Modeling minimum viable population size with multiple genetic problems of small populations
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Updated: Sep 20, 2025

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
Published on: July 4, 2007
Peter Nabutanyi1, Meike J Wittmann1
1Department of Theoretical Biology, Faculty of Biology, Bielefeld University, Bielefeld, Germany.
Abstract:
An important goal for conservation is to define minimum viable population (MVP) sizes for long-term persistence of a species. There is increasing evidence of the role of genetics in population extinction; thus, conservation practitioners are starting to consider the effects of deleterious mutations (DM), in particular the effects of inbreeding depression on fitness. We sought to develop methods to account for genetic problems other than inbreeding depression in MVP estimates, quantify the effect of the interaction of multiple genetic problems on MVP sizes, and find ways to reduce the arbitrariness of time and persistence probability thresholds in MVP analyses. To do so, we developed ecoevolutionary quantitative models to track population size and levels of genetic diversity. We assumed a biallelic multilocus genome with loci under single or multiple, interacting genetic forces. We included mutation-selection-drift balance (for loci with DM) and 3 forms of balancing selection for loci for which variation is lost through genetic drift. We defined MVP size as the lowest population size that avoids an ecoevolutionary extinction vortex. For populations affected by only balancing selection, MVP size decreased rapidly as mutation rates increased. For populations affected by mutation-selection-drift balance, the MVP size increased rapidly. In addition, MVP sizes increased rapidly as the number of loci increased under the same or different selection mechanisms until even arbitrarily large populations could not survive. In the case of fixed number of loci under selection, interaction of genetic problems did not always increase MVP sizes. To further enhance understanding about interaction of genetic problems, there is need for more empirical studies to reveal how different genetic processes interact in the genome.
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