Abstract
Biodiversity studies in ecology often begin with the fitting and documentation of sampling data. This study is conducted to make function approximation on sampling data and to document the sampling information using artificial neural network algorithms, based on the invertebrate data sampled in the irrigated rice field.
Three types of sampling data, i.e., the curve species richness vs. the sample size, the curve rarefaction, and the curve mean abundance of newly sampled species vs.the sample size, are fitted and documented using BP (Backpropagation) network and RBF (Radial Basis Function) network. As the comparisons, The Arrhenius model, and rarefaction model, and power function are tested for their ability to fit these data. The results show that the BP network and RBF network fit the data better than these models with smaller errors.
BP network and RBF network can fit non-linear functions (sampling data) with specified accuracy and don't require mathematical assumptions. In addition to the interpolation, BP network is used to extrapolate the functions and the asymptote of the sampling data can be drawn. BP network cost a longer time to train the network and the results are always less stable compared to the RBF network. RBF network require more neurons to fit functions and generally it may not be used to extrapolate the functions. The mathematical function for sampling data can be exactly fitted using artificial neural network algorithms by adjusting the desired accuracy and maximum iterations. The total numbers of functional species of invertebrates in the tropical irrigated rice field are extrapolated as 140 to 149 using trained BP network, which are similar to the observed richness.
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Brown, K. S. Jr.: 1991, Conservation of Neotropical insects: Insects as indicators. In: Collins, N. M., Thomas, J. A. (eds.), The Conservation of Insects and Their Habitats. Academic Press, London, pp.349–404.
Bunge, J., Fitzpatrick, M.: 1993, Estimating the number of species: a review. Journal of American Statistician Association, 88, 364–373.
Cohen, J. E.: 1978, Food webs and niche space. Monographs in Population Biology 11, Princeton University Press, Princeton, USA.
Cohen, J. E. et al.: 1993, Improving food webs. Ecology, 74, 252–258.
Coleman, B. D., Mares, M. A., Willig, M. R., Hsieh, Y. H.: 1982, Randomness, area, and species richness. Ecology, 63, 1121–1133.
Colwell, R. K., Coddington, J. A.: 1994. Estimating terrestrial biodiversity through extrapolation. Phil. Trans. Roy. Soc. London B, 345, 101–108.
Gotelli, N. J., Graves, G. R.: 1996, Null Models in Ecology. Smithsonian Institution Press, Washington, D.C., USA.
Hagan, M. T., Demuth, H. B., Beale, M. H.: 1996, Neural Network Design. PWS Publishing Company.
Heong, K. L., Aquino, G. B., Barrion, A. T.: 1991, Invertebrate community structures of rice ecosystems in the Philippines. Bulletin of Entomological Research, 81, 407–416.
Hurlbert, S. H.: 1971, The concept of species diversity: a critique and alternative parameters. Ecology, 52, 577–585.
James, F. C., Rathbun, S.: 1981, Rarefaction, relative abundance, and diversity of avian communities. Auk, 98, 785–800.
Krebs, C. J.: 1989, Ecological Methodology. HarperCollinsPublishers, New York, USA, pp. 1–654.
Kremen, C., Colwell, R. K., Erwin, T. L., Murphy, D. D.: 1993, Invertebrate assemblges: their use as indicators in conservation planning. Conservation Biology, 7, 796–808.
Mathworks, 2002. Neural Network Toolbox, MATLAB 6.5.
May, R. M.: 1981, Patterns in multi-species communities. In: May, R.M. (ed.) Theoretical Ecology. Sinauer Associates, Sunderland, MASS., USA, pp. 197–227.
Miller, R. J., White, P. S.: 1986, Condiderations for preserve design based on the distribution of rare plants in Great Smoky Mountains National Park. U.S.A. Journal of Environmental Management, 10, 119–124.
Miller, R. J., and R. G. Wiegert.: 1989, Documenting completeness, species-area relations, and the species-abundance distribution of a regional flora. Ecology, 1, 16–22.
Moreno, C. E., Halffter, G.: 2000, Assessing the completeness of bat biodiversity inventories using species accumulation curves. Journal of Applied Ecology, 37, 149–158.
Ooi, P., Shepard, M.: 1994, Predators and parasitoids of rice insect pests. In: Pedigo, L. P., Buntin, G. D. (eds.), Handbook of Sampling Methods for Arthropods in Agriculture. CRC Press, Boca Raton, Fla., USA.
Sanders, H. L.: 1968, Marine benthic diversity: a comparative study. Am. Nat., 102, 243–282.
Schoenly, K. G., Cohen, M. B., Barrion, A. T., Zhang, W. J., Gaolach, B., Viajante, V. D.: 2003, Effects of Bacillus thuringiensison non-target herbivore and natural enemy assemblages in tropical irrigated rice. Environment and Biosafety Research, 3, 181–206.
Schoenly, K. G., Cohen, J. E., Heong, K. L., Litsinger, J. A., Aquino, G. B., Barrion, A. T., Arida, G.: 1996, Food web dynamics of irrigated rice fields at five elevations in Luzon, Philippines. Bulletin of Entomological Research, 86, 451–466.
Schoenly, K. G., Justo, J. D. Jr., Barrion, A. T., Harris, M. K., Bottrell, D. G.: 1998, Analysis of invertebrate biodiversity in a Philippine farmer's irrigated rice field. Environmental Entomology, 27, 1125–1136.
Schoenly, K. G., Zhang, W. J.: 1999, IRRI Biodiversity Software Series. V. RARE, SPPDISS, and SPPANK: programs for detecting between-sample difference in community structure. IRRI Technical Bulletin No.5. International Rice Research Ins titute, Manila, Philippines, pp. 1–17.
Shahid, S. A., Schoenly, K. G., Haskell, N. H., Hall, R. D., Zhang, W. J.: 2003, Carcass enrichment does not alter decay rates or arthropod community structure: a test of the arthropod saturation hypothesis at the anthropology research facility in Knoxville, Tennessee. Journal of Medical Entomology, 4, 559–569.
Shannon, C. E., Weaver, W.: 1963, The Mathematical Theory of Communication. University of Illinois Press, Urbana, Illinois, USA.
Simberloff, D.: 1972, Properties of the rarefaction diversity measurements. Am. Nat., 196, 414–418.
Simberloff, D.: 1978, Rarefaction as a distribution-free method of expressing and estimating diversity. In: Grassle, J. F., Patil, G. P., Smith, W. K., Taillie, C. (eds.), Ecological Diversity in Theory and Practice. International Cooperative Publishing House, Fairland, Md., USA, pp. 159–176.
Steele, B. B., Bayn, R. L. Jr., ValGrant, C.: 1984. Environmental monitoring using populations of birds and small mammals: analysis of sampling effort. Biological Conservation, 30, 157–172.
Sutherland, W. J.: 1996, Ecological Census Techniques. Cambridge University Press, Cambridge, UK.
Way, M. J., Heong, K. L.: 1994, The role of biodiversity in the dynamics and management of insect pests of tropical irrigated rice-a review. Bulletin of Entomological Research, 84, 567–587.
Zhang, W. J., Schoenly, K. G.: 1999, IRRI Biodiversity Software Series. II. COLLECT1 and COLLECT2: programs for calculating statistics of collectors' curves. IRRI Technical Bulletin No.2. International Rice Research Institute, Manila, Philippines, pp. 1–15.
Zhang, W. J., Schoenly, K. G.: 2001, A randomization test and software to compare ecological communities. International Rice Research Notes, 2, 48–49.
Zhang, W. J., Qi, Y. H.: 2002, Functional link artificial neural network and agri-biodiversity analysis. Biodiversity Science, 3, 345–350.
Zhang, W. J., Qi, Y. H., Schoenly, K. G.: 2002, Randomization tests and computational software on significance of community biodiversity and evenness. Biodiversity Science, 4, 431–437.
Zhang, W. J., Qi, Y. H., Barrion, A. T.: 2004, Neural network approximation of sampling yield-effort curves of rice invertebrates. International Rice Research Notes, 2: 34–36.
Zhang, W. J., Schoenly, K. G.: 2004, Lumping and correlation analyses of invertebrate taxa in tropical irrigated rice field. International Rice Research Notes, 1: 41–43.
Zhao, Z. M., Guo, Y. Q.: 1990, Community Ecology: Principles and Methods. Chongqing Branch Press of Science and Technology Literature Press, Chongqing, China, pp. 129–130.
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Zhang, W., Barrion, A. Function Approximation and Documentation of Sampling Data Using Artificial Neural Networks. Environ Monit Assess 122, 185–201 (2006). https://doi.org/10.1007/s10661-005-9173-6
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DOI: https://doi.org/10.1007/s10661-005-9173-6