1. Discussion Both population and individual level reproductive success were reasonable proxies of recruitment. If there were differences in the quality of cohorts, or differences in survival or transition between years these models would have performed better than the age models. That age models performed best indicates that there are not differences between cohorts. The results of the individual analysis demonstrated that few individuals were missed. The high R 2 indicated a strong correlation between the number of young an individual had and the number of young it recruited into the population. These results give us confidence to continue to monitor and manage habitat to maximize reproductive success. Reproductive Success as a Measurement of Recruitment in Swainson’s hawks Chris W. Briggs 1, Brian Woodbridge 2, Michael W. Collopy 1 1 Natural Resources and Environmental Sciences, University of Nevada, Reno 2 U.S. Fish and Wildlife Service, Yreka, CA Introduction Many studies use reproductive success as a proxy for fitness, and subsequently use reproductive success as a management goal, without the ability to determine the validity of this assumption. We look to take advantage of a long-term dataset of Swainson’s hawks to analyze this assumption to determine if reproductive success is a reasonable proxy for recruitment in a population of Swainson's hawks in the Butte valley of California. Methods We monitored a Swainson’s hawk population from 1990-2005 Individuals were given unique color bands, nest sites were located and nest success was determined annually. Landscape characteristics were measured, including; distance to agriculture, distance to roads and juniper density within a 50 m radius of the nest. We used a multistate model in Program MARK to create models to account for recapture rates of population level recruitment. We examined age, cohort, and time using multistate models at the population level. If there were differences the ability for cohorts to be recruited in the population (i.e. if different cohorts were recruited at different rates), it should be seen by cohort models performing the best. After accounting for individuals that were likely missed, we used a GLM to determine if reproductive success of an individual was predictive of the number of young that individual recruited. Study Area The study was conducted in the Butte Valley, California from 1979-2005. The Butte Valley is primarily agricultural (~53%), alfalfa is the primary agriculture type. The Butte Valley also consists of approximately 21% and 22% sage-steppe and juniper woodland, respectively. Results We were able to recapture 55 individuals who were originally banded as nestlings and whose parents were known. The results of the population level analysis demonstrated that age models of transition from nonbreeder to breeder preformed best (  i = 0.99). Models of cohort and time were poor estimators of transition probability (  i = 0.01). We determined that by adjusting individual recruitment for recapture rates and transition probability that 0.11 ± 0.15 recruits/individual have likely been missed. The GLM results demonstrated that the number of chicks banded per individual is a highly significant predictor of the adjusted number of recruits of an individual, with a coefficient of 0.0882 (p<0.0001), and R 2 = 0.74. Transition parameter AICcΔ AICc AICc Weights Num. Par Deviance Age (T+T2)1761.90700.4306812889.4505 Age (3)1762.6710.7640.2939213888.174 Age (2)1762.8060.8980.2747913888.3087 Cohort (3)1776.41914.510.000318891.6703 Time (T+T2)1776.97415.060.0002312904.5168 Table 1. Multistate model results from population level analysis of all banded fledglings, and their transition probability to the breeding population. Figure1. A) Shows the number of fledglings banded/year, and the number of individuals recruited from that cohort. B) Shows the number of fledglings banded/female and the number of those fledglings that were subsequently recruited to the present. AB Acknowledgements Thanks to Jim Sedinger and Jessi brown for advice and comments on this analysis. Thanks for the US Forest Service for logistic support to help continue this research.