Understanding the effects of endogenous and exogenous factors on population dynamics is essential for assessing the viability of populations, setting recovery goals for endangered species, and evaluating management options. Invertebrates are particularly difficult to monitor and few long-term datasets are available for these species. Additionally, limited resources make it necessary to perform monitoring as efficiently as possible. Here, I use the bivoltine Karner blue butterfly Lycaeides melissa samuelis to demonstrate how analyzing the effects of density-dependent factors and weather on separate life stages can be utilized to understand monitoring data, assess populations and identify critical life-history parameters. My first step was to compare the use of peak numbers as an index of population size with estimates obtained from a more data intensive methodology. Peak numbers proved to be an effective index, and so I utilized this index to analyze 10 and 13 years of monitoring data at two Karner blue butterfly sites in New York, USA. I modeled the effects of weather and density dependence on two distinct population growth rates (λ) per year. Analysis with Akaike's Information Criteria indicated that both sites were primarily influenced by density dependence during the summer period and by weather conditions during the winter period. Large population declines occurred in the winter period and were a result of the previous year's dry summer and cool spring weather. I conclude that recovery goals for this endangered species should include a second brood-carrying capacity, mean winter growth rate and multiple sites with independent populations. This study represents a rare, long-term study on the population dynamics of a polyvoltine species. Understanding the population dynamics of polyvoltine species, such as the Karner blue butterfly, will assist in the conservation of many invertebrate and small mammal species.