The tradeoff between signal detection and recognition rules auditory sensitivity under variable background noise conditions

- A fitness model seeks for best hearing sensitivity under variable ambient noise conditions.
- The model predicts higher sensitivity for species living in quieter habitats.
- The tradeoff between signal detection and recognition determines the best sensitivity level.

Animal acoustic communication commonly takes place under masked conditions. For instance, sound signals relevant for mating and survival are very often masked by background noise, which makes their detection and recognition by organisms difficult. Ambient noise (AN) varies in level and shape among different habitats, but also remarkable variations in time and space occurs within the same habitat. Variable AN conditions mask hearing thresholds of the receiver in complex and unpredictable ways, thereby causing distortions in sound perception. When communication takes place in a noisy environment, a highly sensitive system might confer no advantage to the receiver compared to a less sensitive one. The effects of noise masking on auditory thresholds and hearing-related functions are well known, and the potential role of AN in the evolution of the species' auditory sensitivity has been recognized by few authors. The mechanism of the underlying selection process has never been explored, however. Here I present a simple fitness model that seeks for the best sensitivity of a hearing system performing the detection and recognition of the sound under variable AN conditions. The model predicts higher sensitivity (i.e. lower hearing thresholds) as best strategy for species living in quiet habitats and lower sensitivity (i.e. higher hearing thresholds) as best strategy for those living in noisy habitats provided the cost of incorrect recognition is not low. The tradeoff between detection and recognition of acoustic signals appears to be a key factor determining the best level of hearing sensitivity of a species when acoustic communication is corrupted by noise.