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• W3001190147 abstract "Random environmental fluctuations pose major threats to wild populations. As patterns of environmental noise are themselves altered by global change, there is a growing need to identify general mechanisms underlying their effects on population dynamics. This notably requires understanding and predicting population responses to the colour of environmental noise, in other words its temporal autocorrelation pattern. Here, we show experimentally that environmental autocorrelation has a large influence on population dynamics and extinction rates, which can be predicted accurately provided that a memory of past environment is accounted for. We exposed nearly 1,000 lines of the microalgae Dunaliella salina to randomly fluctuating salinity, with autocorrelation ranging from negative to highly positive. We found lower population growth, and twice as many extinctions, under lower autocorrelation. These responses closely matched predictions based on a tolerance curve with environmental memory, showing that non-genetic inheritance can be a major driver of population dynamics in randomly fluctuating environments. Exposing experimental lines of microalgae to random fluctuations in salinity, the authors show that environmental autocorrelation strongly influences population growth and extinction risk, with accurate predictions only possible when the impact of past environments is taken into account." @default.
• W3001190147 created "2020-01-30" @default.
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• W3001190147 date "2020-01-27" @default.
• W3001190147 modified "2023-10-06" @default.
• W3001190147 title "Phenotypic memory drives population growth and extinction risk in a noisy environment" @default.
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• W3001190147 doi "https://doi.org/10.1038/s41559-019-1089-6" @default.
• W3001190147 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7025894" @default.
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• W3001190147 hasPublicationYear "2020" @default.
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