Optimization of an artificial neural network for identifying fishing set positions from VMS data: An example from the Peruvian anchovy purse seine fishery

Ecological Modelling

Volumn 222, Issue 4, 2011, Pages 1048-1059

  Joo, Rocío ^a,b   Bertrand, Sophie ^a,b   Chaigneau, Alexis ^a,b   Ñiquen, Miguel ^b  

^a INSTITUT DE RECHERCHE POUR LE DÉVELOPPEMENT   (France)

^b INSTITUTO DEL MAR DEL PERÚ   (Peru)

Author keywords

Anchovy purse seine fishery; Artificial neural networks; Fishing set locations; Sensitivity analysis; Vessel Monitoring System

Indexed keywords

ADAPTIVE MANAGEMENT; ANCHOVY PURSE SEINE FISHERY; ARTIFICIAL NEURAL NETWORK; CATCH PER UNIT EFFORTS; ERROR ESTIMATIONS; ESTIMATION ERRORS; FISHING EFFORT; FISHING FLEETS; FISHING SET LOCATIONS; INTERNAL STRUCTURE; PERUVIAN ANCHOVY; SAMPLING DESIGN; SPATIAL BEHAVIORS; SPATIALLY EXPLICIT; SPECIFIC INFORMATION; TRAINING ALGORITHMS; TRUE POSITIVE; VESSEL MONITORING SYSTEMS;

ESTIMATION; FISHING VESSELS; MONITORING; NEURAL NETWORKS; OPTIMIZATION; SENSITIVITY ANALYSIS;

FISHERIES;

ACCURACY ASSESSMENT; ADAPTIVE MANAGEMENT; ALGORITHM; ARTIFICIAL NEURAL NETWORK; DATA SET; ERROR ANALYSIS; FISHING EFFORT; FISHING VESSEL; MONITORING SYSTEM; OPTIMIZATION; PARAMETERIZATION; PURSE SEINING; SAMPLING; SENSITIVITY ANALYSIS; SPATIAL ANALYSIS;

PERU;

ENGRAULIDAE; ENGRAULIS RINGENS;

EID: 78651417952     PISSN: 03043800     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ecolmodel.2010.08.039     Document Type: Article

References (39)

1. Aranda M. Developments on fisheries management in Peru: the new individual vessel quota system for the anchoveta fishery. Fish. Res. 2009, 96(2009):308-312. 10.1016/j.fishres.2008.11.004.
2. Babcock E.A., Pikitch E.K., McAllister M.K., Apostolaki P., Santora C. A perspective on the use of spatialized indicators for ecosystem-based fishery management through spatial zoning. ICES J. Mar. Sci. 2005, 62:469-476. 10.1016/j.icesjms.2005.01.010.
3. Bernardos P.G., Vosniakos G.-C. Optimizing feedforward artificial neural network architecture. Eng. App. Artif. Intell. 2007, 20:365-382.
4. Bertrand A., Segura M., Gutiérrez M., Vásquez L. From small-scale habitat loopholes to decadal cycles: a habitat-based hypothesis explaining fluctuation in pelagic fish populations off Peru. Fish Fish. 2004, 5:296-316.
5. Bertrand S., Díaz E., Ñiquen M. Interactions between fish and fisher's spatial distribution and behavior: the anchovy (Engraulis ringens) fishery of Peru. ICES J. Mar. Sci. 2004, 61:1127-1136.
6. Bertrand S., Burgos J.M., Gerlotto F., Atiquipa J. Lévy trajectories of Peruvian purse seiners as an indicator of the spatial distribution of anchovy (Engraulis ringens). ICES J. Mar. Sci. 2005, 62(3):477-482. 10.1016/j.icesjms.2004.12.002.
7. Bertrand S., Bertrand A., Guevara-Carrasco R., Gerlotto F. Scale-invariant movements of fishermen: the same foraging strategy as natural predators. Ecol. Appl. 2007, 17(2):331-337. 10.1890/06-0303.
8. Bertrand S., Diaz E., Lengaigne M. Patterns in the spatial distribution of Peruvian anchovy (Engraulis ringens) revealed by spatially explicit data. Prog. Oceanogr. 2008, 79(2008):379-389. 10.1016/j.pocean.2008.10.009.
9. Bishop C.M. Neural Networks for Pattern Recognition 1995, Oxford University Press, Inc., New York, NY.
10. Chavez F., Bertrand A., Guevara-Carrasco R., Soler P., Csirke J. The northern Humboldt Current System: Brief history, present status and a view towards the future. Progr. Oceanogr. 2008, 79:95-1051.
11. Chen D.G., Ware D.M. A neural network model for forecasting fish stock recruitment. Can. J. Fish. Aquat. Sci. 1999, 56(12):2385-2396. 10.1139/cjfas-56-12-2385.
12. Cybenko G. Approximation by superpositions of a sigmoidal function. MCSS 1989, 2:303-314. 10.1007/BF02134016.
13. Dedecker A.P., Goethals P.L.M., Gabriels W., De Puw N. Optimization of Artificial Neural Network (ANN) model design for prediction of macroinvertebrates in the Zwalm river basin (Flanders, Belgium). Ecol. Modell. 2004, 174:161-173.
14. Demuth H., Beale M. Neural Network Toolbox for Use with Matlab 1998, The MathWorks, Inc., Natick, MA.
15. Deng R., Dichmont C., Milton D., Haywood M., Vance D., Hall N., Die D. Can vessel monitoring system data also be used to study trawling intensity and population depletion? The example of Australia's northern prawn fishery. Can. J. Fish. Aquat. Sci. 2005, 62(3):611-622. 10.1139/F04-219.
16. Engelhard G.H., Dieckmann U., Godø O.R. Age at maturation predicted from routine scale measurements in Norwegian spring-spawning herring (Clupea harengus) using discriminant and neural network analyses. ICES J. Mar. Sci. 2003, 60(2):304-313. 10.1016/S1054-3139(03)00017-1.
17. Fall M., Samba A., Laloë F. Fishing tactics and strategies in coastal demersal trawling fisheries in Senegal. Aquat. Living Resour. 2006, 19. 10.1051/alr:2007001.
18. Fréon P., Bouchon M., Mullon C., García C., Ñiquen M. Interdecadal variability of anchoveta abundance and overcapacity of the fishery in Peru. Prog. Oceanogr. 2008, 79(2008):401-412. 10.1016/j.pocean.2008.10.011.
19. Goethals P., Dedecker A., Gabriels W., Lek S., De Pauw N. Applications of artificial neural networks predicting macroinvertebrates in freshwaters. Aquat. Ecol. 2007, 41:491-508. 10.1007/s10452-007-9093-3.
20. Hanson P.J., Koenig C.C., Zdanowicz V.S. Elemental composition of otoliths used to trace estuarine habitats of juvenile gag Mycteroperca microlepis along the west coast of Florida. Mar. Ecol. Prog. Ser. 2004, 267:253-265. 10.3354/meps267253.
21. Harrington J.J., Semmens J.M., Haddon M. Spatial distribution of commercial dredge fishing effort: application to survey design and the spatial management of a patchily distributed benthic bivalve species. Mar. Freshwat. Res. 2007, 58:756-764. 10.1071/MF06101.
22. Hornik K., Stinchcombe M., White H. Multilayer feedforward networks are universal approximators. Neural Networks 1989, 2:359-366.
23. Jain A., Mao J. Artificial neural networks: a tutorial. Computer 1996, 29(3):31-44. 10.1109/2.485891.
24. Lek S., Delacoste M., Baran P., Dimopoulos I., Lauga J., Aulagnier S. Application of neural networks to modeling nonlinear relationships in ecology. Ecol. Modell. 1996, 90:39-52. 10.1016/0304-3800(95)00142-5.
25. Lek S., Guégan J.F. Artificial neuronal networks. Application to ecology and evolution. Environmental Sciences 2000, Springer-Verlag, Berlin.
26. Maier H.R., Dandy G.C. Neural networks for the prediction and forecasting of water resources variables: a review of modeling issues and applications. Environ. Model. Software 2000, 15(2000):101-124. 10.1016/S1364-8152(99)00007-9.
27. Mills C.M., Townsend S.E., Jennings S., Eastwood P.D., Houghton C.A. Estimating high resolution trawl fishing effort from satellite-based vessel monitoring system data. ICES J. Mar. Sci. 2007, 64:248-255. 10.1093/icesjms/fsl026.
28. Mühlenbein H. Limitations of multi-layer perceptron networks-steps towards genetic neural networks. Parallel Comput. 1990, 14:249-260.
29. Petitgas, P., 1994. Spatial strategies of fish populations. Statistic committee C.M. 1994/d:14, ICES.
30. Pikitch E.K., Santora C., Babcock E.A., et al. Ecosystem-based fishery management. Science 2004, 305:346-347.
31. Prechelt L. Early stopping-but when?. Neural Networks: Tricks of the Trade 1998, 55-69. Springer-Verlag, London, UK, (Series: Lecture notes in computer science. No. 1524). G.B. Orr, K.-R. Müller (Eds.).
32. Rose G.A., Kulka D.W. Hyperaggregation of fish and fisheries: how catch-per-unit-effort increased as the northern cod (Gadus morhua) declined. Can. J. Fish. Aquat. Sci. 1999, 56(S1):118-127. 10.1139/cjfas-56-S1-118.
33. Sarle W. Stopped training and other remedies for overfitting. Proceedings of the 27th Symposium on the Interface of Computing Science and Statistics 1995, 352-360.
34. Sarle, W., 1997. Neural network FAQ, periodic posting to the Usenet newsgroup comp.ai.neural-nets (online). Available from (accessed 4 February 2008; updated 17 May 2002). http://www.faqs.org/faqs/ai-faq/neural-nets/.
35. Sexton R.S., Dorsey R.E., Johnson J.D. Optimization of neural networks: A comparative analysis of the genetic algorithm and simulated annealing. Eur. J. Op. Res. 1999, 114:589-601.
36. Suryanarayana I., Braibanti A., Rao R.S., Ramam V.A., Sudarsan D., Rao G.N. Neural networks in fisheries resources. Fish. Res. 2008, 92:115-139. 10.1016/j.fishres.2008.01.012.
37. Thorrold S.R., Jones C.M., Swart P.K., Targett T.E. Accurate classification of juvenile weakfish Cynoscion regalis to estuarine nursery areas based on chemical signatures in otoliths. Mar. Ecol. Prog. Ser. 1998, 173:253-265. 10.3354/meps173253.
38. Witt M.J., Godley B.J. A step towards seascape scale conservation: Using vessel monitoring system (VMS) to map fishing activity [online]. PLoS ONE 2007, 2(10):e1111. 10.1371/journal.pone.0001111.
39. Zhang G., Patuwo E., Hu M. Forecasting with artificial neural networks: the state of the art. Int. J. Forecast. 1998, 14:35-62. 10.1016/S0169-2070(97)00044-7.