Page 217 - Special Topic Session (STS) - Volume 2
P. 217

STS486 Tonio D.B. et al.
            4.  Discussion and Conclusion
                This paper focuses on the multivariate nature of biodiversity and aims to
            provide  a  new  methodology  for  overcoming  the  issues  of  the  classical
            indicators  in  a  functional  framework.  Specifically,  we  have  proposed  a
            functional approach to diversity profiles taking into account the constrained
            nature of these data. Then, an inferential approach to diversity profile mean
            estimator is considered. We emphasize the usefulness of the FDA approach to
            overcome some drawbacks typical of an inferential approach for the diversity
            profile based on the abundance vector. The main advantage derives from the
            fact that the profile is a function, that is, a single variable observed on a sample
            unit, rather than a multivariate vector. Moreover, FDA consents an in-deep
            evaluation  of  the  profile  curves  behaviour  through  the  reference  domain,
            showing  different  aspects  of  diversity  as  the  emphasis  shifts  from  rare  to
            common categories. The final goal of this research is to provide Ecologists,
            policymakers, and scholars with additional tools for evaluate biodiversity and
            detect areas with high environmental risk.

            References
            1.  Barabesi, L. and Fattorini, L. Design-Based Approaches for Inference on
                 Diversity, 189-195. Dordrecht: Springer Netherlands, 1998
            2.  J. Burger, M. Gochfeld, C. Powers, J. Clarke, K. Brown, D. Kosson, L. Niles,
                 A. Dey, C. Jeitner, and T. Pitt_eld. Determining environmental impacts for
                 sensitive species: Using iconic species as bioindicators for management
                 and policy. Journal of Environmental Protection, 4:87-95, 2013.
            3.  A. Cuevas, M. Febrero-Bande, R. Fraiman. On the use of bootstrap for
                 estimating functions with functional data. Computational Statistics &
                 Data Analysis 51(2):1063-1074, 2006
            4.  Di Battista, T. and Fortuna, F. Functional confidence bands for lichen
                 biodiversity profiles: A case study in Tuscany region (central Italy).
                 Statistical Analysis and Data Mining: The ASA Data Sci Journal, 10, 21-28,
                 2017.
            5.  T. Di Battista, F. Fortuna, and F. Maturo. Environmental monitoring
                 through functional biodiversity tools. Ecological Indicators, 60:237-247,
                 2016.
            6.  T. Di Battista, S.A. Gattone. Multivariate bootstrap confidence regions for
                 abundance vector using data depth. Environmental and Ecological
                 Statistics, 11, 355-365, 2004.
            7.  Fattorini, L. and Marcheselli, M. Inference on intrinsic diversity profiles of
                 biological populations. Environmetrics, 10, 589-599, 1999.
            8.  M. Febrero-Bande and M. de la Fuente. Statistical computing in
                 functional data analysis: The r package fda.usc. Journal of Statistical
                 Software, Articles, 51(4):1-28, 2012.

                                                               206 | I S I   W S C   2 0 1 9
   212   213   214   215   216   217   218   219   220   221   222