Hidden Markov modelling of occupancy data O. Gimenez, L. Blanc, A. Besnard, R. Pradel, P. Doherty, E. Marboutin, R. Choquet Montpellier, July 4th, 2014
Aug 13, 2015
Hidden Markov modelling of occupancy data
O. Gimenez, L. Blanc, A. Besnard, R. Pradel, P. Doherty, E. Marboutin, R. Choquet
Montpellier, July 4th, 2014
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
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
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
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
Single-season occupancy models as HMMs
• No colonization ( =0g ) and no extinction (e=0) – closure assumption
Single-season occupancy models as HMMs
Initial statesState
processObservation
process
• No colonization ( =0g ) and no extinction (e=0) – closure assumption
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
E-SURGE and occupancy models
• E-SURGE, software developed to analyze CR data with HMMs
Rémi Choquet Roger Pradel
• Model specification via user-friendly syntax
• Numerical tools (random effects, identifiability)
E-SURGE and occupancy models
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
Detection heterogeneity
Random effect Finite mixture
Royle (2006), Gimenez & Choquet (2010) Pledger et al. (2003), Pradel (2005)
Perspectives – based on CR experiences
1. Distribution mapping: unobserved states via Viterbi
2. Accounting for lack of independence:
• Trap-dependence
• Spatial autocorrelation (memory model)