Ruth Dunn, Jonathan Green, Sarah Wanless, Mike Harris, Mark Newell, Maria Bogdanova, Francis Daunt, Cat Horswill, Jason Matthiopoulos
This is a plain language summary of a Functional Ecology research article which is published here.
Wild animals must acquire enough energy from their food to avoid burning through their fat reserves, losing weight, and potentially dying. Although studying how much animals eat and how much they weigh is relatively straightforward in a laboratory environment, it is much more difficult to research this in wild, mobile animals, particularly when we want to do so over long periods of time. These challenges are especially true for species like the common guillemot, a seabird that spends large proportions of its life at sea, out of sight of humans. Furthermore, during their annual cycles, including the time spent at sea, seabirds can be subject to a range of challenging conditions, such as varying temperatures and stormy weather, and must therefore adapt and balance their energy budgets accordingly.
Collecting data via small biologging devices that can be deployed on birds for a whole year allows us to gain insights into wild animals when they are otherwise difficult to observe. These loggers are regularly deployed on seabirds; we weigh the birds and attach loggers to rings around their legs whilst they are at their land-based breeding colonies. A year later, when the birds return to the colony, we then weigh them again, retrieve their loggers, and download the data that they have recorded. The data from these loggers can be used to estimate how much energy guillemots use each day, based on how much time they spend doing different activities such as diving and flying.
In this study, we develop a novel modelling approach (using Bayesian methods) that uses our two mass measurements, the biologging data, and estimates of energy expenditure to recreate time series of energy acquisition and energy reserves. In this way we recreated year-round estimates of components of wild animal energy budgets, namely energy gain and energy reserves, that we have previously been unable to study. The use of biologging data within energetics-focused models like these presents a multitude of opportunities to gain novel insights into the energy budgets of wild animals and how they might respond to environmental change.