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Adaptive maternal effects in early life history traits help to maintain ecological resilience in novel environments for two contrasting Senecio species

Walter, G. M.; du Plessis, S.; Terranova, D.; la Spina, E.; Majorana, M. G.; Pepe, G.; Clark, J.; Cozzolino, S.; Cristaudo, A. E.; Hiscock, S. J.; Bridle, J. R.

2021-02-07 evolutionary biology
10.1101/2021.02.04.429835 bioRxiv
Show abstract

While many organisms shift their development plastically to maintain fitness as environments change, such plasticity has limits. Population mean fitness is expected to decline when novel environments exceed the limits to plasticity, but the level of fitness costs is expected to vary among genotypes that can increase adaptive potential. We lack fundamental insights into how and when changes in early development traits increase adaptive potential in novel environments, which limits our ability to predict the response of natural populations to global change. To test whether genetic variation in development time is associated with increased adaptive potential in novel environments, we used a breeding design to generate c.20,000 seeds of two ecologically contrasting Sicilian species of daisies (Senecio, Asteraceae) adapted to high and low elevations on Mount Etna. We planted the seeds across four elevations that included the native range of each species, and a novel elevation. We tracked seedling mortality and measured development time as the number of days it took seedlings to establish. As predicted, genetic variance in survival increased at novel elevations. However, genetic variance in development time showed the opposite trend, decreasing at novel elevations. A strong negative genetic correlation between development time in the native range and survival at novel elevations suggested that genotypes with faster development in native environments survived better in novel environments. These results were consistent across the two ecologically contrasting species, suggesting that genetic variance in early development in native environments could be used to predict population responses to novel environments.

Published in Evolution Letters (predicted rank #1) · training set

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