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This study explores the acoustic identification of Mexican bats as a crucial tool for monitoring programs due to human impact, climate change, and ecosystem services. The research aims for reliable and cost-effective species identification to capture animal community changes. Despite challenges, bats' acoustic traits make them ideal for monitoring. Techniques include analyzing call types and designing calls for different bat species. Various monitoring tools are utilized, such as real-time detectors and acoustic classification techniques like unsupervised learning and random forest models. Preliminary results show promise but suggest the need for improved species classification methods. Ideas for future research include combining supervised and unsupervised training and pre-grouping species for better classification accuracy.
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Acoustic Identification of Mexican bats PhD Veronica Zamora University of Cambridge Dr Vassilios Stathopoulos University College London Prof. Kate Jones University College London
Why bats? Human Impact Ecosystem services Climate change
Monitoring Programs • Must have reliable species identification • Must be easy, cheap and be able to capture tendencies and changes in animal communities • Bat have several monitoring challenges • They also have other characteristics that make them ideal for acoustic monitoring
Challenges for acoustic monitoring Big acoustic diversity Eptesicusfuscus Whispering bats • Echolocating bats Anourageoffroyi
Pipistrellus sp. Three call types based on function • Feeding buzzes • Social calls • Search calls Eptesicusfuscus Myotis sp.
Coverage of bat call references Areas with potential acoustic monitoring Species calls similarity Walters et al. in press Bat Ecology, Evolution & Conservation
Detector Types Real Time e.g. Pettersson D1000x, Laptop with DAQ card Time Expansion e.g. Pettersson D240x, Tranquillity Transect Frequency division(+ Amplitude) e.g. Batbox Duet, Pettersson D230 Frequency division( - Amplitude) e.g. Anabat Heterodine e.g. BatBox III, Magenta, Skye, many others Russ 2012 British Bat Calls
Detection and call isolation Manual
Antrozouspallidusreal time Semi-automatic software: Sonobat Antrozouspalliduscompressed view
Acoustic Classification Techniques Unsupervised Learning Supervised Learning Clustering Topic Models Mixture Models Classification Logistic regression Discrete Variables Regression Time series forecasting Dimensionality reduction Blind source separation Continuous Variables
Supervised Learning: Machine learning • Example: sex classification Output variable Input variables They are trained and learn from the data
Parameters extracted Natalusstramineus
Parameters optimization in each division or node Group of points in a d-dimensional Branches or terminal nodes, the path generated
Forest Construction • Party package in R: conditional unbiased trees • Default Tree depth • 4 variables selected at the time to build the tree • 5000 trees • Out of bag trainning error measurement • Training 80%, testing 20% • Variables: • Model with 71 variables • Model without amplitud • Model with 20 most important variables
Problems • Not good classification for some species
Ideas? • Unsupervised + supervised training • Pre grouping of species?
THANK YOU Juan Cruzado Cristina MacSwiney Celia Lopez Ricardo Lopez Elizabeth Kalko Gareth Jones Brooke Fenton Michael Barataud SebastienPuechmaille Trust funds
