GPS tracking of the foraging movements of Manx Shearwaters Puffinus puffinus breeding on Skomer Island, Wales

Abstract

We report the first successful use of miniature Global Positioning System loggers to track the ocean‐going behaviour of a c. 400 g seabird, the Manx Shearwater Puffinus puffinus. Breeding birds were tracked over three field seasons during the incubation and chick‐rearing periods on their foraging excursions from the large colony on Skomer Island, Pembrokeshire, UK. Foraging effort was concentrated in the Irish Sea. Likely foraging areas were identified to the north, and more diffusely to the west of the colony. No foraging excursions were recorded significantly to the south of the colony, conflicting with the conclusions of earlier studies based on ringing recoveries and observations. We discuss several explanations including the hypothesis that foraging may have shifted substantially northwards in recent decades. We found no obvious relationship between birds’ positions and water depth, although there was a suggestion that observations at night were in shallower water than those during the day. We also found that, despite the fact that Shearwaters can be observed rafting off‐shore from their colonies in the hours prior to making landfall at night, breeding birds are usually located much further from the colony in the last 8 h before arrival, a finding that has significance for the likely effectiveness of marine protection areas if they are only local to the colony. Short sequences of precise second‐by‐second fixes showed that movement speeds were bimodal, corresponding to sitting on the water (most common at night and around midday) and flying (most common in the morning and evening), with flight behaviour separable into erratic (indicative of searching for food) and directional (indicative of travelling). We also provide a first direct measurement of mean flight speed during directional flight (c. 40 km/h), slower than a Shearwater's predicted maximum range velocity, suggesting that birds are exploiting wave or dynamic soaring during long‐distance travel.

Behavioural mapping of a pelagic seabird: combining multiple sensors and a hidden Markov model reveals the distribution of at-sea behaviour

“…GPS loggers were configured to record location once every 5 or 10 min and were attached dorsally using thin strips of Tesa marine cloth tape underlying a small number of contour feathers [2]. Geolocator-immersion loggers test for saltwater immersion every 3 s and record the proportion of samples immersed in each 10 min interval.…”

“…Advances in miniature telemetry and data-logging systems continue to revolutionize the remote observation of long-distance movements in these and other elusive species [2][3][4][5][6][7], but such systems can be expensive as well as impactful (especially on smaller species), and the resulting data are often under-used. Many tracking studies use recorded locations to examine movement and habitat use only in broad terms [3][4][5][6], leaving key questions about where and when animals engage in specific behaviours unanswered.…”

“…See Brooke [23] for a detailed description of the breeding behaviour. While marine surveys [24] and GPS tracking [2] have provided valuable insights into the routes and destinations of these foraging trips, little is known about individual and colony-based patterns of behaviour at sea.…”

“…We studied the Manx shearwater, a 400 g procellariiform seabird that breeds predominantly in colonies at remote locations off the west coasts of Britain and Ireland. Females lay a single egg in an underground nest and during the incubation period, both parents alternate stints of 5-7 days at the nest with long foraging trips [2] on which they recover body condition. During the chick-rearing period, both parents make repeated, shorter duration foraging trips to sea [2,22], returning at night to provision the chick.…”

“…However, behaviour may be inferred by characterizing patterns of movement based solely on the geometry or complexity of an animal's path using techniques such as tortuosity [8,9], positional entropy [10,11] or first-passage time [12]. In addition, modelling approaches such as Gaussian mixtures have been used to classify animal tracking data into discrete modes of movement [2,13], while state-space models including hidden Markov models (HMMs) [10,[14][15][16][17][18][19] have been used to identify different modes of movement and the dynamics of switching between them.…”