Leslie matrix

In applied mathematics, the Leslie matrix is a discrete, age-structured model of population growth that is very popular in population ecology. It was invented by and named after Patrick H. Leslie. The Leslie matrix (also called the Leslie Model) is one of the best known ways to describe the growth of populations (and their projected age distribution), in which a population is closed to migration and where only one sex, usually the female, is considered.

The Leslie Matrix is used in ecology to model the changes in a population of organisms over a period of time. In a Leslie Model, the population is divided into groups based on age classes. A similar model which replaces age classes with life stage is called a Lefkovitch matrix, whereby individuals can both remain in the same stage class or move on to the next one. At each time step the population is represented by a vector with an element for each age classes where each element indicates the number of individuals currently in that class.

The Leslie Matrix is a square matrix with the same number of rows and columns as the population vector has elements. The (i,j)th cell in the matrix indicates how many individuals will be in the age class i at the next time step for each individual in stage j. At each time step, the population vector is multiplied by the Leslie Matrix to generate the population vector for the following time step.

To build a matrix, some information must be known from the population:

The observations that n_0 at time t+1 is simply the sum of all offspring born from the previous time step and that the organisms surviving to time t+1 are the organisms at time t surviving at probability s_x we get n_{x%2B1} = s_xn_x This then motivates the following matrix representation:

\begin{bmatrix} n_0 \\ n_1 \\ \vdots \\ n_{\omega - 1} \\ \end{bmatrix}_{t%2B1} = \begin{bmatrix} f_0 & f_1 & f_2 & f_3 & \ldots &f_{\omega - 1} \\ s_0 & 0 & 0 & 0 & \ldots & 0\\ 0 & s_1 & 0 & 0 & \ldots & 0\\ 0 & 0 & s_2 & 0 & \ldots & 0\\ 0 & 0 & 0 & \ddots & \ldots & 0\\ 0 & 0 & 0 & \ldots & s_{\omega - 2} & 0 \end{bmatrix} \begin{bmatrix} n_0 \\ n_1 \\ \vdots\\ n_{\omega - 1} \end{bmatrix}_{t}

Where \omega is the maximum age attainable in our population.

This can be written as;

\mathbf{n}_{t%2B1} = \mathbf{L}\mathbf{n}_t

or;

\mathbf{n}_{t} = \mathbf{L}^t\mathbf{n}_0

Where \mathbf{n}_t is the population vector at time t and \mathbf{L} is the Leslie matrix.

The characteristic polynomial of the matrix is given by the Euler–Lotka equation.

The Leslie model is very similar to a discrete-time Markov chain. The main difference is that in a Markov model, one would have f_x%2Bs_x=1 for each x, while the Leslie model may have these sums greater or less than 1.

Stable age structure

This age-structured growth model suggests a steady-state, or stable, age-structure and growth rate. Regardless of the initial population size, N_0, or age distribution, the population tends asymptotically to this age-structure and growth rate. It also returns to this state following perturbation. The Euler–Lotka equation provides a means of identifying the intrinsic growth rate. The stable age-structure is determined both by the growth rate and the survival function (i.e. the Leslie matrix). For example, a population with a large intrinsic growth rate will have a disproportionately “young” age-structure. A population with high mortality rates at all ages (i.e. low survival) will have a similar age-structure. Charlesworth (1980) provides further details on the rate and form of convergence to the stable age-structure.

References