1. Intellectual drivers for behavior Evolution Epigenetics methylation alternative splicing microbiome brain-body interface Ecological context Environmental metadata Individual variation

2. Bottlenecks Technical – New ‘omics’ hard to transfer from model to nonmodel Epigenetics – New theory linking evolution and epigenetics needed – Mapping epigenetics to the genome Behavior genes can be complex – Pleiotropic, complex, background effects, sites of action Databasing – Lack of resources for phenotypes – Lack of culture in the community

3. Bottlenecks II Getting core parties together (Lew grant) – Organismal biologists – Technologists (‘-omics’) – Systems biologists (analysis) Can’t sell a one-behavior paradigm for all species Comparative approach: need many genomes, transcriptomes Complex experimental designs needed – >1 environment – Individual variation – Better statistical tools

4. Networks Networks, rather than individual genes, may be paramount What is a conserved network? (scout bees, forgaing…) Levels of analysis – Molecular – Neurobiological – Pathway Using the environment to interrogate genes and networks

5. Challenges Different actions of conserved networks Need new models for gene action – Threshold – Multiple inputs  similar outputs New experimental designs to better infer causality in behavior – Limitations of dyad comparisons Capturing relevant tissues, behavioral states – Non-invasive monitoring, limitations of organ size – Using species ‘frozen in time’ for specific behavioral comparisons G X E: Changes through time and individual experience (development) Capturing/monitoring the totality of an individual’s current environment

6. Educational challenges Taxonomic vs. conceptual routes to science A ‘Woods Hole’ course for Ecological Genomics Bridging the computational divide – Mentoring graduate students Choosing a particular system or species for particular questions

7. Core issues – the future Dominance of classical Darwinian evolution Noninvasive RNA reporting Optigenomics (brainbow) Interrogating multiple genes, networks Synthesizing multiple sensory inputs – (chemical, visual, etc) Tracking individual variation, mining videos Lab-field interface G X E interactions, changes through time Artificial selection – Monitoring multiple phenotypes simultaneously