The inbreeding depression cost of selfing: Importance of flower size and population size in <i>Collinsia parviflora</i> (Veronicaceae) is a research paper published in American Journal of Botany (2008). On theSindex it has a DataRank of 0.799. It has been cited 18 times, with 17 citing works in its 1-hop citation network.
Inbreeding depression should evolve with selfing rate when frequent inbreeding results in exposure of and selection against deleterious alleles. The selfing rate may be modified by plant traits such as flower size, or by population characteristics such as census size that can affect the probability of biparental inbreeding. Here we quantify inbreeding depression (δ) among different population sizes of Collinsia parviflora, a wildflower with interpopulation variation in flower size, by comparing fitness components and multiplicative fitness of experimentally produced selfed and outcrossed offspring. Selfed offspring had reduced multiplicative fitness compared to outcrossed offspring, but inbreeding depression was low in all combinations of population size and flower size (δ ≤ 0.05) except in large populations of large‐flowered plants (δ = 0.45). The decrement to multiplicative fitness with inbreeding was not affected by population size nested within flower size, but differed between small‐ and large‐flowered plants: small‐flowered populations had lower overall inbreeding depression (δ = 0.04) compared to large‐flowered populations (δ = 0.25). The difference in load with flower size suggests that either selection has removed deleterious recessive alleles or these alleles have become fixed in small‐flowered, potentially more selfing populations, but that purging has not occurred to the same extent in presumably outcrossing large‐flowered populations.
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Base Score Contribution
0.442
From this paper's citation signal
Citation Network Contribution
0.357
From 14 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 55% comes from its base citations and 45% from the citation network (14 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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