Animals use sound to communicate their species-specific messages to each other. Background noise, climate, vegetative structure and interspecific competition can lead to acoustic signal divergence between populations, which can be important in species recognition. My dissertation focuses on the interplay between ecology and animal communication. I explore how interspecific competition and background noise differences between populations lead to song divergence in birds, and how this impacts our understanding of community ecology and evolutionary change. I focus on song variation at different spatial scales---within population, between populations and between species. I found that songs vary sufficiently between individuals of a population to make them individually recognizable based on standard song measurements. Character displacement was observed in song when two related species coexist, accompanied by divergence in their morphological characters. Species recognition appears to drive divergence in the related species studied, and results suggest it occurs to minimize aggressive interactions between heterospecific males during the breeding season. Bird song also varies predictably along an environmental gradient as sound adapts to local climate and habitat conditions. Song frequencies appear to occur within frequency windows below significant levels of ambient noise caused by insects. Both bird song and insect song are highly correlated with environment data obtained from remote sensing, suggesting the environment directly affects ambient noise caused by insects, with indirect effects on bird song, which adapt to avoid masking by ambient noise.