Download - 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

Theoria Metodos Resultados Parciales Retos

Why bats?

Human ImpactEcosystem 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


Two main monitoring techniques


Challenges for acoustic monitoring

1. Big acoustic diversity

Anou

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oyi

Eptesicus fuscus

Echolocating bats

Whispering bats

Three call types based on function


Pipistrellus sp.

Eptesicus fuscus

Myotis sp.

Search calls

Social callsFeeding buzzes


Design of different calls

Walters et al. in press Bat Ecology, Evolution & Conservation

Areas with potential acoustic monitoring


Coverage of bat call references

Species calls similarity

3.- Acoustic Identification Tools

Real Timee.g. Pettersson D1000x, Laptop with DAQ card

Time Expansione.g. Pettersson D240x, Tranquillity Transect

Frequency division(+ Amplitude)e.g. Batbox Duet, Pettersson D230

Frequency division( - Amplitude)e.g. Anabat

Heterodinee.g. BatBox III, Magenta, Skye, many others

Russ 2012 British Bat Calls


Detector Types

Manual

Detection and call isolation



Antrozous pallidus real time

Antrozous pallidus compressed view

Semi-automatic software: Sonobat


SONOBAT: 72 parameters

Supervised Learning Unsupervised LearningDi

scre

te V

aria

bles

Conti

nuou

s Var

iabl

es

ClassificationLogistic regression

RegressionTime series forecasting

ClusteringTopic Models

Mixture Models

Dimensionality reductionBlind source separation


Acoustic Classification Techniques

Supervised Learning: Machine learning

They are trained and learn from the data

Example: sex classification

Altura Peso Tamaño Pies

1.75 78 42

1.62 53 37… … …

1.72 65 39

Sexo

Macho= 1

Hembra= 0

…

Hembra = 0

Input variables Output variable


METODOS

Recording bats


Recordings availability



Nat

alus

stra

min

eus

Parameters extracted


Random Forest: many trees


Branches or terminal nodes, the path generated

Group of points in a d-dimensional

Parameters optimization in each

division or node

• 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


Forest Construction

PRELIMINARY RESULTS

SDM Conceptual Model Statistical Formulation Evaluation and Calibration Software

Accuracy 0.62724Kappa 0.615677AccuracyLower 0.567597AccuracyUpper 0.684149AccuracyNull 0.075269AccuracyPValue 4.06E-122

Model WITH 20 variables, 45 species and 1918 calls

Confusion Matrix by Class

Confusion Table

• Not good classification for some species


Problems

• Unsupervised + supervised training

• Pre grouping of species?

Ideas?


Trust funds

THANK YOU

Juan CruzadoCristina MacSwiney

Celia LopezRicardo LopezElizabeth KalkoGareth Jones

Brooke FentonMichael Barataud

Sebastien Puechmaille