Animal-borne telemetry devices provide essential insights into the life-history strategies of far-ranging species and allow us to understand how they interact with their environment. Many species in the seabird family Alcidae undergo a synchronous moult of all primary flight feathers during the non-breeding season, making them flightless and more susceptible to environmental stressors, including severe storms and prey shortages. However, the timing and location of moult remains largely unknown, with most information coming from studies on birds killed by storms or shot at sea. Using light-level geolocators with saltwater immersion loggers, we develop a method for determining flightless periods in the context of the annual cycle. Four Atlantic puffins (Fratercula arctica) were equipped with geolocator/immersion loggers on each leg to attempt to overcome issues of leg-tucking in plumage while sitting on the water, which confounds the interpretation of logger data. Light level and saltwater immersion time-series data were combined to correct for this issue. This approach was adapted and applied to 40 puffins equipped with the standard practice deployments of geolocators on one leg only. Flightless periods consistent with moult were identified in the dual-equipped birds, whereas moult identification in single-equipped birds was less definitive and should be treated with caution. Within the dual-equipped sample, we present evidence for two flightless moult periods per non-breeding season in two puffins that undertook more extensive migrations (> 2000km), and were flightless for up to 76 days in a single non-breeding season. A biannual flight feather moult is highly unusual among non-passerine birds, and may be unique to birds that undergo catastrophic moult, i.e. become flightless when moulting. Though our conclusions are based on a small sample, we have established a freely available methodological framework for future investigation of the moult patterns of this and other seabird species.

Many of the species in decline around the world are subject to different environmental stressors across their range, so replicated large-scale monitoring programmes, are necessary to disentangle the relative impacts of these threats. At the same time as funding for long-term monitoring is being cut, studies are increasingly being criticised for lacking statistical power. For those taxa or environments where a single vantage point can observe individuals or ecological processes, time-lapse cameras can provide a cost-effective way of collecting time series data replicated at large spatial scales that would otherwise be impossible. However, networks of time-lapse cameras needed to cover the range of species or processes create a problem in that the scale of data collection easily exceeds our ability to process the raw imagery manually. Citizen science and machine learning provide solutions to scaling up data extraction (such as locating all animals in an image). Crucially, citizen science, machine learning-derived classifiers, and the intersection between them, are key to understanding how to establish monitoring systems that are sensitive to – and sufficiently powerful to detect –changes in the study system. Citizen science works relatively ‘out of the box’, and we regard it as a first step for many systems until machine learning algorithms are sufficiently trained to automate the process. Using Penguin Watch (www.penguinwatch.org) data as a case study, we discuss a complete workflow from images to parameter estimation and interpretation: the use of citizen science and computer vision for image processing, and parameter estimation and individual recognition for investigating biological questions. We discuss which techniques are easily generalizable to a range of questions, and where more work is needed to supplement ‘out of the box’ tools. We conclude with a horizon scan of the advances in camera technology, such as on-board computer vision and decision making.