Implications of uncertainty: Bayesian modelling of aquatic invasive species spread

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

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/
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
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
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