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Relict population recovers from extreme event: genomic insights from a marine forest

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NIAID Data Ecosystem2026-05-01 收录
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https://figshare.com/articles/dataset/Relict_population_recovers_from_extreme_event_genomic_insights_from_a_marine_forest/25524181
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Abstract Extreme climatic events, such as marine heatwaves (MHWs), have devastating consequences for ecosystems, including degradation of genetic diversity, local extinctions and ecosystem collapse and transformation. Resilience comprises resistance to withstand and the ability to recover, which depends on factors such as remaining genetic diversity and population connectivity. In 2011, a MHW caused a 100 km range contraction of kelp (Ecklonia radiata) off Western Australia, but recently recovering kelp forests were discovered. To understand mechanisms of recovery and determine if recovering populations are survivors or immigrants, we used genotyping-by-sequencing to assess patterns of genetic diversity and connectivity. We found that two of the three recovering kelp forests (PG1 and 2) were likely survivors whereas a third smaller population (PGCr 1) was likely produced through re-colonisation from nearby surviving forests. Connectivity was high among populations and migration analysis identified one population (Horrocks) as the most important source for the recovering kelps. All recovering populations had higher neutral genetic diversity, and similar putative adaptive diversity to surrounding surviving populations, suggesting local adaptation. Our results elucidate how mixed processes can contribute to kelp forest resilience following MHWs but cryptic survival and maintenance of population connectivity is key to recovery. Data PG2.inclKalFinal.popmap.clean.csv: popmap for dataset including Kalbarri data (175 individuals) PG2.inclKalFinal.vcf.gz: filtered vcf-file for dataset including Kalbarri data (175 individuals, 663 SNPs) PG3.noKalFinal.popmap.clean: popmap file for dataset not including KAlbarri data (155 individuals) PG3.noKalFinal.vcf: filtered vcf-file for dataset not including Kalbarri data (155 individuals, 6133 SNPs)
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2024-04-05
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