Using Molecular Genetics to Learn About the Ecology of Threatened Species: The Allure and The Illusion of Measuring Genetic Structure In Natural Populations

  • Eleanor K. Steinberg
  • Christopher E. Jordan


Conservation of threatened and endangered species depends upon understanding the contribution of migration and local demography to population change. Unfortunately, studies of species at risk tend to be plagued by logistic problems, including limited access to populations, small sample sizes, and restrictions prohibiting manipulative experimentation. Thus, even the most basic demographic data (e.g., birth and death rates) — and certainly data regarding migration — can be difficult to acquire. Technological advances such as radio telemetry and geographic positioning systems have improved somewhat our ability to pursue field demography (i.e., McKelvey et al. 1993; Lahaye et al. 1994), but in general studies that employ such technology remain extremely expensive and logistically difficult. In contrast, recent technological advances in molecular population genetics have greatly reduced the cost and simultaneously increased the ease of field genetic studies. For example, the recent development of the polymerase chain reaction (PCR) allows amplification of DNA from tiny skin biopsies, individual hairs, or even scat. These non-intrusive sampling methods mean we can obtain genetic data on highly endangered species without sacrificing a single individual. In addition, easy-to-use computer packages are readily available to translate genetic data from individuals into assessments of population genetic structure (i.e., BIOSYS by Swofford and Selander 1981; GDA by Lewis and Zaykin 1996). Most importantly, the analysis of genetic structure does not require tracking the fate of individuals, or even capturing individuals more than once.


Genetic Structure Effective Population Size Population Genetic Structure Conservation Biology Conservation Genetic 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media Dordrecht 1998

Authors and Affiliations

  • Eleanor K. Steinberg
  • Christopher E. Jordan

There are no affiliations available

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