Parametric functional analysis of variance for fish biodiversity

Tonio Di Battista, Francesca Fortuna, Fabrizio Maturo


The conservation and restoration of biodiversity in the marine environment is a crucial aspect of fishing and related activities. Human activities cause changes in fish population and deep transformation in the type and quality of the water. Fishing, restocking and pollution often bring to reduction and distribution changes of  indigenous fish species to the benefit of the diffusion of exotic species. In this context protection and management of water environments become a primary objective. Therefore it is necessary to implement initiatives for protecting and restoring the quality and integrity of native species. Any decision-making process must be based on a careful analysis of the collected data. In this paper we propose a parametric functional approach to study the biodiversity in marine environment.

Full Text:

 Subscribers Only


Burger, J., Gochfeld, M., Powers, C., Clarke, J., Brown, K., Kosson, D., Niles, L., Dey, A., Jeitner, C., Pittfield, T. (2013). Determining environmental impacts for sensitive species: Using iconic species as bioindicators for management and policy. Journal of Environmental Protection 4, 87–95.

De Sanctis, A., Di Battista, T. (2012). Functional analysis for parametric families of functional data. International Journal of Bifurcation and Chaos 22 (9), 1250226–1–1250226–6.

Di Battista, T., Fortuna, F. (2013). Assessing biodiversity profile through fda. Statistica 1, 69–85.

Ferraty, F., Vieu, P. (2006). Nonparametric functional data analysis. Springer, New York.

Gattone, S., Di Battista, T. (2009). A functional approach to diversity profiles. Journal of the Royal Statistical Society 58, 267–284.

Gove, J., Patil, G., Swindel, D., Taillie, C. (1994). Ecological diversity and forest management. In: Patil, G., Rao, C. (Eds.), Handbook of Statistics, vol.12, Environmental Statistics. Elsevier, Amsterdam, pp. 409–462.

Henderson, B. (2006). Exploring between site differences in water quality trends: a functional data analysis approach. Environmetrics 17, 65–80.

Heywood, V., Watson, R. (1995). Global biodiversity assessment. Cambridge University Press, Cambridge, UK.

Hill, M. (1973). Diversity and evenness: a unifying notation and its consequences. Ecology 54, 427–432.

Huet, M. (1949). Apercu des relations entre la pente et les populations piscicoles des eaux courantes. Schweitz. Zeitschr. Hydrol. 11, 333–351.

Paoletti, M. (1999). Using bioindicators based on biodiversity to assess landscape sustainability. Agriculture, Ecosystems and Environment 74, 1–18.

Patil, G., Taillie, C. (1979). An overview of diversity. In: Grassle, J., Patil, G., Smith, W., Taillie, C. (Eds.), Ecological Diversity in Theory and Practice. International Co-operative Publishing House, Fairland, MD, pp. 23–48.

Patil, G., Taillie, C. (1982). Diversity as a concept and its measurement. Journal of the American Statistical Association 77, 548–567.

Pavoine, S., Doledec, S. (2005). The apportionment of quadratic entropy: a useful alternative for partitioning diversity in ecological data. Environmental and Ecological Statistics 12, 125–138.

Ramsay, J., Silverman, B. (2005). Functional Data Analysis, 2nd edn. Springer, New York.

Rudin, W. (2006). Real and complex analysis. McGraw-Hill, New York.

Shannon, C. (1948). A mathematical theory of communication. Bell Syst. tech. J. 27, 379–423.

Simpson, E. (1949). Measurement of diversity. Nature 163, 688.


  • There are currently no refbacks.