Acoustic identification of Mexican bats based on taxonomic and ecological constraints on call design

Veronica Zamora-Gutierrez*, Celia Lopez-Gonzalez, M. Cristina Macswiney Gonzalez, Brock Fenton, Gareth Jones, Elisabeth K V Kalko, Sebastien J. Puechmaille, Vassilios Stathopoulos, Kate E. Jones

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

37 Citations (Scopus)
334 Downloads (Pure)



1. Monitoring global biodiversity is critical for understanding responses to anthropogenic change, but biodiversity monitoring is often biased away from tropical, megadiverse areas that are experiencing more rapid environmental change. Acoustic surveys are increasingly used to monitor biodiversity change, especially for bats as they are important indicator species and most use sound to detect, localise and classify objects. However, using bat acoustic surveys for monitoring poses several challenges, particularly in megadiverse regions. Many species lack reference recordings, some species have high call similarity or differ in call detectability, and quantitative classification tools, such as machine learning algorithms, have rarely been applied to data from these areas. 

2. Here, we collate a reference call library for bat species that occur in a megadiverse country, Mexico. We use 4685 search-phase calls from 1378 individual sequences of 59 bat species to create automatic species identification tools generated by machine learning algorithms (Random Forest). We evaluate the improvement in species-level classification rates gained by using hierarchical classifications, reflecting either taxonomic or ecological constraints (guilds) on call design, and examine how classification rate accuracy changes at different hierarchical levels (family, genus and guild). 

3. Species-level classification of calls had a mean accuracy of 66%, and the use of hierarchies improved mean species-level classification accuracy by up to 6% (species within families 72%, species within genera 71·2% and species within guilds 69·1%). Classification accuracy to family, genus and guild-level was 91·7%, 77·8% and 82·5%, respectively. 

4. The bioacoustic identification tools we have developed are accurate for rapid biodiversity assessments in a megadiverse region and can also be used effectively to classify species at broader taxonomic or ecological levels. This flexibility increases their usefulness when there are incomplete species reference recordings and also offers the opportunity to characterise and track changes in bat community structure. Our results show that bat bioacoustic surveys in megadiverse countries have more potential than previously thought to monitor biodiversity changes and can be used to direct further developments of bioacoustic monitoring programs in Mexico.

Original languageEnglish
Pages (from-to)1082-1091
Number of pages10
JournalMethods in Ecology and Evolution
Issue number9
Early online date14 Apr 2016
Publication statusPublished - Sep 2016


  • Acoustic identification
  • Guild
  • Hierarchical classification
  • Machine learning
  • Neotropical
  • Random forest
  • Whispering bats


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