{
  "id": 13357313,
  "title": "Demographic stochasticity can drive population variability even in large populations",
  "url": "https://urgent.news/2026/10/09/demographic-stochasticity-can-drive-population-variability-even-in",
  "topic": "science",
  "section": "Science",
  "published": "2026-10-09T00:00:00.000Z",
  "source": {
    "name": "bioRxiv",
    "slug": "biorxiv",
    "url": "https://www.biorxiv.org/content/10.64898/2026.10.07.757207v1?rss=1"
  },
  "original_language": "en",
  "account": "Demographic stochasticity, the random variation caused by the unpredictable nature of individual births and deaths, can still produce noticeable population fluctuations even in large groups of organisms. This counterintuitive finding arises because the contributions of individual organisms to reproduction are often unevenly distributed. In some natural populations, only a small fraction of breeders—referred to as the effective number of breeders (Nb)—actually contribute to the next generation, while the rest of the population serves merely to increase the overall census size. Genetic analyses can estimate Nb by studying a single generation's offspring, even if detailed demographic information is lacking.\n\nThe relationship between the census number of mature females (N) and the actual breeding potential (Nb) determines how much demographic variability—fluctuations in population size from one generation to the next—is present. When reproductive success is highly skewed, with a few individuals dominating reproduction, a large census size does not automatically mean that demographic variability is negligible. Instead, the effective number of breeders (Nb) captures the portion of the population that actively contributes to population change. The way individuals contribute to reproduction, along with factors like survival and maturation rates, influences the overall variability in population abundance.\n\nResearchers have developed a theoretical model that connects Nb to the temporal variability in mature population numbers. By considering recruitment variability as a result of non-Poisson reproductive variation and environmental factors, the model demonstrates how Nb can inform predictions about population dynamics. Under certain conditions—when the random variations in recruitment are not overwhelming and Nb is sufficiently small relative to the census size—population fluctuations can be predicted using just the effective number of breeders, the rate of environmental change, and a life-history coefficient that accounts for survival, maturation, and overlapping generations. This approach does not require knowing the exact relationship between adult population size and recruitment rates.\n\nStochastic simulations that incorporate these factors have shown that even with a census size of around 100,000 individuals, substantial population fluctuations can occur when the effective number of breeders is small. Overlapping generations of individuals help smooth out the fluctuations caused by recruitment variability, but the underlying genetic differences between individuals still play a crucial role. This finding is significant because it highlights the potential of genetic data to provide insights into population dynamics when detailed demographic information is scarce. By linking genetic monitoring—such as estimating Nb from a representative sample of a single cohort—to predictions of population fluctuations, conservationists and biologists can better understand and manage the variability inherent in natural populations, even when populations appear large on the surface.",
  "summary": "Demographic stochasticity is generally expected to contribute little to population variability when census abundance is large, because random demographic events are increasingly averaged across many individuals. This expectation, however, depends on how evenly individuals contribute to reproduction. In many natural populations, reproductive success is highly skewed, so that a large census…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}