| Summary: | Collecting spatially extensive data on phenology and reproductive success is important for seabird conservation and management, but can be logistically challenging in remote regions. Autonomous time-lapse camera systems offer an opportunity to provide such coverage. We describe a method to estimate nest-level breeding phenology and reproductive success of colonial pygoscelid penguins using photographs from time-lapse cameras. The method derives from stereotypical patterns of nest attendance, where predominantly two adults are present before and during laying, but switch to one adult during incubation. The switch approximates the date of clutch completion and is estimated by fitting a smoothing spline to daily nest attendance data, identifying candidate dates that switch from two adults to one and selecting the date when the first derivative of the spline is minimized. Clutch initiation and hatch dates are then estimated from the mean, species-specific interval between laying (pygoscelid penguins typically lay two eggs) and the duration of the incubation period. We estimated these intervals for each species from historical field data. The phenology is adjusted when photographs indicate egg or chick presence prior to their estimated lay or hatch dates. The number of chicks alive in each study nest on its crèche date determines reproductive success estimates. The method was validated with concurrent direct observations for each species and then applied to a camera network in the Antarctic Peninsula region to demonstrate its utility. Mean egg laying and incubation intervals from direct observations were similar within species across sites. In the validation study, the mean clutch initiation, hatch and crèche dates were generally equivalent between photographs and direct observations. Estimates of reproductive success were identical. Applying the method to a time-lapse network suggested relatively high reproductive success for all species across the region and corroborated general understanding of latitudinal trends ...
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