1. Hidden Markov modelling of occupancy data
O. Gimenez, L. Blanc, A. Besnard, R. Pradel,
P. Doherty, E. Marboutin, R. Choquet
Montpellier, July 4th, 2014

2. Occupancy models

3. Occupancy models

4. My conversion to occupancy models
• I’m more of a capture-recapture (CR) guy
• Can we use what we know from CR for occupancy?
• Individuals in CR = Sites in occupancy
• Two sides of same coin: hidden Markov models (HMM)
• Suggested by M. Kéry, I. Fiske, W. Challenger, …
• Flexible framework, well developed in other areas

5. Dynamic occupancy models as HMMs
O U
O = occupied; U = unoccupied
extinction
e

6. Dynamic occupancy models as HMMs
1
O U
O = occupied; U = unoccupied
1 = species detected; 0 = species undetected
0 1
extinction
e
1-pp p

7. Dynamic occupancy models as HMMs
1
O U
O = occupied; U = unoccupied
1 = species detected; 0 = species undetected
0 1 0 0 0
1-pp
extinction
0
e
p 0 0

8. Dynamic occupancy models as HMMs
0
U O
O = occupied; U = unoccupied
1 = species detected; 0 = species undetected
0 0 1 1 1
colonization
g
0 0 0 p p p

9. Dynamic occupancy models as HMMs
Initial states
State
process
Observation
process

10. Single-season occupancy models as HMMs
• No colonization (g=0) and no extinction (e=0) – closure assumption

11. Single-season occupancy models as HMMs
Initial states
State
process
Observation
process
• No colonization (g=0) and no extinction (e=0) – closure assumption

12. Advantages of the HMM formulation
1. (Almost?) all occupancy models in a unified framework
• Single-season, dynamic models
• Mixtures and random effects (see case study)
• Multistate models, with uncertainty
• Multispecies models
• False-positives (Chambert & Miller, submitted)
• …
2. Formal link between occupancy and CR communities

13. E-SURGE and occupancy models
• E-SURGE, software developed to analyze CR data with HMMs
Rémi Choquet Roger Pradel

14. The E-SURGE of occupancy models

15. • Model specification via user-friendly syntax
• Numerical tools (random effects, identifiability)
E-SURGE and occupancy models

16. occupancyinesurge.wikidot.com

17. Case study with
Eurasian lynx in
France
• Signs of presence
between 2002 and 2006
• 197 sites, 5 1-y periods
• Single-season
occupancy & detection
heterogeneity
Van Gogh

18. Detection heterogeneity
Random effect Finite mixture
Royle (2006), Gimenez & Choquet (2010) Pledger et al. (2003), Pradel (2005)

19. Results
Random effect Finite mixture
Average detection probability 0.5 in both models

20. Perspectives – based on CR experiences
1. Distribution mapping: unobserved states via Viterbi
2. Accounting for lack of independence:
• Trap-dependence
• Spatial autocorrelation (memory model)

21. Self-promotion…

22. Bonus slides

23. Finite mixture model

24. Model selection in Lynx case study

25. Multistate occupancy model - 1

26. Multistate occupancy model - 2

27. Multistate occupancy model - 3