Developing Integrated Database Systems for the ... -

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automated tasks and user interfaces for uploading data and quality control. ... software written specifically to handle a particular tag format. ...... e-mail: Alistair.

Developing Integrated Database Systems for the Management of Electronic Tagging Data Jason R. Hartog, Toby A. Patterson, Klaas Hartmann, Paavo Jumppanen, Scott Cooper and Rossen Bradford

Abstract Recent advances in electronic tag technology have resulted in an explosion of data for marine biologists. Providing descriptions of data management systems and discussion of their strengths and weaknesses will be important in promoting a dialogue with the ultimate goal of building better systems to support research. The importance of this will only increase as large multinational, multiinstitutional studies become more common. Modem memory components permit a single archival tag to collect many megabytes of data. Effective handling of the volume of data generated by multiple tag deployments is a major challenge, and an essential step before data analysis can be performed. Tags are deployed on multiple species and a single animal may carry several different tag types. Data handling systems must be flexible enough to accommodate the variety of ways tags are used, as well as the changes to tag specifications over time. This paper describes a relational database system that has been developed to meet these requirements. This database stores data from a variety of electronic tags from different manufacturers, inclUding archival tags, satellite tags and acoustic tags. The data are downloaded, processed and stored automatically where possible. Centralising data storage allows flexibility in data access, quality control, exploration and analysis. Software programs allow access to the data from local servers or via the internet and include initial visualization of animal tracks via a mapping system. A suite of environmental data and products can also be matched to the selected track(s), which aids initial analyses and development and refinement of scientific hypotheses. . Relational database . Information management .

. Hartog (181) IRO Marine and Atmospheric Research, GPO Box 1538, Hobart, Tasmania, 7001, Australia 01: [email protected] artmann (181)

'Dian ICf Centre, CSIRO, GPO Box 1538, Hobart, Tasmanian 7001, Australia

Nielsen et al. (eds.), Tagging and Tracking ofMarine Animals with Electronic Devices, ws: Methods and Technologies in Fish Biology and Fisheries 9, ~O.l0071978-1-4020-9640_222, 10 Springer Science+Business Media B.V. 2009



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Introduction Historically, tagging studies have been limited to mark-recapture experiments. The data collected from these studies has been used in a variety of applications: estimating abundance (Seber, 1982); estimating mortality rates (Brownie et aI., 1985; Hearn et aI., 1987; Polacheck et aI., 2006); determining spatial movement (Hilborn, 1990); determining growth rates (Polacheck et aI., 2004); and in a variety of fisheries stock assessment models (Beverton and Holt, 1957; Quinn et aI., 1990; Hampton and Fournier, 2001; Butterworth et aI., 2003). Advances in electronic tag technology (Gunn and Block, 2001), deployment of large acoustic arrays (Welch et aI., 2002,!) and the increase in computing power and memory storage have resulted in an explosion of marine telemetry and biologging data (Hooker et aI., 2007). In this paper we consider the design and construction of databases suitable for handling both mark recapture data, electronic and satellite tag data, and also the integration of these data with marine habitat data. The development of electronic tags has enabled researchers to tackle increasingly complicated problems (Arnold and Dewar, 2001). Electronic tags have been deployed on a large variety of species including tunas (Block et aI., 1998; Stokesbury et aI., 2004; Domeier et aI., 2005; Block et aI., 2001; Wilson et aI., 2005), sharks (Weng et aI., 2005; Wilson et aI., 2006), turtles (Swimmer et aI., 2006), marine mammals (Halpin et aI., 2006) and large squid (Gilly et aI., 2006). Animals tagged with electronic tags are also often conventionally tagged. Therefore, an effective data storage system must hold both the conventional release and recapture information as well as the sensor data from electronic instruments. Additionally, the system should link mark-recapture and sensor data with fisheries or ocean data, such as catch per unit effort and sea surface temperature. CSIRO initially developed a database system for southern bluefin tuna (SBT; Thunnus macoyii), holding catch, effort and conventional tagging information. This system was implemented using ~acle® (Oracle Corporation, 500 Oracle Parkway, Redwood Shores, CA, USA) and the data accessed using a Microsoft® (Microsoft Corporation, Redmond, WA, USA) Access interface. Keeping all the fisheries related data in a single centralised database enabled researchers to perform fisheries analyses and assessments with greater ease. From an information management perspective, data derived from electronic tags present several challenges. When many tags are deployed, manual data processing becomes unwieldy. As a result of the developments in tag technology, the database for simple mark/recapture information has evolved the capability to store release and recapture of multiple tag types, as well as the data streams that accompany these new tag types. This paper describes the current state of the database system, detailing its flexibility and the plans for future development. Despite growing demand for data management systems for electronic tag data, there is little discussion of their merits and associated development challenges in the literature (Coyne and Godley, 2005; Halpin et aI., 2006). Storage and access to data is, understandably, secondary to the science which it supports. However, descriptions of database systems are particularly topical given the very large ongoing investment in marine


Electronic Tagging Database System


tracking (Boehlert et aI., 200 I, Block et aI., 2002; Sibert et aI., 2006) which will increasingly rely on such systems. With the advent of large tagging programs between many institutions that may be regional or global, data management systems will increasingly be required to integrate disparate kinds of information and support a greater range of data types. In this paper we do not advocate a particular design methodology, software or technology, but simply aim to document the planning and effort required to consolidate large electronic tag datasets into a useful format. We hope that by describing our system in the literature, we may stimulate interest in the question of how to store tagging data and thereby aid in developing better tools to assist research.

System Overview Several challenges presented themselves from the outset. Tag manufacturers are continually developing new models with different sensors and formats. Therefore, our database required a degree of flexibility to store new types of tag data. Additionally, to cope with a variety of electronic tags, we required development of both automated tasks and user interfaces for uploading data and quality control. These constraints demanded a system that could be configured and reconfigured easily and quickly in the case of either changes to data streams or corruption of data. Additionally, electronic tag data formats are often proprietary and data can only be accessed with third-party software. Therefore, the system required many pieces of software written specifically to handle a particular tag format. This situation is not ideal for processing data as tag-specific applications are required in each case, but it does highlight the need for developing a central repository that can be accessed using a single protocol - in this case database queries written in Structured Query Language (SQL). This is crucial in avoiding a fragmented approach to data collation for analysis. Importantly, the system we describe makes no distinction between what is often termed "meta-data" (i.e. release data, comments on tag insertion, species information) and "primary" sensor data. This avoids artificial segregation of what is just another type of data from the sensor streams.

Database Design A relational database design was implemented in Oracle, with an Access database front-end for ease of data entry. Originally, the tag was the most basic entity in the design of the database - i.e. tags were the unique basic element that allowed records to be linked across tables. But because animals may carry several tags and also because tags may be reused, when the database structure was redesigned to handle electronic tag data, the "tag" entity was replaced by the concept of a "release" (Fig. I). Each time an animal is caught, tagged and released, an entry into the database release table is created. This unique identification number links the release

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Electronic Tagging Database System





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Fig. 1 Simplified schema of the database table structure. SAT are the types of satel1ite tags that transmit during deployment, and PSAT are the type of satel1ite tag that transmits after detaching from the animal. Central to the database schema is the concept of a release. and all table linkages run through this important table

event with any number of tag types that may be attached to the animal (e.g. an animal may be released with a conventional dart tag as well as an archival tag, acoustic tag or a satellite tag). In the system, tagging data tables are segregated into four broad components based on tag type: conventional tags; acoustic tags; satellite tags and archival tags. Due to the different type and volume of data transmitted from each tag type, the tables storing satellite tag data were split into those handling regular satellite tags (i.e. those where Argos location is used to determine animal movement) and pop-up satellite tags. The conventional tag data table contains basic information including the tag identification number and colour, and notes on the quality of insertion. Generic data, such as setup information for the satellite tags, are captured and stored in the first level tables ill the system. The actual tag data are stored in secondary tables (Fig. I). Our database stores individual records in tabular format (as opposed to say, storing binary files within tables). While this makes the structure of the database more amenable to user-queries, it trades off simplicity and flexibility against compactness, as tables store each sensor reading as a separate record. For the purposes of this paper, the full database schema has been omitted, and has been represented with a much simpler design schema (Fig. I). The full schema is available by contacting the corresponding author. The Access user interface to the database enables the user to enter release and recapture information and to generate a variety of reports and standard queries. Researchers with basic computing skills can access their data through the Microsoft Access front end and an internet mapping website that is described below. Users can also access the data through their choice of computer language or analysis program (Fig. 2) using database connectivity protocols such as Open Database Connectivity (ODBC). Predefined reports allow for the automatic generation of reward certificates for individuals and organisations that have returned tags. Where possible all data handling and archiving has been automated. Where automation of tasks is not



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Fig. 2 Synopsis of the processes that have been developed to form the tagging database system. Deployment data is captured using paper or database forms. The electronic tag data are processed and made available for analysis using a variety of tools. Environmental data are downloaded and indexed for lookup in the scientific output phase. SST is sea surface temperature. Data storage is achieved using a combination of databases and file systems. SQL is the Structured Query Language used for database input and output. Results are stored back to the database for archiving and further analysis

possible, data handling protocols and purpose-built software has been developed to ensure the data are handled and maintained in a systematic way. Satellite Tags Data from satellite tags and pop-up satellite archival tags are managed via a fully automated system driven by a series of functions and scripts executed in Perl and Bash in a Linux environment. On a daily basis Control scripts are executed daily to automatically download, decode and archive tag data received from Service Argos. Remote Access and Download of Data Each satellite tag is allocated a unique number by Service Argos which is used to identify the transmissions made by it. This number is referred to as the Platform Terminal Transmitter (PTT) identification number. A connection to Service Argos is made via telnet and the last nine days of data for all PTT identification numbers registered to a particular Argos program number are downloaded. By adding a single line to the main control script the system is easily extendable to request data for an additional Argos program number if required. The output from the telnet session is automatically saved to a log file with a unique name based on the day and the year the data are downloaded.

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Split and Decode The P1T identification number of each tag is used to split the data into separate files corresponding to each tag (Fig. 3). Using a fixed width format specifier as described in the Argos user manual (Argos, 1996), the system parses out the transmission locations from each message header. For tag types that have an associated data stream (e.g. PSATs which transmit summarised light, temperature and depth data rather than location only data), the system reads an Extensible Markup Language (XML) based configuration file which describes to the system how to decode the incoming data. As new tag types are developed, they can be incorporated into the system by simply providing an associated XML configuration file. The system then parses the data into each data type (e.g. time at depth, time at temperature histograms), and stores the data in a delimited file for upload to the database (Fig. 3). Data are never discarded; data transmissions that are corrupt or incomplete are removed from the final data file to be decoded and stored in a "Lost Data" folder (Fig. 3). This is adopted as an insurance policy in case of errors in decoding or if methods for data recovery are developed. When data associated with a new P1T identification number are downloaded the system sends an alert email to several pre-specified email addresses notifying the recipients of new data. If certain key information associated with the release of the tag are missing from the database the data are still downloaded from Service Argos and stored, but decoding will only occur once the P1T identification number of the transmitting tag can be matched completely to a release in the database. This is to ensure that the P1T is correctly linked to a unique key in the database tables, ensuring database integrity. Matching of the P1T identification number also ensures that pre-deployment transmissions (e.g. test transmissions by tag manufacturers) are not decoded and presented as real deployment data. In order to ensure that data

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are decoded in a near real time fashion, the system also maintains an inventory of releases and a warning email is automatically sent to the owner of the tag to remind them to enter in the release information for tags that have incomplete release data.

Uploading After the download, decoding and parsing steps have been completed, the system updates the Oracle tables with the new data. Each file that is created in the folder for a tag has a one-to-one mapping to a table in the database. Using ODBC and Perl scripting, the system replaces the decoded data for that tag in the database, thus ensuring that the database always has the most recent data present. Calculating geographical positions from PSAT data requires a user to run some proprietary software provided by the tag manufacturer. The results from this process are then placed on a server, and the system retrieves the data from that server location.

Data Security The file structure of the whole system (Fig. 3) is compressed and saved to a dedicated user account on a separate machine, which is then backed up and stored at another location. As a second level of data security, a scheduled task is run once a week which downloads all transmission data available for an Argos program. These downloaded data are stored in a different user account which is routinely backed up. In this way, all the raw, unprocessed data from Service Argos are maintained should either the Oracle database or the database backup fail. The whole system can quickly be restored using only the raw Argos downloads in the unlikely event that the database or decoding has been corrupted.



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Fig. 3 Schematic of the file system structure used to manage Argos data. Panel A shows how the data is organised and separated under an individual Argos user identification. The data downloaded from the Service Argos ftp site is stored in the Raw Argos Logs, and are then split into individual files for each Platform Terminal Transmitter (PTf). Incomplete transmissions are archived in the Lost Data folder for each PTT. Lost data are archived in case better decoding techniques are developed in the future. Each PTT is associated with a unique ill from the database and a folder is created for each of these. Panel B shows the types of output files that result from the decode process. These files are uploaded to the database as part of the daily maintenance schedule


Acoustic Tags Acoustic tags are managed using a combination of manufacturer provided software and manual processes. The tag database has the necessary tables defined to record tag specifications, receiver specifications and the actual detection data. Currently, the software provided by Vemco (VEMCO Division, AMIRIX Systems Inc., Halifax, Nova Scotia, Canada) is the only method available to decode the acoustic tag detections, receiver events log and receiver header obtained from the acoustic listening stations. An export facility in the software allows the decoded data to be saved to a text file. A utility program is then used to interpret and insert the data into our database format. The receiver header is first imported and the receiver deployment is added to the database. The detection data will then match to the receiver and an acoustic tag release if the release resides in our database. As with conventional tags, all acoustic tag release and receiver deployment information must be manually entered into the database. Some acoustic tags and receivers also collect data from sensors which can also be stored in the database.

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Archival tags are managed using a combination of manual and automatic processes defined through the use of standardised protocols. Once data have been downloaded from an archival tag and decoded into CSV format using proprietary software supplied by the tag manufacturer, the sensor data are visualised using custom software written in C++. This software is also available by request from the corresponding author. The internal data structures in the viewing software have a flexible design such that they can read data output from a variety of manufacturer provided software. The viewing software allows the data retrieved from the tags to be trimmed to the actual period that the tag was deployed on the animal and any data corrupted due to sensor malfunction can be flagged, or corrected in the case of depth sensor drift. Corrupted data are never discarded, but simply marked with a quality flag, allowing filtering as required. The processed tag data are then saved to a server location and scheduled for upload. Geolocation methods utilising threshold methods (Hill, 1994) and calculation of latitude using sea surface temperature (SST; Teo et al., 2004, Patterson et al., 2008) are applied to the light data, producing estimates of position from which a "best estimate" movement path can be generated. Alternative geolocation methods can be incorporated as they are developed. SST data required for geolocation are retrieved using generic MATLAB® (Mathworks™, Natick, MA, USA) functions to access the data from an environmental data management system described in the next section. The calculated locations are then saved to a server location and scheduled for upload.

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Access to local and global ocean da~ products is crucial to light-based geolocation method refinement and for deterrniri'ing behaviour in marine animals. A series of MATLAB functions and bash scripts automatically maintains a file system of ocean products. On a daily basis, the most recent ocean product files from local servers containing CSIRO products or remote File Transfer Protocol (FTP) sites containing ocean products generated by other institutions (such as, http://noaa.govl) are downloaded. After the file system has been updated, a spatial and temporal dimensioning process is initiated that maintains an index of the dimensions of the environmental products, allowing users to easily query available products. Access to the environmental data is provided through a graphical user interface to enable the user to load a data product for a user-defined area of interest. Functions are also provided to enable command line requests, from both R (http://www.rproject.orgl) and MATLAB. For instance this is used for refining location estimates using SST (see above).


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Mapping Solutions Interactive mapping and visualisation of tagging data is provided through a MapServer ( based web application. Implemented in Perl script, the Common Gateway Interface (CGI) application allows users to select a subset of data products to be mapped on an interactive web page, allowing the user to zoom and pan the map, toggle the display of layers, perform spatial queries, extract spatial queries to comma separated text files and create tag track animations (Fig. 4). The list of possible data products is large and includes many that are maintained by the environmental data system described in the previous section. Specific locations can be highlighted utilising a drop-down menu of dates specific to the tag deployment. If there is a time component to the chosen data layer (e.g. sea surface temperature), the application automatically extracts the GIS data that corresponds to the selected date. Available ocean data layers and their specifications are defined within an XML file and the HyperText Markup Language (HTML) pages are generated from HTML template files using text substitution. This strategy allows the layout of the pages to be changed without the need to alter the Perl code used to generate them. The mapping system was initially developed as a local web application accessible via the intranet, but has been migrated to an internet accessible web server ( has enabled access to summary data by external stakeholders and will allow researchers to showcase tagging data offsite.

Conclusion Effective data handling is an ikportant component even for a modest tagging program but becomes especially important in large tagging programs involving many different species, double tagging experiments, and electronic data streams. There is a growing demand for data management systems as a tool for research. Developing systems to handle all the different types of tagging data is not an easy task, and it is our hope that our experiences will aid others seeking to do the same. The database system described in this paper provides a central storage facility for all data associated with tagging of a variety of species. The system ensures data quality and control, and reliably provides a solution for long term archiving and backup of electronic tag data. A major benefit of the system is the ability to add new tag types as tag technology is developed, thus increasing the utility and lifespan of the system. In addition, new software can be developed to build onto the core, stable system that houses the data. At the time of writing, a prototype web interface has been developed that will enable release information to be entered via the internet while working in the field. When this is complete, it will ensure that the subsystem that is checking for new satellite tag transmissions is constantly up to date.


Electronic Tagging Database System


The original motivation for developing the decoding and parsing Perl code for Argos data was that the manufacturer's software was user driven. This made simply extracting and storing tag data a very time consuming and expensive task. Additionally, it introduced subjectivity into quality control and geolocation processing by allowing users to determine inputs to the geolocation process by eye. While this is unavoidable in some cases, we aimed to reduce overheads and ad hoc processing by automating these tasks where possible. However, to automate decoding of satellite data from Service Argos output often requires knowledge of proprietary data formats. Wildlife Computers Inc. (Redmond, WA, USA) has recently provided utilities that can be called in batch mode. These software programs have been incorporated, in production mode, into the set of system scripts to deal with recent tag releases of tags manufactured by Wildlife Computers. We note that automatic handling of other versions of satellite tags will be reliant on collaboration between the tag users and the manufacturers - either through the disclosure of algorithms or the release of batch mode decoding utilities. We strongly recommend that manufacturers provide either access to data formats or suitable utilities that can be easily incorporated into a larger system. Development costs for this system have been minimal compared to the cost of the hardware that has been deployed on marine animals. While a commercial database (Oracle) has been used, it is perfectly feasible to implement the system in an open source equivalent. Currently the system holds records for 138,588 tagged animals, resulting in the database containing 139,590 tag releases: 137,452 of these are conventional tag releases; 2,138 are electronic tag releases, noting that some animals were tagged with more than one tag type. The database contains 20,569 recaptures: 20,385 of these are conventional tag recaptures; 212 are archival recaptures and there have been 174 satellite tags that have transmitted data. The 212 archival recaptures have contributed to 44 million rows of data containing time, temperature, light and depth readings, as well as more than 50,000 location estimates for those animals. The 174 satellite transmitting tags have combined to produce 38,000 rows of binned sensor data (e.g. depth and temperature histograms), and 42,000 satellite transmission locations. Thus a small number of electronic tag "recaptures" can generate a substantial amount of data. The real time nature of the system is shown in application through advice being given to the fisheries managers in the Eastern Australian Tuna and Billfish Fishery (Hobday and Hartmann, 2006; Hobday et al., 2009). In this example, habitat preference for SBT is determined from the data transmitted by PSATs deployed on SBT in the management region. These preferences are then compared to satellite imagery for SST and a modelled vertical temperature data product (synTS _ synthetic Temperature and Salinity, CSIRO Marine and Atmospheric Research). This produces a habitat map, which is one of the tools used by managers to place restrictions on fishing effort in certain areas. Applications such as this benefit from the data being rapidly processed thereby allowing its inclusion in the management process. The system described in this paper has made this possible. Systems like those described in this paper can facilitate partnerships with other organisations in electronic tagging studies as has been demonstrated in other


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systems (Coyne and Godley, 2005). For example, each organisation can operate a local tagging program, and the database system can be leveraged to perform the data management, an operation which requires resources that many smaller institutions do not have. All that is required for the system to manage a research group's Argos data is an Argos account name and a password. As an organisation, we are not seeking to be a data management service provider, nor is the system described available in a pre-packaged form; we would, however, welcome collaboration and discussion with others in the electronic tagging community. The early days of fish telemetry involved a few specialist researchers deploying small numbers of experimental tags. At that time the size of electronic tagging datasets was limited by the technological limitations, such as memory and power, on the tags themselves. Today, large numbers of tags continue to be released and electronic tagging has been transformed from a prototype technology to a standard tool for biologists. The volume of data being collected and the monetary investment of grant money into tagging programs, mandates that the fish tracking community give the processing, storage and uptake of this wealth of data high priority. Yet few papers in the area (with the exception of Coyne and Godley, 2005; Halpin et al., 2006) have discussed the need for data management protocols and systems. We believe much greater discussion of the merits (and pitfalls) of building such systems is needed and that developing appropriate data management be recognised as an important component of marine tracking and biologging science. Without such discussion, the risks of needless replication, or worse, significant investment of time and money in poorly structured systems are increased. While, this paper has given a high-level overview of a particular system, some general points arise. Centralisation within a relational database is probably the most powerful way to integrate data"{ We believe similar systems to this are required even for deployments of modest numbers of tags. Additionally, data management systems must be able to quickly adapt to advances in both tagging technology and also analysis methods. Therefore, it is important to construct systems that are scalable and that can be easily reconfigured. This also requires manufacturers to develop software with large automated systems in mind. The major advances in marine biologging science are now being made through large multi national and multi institutional collaborative efforts which pool data from many smaller scale studies (Boehlert et al., 2001; Biuw et al., 2007). These efforts rely on similar systems to that described here. We believe that there are many benefits to be gained from further published descriptions of different approaches to the problems of storing electronic tag data, ultimately leading to better tools for advancing marine tracking science. Acknowledgements Funding for development of the database system was provided by CSIRO. The CSIRO Blueliok project provided sea surface temperature data and synTS. Grant West, Clive Stanley, Karen Evans and Katherine Tattersall provided valuable data input and testing. We thank all the taggers and tagging projects that have provided data to the system. Karen Evans provided valuable comments on earlier drafts of this manuscrr·

References Argos. (1996) User's Manual. CLS/Service Argos, Toulouse, 176pp. Arnold, G. and Dewar, H. (200I) Electronic tags in marine fisheries research: a 30 year perspective. In: Sibert, J.R and Nielsen, J.L. (eds.) Electronic Tagging and Tracking in Marine Fisheries. Kluwer Academic Publishers, Dordrecht, pp. 7-64. Beverton, RJ.H. and Holt, S.J. (1957) On the Dynamics of Exploited Fish Populations. UK Ministry of Agriculture and Fisheries, Fishery Investigations (Ser 2), No. 19, 533pp. Biuw, M., Boehme, L., Guinet, C., Hindell, M., Costa, D., Charrassin, J.-B., Roquet, E, Bailleul, E, Meredith, M., Thorpe, S., Tremblay, Y, McDonald, B., Park, Y-H., Rintoul, S.R., Bindoft', N., Goebel, M., Crocker, D., Lovell, P., Nicholson, I., Monks, E and Fedak, M.A. (2007) Variations in behavior and condition of a Southern Ocean top predator in relation to in situ oceanographic conditions. P. Natl. Acad. Sci. 104, 13705-13710. Block, B.A., Costa, D.P., Boehlert, G.W. and KOChevar, R.E. (2002) Revealing pelagic habitat use: the tagging of Pacific pelagics program. Oceanol. Acta. 25, 255-266. Block, B.A., Dewar, H., Blackwell, S.B., Williams, T.D., Prince, E.D., Farwell, C.J., Boustany, A., Teo, S.L.H., Seitz, A., Walli, A. and FUdge, D. (2001) Migratory movements, depth preferences and thermal biology of Atlantic bluefin tuna. Science 293,1310-1314. Block, B.A., Dewar, H., Williams, T., Prince, E., Farwell, C. and FUdge, D. (1998) Archival tagging of Atlantic bluefin tuna (Thunnus thYMUS). Mar. Tech. Soc. J. 32, 37-46. Boehlert, G.W, Costa, D.P., Crocker, D.E., Green, P., O'Brien, T., Levitus, S. and Le Boeuf, B.J. (2001) Autonomous pinniped environmental samplers: Using instrumented animals as oceanographic data collectors. J. Almos. Ocean. Tech. 18, 1882-1893. Brownie, C., Anderson, D.R, Burnham, K.P. and Robson, D.S. (1985) Statistical inference from band recovery data: a handbook. U.S. Fish. Wildl. Serv., Resource Publication 156, 305pp. Butterworth, D.S., lanelIi, J.N. and Hilborn, R (2003) A statistical model for stock assessment of Southern Bluefin Tuna with temporal changes in selectivity. Afr. J. Mar. Sci. 25,331-361. Coyne, M.S. and Godley, B.J. (2005) Satellite Tracking and Analysis Tool (STAT): an integrated system for archiving, analysing and mapping animal tracking data. Mar. Ecol. Prog. Ser. 301, 1-7. Domeier, M., Kiefer, D., Nasby-Lucas, N., Wagschal, A. and O'Brien, E (2005) Tracking Pacific bluefin tuna (Thunnus thynnus orientalis) in the northeastern Pacific with an automated algorithm that estimates latitude by matching sea-surface-temperature data from satellites with temperature data from tags on fish. Fish. Bull. 103,292-306. Gilly, WE, Markaida, U., Baxter, c.H., Block, B.A., Boustany, A., Zeidberg, L., Reisenbichler, K., Robison, B., Bazzino, G. and Salinas, C. (2006) Vertical and horizontal migrations by the jumbo squid Dosidicus gigas revealed by electronic tagging. Mar. EcoI. Prog. Ser. 324, 19-35. Gunn, J.S. and Block, B.A. (2001) Advances in acoustic, archival and satellite tagging of tunas. In: Block, B.A. and Stevens, E.D. (eds.) Tunas: Ecological Physiology and Evolution. Academic Press, San Diego, CA, pp. 167-224. Halpin, P.N., Read, A.J., Best, B.D., Hyrenback, K.D., Fujioka, E., Coyne, M.S., Crowder, L.B., Freeman, S.A. and Spoerri, C. (2006). OBIS-SEAMAP: developing a biogeographic data commons for the ecological studies of marine mammals, seabirds and sea turtles. Mar. Ecol. Prog. Ser. 316, 239-246. Hampton, J. and Fournier, D.A. (200 I) A spatially disaggregated, length-based, age-structured population model of yellowfin tuna (ThuMus albacares) in the western and central Pacific Ocean. Mar. Freshw. Res.52, 937-963. Hearn, W.S., Hampton, J.W and Sandland, RL. (1987) Robust estimation of the natural mortality rate in a completed tagging experiment with variable fishing intensity. Journal Du Conseil Permanent International Pour L 'Exploration De Lo Mer. 43, 107-II 7. Hilborn, R (1990) Determination of fish movement patterns from tag recoveries using maximum likelihood estimators. Can. J. Fish. Aquat. Sci. 47, 635-643.

J.R. Hartog et al.


Electronic Tagging Data Supporting Flexible Spatial Management in an Australian Longline Fishery

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Alistair J. Hobday, Nicole Flint, Trysh Stone and John S. Gunn

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Abstract Despite widespread claims for the importance of electronic tagging and the resulting data for improved management of fished species, there are few examples where this has occurred. In this contribution we describe how pop-up satellite archival tagging data have been 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 (SBT, Thunnus maccoyii). This model, and its practical application, has evolved over time (2002-present), due to the successful cooperation between scientists, fishery managers and stakeholders. To illustrate this example of successful use of tag data in management, we review the appropriate biology of SBT and its' interaction with the fishery, the management challenge that tagging and the habitat model helped resolve, and how this approach has been implemented. Discussion of the management costs associated with this management system shows that fine-scale spatial management is a suitable approach for this bycatch issue. We conclude with some general lessons for the application of flexible spatial management approaches, based on management and science constraints.

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Keywords Management tool . Pelagic fishery. Southern bluefin tuna. Bycatch


Introduction In the majority of marine fisheries, capture of the desirable (target) species is accompanied by the incidental capture of other species (bycatch) (e.g. Poiner and Harris, 1996; Hall, 1998; Tasker et al., 2000; Kock, 2001). Impacts on the population size A.J. Hobday (181) Wealth from Oceans National Research Flagship, CSIRO Marine and Atmospheric Research, GPO Box 1538, Hobart, Tasmania 7000, Australia e-mail: [email protected]

I .L. Nielsen et al. (eds.), Tagging and Tracking ofMarine Animals with Electronic Devices, .eviews: Methods and Technologies in Fish Biology and Fisheries 9, '110.10071978-1-4020-9640-2.23, © Springer Science+Business Media B.Y. 2009