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.

Keywords: annual plant; climate change; genomic prediction; season.

Fig. 1.

(A) Structure of traits network. This topology was inferred through iterative mixed modeling by jointly using all climates and seasonal plantings. The different colors highlight four different trait functional clusters: life history timing, growth, reproductive fitness, and offspring development. A dynamic view of the network is available at traitnet.adaptive-evolution.org/DynAdapt/TraitNetwork.html. (B) Phenological and fitness model. Details are provided in Materials and Methods.

Fig. 2.

Change in mean estimated seed number per plant over the year after 50 y of simulation in four experimental climates. The seed number is expressed relative to its mean value in the first year of simulation. Each of the 250 lines corresponds to an independent simulation. The x axis represents a set of discrete cohorts germinating at the given dates. Dynamic analysis for all traits can be viewed at traitnet.adaptive-evolution.org/DynAdapt/VideoLauncher.html.

Fig. 3.

Seasonal reaction norms of the estimated seed number (Left) and mean estimated seed number (Right) across replicated simulations over time. For the seasonal reaction norms, each segment of the radar plot represents the normalized mean relative fitness (y axis) calculated from 2001 for each germination date (circularized x axis), arbitrarily ranging from 0 to 1. A minimum value indicates that the seed number is reaching the minimal seed number for this specific climate at this specific germination date, whereas a maximum value indicates the seed number reaches its maximum over the years. For the mean seed number plot, each curve represents a different cohort transplanted over the year: March 1 spring cohort is shown in solid green, July 1 summer cohort is shown in dashed yellow, and November 15 fall cohort is shown in dotted brown. The x axes are identical to the x axes in Fig. 2. A dynamic real-time analysis for all traits can be viewed at traitnet.adaptive-evolution.org/DynAdapt/VideoLauncher.html.

Fig. 4.

Evolution of Nei diversity index over 50 y of simulation and histogram of the distribution in 2050. The simulations all started from the same level in 2001, but curves were only plotted from 2005 onward to improve readability.

Fig. 5.

Probability of fixation for each individual SNP allele in the different climates against the initial allele frequency in the starting population. This probability is calculated as the number of times a given allele reaches fixation across simulation replicates. The dashed line corresponds to the expected values of fixation probability proposed by Fisher and obtained from the diffusion approximation theory for different coefficient of selection in units of 4N_es. Colored crosses correspond to the alleles considered to have reached fixation more often than expected under neutrality, and were thus considered as selected alleles. Colored dots correspond to those selected alleles that also showed association with a trait through either simple GLMs (P < 0.001) or genomic prediction models (SNP effect in top 0.1 percentile). FRI_Col, FRIGIDA allele of the Columbia parent; FRI_Ier, FRIGIDA allele of the Landsberg parent.

Fig. 6.

Absolute mean seed number per plant over the year for 100 y of simulation for three scenarios transitioning from the current REF climate to a novel climate under climate change. Solid lines represent the seed number for the initial population in 2001 under the current climate (black/gray) and for the final population in 2100 after experiencing the future climate (red/pink). Dashed lines represent the reciprocal transplant of the 2001 population directly to the future climate without adaptation (blue/cyan) and of the 2100 population back to the current climate (green/emerald). For each scenario, the thin light lines represent each of the 500 replicated runs and the thick dark lines represent the mean.