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Testing Predictive Performance of Ecological Niche Models

Testing Predictive Performance of Ecological Niche Models. A. Townsend Peterson, STOLEN FROM Richard Pearson. Niche Model Validation. Diverse challenges … Not a single loss function or optimality criterion Different uses demand different criteria

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Testing Predictive Performance of Ecological Niche Models

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  1. Testing Predictive Performance of Ecological Niche Models A. Townsend Peterson, STOLEN FROM Richard Pearson

  2. Niche Model Validation • Diverse challenges … • Not a single loss function or optimality criterion • Different uses demand different criteria • In particular, relative weights applied to omission and commission errors in evaluating models • Nakamura: “which way is relevant to adopt is not a mathematical question, but rather a question for the user” • Asymmetric loss functions

  3. Where do I get testing data????

  4. Model calibration and evaluation strategies: resubstitution Projection Calibration Same region Different region Different time Different resolution All available data 100% Evaluation (after Araújo et al. 2005 Gl. Ch. Biol.)

  5. Model calibration and evaluation strategies: independent validation Projection Same region Different region Different time Different resolution All available data Calibration 100% Evaluation (after Araújo et al. 2005 Gl. Ch. Biol.)

  6. Model calibration and evaluation strategies: data splitting Projection Calibration Calibration data Same region Different region Different time Different resolution 70% Test data 30% Evaluation (after Araújo et al. 2005 Gl. Ch. Biol.)

  7. Types of Error

  8. The four types of results that are possible when testing a distribution model (see Pearson NCEP module 2007)

  9. Presence-absence confusion matrix Recorded (or assumed) absent Recorded present Predicted present a (true positive) b (false positive) Predicted absent c (false negative) d (true negative)

  10. Thresholding

  11. Selecting a decision threshold (p/a data) (Liu et al. 2005 Ecography 29:385-393)

  12. Selecting a decision threshold (p/a data)

  13. Omission (proportion of presences predicted absent) (c/a+c) Commission (proportion of absences predicted present) (b/b+d) Selecting a decision threshold (p/a data)

  14. LPT T10 Selecting a decision threshold (p-o data)

  15. Threshold-dependent Tests(= loss functions)

  16. The four types of results that are possible when testing a distribution model (see Pearson NCEP module 2007)

  17. Presence-absence test statistics Recorded (or assumed) absent Recorded present Predicted present a (true positive) b (false positive) Predicted absent c (false negative) d (true negative) Proportion (%) correctly predicted (or ‘accuracy’, or ‘correct classification rate’): (a + d)/(a + b + c + d)

  18. Presence-absence test statistics Recorded (or assumed) absent Recorded present Predicted present a (true positive) b (false positive) Predicted absent c (false negative) d (true negative) Cohen’s Kappa:

  19. Presence-only test statistics Recorded (or assumed) absent Recorded present Predicted present a (true positive) b (false positive) Predicted absent c (false negative) d (true negative) Proportion of observed presences correctly predicted (or ‘sensitivity’, or ‘true positive fraction’): a/(a + c)

  20. Presence-only test statistics Recorded (or assumed) absent Recorded present Predicted present a (true positive) b (false positive) Predicted absent c (false negative) d (true negative) Proportion of observed presences correctly predicted (or ‘sensitivity’, or ‘true positive fraction’): a/(a + c) Proportion of observed presences incorrectly predicted (or ‘omission rate’, or ‘false negative fraction’): c/(a + c)

  21. Leaf-tailed gecko (Uroplatus) Presence-only test statistics:testing for statistical significance U. sikorae U. sikorae Success rate: 4 from 7 Proportion predicted present: 0.231 Binomial p = 0.0546 Success rate: 6 from 7 Proportion predicted present: 0.339 Binomial p = 0.008

  22. Absence-only test statistics Recorded (or assumed) absent Recorded present Predicted present a (true positive) b (false positive) Predicted absent c (false negative) d (true negative) Proportion of observed (or assumed) absences correctly predicted (or ‘specificity’, or ‘true negative fraction’): d/(b + d)

  23. Absence-only test statistics Recorded (or assumed) absent Recorded present Predicted present a (true positive) b (false positive) Predicted absent c (false negative) d (true negative) Proportion of observed (or assumed) absences correctly predicted (or ‘specificity’, or ‘true negative fraction’): d/(b + d) Proportion of observed (or assumed) absences incorrectly predicted (or ‘commission rate’, or ‘false positive fraction’): b/(b + d)

  24. Recorded present Recorded (or assumed) absent a (true positive) b (false positive) Predicted present Predicted absent c (false negative) d (true negative) AUC: a threshold-independent test statistic (1 – omission rate) (fraction of absences predicted present) sensitivity = a/(a+c) specificity = d/(b+d)

  25. Threshold-independent assessment: The Receiver Operating Characteristic (ROC) Curve A B set of ‘absences’ set of ‘presences’ 1 Frequency 0 1 Predicted probability of occurrence sensitivity C set of ‘presences’ set of ‘absences’ Frequency 0 0 1 0 1 Predicted probability of occurrence 1 - specificity (check out: http://www.anaesthetist.com/mnm/stats/roc/Findex.htm)

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