Adaptive significance of flowering time variation across natural seasonal environments in Arabidopsis thaliana

Abstract

The relevance of flowering time variation and plasticity to climate adaptation requires a comprehensive empirical assessment. We investigated natural selection and the genetic architecture of flowering time in Arabidopsis through field experiments in Europe across multiple sites and seasons.We estimated selection for flowering time, plasticity and canalization. Loci associated with flowering time, plasticity and canalization by genome-wide association studies were tested for a geographic signature of climate adaptation.Selection favored early flowering and increased canalization, except at the northernmost site, but was rarely detected for plasticity. Genome-wide association studies revealed significant associations with flowering traits and supported a substantial polygenic inheritance. Alleles associated with late flowering, including functional FRIGIDA variants, were more common in regions experiencing high annual temperature variation. Flowering time plasticity to fall vs spring and summer environments was associated with GIGANTEA SUPPRESSOR 5, which promotes early flowering under decreasing day length and temperature.The finding that late flowering genotypes and alleles are associated with climate is evidence for past adaptation. Real-time phenotypic selection analysis, however, reveals pervasive contemporary selection for rapid flowering in agricultural settings across most of the species range. The response to this selection may involve genetic shifts in environmental cuing compared to the ancestral state.

Precipitation timing and soil substrate drive phenology and fitness of Arabidopsis thaliana in a Mediterranean environment

“…From these readings we extracted hourly average temperatures and used them to calculate hourly accumulation of photothermal units (PTUs) from germination to bolting and flowering, with a base temperature of 3°C (Granier et al, 2002; Wilczek et al, 2009) (Figure S6). This metric allowed us to compare time to phenological events in common developmental units across cohorts in different seasonal environments (Fournier‐Level et al, 2013, 2022; Wilczek et al, 2009). Hourly temperature data were also used to calculate accumulation of vernalization units using the vernalization effectiveness function of Wilczek et al (2009; Figure S7).…”

“…DOG1 plays an important role in controlling dormancy levels (Bentsink et al, 2010; Footitt et al, 2015; Martínez‐Berdeja et al, 2020) and has a strong influence on the seasonal timing of germination in the field (Chiang et al, 2011; Postma & Ågren, 2015; Postma & Agren, 2016; Taylor et al, 2017); it may directly or indirectly influence flowering time (Chiang et al, 2013; Huo et al, 2016). FRI confers a vernalization requirement for flowering, and loss‐of‐function mutations at this locus are strongly associated with early flowering (Bloomer & Dean, 2017; Caicedo et al, 2004; Fournier‐Level et al, 2022; Lempe et al, 2005; Méndez‐Vigo et al, 2011; Michaels et al, 2003; Scarcelli et al, 2007; Shindo et al, 2005). However, in natural environments, the phenotypic effects of FRI depend strongly on germination timing (Burghardt et al, 2015; Wilczek et al, 2009).…”

Taking quantitative genomics into the wild

“…These advances have created opportunities to better understand selection and evolution in wild populations. In particular, they have provided a foundation for detailed dissection of the genomic architecture of additive genetic variation [26,31,34], local adaptation [35,36], inbreeding depression [37,38], indirect genetic effects and multi-genomic architectures [24,39], genotype-by-environment interactions [40] and multivariate genetic architectures and selection [41][42][43], as well as more accurate predictions of evolutionary responses and constraints [44][45][46] and the genetic pathways important to local adaptation and fitness [47]. In this special issue, we present 12 contributed publications that encompass broad themes in cutting-edge wild quantitative genomics.…”

The Boechera model system for evolutionary ecology

“…Cellular and molecular biology, development, genetics and genomics, evolution, ecology (Atwell et al, 2010;Fournier-Level et al, 2011;Wilczek et al, 2014;Alonso-Blanco et al, 2016;Exposito-Alonso et al, 2019;Kinoshita and Richter, 2020;Fournier-Level et al, 2022;Monroe et al (Siemens et al, 2002;Franks et al, 2007Franks et al, , 2016Franks, 2011;The Brassica rapa Genome Sequencing Project Consortium et al, 2011;Yu et al, 2013;Kitashiba and Nasrallah, 2014;O'Hara et al, 2016;Hamann et al, 2018;Qiao et al, 2020;Kaur et al, 2021;Mabry et al, 2021) (S. Turner-Hissong, Bayer, pers. comm.…”