Inbreeding rate modifies the dynamics of genetic load in small populations is a research paper published in Ecology and Evolution (2012). On theSindex it has a DataRank of 1.9. It has been cited 48 times, with 45 citing works in its 1-hop citation network.
AbstractThe negative fitness consequences of close inbreeding are widely recognized, but predicting the long‐term effects of inbreeding and genetic drift due to limited population size is not straightforward. As the frequency and homozygosity of recessive deleterious alleles increase, selection can remove (purge) them from a population, reducing the genetic load. At the same time, small population size relaxes selection against mildly harmful mutations, which may lead to accumulation of genetic load. The efficiency of purging and the accumulation of mutations both depend on the rate of inbreeding (i.e., population size) and on the nature of mutations. We studied how increasing levels of inbreeding affect offspring production and extinction in experimental Drosophila littoralis populations replicated in two sizes, N = 10 and N = 40. Offspring production and extinction were measured over 25 generations concurrently with a large control population. In the N = 10 populations, offspring production decreased strongly at low levels of inbreeding, then recovered only to show a consistent subsequent decline, suggesting early expression and purging of recessive highly deleterious alleles and subsequent accumulation of mildly harmful mutations. In the N = 40 populations, offspring production declined only after inbreeding reached higher levels, suggesting that inbreeding and genetic drift pose a smaller threat to population fitness when inbreeding is slow. Our results suggest that highly deleterious alleles can be purged in small populations already at low levels of inbreeding, but that purging does not protect the small populations from eventual genetic deterioration and extinction.
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Base Score Contribution
0.584
From this paper's citation signal
Citation Network Contribution
1.3
From 38 citing papers with measurable signal
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 31% comes from its base citations and 69% from the citation network (38 citing papers contributed measurable signal).
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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