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Abstract The black abalone,Haliotis cracherodii, is a large, long‐lived marine mollusc that inhabits rocky intertidal habitats along the coast of California and Mexico. In 1985, populations were impacted by a bacterial disease known as withering syndrome (WS) that wiped out >90% of individuals, leading to the closure of all U.S. black abalone fisheries since 1993. Current conservation strategies include restoring diminished populations by translocating healthy individuals. However, population collapse on this scale may have dramatically lowered genetic diversity and strengthened geographic differentiation, making translocation‐based recovery contentious. Additionally, the current prevalence of WS remains unknown. To address these uncertainties, we sequenced and analysed the genomes of 133 black abalone individuals from across their present range. We observed no spatial genetic structure among black abalone, with the exception of a single chromosomal inversion that increases in frequency with latitude. Outside the inversion, genetic differentiation between sites is minimal and does not scale with either geographic distance or environmental dissimilarity. Genetic diversity appears uniformly high across the range. Demographic inference does indicate a severe population bottleneck beginning just 15 generations in the past, but this decline is short lived, with present‐day size far exceeding the pre‐bottleneck status quo. Finally, we find the bacterial agent of WS is equally present across the sampled range, but only in 10% of individuals. The lack of population genetic structure, uniform diversity and prevalence of WS bacteria indicates that translocation could be a valid and low‐risk means of population restoration for black abalone species' recovery.
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What the Average Really Means: Dissociating Effect Size and Effect Prevalence using p -curve Mixtures
Abstract Most research in the behavioral sciences aims to characterize effects of interest using sample means intended to describe the “typical” person. A difference in means is usually construed as a size difference in an effect common across subjects. However, mean effect size varies with bothwithin-subject effect sizeandpopulation prevalence(proportion of population showing the effect) in compared groups or across conditions. Few studies consider how prevalence affects mean effect size measurements and existing estimators of prevalence are, conversely, confounded by uncertainty about within-subject power. We introduce a widely applicable Bayesian method, thep-curve mixture model, that jointly estimates prevalence and effect size. Our approach outperforms existing prevalence estimation methods when within-subject power is uncertain and is sensitive to differences in prevalence or effect size across groups or experimental conditions. We present examples, extracting novel insights from existing datasets, and provide a user-facing software tool.
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- Award ID(s):
- 2024923
- PAR ID:
- 10542111
- Publisher / Repository:
- bioRxiv
- Date Published:
- Format(s):
- Medium: X
- Institution:
- bioRxiv
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Posted Content:
- https://doi.org/10.1101/2024.07.31.606048
