In Mediterranean climates, the timing of seasonal rains determines germination and flowering phenology, which in turn may affect fitness. As climate change alters seasonal precipitation patterns, it is important to ask how these changes will affect the phenology and fitness of plant populations. We addressed this question experimentally with the annual plant species Arabidopsis thaliana. In a first experiment, we manipulated the date of rainfall onset and recorded germination phenology on sand and soil substrates. In a second experiment, we manipulated germination date, growing season length, and mid-season drought to measure their effects on flowering time and fitness. Within each experiment, we manipulated seed dormancy and flowering time using multilocus near-isogenic lines segregating strong and weak alleles of the seed dormancy gene DOG1 and the flowering time gene FRI.  We synthesized germination phenology data from the first experiment with fitness functions from the second experiment to project population fitness under different seasonal rainfall scenarios. Germination phenology tracked rainfall onset but was slower and more variable on sand than on soil. Many seeds dispersed on sand in spring, and summer delayed germination until the cooler temperatures of autumn. The high-dormancy DOG1 allele also prevented immediate germination in spring and summer. Germination timing strongly affected plant fitness. Fecundity was highest in the October germination cohort and declined to extremely low levels in spring germinants. The late flowering FRI allele had lower fecundity.  Projections of population fitness revealed that: 1) Later onset of autumn rains will negatively affect population fitness. 2) Slow, variable germination on sand buffers populations against fitness impacts of variable spring and summer rainfall. 3) Seasonal selection favors high dormancy and early flowering genotypes in a Mediterranean climate.  In particular, the high-dormancy DOG1 allele delayed germination of spring-dispersed fresh seeds until more favorable early fall conditions, resulting in higher fecundity and projected population fitness. These findings suggest that Mediterranean annual plant populations are vulnerable to changes in seasonal precipitation, especially in California where rainfall onset is already occurring later. The fitness advantage of highly dormant, early flowering genotypes helps explain the prevalence of this strategy in Mediterranean populations.

The seasonal timing of seed germination determines a plant's realized environmental niche, and is important for adaptation to climate.  The timing of seasonal germination depends on patterns of seed dormancy release or induction by cold and interacts with flowering time variation to construct different seasonal life histories. To characterize  the genetic basis and climatic associations of natural variation in seed chilling responses and associated life history syndromes, we selected 559 fully-sequenced accessions of the model annual species Arabidopsis thalianafrom across a wide climate range and scored each for seed germination across a range of 13 cold stratification treatments as well as the timing of flowering and senescence. Germination strategies varied continuously along two major axes: 1) overall germination fraction and 2) induction vs release of dormancy by cold. Natural variation in seed responses to chilling was correlated with flowering time and senescence to create a range of seasonal life history syndromes. Genome-wide association (GWA) identified several loci associated with natural variation in seed chilling responses, including a known functional polymorphism in the self-binding domain of the candidate gene DOG1. A phylogeny of DOG1haplotypes revealed ancient divergence of these functional variants associated with periods of Pleistocene climate change, and Gradient Forest analysis showed that allele turnover of candidate SNPs was significantly associated with climate gradients. These results provide evidence that Arabidopsis thaliana’s germination niche and correlated life history syndromes are shaped by past climate cycles as well as local adaptation to contemporary climate.

1.Many short-lived species complete their life cycles during brief seasonal windows of favorable environmental conditions. Such species may persist in the face of climate warming by migration to track their seasonal climate niche in space and/or by phenological shifts to track favorable conditions in time within the year. To describe the seasonal climate niche of the short-lived annual Mollugo verticillata in California, we used data from herbarium specimens and historic climate records to estimate environmental conditions at the location, month and year of each collection. 2.We used these data in a MaxEnt framework to construct a seasonal species distribution model (SDM) of the species’ climate niche within the total climate space available across all seasons and locations in California. The model provides fine-scale spatial and temporal predictions of habitat suitability, predicting both where and when the species should be observed. 3.We compared the predictions of the model to those from a conventional SDM based on mean annual climate data. Both models showed that M. verticillata is limited to warm environments within California. However, the seasonal SDM also predicted phenology by mapping climate suitability across the state for each month of the year. Mollugo verticillata is limited to warm months, and its seasonal climate niche shifts in space across California in the course of the year. 4.We used the seasonal SDM to map the predicted future species distribution for each month of the year under three warming scenarios. The species is predicted to expand its range and occur earlier in the year in most locations; in the warmest locations seasonal suitability is predicted to decline in the warmest months, which may result in bimodal phenology with a mid-summer gap. 5.Synthesis - We developed a novel species distribution model using herbarium records and monthly weather data, which predicts not only where a short-lived species should be found, but when during the year it is predicted to occur in those areas. This model can be used to predict how climate change will affect the species distribution in space as well as seasonal phenology across the landscape.