Upgrade to Pro — share decks privately, control downloads, hide ads and more …

Implications of uncertainty: Bayesian modelling of aquatic invasive species spread

Implications of uncertainty: Bayesian modelling of aquatic invasive species spread

Talk given at the International Aquatic Invasive Species (ICAIS) conference on April 23rd, 2013.

Corey Chivers

April 23, 2013
Tweet

More Decks by Corey Chivers

Other Decks in Science

Transcript

  1. Implications of uncertainty: Bayesian modelling of aquatic invasive species spread

    Corey Chivers & Brian Leung Dept. of Biology, McGill University http://madere.biol.mcgill.ca/cchivers/
  2. None
  3. 3 Difficulties in forecast modelling of invasive species • Limited

    Data – Finite resources – Rare events – Long Distance Dispersal – Large scale phenomena • Incomplete knowledge of how the processes work – How well is 'reality' captured through our abstraction? • Stochasticity – Invasion/spread are probabilistic phenomena – Noise and non-determinism
  4. Prediction is very hard, Prediction is very hard, especially about

    the future especially about the future -Niels Bohr, physicist (1885-1962) -Niels Bohr, physicist (1885-1962)
  5. 5 Why does uncertainty matter? • Rather than a single

    estimate about a future state of nature, a forecast should be a probability distribution over the range of possible future states. t=0 t=T Probability Density
  6. 6 Why does uncertainty matter? t=0 t=T Probability Density Risk

    =( Probability) x(Consequence)
  7. Outcome A Outcome B Outcome C Outcome E Outcome F

    Do A Do B Do C Do D Do Nothing t = 0 t = T Time In a changing world, not making a decision has consequences, intended or otherwise.
  8. 8 Forecasting aquatic species spread

  9. 9

  10. 10 O O O O = known uninvaded

  11. 11 X X X O O O X = known

    invaded O = known uninvaded
  12. 12 X X X O O O ? ? ?

    ? ? ? ? ? ? X = known invaded O = known uninvaded ? = Unknown ?
  13. 13 X X X O O O ? ? ?

    ? ? ? ? ? ? X = known invaded O = known uninvaded ? = Unknown ?
  14. 14 X X X O O O ? ? ?

    ? ? ? ? ? ? X = known invaded O = known uninvaded ? = Unknown ?
  15. 15 X X X O O O ? ? ?

    ? ? ? ? ? ? X = known invaded O = known uninvaded ? = Unknown ?
  16. 16 311/1600 = 19% data coverage

  17. 17 Bayesian modelling of dispersal and environmental suitability Non-equilibrium species

    distribution modelling Q it E it α i GM X i β αi =−log(1−p i ), P i = 1 1+e−z i , z i =β 0 +∑ j=1 E β j X ij . E(Q it ,αi )=1−e−(α i Q it )c , Q it = propagule pressure generated by underlying dispersal network c > 1 indicates an Allee effect α i = habitat suitability X ij = Environmental condition j at site i. β j = Estimated coefficients
  18. 18 Environmental Variables: Sodium (mg/L) Potassium (mg/L) Magnesium (mg/L) Calcium

    (mg/L) Total Phosphorus (μg/L) SiO3 (mg/L as Si) Dissolved Organic Carbon (mg/L) True Colour (TCU) Total inflection point alkalinity (mg/L as CaCO3) Total fixed end point alkalinity to pH 4.5 (mg/L as CaCO3) pH Conductivity @ 25*C (μS/cm) Secchi Depth
  19. 19 Results

  20. 20

  21. 21 Validation • We can evaluate the performance of this

    model using AUC. (~0.85) • What we really want to know are probabilities. – Expressions of uncertainty • Ongoing work into a validation metric which assesses model performance in terms of probability across the entire prediction range. • Will use 102 new sample points from 2010.
  22. Code available on Github https://github.com/cjbayesian/grav_mod

  23. Thank you Supervisors: Dr. Brian Leung Dr. Elena Bennett Dr.

    Claire De Mazancourt Dr. Gregor Fussman 300 Lakes Survey Team Lab Mates: Johanna Bradie Paul Edwards Kristina Marie Enciso Andrew Sellers Lidia Della Venezia Erin Gertzen