A functional approach to diversity profiles
Stefano A. Gattone and
Tonio Di Battista
Journal of the Royal Statistical Society Series C, 2009, vol. 58, issue 2, 267-284
Abstract:
Summary. Diversity 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.
Date: 2009
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)
Downloads: (external link)
https://doi.org/10.1111/j.1467-9876.2009.00646.x
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:bla:jorssc:v:58:y:2009:i:2:p:267-284
Ordering information: This journal article can be ordered from
http://ordering.onli ... 1111/(ISSN)1467-9876
Access Statistics for this article
Journal of the Royal Statistical Society Series C is currently edited by R. Chandler and P. W. F. Smith
More articles in Journal of the Royal Statistical Society Series C from Royal Statistical Society Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().