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Though it is possible to estimate capture probability using only marked animals known to be alive because of subsequent recaptures, as in the Jolly-Seber model (Jolly, 1965; Seber, 1965), this method limits the sample size available to estimate capture probability and does not help with parameter con- founding in the terminal year. In ASMR, the virtual population analysis structure allows the use of both marked and unmarked animals in the calculation of capture probability, but confound- ing between mortality tmd capture probability is not explic- itly minimized via model structure. Retrospective analyses illustrate how perceptions of key population parameters, such as mortality rate, have been modified as more information becomes available, particularly following publication of Cog- gins and others (2006a and 2006b). These analyses are also useful in understanding how large changes in sampling inten- sity and protocols (e.g., minimal sampling in the LCR during 1996-99) may bias or otherwise distort understanding of HBC population dynamics based on capture-recapture analyses. The primary objective of this report is to provide an updated stock assessment for the LCR population of HBC using data collected during 1989-2006. This assessment includes catch-rate indices, closed population mark-recapture model abundance estimates, the original ASMR model (Cog- gins and others, 2006b), and a newly refined ASMR model (detailed below). Coggins and others (2006a) used data collected during 1989-2002, and this report includes that data and additional data collected through 2006. Supporting objectives include (1) formally evaluating alternative stock assessment models using Pearson residual statistics and information theoretic metrics (Burnham and Anderson, 2(02), (2) using mark-recapture data to estimate the relationship between HBC age and length, (3) translating uncertainty in the assignment of individual tish age to resulting estimates of recruitment and abundance from the ASMR model, and (4) evaluating past and present stock assessments considering the available data sources and analyses, recognizing the limita- tions inherent in both. The ongoing monitoring program for HBC in Grand Can- yon has varied in intensity over the years, but the primary sam- ple locations, techniques, and personnel have remained remark- ably consistent (Coggins and others, 2006a). Insight into the performtmce of the model is provided by conducting the annual stock assessment and continuously evaluating the performance of the assessment with retrospective analyses, independent peer evaluations, and tests of the model with simulated data. This comprehensive examination may prove useful to other adaptive management programs that seek to develop a robust monitor- ing component. In particular, the model may provide insight into (1) the limitations of monitoring alone in assigning cause and ,effect in association with prescriptive management actions, (2) the pathologies associated with large changes in monitor- ing protocols, and (3) a realistic assessment of the considerable uncertainty in results for a rare, elusive, long-lived organism, even after many years of intensive monitoring. 3 Methods The methods employed for this analysis are presented in three separate sections. Section 1 describes methods used to update the 2002 HBC assessment metrics as presented in Coggins and others (2006a) and to refine the ASMR models. Additionally, section 1 describes the criteria used to assess model fit for each of the ASMR models. Section 2 outlines the methods used to estimate the relationship between HBC age and length based on mark-recapture information. Finally, section 3 describes the Monte Carlo simulations conducted to capture the uncertainty in the abundance and recruitment estimates that result from uncertainty in age assignment. Section 1-2006 Humpback Chub Assessment Update with Refinements Monitoring efforts for the humpback chub began in 1987 when a standardized hoop-net sampling program was implemented in the lower reaches of the Little Colorado River. During the subsequent 19 years, four stmlpling periods can be generally defined that correspond to different levels of sampling effort and protocol (Coggins and others 2006a). The initial sampling period (1987-91> consisted mainly of lim- ited hoop netting in the lower 1,200 m of the LCR. Sampling period 2 (1991-95) involved an intensive sampling effort in both the LCR and the mainstem Colorado River as pmt of an environmental impact statement on the operation of Glen Can- yon Dam (U.S. Department of the Interior, 1995). The third smnpling period (1996-2000) also included both the Colo- rado River and the LCR but with severely reduced intensities compared to period 2. The tinal sampling period (2000-06) involved a higher sampling intensity relative to period 3 but decreased relative to period 2. During each of these sampling periods, HBC have been collected using multiple types of gear, including hoop nets and trammel nets in the LCR. and this same gear plus pulsed-DC eIectrofishing in the mainstem Colorado River (Valdez tmd Ryel, 1995; Douglas and Marsh, 1996; GOIman and Stone. 1999; Coggins and others, 2006a). Index-Based Metrics Although index-based metrics (e.g., catch rate) can be unreliable in tracking trends in population size (MacKenzie and others, 2006), these indices are frequently examined and are potentially useful for comparison with previous assess- ment efforts. With this caveat in mind and following Coggins and others (2006a), two long-term catch-rate time series were updated with data from 20m through 2006, including (1) hoop-net catch rate ofHBC in the lower 1,200 m oUhe LCR and (2) trammel-net catch rate of HBC in the LCR inflow reach of the Colorado River (defined as approximately 9 km upstream and 11 km downstream of the confluence; Valdez