Estimating abundance of endangered marine benthic species using distance sampling through SCUBA diving: The Pinna nobilis (Mollusca: Bivalvia) example

Endangered Species: New Research

Volumn , Issue , 2009, Pages 81-115

  Katsanevakis, Stelios ^a  

^a INSTITUTE OF OCEANOGRAPHY   (Greece)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 67349279470     PISSN: None     EISSN: None     Source Type: Book    
DOI: None     Document Type: Chapter

References (62)

1. Ackerman, J.L. and Bellwood, D.R. (2000). Reef fish assemblages: a re-evaluation using enclosed rotenone stations. Marine Ecology-Progress Series, 206, 227-237.
2. Akaike, H. (1973). Information theory as an extension of the maximum likelihood principle. In: B.N. Petrov and F. Csaki (Eds.) Proceedings of the second international symposium on information theory. Budapest: Akademiai Kiado; pp. 267-281.
3. Akaike, H. (1983). Information measures and model selection. Bulletin of the International Statistical Institute, 44, 277-290.
4. Borchers, D.L. and Burnham, K.P. (2008). General formulation for distance sampling. In: S.T. Buckland, D.R. Anderson, K.P. Burnham, J.L. Laake, D.L. Borchers and L. Thomas (Eds.) Advanced distance sampling: estimating abundance of biological populations. New York: Oxford University Press; pp. 6-30.
5. Borchers, D.L., Buckland, S.T. and Zucchini, W. (2004). Estimating animal abundance. London: Springer-Verlag.
6. Buckland, S.T., Burnham, K.P. and Augustin, N.H. (1997). Model selection: An integral part of inference. Biometrics, 53(2), 603-618.
7. Buckland, S.T., Goudie, I.B.J. and Borchers, D.L. (2000). Wildlife population assessment: Past developments and future directions. Biometrics, 56(1), 1-12.
8. Buckland, S.T., Anderson, D.R., Burnham, K.P., Laake, J.L., Borchers, D.L. and Thomas, L. (2001). Introduction to distance sampling: Estimating abundance of biological populations. New York: Oxford University Press.
9. Buckland, S.T., Anderson, D.R., Burnham, K.P., Laake, J.L., Borchers, D.L. and Thomas, L. (2008). Advanced distance sampling: Estimating abundance of biological populations. New York: Oxford University Press.
10. Burnham, K.P. and Anderson, D.R. (1984). The need for distance data in transect counts. Journal of Wildlife Management, 48(4), 1248-1254.
11. Burnham, K.P. and Anderson, D. (2002). Model selection and multi-model inference. Berlin: Springer.
12. Burnham, K.P., Buckland, S.T., Laake, J.L., Borchers, D.L., Marques, T.A., Bishop, J. R. B. and Thomas, L. (2008). Further topics in distance sampling. In: S.T. Buckland, D.R. Anderson, K.P. Burnham, J.L. Laake, D.L. Borchers and L. Thomas (Eds.) Advanced distance sampling: estimating abundance of biological populations. New York: Oxford University Press; pp. 307-392.
13. Caddy, J.F. (1968). Underwater observations on scallop (Placopecten magellanicus) behaviour and drag efficiency. Journal of the Fisheries Research Board of Canada, 25(10), 2123-2141.
14. Caddy, J.F. (1989). A perspective on the population dynamics and assessment of scallop fisheries, with special reference to the sea scallop, Placopecten magellanicus Gmelin. In: J.F. Caddy (Ed.) Marine Invertebrate Fisheries: Their Assessment and Management. New York: Wiley; pp. 559-589.
15. Chabanet, P., Dufour, V. and Galzin, R. (1995). Disturbance impact on reef fish communities in reunion island (Indian Ocean). Journal of Experimental Marine Biology and Ecology, 188(1), 29-48.
16. Chapman, C.J. (1980). Ecology of juvenile and adult nephrops. In: S. Cobb and B. Philips (Eds.) The biology and management of lobsters, vol. II. New York: Academic Press; pp. 143-178.
17. De Girolamo, M. and Mazzoldi, C. (2001). The application of visual census on Mediterranean rocky habitats. Marine Environmental Research, 51(1), 1-16.
18. Denny, C.M. and Babcock, R.C. (2004). Do partial marine reserves protect reef fish assemblages? Biological Conservation, 116(1), 119-129.
19. Edgar, G.J. and Barrett, N.S. (1997). Short term monitoring of biotic change in Tasmanian marine reserves. Journal of Experimental Marine Biology and Ecology, 213(2), 261-279.
20. Efron, B. and Tibshirani, R.J. (1993). An introduction to the bootstrap (Monographs on statistics and applied probability). New York: Chapman & Hall/CRC.
21. Ensign, W.E., Angermeier, P.L. and Dolloff, C.A. (1995). Use of line transect methods to estimate abundance of benthic stream fishes. Canadian Journal of Fisheries and Aquatic Sciences, 52(1), 213-222.
22. Galinou-Mitsoudi, S., Vlahavas, G. and Papoutsi, O. (2006). Population study of the protected bivalve Pinna nobilis (Linaeus, 1758) in Thermaikos Gulf (North Aegean Sea). Journal of Biological Research, 5, 47-53.
23. Giguere, M. and Brulotte, S. (1994). Comparison of sampling techniques, video and dredge, in estimating sea scallop (Placopecten magellanicus, Gmelin) populations. Journal of Shellfish Research, 13(1), 25-30.
24. Guidetti, P., Fanelli, G., Fraschetti, S., Terlizzi, A. and Boero, F. (2002). Coastal fish indicate human-induced changes in the Mediterranean littoral. Marine Environmental Research, 53(1), 77-94.
25. Hall-Spencer, J.M., Froglia, C., Atkinson, R.J.A. and Moore, P.G. (1999). The impact of Rapido trawling for scallops, Pecten jacobaeus (L.), on the benthos of the Gulf of Venice. ICES Journal of Marine Science, 56(1), 111-124.
26. Hastie, T.J. and Tibshirani, R.J. (1990). Generalized additive models (Monographs on statistics and applied probability). London: Chapman & Hall/CRC.
27. Hedley, S.L. (2000). Modelling heterogeneity in cetacean surveys, PhD thesis, University of St Andrews, UK.
28. Hedley, S.L. and Buckland, S.T. (2004). Spatial models for line transect sampling. Journal of Agricultural Biological and Environmental Statistics, 9(2), 181-199.
29. Hedley, S.L., Buckland, S.T. and Borchers, D.L. (2008). Spatial distance sampling models. In: S.T. Buckland, D.R. Anderson, K.P. Burnham, J.L. Laake, D.L. Borchers and L. Thomas (Eds.) Advanced distance sampling: estimating abundance of biological populations. New York: Oxford University Press; pp. 48-70.
30. Hilborn, R. and Walters, C.J. (1992). Quantitative fisheries stock assessment: choice, dynamics and uncertainty. New York: Chapman & Hall.
31. Hurvich, C.M. and Tsai, C.L. (1989). Regression and time-series model selection in small samples. Biometrika, 76(2), 297-307.
32. Katsanevakis, S. (2005a). Habitat use by the pearly razorfish, Xyrichtys novacula (Pisces: Labridae). Scientia Marina, 69(2), 223-229.
33. Katsanevakis, S. (2005b). Abundance and spatial distribution of the Mediterranean scallop, Pecten jacobaeus, in a marine lake. Fisheries Research, 76(3), 417-429.
34. Katsanevakis, S. (2006). Population ecology of the endangered fan mussel Pinna nobilis in a marine lake. Endangered Species Research, 1, 51-59.
35. Katsanevakis, S. (2007a). Density surface modeling with line transect sampling as a tool for abundance estimation of marine benthic species: the Pinna nobilis example in a marine lake. Marine Biology, 152(1), 77-85.
36. Katsanevakis, S. (2007b). Growth and mortality rates of the fan mussel Pinna nobilis in Lake Vouliagmeni (Korinthiakos Gulf, Greece): a generalized additive modeling approach. Marine Biology, 152(6), 1319-1331.
37. Katsanevakis, S. and Verriopoulos, G. (2004a). Abundance of Octopus vulgaris on soft sediment. Scientia Marina, 68(4), 553-560.
38. Katsanevakis, S. and Verriopoulos, G. (2004b). Den ecology of Octopus vulgaris Cuvier, 1797, on soft sediment: availability and types of shelter. Scientia Marina, 68(1), 147-157.
39. Katsanevakis, S. and Thessalou-Legaki, M. (2007). First record of Alicia mirabilis (Anthozoa: Actiniaria) from the Aegean Sea and density assessment with distance sampling in a site of high abundance. Marine Biology Research, 3(6), 468-472.
40. Kim, Y.J. and Gu, C. (2004). Smoothing spline Gaussian regression: more scalable computation via efficient approximation. Journal of the Royal Statistical Society Series B-Statistical Methodology, 66, 337-356.
41. Kinne, O. (2006). Protecting nature. Endangered Species Research, 1, 1-2.
42. Kulbicki, M. and Sarramegna, S. (1999). Comparison of density estimates derived from strip transect and distance sampling for underwater visual censuses: a case study of Chaetodontidae and Pomacanthidae. Aquatic Living Resources, 12(5), 315-325.
43. Letourneur, Y., Kulbicki, M. and Labrosse, P. (1998). Spatial structure of commercial reef fish communities along a terrestrial runoff gradient in the northern lagoon of New Caledonia. Environmental Biology of Fishes, 51(2), 141-159.
44. Marques, F.F.C. and Buckland, S.T. (2003). Incorporating covariates into standard line transect analyses. Biometrics, 59(4), 924-935.
45. Marques, F. F. C. and Buckland, S.T. (2008). Covariate models for the detection function. In: S.T. Buckland, D.R. Anderson, K.P. Burnham, J.L. Laake, D.L. Borchers and L. Thomas (Eds.) Advanced distance sampling: estimating abundance of biological populations. New York: Oxford University Press; pp. 31-47.
46. McCullagh, P. and Nelder, J.A. (1989). Generalized linear models, second edition (Monographs on statistics and applied probability). London: Chapman & Hall/CRC.
47. McLoughlin, R.J., Young, P.C., Martin, R.B. and Parslow, J. (1991). The Australian scallop dredge -estimates of catching efficiency and associated indirect fishing mortality. Fisheries Research, 11(1), 1-24.
48. Pet-Soede, C., van Densen, W.L.T., Pet, J.S. and Machiels, M.A.M. (2001). Impact of Indonesian coral reef fisheries on fish community structure and the resultant catch composition. Fisheries Research, 51(1), 35-51.
49. Pink, M., Pratt, T.C. and Fox, M.G. (2007). Use of underwater visual distance sampling for estimating habitat-specific population density. North American Journal of Fisheries Management, 27(1), 246-255.
50. R Development Core Team. (2007). R: A language and environment for statistical computing. R Foundation for Statistical Computing: Vienna, Austria. ISBN 3-900051-07-0, http://www.R-project.org.
51. Seber, G.A.F. (1982). The estimation of animal abundance and related parameters. New York: Macmillan.
52. Shafee, M.S. (1979). Underwater observation to estimate the density and spatial distribution of black scallop, Chlamys varia (L.) in Lanveoc (Bay of Brest). Bulletin Off. Natl. Pêches (Tunisie), 3, 143-156.
53. Thomas, L., Laake, J.L., Rexstad, E., Strindberg, S., Marques, F.F.C., Buckland, S.T., Borchers, D.L., Anderson, D.R., Burnham, K.P., Burt, M.L., Hedley, S.L., Pollard, J.H., Bishop J.R.B. and Marques, T.A. (2006). Distance 6.0. Release Beta 3. Research Unit for Wildlife Population Assessment, University of St. Andrews: St. Andrews, UK. http://www.ruwpa.st-and.ac.uk/distance/.
54. Thompson, W.L. (2004). Sampling rare or elusive species: concepts, designs, and techniques for estimating population parameters. Washington: Island Press.
55. Thompson, W.L., White, G.C. and Gowan, C. (1998). Monitoring vertebrate populations. San Diego: Academic Press.
56. Thresher, R.E. and Gunn, J.S. (1986). Comparative-analysis of visual census techniques for highly mobile, reef-associated piscivores (Carangidae). Environmental Biology of Fishes, 17(2), 93-116.
57. Tuck, I.D., Chapman, C.J., Atkinson, R.J.A., Bailey, N. and Smith, R.S.M. (1997). A comparison of methods for stock assessment of the Norway lobster, Nephrops norvegicus, in the Firth of Clyde. Fisheries Research, 32(1), 89-100.
58. Tuya, F.C., Soboil, M.L. and Kido, J. (2000). An assessment of the effectiveness of Marine Protected Areas in the San Juan Islands, Washington, USA. ICES Journal of Marine Science, 57(4), 1218-1226.
59. Williams, B.K., Nichols, J.D. and Conroy, M.J. (2002). Analysis and management of animal populations. San Diego: Academic Press.
60. Willis, T.J. (2001). Visual census methods underestimate density and diversity of cryptic reef fishes. Journal of Fish Biology, 59(5), 1408-1411.
61. Wood, S.N. (2000). Modelling and smoothing parameter estimation with multiple quadratic penalties. Journal of the Royal Statistical Society Series B-Statistical Methodology, 62, 413-428.
62. Wood, S. (2006). Generalized additive models: An introduction with R (Texts in Statistical Science). Florida: Chapman & Hall/CRC.