Understanding the mechanisms by which organisms adapt to environmental conditions is a fundamental question for ecology and evolution. In this study, we evaluate changes in gene expression of a marine mollusc, the eastern oyster Crassostrea virginica, associated with the physico-chemical conditions and the levels of metals and other contaminants in their environment. The results indicate that transcript signatures can effectively disentangle the complex interactive gene expression responses to the environment and are also capable of disentangling the complex dynamic effects of environmental factors on gene expression. In this context, the mapping of environment to gene and gene to environment is reciprocal and mutually reinforcing. In general, the response of transcripts to the environment is driven by major factors known to affect oyster physiology such as temperature, pH, salinity, and dissolved oxygen, with pollutant levels playing a relatively small role, at least within the range of concentrations found in the studied oyster habitats. Further, the two environmental factors that dominate these effects (temperature and pH) interact in a dynamic and nonlinear fashion to impact gene expression. Transcriptomic data obtained in our study provide insights into the mechanisms of physiological responses to temperature and pH in oysters that are consistent with the known effects of these factors on physiological functions of ectotherms and indicate important linkages between transcriptomics and physiological outcomes. Should these linkages hold in further studies and in other organisms, they may provide a novel integrated approach for assessing the impacts of climate change, ocean acidification and anthropogenic contaminants on aquatic organisms via relatively inexpensive microarray platforms.

Summary1. Increasing habitat connectivity is important for mitigating the effects of climate change, landscape fragmentation and habitat loss for biodiversity conservation. However, modelling connectivity at the relevant scales over which these threats occur has been limited by computational requirements. 2. Here, we introduce the open-source software GFLOW, which massively parallelizes the computation of circuit theory-based connectivity. The software is developed for high-performance computing, but scales to consumergrade desktop computers running modern Linux or Mac OS X operating systems. 3. We report high computational efficiency representing a 1739 speedup over existing software using highperformance computing and a 8Á49 speedup using a desktop computer while drastically reducing memory requirements. 4. GFLOW allows large-extent and high-resolution connectivity problems to be calculated over many iterations and at multiple scales. We envision GFLOW being immediately useful for large-landscape efforts, including climate-driven animal range shifts, multitaxa connectivity, and for the many developing use-cases of circuit theorybased connectivity.

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