Time-lapse imagery is cheap and timely in the fight against colonial species' decline

Abstract

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.

Phenology and reproductive biology of the southern rockhopper penguin (Eudyptes chrysocome chrysocome) at Isla Pingüino (Santa Cruz province, Argentina)

“…Time-lapse cameras allow researchers to study many aspects of the breeding cycle without having to be physically at the colony (Hart et al 2021), that come from the possibility to detect events of predation, stealing of nests, storms or any other situation that results in the death of chicks, making it viable to assess the importance of each factor in the overall population trend. Each methodology has its pros and cons but, in both cases, if repeated year after year, they are useful to detect interannual variations (Hinke et al 2018).…”

“…The primary aim of this study is to provide life history information on breeding aspects of the southern rockhopper penguin population of Isla Pingüino. We used a methodology based on autonomous timelapse cameras to record the activity of the colony (Hinke et al 2018;Dodino et al 2018;Hart et al 2021), combined with individual marking. This developing technology is useful to monitor colonies in remote areas, particularly in this study, in which nest distribution and the rocky, not vegetated landscape make it possible to carry out a follow-up work with panoramic pictures, showing an important amount of valuable information.…”