Ecological estimates of the number of species at large scales come from a hypothetical curve based on fractals, which predicts that the number of species will increase with area, but increase more slowly for larger and larger areas - a power-law rise with the number of species proportional to the power of the area.
"You can sample an area and count the number of species, and then double the area and find more species, but not twice as many, because the species overlap," Harte said.
He and colleague Jessica Green showed in 2003 that the theory of fractals, which posits that physical patterns such as the distribution of plants look similar on small and large scales, does not explain species richness over large areas. In addition, experimental tests of the species-area relationship showed that the curve has to be tweaked for every class of organisms and habitat studied.
Harte and colleagues Adam Smith of UC Berkeley and David Storch of Charles University in Prague, Czech Republic, decided to approach the problem from the perspective of information theory, which has provided key insights into thermodynamics and statistical mechanics.
In their report, they say that maximizing the information entropy - making full use of what is known from small plots without assuming anything about the unknown, larger areas - "provided a formal and robust derivation of the relationship between number of species and area."
The method not only scales up from measurements in small plots to provide more precise estimates of the number of species over large areas, but provides a universal species-area relationship, Harte emphasized.
"People have been finding different curves when looking at different organisms or in different habitats, but in
|Contact: Robert Sanders|
University of California - Berkeley