Predicting fish assemblages and diversity in shallow lakes in the Yangtze River basin

Limnologica

Volumn 42, Issue 2, 2012, Pages 127-136

  Cheng, Lin ^a,b,c   Lek, Sovan ^c   Lek Ang, Sithan ^c   Li, Zhongjie ^a  

^a INSTITUTE OF HYDROBIOLOGY   (China)

^b UNIVERSITY OF CHINESE ACADEMY OF SCIENCES   (China)

^c UNIVERSITÉ DE TOULOUSE   (France)

Author keywords

CART model; Floodplain basin; Indicator species; RF model; Self organizing map

Indexed keywords

BIODIVERSITY; DATA SET; FISH; FRESHWATER ECOSYSTEM; HABITAT; LAKE; MACROPHYTE; SELF ORGANIZATION; SPECIES DIVERSITY; SPECIES RICHNESS; TEMPORAL DISTRIBUTION;

CHINA; YANGTZE BASIN;

CULTER; ODONTOBUTIS; RHINOGOBIUS GIURINUS; ROTIFERA;

EID: 84857795305     PISSN: 00759511     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.limno.2011.09.007     Document Type: Article

References (88)

1. Alhoniemi E., Himberg J., Parhankangas J., Vesanto J. SOM Toolbox 2000, Laboratory of Information and Computer Science, The Helsinki University of Technology.
2. Amarasinghe U., Welcomme R. An analysis of fish species richness in natural lakes. Environ. Biol. Fishes 2002, 65:327-339.
3. Angermeier P., Winston M. Characterizing fish community diversity across Virginia landscapes, prerequisite for conservation. Ecol. Applicat. 1999, 9:335-349.
4. Appelberg M. Swedish standard methods for sampling freshwater fish with multimesh gillnets. Fiskeriverket Inform. 2000, 1:1-32.
5. Archer K., Kimes R. Empirical characterization of random forest variable importance measures. Comput. Stat. Data Anal. 2008, 52:2249-2260.
6. Barbour C., Brown J. Fish species diversity in lakes. Am. Nat. 1974, 73-489.
7. Brazner J., Beals E. Patterns in fish assemblages from coastal wetland and beach habitats in Green Bay, Lake Michigan: a multivariate analysis of abiotic and biotic forcing factors. Can. J. Fish. Aquat. Sci. 1997, 54:1743-1761.
8. Breiman L. Classification and Regression Trees 1984, Chapman & Hall/CRC.
9. Breiman L. Random forests. Machine Learn. 2001, 45:5-32.
10. Breiman, L., 2003. Manual-Setting Up, Using, and Understanding Random Forests V4.0. ftp://ftp.stat.berkeley.edu/pub/users/breiman.
11. Brosse S., Lek S., Townsend C. Abundance, diversity, and structure of freshwater invertebrates and fish communities: an artificial neural network approach. NZ J. Mar. Freshwater Res. 2001, 35:135-145.
12. Caughley G. Directions in conservation biology. J. Anim. Ecol. 1994, 63:215-244.
13. Cheng L., Lek S., Loot G., Lek-Ang S., Li Z. Variations of fish composition and diversity related to environmental variables in shallow lakes in the Yangtze River basin. Aqua. Liv. Resources 2010, 23:417-426.
14. Chon T., Park Y., Moon K., Cha E. Patternizing communities by using an artificial neural network. Ecol. Model. 1996, 90:69-78.
15. Crowder L., Cooper W. Habitat structural complexity and the interaction between bluegills and their prey. Ecology 1982, 63:1802-1813.
16. Crowder A., Painter D. Submerged macrophytes in Lake Ontario: current knowledge, importance, threats to stability, and needed studies. Can. J. Fish. Aquat. Sci. 1991, 48:1539-1545.
17. Cui Y., Li Z. Fishery Resources and Conservation of Environment in Lakes of the Chongjiang River Basin 2005, Science Publishing Company, Beijing.
18. Cutler D., Edwards T., Beard K., Cutler A., Hess K., Gibson J., Lawler J. Random forests for classification in ecology. Ecology 2007, 88:2783-2792.
19. De'ath G., Fabricius K. Classification and regression trees: a powerful yet simple technique for ecological data analysis. Ecology 2000, 81:3178-3192.
20. Diehl S. Fish predation and benthic community structure: the role of omnivory and habitat complexity. Ecology 1992, 73:1646-1661.
21. Dufrêne M., Legendre P. Species assemblages and indicator species definition: the need of an asymmetrical and flexible approach. Ecol. Monogr. 1997, 67:345-366.
22. Dudgeon D., Arthington A., Gessner M., Kawabata Z., Knowler D., Lévêque C., Naiman R., Prieur-Richard A., Soto D., Stiassny M. Freshwater biodiversity: importance, threats, status and conservation challenges. Biol. Rev. 2006, 81:163-182.
23. Dugan J., Davis G. Applications of marine refugia to coastal fisheries management. Can. J. Fish. Aquat. Sci. 1993, 50:2029-2042.
24. Fang J., Rao S., Zhao S. Human-induced long-term changes in the lakes of the Jianghan Plain, Central Yangtze. Front. Ecol. Environ. 2008, 3:186-192.
25. Fischer P., Eckmann R. Seasonal changes in fish abundance, biomass and species richness in the littoral zone of a large European lake, Lake Constance, Germany. Arch. Hydrobiol. 1997, 139:433-448.
26. Fu C., Wu J., Chen J., Wu Q., Lei G. Freshwater fish biodiversity in the Yangtze River basin of China: patterns, threats and conservation. Biodivers. Conserv. 2003, 12:1649-1685.
27. Gilinsky E. The role of fish predation and spatial heterogeneity in determining benthic community structure. Ecology 1984, 65:455-468.
28. Giraudel J., Lek S. A comparison of self-organizing map algorithm and some conventional statistical methods for ecological community ordination. Ecol. Model. 2001, 146:329-339.
29. Giraudoux, P., 2010. Pgirmess: Data Analysis in Ecology. R Package Version 1.4.5.
30. Guisan A., Zimmermann N. Predictive habitat distribution models in ecology. Ecol. Model. 2000, 135:147-186.
31. Harris H., McAllister L., McLaughlin D. Impacts of nutrients, sediment, and turbidity on coastal marshes of Green Bay, Lake Michigan. Great Lakes Wetlands 1991, 2:1-3.
32. He Y., Wang J., Lek S., Cao W., Lek-Ang S. Structure of endemic fish assemblages in the upper Yangtze River Basin. River Res. Applicat. 2009, http://www.interscience.wiley.com/, 10.1002/rra.1339.
33. Heegaard E., Birks H., Gibson C., Smith S., Wolfe-Murphy S. Species-environmental relationships of aquatic macrophytes in Northern Ireland. Aquat. Botany 2001, 70:175-223.
34. Heino J., Muotka T., Mykrä H., Paavola R., Hämäläinen H., Koskenniemi E. Defining macroinvertebrate assemblage types of headwater streams: implications for bioassessment and conservation. Ecol. Applicat. 2003, 13:842-852.
35. Ibarra A., Gevrey M., Park Y., Lim P., Lek S. Modelling the factors that influence fish guilds composition using a back-propagation network: assessment of metrics for indices of biotic integrity. Ecol. Model. 2003, 160:281-290.
36. Joy M., Death R. Predictive modelling and spatial mapping of freshwater fish and decapod assemblages using GIS and neural networks. Freshwater Biol. 2004, 49:1036-1052.
37. - and conductivity. Jpn. J. Ecol. 1982, 32:39-44.
38. Karr J. Assessment of biotic integrity using fish communities. Fisheries 1981, 6:21-27.
39. Kessler W., Salwasser H., Cartwright C., Caplan J. New perspectives for sustainable natural resources management. Ecol. Applicat. 1992, 2:221-225.
40. Kohonen T. Self-Organizing Maps. Springer Series in Information Sciences 2001, Springer, Berlin.
41. Konan F., Leprieur F., Ouattara A., Brosse S., Grenouillet G., Gourène G., Winterton P., Lek S. Spatio-temporal patterns of fish assemblages in coastal West African rivers: a self-organizing map approach. Aquat. Living Resour. 2006, 19:361-370.
42. Kruk A., Lek S., Park Y., Penczak T. Fish assemblages in the large lowland Narew River system (Poland): application of the self-organizing map algorithm. Ecol. Model. 2007, 203:45-61.
43. Lasne E., Bergerot B., Lek S., Laffaille P. Fish zonation and indicator species for the evaluation of the ecological status of rivers: example of the Loire basin (France). River Res. Applicat. 2007, 23:877-890.
44. Liaw A., Wiener M. Classification and Regression by randomForest 2002, R News, pp. 18-22.
45. Margules C., Pressey R. Systematic conservation planning. Nature 2000, 405:243-253.
46. McAllister L. Factors Influencing the Distribution of Submerged Macrophytes in Green Bay, Lake Michigan: A Focus on Light Attenuation and Vallisneria americana 1991, University of Wisconsin-Green Bay.
47. Oh J., Laubach M., Luczak A. Estimating neuronal variable importance with random forest. Proceedings of the 29th Annual Northeast Bioengineering Conference 2003, 33-34. IEEE.
48. Olden J. A species-specific approach to modeling biological communities and its potential for conservation. Conserv. Biol. 2003, 17:854-863.
49. Olden J., Rooney T. On defining and quantifying biotic homogenization. Global Ecol. Biogeogr. 2006, 15:113-120.
50. Olden J., Joy M., Death R. Rediscovering the species in community-wide predictive modeling. Ecol. Applicat. 2006, 16:1449-1460.
51. Park Y., Céréghino R., Compin A., Lek S. Applications of artificial neural networks for patterning and predicting aquatic insect species richness in running waters. Ecol. Model. 2003, 160:265-280.
52. Park Y., Kwak I., Chon T., Kim J., Jørgensen S. Implementation of artificial neural networks in patterning and prediction of exergy in response to temporal dynamics of benthic macroinvertebrate communities in streams. Ecol. Model. 2001, 146:143-157.
53. Perdiguero-Alonso D., Montero F., Kostadinova A., Raga J., Barrett J. Random forests, a novel approach for discrimination of fish populations using parasites as biological tags. Int. J. Parasitol. 2008, 38:1425-1434.
54. Peters J., Baets B., Verhoest N., Samson R., Degroeve S., Becker P., Huybrechts W. Random forests as a tool for ecohydrological distribution modelling. Ecol. Model. 2007, 207:304-318.
55. Petry P., Bayley P., Markle D. Relationships between fish assemblages, macrophytes and environmental gradients in the Amazon River floodplain. J. Fish Biol. 2003, 63:547-579.
56. Phillips D., MacMahon J. Gradient analysis of a Sonoran Desert bajada. Southwestern Nat. 1978, 23:669-679.
57. Prasad A., Iverson L., Liaw A. Newer classification and regression tree techniques: bagging and random forests for ecological prediction. Ecosystems 2006, 9:181-199.
58. R Development Core Team R: A Language and Environment for Statistical Computing 2009, R Foundation for Statistical Computing, Vienna, Austria.
59. Rahel F. Factors structuring fish assemblages along a bog lake successional gradient. Ecology 1984, 65:1276-1289.
60. Rashleigh B. Relation of environmental characteristics to fish assemblages in the upper French broad river basin, North Carolina. Environ. Monit. Assess. 2004, 93:139-156.
61. Razi M., Athappilly K. A comparative predictive analysis of neural networks (NNs), nonlinear regression and classification and regression tree (CART) models. Expert Syst. Applicat. 2005, 29:65-74.
62. Roberts, D., 2010. labdsv: Ordination and Multivariate Analysis for Ecology. R Package Version 1.4-1. http://cran.r-project.org/package=labdsv.
63. Roth N., Allan J., Erickson D. Landscape influences on stream biotic integrity assessed at multiple spatial scales. Landscape Ecol. 1996, 11:141-156.
64. Savino J., Stein R. Predator-prey interaction between largemouth bass and bluegills as influenced by simulated, submersed vegetation. Trans. Am. Fish. Soc. 1982, 111:255-266.
65. Savino J., Stein R. Behavior of fish predators and their prey: habitat choice between open water and dense vegetation. Environ. Biol. Fishes 1989, 24:287-293.
66. Schleiger S. Use of an index of biotic integrity to detect effects of land uses on stream fish communities in west-central Georgia. Trans. Am. Fish. Soc. 2000, 129:1118-1133.
67. Schriver P., Bøgestrand J., Jeppesen E., Søndergaard M. Impact of submerged macrophytes on fish-zooplanktonphytoplankton interactions: large-scale enclosure experiments in a shallow eutrophic lake. Freshwater Biol. 1995, 33:255-270.
68. Seddon B. Aquatic macrophytes as limnological indicators. Freshwater Biol. 1972, 2:107-130.
69. Seghers B. Schooling behavior in the guppy (Poecilia reticulata): an evolutionary response to predation. Evolution 1974, 28:486-489.
70. Simberloff D. Flagships, umbrellas, and keystones: is single-species management passé in the landscape era?. Biol. Conserv. 1998, 83:247-257.
71. Sneath, P., Sokal, R., 1973. Numerical Taxonomy: The Principles and Practice of Numerical Classification. San Francisco, p. 21.
72. Soulé, M., Orians, G., 2001. Conservation Biology: Research Priorities for the Next Decade (Island Pr).
73. Spence D. Factors controlling the distribution of freshwater macrophytes with particular reference to the lochs of Scotland. J. Ecol. 1967, 55:147-170.
74. Stuckey R. Western Lake Erie Aquatic and Wetland Vascular-Plant Flora: Its Origin and Change in Lake Erie Estuarine Systems: Issues, Resources, Status, and Management 1989, National Oceanographic and Atmospheric Administration, Washington, DC.
75. Takamura N., Kadono Y., Fukushima M., Nakagawa M., Kim B. Effects of aquatic macrophytes on water quality and phytoplankton communities in shallow lakes. Ecol. Res. 2003, 18:381-395.
76. Teixeira-de Mello F., Meerhoff M., Pekcan-Hekim Z., Jeppesen E. Substantial differences in littoral fish community structure and dynamics in subtropical and temperate shallow lakes. Freshwater Biol. 2009, 54:1202-1215.
77. The Mathworks Inc., 2001. MATLAB, Version 6.1. The Mathworks, Inc., MA.
78. Therneau, T., Atkinson, B., 2010. rpart: Recursive Partitioning. R Package Version 3.1-46. http://cran.r-project.org/package=rpart.
79. Tonn W., Magnuson J. Patterns in the species composition and richness of fish assemblages in northern Wisconsin lakes. Ecology 1982, 149-1166.
80. Tonn W., Paszkowski C., Holopainen I. Piscivory and recruitment: mechanisms structuring prey populations in small lakes. Ecology 1992, 73:951-958.
81. Walley W., Martin R., O'Connor M. Self-organising maps for the classification of river quality from biological and environmental data. Environmental Software Systems: Environmental Information and Decision Support 2000, Kluwer Academic, Dordrecht, The Netherlands, pp. 27-41.
82. Wong, C., 2007. World's Top 10 Rivers at Risk. Working Papers.
83. Xie P., Chen Y. Threats to biodiversity in Chinese inland waters. Ambio 1999, 67:4-681.
84. Xie S., Cui Y., Li Z. Small fish communities in two regions of the Liangzi Lake, China, with or without submersed macrophytes. J. Appl. Ichthyol. 2001, 17:89-92.
85. Ye S., Li Z., Lek-Ang S., Feng G., Lek S., Cao W. Community structure of small fishes in a shallow macrophytic lake (Niushan Lake) along the middle reach of the Yangtze River, China. Aquat. Living Resour. 2006, 19:349-359.
86. Ye, S., 2007. Studies on fish communities and trophic network model of shallow lakes along the middle reach of Yangtze River. Ph.D. Thesis, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan (in Chinese with English abstract).
87. Zeng X. Fishery Resources of the Yangtze River Basin 1990, Marine Press, Beijing, China, (in Chinese).
88. Zhao S., Fang J., Peng C., Tang Z., Piao S. Patterns of fish species richness in China's lakes. Global Ecol. Biogeogr. 2006, 15:386-394.