Survivorship is a fundamental concept used across population biology and epidemiology to measure the probability of an individual surviving from birth to a specific age. This measurement provides a powerful tool for analyzing a species’ health, predicting human demographic trends, and guiding conservation efforts for vulnerable populations. The calculation involves tracking individuals across their lifespan to determine patterns of survival and mortality. Understanding this process, which centers on constructing and analyzing a statistical framework called a life table, is essential for gaining deep insight into a population’s dynamics.
Defining Survivorship in Biology
In biological terms, survivorship, symbolized as \(l_x\), is the proportion of a group of individuals born at the same time that are still alive at the beginning of a given age class \(x\). It tracks a birth cohort from its origin until the last individual has died. This proportion is a normalized value, ranging from 1.0 (100% survival at birth) down to 0.0 at the maximum lifespan. The complementary concept is mortality, or the death rate, which is the probability of an individual dying within that same age interval. Analyzing survivorship reveals the specific ages or life stages where a species faces the greatest risks.
Data Collection Methods for Survivorship Studies
Researchers primarily use two methods to gather the raw data needed to construct a life table.
Cohort Life Table
The cohort life table, also known as a dynamic or longitudinal table, involves tracking a group of individuals born during a short, defined period. Scientists monitor this specific group until every member has died, recording the exact age of death for each one. This method yields the most accurate data on age-specific survival rates but is impractical for species with very long lifespans, such as elephants or redwood trees.
Static Life Table
The second approach, the static life table, uses a time-specific cross-section of a population. Researchers sample a large population at a single point in time and determine the age of each individual. This method is practical for long-lived or mobile species where tracking a single cohort is unfeasible. However, the static method requires the assumption that both the birth rate and the age-specific mortality rates have remained constant over the years spanned by the oldest individuals in the sample.
Organizing Data into a Life Table
The life table provides the organizational structure for the raw data, forming the foundation for all subsequent calculations. The table begins with the age interval, designated as \(x\), which can represent a year, a month, or a specific life stage.
The \(N_x\) column records the number of individuals from the original cohort that are still alive at the start of age interval \(x\). This number is the direct output of the data collection method.
The third column, \(D_x\), represents the raw count of deaths that occurred during the age interval \(x\). For a cohort life table, \(D_x\) is found by subtracting the number of survivors in the next age class (\(N_{x+1}\)) from the number of survivors in the current age class (\(N_x\)). This structure links the number of survivors at the start of an interval to the number of deaths that occur before the next interval begins.
Calculating the Survivorship Rate
The core of the analysis is calculating the survivorship rate, \(l_x\), which transforms the raw count of survivors into a standardized proportion. The process begins by defining the starting population, \(N_0\), which is the total number of individuals in the initial cohort at age zero.
The survivorship rate, \(l_x\), for any age class \(x\) is calculated by dividing the number of individuals surviving to that age, \(N_x\), by the starting cohort size, \(N_0\). The formula is \(l_x = N_x / N_0\).
For example, if 85 individuals from a starting cohort of 100 survive to age class \(x=5\), the survivorship rate \(l_5\) is 0.85. This means 85% of the original population survived to the beginning of age five. A related measure is the mortality rate (\(q_x\)), calculated by dividing the number of deaths during the interval (\(D_x\)) by the number alive at the start (\(N_x\)). This proportion provides the risk of dying within that specific age interval.
Interpreting Survivorship Curves
The calculated \(l_x\) values are plotted against age (\(x\)) to create a survivorship curve, allowing for immediate biological interpretation. These curves reflect different life history strategies and fall into three generalized categories:
Type I Curve
A Type I curve is convex, showing high survival throughout early and middle life, with a sharp decline only in old age. This pattern is typical of humans and large mammals. It indicates that the species invests heavily in a few offspring, providing extensive parental care to ensure high survival rates early in life.
Type II Curve
A Type II curve appears as a straight, diagonal line on a logarithmic scale, signifying a constant rate of mortality throughout the entire lifespan, independent of age. This pattern is found in some bird species and hydras, where the risk of death is consistent from youth to old age.
Type III Curve
A Type III curve is concave, showing extremely high mortality rates early in life, followed by a much lower death rate for the few individuals who survive the initial bottleneck. This pattern is common among species that produce a large number of offspring with minimal parental investment, such as many marine invertebrates and fish.