Species extinction rates are causing alarm. Anthropogenic distortion of the climate system is rapidly altering the natural environment. Arabidopsis thaliana is a model species in molecular biology with widespread wild populations showing functional diversity however its ecology and evolution is poorly understood. Faced with a changing natural world, what is the adaptive potential of the model plant species Arabidopsis thaliana? This thesis focuses on the interactions of genotypes, phenotypes and environments to assess the current state of adaptation in this vagile species and to identify mechanisms for rapid adaptation to future stress, focusing on plant pathogens. Here I show that A. thaliana populations in England exhibit evidence of local adaptation and genetic structure. A large common garden experiment using genotypes gathered in natural habitats revealed functional fitness differences in genotype-by-environment interactions. Wild populations showed differential representation of RPM1 alleles suggesting non-random processes are responsible for the exhibited patterns. A further common garden experiment demonstrated ‘home site advantage’ through a correlation between fitness and home site climate, which suggests that local adaptation had occurred. Phenotypic plasticity and mechanisms for rapid adaptation could be essential for plant survival under predicted climate change. Using Xanthomonas spp. as xenopathogens, I show differing levels of pre-adaptation for pathogen response exists in wild UK populations of A. thaliana. By using a multi-generation study, I found some evidence that epigenetic modification enabled rapid adaptation to pathogen stress. Finally, I compared the metabolic expressions of phenotype among genotypes in two artificial environments. Environmental effects detected by this method are far greater than genetic ones, suggesting that metabolic plasticity can underpin environmental adaptation. Taken together, my results suggest that wild populations of A. thaliana contain a range of mechanisms for rapid adaptation to environmental change. If these capacities are general, my work offers a note of optimism about the fate of some wild plant species in the face of global climate change. Additionally, as A. thaliana is a model species in genomics, my findings may facilitate future exploitation of these traits by crop geneticists.

The use of Arabidopsis thaliana populations to understand the genetic basis for natural variation has been highlighted in recent years. The role of adaptation in natural variation remains of key interest. Here, natural variation in growth rate, flowering time and seed production were examined in local populations of A. thaliana from the Edinburgh area using a common garden approach. Growth rate and seed production were found to be highly genetically determined and sometimes correlated, and some genotypes were found to perform consistently better as winter annuals and others as summer annuals, suggesting that adaptation to different seasons might maintain natural variation locally. In order to dissect the environmental factors that could affect growth, these genotypes were also grown under controlled conditions. Photoperiod and temperature were identified as two of the seasonal variables to which different genotypes may be adapted. The relationship between growth rate and competition was also examined. In general, competition exaggerated the differences in performance between genotypes, although the identity of neighbours was observed to have an effect on both growth rate and fitness of A. thaliana in competition. To understand the genetic basis of growth rate variation, the genetic relationships between local populations was examined. Local accessions were usually found to be more closely related to each other than to world-wide accessions, suggesting that their variation did not reflect recent immigration. To examine the genetic architecture of growth rate variation, hybrids between local genotypes with different growth rates were used in QTL analysis. Four chromosomal regions were detected; these regions represent potential growth-rate associated QTL.