Premium
Reintroduction programmes: genetic trade‐offs for populations
Author(s) -
Earnhardt Joanne M.
Publication year - 1999
Publication title -
animal conservation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.111
H-Index - 85
eISSN - 1469-1795
pISSN - 1367-9430
DOI - 10.1111/j.1469-1795.1999.tb00074.x
Subject(s) - genetic diversity , biology , captive breeding , selection (genetic algorithm) , population , genetic variation , effective population size , ecology , evolutionary biology , genetic variability , conservation genetics , zoology , endangered species , demography , genetics , microsatellite , genotype , habitat , allele , artificial intelligence , sociology , computer science , gene
Reintroductions of captive‐managed animals are a vital tool for the conservation of populations and species extinct in the wild. Because genetic diversity may impact population persistence, the genetic composition of reintroduced and captive populations is critical. For captive‐managed species with known pedigrees, individual animals can be selected to create reintroduced populations with specific genetic compositions. Five genetic and demographic strategies for selecting animals were tested on four species. Selection strategies were based on criteria identified in field and theoretical studies. Simulated reintroductions of animals from captive‐breeding programmes created a genetic conflict. Among the different strategies, increases in the genetic diversity of one subpopulation were negatively correlated with changes in the genetic diversity of the other subpopulation. For example, the release of genetically over‐represented animals was the most beneficial strategy for a captive‐breeding programme, but provided the least genetic diversity for the reintroduced population. However, gains and losses in genetic diversity between populations varied with different selection criteria and different population structures. Because captive‐breeding histories vary for each species, changes in genetic composition cannot be accurately predicted and no one strategy is universally optimal. Thus, genetic trade‐offs must be assessed for each population relative to specific programme goals.