Otolith contours were used mainly for discriminating between stocks (4, 5, 6, 7, 8). However, we could show in a preliminary otolith shape analysis with eel that 92 % of the specimen were aged correctly with a maximum error of two years (9). The mean age was 6 years. These preliminary findings and results from other authors (4, 5, 10) lead us to our basic working hypotheses that fish age information is coded in the three-dimensional shape and density distribution of otoliths.
We intend to develop a technique based on the measurement of the external shape of otoliths in combination with other directly available data, such as otolith weight (11)
Present fish catches have reached 8 %, and in some ecosystems even as much as 35 % of the total aquatic primary production. In consequence, many ecologically and commercially important fish stocks are collapsing. While the ecological losses due to this stock-management failure are inestimable, world-wide economic losses can be estimated - and exceed $25,000,000,000 annually (1, 2).
In the new Common Fisheries Policy (3), "a fundamental priority" is given to the collection of reliable, basic data on .... the detailed composition of ...... catches". In full accordance with this policy, fast and objective ageing criteria are important. Age distribution data is an important factor in fish stock assessment for determining the total allowable catch (TAC).
Presently, annulus (yearly growth ring) detection techniques are used for assessing the age of fish. These techniques are either visual or computer-assisted, and are used by thousands of experts world-wide. Unfortunately, these techniques are relatively subjective and involve man-power-intensive work requiring skilled handling of otoliths.
The methodology we want to establish must be simple, fast, non-destructive and applicable on a large scale. In developing and validating this methodology we will, however, also exploit possibly complex, state-of-the-art technology to achieve both a complete description of the three-dimensional (3D) outer surface (12, 13) of the otoliths, and a reconstruction of the entire 3D density distributions of the otoliths (14, 15) using a recently developed micro X-ray tomography technique. Both of these 3D approaches, which will be used to justify the simpler and faster 2D techniques, are novel in the field of otolith analysis. The 3D techniques may themselves yield new scientific insights into how age, environmental factors, and genotype influence the growth and shape of otoliths.
In order to calibrate and validate the proposed methodology, we will use otoliths of known-age of cod (Gadus morhua), eel (Anguilla anguilla), turbot (Psetta maxima), sea trout (Salmo trutta) and salmon (Salmo salar). For calibrating the new methodology for other commercially important species such as anchovy (Engraulis encrasicolus), for which no known-age reference data is available, we will use existing ageing techniques. A data base containing contours and 3D surfaces/densities will be collected and processed for each of the species studied. These databases will form the frame of reference for our rapid age determination technology, and will help in characterising the multiple factors which influence otolith shape.
The proposed project is highly innovative (3, 16) in its 3D methodology and, if successful, the simple and fast 2D instrumentation and measurement techniques envisaged could have a considerable economic impact.
1. SAFINA, C., 1995, The world's imperilled fish. Sci. Am. Nov. 1995: 30-37.
2. PAULY, D. & V. CHRISTENSEN, 1995, Primary production required to sustain global fisheries. Nature 374: 255-257.
3. European Commission, 1994, The new common fisheries policy. Luxembourg: Office for Official Publ. of the EC, 46 pp.
4. CASTONGUAY, M., P. SIMARD & P. GAGNON, 1991, Usefulness of Fourier analysis of otolith shape for Atlantic mackerel (Scomber scombrus) stock discrimination. Can. J. Fish Aquat. Sci. 48: 296-302.
5. CAMPANA, S. E. & J. M. CASSELMAN, 1993, Stock discrimination using otolith shape analysis. Can. J. Fish. Aquat. Sci. 50: 1062-1083.
6. FRIEDLAND, K. D. & D. G. REDDIN, 1994, Use of otolith morphology in stock discriminations of Atlantic salmon, Can. J. Fish. Aquat. Sci. 51: 91-98.
7. COLURA, R. L. & T. L. KING, 1995, Using scale and otolith morphologies to separate spotted sea trout (Cynoscion nebulosus) collected from two areas within Galveston Bay. In: Recent developments in fish otolith research, D. H. SECOR, J. M. DEAN & S. E. CAMPANA (eds.), Univ. of South Carolina Press, Columbia, South Carolina, 617-628.
8. MACINTYRE, F. & T. T. NOJI, 1996, Pattern recognition. In: Computers in fisheries research, B. A. MEGREY & E. MOKSNESS (eds.), Chapman & Hall, London, 143-175.
9. DOERING, P., J. LUDWIG & G. GMEL (1992): Preliminary results of otolith shape analysis with eels of known age. - Irish Fish. Invest. Ser. A (Freshw.) 36: 105.
10. APS, R., A. PAAT & Y. O. UDER, 1990, Variability of the Baltic sprat otolith shape. Fisch.-Forsch. Rostock, 28: 32-33.
11. FLETCHER, W. J., 1995, Application of the otolith weight - age relationship for the pilchard, Sardinops sagax neopilchardus. Can. J. Fish. Aquat. Sci. 52: 657-664
12. BARKER, T. M., W. J. EARWAKER, N. FROST & G. WAKELY, 1993, Integration of three-dimensional medical imaging and rapid prototyping to create stereolithographic models. Australas. Phys. Eng. Sci. Med. 16 (2): 79-85.
13. POON, C. Y., R. S. SAYLES & T. A. JONES, 1992, Surface Measurement and Fractal Characterisation of Naturally Fractured Rocks. J. Phys. D., Appl. Phys. 25: 1269-1275.
14. BAILEY, K.M. A.L. BROWN, A. NISHIMURA, & M.T. REILLY, 1995, Three-dimensional imaging of walley pollock otoliths: reconstruction from serial sections and fluorescent laser cytometry, J. Fish Biol. 47: 671-678.
15. LAGARDERE, F., G. CHAUMILLON, R. AMARA, G. HEINEMAN & J. M. LAGO, 1995, Examination of otolith morphology and microstructure using lasar scanning microscopy. In: SECOR, D. H., J. M. DEAN & S. E. CAMPANA (eds.), 1995, Recent developments in fish otolith research. Univ. of South Carolina Press, Columbia, 7-26.
16. COM (93) 95, 1993, European Fisheries Research - Current Position and Prospects. Doc.: CB-CO-93-115-EN-C.
Fields of science
- natural sciencesmathematicspure mathematicsmathematical analysisfourier analysis
- natural sciencesphysical sciencesopticsmicroscopy
- social sciencessociologysocial issuessocial inequalities
- natural sciencescomputer and information sciencesartificial intelligencepattern recognition
- natural sciencesphysical sciencesopticslaser physics
Call for proposalData not available
Funding SchemeCSC - Cost-sharing contracts
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