An optimal and near-optimal strategy to selecting individuals for transfer in captive breeding programs

Abstract

As species extinction rates continue to rise, zoos have adopted a more active role in the conservation of endangered species. A central concern is to preserve genetic diversity of zoological populations. Accordingly, when selecting individuals to transfer to new or existing populations, zoo managers must consider the genetic effects on all populations involved. We propose a quadratic integer programming (IP) model to identify a group of individuals to transfer that maximizes genetic diversity within two subpopulations. We then reduce this model to a linear IP formulation and apply it to the California condor ( Gymnogyps californianus) studbook. After simplifying the linear IP model, optimality is achieved within a reasonable time limit when a limited number of individuals are relocated. We also develop a local improvement algorithm (LIA) to efficiently provide near-optimal solutions when we increase the number of transferred individuals. The LIA quickly obtains optimal solutions when few individuals are transferred and in most cases, the LIA outperforms MetaMK, an existing program used to select animals for transfer.