Our lab studies the ecological and genomic mechanisms of plastic responses and evolutionary adaptation to environmental change. We are interested in questions such as: What is the genetic basis of adaptive trait variation in the wild? Can information on the genetic basis of environmentally plastic traits allow us to predict trait expression in novel environments? How will natural selection act on the resulting phenotype in those environments? Can we predict how species will evolve or persist in the face of ongoing rapid environmental change?
We address these questions using the genomic toolbox of Arabidopsis thaliana, combined with field experiments, controlled environmental manipulations, climate data, and modeling. A. thaliana, an inbreeding annual weed of bare ground and arable fields, is ideal for this investigation not only because of the wealth of genomic tools available, but also because its fugitive natural history and broad geographic distribution have exposed it to variable natural selection for adaptation to a wide range of environments. Using this model system, our ultimate goal is to develop a general framework to predict trait expression in novel environments and forecast patterns of population persistence and adaptive evolution in response to changing climate.
Current areas of investigation include:
- The genetic basis of complex traits in natural environments
- Testing for adaptation to climate
- Predictive models of life history expression in natural environments
- Plant conservation biology
Photos of Arabidopsis in the field, Northern Blazing Star, Impatiens capensis
The genetic basis of complex traits in natural environments:
Screens of natural variation in A. thaliana are widely used as a tool for gene discovery, but have largely been confined to controlled conditions. We therefore study the genetic basis of life history traits in natural environments, using field experiments with inbred lines from natural populations (ecotypes) or recombinant inbred lines (RILs) in different seasons and geographic sites (Weinig et al. 2002.2003a,b, Stinchcombe et al. 2004, 2009, Donohue et al. 2005 a,b,c, Korves et al. 2007, Huang et al. 2010, Fournier-Level et al. 2011). We recently completed a set of large-scale experiments, funded by an NSF FIBR grant, in which we grew a large panel of ecotypes, mutants, RILs, and near-isogenic lines (NILs) in multiple plantings(synchronized with natural germination in local populations) in 5 sites spanning the native European climate range (Wilczek et al. 2009, Fournier-Level et al. 2011). Our sites in Spain and Finland represent the species’ southern and northern range limits, respectively, whilst the other three sites lie 
on a longitudinal transect from the oceanic climate of England to the continental climate of eastern Germany. Our data provide a comprehensive study of the genetic basis of flowering time, life history expression, and fitness in multiple natural environments across the native climate range of A. thaliana. We are now engaged in comprehensive analysis of these unique data, which allow us to address a number of important questions about the genetic basis of adaptation to climate in this model species.
We continue to conduct field experiments, coupled with controlled chamber manipulations of specific environmental factors, to test functional hypotheses about the effects of allelic variation at specific candidate loci on life history expression in different photothermal environments, and to measure natural selection on that variation. Because A. thaliana is an inbreeding species, we can combine our field data with genotypes and phenotypic data for the same inbred lines from laboratory studies, as well as geographic and climatic data from the site of origin for ecotypes. These combined data allow us to address a number of specific questions, such as:How is allelic variation at candidate flowering time loci expressed in different natural conditions, and what are the underlying mechanisms? How does the expression of genetic variation depend upon genetic background? What is the genome-wide architecture of life history variation in the wild?
Testing for adaptation to climate:
Latitudinal clines in life history traits observed in common gardens provide strong circumstantial evidence of adaptation to climate on a broad geographic scale in A. thaliana (Stinchcombe et al. 2004. However, to test directly for adaptation to climate, it is necessary to grow genotypes from a large number of populations spanning a range of climates in multiple common gardens across a species’ range, and to measure the fitness of each genotype in each site. Our European field experiments with A. thaliana are, to our knowledge, the first such explicit test of adaptation to climate outside of forestry provenance tests. We can moreover test whether local adaptation to climate is lagging behind rapid recent climate change, an important question for forestry and conservation biology.
Our data also allow us to dissect the ecological and genetic mechanisms underlying local adaptive evolution. Using genotypic selection analysis and structural equation modeling, we are now elucidating how natural selection acts on specific life history traits in different sites and seasons. Our field data also allow us to measure natural selection at specific loci in different natural environments, using genotypic data from several collaborating laboratories. We do this by testing for fitness associations with candidate gene polymorphisms in ecotypes (Korves et al. 2007), as well as genomewide scans for fitness-associated SNPs (Fournier-Level et al. 2011). We can moreover use climate data from the site of ecotype origin to ask whether specific alleles occupy distinct climate niches, using climate envelope analysis tools used by landscape ecologists to predict species range limits (Fournier-Level et al. 2011). We find that entirely different loci are most strongly associated with fitness in each field site. Moreover, the alleles associated with fitness in each site are significantly more differentiated in climate space than genomic controls, suggesting that they may contribute to climatic adaptation. Thus, by combining our field fitness data with climatic information and genomic data for the same ecotypes, we can begin to dissect the underlying genetic mechanisms of local adaptation.
Predictive models of life history expression in natural environments
If we can identify the genetic basis of life history variation in natural conditions, can we then use this information to predict trait expression in novel environments? To this end, in collaboration with Stephen Welch at Kansas State University, we recently developed a genetically informed photothermal model to predict the flowering time of A. thaliana mutants impaired in different environmental signaling pathways in natural seasonal environments (Wilczek et al. 2009). Our approach is to model the daily developmental progress of each genotype from hourly field environmental data on daylength, thermal time, and exposure to chilling, using functions that describe the ability of each genotype to respond to those signals. By manipulating the daily photothermal inputs into the model, we can do thought experiments to predict when any germination cohort of a given genotype will flower in different regions and seasonal climates. For example, by using publicly available climate data as inputs to our model, we can map the predicted flowering time of a given genotype germinating on a given date across the species range (Wilczek et al. 2010). We can moreover predict how flowering time will change within a site or across the species native range under scenarios of future climate change (Wilczek et al. 2010).
Repeated planting experiment
graph of cologne MTTY curve/repeated planting from Wilczek 2009, map of flowering dates across Europe from Wilzcek et al. 2010,
Our next objective is to generalize the model to predict the flowering time of any A. thaliana genotype in any photothermal environment. Toward this end, we are working with collaborators Stephen Welch (KSU) and former postdoc Amity Wilczek (Deep Springs College), to extend the model to predict flowering time of recombinant inbred lines (RILs) and natural ecotypes in our field experiments based upon allelic variation at major flowering time loci. We are refining the model to describe the dynamics of vernalization more accurately, allowing for genetic variation in parameters such as rates of response to chilling. We are currently quantifying these parameters in selected RILs that segregate known polymorphisms in candidate genes affecting vernalization behavior. To test model predictions, we are also growing selected RILs in controlled chamber experiments designed to replicate annual cycles of daylength and daily and seasonal temperature changes experienced by different germination cohorts in present-day Norwich England, as well as predicted temperature profiles in Norwich in 2100 under a representative scenario of predicted climate change.
Photos of Col X Kas experiments,
Another objective is to develop an integrated model of life history expression and population dynamics in natural environments. A major result from our photothermal model, validated by field experiments, is the critical dependence of flowering time on germination timing in natural environments (Wilczek et al. 2009, 2010). Simultaneously, former postdoc Kathleen Donohue (Duke University) has demonstrated experimentally that germination timing in the field in turn depends critically upon the timing of flowering and seed dispersal (Donohue et al. 2005), and can experience strong selection (Donohue et al. 2005, Huang et al. 2010). To predict the actual life history expression of different genotypes under field conditions requires an integrated life-cycle model of germination and flowering dynamics. Hydrothermal models have been developed to predict the dormancy release and germination date of weed seeds as a function of afterripening, chilling, thermal time, and soil water potential, but they have never been informed by genetic studies of dormancy and germination loci (e.g. Huang et al. 2010) or linked to photothermal models of flowering time. We are collaborating with Kathleen Donohue, Amity Wilczek, former postbac Liana Burghardt (now a Ph.D student in the Donohue lab), and Jessica Metcalf (Oxford) to develop such an integrated model for A. thaliana and use it to predict life cycle dynamics of different genotypes in different climates. We hypothesize that genetic variation in the dynamics of dormancy release and photothermal development will interact to determine the seasonal germination timing and consequent life cycle and population dynamics of different genotypes in different environments. For example, the seasonal timing of germination may synchronize or desynchronize flowering time, and vice versa, thus determining the geographic distribution of winter annual and rapid cycling life histories and thus population dynamics. We plan to extend and integrate our photothermal model of flowering with a genetically informed hydrothermal model of germination time (to be parameterized by the Donohue lab) that includes feedbacks of flowering time on germination. The ultimate goal is to create a genetically-informed process-based life cycle model to predict the geographic distribution and adaptive evolution of germination and flowering time in current and future climates.
photo of germinating seedlings, repeated planting experiment?
Plant conservation biology My lab also has an ongoing interest in applying evolutionary ecology to rare plant conservation and invasive species management. We are particularly interested in the role of local adaptation, inbreeding depression, and phenotypic plasticity in population responses to climate change and environmental stress. Several undergraduate theses have explored these issues in a variety of species, including several threatened New England plants (Kane and Schmitt 2001; Gravuer et al. 2003, 2005; Riginos et al. 2007; McGeoch et al. 2008; Mandle et al. 2010).