Understanding physical mechanisms underlying seabird foraging is fundamental to predict responses to coastal change. For instance, turbulence in the water arising from natural or anthropogenic structures can affect foraging opportunities in tidal seas. Yet, identifying ecologically important localised turbulence features (e.g. upwellings ~10-100 m) is limited by observational scale and this knowledge gap is magnified in volatile predators. Here, using a drone-based approach, we present the tracking of surface-foraging terns (143 trajectories belonging to three tern species) and dynamic turbulent surface flow features in synchrony. We thereby provide the earliest evidence that localised turbulence features can provide physical foraging cues. Incorporating evolving vorticity and upwelling features within a hidden Markov model, we show that terns were more likely to actively forage as the strength of the underlying vorticity feature increased, while conspicuous upwellings ahead of the flight path presented a strong physical cue to stay in transit behaviour. This clearly encapsulates the importance of prevalent turbulence features as localised foraging cues. Our quantitative approach therefore offers the opportunity to unlock knowledge gaps in seabird sensory and foraging ecology on hitherto unobtainable scales. Finally, it lays the foundation to predict responses to coastal change to inform sustainable ocean development. 

The processed input dataset (.csv) includes the tern tracking data and associated environmental data. The R code (.txt) contains the statistical analysis (HMM - hidden Marcov model) and plotting code (ggplot) for the figures supporting the article’s results. 

The R code has been run in the R version R-3.5.2 and requires the following packages:

library(moveHMM)
library(mvtnorm)
library(ggplot2)
library(patchwork)