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Acoustic Identification of Mexican bats

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.

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Acoustic Identification of Mexican bats

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  1. Acoustic Identification of Mexican bats PhD Veronica Zamora University of Cambridge Dr Vassilios Stathopoulos  University College London Prof. Kate Jones University College London

  2. Why bats? Human Impact Ecosystem services Climate change

  3. 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

  4. Two main monitoring techniques

  5. Challenges for acoustic monitoring Big acoustic diversity Eptesicusfuscus Whispering bats • Echolocating bats Anourageoffroyi

  6. Pipistrellus sp. Three call types based on function • Feeding buzzes • Social calls • Search calls Eptesicusfuscus Myotis sp.

  7. Design of different calls

  8. Coverage of bat call references Areas with potential acoustic monitoring Species calls similarity Walters et al. in press Bat Ecology, Evolution & Conservation

  9. 3.- Acoustic Identification Tools

  10. 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

  11. Detection and call isolation Manual

  12. Antrozouspallidusreal time Semi-automatic software: Sonobat Antrozouspalliduscompressed view

  13. SONOBAT: 72 parameters

  14. 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

  15. Supervised Learning: Machine learning • Example: sex classification Output variable Input variables They are trained and learn from the data

  16. METODOS

  17. Recording bats

  18. Recordings availability

  19. Parameters extracted Natalusstramineus

  20. Random Forest: many trees

  21. Parameters optimization in each division or node Group of points in a d-dimensional Branches or terminal nodes, the path generated

  22. 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

  23. PRELIMINARY RESULTS

  24. Model WITH 20 variables, 45 species and 1918 calls

  25. Problems • Not good classification for some species

  26. Ideas? • Unsupervised + supervised training • Pre grouping of species?

  27. THANK YOU Juan Cruzado Cristina MacSwiney Celia Lopez Ricardo Lopez Elizabeth Kalko Gareth Jones Brooke Fenton Michael Barataud SebastienPuechmaille Trust funds

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