Below you will find links to a number of different Introductory Lecttures covering a diverse range of topics related to Biologging Studies and the analysis of Biologging data. The lectures introduce worked examples using case studies from both the marine and terrestrial environments. Lectures and materials have been prepared by international experts in their respective fields.

(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


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


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


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


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


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


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

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


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


Download: Lecture 9: Inferring behaviour: a case study