A functional approach to diversity profiles
AbstractDiversity plays a central role in ecological theory and its conservation and management are important issues for the wellbeing and stability of ecosystems. The aim of this work is to provide a reliable theoretical framework for performing statistical analysis on ecological diversity by means of the joint use of diversity profiles and functional data analysis. We point out that ecological diversity is a multivariate concept as it is a function of the relative abundances of species in a biological community. For this, several researchers have suggested using parametric families of indices of diversity for obtaining more information from the data. Patil and Taillie introduced the concept of intrinsic diversity ordering which can be determined by using the diversity profile. It may be noted that the diversity profile is a non-negative and convex curve which consists of a sequence of measurements as a function of a given parameter. Thus, diversity profiles can be explained through a process that is described in a functional setting. Recent developments in environmental studies have focused on the opportunity to evaluate community diversity changes over space and/or correlation of diversity with environmental characteristics. For this, we develop an innovative analysis of diversity based on a functional data approach. Whereas conventional statistical methods process data as a sequence of individual observations, functional data analysis is designed to process a collection of functions or curves. Moreover, unconstrained models may lead to negative and/or non-convex estimates for the diversity profiles. To overcome this problem, a transformation is proposed which can be constrained to be non-negative and convex. We focus on some applications showing how functional data analysis provides an alternative way of understanding biological diversity and its interaction with natural and/or human factors. Copyright (c) 2009 Royal Statistical Society.
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Bibliographic InfoArticle provided by Royal Statistical Society in its journal Journal of the Royal Statistical Society: Series C (Applied Statistics).
Volume (Year): 58 (2009)
Issue (Month): 2 ()
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