Simulating Retention of Rare Alleles in Small Populations to Assess Management Options for Species with Different Life Histories

Preserving allelic diversity is important because it provides the capacity for adaptation and thus enables long-term population viability. Allele retention is difficult to predict in animals with overlapping generations, so we used a new computer model to simulate retention of rare alleles in small populations of 3 species with contrasting life-history traits: North Island Brown Kiwi (Apteryx mantelli; monogamous, long-lived), North Island Robins (Petroica longipes; monogamous, short-lived), and red deer (Cervus elaphus; polygynous, moderate lifespan). We simulated closed populations under various demographic scenarios and assessed the amounts of artificial immigration needed to achieve a goal of retaining 90% of selectively neutral rare alleles (frequency in the source population = 0.05) after 10 generations. The number of immigrants per generation required to meet the genetic goal ranged from 11 to 30, and there were key similarities and differences among species. None of the species met the genetic goal without immigration, and red deer lost the most allelic diversity due to reproductive skew among polygynous males. However, red deer required only a moderate rate of immigration relative to the other species to meet the genetic goal because nonterritorial breeders had a high turnover. Conversely, North Island Brown Kiwi needed the most immigration because the long lifespan of locally produced territorial breeders prevented a large proportion of immigrants from recruiting. In all species, the amount of immigration needed generally decreased with an increase in carrying capacity, survival, or reproductive output and increased as individual variation in reproductive success increased, indicating the importance of accurately quantifying these parameters to predict the effects of management. Overall, retaining rare alleles in a small, isolated population requires substantial investment of management effort. Use of simulations to explore strategies optimized for the populations in question will help maximize the value of this effort.