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Setting up for modeling. Remember goals: Desired model and uncertainty Sample area selection What resolution will we model at? What are we modeling? How will we validate the model? Do we have enough sample data or too much? Do we have the predictor variables we need?.

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setting up for modeling
Setting up for modeling
  • Remember goals:
    • Desired model and uncertainty
  • Sample area selection
  • What resolution will we model at?
  • What are we modeling?
  • How will we validate the model?
  • Do we have enough sample data or too much?
  • Do we have the predictor variables we need?
sample area selection
Sample Area Selection
  • What is the area of interest?
  • How was the area sampled?
    • Optimal: rigorously sampled, randomly spaced plots
    • Worse case: data integrated from multiple sources with little documentation on collection methods
  • How far can we extent the study area realistically?
  • Best to pad the extent a bit
sample area selection1
Sample Area Selection
  • CA – Douglas fir
sample area selection2
Sample Area Selection
  • CA – Redwood (coastal)
sample area selection3
Sample Area Selection
  • CA - Giant Sequoia
resolution
Resolution
  • Higher resolution:
    • Better detail
    • Can be misleading
    • Performance problems
  • Lower resolution:
    • Much faster processing
    • Can hide “niche environments”
  • Recommendation:
    • Use lowest predictor layer resolution for all layers, or lower
    • Grid to lower resolution as needed
what are we modeling
What are we modeling?
  • Need a relationship we can model
  • Measured variables:
    • Height, DBH, Canopy size
    • Weight, length, grain size, proportion
    • Income, longevity
  • Presence/Absence:
    • Logistic regression
  • Occurrences:
    • Density?