NO-9037 Tromsø, Norway Klaas Hartmann Tasmanian ICT Centre, CSIRO, Hobart, Tasmania, 7001, Australia John Hampton Oceanic Fisheries Programme, Secretariat of the Pacific Community, B.P. D5, 98848 Noumea Cedex, New Caledonia Jason R. Hartog CSIRO Marine and Atmospheric Research, Hobart, Tasmania, 7001, Australia Graeme C. Hays Institute of Environmental Sustainability, School of the Environment and Society, Swansea University, Singleton Park, Swansea, SA2 8PP, UK Stuart Hetherington Centre for Environment, Fisheries and Aquaculture Science, Lowestoft Laboratory, Pakefield Road, Lowestoft, Suffolk, NR33 0HT, UK Alistair J. Hobday Wealth from Oceans National Research Flagship, CSIRO Marine and Atmospheric Research, Hobart, Tasmania 7000, Australia Kim N. Holland Hawaii Institute of Marine Biology, University of Hawaii, Kaneohe, Hawaii 96744, USA Jonathan D.R. Houghton Institute of Environmental Sustainability, School of the Environment and Society, Swansea University, Singleton Park, Swansea, SA2 8PP, UK; School of Biological Sciences, Queen’s University Belfast, Medical Biology Centre, Belfast, BT9 7BL, UK Hunt Howell Department of Zoology, University of New Hampshire, Spaulding Hall, Durham, New Hampshire 03824 USA David G. Itano Pelagic Fisheries Research Program, University of Hawaii, MSB 312, Honolulu, Hawaii 96822, USA Tomoyuki Itoh National Research Institute of Far Seas Fisheries, Fisheries Research Agency, Shizuoka, Japan Aurélie Jolivet Ifremer, Laboratoire de Sclérochronologie des Animaux Aquatiques STH/LASAA, Z.I. Pointe du diable, BP 70, F-29280 Plouzané, France

xxiv

Contributors

Paavo Jumppanen CSIRO Marine and Atmospheric Research, Hobart, Tasmania, 7001, Australia Ryo Kawabe Institute for East China Sea Research, Nagasaki University, Nagasaki, Japan Suzanne Kohin NOAA Fisheries Southwest Fisheries Science Center, 8604 La Jolla Shores Dr., La Jolla, CA 92037, USA Bruno Leroy Oceanic Fisheries Programme, Secretariat of the Pacific Community, B.P. D5, 98848 Noumea Cedex, New Caledonia Ainhoa Lezama AZTI-Tecnalia, Marine Research Division, Herrera kaia portualdea z/g, 20110 Pasaia, Gipuzkoa, Spain Nikolai Liebsch Institute of Environmental Sustainability, School of the Environment and Society, Swansea University, Singleton Park, Swansea, SA2 8PP, UK Martin K.S. Lilley Institute of Environmental Sustainability, School of the Environment and Society, Swansea University, Singleton Park, Swansea, SA2 8PP, UK Pedro G. Lino Instituto Nacional dos Recursos Biológicos (INRB) / IPIMAR, Av. 5 de Outubro S/N, 8700-305 Olhão, Portugal Ivan Locci Department of Animal Biology and Ecology, University of Cagliari, Viale Poetto 1, 09126 Cagliari, Italy Molly Lutcavage Large Pelagics Research Center, University of New Hampshire, Durham, New Hampshire, USA 03824 Henrik Madsen Institute for Informatics and Mathematical Modelling, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark Julian D. Metcalfe Centre for Environment, Fisheries and Aquaculture Science, Lowestoft Laboratory, Lowestoft, Suffolk, England UK Kazushi Miyashita Field Science Center for Northern Biosphere, Hokkaido University, Hakodate, Japan Gala Moreno AZTI-Tecnalia, Marine Research Division, Herrera kaia portualdea z/g, 20110 Pasaia, Gipuzkoa, Spain Michael K. Musyl Pelagic Fisheries Research Program, Joint Institute for Marine and Atmospheric Research, University of Hawai’i at Manoa, MSB 312, Honolulu, HI 96822, USA Anders Nielsen Institute for Aquatic Resources, Technical University of Denmark, Jægersborg Allé 1, 2920 Charlottenlund, Denmark, Pelagic Fisheries Research Program, Joint Institute for Marine and Atmospheric Research, University of Hawai’i at Manoa, MSB 312, Honolulu, HI 96822, USA

Contributors

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John D. Neilson Fisheries and Oceans Canada, St. Andrews Biological Station, St. Andrews, New Brunswick, Canada E5B 2L9 Simon J. Nicol Oceanic Fisheries Programme, Secretariat of the Pacific Community, B.P. D5, 98848 Noumea Cedex, New Caledonia Ronald K. O’Dor Consortium for Ocean Leadership, 1201 New York Ave., Washington, DC 20003, USA Toby A. Patterson CSIRO Marine and Atmospheric Research, GPO Box 1538, Hobart, Tasmania, 7001, Australia Stacey D. Paul Fisheries and Oceans Canada, St. Andrews Biological Station, St. Andrews, New Brunswick, Canada E5B 2L9 Martin Wæver Pedersen Institute for Informatics and Mathematical Modelling, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark Graham Pickett CEFAS, Lowestoft Laboratory, Pakefield Road, Lowestoft, Suffolk, NR33 0HT, UK Hélène de Pontual Ifremer, Laboratoire de Sclérochronologie des Animaux Aquatiques STH/LASAA, Z.I. Pointe du diable, BP 70, F-29280 Plouzané, France Julie M. Porter Fisheries and Oceans Canada, St. Andrews Biological Station, St. Andrews, New Brunswick, Canada E5B 2L9 Victoria A. Quayle CEFAS, Lowestoft Laboratory, Pakefield Road, Lowestoft, Suffolk, NR33 0HT, UK David Righton CEFAS, Lowestoft Laboratory, Pakefield Road, Lowestoft, Suffolk, NR33 0HT, UK Audun H. Rikardsen Institute of Aquatic Biology, Norwegian College of Fishery Science, University of Tromsø, NO-9037 Tromsø, Norway Nathan Rennels Department of Zoology, University of New Hampshire, Spaulding Hall, Durham, New Hampshire 03824 USA François Royer Large Pelagics Research Center, University of New Hampshire, Durham, New Hampshire, USA 03824 and Collecte Localisation Satellites, 8-10 rue Hermes, Parc Technologique du Canal, 31520 Ramonville, France Miguel Neves dos Santos Instituto Nacional dos Recursos Biológicos (INRB) / IPIMAR, Av. 5 de Outubro S/N, 8700-305 Olhão, Portugal Ricardo S. Santos IMAR/DOP – University of the Azores; Cais de Santa Cruz, 9901-862 Horta, Portugal Kurt M. Schaefer Inter-American Tropical Tuna Commission, 8604 La Jolla Shores Drive, La Jolla, California 92037-1508, USA

xxvi

Contributors

John R. Sibert Pelagic Fisheries Research Program, Joint Institute for Marine and Atmospheric Research, University of Hawai’i at Manoa, MSB 312, Honolulu, HI 96822, USA Sean Smith Fisheries and Oceans Canada, St. Andrews Biological Station, St. Andrews, New Brunswick, Canada E5B 2L9 Michael J.W. Stokesbury Dalhousie University, Halifax, NS, Canada B3H 4J1 Trysh Stone Australian Fisheries Management Authority, Canberra Yoshimi Takao NRIFE, Ibaraki, Japan Fernando Tempera IMAR/DOP – University of the Azores; Cais de Santa Cruz, 9901-862 Horta, Portugal Eva B. Thorstad Norwegian Institute for Nature Research (NINA), NO-7485 Trondheim, Norway Uffe Høgsbro Thygesen Institute for Aquatic Resources, Technical University of Denmark, Jægersborg All’e 1, 2920 Charlottenlund, Denmark T. Usu National Fisheries Authority, PO Box 2016, Port Moresby, Papua New Guinea Rory P. Wilson Institute of Environmental Sustainability, Biological Sciences, Swansea University, Singleton Park, SA1 8PP, UK

Using Telemetry to Monitor Movements and Habitat Use of Cultured and Wild Juvenile Winter Flounder in a Shallow Estuary Elizabeth A. Fairchild, Nathan Rennels and Hunt Howell

Abstract Home ranges, dispersal patterns, and habitat associations of juvenile winter flounder (Pseudopleuronectes americanus) were studied using acoustic tags and three tracking systems in the Hampton-Seabrook Estuary in New Hampshire, USA. This is the first study that we know of to use electronic tags on juvenile winter flounder, and on flatfish ≤ 19 cm. For the first two tracking periods, both wild and cultured age 1 fish were followed by a combination of handheld hydrophones and a VRAP system. Although both cultured and wild fish maintained similar home ranges, the cultured fish immediately emigrated approximately 1000 m out of the release area while the wild fish maintained high release site fidelity. Cultured fish acclimated to the release site using in-situ cages displayed higher site fidelity after release. Cultured flounder habitat use was very similar to wild flounder habitat use in terms of bottom water temperature, dissolved oxygen, salinity, depth, and sediment composition. For the third tracking period only wild juvenile flounder were followed to discern when and at what size fish leave the estuary. Fish (n = 10) were tracked passively by an array of six submersible receivers stationed throughout the estuary. A total of 244,985 fixes were recorded by the receivers during the lifespan of the tags. The majority of these fixes were recorded by the receivers at the release site (72%) and immediately down-estuary (25%). Eighty percent of the fish remained in the immediate release area for the first two weeks, but as time at large increased, several fish dispersed down-estuary, and two individuals left the estuary for the sea. Estuary exits occurred in the winter by the largest individuals. Final positions of the tagged fish indicated that 20% had left the estuary all together, 30% of the tagged fish were still at the release site, 20% were approximately 500 m down-estuary from the release site, and 30% were unaccounted for. Understanding these movement patterns and habitat associations of both cultured and wild juvenile winter flounder is significant for developing techniques for enhancement programs and for defining essential fish habitats within this important nursery area. E.A. Fairchild (B) Department of Zoology, University of New Hampshire, Durham, NH 03824, USA e-mail: [email protected] J.L. Nielsen et al. (eds.), Tagging and Tracking of Marine Animals with Electronic Devices, Reviews: Methods and Technologies in Fish Biology and Fisheries 9, C Springer Science+Business Media B.V. 2009 DOI 10.1007/978-1-4020-9640-2 1,

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E.A. Fairchild et al.

Keywords Telemetry · Habitat · Release strategies · Stock enhancement · Flatfish · Movements

Introduction Winter flounder (Pseudopleuronectes americanus) is one of several species along the US east coast that management agencies continuously strive to understand better in terms of movements and habitat use (Able and Grothues, 2007). In addition, winter flounder is a potential stock enhancement candidate and the subject of an ongoing research program in New Hampshire (Fairchild and Howell, 2004; Fairchild et al., 2005; Fairchild et al., 2008a). Understanding the temporal and spatial dynamics of the wild population and how released cultured fish compare is essential for a successful enhancement program. Using electronic tags to track winter flounder may be a useful technique to gather valuable data for both management decisions and enhancement strategies. However, few studies have used electronic tags on winter flounder. Adult flatfish tracking studies include quantifying habitat and residency time of summer flounder (Paralichthys dentatus) in an off-shore area (Fabrizio et al., 2005); mapping the spatial dynamics of plaice (Pleuronectes platessa; see Arnold and Dewar, 2001) and their selective tidal stream transport movement patterns (Buckley and Arnold, 2001); examining the movements of European flounder (Platichthys flesus) associated with feeding (Wirjoatmodjo and Pitcher, 1984); and determining spawning habitat of winter flounder (Pereira et al., 1994). The challenges of tagging small fish with electronic tags has been addressed by others (Bridger and Booth, 2003; Sheridan et al., 2007). For juvenile flatfish, acoustic tracking studies generally occur within estuaries and examine residency times (Szedlmayer and Able, 1993; Sackett et al., 2007) and movements associated with tidal cycles (Szedlmayer and Able, 1993), photoperiod (Lagardere et al. 1988), or other environmental factors (Lagardere et al. 1988; Lagardere and Sureau, 1989). Species studied include sole (Solea vulgaris; Lagardere et al., 1988; Lagardere and Sureau, 1989) and summer flounder (Szedlmayer and Able, 1993; Sackett et al., 2007). Ours is the first study that we know of to use electronic tags on juvenile winter flounder, and on flatfish ≤ 19 cm. Tracking fishes electronically can be helpful in understanding the effects of stocking cultured fishes and improving release strategies. For example, acoustically tagged and released wild and cultured coho salmon (Onchorhyncus kisutch) were monitored in an estuary in British Columbia (Chittenden et al., 2008). Researchers found that the physiology and migratory behavior of the fishes differed; the cultured fish remained in the estuary longer than the wild fish, had slower swimming speeds, and dispersed from the estuary into the ocean both to the north and the south unlike their tagged wild counterparts which moved northward. In another salmonid study, wild and cultured Altantic salmon (Salmo salar) smolts were tracked over 14 h in an estuary in the Baltic Sea (Hyvärinen et al., 2006). No differences were detected between the survival rates and swimming speeds of cultured and wild

Tracking Acoustically Tagged Cultured and Wild Juvenile Flounder

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fish, however, sample sizes were small and cultured fish were larger than wild fish which confounded analyses. To further the development of release strategies for red tilefish (Branchiostegus japonicus) enhancement in Japan, cultured and wild fish were tracked after release (Yokota et al., 2006). Although generally both fish types showed similar territorial and diel activity behaviors, there were differences in habitats used (water temperature) and one cultured fish showed a nocturnal activity pattern. Despite the differences observed between cultured and wild released fish in these studies, these telemetry findings aid enhancement programs by highlighting which release protocols require refinement. The objective of this study was to determine the best acoustic tracking method for following juvenile winter flounder movements in a shallow estuary. Other goals of the study were to compare cultured and juvenile fish movements and habitat use, and to examine the residency time of wild juveniles in the estuary.

Materials and Methods Study Site The Hampton-Seabrook Estuary (HSE), one of two estuaries in New Hampshire, is located in the southeastern corner of the state. It is a small (approx. 192 hectares at MHW), tidally dominated, shallow estuary (Fig. 1), comprised of several rivers and numerous creeks; all tidal waters enter and exit through the sole harbor entrance. Water depth is relatively shallow in the estuary, ranging from < 1 m in the tidal creeks to > 6 m at the harbor entrance at MLW, with 1–3 m of water in most channels at mean low tide (Jones, 2000). Over 88% of the water in the estuary is replaced on each tide, with the average tidal flow equaling 623,000 l s–1 (PSNH, 1973). Due to this strong tidal flushing, low dissolved oxygen is not a limiting factor in the estuary (Jones, 1997). An approximately 1 km2 area in the Hampton River in the HSE has been selected as a potential release site for winter flounder enhancement efforts (Fairchild et al., 2008a). It was chosen because of its favorable characteristics for juvenile winter flounder, including a fine to medium grained, sandy substrate (Phelan et al., 2001; Fairchild and Howell, 2004), shallow areas (< 2 m) at low tide, being bounded by a deep channel (Saucerman and Deegan, 1991), having relatively high salinities (28–30 ppt), and an appropriate thermal regime. In this study, all wild fish used were captured from this location, and all tagged fish were released at this location.

Tagging Protocol Vemco V7-2L-R256 coded acoustic tags (17 × 7 mm; 0.5 g in water; Vemco Ltd., Nova Scotia) were used for tracking juvenile flounder movements. Each acoustic

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Fig. 1 The Hampton-Seabrook Estuary in New Hampshire and an inset map showing the position (denoted by black arrow) of the estuary relative to the US eastern seaboard. Triangles (Δ) denote VR2 receivers stationed along the Hampton River and at the mouth of the estuary. The circled receiver denotes the release site for the three tracking studies. The gray shaded triangular area represents the approximate location of the VRAP system

transmitter emitted a distinctive coded pulse (frequency of 69 khz) that was detected by a hydrophone, thereby allowing the fish’s location to be determined, and the fish’s movements to be tracked over time. Predicted tag battery lifespan was 40 days, though many tags were active 2–4 times longer (see results). Because juvenile winter flounder were too small to be surgically implanted with these tags, the tags were attached externally to the fish just above the lateral line and laying perpendicular to the fish’s axis (Fig. 2). To attach these tags securely, a harness was made of a 15 mm length of TygonTM tubing cut lengthwise. Two holes on each side of the cut were made by heating a needle and melting the tubing; these provided attachment points from the harness to the fish. The tag then was glued to the inside of the tube and allowed to cure for 24 h. The final product added 0.3 g to the tag weight but for an average age 1, cultured, juvenile fish (22.9 ± 4.7 g), the total tag configuration was 3.6 % (± 0.7%) of the fish’s weight in water. Fish were anesthetized with MS-222 (0.5 g/l) for 2 min, and then each tag was sutured to the fish using 2–0 Dermalon blue thread. Laboratory studies indicated that the tag did not interfere with the fish’s swimming abilities; no differences were observed in either the burying behavior, burst swimming speeds, or the amount of moves made by tagged and untagged cultured fish (Fairchild, unpublished data). In addition, a subsample of tagged fish (n = 6) were retained in laboratory for tag retention and survival studies. On average, tags stayed on for 47 days (range = 35–70 days), and there was no tag-induced mortality.


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Fig. 2 Juvenile winter flounder were tagged externally with VEMCO V7 acoustic tags. The tags were glued to a tubing sleeve which was sewn onto the fish above and perpendicular to the lateral line

Comparison of Cultured and Wild Juvenile Movements Simultaneous releases of cultured and wild age 1 winter flounder occurred in the Hampton River on 23 August 2005 and on 28 November 2005. Cultured juvenile winter flounder were reared at the University of New Hampshire’s Coastal Marine Laboratory (CML) per the methodology of (Fairchild et al., 2007). Wild fish were caught with a 1-m beam trawl (4 mm mesh size) at the release site and brought back to the CML where they and the cultured fish were fitted with acoustic tags. After 48 h, all fish were released back into the Hampton River. Active Tracking For the first study, two cultured (47.3 ± 0.4 g; 137 ± 7 mm TL) and two wild (39.6 ± 0.8 g; 147 ± 1 mm TL) fish were released by gently placing them into the river (Table 1). Tracking began one hour after the fish were released and continued daily until either the fish could not be found or we suspected that the tag battery had expired. Fish were located manually using a VH165 omni-directional and a VH110 directional handheld hydrophone, and a VR100 receiver (Vemco Ltd., Nova Scotia). Once a tag was detected, the fish was located using the directional hydrophone which was mounted on a 3 m pvc pipe and submerged 0.5 m below the surface. Tagged fish locations were based on the strongest signal recorded by the

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Table 1 Summary of VEMCO V7 tagged juvenile winter flounder released and tracked in the Hampton-Seabrook Estuary, New Hampshire. C = cultured fish; W = wild fish; AC = acclimated in-situ cultured fish Release date

Fish type

Weight(g)

Length (mm)

Tag no.

Days tracked

No. fixes

8/23/2005

C C W W

47.3 47.8 40.1 39

132 142 146 147

137 135 132 133

38 65 17 57

18 37 17 35

11/28/2005

AC AC C C C C W W

81.4 86.3 19.5 39 96.5 38.9 70.2 18.4

165 162 155 130 175 133 186 117

149 146 143 142 141 140 145 144

86 0 1 1 0 0 86 77

13,082 88 27 7 123 34 1,378 175

10/4/2006

W W W W W W W W W W

– – – – – – – – – –

168 185 145 150 160 191 128 110 108 122

3981 3982 3983 3984 3985 3986 3987 3988 3989 3990

139 168 70 95 168 72 3 1 47 111

66,338 59,543 4,131 11,098 43,347 1,391 1,174 71 4,855 53,037

receiver. When a fish was located, the position (latitude, longitude), depth, and tidal stage were recorded. Using a Niskin sampling bottle, a sample of bottom water was retrieved and salinity, temperature, and dissolved oxygen were measured using a refractometer and a dissolved oxygen meter (YSI Inc., Yellow Springs, OH, USA). In addition, a core was taken with a grab sampler for sediment composition analyses. Sediment grain size composition analyses were processed as previously described in Fairchild et al. (2005). Percent sediment composition data were arcsine transformed (Zar, 1996). All habitat data (depth, temperature, salinity, dissolved oxygen, and sediment particle size) were tested for normality and homogeneity, and compared between fish treatments with analyses of variance. Fish location data were analyzed using ArcView GIS 3.3 software (ESRI, Redlands, CA, USA). Home ranges were calculated using kernel analyses, and minimum convex polygon analyses were used to calculate total area of home ranges. Total distance and mean distance/day were calculated from the polylines from points generated by Arc View. Differences between fish treatments were tested using one way analysis of variance. VRAP Tracking In the second study, a total of two wild fish (44.3 ± 36.6 g; 151 ± 49 mm TL) and six cultured fish (60.3 ± 31.6 g; 153 ± 18 mm TL) were released and tracked (Table 1). Two of the cultured fish were acclimated (AC) in situ at the release site in a


11

91 × 46 × 32 cm cage constructed of 1.3 cm plastic-coated wire and weighted with 15 kg of chain. After 48 h, the cage was hauled to the surface and the AC fish were released. At this time, the other six fish were released into the river. Fish were located using the Vemco VRAP system (Vemco, Ltd., Nova Scotia), a triangular array of receiving buoys which automatically measured real-time, detailed position information from underwater acoustic transmitters, and uploaded the data via a twoway radio link to a computer at a base station on land about 300 m away. The buoys were configured in the Hampton River at the release site and spaced approximately 100 m apart, the furthest distance possible accounting for the 3–4 m tidal cycle (Fig. 1). One reference tag was placed within the buoy array, and another just outside of the triangle so that buoy movements due to current and wind could be compensated for (see Golet et al., 2006). Movement data were analyzed using VRAP5 software (Vemco, Ltd., Nova Scotia).

Wild Sub-adult Movements Ten juvenile wild winter flounder (147 ± 30 mm TL) were caught with the 1-m beam trawl in the Hampton River on 2 October 2006 and tagged on site. Tagged fish were then impounded in acclimation cages for 48 h to ensure that the tags were secure and fish were healthy. Fish were released from the cages (as previously described) and tracking was done using an array of six submersible receivers (VR2s; Vemco Ltd., Nova Scotia; Table 1). The VR2 receivers were attached 1–2 m off the bottom and pointed upward on lines anchored by either 19-kg mushroom anchors in high current areas or customized lighter anchors in slower current areas. The range of the VR2s was determined by listening with a receiver to a fixed acoustic tag at increasing distances. The maximum range at several tidal heights and currents was 150 m. Because we only had a limited number of VR2’s, we spaced them out at least 300 m in the Hampton River to cover more area. Positions in the estuary included the release site (6625) in the Hampton River as well as two upriver sites (6623 and 6624a), one downriver site (6622), and two on either side of the mouth of the estuary (6620 and 6621; Fig. 1). The furthest upriver receiver (6624a) was moved to the outer edge of the breakwater at the extreme edge of the mouth of the estuary (6624b) when it was apparent that no fish were traveling that far up-estuary. Receivers were downloaded to a laptop computer in the field periodically, and data were analyzed using ArcView 3.3 software.

Results Comparison of Cultured and Wild Juvenile Movements Active Tracking The two cultured and two wild fish were tracked daily for 78 days. Due to small sample sizes, no significant differences were observed between cultured and wild fish

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Fig. 3 Movements made by 2 wild (fish 132 = @, fish 133 = ) and 2 cultured (fish 135 = +, fish 137 = •) winter flounder released on 23 August 2005. Symbols represent known locations of each fish. Arrow vectors show the direction of movement

home ranges (50% probability area p = 0.33; 95% probability area p = 0.45; total area p = 0.07). Daily movements were similar too. No significant differences in total distance moved (p = 0.86) nor in mean daily distance (p = 0.53) were seen. However, within the first two days, cultured fish emigrated approximately 1000 m out of the release site, whereas wild fish generally maintained high release site fidelity (Fig. 3). Because of this trend, movement data were modified to exclude this initial movement and re-analyzed. The modified mean daily movement of the wild fish was significantly (p < 0.01) greater than that of the cultured fish (Table 2). This difference also was detected by kernel analyses of the home ranges. In the 95% probability area, cultured fish maintained a smaller home range than wild fish (p = 0.01; Table 2). However, there were no significant differences in the 50% probability area (p = 0.14), nor in the overall area (p = 0.10) of the home ranges (Table 2). Habitat use by cultured fish was very similar to that of wild fish (Fig. 4). There were no differences in dissolved oxygen (p = 0.64) or salinity (p = 0.67) where cultured and wild fish were located. Depths at the locations where fish were found reflected individual variability. Differences existed between depths where all fish were found, except for cultured 137 (mean = 3.4 m) and wild 132 (mean = 2.9 m; p = 0.198). Cultured 135 and wild 133 were found in depths of 5.0 and 4.0 m, respectively on average. Typically temperature at these locations was similar, except for the warmer waters that wild fish 132 occupied on average (16.65◦ C; p < 0.05). Fishes 133, 135, and 137 were found, on average, in 11.6, 11.4, and 12.0◦ C water, respectively. No significant (p > 0.05) differences were found between any of the grain size fractions of the sediment samples collected where cultured and wild fish


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Table 2 Home range and mean daily movements for cultured and wild juvenile winter flounder released in the Hampton-Seabrook Estuary on 23 Aug. 2005. Listed are the values for modified (76 days) time at large which excludes the first two days. Kernal analyses were used to calculate 50% and 95% probability areas; minimum convex polygon analyses were used to calculate total areas. Total and mean distances were calculated from the polylines from point generated by Arc View software 50% Area 2

95% Area 2

Total area 2

Total distance

Mean distance

Fish type

Tag no.

(m )

(m )

(m )

(m)

(m/d)

sd

C C W W C W

137 135 132 133 all all

217.7 232.0 499.6 338.3 224.9 419.0

1427.2 942.7 4090.3 3807.8 1185.0 3949.1

928.9 666.3 2094.8 3428.4 797.6 2761.6

251.3 371.7 256.8 791.7 623.0 1048.5

9.3 10.4 22.6 22.3 10.0 22.4

2.3 2.7 5.3 4.4 13.6 22.2

were located, except for clay (Table 3). Cultured fish were found in sediments that contained more clay (p = 0.015) than where wild fish were found. Overall, these results indicate that released cultured winter flounder will inhabit the same sediment types as similarly-sized wild fish.

VRAP Tracking The second group of fish was tracked for a total of 74 days until 10 February 2006 (Table 1). Though the receivers recorded continuously, the coded tags only pinged every 1–2 min. As such, there were times when the signals coincided (e.g. code collisions) and the receiver could not distinguish the tags. Therefore, periodically fish were not located by the VRAP system even if they were within the range of the buoys. Also, due to the physical parameters of the narrow channel and the large tidal flux, we could not stretch the buoy triangle out very far. The fish did not, for the most part, remain within the VRAP range, and so we manually moved the receiver buoys so that we were tracking at least two fish at all times. This resulted in limited detailed information on most of the fish released; however, some activity patterns were discerned. All non-acclimated cultured fish followed a similar pattern in which they quickly left the triangulation area. Within the first day, 75% of the fish went downriver on the falling tide. One non-acclimated cultured fish (142) went upriver out of range of the VRAP system on the first day after release, then returned to the release spot three days later, and proceeded downriver. After this, these non-acclimated cultured fish were never located again. Although both AC fish left the triangulation area too, it appears that they did not emigrate as far as the non-acclimated cultured fish. AC fish 146 went downriver while the other, fish, 149, went upriver. We moved the VRAP system approximately


mg/l

14

12 10 8 6 4 2 0 8/16/2005

Dissolved Oxygen

9/5/2005

9/25/2005

10/15/2005

11/4/2005

10/15/2005

11/4/2005

10/15/2005

11/4/2005

Salinity 40

ppt

30 20 10 0 8/16/2005

9/5/2005

9/25/2005

Depth Meters

8 6 4 2 0 8/16/2005

9/5/2005

9/25/2005

Celsius

Temperature 25 20 15 10 5 0 8/16/2005

9/5/2005

9/25/2005 10/15/2005 11/4/2005

Fig. 4 Dissolved oxygen, salinity, depth, and temperature of habitats where cultured and wild tagged fish were located 23 August to 27 October 2005. Represented are: fish 132 = •, fish 133 = , 135 = ♦, and fish 137 =

300 m upriver to track fish 149 since a wild fish was also nearby. Interestingly, AC fish 146 was located later at the acclimation cage site. The two wild fish remained in the general area throughout the entire tracking period. Fish 144 was tracked regularly for the first 16 days but then headed downriver. This fish did move back in and out of the triangulation area later in the tracking period. Fish 145 was tracked fairly regularly throughout the entire period. Data analyses are underway to compare cultured and wild fish swimming speeds in relation to diel and tidal cycles.

mean (sd)

0.031 (0.000) 0.003 (0.004) 0.012 (0.011)

Location

release site cultured fish wild fish

Gravel

0.348

p value 0.928 (0.000) 0.942 (0.021) 0.956 (0.001)

mean (sd)

Sand

0.174

p value 0.005 (0.000) 0.007 (0.466) 0.001 (0.084)

mean (sd)

Silt

0.084

p value

0.035 (0.000) 0.049 (0.415) 0.031 (0.015)

mean (sd)

Clay

0.015

p value

1 5 3

n

Table 3 The mean proportion of gravel, sand, clay, and silt of the sediments at the location where fish were released and the locations where cultured and wild fish were found. Statistical differences of sediment fractions between cultured and wild fish locations are denoted by p ≤ 0.05

Tracking Acoustically Tagged Cultured and Wild Juvenile Flounder 15

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Table 4 Total number of fixes recorded by each VR2 receiver and total number of locations where each tagged fish was heard. Receivers are listed in order from up estuary to down estuary Fish ID VR-6624a VR-6623 VR-6625 VR-6622 VR-6620 VR-6621 VR-6624b Total Fixes 3981 3982 3983 3984 3985 3986 3987 3988 3989 3990

179 0 7 143 32 0 0 0 1 0

362 62 201 4,806 1,060 2 9 6 17 146

59,373 10,555 3,090 6,096 40,924 560 465 65 3,001 52,715

6,423 48,926 833 53 1,331 344 700 0 1,836 176

0 0 0 0 0 27 0 0 0 0

0 0 0 0 0 458 0 0 0 0

1 0 0 0 0 0 0 0 0 0

66,338 59,543 4,131 11,098 43,347 1,391 1,174 71 4,855 53,037

Total

362

6,671

176,844

60,622

27

458

1

244,985

Wild Sub-adult Movements A total of 244,985 “fixes” were recorded by the six VR2 receivers during the lifespan of the tags (Table 4). Individual fish were tracked from as few as one day post release to as many as 168 days post release. The majority of these fixes were recorded by the receivers at the release site (72%) and immediately downriver (25%). Because daily fish movements for sub-adult winter flounder were relatively small (99% km–1 for both females and males. In addition, fishing mortality of all tagged kelts was low

48

E. Halttunen et al.

(1%) even though they were exposed to fishing pressure both in the river and in the fjord during their outward migration. The observed swimming speeds and depths of Atlantic salmon kelts were consistent with previous findings on mature salmon indicating that the low body condition of kelts does not affect their swimming behaviour. Further study is needed to be able to identify critical phases to this potentially important subpopulation. Acknowledgments The study was financed by the University of Tromsø, Norwegian Research Council and the Norwegian Institute for Nature Research. We thank especially River Alta Sports Fishing Association (Alta Laksefiskeri Interessentskap) for in-river fishery data and co-operation during fieldwork. Statistics Norway kindly provided in-fjord fishery data. We also thank Jenny Jensen and Amund Suhr for their extensive help during catch and tagging of the fish, and Øystein Aas and Raul Primicerio for their valuable comments on the manuscript. The experimental procedures were approved by the Norwegian Animal Research Authority.

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Small Scale Vertical Behaviour of Juvenile Albacore in Relation to Their Biotic Environment in the Bay of Biscay ˜ Igor Arregui, Ainhoa Lezama, Haritz Arrizabalaga Nicolas Goni, and Gala Moreno

Abstract The goal of the present study is to analyze the small scale vertical behaviour of juvenile albacore tuna (Thunnus alalunga) in relation to the abundance and distribution of their main prey, which has particular importance regarding catchability by surface fishing gears, such as trolling. A total of six juvenile albacore were tracked in the south east Bay of Biscay in July and August 2005, using ultrasonic transmitters. Two echosounders working at 38 and 120 kHz on the tracking vessel were used to collect data on the biotic environment (krill, small pelagic fish and planktonic layers) between the surface and 200 m depth. These data were echo-integrated in order to relate tuna vertical movements to food availability. The stomach contents of 97 albacore caught during the surveys were analyzed, the comparison of prey occurrences respectively in the stomachs and on the echograms showed selectivity for blue whiting. However, the biotic factors considered in this study had no significant influence on the depth of albacore, which possibly feed during night-time in surface waters. The tracked albacore had a shallow depth distribution and did not exhibit any regular deep-diving behaviour. A significant effect of time of day and body size on albacore depth was shown, all fish remaining deeper during daytime, and smaller fish having a shallower vertical distribution. Keywords Albacore · Behaviour · Ultrasonic telemetry · Echosounding · Prey · Bay of Biscay · Atlantic

Introduction Albacore (Thunnus alalunga) is a highly migratory tuna species, found in both tropical and temperate waters of the three oceans. It is considered the most pelagic of all tuna species (Bard, 1981) and the juveniles make large scale seasonal migrations N. Goñi (B) AZTI-Tecnalia, Marine Research Division, Herrera kaia portualdea z/g, 20110 Pasaia, Gipuzkoa, Spain e-mail: [email protected]

J.L. Nielsen et al. (eds.), Tagging and Tracking of Marine Animals with Electronic Devices, Reviews: Methods and Technologies in Fish Biology and Fisheries 9, C Springer Science+Business Media B.V. 2009 DOI 10.1007/978-1-4020-9640-2 4,

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between subtropical and temperate waters (Bard, 1981; Santiago, 2004). In the North Atlantic, juveniles make a trophic migration in summer, the period in which they show higher growth rates than during the rest of the year when in wintering grounds (Santiago and Arrizabalaga, 2005). This migration occurs towards productive zones in the Bay of Biscay and surrounding waters, where they are exploited from June through October by surface gears, mainly Spanish trolling and baitboat fisheries, which delivered more than 50% of the total North Atlantic albacore catch in the last decade (ICCAT, 2007). Knowledge on fish behaviour is highly important for a better understanding of their catchability (Fréon and Misund, 1999), and the study of their vertical behaviour in relation to their biotic environment is particularly relevant regarding catchability by surface fishing gear. This is especially the case for trolling CPUE series, which are used as abundance indices for the North Atlantic albacore stock assessment (ICCAT, 2007). Possible biotic influences on albacore vertical behaviour could bias these abundance indices, and need therefore to be identified. Albacore is one of the the main commercial tuna species, with a particularly high commercial importance in the North-East Atlantic (Santiago, 2004). However, little is known about albacore behaviour through tagging and/or tracking experiments (Childers et al., 2007; Laurs et al., 1977; Uosaki, 2004), compared with the information collected on Atlantic bluefin tuna (Thunnus thynnus) (Brill et al., 2002; Stokesbury et al., 2004; Wilson et al., 2005), Pacific bluefin tuna (Thunnus orientalis) (Itoh et al., 2003; Kitagawa et al., 2004; Kitagawa et al., 2007; Marcinek et al., 2001), or to a lesser extent on yellowfin (Thunnus albacares) and bigeye tuna (Thunnus obesus) (Brill et al., 1999; Dagorn et al., 2000a; Musyl et al., 2003; Schaeffer and Fuller, 2005). In most of these experiments on tuna behaviour, the movements of the tracked or tagged individuals have been mainly interpreted in relation to variations of abiotic variables, such as temperature, salinity or dissolved oxygen. In the case of albacore, the horizontal movements of 17 juvenile (age-3 to age-5) albacore in the Eastern North Pacific were related to sea surface temperatures between 15◦ C and 19◦ C (Childers et al., 2007), which confirms the observations previously made by Laurs et al. (1977) in the same area on 84–87 cm individuals. As for their vertical movements, Childers et al. (2007) report a shallower distribution in summer months related to shallow thermoclines associated with surface mixing, but with dives to waters with temperatures as low as 9◦ C. This shallower distribution in summer months is confirmed by Uosaki (2004), who also reports a distribution related to ambient temperatures between 14◦ C and 18◦ C in summer months. Little interpretation involving biotic environment has been given, either in experiments on albacore or on other species, as most experiments did not consider or collect relevant observations of tuna prey. However, in studies in which potential prey were observed and analyzed in relation to the movements or habitat of tuna or large pelagic fish (Carey, 1992; Josse et al., 1998; Bertrand et al., 2002; Domokos et al., 2007), prey distribution had an important influence on the behaviour of tuna or large pelagic fish. Tunas have high metabolic rates due to their obligate continuous activity (Graham and Laurs, 1982), and high standard metabolic rates compared to strictly

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poikilotherm fish species (Korsmeyer and Dewar, 2001). This metabolic rate may be particularly high in juvenile individuals – i.e. in rapid growth phase – and in individuals that perform long-distance seasonal migrations. For juvenile albacore performing a trophic migration from subtropical waters to the Bay of Biscay and surrounding areas, we hypothesize that, due to their energetic needs, prey abundance and distribution will have a significant influence on their behaviour. The aim of this study is to analyze the small scale vertical behaviour of juvenile albacore tuna, as inferred from ultrasonic telemetry, in relation to the abundance and distribution of their main prey, as inferred from acoustic surveys. As this is the first tracking experiment on the North Atlantic albacore population, we also aim to compare the vertical distribution of albacore to that observed in other areas, and discuss implications regarding catchability by trolling gear.

Materials and Methods Echosounding and Echointegration Two Simrad EY60 split-beam scientific echosounders were used, working respectively at 38 and 120 kHz. The depth range of signal detection was from 5 to 400 m in this experiment, and the detection angle of both echosounders was 7◦ . The software Simrad was used to record echosounding data to a depth of 200 m. The use of two frequencies allowed us to perform an echointegration of the recorded data, in order to estimate the relative biomass of the prey groups observed. Multi-frequency analysis uses the response of different targets to the different operative frequencies to distinguish between species. For instance the different frequency response of swim-bladdered fish from that of plankton or krill is well known (Madureira et al., 1993; Kang et al., 2002). In the present work two coarse groups of prey were distinguished using multifrequency analysis: swim-bladdered fish and planktonic organisms (including krill), this last group being further divided into krill and plankton. The energy scattered by a given target, at both 38 and 120 kHz, was compared between acoustic samples, in order to create different filters suited for the discrimination of a single prey group. The filters are applied to the echograms, to obtain virtual channels which contain the energy attributed only to the corresponding prey group. For fish, the virtual channel was obtained by subtracting the 120 kHz channel from the 38 kHz channel. A constant value of –55 dB was then added to this new channel, and a –55 dB threshold was applied during echointegration. The resulting channel is a filter that was applied to the 38 kHz channel, leaving the fish detections unaltered and subtracting the other prey groups. For krill, the virtual channel was obtained by subtracting the 38 kHz channel from the 120 kHz channel. As for fish, a constant value of –55 dB was then added to this new channel, and a –55 dB threshold was applied during echointegration. The resulting channel is a filter that was applied to the 120 kHz channel, separating krill echoes from other elements. For plankton, the virtual channels of fish and of krill were summed, and this sum

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divided by two in order to shift the minimal threshold from –160 dB to –80 dB. The result is then subtracted from the channel where the majority of the plankton appears (38 kHz channel in our case). A –55 dB threshold was applied during echointegration. The resulting channel is a filter that was applied to the 38 kHz channel in order to separate the echoes of plankton layers. The echointegration was realized by shoals (krill and small pelagic fish), and by groups within layers (krill, small pelagic fish, and plankton). Ten layers were considered, nine layers of 10 m between 5 m and 95 m, and a single layer from 95 and 200 m. The acoustic back-scattered energy by surface unit (sA , MacLennan et al. 2002) of each layer was computed every 0.5 geo-referenced nautical mile, for fish, krill and plankton separately. For the echointegration by shoals, the following data were computed for each shoal: position, time of detection, depth of the “backscatter centroid” (average of the latitude, longitude and depth of all the pixels in a shoal, weighted by the backscatter energy of each pixel), length, height, area of the vertical section, and volume reverberation index of the shoal (Sv, MacLennan et al. 2002). Extracted shoal objects with a length smaller than two beam widths at depth were rejected as being too small to be characterized adequately (Diner, 2001). Shoal objects between the surface and 5 m depth were also rejected to avoid the risk of confusion with echoes generated by surface-related turbulence. The relative biomass (within each prey group) of each shoal was estimated as Sv

biomass = ar ea · 10 10 with area being the area of the vertical section of the shoal. The echointegration, or “EI by shoal” algorithm, implemented in Movies+ 3.4. software (Weill et al. 1993; Diner et al. 2006), was applied to the acoustic data. Among fish shoals, the identification at the species level was not made automatically but visually through inspection of echo trace characteristics (Boyra et al., 2006). The species we focused on in the echograms were determined after stomach content analysis (see below). Mackerel (Scomber scombrus) shoals were characterized by a higher reflected energy in the 120 kHz channel than on the 38 kHz channel, and a variable shape due to vertical movements within schools (Boyra, pers. comm.). Krill shoals were also characterized by a higher reflected energy in the 120 kHz channel, but by a round shape. Anchovy (Engraulis encrasicolus) and sardine (Sardina pilchardus) shoals were both characterized by a high reflected energy (–50 to –25 dB) in the two channels, and were mostly located between 5 and 20 m depth. Blue whiting (Micromesistius poutassou) shoals appeared as consecutive small elements, located below 60 m depth, with a higher reflected on the 38 kHz channel than on the 120 kHz channel, this reflected energy being lower than for the previously mentioned groups. Horse mackerel (Trachurus trachurus) shoals appeared as large shoals located mostly below 30 m depth, with a higher reflection on the 38 kHz channel than on the 120 kHz channel, this reflected energy being relatively low as for blue whiting. Horse mackerel shoals appeared only rarely in the echograms, thus, they were not considered in the analysis of albacore behaviour. Other shoals not clearly fitting with any of the previously described characteristics were also

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discarded. Discarded shoals represented 5.4% of the number of visualized shoals. The software Movies+ 3.4 was also used for the visualization of shoals.

Fishing Operations Fishing operations were conducted from a 16 m commercial fishing boat (FV Handik, Pasaia) in the south east Bay of Biscay. The fishing gear used in this experiment was trolling lines. Individuals that were injured by the hooks or too small for tagging were kept on board and their stomachs stored for diet analysis. The stomach contents of 97 non-tagged albacore were analyzed, with the weight and number of individuals of each identified prey species recorded.

Tagging and Tracking The tracking equipment was a VEMCO VR28 system. Fish were tagged with V16 and V22 ultrasonic transmitters with frequencies of 51 and 50 kHz, which minimised interference with the echosounder. The tags were attached to the tunas using two nylon tie-wraps that were implanted behind the second dorsal fin, as described by Holland et al. (1990). A VH40 hydrophone, mounted within a VFIN (i.e. a v-shaped fin that allows the hydrophone to be towed by the vessel at a uniform depth), was used to detect the signals of the transmitters in each horizontal direction (bow, aft, starboard, and port). The V16 TP-5H transmitters were fitted with pressure and temperature sensors. V22 P-5XS (used because of their more powerful signal) and V16 P-4H transmitters were fitted with pressure sensors only. During tracks performed using V22 P-5XS and V16 P-4H tags, temperature profiles of the water column were measured using XBTs. Depending on the background noise, mainly related to sea surface turbulence, the maximum distance between the tracked fish and the vessel was 300 m for V16P-4H transmitters (Girard et al., 2007) and up to 1000 m for V22 P-5XS transmitters (Moreno, unpublished data). Tracks were terminated when the signal of the transmitter was lost.

Behaviour Analyses The vertical behaviour of the tracked individuals was analyzed at two time-scales. By minute, the mean depth of the tracked individuals was related to the depth of shoals of each prey group used, in order to look for immediate reactions of albacore to the presence of prey. By hour, in order to look for influences of potential prey on the preferential depths of the tracked individuals, the mean depth of the tracked individuals was related to the mean depth, mean relative biomass and hourly number of shoals of each prey group used (anchovy/sardine, mackerel, blue whiting

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and krill), and to the biomass (as derived from the reflected energy by layers) and weighted mean depth of the biomass of krill, fish and plankton. The weighted mean depth of the biomass of krill, fish and plankton was defined as the average depth of the 10 layers, weighted by the relative back-scattered energy by surface unit (sA ) of each layer. Separate analyses were performed for diving periods. The start and end of arbitrary diving periods were defined respectively as the first minute before a depth increase greater than 25 m in three minutes, and as the last minute of a depth decrease greater than 25 m in three minutes. The mean depth, maximum depth and duration of the dives were related to the depth and relative biomass of shoals of each taxonomic group considered. The water temperature was not taken into account, as the measured temperatures during the diving periods observed in this study were above the minimal temperature threshold allowing thermoregulation (i.e. 11.5◦ C) – according to the observations reported by Graham and Dickson (1981) in experimental tanks – except for one individual at 11.34◦ C for a few seconds. Time at liberty, hour-of-day, and fork length of the tracked fish were considered likely to affect their depth, and consequently considered in the analyses. These relationships were analyzed using generalized additive models (GAMs), which are nonparametric generalizations of multiple linear regression techniques (Hastie, 1992). A Gaussian distribution was assumed for all random variables. A stepwise model selection procedure was adopted, where the explanatory variables could either be modelled as absent, as a linear function or as a spline function. Rows with missing values were omitted before the stepwise model selection, so that all models were based on the same observations. The model with lowest Akaike Information Criteria (AIC) was selected, following Hastie (1992). To assess the non-spuriousness of each significant relationship encountered, the same model was tested on 1000 random re-samples of the original data; the relationship was considered non-spurious when it was significant for fewer than 50 re-samples. The generalized additive models were built stepwise using the R v. 2.6.0. (R Development Core Team 2007) statistical software (available online at http://www.r-project.org/) and the gam 0.98 package. The corresponding analyses of variance were performed using the mgcv 1.3-29 package.

Results Environment at the Time of the Tracking During all tracks the weather was sunny or with light cloud cover, the wind speed was less than force 4 on Beaufort scale (i.e.

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