My talk at ISEC 2014 ( on how to model occupancy data using hidden Markov models

Hidden Markov modelling of occupancy data

O. Gimenez, L. Blanc, A. Besnard, R. Pradel, P. Doherty, E. Marboutin, R. Choquet

Montpellier, July 4th, 2014

Occupancy models

Occupancy models

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

O U

O = occupied; U = unoccupied

extinction

e

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

Dynamic occupancy models as HMMs

Initial statesState

processObservation

process

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

The E-SURGE of occupancy models

• Model specification via user-friendly syntax

• Numerical tools (random effects, identifiability)

E-SURGE and occupancy models

occupancyinesurge.wikidot.com

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)

Results

Random effect Finite mixture

Average detection probability 0.5 in both models

Perspectives – based on CR experiences

1. Distribution mapping: unobserved states via Viterbi

2. Accounting for lack of independence:

• Trap-dependence

• Spatial autocorrelation (memory model)

Self-promotion…

Bonus slides

Finite mixture model

Model selection in Lynx case study

Multistate occupancy model - 1

Multistate occupancy model - 2

Multistate occupancy model - 3