<![CDATA[Maximum Entropy and Ecology]]>

<![CDATA[Maximum Entropy and Ecology]]>I already talked about John Harte’s book on how to stop global warming. Since I’m trying to apply information theory and thermodynamics to ecology, I was also interested in this book of his:

• John Harte, Maximum Entropy and Ecology, Oxford U. Press, Oxford, 2011.

There’s a lot in this book, and I haven’t absorbed it all, but let me try to briefly summarize his maximum entropy theory of ecology. This aims to be “a comprehensive, parsimonious, and testable theory of the distribution, abundance, and energetics of species across spatial scales”. One great thing is that he makes quantitative predictions using this theory and compares them to a lot of real-world data. But let me just tell you about the theory.

It’s heavily based on the principle of maximum entropy (MaxEnt for short), and there are two parts:

Two MaxEnt calculations are at the core of the theory: the first yields all the metrics that describe abundance and energy distributions, and the second describes the spatial scaling properties of species’ distributions.

Abundance and energy distributions

The first part of Harte’s theory is all about a conditional probability distribution

$R(n,\epsilon | S_0, N_0, E_0)$

which he calls the ecosystem structure function. Here:

• $S_0$ : the total number of species under consideration in some area.

• $N_0$ : the total number of individuals under consideration in that area.

• $E_0$ : the total rate of metabolic energy consumption of all these individuals.

Given this,

$R(n,\epsilon | S_0, N_0, E_0) \, d \epsilon$

is the probability that given $S_0, N_0, E_0,$ if a species is picked from the collection of species, then it has $n$ individuals, and if an individual is picked at random from that species, then its rate of metabolic energy consumption is in the interval $(\epsilon, \epsilon + d \epsilon).$

Here of course $d \epsilon$ is ‘infinitesimal’, meaning that we take a limit where it goes to zero to make this idea precise (if we’re doing analytical work) or take it to be very small (if we’re estimating $R$ from data).

I believe that when we ‘pick a species’ we’re treating them all as equally probable, not weighting them according to their number of individuals.

Clearly $R$ obeys some constraints. First, since it’s a probability distribution, it obeys the normalization condition:

$\displaystyle{ \sum_n \int d \epsilon \; R(n,\epsilon | S_0, N_0, E_0) = 1 }$

Second, since the average number of individuals per species is $N_0/S_0,$ we have:

$\displaystyle{ \sum_n \int d \epsilon \; n R(n,\epsilon | S_0, N_0, E_0) = N_0 / S_0 }$

Third, since the average over species of the total rate of metabolic energy consumption of individuals within the species is $E_0/ S_0,$ we have:

$\displaystyle{ \sum_n \int d \epsilon \; n \epsilon R(n,\epsilon | S_0, N_0, E_0) = E_0 / S_0 }$

Harte’s theory is that $R$ maximizes entropy subject to these three constraints. Here entropy is defined by

$\displaystyle{ - \sum_n \int d \epsilon \; R(n,\epsilon | S_0, N_0, E_0) \ln(R(n,\epsilon | S_0, N_0, E_0)) }$

Harte uses this theory to calculate $R,$ and tests the results against data from about 20 ecosystems. For example, he predicts the abundance of species as a function of their rank, with rank 1 being the most abundant, rank 2 being the second most abundant, and so on. And he gets results like this:

The data here are from:

• Green, Harte, and Ostling’s work on a serpentine grassland,

• Luquillo’s work on a 10.24-hectare tropical forest, and

• Cocoli’s work on a 2-hectare wet tropical forest.

The fit looks good to me… but I should emphasize that I haven’t had time to study these matters in detail. For more, you can read this paper, at least if your institution subscribes to this journal:

• J. Harte, T. Zillio, E. Conlisk and A. Smith, Maximum entropy and the state-variable approach to macroecology, Ecology 89 (2008), 2700–2711.

Spatial abundance distribution

The second part of Harte’s theory is all about a conditional probability distribution

$\Pi(n | A, n_0, A_0)$

This is the probability that $n$ individuals of a species are found in a region of area $A$ given that it has $n_0$ individuals in a larger region of area $A_0.$

$\Pi$ obeys two constraints. First, since it’s a probability distribution, it obeys the normalization condition:

$\displaystyle{ \sum_n \Pi(n | A, n_0, A_0) = 1 }$

Second, since the mean value of $n$ across regions of area $A$ equals $n_0 A/A_0,$ we have

$\displaystyle{ \sum_n n \Pi(n | A, n_0, A_0) = n_0 A/A_0 }$

Harte’s theory is that $\Pi$ maximizes entropy subject to these two constraints. Here entropy is defined by

$\displaystyle{- \sum_n \Pi(n | A, n_0, A_0)\ln(\Pi(n | A, n_0, A_0)) }$

Harte explains two approaches to use this idea to derive ‘scaling laws’ for how $n$ varies with $n$ . And again, he compares his predictions to real-world data, and get results that look good to my (amateur, hasty) eye!

I hope sometime I can dig deeper into this subject. Do you have any ideas, or knowledge about this stuff?

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