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Effective population size

  • Biodiversity

  • Structure


Effective population size is a key parameter that influences wildlife conservation and management decisions (Luikart et al. 2010). Effective population size is one of the most effective measures of genetic erosion and provides information on the rate of inbreeding and loss of genetic variation (Leroy et al. 2018, Hoban et al. 2020, Frankham 1995). Effective population size influences other important drivers of genetic diversity such as efficacy of mutation, selection and migration (Wang et al. 2016).

Methodology summary

The process involved in establishing effective population size (Ne) is methodologically complex and requires specialist expertise that is likely to be beyond the reach of the average Nature-based Solutions project. Ne is relevant in projects that target the conservation of a specific species and will have an impact on a distinct population. An outline of the process involved in calculating Ne is given below.

There are two main approaches to estimating effective population size (Wang et al. 2016):

  • Prediction from demographic parameters e.g. census size, variance of reproductive success
  • Prediction from genetic properties e.g. changes in allele frequency (an allele is a variant of a gene, in some cases different alleles are associated with different traits e.g. for different eye colours) and linkage disequilibrium (association between alleles at different loci)

Linkage disequilibrium is a widely used and well-evaluated measure, and can be calculated from samples (e.g. DNA extracted from faeces, hair, blood, animal/plant tissue) from multiple individuals within a population taken at a single point in time:

  • Simple to calculate from samples of multilocus genotypes (alleles at multiple loci that are transmitted together from a parent to an offspring)
  • Can be used to track population trajectories on a yearly basis
  • Risk of inaccuracies with small populations and non-random mating/population structure

Change in allele frequencies also tracks Ne:

  • Reflects genetic drift
  • Mutation, selection and migration can also influence genetic drift, which can complicate interpretation
  • Estimates are based on two samples of a set of 10-20 microsatellites (small pieces of repeating DNA that serve as markers), taken at least one generation apart

Demographic parameters:

  • Require data on census sizes, variances of progeny numbers, type of mating system and other demographic data
  • These data are rarely available and the increasing availability of genetic data makes genetic estimation the leading approach

Additional related metrics can be calculated:

  • Nei inbreeding effective population size (loss of Heterozygosity, doesn’t change until inbreeding accumulated, influenced by number of parents)
  • Nev variance effective size = rate of genetic drift (change in allele frequencies through time, early detection, influenced by number of offspring)

Effective population size, Allelic diversity and rate of genetic drift are three interrelated concepts and metrics that give slightly different pieces information on the underlying genetic properties of populations.

Metric threshold or direction of change

The desired effective population size will be species/project specific. A higher effective population size is better, reflecting greater underlying genetic diversity.

Technological innovations

  • Improvements in genetic and statistical methods complement or improve on traditional population census methods. Combining genotyping 100-1000s of loci with new computational approaches will bring greatest advancement in estimating Ne (Luikart et al. 2010).
  • As SNP (Single Nucleotide Polymorphism – a type of genetic marker) discovery and genotyping costs decline many species will soon have 100s of SNPs available, which will improve estimation of Ne. This will be coupled to improved statistical methods and computer efficiency (Wang et al. 2016).

  • Agricultural
  • Forest
  • Grassland
  • Heathland
  • Other
  • Peatland
  • Saltmarsh
  • Wetland


  • Genetic


  • High


  • Future

Technical expertise

  • High

Standardised methodology

  • No