Machine Learning Problems in Species Occupancy Modeling

1. Machine Learning Problems in Species Occupancy Modeling Rebecca Hutchinson March 25, 2010

2. Toy Example

3. Adding Covariates

4. Challenge #2

5. Birds move. And hide.

6. Multiple Visits • Visit each site more than once, recording detection histories Yit • E.g. • Population closure assumption: the species occupancy status does not change over the course of the visits to a site.

7. Assumptions • Species is never misidentified. • Occupancy status is constant across visits. • Visits are separated enough to be conditionally independent, given the occupancy status. • Sites are independent.

8. Xi Wit oi Zi Yit dit t=1,…,T i=1,…,M a b Key: square=discrete circle=continuous unshaded=latent grey=observed pink=parameter blue=deterministic function of inputs dashed=repeated section Xi = occupancy covariates at site i oi = probability of occupancy at site i Zi = true, unobserved occupancy status of site i a = parameters of occupancy model Wit = detection covariates at site i, visit t dit = probability of detection at site i, visit t Yit = observed presence/absence at site i, visit t b = parameters of detection model

9. Some details • Conditional distributions: • Conditional log-likelihood • Expected joint log-likelihood

10. Typical Usage • Fit a small number of models with differing (small) sets of covariates, using the conditional log-likelihood objective • E.g. model 1 vs. model 2 where • o1 ~ rainfall + elevation, d1 ~ weather + time-of-day • o2 ~ rainfall + temperature, d2 ~ underbrush-density • Evaluate models with AIC • Books on this approach: Mackenzie et al 2006, Royle et al 2007.

11. Outline • Citizen Science: 2 motivating datasets • Problem 1: Integrating more flexible models for occupancy and detection • Regularization • Boosted regression trees • (Joint work with Tom Dietterich) • Problem 2: Alternative detection models • Experts vs. novices • Relaxing assumptions • (Joint work with Weng-Keen Wong and Jun Yu)

12. Cornell Lab of Ornithology Mission: To interpret and conserve the earth’s biological diversity through research, education, and citizen science focused on birds.

13. Birds in Forested Landscapes (BFL) • Goals: • Determine habitat/landscape requirements of forest-dwelling birds (especially thrushes) • Translate results into management recommendations for conservation • Develop a network of experienced citizen scientists • BFL is a continent-wide project that has engaged over 1,000 volunteers who surveyed over 3,000 study sites. • Have data from 1997-2006 • Participants follow a rigorously tested protocol that includes: • selecting suitable study sites • visiting these sites at least twice during the breeding season and • measuring a variety of habitat variables. • http://www.birds.cornell.edu/bfl/

14. BFL data • Select forest patches, then survey points, and one or more species of interest. • Visit 1: earliest date when all your study species have arrived • Want beginning of breeding period, but no birds still migrating. • Visit 2: 2-4 weeks later • Breeding should be underway, different evidence available. • Record presence/absence of 22 possible breeding behaviors observed in each period on each visit. • Record presence/absence of competitors/predators on each visit. • Record environmental variables at large, medium, and small scales. • Observers work in teams of 1-4 people.

15. BFL data: visit protocol example • Observation Period (mandatory 10 minutes) Look and listen for predators, cowbirds, and study species • Playback Period (mandatory 5 minutes per species) Species 1: play songs, calls, or drums for 1 minuteSpecies 1: watch/listen for 1 minuteSpecies 1: repeat songs, calls, or drums for 1 minuteSpecies 1: watch/listen for 2 minutesSpecies 2: play songs, calls, or drums for 1 minuteSpecies 2: watch/listen for 1 minuteSpecies 2: repeat songs, calls, or drums for 1 minuteSpecies 2: watch/listen for 2 minutes • Behavior Watch Period (mandatory 10 minutes)Play eastern or western mobbing calls for 5 minutes while looking and listening for study speciesWatch/listen for 5 minutes

16. BFL data: habitat characteristics • Survey point (where observer stands) • Latitude/longitude • Elevation • Distance to nearest edge, road, water, occupied building • Study site (radius=150m) • Hydrology during breeding season • Forest cover type • Slope • Land use • Land ownership • Canopy characteristics • Low vegetation characteristics • Landscape level (2500 acres) • Patch edge (what habitats are adjacent) • Forest patch size • Percentage of forest • Linear distance of edge • Distance to nearest 100 & 500 acre patches (if patch is less than 1000 acres)

17. Increasing model flexibility • Why? • Many possible habitat variables • interactions? • Exploratory modeling with many covariates rather than hypothesis testing with few • 2 ideas: • Regularization • Boosted regression trees

18. How to regularize these models? • One possible penalty: • How should the two components be weighted? • tug-of-war between occupancy and detection to explain the all-zero detection histories

19. Preliminary synthetic data results • 8 covariates for each model, half of which truly had non-zero coefficients • Choice of objective function seems more important than regularization parameters

20. Posterior Regularization • [Ganchev, Gillenwater, Graca, and Taskar, 2009] • Regularization constraints on posterior expectations instead of parameters, for example: • Expected occupancy is less than 60% • Of the all-zero detection histories, only half can be ‘explained away’ by the detection model

21. Boosted Regression Trees • Popular in species distribution modeling • [Elith et al 2006] • Functional gradient ascent [Friedman 2001] • regression trees predict F(X) and G(W) • F and G are fed through logistic() to get o and d • Current challenge: tuning • learning rate (shrinkage) • number of trees to grow at each stage • depth of trees • number of stages

22. Where Birding Meets Science!

23. eBird—Current Stats (2009) • ~70,000 users • ~540,000 site visitors • 173 countries/territories • >1,500,000 checklists submitted • 2,945 species reported • 21 million observations reported

24. Northern Cardinal Distribution (Frequency of Detection) • Gray – not reported • Tan – insufficient data • White – not covered

25. Extensions needed for eBird? • Alternative detection model • add a node for expertise of observer • Relax the assumption of no-misidentifications • Y|Z=1 ~ Bernoulli(d) • Y|Z=0 ~ Bernoulli(h) • (instead of 0)

26. Model with expertise node Bic Zis Yics Ej Uj Xi s j Wics c i

27. Preliminary results: Synthetic data Synthetic data generated from EOM with different levels of false positives Area under ROC curve Slide courtesy of Jun Yu

28. Preliminary results: eBird data • data from New York from May and June in year 2006, 2007 and 2008. • 27 by 64 Checkerboarding [New York State: Width-285 miles (455 km) and Length-330 miles (530 km): • Each Cell is roughly 16.8 km by 8.3 km. • There are roughly 200 sites generated during training. Slide courtesy of Jun Yu

29. More challenges • Sampling bias • Spatial autocorrelation • For BFL, modeling multiple occupancy states • For eBird, modeling abundance • Multi-species approaches • Dynamic models • migration • range shift

30. Questions?Comments?Suggestions?