BRIEF INTRODUCTION TO
ROBUST DESIGN
CAPTURE-RECAPTURE
Original Motivation
CJS models permit estimation of survival and are
robust to heterogeneity in capture probabilities.
JS models allow abundance estimation and
recruitment but…
• Are not robust to capture heterogeneity/
behavioral effects
• Potential for serious bias in the estimates of
abundance and recruitment
Solution
Estimate survival using CJS between periods when the
population is considered open
Estimate abundance using closed capture models over
shorter periods when the population is considered
closed.
Combine the estimates to estimate recruitment
Sample design
Sampling at 2 temporal scales:
Primary periods
Periods longer-term sampling over which
population is assumed to be open (gains and
losses may occur, birth death, emigration)
Secondary periods
Periods short-term sampling during which the
population is assumed to be closed (no birth,
death, emigration)
The best of both worlds:
Robust Design
Combination of open and closed population models
Parameters: survival, emigration, immigration,
detection, population size
The Robust Design
Survival, emigration, immigration
Population size, capture probability
Robust design capture histories
Encounter history ordered by primary period and
secondary period within primary period
e.g., 3 primary periods, 4 secondary periods
0001 1001 1100 0000
note: NO SPACES in MARK data file
Interpretation:
In primary period 1: caught only in secondary sample period 4
In primary period 2: caught in secondary sample periods 1 and 4
In primary period 3: caught in secondary sample periods 1 and 2
In primary period 4: never caught
Likelihood based approach in
program MARK
Full likelihood using data from both primary and
secondary periods
Models
Can include virtually any of the open models
Additional parameter temporary emigration
Closed abundance estimation
Maximum likelihood models, including Huggins
variation
Covariates, time, and individual effects
Temporary emigration
Super population of animals Ni0
Subset of population Ni in sample area and available for
capture with probability p*i
Spawning sturgeon
e.g., spawning sturgeon
All adult
Sturgeon
Spawning
and
non spawning
N i0
available for capture
Ni
Temporary emigration
Parameters
g”i: probability that the animal leaves the study area
(an estimate for each interval)
g’i: probability stays away (i.e., is not available for capture),
given that the animal was not present during primary trapping
period i—1 (no estimate for the first interval)
No emigration:
g”i = g’i= 0
Immigration only: g”i = 0
Random emigration: g”i = g’i
Advantages of Robust Design
In comparison with designs with dispersed effort:
Permits the assumptions of closed population to be satisfied
closely during the secondary periods with concentrated effort
The separation between primary periods is more appropriate
for estimating survival and other parameters of population
dynamics
Dispersed sampling effort frequently will result in a failure of
the study estimates
Insufficient data to estimate parameters with precision
Failure to satisfy assumptions of either the closed or open
model
RD is recommended over dispersed sampling
Robust Design in MARK
Multiple options in MARK
We’ve barely scratched the surface
Planning a CMR study
How many marked fish needed?
How many capture occasions (primary/secondary)?
Effort per occasion?
“Power” to detect differences/change or precision of
estimates?
Planning a CMR study
Evaluate tradeoffs via simulation
values from previous studies
preliminary data
best guess
costs constraints
Simulations in MARK
ON TO MARK