The document discusses hierarchical models for species distributions and the importance of including sampling models in species distribution modeling. It presents different observation models (Binomial, Poisson, Bernoulli, no observation model) that account for sampling effort and compares their predictions on species distribution data from the UK. The key points are that sampling effort needs to be measured and accounted for, different effort estimates can lead to different sampling models, and sampling models should be included in species distribution modeling to improve predictions.

1. Hierarchical Models for Species Distributions Bob O'Hara BiK-F Frankfurt am Main, Germany

2. Stuff I'd like to talk about Dispersal Dynamics Processes

3. Sampling & Observation http://www.flickr.com/photos/slowloris/

4. Presence Only Models Pseudo-absences MaxEnt Dave Wharton : Both approximations to point processes

5. Problem: False Absences Some things are just not seen

6. We need to measure effort

7. Different ways of estimating effort lead to different sampling models

8. Estimating Sampling Effort Repeated visits: can use absences Use reports of other species as estimate of effort?

9. Data Database of UK butterfly records Year 2000 10km x 10km squares Covariates: proportion of habitat, latitude & longitude

10. The Model Process Model and Sampling Model http://pixdaus.com/single.php?id=235258

11. Process Model I i – presence of species s at site i X ij – proportion of habitat of type j at location i G ij – Latitude/longitude at location i

12. Observation Models Binomial Poisson Incidence Compare several: None

13. Binomial Observation Model Photo:USFWS r ~ Bin( N , p )

14. Poisson Observation Model Photo:USFWS r ~ Po( N l )

15. Bernoulli Observation Model Photo:USFWS At least 1 razorbill I ~ Bin( 1 , 1-e - N l )

16. No Observation Model

17. Adonis Blue (possibly) http://www.flickr.com/photos/markkilner/3867177467/

18. Predicted Ranges

19. Comparing Predictions

20. Gatekeeper http://www.flickr.com/photos/duncanh1/3859133643/

21. Predicted Ranges (presences slightly offset)

22. Comparing Predictions

23. So? We should include sampling models in our SDMs Might have to be imaginative about measuring effort There are several models we could use

24. For the Future Better process model http://services.niagaracollege.ca/unitedway/virtual%20break.htm Check which model is actually best