Predicting species diversity in tropical forests
Author(s) -
Joshua B. Plotkin,
Matthew D. Potts,
Douglas W. Yu,
Sarayudh Bunyavejchewin,
Richard Condit,
Robin B. Foster,
Stephen P. Hubbell,
James V. LaFrankie,
N. Manokaran,
Lee Hua Seng,
Raman Sukumar,
Martin A. Nowak,
Peter S. Ashton
Publication year - 2000
Publication title -
proceedings of the national academy of sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.011
H-Index - 771
eISSN - 1091-6490
pISSN - 0027-8424
DOI - 10.1073/pnas.97.20.10850
Subject(s) - ecology , exponent , tropical forest , species diversity , similarity (geometry) , power law , scale (ratio) , ecosystem , geography , mathematics , biology , statistics , computer science , cartography , image (mathematics) , artificial intelligence , philosophy , linguistics
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 </=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.
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom