SynthesisThe Journal of heredity2026
Embracing the power of genomics to inform evolutionary significant units.
Synthesis in The Journal of heredity, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
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Authors and funding
3 authors.
Funding
Abstract
Appropriate identification of evolutionary significant units (ESUs) is essential for effective conservation planning. Genomic data has emerged as a key tool to inform ESU decisions, yet it remains unclear how genomic data are being used in practice to identify the number of ESUs. To address this, we conducted a systematic literature review and found that genomic data are increasingly being used to suggest numbers of ESUs globally across plant and animal taxa. However, our review revealed inconsistencies in how ESUs are defined, with many studies not providing a definition. We also found inconsistencies in the methods used to analyze genomic data, highlighting the need for greater standardization to ensure studies adequately address both the discreteness and evolutionary significant components of ESUs. For example, separation on a principal component analysis or a high Fst cannot ensure evolutionary significance as they may be due to recent processes such as habitat fragmentation. Adaptive loci, a key advantage of genomic data, need to be interpreted with caution and simply identifying these loci may lead to inflated ESU estimates. Overall, we found that 68% of studies suggested an increase in the number of ESUs, and that the amount of gene flow detected did not appear to influence this conclusion. We provide clear definitions of the two key components of ESUs and specify which analyses can be used to assess each, as well as provide recommendations for future studies aiming to identify ESUs with genomic data.
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