© KE Adventure
Multi-state open robust design models for dealing with incomplete sampling and imperfect detection: an example with sperm whales and opportunistic photo-ID data Rebecca M Boys, Cláudia Oliveira, Sergi Pérez-Jorge, Rui Prieto, Lisa Steiner, Mónica A. Silva
Estimating demographic parameters of highly-mobile species
• Wide-ranging
Azores
Steiner et al., 2015
Estimating demographic parameters of highly-mobile species
• Wide-ranging
Azores
Steiner et al., 2015
• Dedicated sampling
Estimating demographic parameters of highly-mobile species
• Wide-ranging
Azores
Steiner et al., 2015
• Dedicated sampling
• Opportunistic sampling Eco-tourism (whale watching) Citizen Science
R. Prieto@ImagDOP
Problem: Heterogeneity
Uneven sampling Movements Heterogeneity in capture probability
Biased estimates Lower precision
Problem: Heterogeneity
Uneven sampling Movements Heterogeneity in capture probability
Biased estimates Aim: • Open covariate-based model • Multi-state model
Lower precision
Solution: Open covariate-based POPAN model
• Open POPAN model with covariate describing variation in capture
• PriorCapL function – Was the animal seen in previous time periods?
Time 1
Time 2
Time 3
?
?
? Lisa Steiner
Solution: Multi-State Open Robust Design (MSORD)
• Multi-state models with Pollock’s Robust Design • Accounts for imperfect detection
Inside study area: Observable
Outside study area: Unobservable
(Pollock 1982; Kendall et al. 1997; Schwarz and Stobo 1997; Kendall and Bjorkland 2001)
POPAN: Demographic parameters
Outside study area Inside study area • Capture probability • Survival • Annual abundance
Super-population size
Entry probability
MSORD: Demographic parameters
Outside study area Inside study area • Capture probability • Survival • Annual abundance • Remaining probability • Residence time
Temporary emigration
Entry probability
Study site and dataset no. survey days
2500
cum no. ind
100 90
2000
80
70 1500
60 50
1000
40 30
500
20
10
Year
R. Medeiros@ImagDOP
2014
2012
2010
2008
2006
2004
2002
2000
1998
1996
1994
1991
1989
0 1987
0
Number survey days
Cumulative number individuals
• July-start September • POPAN:2009-15 (7 years) • MSORD:2011-15 (5 years)
Study site and dataset no. survey days
2500
cum no. ind
100 90
2000
80
70 1500
60 50
1000
40 30
500
20
10
Year
R. Medeiros@ImagDOP
2014
2012
2010
2008
2006
2004
2002
2000
1998
1996
1994
1991
1989
0 1987
0
Number survey days
Cumulative number individuals
• July-start September • POPAN:2009-15 (7 years) • MSORD:2011-15 (5 years)
MSORD: Estimates of inter-annual movement
Temporary emigration: Even-flow
Inside study area Observable
=
Outside study area Unobservable
Varied between years from 0.22-0.66
MSORD: Estimates of intra-annual movement
Probability of entry, remaining and average residence Start summer 0.40 (SE=0.047)
Study area Residence: 3 weeks Remaining: low 0.053 (SE=0.025)
End summer 0.33 (SE=0.041)
MSORD and POPAN: Survival
POPAN PriorCapL
MSORD Time Since Marking
0.95 (95%CI=0.56-0.99)
0.93 (95%CI=0.73-1.00)
High and constant Consistent with results from other studies
POPAN: Abundance
Total abundance estimate
Super-population estimate: 1468 (95%CI: 1202.8 – 1791.0) 1200 1000 800
600 POPAN 400 200 0 2009
2010
2011
2012
Year
2013
2014
2015
2016
Total abundance estimate
MSORD: Abundance 1200 1000 800
600
MSORD POPAN
400 200 0 2009
2010
2011
2012
Year
2013
2014
2015
2016
Total abundance estimate
MSORD: Abundance 1200
More Precise
Less Biased
1000 800
600
MSORD POPAN
400 200 0 2009
2010
2011
2012
Year
2013
2014
2015
2016
Model comparison
MSORD Imperfect detection Transience (Time Since Marking) Temporary emigration Intra-annual pooling of data Coarse estimates
Model comparison
MSORD
POPAN
Imperfect detection Transience (Time Since Marking) Temporary emigration
Super-population Transience (PriorCapL)
Intra-annual pooling of data Coarse estimates
Temporary emigration Biased annual abundance
Importance and applicability
• Permitted us to derive key population parameters
JorgeFontes@ImagDOP
Importance and applicability
• Permitted us to derive key population parameters • Importance of using appropriate CMR methods for modelling: – Opportunistic data – Wide-ranging species
JorgeFontes@ImagDOP
Importance and applicability
• Permitted us to derive key population parameters • Importance of using appropriate CMR methods for modelling: – Opportunistic data – Wide-ranging species JorgeFontes@ImagDOP
• MSORD reduces bias, improves precision and reliability of estimated parameters
Thank you for your attention
[email protected] Azores Whale Lab whales.scienceontheweb.net We acknowledge IFAW for providing photo-identification data from 1987-1993 Biosphere Expeditions and clients of Whale Watch Azores for making data collection possible Sara Magalhães, Tiago Sá, João Medeiros, Yves Cuenot, Pablo Chevallard Navarro, and numerous volunteers that over the years helped with data collection and organization of the photo-identification catalogue. We are deeply grateful to Gary White, Bill Kendall, Jim Hines, James Nichols and Paul Conn for offering guidance and advice on CMR modelling.
JorgeFontes@ImagDOP
