“Why shouldn’t an ecosystem be just as beautifully perfect as an ideal gas, and why can’t ecologists have as much predicting power as a physicist? The answers to these questions just might be “it is,” and “they can.” But only when viewed from a particular perspective.”
“In the 1980s, two ecologists, Jim Brown at the University of New Mexico and Brian Maurer at Brigham Young University, coined the term macroecology, which gave a name and intellectual home to researchers searching for emergent patterns in nature.”
“Brown and Maurer had been influenced heavily by regularities in many ecological phenomena. One of these, called the species-area curve, was discovered back in the 19th century, and formalized in 1921. That curve emerged when naturalists counted the number of species (of plants, insects, mammals, and so on) found in plots laid out in backyards, savannahs, and forests.”
“They discovered that the number of species increased with the area of the plot, as expected. But as the plot size kept increasing, the rate of increase in the number of species began to plateau. Even more remarkable, the same basic species-area curve was found regardless of the species or habitat. To put it mathematically, the curve followed a power law, in which the change in species number increased proportionally to the square root of the square root of the area.”
“Power laws are common in science, and are the defining feature of universality in physics. They describe the strength of magnets as temperature increases, earthquake frequency versus size, and city productivity as a function of population. For many ecologists, the species-area curve strikes a nerve. It suggests that at a large enough scale, the specific detail of an ecosystem—the “entangled bank” that lies so near and dear to the ecologist’s heart—simply doesn’t matter. The idiosyncrasies wash out, and ecological systems start to look surprisingly similar to a broad swathe of disparate systems in other sciences.”
“Through a combination of analytical equations and computer simulations, his model, called the unified neutral theory of biodiversity, predicted a species-area curve that looked surprisingly realistic. Its success was built on this brutally simplified version of real ecosystems, with plants, animals, and organisms replaced by nearly identical statistical placeholders.”
“John Harte, wondered if the species-area curve could be understood with even less ecological mechanism than the neutral theory supposed. Harte developed the maximum entropy theory of ecology, based around ideas taken from thermodynamics and information theory.”
“Entropy is a measure of the disorder of a system, and is used in thermodynamics to calculate the most likely arrangement of identical gas molecules in a fixed volume. More disorder usually wins. By playing with the spatial distribution of species under certain constraints, Harte used maximum entropy theory to predict the number of tree species across the entire Western Ghats mountain range in India. Their estimate, published in Ecology Letters, fell within a respectable 10 percent of some 900 types of counted trees.2 Harte wasn’t considering the details of individual trees and their reproduction or seed dispersal—his work was purely driven by principles from the realm of information theory.”
“By ignoring the details of how species compete with and differ from each other, maximum entropy and neutral theory transform the messy, complex tangle of an ecosystem into the idealized perfection of an ideal gas. In doing so, they let ecologists gain a physics-like ability to predict and explain. But both models are also controversial. These details are precisely what ecologists can spend a lifetime investigating, and here are two theories suggesting that they don’t matter.”
“What if these spatial predictions were correct, but for the wrong reasons? Maybe general ecological models do need to include real ecological detail and mechanisms, and models that ignore these details but still succeed are flukes, restricted just to spatial patterns like the species-area curve. One way to find out was to try to extend these models to another dimension. I chose time.”
“When we plotted average evolutionary distance against species number, we found the power law lurking in yet another dimension of ecology: The distance increased rapidly at first, then began to slow in the same manner as the species-area curve.3 The reasons for this behavior are not clear at the moment. One possibility is that both spatial and temporal scaling behaviors are affected by a “burstiness,” in which periods of stasis are punctuated by rapid periods of diversification. In our bacterial trees we found that these bursty expansions have a fractal distribution, also described by a power law, and they could point to radiations of species through both time and space.”