Predicting the evolutionary dynamics of seasonal adaptation to novel climates in Arabidopsis thaliana

Abstract

Predicting whether and how populations will adapt to rapid climate change is a critical goal for evolutionary biology. To examine the genetic basis of fitness and predict adaptive evolution in novel climates with seasonal variation, we grew a diverse panel of the annual plant Arabidopsis thaliana (multiparent advanced generation intercross lines) in controlled conditions simulating four climates: a present-day reference climate, an increased-temperature climate, a winter-warming only climate, and a poleward-migration climate with increased photoperiod amplitude. In each climate, four successive seasonal cohorts experienced dynamic daily temperature and photoperiod variation over a year. We measured 12 traits and developed a genomic prediction model for fitness evolution in each seasonal environment. This model was used to simulate evolutionary trajectories of the base population over 50 y in each climate, as well as 100-y scenarios of gradual climate change following adaptation to a reference climate. Patterns of plastic and evolutionary fitness response varied across seasons and climates. The increased-temperature climate promoted genetic divergence of subpopulations across seasons, whereas in the winter-warming and poleward-migration climates, seasonal genetic differentiation was reduced. In silico "resurrection experiments" showed limited evolutionary rescue compared with the plastic response of fitness to seasonal climate change. The genetic basis of adaptation and, consequently, the dynamics of evolutionary change differed qualitatively among scenarios. Populations with fewer founding genotypes and populations with genetic diversity reduced by prior selection adapted less well to novel conditions, demonstrating that adaptation to rapid climate change requires the maintenance of sufficient standing variation.climate change | annual plant | genomic prediction | season O ngoing climate change is causing rapid shifts in environmental selective pressures within local populations (1, 2). To persist, populations must track the shifting multivariate trait optimum by phenotypic plasticity or adaptive evolution (3, 4), or migrate to keep up with poleward shifts in their original climate niche (1). The outcome of these responses to climate change will depend upon the seasonal variation a population experiences, particularly in temperate climates, where seasonality is a major source of environmental heterogeneity (5, 6). Understanding adaptation to seasonal environments is critical for predicting the response to climate change in short-lived organisms with multiple generations per year, like many insects and annual plants. Such prediction requires theoretical projections based on solid empirical foundations, tracking phenotypic change in complex traits as well as in the molecular variation present within populations as they adapt to different seasons. Here, we use a genomic prediction model, based on experimental data from Arabidopsis thaliana, to simulate trajectories of adaptation to novel climate scenarios in season...

Control of seed dormancy in Arabidopsis by a cis -acting noncoding antisense transcript

“…seed dormancy | DOG1 gene | cis-acting ncRNA | antisense transcript P lants have evolved elaborate adaptation mechanisms to cope with unexpected and rapid changes in their natural environment (1). The division of the plant life cycle into consecutive developmental phases can be viewed as one such mechanism.…”

Natural variation on whole‐plant form in the wild is influenced by multivariate soil nutrient characteristics: natural selection acts on root traits

“…At the PSC and NAV common garden sites, we harvested plants when spring precipitation ceased (early May), such that we were able to excavate intact root structures, take photographs and measure phenotypes using WinRhizo as described above before root senescence. Implementing a maturity index (MI of Fournier‐Level et al., ), we combined survival, quantitative reproductive output, and aboveground maturity as a measure of fitness in our garden plants. As above, we used standardized traits and relative fitness and regressed relative fitness on root traits to evaluate phenotypic selection.…”

Evolutionary and ecological functional genomics, from lab to the wild

“…The development of systems biology has also provided the opportunity to use transcriptome data, in conjunction with other data types (Bonneau et al ., ), to infer key regulatory networks that display the genetic underpinnings of plant responses to the environment. Such regulatory networks include environmental gene regulatory influence networks (EGRINs; Wilkins et al ., ; Figure ), as well as other representations of gene regulatory modules reacting to environmental signals (Nagano et al ., ; Plessis et al ., ; Fournier‐Level et al ., ; Des Marais et al ., ; Miao et al ., ).…”

“…While transcriptome studies have become a convention for model species and crop plants in an EEFG context, studies on wild plant species place more emphasis on the basic mechanisms for survival and adaptation. The attention to different environments goes hand‐in‐hand with increased interest in modeling evolutionary responses in relation to climate change and environmental stress (Nagano et al ., ; Plessis et al ., ; Fournier‐Level et al ., ; Watson‐Lazowski et al ., ). Because transcriptional reprogramming was the standard in examining plant defense responses, in the EEFG framework, the transcriptional implications of responses to biotic interactors are more rigorously being considered at the systems level in natural environments (Turner et al ., ; Liao et al ., ; Nobori et al ., ; Young et al ., ).…”