4.7 Article

Pedigree-based assignment tests for reversing coyote (Canis latrans) introgression into the wild red wolf (Canis rufus) population

Journal

MOLECULAR ECOLOGY
Volume 12, Issue 12, Pages 3287-3301

Publisher

WILEY-BLACKWELL
DOI: 10.1046/j.1365-294X.2003.02003.x

Keywords

assignment test; hybridization; likelihood ratio; microsatellite; Monte Carlo simulation; red wolf

Ask authors/readers for more resources

The principal threat to the persistence of the endangered red wolf (Canis rufus) in the wild is hybridization with the coyote (Canis latrans). To facilitate idengification and removal of hybrids, assignment tests are developed which use genotype data to estimate identity as coyote, (1)/(4), (1)/(2), (3)/(4) or full red wolf. The tests use genotypes from the red wolves that founded the surviving population and the resulting pedigree, rather than a contemporary red wolf sample. The tests are evaluated by analysing both captive red wolves at 18 microsatellite loci, and data simulated under a highly parameterized, biologically reasonable model. The accuracy of assignment rates are generally high, with over 95% of known red wolves idengified correctly. There are, however, tradeoffs between ambiguous assignments and misassignments, and between misidengifying red wolves as hybrids and hybrids as red wolves. These result in a compromise between limiting introgression and avoiding demographic losses. The management priorities and level of introgression determine the combination of test and removal strategy that best balances these tradeoffs. Ultimately, we conclude that the use of the assignment tests has the capacity to arrest and reverse introgression. To our knowledge, the presented approach is novel in that it accounts for genetic drift when the genotypes under analysis are temporally separated from the reference populations to which they are being assigned. These methods may be valuable in cases where reference databases for small populations have aged substantially, pedigree information is available or data are generated from historical samples.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available