Imagine a population of 1,000 individuals born at the same time in the same place. As time progresses,
some individuals die, so there are fewer and fewer individuals present each year. But when do most
individuals die? Do most individuals live to old age or do many individuals die at young ages? Ecologists
use survivorship curves to visualize how the number of individuals in a population drops off with time. In order to measure a population, ecologists identify a cohort, which is a group of individuals of the same
species, in the same population, born at the same time. Data is then collected on when each individual
in a population dies. Survivorship curves can be used to compare generations, populations, or even
different species. Survivorship curves actually describe the survivorship in a cohort: If cohorts are similar
through time, they can be considered to describe the survivorship of a population. Because survivorship
can be drastically different in different environments, this metric is not usually considered to be a
property of a species. Besides the constraint of the general life history strategy of a species, the shape of
survivorship curves can be affected by both biotic and abiotic factors, such as competition and
By plotting the number of survivors per 1,000 individuals on a log scale versus time, three basic patterns
emerge (Pearl 1944, Deevey 1947; Figure 1). Individuals with Type I survivorship exhibit high
survivorship throughout their life cycle. Populations with Type II survivorship have a constant proportion of individuals dying over time. Populations with Type III survivorship have very high mortality at young ages. Most real populations are some mix of these three types. For example, survivorship of juveniles fo some species is Type III, but is followed by type II survivorship for the long-lived adults.
Note that survivorship curves must be plotted on a log scale to compare with idealized Type I, II,
and III curves; they will look different on a linear scale. The use of a log scale better allows a
focus on per capita effects rather than the actual number of individuals dying. For example, the
type II curve has a constant proportion of individuals dying each time period. Starting with 1,000
individuals, in the first time period, if 40% survive, then only 400 will be left. In the second time
period, 40% of the remaining 600 will be left: 160. Plotting this on a linear scale, these three
points are not a straight line: The biggest drop occurs when 60% of the original 1,000 die in the
first time period. Nonetheless, the same proportion of individuals died both times. On a log scale,
the relationship of survivorship with time is linear; this scale highlights that the same proportion
dies in the second time period as in the first.
