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Population size

Population size refers to the total number of individuals comprising a given population, serving as a fundamental metric in both ecological and demographic studies. In ecology, it represents the count of organisms of a specific species within a defined geographic area or habitat, influencing factors such as reproduction, resource use, and survival rates.^[1] In demography, particularly for human populations, it denotes the aggregate number of people residing in a particular region, country, or globally, which shapes social, economic, and environmental dynamics.^[2] The significance of population size lies in its role as a key indicator of stability and vulnerability across biological and human systems. In ecological contexts, larger population sizes enhance genetic diversity and resilience against stochastic events like disease outbreaks or habitat loss, reducing the risk of extinction for species.^[3] Smaller populations, conversely, face heightened threats from inbreeding depression and environmental fluctuations, making conservation efforts critical for their persistence.^[4] For human demography, population size directly impacts resource demands, including food security, infrastructure needs, and public services; for instance, rapid growth in certain regions strains healthcare and education systems, while aging populations in others challenge pension and labor frameworks.^[5] Globally, the human population size underscores broader challenges like climate change adaptation and sustainable development, as projected peaks around 10.3 billion by the mid-2080s will amplify these pressures.^[6] Measuring population size involves a combination of direct enumeration and indirect estimation methods to account for its dynamic nature. In ecology, techniques range from mark-recapture sampling for mobile species to quadrat surveys for sessile organisms, often yielding estimates rather than exact counts due to logistical constraints.^[7] In human demography, national censuses provide periodic snapshots, supplemented by vital registration systems tracking births, deaths, and migrations to model ongoing changes.^[8] These measurements reveal trends driven by demographic processes: fertility rates determine growth potential, mortality rates affect decline, and net migration alters composition.^[9] As of November 2025, the world's human population size stands at approximately 8.26 billion, reflecting a slowdown from earlier explosive growth rates due to declining fertility in many countries.^[10]

Fundamental Concepts

Census Population Size

Census population size, denoted as N, refers to the total number of living individuals within a defined population at a specific point in time, typically encompassing all organisms of a species in a designated geographic area.^[3] This measure provides a straightforward count of abundance, distinct from population density, which quantifies individuals per unit area or volume, or biomass, which assesses total living mass rather than numerical headcount.^[7] In ecological studies, N serves as a foundational metric for understanding community structure and resource dynamics without incorporating adjustments for reproductive or genetic contributions.^[11] Estimating census population size in field studies varies by scale and organism mobility. For small, accessible populations, direct counts involve systematically enumerating all individuals, such as tallying plants in a plot or observing sessile organisms. In scenarios where complete enumeration is impractical, mark-recapture techniques are employed, particularly for mobile animals; the Lincoln-Petersen estimator calculates N as N = \frac{M \times C}{R}, where M is the number of initially marked individuals, C is the total captured in a second sample, and R is the number of recaptured marked individuals, assuming equal capture probabilities and no migration or mortality between samples. For large or elusive populations, sampling extrapolations use quadrats—randomly placed plots—to count individuals and scale up via statistical models, or integrate multiple visits to account for temporal variability.^[7] Practical applications of these methods appear across diverse taxa. Direct counts suit dense insect swarms, where researchers can visually tally clusters in confined areas like forest clearings.^[12] Wildlife censuses often rely on camera traps to capture images of animals, enabling identification and abundance estimation through spatial capture-recapture models that infer N from detection patterns without physical handling.^[13] In human or large-mammal contexts, surveys combine aerial counts or ground transects with statistical adjustments to approximate total numbers in expansive regions.^[14] The concept of census population size gained prominence in early 20th-century ecology through the predator-prey models developed by Alfred J. Lotka in 1920 and Vito Volterra in 1926, which incorporated N as a dynamic variable to predict oscillatory population interactions.^[15] These foundational works emphasized direct measures of abundance to parameterize differential equations simulating ecological balances. While census size offers an observable baseline, it relates to effective population size as an adjusted metric that accounts for variances in reproductive success in genetic contexts.^[3]

Effective Population Size

The effective population size, denoted N_e, represents the size of an idealized Wright-Fisher population that would exhibit the same magnitude of genetic drift or rate of inbreeding as the actual population of interest. This measure adjusts the raw census count to reflect the population's true genetic dynamics, providing a more accurate predictor of evolutionary processes like allele frequency changes.^[16] The concept was first introduced by Sewall Wright in 1931 to bridge theoretical models with real-world demographic variations. Subsequent refinements in the 1950s by James Crow and colleagues distinguished between inbreeding and variance components of N_e, enhancing its applicability in population genetics.^[17] One key formulation is the inbreeding effective size, which equates the rate of inbreeding in the real population to that in an ideal one:
N_e = \frac{1}{2 \Delta F},
where \Delta F is the increase in the inbreeding coefficient per generation.^[18] This captures how quickly relatedness accumulates among individuals due to non-random mating or small breeding numbers. The variance effective size, by contrast, focuses on the stochastic variance in allele frequencies caused by sampling error in reproduction. It is derived from the distribution of offspring numbers per parent, where \sigma_k^2 is the variance in offspring number and \mu_k is the mean offspring number. A common expression is
N_e = \frac{N \mu_k - 1}{\mu_k - 1 + \frac{\sigma_k^2}{\mu_k}},
with N as the census size; for the ideal diploid case where \mu_k = 2 (replacement reproduction) and \sigma_k^2 = 2 (binomial sampling of gametes), this simplifies to N_e \approx N.^[18] Higher \sigma_k^2 relative to \mu_k amplifies drift, reducing N_e. Several demographic factors typically cause N_e to be smaller than the census size N. Unequal sex ratios diminish N_e according to
N_e = \frac{4 N_m N_f}{N_m + N_f},
where N_m and N_f are the numbers of breeding males and females; for example, if males are far fewer, their higher per capita variance in success dominates.^[18] Variance in reproductive success among individuals further lowers N_e by increasing the skew in genetic contributions. Population size fluctuations over time are summarized by the harmonic mean:
N_e = \frac{t}{\sum_{i=1}^t \frac{1}{N_i}},
where t is the number of generations and N_i is the size in generation i; even brief bottlenecks can severely depress the long-term N_e.^[18] In conservation biology, the cheetah (Acinonyx jubatus) exemplifies low N_e stemming from historical bottlenecks around 10,000–12,000 years ago, which reduced genetic diversity and elevated inbreeding risks despite a current census size of several thousand.^[19]

Role in Genetic Drift

Intensity and Mechanisms of Genetic Drift

Genetic drift is the random fluctuation in allele frequencies within a population arising from sampling error during reproduction in finite populations.^[20] This process occurs because the gametes contributing to the next generation represent a finite sample of the parental gene pool, leading to stochastic changes rather than deterministic shifts driven by selection or other forces.^[20] The intensity of genetic drift is inversely proportional to the effective population size, N_e, with smaller populations exhibiting more pronounced random changes in allele frequencies. Specifically, the variance in the change of allele frequency per generation, \Delta p, for a neutral allele with initial frequency p is given by

\text{Var}(\Delta p) = \frac{p(1-p)}{2 N_e},

demonstrating that drift accelerates as N_e decreases, potentially causing rapid shifts toward fixation or loss of alleles. The effective population size serves as the key determinant of this drift intensity, as it reflects the number of individuals effectively contributing to the gene pool in terms of genetic variability. The primary mechanism underlying genetic drift is binomial sampling of gametes during reproduction, where the number of copies of an allele passed to offspring follows a binomial distribution based on the parental frequencies.^[20] This sampling error accumulates over generations, increasing the likelihood of alleles reaching fixation (frequency of 1) or loss (frequency of 0). For a neutral allele, the probability of eventual fixation equals its initial frequency p, independent of population size but with the rate of approach to fixation scaling inversely with N_e. In infinite populations, genetic drift is negligible, and allele frequencies remain constant across generations, adhering to Hardy-Weinberg equilibrium under random mating and absence of other evolutionary forces.^[21] Finite population sizes disrupt this equilibrium through drift, introducing variance that can override weak selective pressures.^[21] From an evolutionary perspective, genetic drift in small populations erodes genetic variation by randomly eliminating alleles, thereby reducing the raw material for adaptation and heightening susceptibility to environmental changes or strong selection.^[21] This loss also elevates the risk of inbreeding depression, as fixed deleterious alleles accumulate without counterbalancing diversity.^[21] The foundational descriptions of genetic drift emerged in the works of Sewall Wright and Ronald A. Fisher during the 1920s and 1930s, integrating stochastic processes into the modern evolutionary synthesis alongside mutation, selection, and migration.^[22]

Population Bottlenecks and Founder Effects

A population bottleneck refers to a sharp and drastic reduction in the size of a population, often caused by environmental catastrophes, human activities, or disease outbreaks, which persists for at least one generation and intensifies the effects of genetic drift.^[23] This event leads to a substantial loss of genetic variation through random sampling error, as the surviving individuals represent only a small, non-random subset of the original gene pool, resulting in decreased allelic diversity and increased homozygosity.^[24] Post-bottleneck effective population size (N_e) can be estimated using the temporal method, which examines changes in allele frequencies across time points. A standard estimator is \hat{N_e} \approx \frac{t}{2F_c}, where t is the number of generations between samples, and F_c is the coefficient of standardized temporal variance in allele frequencies, F_c = \frac{\sum (p_2 - p_1)^2}{\sum p_1(1-p_1)} averaged over loci.^[25] The founder effect is a related phenomenon that mimics a bottleneck, occurring when a small group of individuals from a larger population colonizes a new habitat, such as an island or isolated region, establishing a new population with reduced genetic variation.^[26] The genetic composition of this founding group is not representative of the source population, leading to altered allele frequencies and lower overall heterozygosity in the derived population, which can promote rapid evolutionary divergence.^[27] Both bottlenecks and founder effects accelerate genetic drift, causing a pronounced decline in heterozygosity over generations, described by the formula H_t = H_0 \left(1 - \frac{1}{2N_e}\right)^t, where H_t is heterozygosity at time t, H_0 is initial heterozygosity, N_e is effective population size, and t is the number of generations.^[28] This loss of diversity increases homozygosity, elevates the fixation probability of deleterious alleles, and diminishes the population's adaptive potential by limiting the raw material for natural selection.^[23] A classic example of a population bottleneck is the northern elephant seal (Mirounga angustirostris), hunted nearly to extinction in the late 19th century, reducing to approximately 20 individuals around the 1890s; despite recovery to over 220,000 today, modern populations exhibit profoundly low genetic diversity, with genome-wide heterozygosity at 0.000176 compared to 0.00142 pre-bottleneck, alongside elevated inbreeding and reduced fitness traits like reproductive success and foraging efficiency.^[29] For the founder effect, the colonization of the Galápagos Islands by Darwin's finches illustrates how a small founding group of at least 30 individuals carried limited genetic variation, contributing to the rapid speciation and morphological diversification observed among the 15 extant species through amplified drift in isolated island populations.^[30] Detection of these events relies on genetic signatures in allele frequency data, such as transient excess heterozygosity relative to mutation-drift equilibrium, which can be identified using software like BOTTLENECK; this program simulates expected heterozygosity under a stable population model and tests for deviations indicating recent reductions in effective population size via methods like the sign test or Wilcoxon test on microsatellite loci.^[31]

Modeling and Dynamics

Mathematical Models of Genetic Drift

The Wright-Fisher model represents a foundational idealized framework for understanding genetic drift in a finite population of constant size N, assuming diploid organisms and random mating. In this model, the next generation is formed by drawing $2N gametes randomly from the current generation's gene pool, with no overlap between generations. The frequency of a neutral allele in the next generation, denoted p_{t+1}, follows a binomial distribution: p_{t+1} = K / (2N), where K \sim \text{Binomial}(2N, p_t) and p_t is the allele frequency in generation t. This discrete process captures the stochastic variance in allele frequencies due to sampling, with the expected frequency remaining p_t under neutrality, but variance increasing as \text{Var}(p_{t+1}) = p_t (1 - p_t) / (2N). For large N, the model approximates a diffusion process in continuous time, facilitating analytical solutions for long-term behavior. The Moran model offers an alternative stochastic framework with overlapping generations, maintaining a constant population size N through a continuous-time birth-death process. In each step, one individual is chosen proportional to its fitness to reproduce, and its offspring replaces a randomly selected individual, leading to gradual allele frequency changes. For neutral alleles, the fixation probability matches that of the Wright-Fisher model (1/(2N) for a single copy), but the variance effective population size differs, with drift occurring at twice the rate compared to Wright-Fisher due to more frequent updates. This model is mathematically tractable for exact computations, particularly in structured populations, and is often used to derive analytical results for small N. Diffusion approximations provide a continuous-time limit for both models, transforming the discrete stochastic processes into partial differential equations that describe allele frequency evolution. The forward Kolmogorov equation governs the probability density f(p, t) of frequency p at time t: \frac{\partial f}{\partial t} = \frac{1}{4N_e} \frac{\partial^2}{\partial p^2} [p(1-p) f] for neutral drift, where N_e is the effective population size; the backward equation, conversely, addresses absorption probabilities and times. Under neutrality, the mean time to fixation for an allele starting at frequency p is approximately -4N_e [p \ln p + (1-p) \ln (1-p)] / [p(1-p)], simplifying to about $4N_e generations for a new mutant (p = 1/(2N_e)). These equations enable predictions of coalescence times and heterozygosity decay, central to coalescent theory. Extensions of these models incorporate additional forces while emphasizing neutral drift dynamics, such as weak selection or migration, often via modified diffusion coefficients. For instance, the diffusion equation adjusts the drift term to include selection as \mu(p) = s p (1-p), but neutral cases (s=0) remain the baseline for drift quantification. Computational implementations facilitate simulations beyond analytical limits; the ms program generates coalescent-based samples under neutral Wright-Fisher-like models, enabling efficient inference of demographic parameters from genetic data, while SLiM supports forward-time simulations of individual-based drift in complex scenarios, including spatial structure. These models assume panmictic populations with constant size and no spatial or demographic structure, limiting their applicability to real-world scenarios with varying N_e. Historically, they have been pivotal in predicting coalescence times, scaling drift to effective population size as a key input parameter.

Critical Mutation Rate

The critical mutation rate, often denoted as U_{\text{crit}}, represents the threshold genomic mutation rate beyond which deleterious mutations accumulate irreversibly in asexual populations, overwhelming the rare back-mutations that could restore higher-fitness genotypes. This threshold is approximated as U_{\text{crit}} \approx \ln N_e, where N_e is the effective population size (assuming normalized selection coefficient s=1), because at this level, the expected size of the fittest genotype class N_e e^{-U} approaches unity, facilitating the loss of the highest-fitness individuals through stochastic processes.^[32] In sexual populations, recombination mitigates this effect, but in asexuals, the linkage of mutations across the genome exacerbates the problem. The concept of the critical mutation rate is closely tied to Muller's ratchet, a process first proposed by Hermann J. Muller in 1964 to illustrate the evolutionary advantage of sexual recombination over asexuality. Muller's ratchet describes the irreversible loss of the fittest genotype class in finite asexual populations due to the stochastic fixation of deleterious mutations, with subsequent classes becoming the new "fittest" but at lower mean fitness. The ratchet "clicks" when the fittest class is entirely lost to mutation and drift, and the clicking rate is approximately U / \ln N_e, where U is the total genomic mutation rate; this rate increases as U approaches or exceeds U_{\text{crit}}, leading to rapid fitness decline.^[32] The term "Muller's ratchet" was formalized by Joe Felsenstein in 1974, building on Muller's idea to quantify how finite population size amplifies mutation accumulation in non-recombining lineages.^[33] Complementing Muller's framework, the quasispecies model introduced by Manfred Eigen in 1971 provides another perspective on critical mutation rates, particularly for high-fidelity replication in evolving populations. In this model, the error threshold marks the maximum per-site mutation rate \mu_c beyond which the population cannot maintain the master sequence (the optimal genotype), given by \mu_c L \approx \ln(\sigma), where L is the genome length and \sigma is the fitness advantage of the master sequence over mutants. Although the classic infinite-population formulation is independent of N_e, finite population size influences the maintenance of the master sequence; specifically, if N < 1/\mu (adjusted for genome-wide effects), stochastic loss becomes probable, shifting the effective threshold lower in small populations and promoting error catastrophe. When the mutation rate exceeds the critical threshold in small populations, it can trigger mutational meltdown, a synergistic decline in fitness where accumulated deleterious mutations reduce population growth rates below replacement levels, hastening extinction.^[34] This process interacts with genetic drift to amplify the fixation of mildly deleterious alleles, particularly in asexuals where recombination cannot purge them.^[34] In RNA viruses, which often operate near their error thresholds due to high U (around 0.1–1 per genome), experimental elevation of mutation rates via nucleoside analogs induces meltdown, as seen in poliovirus populations where increased mutagenesis leads to non-viable quasispecies clouds. Similarly, endangered species with small N_e (e.g., cheetahs or island endemics) face heightened meltdown risk, as low genetic diversity and drift facilitate deleterious accumulation, contributing to inbreeding depression and reduced adaptability.^[34]

Applications in Evolution

Factors Affecting Effective Population Size

The effective population size (N_e) often deviates from the census population size (N) due to demographic and biological factors that increase the variance in reproductive success or alter the genetic contribution of individuals to future generations. These deviations arise because N_e reflects the rate of genetic drift in an idealized Wright-Fisher population, where deviations amplify drift and reduce N_e relative to N.^[35] Variance in family size, or reproductive success, is a primary factor reducing N_e. In an ideal population with Poisson-distributed offspring (variance equal to mean), N_e \approx N; however, higher variance, such as from polygamous mating systems where few males sire most offspring, substantially lowers N_e. The approximate formula is N_e = \frac{N}{1 + \frac{V_k}{\bar{k}}}, where V_k is the variance in offspring number and \bar{k} is the mean offspring number (typically \bar{k} = 2 for stable diploid populations). For example, in species with high polygyny like elephant seals, V_k can exceed 10, yielding N_e/N < 0.1. This relationship was formalized by Sewall Wright and elaborated in foundational models.^[36]^[35] Unequal sex ratios further diminish N_e by limiting contributions from the underrepresented sex. The formula for a dioecious population is N_e = \frac{4 N_m N_f}{N_m + N_f}, where N_m and N_f are the numbers of breeding males and females, respectively; this reaches a maximum of N only at equal ratios (1:1) and drops sharply with skew, such as to N/4 in extreme cases like 1:99. In haplodiploid systems (e.g., bees), where males are haploid and arise from unfertilized eggs, N_e is even lower due to hemizygosity, often approaching $0.75N or less under balanced ratios. This effect is pronounced in species with male-biased harvesting or sexual dimorphism in dispersal.^[37]^[38] Temporal fluctuations in population size, particularly bottlenecks, have a disproportionate impact on N_e because it is determined by the harmonic mean over generations: N_e = \frac{t}{\sum_{i=1}^t \frac{1}{N_i}}, where t is the number of generations and N_i is the size in generation i. Small sizes in any generation weigh heavily, so a single bottleneck (e.g., N = 10 amid otherwise large populations) can reduce multigenerational N_e by orders of magnitude, accelerating drift even if the current N is large. This explains persistent low genetic diversity in species like northern elephant seals despite recovery to thousands of individuals.^[37]^[36] Spatial structure and kin competition also reduce N_e by promoting local mating, which increases the variance in allele transmission via the Wahlund effect—where subpopulation admixture mimics inbreeding and inflates homozygosity. In continuously distributed or subdivided populations with limited dispersal, this local kin competition elevates reproductive variance, lowering N_e below the global N; for instance, in island models with low migration, N_e per deme can be as low as N/2 due to heightened local drift.^[39]^[40] Overlapping generations in age-structured populations require adjustments to N_e estimates, as lifetime reproductive success replaces per-generation contributions. For iteroparous species, an adjustment factor incorporates generation length and variance in lifetime offspring: N_e \approx \frac{4 N_g}{\sigma_{k\bullet}^2 + 2}, where N_g is the number of breeding adults per generation and \sigma_{k\bullet}^2 is lifetime variance; this often yields N_e < N if age-specific survival varies, as older cohorts contribute disproportionately.^[36]^[41] In fisheries, selective harvesting skews age and size distributions toward younger or smaller individuals, increasing variance in reproductive success and reducing N_e by up to 50% or more compared to unharvested populations; for example, in Atlantic cod, intense fishing has lowered N_e/N ratios below 0.1 through truncated maturation. Similarly, in plants, high selfing rates halve N_e to approximately N/2 by reducing heterozygosity and effective allele diversity, as seen in fully self-compatible species like Arabidopsis thaliana, where drift erodes variation twice as fast.^[42]^[43]^[35]

Implications for Genetic Diversity and Adaptation

Population size, particularly the effective population size N_e, plays a critical role in maintaining genetic diversity, which is essential for long-term evolutionary potential. Under the infinite alleles model, the expected heterozygosity H at equilibrium is given by H = \frac{4 N_e \mu}{1 + 4 N_e \mu}, where \mu is the mutation rate per locus per generation. In small populations with low N_e, genetic drift accelerates the loss of alleles, causing heterozygosity to decay exponentially at a rate of approximately $1/(2 N_e) per generation, thereby eroding genetic variation faster than it can be replenished by mutation. This rapid decline in diversity reduces the raw material available for natural selection, limiting a population's ability to adapt to changing environments. Adaptation rates are particularly compromised in small populations where genetic drift dominates over selection. Beneficial alleles with selection coefficients s < 1/N_e are likely to be lost due to random fluctuations rather than fixed by selection, as the product N_e s < 1 renders selection ineffective. Additionally, Hill-Robertson interference, where linkage between selected loci hinders the independent fixation of favorable alleles, is exacerbated in small N_e because reduced recombination efficiency relative to stronger drift amplifies negative epistatic interactions across the genome. These effects collectively slow the rate of adaptive evolution, making populations more vulnerable to environmental shifts. Inbreeding depression further compounds these challenges, with the inbreeding coefficient increasing by \Delta F = 1/(2 N_e) per generation, leading to cumulative fitness declines from the expression of deleterious recessive alleles. In conservation biology, this informs the "50/500 rule," where a short-term N_e of at least 50 is needed to avoid immediate inbreeding depression, while a long-term N_e of 500 is required to maintain evolutionary potential against mutation load.00162-0) The International Union for Conservation of Nature (IUCN) incorporates N_e estimates into Red List criteria for assessing endangered status, emphasizing genetic viability alongside census size. A notable application is the 1995 translocation of eight Texas pumas into the Florida panther population, which increased heterozygosity, reduced inbreeding, and boosted N_e by over 20-fold, contributing to population recovery from near extinction. Looking forward, climate change poses additional threats by fragmenting habitats and reducing population sizes, which can limit adaptive potential in isolated patches. Small, fragmented populations face heightened drift and reduced gene flow, predicting adaptation limits as environmental pressures like shifting temperatures outpace the maintenance of adaptive variation. These dynamics underscore the need for connectivity restoration to sustain N_e and enhance resilience in an era of rapid global change.

References

[1]
Demography - National Geographic Education
Oct 1, 2024 · Demography is the statistical study of human populations. Demography examines the size, structure, and movements of populations over space ...
[2]
Introduction to Population Demographics | Learn Science at Scitable
How do we know if a species is heading towards extinction? Demographics help us understand the size, status, and behavior of populations.
[3]
Species populations - Understanding Global Change
Species with large population sizes and geographic ranges are less likely to go extinct than species with small populations and limited geographic ranges.
[4]
Lesson Plans on Human Population and Demographic Studies | PRB
Aside from the total size, the most important demographic characteristic of a population is its age and sex structure, or the proportion of people at each age, ...<|control11|><|separator|>
[5]
Population - the United Nations
The world's population is projected to continue growing for the next 50 to 60 years, peaking at approximately 10.3 billion by the mid-2080.
[6]
Population size, density, & dispersal (article) - Khan Academy
Population size is the number of individuals, density is the number per unit area, and dispersal is how individuals are distributed (uniform, random, or ...
[7]
Population Demography | Introduction to Ecology & Evolutionary ...
Within a particular habitat, a population can be characterized by its population size (N), the total number of individuals, and its population density, the ...Population Research Methods · Demography · Life Tables
[8]
Human population growth and the demographic transition - PMC
This paper summarizes key trends in population size, fertility and mortality, and age structures during these transitions.2. Future Population Trends · 3. Drivers Of Population... · 4. Changing Population Age...
[9]
World Population Clock: 8.2 Billion People (LIVE, 2025) - Worldometer
The current world population is 8,256,206,983 as of Monday, November 10, 2025 according to the most recent United Nations estimates [1] elaborated by ...World Population Projections · Countries · Year · U.S.A.
[10]
[PDF] Estimation of census and effective population sizes
Feb 27, 2010 · Estimation of census size (NC). We define population census size (NC) as the number of adults in a study area or population, unless stated other ...
[11]
Field Techniques for Population Sampling and Estimation
Spot mapping, also referred to as territory mapping, often is used to estimate avian population densities by locating singing males during a number of visits ...
[12]
Camera Trap Methods and Drone Thermal Surveillance Provide ...
Jun 5, 2023 · Wildlife researchers and managers can choose from several techniques to estimate the number of animals in a population. Camera traps and ...
[13]
Mark, Release and Recapture - Field Studies Council
The size of populations of invertebrates or small mammals in an area can be estimated using mark-release-recapture technique.
[14]
Alfred J. Lotka and the origins of theoretical population ecology - PMC
Aug 4, 2015 · The equations describing the predator–prey interaction eventually became known as the “Lotka–Volterra equations,” which served as the starting ...
[15]
[PDF] Understanding and Estimating Effective Population Size for Practical ...
The concept of Ne was first introduced by Sewall. Wright (1931), who defined it as the size of an imaginary, theoretically ideal population affected by ...
[16]
Inbreeding and Variance Effective Population Numbers - jstor
There has been increased in- terest in effective population size because of its use in the study of rare and endangered species. The effective number of a ...
[17]
[PDF] 4 Genetic Drift and Effective Population Size - JBLearning
In this case, the effective population size is (Wright, 1931). (4.8a). If there are equal numbers of females and males. ) then as expected because the males ...
[18]
Gene pseudogenization in fertility-associated genes in cheetah ...
The cheetah has experienced severe genetic bottlenecks and a prolonged low effective population size for millions of years. The resulting accumulation of ...Abstract · Introduction · Materials and methods · Discussion
[19]
Genetic Drift - Stanford Encyclopedia of Philosophy
Sep 15, 2016 · The standard mathematical model of drift found in textbooks is the Wright-Fisher model, the core of which is the binomial distribution, and ...Origins of the Concept of... · What Is Drift, and Can It Be...
[20]
Natural Selection, Genetic Drift, and Gene Flow Do Not Act in ...
At any given time, the probability that one of these neutral alleles will eventually be fixed equals that allele's frequency in the population. We can think ...
[21]
The “New Synthesis” - PNAS
Jul 18, 2022 · During the 1920s, Fisher, Haldane, and Wright established the principles of population genetics, emphasizing that Mendelian genetics facilitates ...
[22]
Bottlenecks and founder effects - Understanding Evolution
Population bottlenecks occur when a population's size is reduced for at least one generation. Because genetic drift acts more quickly to reduce genetic ...
[23]
Genetic Bottleneck - National Geographic Education
Oct 19, 2023 · A genetic bottleneck occurs when a population is greatly reduced in size, limiting the genetic diversity of the species.
[24]
generalized approach for estimating effective population size from ...
Feb 1, 1989 · The temporal method for estimating effective population size (Ne) from the standardized variance in allele frequency change (F) is presented ...<|separator|>
[25]
Founder Effect - National Human Genome Research Institute (NHGRI)
The founder effect is the reduction in genetic variation that results when a small subset of a large population is used to establish a new colony.
[26]
Founder Effect: Definition, Examples, Significances - Microbe Notes
Dec 3, 2023 · The founder effect occurs when a small group of individuals establishes a new population, and their genetic makeup influences the new population ...What is Genetic Drift? · What is Founder Effect? · Examples of Founder Effect
[27]
Genetic signatures of small effective population sizes and ...
Specifically, James (1970) proposed using the equation Ht = H0 (1−1/2Ne)t ... heterozygosity t generations after the instantaneous decline of a large ...
[28]
Genomics of post-bottleneck recovery in the northern elephant seal
Feb 21, 2024 · We sequenced 260 modern and 8 historical northern elephant seal nuclear genomes to assess the impact of the population bottleneck on individual northern ...
[29]
How large was the founding population of Darwin's finches? - Journals
For such effects to occur, founding populations must be very small, and remain small for some time after founding. This assumption has, however, rarely been ...
[30]
a computer program for detecting recent reductions in the effective ...
Computer note. BOTTLENECK: a computer program for detecting recent reductions in the effective size using allele frequency data. S Piry,. S Piry.Missing: analysis | Show results with:analysis
[31]
Impacts of mutation effects and population size on mutation rate in ...
Sep 30, 2010 · However, the influx of favorable mutations is the critical factor and largely determines the optimum mutation rate in large asexual populations.
[32]
Mutation Accumulation and the Extinction of Small Populations
We develop a general theoretical approach for evaluating the risk of small populations to extinction via the accumulation of mildly deleterious mutations.
[33]
Prediction and estimation of effective population size | Heredity
Jun 29, 2016 · The effective population size is defined in reference to the Wright–Fisher idealised population, that is, a hypothetical population with very ...
[34]
The Idiot's Guide to Effective Population Size - Wiley Online Library
Feb 10, 2025 · Using this information, it is easy to compute mean and variance in offspring number and from that, effective size. The limiting factor here ...
[35]
Genetic Drift and Effective Population Size | Learn Science at Scitable
### Summary of Factors Affecting Effective Population Size
[36]
[PDF] Effective population size N Factors affecting N
Sep 22, 2014 · Effective population size N e. ❖ the number of individuals in a theoretically ideal population having the same magnitude of random genetic ...
[37]
Estimation of effective population size in continuously distributed ...
May 8, 2013 · Results indicate that caution is needed in applying standard methods for estimating effective size to continuously distributed populations.
[38]
Low‐coverage sequencing and Wahlund effect severely bias ...
May 24, 2024 · At individual loci, the Wahlund effect manifests as a deficiency of heterozygotes when a sample includes individuals from multiple genetically- ...2 Inbreeding And... · 4 Recommendations · 4.1 Heterozygosity And...
[39]
A note on effective population size with overlapping generations
A simple derivation is given for a formula obtained previously for the effective size of random-mating populations with overlapping generations.
[40]
Harvest‐induced evolution and effective population size - PMC - NIH
Adding harvest on top of natural mortality roughly doubled the total adult mortality experienced by the population. As a consequence, adult N declined sharply ...
[41]
A practical introduction to effective population size for fisheries ...
Jul 18, 2025 · Effective population size was introduced by Wright (1931) as the size of an ideal population (Box 1) that follows the same rate of genetic drift ...