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Maximum Entropy spatial modeling with imperfect data

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Spatial modeling with imperfect data Example: species distribution models What’s wrong with available data and why we want to use it anyway Comparison of different approaches Maximum Entropy approach: Maxent Model evaluation: AUC

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Building models with imperfect data Example: constructing (or reconstructing) species distributions Paleoecology, conservation, speciation, invasion Often data is presence-only (Elith et al. 2006): – Museum records – Herbaria – Fossil locals – Reported sightings Sparse data Spatial bias Temporal bias Uncertainty in absence records

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Building models with imperfect data Elith et al. 2006: Evaluated methods for modeling species distributions using presence-only data Compared 16 methods for 226 species in 6 geographic regions Models were built using presence-only data and climate and environmental layers Evaluated against independent presence- absence datasets

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Building models with imperfect data Elith et al. 2006: Models that use only occurrences: – Envelope BIOCLIM – Distance based DOMAIN, LIVES Models that characterize background (psuedo-absence): – GLM, GAM, MARS, GARP, MAXENT, BRT, GDM, MARS- COMM Some models implemented in several ways

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Building models with imperfect data Elith et al. 2006:

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Building models with imperfect data Elith et al. 2006:

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Building models with imperfect data Elith et al. 2006:

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Maxent: A maximum entropy approach Occurrence is a Lat-Long pair denoting location of observation\collection Layers that inform the model are from same geographic area in raster format Model represents approximation of the realized niche for species Assumed that the realized niche and the fundamental niche for the species coincide Increasing sampling in larger geographic area (and thus including more variation in environmental conditions encountered by the species) may increase the fraction of fundamental niche represented by occurrences

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Fundamental Niche

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Realized Niche

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Fundamental Niche

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Maxent methods The approximation of an unknown probability distribution should satisfy any known constraints, and subject to those constraints should have maximum entropy (Jaynes, 1957) Maximum Entropy is an epistemic approach to Bayes’ rule The Monkey Example: – A team of monkeys are employed to create images by throwing balls at a grid of bins – Every so often the grid is removed and replaced by a new one – Eventually the monkeys will create multiple copies of each possible arrangement

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Maxent methods The Monkey Example (cont.): – Given some evidence about true grid some of the monkey’s grids can be ruled out – Those left constitute the feasible set, and that which appears most often is a reasonable choice – Assuming the monkeys are not biased, this choice is consistent with the data but noncommittal about information we do not have

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Maxent methods π is the unknown probability distribution over a finite set X (the set of pixels or points in the study area) The distribution defines a non-negative probability π(x) to each point x These probabilities sum to 1 Best approximation of π is the probability distribution π(hat) The entropy of π(hat) is:

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Maxent methods Constraints on π(hat) for layers informing model: – Linear features- continuous variables should be close to their observed values (their mean at occurrence localities) – Quadratic features- variance of continuous variables should be close to observed values – Product features- covariance of two continuous variables should be close to observed values – Threshold feature- proportion of model that has values above a threshold for a continuous variable should be close to observed proportion – Binary feature- the proportion of each category in a categorical feature should be close to the observed proportions

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Maxent methods Regularization parameter B j governs how close the constraints need to match the observed value (without regularization they must be equal) Program allows a user-specified proportion of occurrence locals to be reserved from model training for model testing absences can be randomly selected (pseudo-absences for presence only) or specified by user (if P-A data available) Model will run for either a set number of iterations or until the gain from each iteration falls below a set threshold

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Maxent example: brown-throated three-toed sloth, Bradypus variegatus

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Log contribution of each variable to the raw prediction value

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Maxent example : brown-throated three-toed sloth, Bradypus variegatus

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Other Maxent Applications (Siva 1990)

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Model Evaluation Area under ROC curve (AUC) Receiver Operating Characteristic Contingency Table: Actual Value (Data) Predicted Outcome (Model) Presence (pos) Absence (neg) Presence (pos) True Positive (TP) False Positive (FP) Absence (neg) False Negative (FN) True Negative (TN)

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Model Evaluation Area under ROC curve (AUC) Sensitivity- True Positive Rate (TPR) Actual Value (Data) Predicted Outcome (Model) Presence (pos) Absence (neg) Presence (pos) True Positive (TP) False Positive (FP) Absence (neg) False Negative (FN) True Negative (TN)

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Model Evaluation Area under ROC curve (AUC) Specificity- True Negative Rate (TNR) Actual Value (Data) Predicted Outcome (Model) Presence (pos) Absence (neg) Presence (pos) True Positive (TP) False Positive (FP) Absence (neg) False Negative (FN) True Negative (TN)

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Model Evaluation Area under ROC curve (AUC) Specificity- True Negative Rate (TNR) ROC is Sensitivity by (1- Specificity)=(FPR) Actual Value (Data) Predicted Outcome (Model) Presence (pos) Absence (neg) Presence (pos) True Positive (TP) False Positive (FP) Absence (neg) False Negative (FN) True Negative (TN)

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Model Evaluation Area under ROC curve (AUC) An example: TPR = 63/100 =.63 FPR = 28/100 =.28 Actual Value (Data) Predicted Outcome (Model) P=100N=100 P=91TP=63FP=28 N=109FN=37TN=72

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Image from wikipedia A P=100N=100 P=91TP=63FP=28 N=109FN=37TN=72 B P=100N=100 P=154TP=77FP=77 N=46FN=23TN=23 C P=100N=100 P=112TP=24FP=88 N=88FN=76TN=12

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Image from wikipedia A P=100N=100 P=91TP=63FP=28 N=109FN=37TN=72 B P=100N=100 P=154TP=77FP=77 N=46FN=23TN=23 C P=100N=100 P=112TP=24FP=88 N=88FN=76TN=12 C’ P=100N=100 P=112TP=88FP=24 N=88FN=12TN=76

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TPTN FP FN

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TPTN FP FN TP TN FN

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TPTN FP FN TP TNFP

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AUC > 0.5 Higher Predictive Power AUC = 0.5 Random Chance AUC < 0.5 Worse than Random False Positive Rate True Positive Rate 1 1 False Positive Rate True Positive Rate 1 0 False Positive Rate True Positive Rate 1 1 0 0 Model Evaluation Area under ROC curve (AUC)

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References: Elith, J., Graham, C. H., Anderson, R. P., Dudı´k, M., Ferrier, S., Guisan, A., Hijmans, R. J.,Huettmann, F., Leathwick, J. R., Lehmann, A., Li, J., Lohmann, L. G., Loiselle, B. A., Manion, G.,Moritz, C., Nakamura, M., Nakazawa, Y., Overton, J. McC., Peterson, A. T., Phillips, S. J.,Richardson, K. S., Scachetti-Pereira, R., Schapire, R. E., Sobero´n, J., Williams, S., Wisz, M. S. and Zimmermann, N. E. 2006. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29: 129 -151 Jaynes, E.T., 1957. Information theory and statistical mechanics. Phys. Rev. 106, 620 - 630 Lobo, J. M., Jiménez-Valverde, A. and Real R. 2007. AUC a misleading measure of the performance of predictive distribution models. Global Ecology and Biogeography Phillips, S. J., Dudik, M. and Schapire, R. E. 2004. A maximum entropy approach to species distribution modeling. Proceedings of the 21 st International Conference on Machine Learning, Banff, Canada Phillips, S. J., Anderson, R. P. and Schapire, R. E. 2006. Maximum entropy modeling of species geographic distributions. Ecological Modeling 190: 231-259 Siva, D. S., 1990. Bayesian Inductive Inference Maximum Entropy & Neutron Scattering. Los Alamos Science, Summer: 180 – 206 Maxent program website (its free): http://www.cs.princeton.edu/~schapire/maxent/

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