Blending molecular genetics with pedigree data to breed endangered species — ASN Events

Blending molecular genetics with pedigree data to breed endangered species (#62)

Carolyn J Hogg 1 , Jamie Ivy 2 , Rob Ogden 3 , Rebecca Johnson 4
  1. Zoo and Aquarium Association, Mosman, NSW, Australia
  2. Sand Diego Zoo Global, San Diego, CA, USA
  3. Royal Zoological Society of Scotland, Edinburgh, Scotland
  4. Australian Centre for Wildlife Genomics , Australian Museum, Sydney, NSW, Australia

Applying traditional species management methods to zoo populations with unknown pedigrees, or to species housed in group management scenarios has always been problematic. With the development of the new species management software, PMx, a number of the more challenging areas have been resolved. However for those species with a high number of unknown pedigrees long-term management is still difficult. Molecular genetics is evolving rapidly with vastly increased volumes of data becoming increasingly inexpensive to obtain for many non-model species. The application of this data means that we are now in a position to generate and apply data as one of the newer tools in the management of small populations. A successful partnership has been developed between the Zoo and Aquarium Association, San Diego Zoo Global and the Royal Zoological Society of Scotland, who are strong advocates of the use of genetic data to inform species management, and the Australian Centre for Wildlife Genomics at the Australian Museum, who have expertise in applying genetic techniques for real world applications for non-model animal species.

 

This talk will outline some of the more successful applications of microsatellite data with pedigree data to improve the breeding of critically endangered species, in particular the orange-bellied parrot, honeyeaters and scimitar-horned Oryx. Issues surrounding the use of relatedness estimators for small, closed populations will be discussed, and how we can better manage both zoo-based populations and small isolated wild populations long-term.