Lark Sparrows have Ecogeographic Song Variation across North America
Fuertes, S. H.; Provost, K. L.
Show abstract
Machine learning models can be used to analyze large bioacoustics datasets and explore variation due to geography or habitat. We find that in the monotypic Lark Sparrow (Chondestes grammacus), both environmental variables and geographic distance influence song variation in this species. Bird song is an important method of communication within avian species. The variation in bird song within a species can be due to a variety of factors, including genetic drift and isolation by distance. However, it remains unclear in species with wide ranges how environmental factors in particular can cause changes to the song. In this study, the song C. grammacus was analyzed via machine learning to determine if it had significant variation based on multiple geographical metrics. We trained a convolutional neural network to segment individual syllables of 91 C. grammacus recordings, then extracted song characteristics. We found that ecoregion and state explain variation in C. grammacus songs. Our results demonstrate the efficacy of using machine learning models to analyze large datasets, as well as the impact that ecogeographic variation has on song variance.
Matching journals
The top 10 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Allopatric montane wren-babblers exhibit similar song notes but divergent vocal sequences 94%
- Testing the maintenance of natural responses to survival-relevant calls in the conservation breeding population of a critically endangered corvid (Corvus hawaiiensis) 93%
- Food-associated calls in disc-winged bats 91%
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- Solo songs, duets, and territory defense across seasons in female Galapagos Yellow Warblers (Setophaga petechia aureola) 94%
- A densely sampled and richly annotated acoustic dataset from a wild bird population 93%
- Galapagos yellow warblers differ in behavioural plasticity in response to traffic noise depending on proximity to road. 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.