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
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
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
(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
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.
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