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

Survival, emigration, immigration Population size, capture probability The Robust Design

Robust design capture histories Encounter history ordered by primary period and secondary period within primary period e.g., 3 primary periods, 4 secondary periods 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 N i 0 Subset of population N i in sample area and available for capture with probability p* i e.g., spawning sturgeon All adult Sturgeon Spawning and non spawning N i 0 Spawning sturgeon available for capture N i

Temporary emigration Parameters  ” i : probability that the animal leaves the study area (an estimate for each interval)  ’ 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 :  ” i =  ’ i = 0 Immigration only :  ” i = 0 Random emigration:  ” i =  ’ 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

Multiple options in MARK Robust Design 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