• Open Access

Early detection of population declines: high power of genetic monitoring using effective population size estimators


  • Tiago Antao,

    1.  Liverpool School of Tropical Medicine, Liverpool, UK
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  • Andrés Pérez-Figueroa,

    1.  Departamento de Bioquímica, Genética e Inmunología, Facultad de Biología, Universidad de Vigo, Vigo, Spain
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  • Gordon Luikart

    1.  Flathead Lake Biological Station and Division of Biological Sciences, University of Montana, Polson, MT, USA
    2.  CIBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, Universidade do Porto, Campus Agrário de Vairão, Vairão, Portugal
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Tiago Antao, Liverpool School of Tropical Medicine, Liverpool, UK. Tel: +44(0)151 705 3100; fax: +44(0)151 705 3370; e-mail: tra@popgen.eu


Early detection of population declines is essential to prevent extinctions and to ensure sustainable harvest. We evaluated the performance of two Ne estimators to detect population declines: the two-sample temporal method and a one-sample method based on linkage disequilibrium (LD). We used simulated data representing a wide range of population sizes, sample sizes and number of loci. Both methods usually detect a population decline only one generation after it occurs if Ne drops to less than approximately 100, and 40 microsatellite loci and 50 individuals are sampled. However, the LD method often out performed the temporal method by allowing earlier detection of less severe population declines (Ne approximately 200). Power for early detection increased more rapidly with the number of individuals sampled than with the number of loci genotyped, primarily for the LD method. The number of samples available is therefore an important criterion when choosing between the LD and temporal methods. We provide guidelines regarding design of studies targeted at monitoring for population declines. We also report that 40 single nucleotide polymorphism (SNP) markers give slightly lower precision than 10 microsatellite markers. Our results suggest that conservation management and monitoring strategies can reliably use genetic based methods for early detection of population declines.