Common garden experiments are a powerful way to both test for local adaptation and apply climate change manipulations. We have used this approach to determine how populations of two co-occurring butterflies of differing specialization and flight capacity (Erynnis propertius and Papilio zelicaon) respond to conditions of the center and poleward periphery of their geographic range. These experiments reveal if climate warming will enhance poleward populations and thus enable poleward range expansion. In both study species, based on measurements of survival, growth, and metabolic rate, we found little evidence for peripheral population enhancement due to warming, and in one species, we found evidence for local adaptation in peripheral populations to winter conditions (Pelini et al. 2009).

We now seek the genetic basis of these fitness differences; therefore, we sequenced the larval transcriptome of both species with 454 FLX Titanium. Data were assembled to maximize assembly quality via comparison with Bombyx mori, and putative gene sequences were spotted onto microarrays. Individuals from a repeat common garden experiment were hybridized to these arrays so that differential gene expression by source population (central versus peripheral) and rearing condition (central versus peripheral) at the end of the growing season could be revealed. Genes that were significantly up or down regulated were compared to B. mori and other whole-genome datasets to identify possible function.

Results/Conclusions  

Greater than 95% of the 454 sequence tags of both species assembled into long (>700 bp on average) and highly covered (>9.5x on average) contigs (O'Neil et al. in review). These contigs, along with unassembled sequences, resulted in 28,044 (E. propertius) and 37,957 (P. zelicaon) putative gene sequences. Further, greater than 50% of B. mori genes had homologous sequences within our putative gene set, indicating broad sampling of transcripts. Results from microarray hybridization will be presented, including preliminary identification of those transcripts that are differentially expressed.

This study is an example of how genomic biology can be used to identify specific functions involved in climatic tolerance or intolerance of non-model organisms. Such information is essential to identify the relative vulnerability of populations and species to climate change and the mechanisms that determine that vulnerability. Such tools also can reveal the extent of genetic diversity at key genes that affect performance under climate change, information needed to predict evolution due to global warming.