Brood parasites and nest predators reduce the seasonal fecundity and, hence, the population growth rates of their victims. However, most field studies do not measure directly how parasites and predators decrease seasonal fecundity, but instead measure the impact of these organisms on individual nesting attempts. Because a female may renest after losing a nest to predation, abandoning a parasitized nest, or successfully fledging a brood, knowing how brood parasites and nest predators reduce the number of offspring fledged from individual nesting attempts is not equivalent to knowing their impact on seasonal fecundity. We address this problem by developing a mathematical model that: estimates several parameters describing the natural history of this system, including the brood-parasitism rate, nest-predation rate, and probability of nest abandonment in response to a parasitism event; and extrapolates to seasonal fecundity from these parameters and others describing the length of the breeding season, the timing of events in the nesting cycle, and the productivity of parasitized and unparasitized nests. We also show how different researchers using different observational methodologies to study exactly the same population likely would arrive at noticeably different conclusions regarding the intensity of brood parasitism, and we provide mathematical formulas for comparing among several of these measures of parasitism. Our procedures extend Mayfield's method for calculating nest-success rates from nest-history data in that we simultaneously estimate parameters describing nest predation and brood parasitism, predict seasonal fecundity from these parameters, and provide confidence intervals on all parameter estimates. The model should make the design and interpretation of logistically difficult empirical studies more efficient. It also can be specialized to species affected by nest predators but not brood parasites. We use the model to analyze prairie Warbler (Dendroica discolor) and Black-capped Vireo (Vireo atricapillus) nesting data. We estimate the model's parameters for these species and use the resulting estimates to predict seasonal fecundity. For both species, the predicted seasonal fecundity closely matches the value measured directly.