Introductory lectures

(1) Introductory Lecture

Stacy De Ruiter

1. Why tag?
2. Ethics of tagging?
3. Ancillary data & data challenges
4. Data sharing
5. The pros and cons of different tag types
6. Getting started data analysis and software environments?
7. Getting started Tag Tools and Importing data
8. Running example: Controlled Exposure of Beaked whales to Mid-frequency Active Sonar

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Download: Lecture 1: Introduction

(2) Sensors, calibration & orientation

Mark Johnson

1. Degrees of freedom in animal movement
2. Biologging sensors for animal movement
3. Reference frames & axes
4. Accelerometers & Magnetometers
5. Animal orientation & Euler angles
6. Gyroscopes

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Download: Lecture 2: Sensors, calibration & orientation

(3) Track reconstruction

Tiago Marques

1. Why reconstruct tracks?
2. Spatial and temporal scales of movement
3. Relative vs. absolute positions
4. Representing movement, pseudo-tracks
5. Dead reckoning
6. Join the dots using Hidden Markov Models & State-Space models

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Download: Lecture 3: Track reconstruction

(4) Fine-scale tracking of animals

Mark Johnson

1. Why measure animal tracks?
2. Tracking animals & GPS tags for marine mammals
3. Co-ordinate systems, frames of reference & projections
4. Dead-reckoning & estimating velocity
5. Sources of error
6. Merging GPS and Dead-reckoned tracks
7. Group movements

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Download: Lecture 4: Fine-scale tracking of animals

(5) Signal processing in Biologging

Mark Johnson

1. What are signals?
2. Welcome to the frequency domain
3. Fourier analysis?
4. Real signals and spectrograms
5. Filters and how to use them
6. Sampling continuous signals

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Download: Lecture 5: Signal processing in Biologging

(6) Change point detection

Stacy De Ruiter

1. Detection & Classification?
2. When to supervise? From un-supervised all the way to super-supervised.
3. Examples. Excursions such as dives or flights.
4. Challenges
5. Detecting change points.
6. Single chage points vs. multiple change points.
7. Frustrations

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Download: Lecture 6: Change point detection

(7) Event detection

David Sweeney

1. Detection theory
2. Signal vs. noise
3. Thresholds
4. Blanking times
5. ROC curves
6. Case study with examples: detecting lunge feeding events

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Download: Lecture 7: Event detection

(8) Statistics for tag data

Stacy de Ruiter & Tiago Marques

1. What is statistics?
2. Models vs. Reality
3. Hypothesis testing
4. Autocorrelation & model assessment
5. Dealing with dependence
6. Regression, LM, GLM, GLMM, GAM, GAMM
7. GEE vs Random Effects
8. Multivariate anlaysis, PCA & Mahalanobis distance
9. Rotation tests & Hidden Markov Models
10. Sampling continuous signals

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Download: Lecture 8: Statistics for tag dat

(9) Inferring behaviour: A case study

Saana Isojunno

Title: Activity time budgets in long-finned pilot whales. Pros and challenges of applying mixture & hidden Markov models to classify behaviour.

1. Multivariate mixture models vs. Hidden Markov Models
2. Data discretization, selection & classification
3. Modelling variation
4. Summary

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Download: Lecture 9: Inferring behaviour: a case study