Predicting species diversity in tropical forests

Plotkin, Joshua B and Potts, Matthew D and Yu, Douglas W and Bunyavejchewin, Sarayudh and Condit, Richard and Sukumar, Raman and Foster, Robin B and Hubbell, Stephen and LaFrankie, James and Manokaranj, N and Leek, Hua-Seng and Nowaka, Martin A and Ashtonm, Peter S (2000) Predicting species diversity in tropical forests. In: Proceedings of The National Academy of Sciences of The United States of America, 97 (20). pp. 10850-10854.

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Abstract

A fundamental question in ecology is how many species occur within a given area. Despite the complexity and diversity of different ecosystems. there exists a surprisingly simple, approximate answer: the number of species is proportional to the size of the area raised to some exponent. The exponent often turns out to be roughly 1/4. This power law can be derived from assumptions about the relative abundances of species or from notions of self-similarity. Here we analyze the largest existing data set of location-mapped species: over one million, individually identified trees from five tropical forests on three continents. Although the power law is a reasonable, zeroth-order approximation of our data, we find consistent deviations from it on all spatial scales. Furthermore, tropical forests are not self-similar at areas less \leq 50 hectares. We develop an extended model of the species-area relationship, which enables us to predict large-scale species diversity from small-scale data samples more accurately than any other available method.

Item Type:	Journal Article
Publication:	Proceedings of The National Academy of Sciences of The United States of America
Publisher:	National Academy of Sciences of The United States of America
Additional Information:	Copyright of this article belongs to National Academy of Sciences of The United States of America.
Department/Centre:	Division of Biological Sciences > Centre for Ecological Sciences
Date Deposited:	04 Nov 2009 09:45
Last Modified:	19 Sep 2010 05:00
URI:	http://eprints.iisc.ac.in/id/eprint/18101

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