Quantifying sea turtle nesting behavior is essential for recovery planning and evaluating management actions. Traditional monitoring approaches, based on nest counts from beach surveys, can misclassify non-nesting emergences, obscure true fecundity, and underestimate clutch frequency, metrics that directly influence population models and regulatory decisions. Here, we demonstrate that high-resolution acceleration data loggers (ADLs) can reliably discriminate nesting from non-nesting emergences across four imperiled species of sea turtles at sites in the Gulf of America, southeast USA, and Caribbean. From 60 recovered ADL deployments on green (Chelonia mydas; N = 10), hawksbill (Eretmochelys imbricata; N = 7), Kemp’s ridley (Lepidochelys kempii; N = 21), and loggerhead sea turtles (Caretta caretta; N = 22) lasting on average 17.5 ± 8.7 days (range 2–43 days), we identified 54 nesting events and 76 non-nesting emergences, with >97% accuracy when compared to direct observations. These data provide the first observer-validated, species-specific behavioral signatures of nesting phases and reveal correlations between egg-laying duration and clutch size. All non-nesting emergences occurred within 72 hours of subsequent nesting, allowing managers to anticipate nest deposition windows. By refining inter-nesting intervals and fecundity estimates, ADLs offer a practical path to reduce error in clutch frequency estimates. The integration of ADL-derived algorithms with satellite-transmitting tags would enable the remote, real-time monitoring of nesting activity, creating a system for the remote monitoring of inter-nesting intervals and nest fecundity that are crucial to quantify the impacts of climate change and other threats to sea turtle nesting habitat.