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Negotiating social dynamics among allies and enemies is a complex problem that often requires individuals to tailor their behavioral approach to a specific situation based on environmental and/or social factors. One way to make these contextual adjustments is by arranging behavioral output into intentional patterns. Yet, few studies explore how behavioral patterns vary across a wide range of contexts, or how allies might interlace their behavior to produce a coordinated response. Here, we investigate the possibility that resident female and male downy woodpeckers guard their breeding territories from conspecific intruders by deploying defensive behavior in context-specific patterns. To study whether this is the case, we use correlation networks to reveal how suites of agonistic behavior are interrelated. We find that residents do organize their defense into definable patterns, with female and male social mates deploying their behaviors non-randomly in a correlated fashion. We then employ spectral clustering analyses to further distill these responses into distinct behavioral motifs. Our results show that this population of woodpeckers adjusts the defensive motifs deployed according to threat context. When we combine this approach with behavioral transition analyses, our results reveal that pair coordination is a common feature of territory defense in this species. However, if simulated intruders are less threatening, residents are more likely to defend solo, where only one bird deploys defensive behaviors. Overall, our study supports the hypothesis that nonhuman animals can pattern their behavior in a strategic and coordinated manner, while demonstrating the power of systems approaches for analyzing multiagent behavioral dynamics. 

Emerging large-scale biobanks pairing genotype data with phenotype data present new opportunities to prioritize shared genetic associations across multiple phenotypes for molecular validation. Past research, by our group and others, has shown gene-level tests of association produce biologically interpretable characterization of the genetic architecture of a given phenotype. Here, we present a new method, Ward clustering to identify Internal Node branch length outliers using Gene Scores (WINGS), for identifying shared genetic architecture among multiple phenotypes. The objective of WINGS is to identify groups of phenotypes, or “clusters,” sharing a core set of genes enriched for mutations in cases. We validate WINGS using extensive simulation studies and then combine gene-level association tests with WINGS to identify shared genetic architecture among 81 case-control and seven quantitative phenotypes in 349,468 European-ancestry individuals from the UK Biobank. We identify eight prioritized phenotype clusters and recover multiple published gene-level associations within prioritized clusters.