*What it means**N*is population density and

*t*is time. This is the simplest model of population growth and assumes that the per capita growth rate, i.e., the difference between per capita birth and death rates, is a constant,

*r*, often referred to as the intrinsic (per capita) growth rate. The solution of the differential equation above is

where

*N*(0) is the population density at time zero. If

*r*> 0 the population will grow to infinity, whereas if

*r*< 0 it will decline towards zero.

**Implications and importance**The 18th century reverend Thomas Malthus is often cited as the founder of the exponential growth model. This model is sometimes referred to as the exponential law (Turchin 2003); it certainly has similarities with the law of intertia in physics, and it is generally considered to be the first principle of population dynamics (e.g. Ginzburg 1986; Berryman 1999). Since it describes the dynamics of a population in a constant environment with no forces acting upon it, it efficiently serves as a starting point for more detailed models including e.g. structure (age, stage, space, etc), interactions and stochasticity. The stochastic version of the exponential growth model, which is a random walk on the log scale, is sometimes used in conservation biology for estimating extinction risks of populations at low density.

**Further reading**Berryman, A. A. 1999. Principles of population dynamics and their applications. Stanley Thornes Publishers, Cheltenham, UK.

Ginzburg, L. R. 1986. The theory of population dynamics. I. Back to first principles. Journal of Theoretial Biology 122:385-399.

Turchin, P. 2003. Complex population dynamics. A theoretial/empirical synthesis. Princeton University Press, Princeton, NJ.