Spatial and within-season variation in the diet of a declining seabird described through digital photography and citizen science

Abstract

Understanding an animal’s diet is a crucial component of conservation, but diet data are often labor intensive to collect and are frequently scarce. Atlantic Puffins (Fratercula arctica; hereafter Puffins) are vulnerable to global extinction and have declined in some parts of their UK and Irish range. Differences in population trajectories may relate to diet, but Puffin diet data are currently only collected at a handful of colonies. We explored whether citizen science could address this data gap by inviting visitors to Puffin colonies in 2017 to submit their photographs of Puffins carrying prey. In total, 602 people submitted 1402 images from 35 colonies. We identified the species group, size, and number of prey items in each bill load. Photograph quality was excellent, with 89% of birds in images providing useable diet information. In total 11,150 prey items were counted and measured from 1198 Puffins across 27 colonies. We demonstrated a lack of bias in the sample of photos provided by citizen scientists and described how Puffin chick diet varies in prey composition, prey length, number of prey per bill load, and load biomass over large spatial scales and throughout the breeding season. The diet of Puffin chicks from regions where severe declines have occurred, most notably Shetland, were characterized by a lower prey biomass, higher numbers of fish per load, and a high proportion of small, transparent sandeels consistently through the season. By contrast, in regions where Puffin populations are thought to be increasing, load biomass was high, the number of prey per load low, and larger non-transparent sandeels were the dominant prey, which persisted right through the breeding season. Results from our study show colonies and regions where birds may be expending more effort (collecting more prey items) for lesser returns (lower load biomass) and emphasize the value of collecting diet data across large spatial scales.

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