Curso lichos dia1
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Presentations 20 de junio del 2016, Town Peterson
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- 1. BEGIN WITH A DETOUR … NSB
- 11. No Silver Bullets in ENM • Single algorithms may perform ‘best’ on average • The best algorithm in any given situation, however, may be other than the ‘best’ • NSB thinking suggests that we should not use a single approach • Use a suite of approaches (e.g., as implemented in OM, BIOMOD, BIOENSEMBLES, etc.), challenge to predict, choose best for that situation • Maxent is good, but it is not the only algorithm …
- 12. COMPLEXITY OF MODELS AND OVERFITTING
- 17. Which Model is Best?
- 18. Which Model is Best? Least calibration error Most calibration error Moderate calibration error
- 19. Which Model is Best? Least calibration error Most calibration error Moderate calibration error
- 20. Five Goals of Niche Modeling 1. ESTIMATE THE FUNDAMENTAL NICHE 2. ESTIMATE THE FUNDAMENTAL NICHE 3. ESTIMATE THE FUNDAMENTAL NICHE 4. ESTIMATE THE FUNDAMENTAL NICHE 5. ESTIMATE THE FUNDAMENTAL NICHE
- 21. How Would the Fundamental Niche Look? • In any one dimension, expected to be unimodal • In multiple dimensions, expected to be convex • So, simple models are probably better • Need to take sampling and incomplete representation into account carefully
- 25. The Area of Distribution G Physiological requirements (Abiotic) A Favorable biotic environment (Biotic) B Accessible to dispersal (Movements) M
- 30. Existing niches are irregular, concave, and complex Fundamental niches should be simple and convex
- 33. Current Methods • Popular methods fit highly complex objects to estimate niches … but which niche? • Complex objects are more likely to correspond to the existing niche, rather than the fundamental
- 34. Simplify: • Complex response forms created by many current algorithms do not fit well with our understanding of the fundamental niche • Simple, convex response forms may be much more appropriate as approximations to the fundamental niche • This thinking will most likely require algorithms that can incorporate incomplete data, as well as uncertainty in inference, but fit simple response forms
- 36. Which Model is Best? UNDERFITTING OVERFITTING
- 42. AIC: A Means of Choosing Complex Models • Fit better with realized niche and SDM • Risk overfitting • Frequently will be limited in predictive ability Simple Models • Fit better with ecological niche theory • Risk underfitting • May not address the full complexity of distributional ecology
- 54. Absence Data Abiotic niche Biotic interactionsAccessibility
- 55. “Presence”
- 58. E parameter
- 59. ENM Thresholding • Least Training Presence Thresholding seeks the highest threshold level that includes all calibration data, or T100 • The T100 approach fails when there is error in the occurrence data set • E is a summary of the expected proportion of data in the calibration dataset that will include significant errors • And “Adjusted LTPT” approach would seek T100-E as a thresholding approach ideal for ENM
- 62. You Fit Your ENM… And Now? • Niches to distributions • Consideration of dispersal (non-equilibrium situations) • Add consideration of factors not able to be incorporated directly in the model – E.g., land-use change, human presence
- 63. You Fit Your ENM… And Now? • Niches to distributions • Consideration of dispersal (non-equilibrium situations) • Add consideration of factors not able to be incorporated directly in the model – E.g., land-use change, human presence
- 64. Potential Distribution Abiotic niche Biotic interactions
- 65. Potential and Actual Distributions Abiotic niche Biotic interactionsAccessibility
- 66. Niches to Distributions • In geographic space, distributions are the results of the BAM intersection: GO = A ∩ B ∩ M • In “canonical” ENM, the model output is something like A, or A∩B if the Eltonian Noise Hypothesis holds • What does this say about the semantics? – Ecological niche modeling – Species distribution modeling
- 69. Niches to Distributions GO = A ∩ B ∩ M • Need to restrict model outputs via some hypothesis of M … post hoc?
- 72. Test Arena: The Lawrence Species
- 73. M and Model Training
- 74. M and Model Validation
- 75. Model Evaluation
- 76. M and Model Comparison
- 77. Model Comparison
- 78. The Area of Distribution G Physiological requirements (Abiotic) A Favorable biotic environment (Biotic) B Accessible to dispersal (Movements) M Sampling Effort
- 79. Interesting Result • If model calibration is constrained to M, and if the Eltonian Noise Hypothesis holds, then … GO = A • Simplifies the modeling process enormously, and eliminates the need for post-modeling assumptions about M
- 80. You Fit Your ENM… And Now? • Niches to distributions • Consideration of dispersal (non-equilibrium situations) • Add consideration of factors not able to be incorporated directly in the model – E.g., land-use change, human presence
- 81. What About Non-equilibrium Situations? • Non-equilibrium: – Species does not inhabit the entire spatial footprint of its habitable area • E.g., a projection of future-climate potential distribution of a species • E.g., an invading species that has only established populations in a small part of its potential range • E.g., a species being evaluated in terms of invasive potential on other continents • Need to bring in consideration of dispersal
- 88. You Fit Your ENM… And Now? • Niches to distributions • Consideration of dispersal (non-equilibrium situations) • Add consideration of factors not able to be incorporated directly in the model – E.g., land-use change, human presence
- 89. Often Cannot Include Land Cover… • Occurrence data are too old, or are heterogeneous temporally • Occurrence data are not sufficiently finely resolved spatially to permit link to land cover • Land use change is ongoing, such that there is not a single, stable state • Solution: incorporate after model calibration
- 93. Adding Fine-scale Effects • When ENMs cannot be calibrated at the fine resolutions of land use • Can make the assumption that species have particular land-use associations, and that they will be found only under certain land use types • The coarse, climate-based ENM output can be “clipped” by the finer-resolution land use outputs
- 94. town@ku.edu