DOI: 10.5061/dryad.760r8
title: Data from: Acoustic identification of Mexican bats based on taxonomic and ecological constraints on call design

description: 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.
 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). 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. 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.
description: Data for each of the 4
 classifiersThere are 4 sheets in
 the excel file. Each sheet contains the data used for each one of the 4
 classifiers. Classifier 1 - Species-level without a hierarchy. Classifier
 2 - Species-level within a family hierarchy. Classifier 3 - Species-level
 within a genus hierarchy. Classifier 4 - Species-level within a guild
 hierarchy. The species acronyms can be found in the supplementary material
 of the paper. The column ‘Fold’ indicates to which one of the 5 folds each
 call was assigned for the 5-folds cross-validation procedure used to
 estimate the accuracy of each classifier.Data-All-Classifiers.xlsx