Effective population size ($N_e$) is a theoretical concept in population genetics that quantifies the genetic behavior of a real-world population. It represents the size of an idealized population that would experience the same magnitude of random fluctuations in allele frequencies (genetic drift) or the same rate of inbreeding as the population being studied. This metric directly relates a species’ demographic reality to its long-term genetic health. The concept is relevant in conservation biology for assessing a population’s vulnerability to extinction and its ability to adapt. Importantly, $N_e$ is almost always significantly lower than the total number of individuals in the population.
The Distinction Between Census Size and Effective Size
The most fundamental distinction in population genetics is between the census size ($N$) and the effective population size ($N_e$). Census size ($N_c$) is simply the total count of individuals within a defined population, often restricted to reproductively mature adults. This number reflects the immediate, visible size of a population but does not account for how those individuals contribute to the next generation’s gene pool.
Effective population size, in contrast, is the actual number of individuals successfully breeding and passing on their genes. $N_e$ is the size of a hypothetical, idealized population that adheres to strict assumptions like equal sex ratios and random mating. This idealized population would exhibit the same loss of genetic variation as the real population, making $N_e$ a more accurate predictor of genetic change than $N$.
For example, a census might count 1,000 adult sea turtles, but if only 50 females successfully nest and 5 dominant males sire all the offspring, $N_e$ will be a small fraction of 1,000. A large disparity between $N$ and $N_e$ indicates that a small fraction of the population is responsible for carrying the genetic material forward. Understanding this difference is foundational because the key evolutionary forces—drift, mutation, and selection—are primarily governed by $N_e$, not $N$.
Demographic Factors That Reduce Effective Population Size
Several biological and demographic factors cause the effective population size to drop far below the census size.
Unequal Sex Ratios
One factor is an unequal sex ratio in the breeding pool, common in species with polygynous or polyandrous mating systems. For example, if a few dominant males mate with many females, the genetic contribution of the males is highly skewed. The calculation for $N_e$ is heavily influenced by the smaller number of the sexes. A population with a 1:9 male-to-female ratio will have a much lower $N_e$ than one with a balanced 1:1 ratio, even if the total census size is the same.
Variance in Reproductive Success
Another factor is the variance in reproductive success, which refers to the uneven distribution of offspring among parents. In an idealized population, every breeding individual contributes an equal number of offspring. In nature, however, some individuals produce many offspring while others produce few or none, increasing the variance in family size. This heightened variance reduces $N_e$ because fewer individuals disproportionately contribute their genes, accelerating the rate of genetic drift.
Population Fluctuations and Bottlenecks
A third factor is the fluctuation in population size over generations, particularly the occurrence of population bottlenecks. The effective population size over multiple generations is determined by the harmonic mean of the census sizes, which places disproportionate weight on the smallest numbers. A single generation with a severely reduced population size—a bottleneck event—will have a lasting negative impact on the long-term $N_e$. The genetic loss incurred during that low point will govern the genetic trajectory of the population for many subsequent generations, even after the census size recovers.
Genetic Consequences of a Low Effective Size
A low effective population size directly translates into two major genetic consequences that threaten a population’s long-term survival.
Accelerated Genetic Drift
The first consequence is an accelerated rate of genetic drift, which is the random fluctuation of allele frequencies due to chance sampling. In a small $N_e$, random events can quickly lead to the loss of certain alleles or the fixation of others, regardless of whether they are beneficial or harmful. This process rapidly erodes the population’s overall genetic diversity, which is the raw material required for evolution and adaptation.
Increased Inbreeding
The second consequence is an increase in inbreeding, defined as the mating of closely related individuals. As $N_e$ decreases, the probability that any two individuals share a recent common ancestor increases, leading to a rise in the inbreeding coefficient. This heightened inbreeding often results in inbreeding depression, where increased homozygosity exposes deleterious recessive alleles to selection. The result is a measurable reduction in fitness, manifesting as lower reproductive success, decreased survival rates, and reduced resistance to disease, which increases the risk of the population entering an extinction vortex.