| Summary: | 1. In capture-recapture studies, recycled individuals occur when individuals lose all of their tags and are recaptured as though they were new individuals. Typically, the effect of these recycled individuals is assumed negligible. 2. Through a simulation-based study of double tagging experiments, we examined the effect of recycled individuals on parameter estimates in the Jolly-Seber model with tag loss (Cowen & Schwarz, 2006). We validated the simulation framework using long-term census data of elephant seals. 3. Including recycled individuals did not affect estimates of capture, survival, and tag-retention probabilities. However, with low tag-retention rates, high capture rates, and high survival rates, recycled individuals produced over estimates of population size. For the elephant seal case study, we found population size estimates to be between 8 and 53% larger when recycled individuals were ignored. 4. Ignoring the effects of recycled individuals can cause large biases in population size estimates. These results are particularly noticeable in longer studies. The simulated data is available for all 6 considered scenarios are available in the files with the prefixes DATA. Six different scenarios for each model were considered: GJSTL1 - scenario with N=1000, double-tagging=100%, 10 time periods GJSTL2 - scenario with N=100000, double-tagging=100%, 10 time periods GJSTL3 - scenario with N=100000, double-tagging=50%, 10 time periods GJSTL4 - scenario with N=1000, double-tagging=50%, 10 time periods GJSTL5 - scenario with N=1000, double-tagging=100%, 5 time periods GJSTL6 - scenario with N=1000, double-tagging=100%, 7 time periods The following following number (1-27) represents the combination of survival, capture, and tag retention probabilities. There were 3 levels for each of the 3 parameters, giving 3^3=27 different combinations. Parameter Estimates (ests) and Standard Errror (se) are given for the model including (RECYCLED) and excluding the recycled individuals. For example, GJSTL1_4.seRECYCLED.txt ...
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