Dataset Information

Manx shearwater (Puffinus puffinus) rafting behaviour revealed by GPS tracking and behavioural observations.

ABSTRACT: Before visiting or leaving their remote island colonies, seabirds often engage in a behaviour termed 'rafting', where birds sit, often in groups, on the water close to the colony. Despite rafting being a widespread behaviour across many seabird taxa, the functional significance of rafting remains unknown. Here we combine global positioning system (GPS) tracks, observational and wind condition data to investigate correlates of rafting behaviour in Manx shearwaters (Puffinus puffinus) at a large colony on Skomer Island, Wales. We test (1) the influence of wind direction on rafting location and (2) whether raft size changes with respect to wind speed. Our approach further allows us to describe day-night trends in (3) raft distance from shore through time; (4) the number of birds present in the nearshore waters through time; and (5) spatial patterns of Manx shearwater rafts in marine waters adjacent to the breeding colony. We find no evidence that wind direction, for our study period, influences Manx shearwater rafting location, yet raft size marginally increases on windier days. We further find rafting birds closer to the shore at night than during the day. Thus, before sunset, birds form a "halo" around Skomer Island, but this halo disappears during the night as more individuals return from foraging trips and raft nearer the colony on Skomer Island. The halo pattern reforms before sunrise as rafts move away from land and birds leave for foraging. Our results suggest that wind conditions may not be as ecologically significant for rafting locations as previously suspected, but rafting behaviour may be especially important for avoiding predators and cleaning feathers.

SUBMITTER: Richards C

PROVIDER: S-EPMC6812691 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Manx shearwater (<i>Puffinus puffinus</i>) rafting behaviour revealed by GPS tracking and behavioural observations.

Richards Cerren C Padget Oliver O Guilford Tim T Bates Amanda E AE

PeerJ 20191021

Before visiting or leaving their remote island colonies, seabirds often engage in a behaviour termed 'rafting', where birds sit, often in groups, on the water close to the colony. Despite rafting being a widespread behaviour across many seabird taxa, the functional significance of rafting remains unknown. Here we combine global positioning system (GPS) tracks, observational and wind condition data to investigate correlates of rafting behaviour in Manx shearwaters (<i>Puffinus puffinus</i>) at a ...[more]

PMID: 31656697

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Project description:BackgroundState-space models, such as Hidden Markov Models (HMMs), are increasingly used to classify animal tracks into behavioural states. Typically, step length and turning angles of successive locations are used to infer where and when an animal is resting, foraging, or travelling. However, the accuracy of behavioural classifications is seldom validated, which may badly contaminate posterior analyses. In general, models appear to efficiently infer behaviour in species with discrete foraging and travelling areas, but classification is challenging for species foraging opportunistically across homogenous environments, such as tropical seas. Here, we use a subset of GPS loggers deployed simultaneously with wet-dry data from geolocators, activity measurements from accelerometers, and dive events from Time Depth Recorders (TDR), to improve the classification of HMMs of a large GPS tracking dataset (478 deployments) of red-billed tropicbirds (Phaethon aethereus), a poorly studied pantropical seabird.MethodsWe classified a subset of fixes as either resting, foraging or travelling based on the three auxiliary sensors and evaluated the increase in overall accuracy, sensitivity (true positive rate), specificity (true negative rate) and precision (positive predictive value) of the models in relation to the increasing inclusion of fixes with known behaviours.ResultsWe demonstrate that even with a small informed sub-dataset (representing only 9% of the full dataset), we can significantly improve the overall behavioural classification of these models, increasing model accuracy from 0.77 ± 0.01 to 0.85 ± 0.01 (mean ± sd). Despite overall improvements, the sensitivity and precision of foraging behaviour remained low (reaching 0.37 ± 0.06, and 0.06 ± 0.01, respectively).ConclusionsThis study demonstrates that the use of a small subset of auxiliary data with known behaviours can both validate and notably improve behavioural classifications of state space models of opportunistic foragers. However, the improvement is state-dependant and caution should be taken when interpreting inferences of foraging behaviour from GPS data in species foraging on the go across homogenous environments.

Project description:The increasing spatiotemporal accuracy of Global Navigation Satellite Systems (GNSS) tracking systems opens the possibility to infer animal behaviour from tracking data. We studied the relationship between high-frequency GNSS data and behaviour, aimed at developing an easily interpretable classification method to infer behaviour from location data. Behavioural observations were carried out during tracking of cows (Bos Taurus) fitted with high-frequency GPS (Global Positioning System) receivers. Data were obtained in an open field and forested area, and movement metrics were calculated for 1 min, 12 s and 2 s intervals. We observed four behaviour types (Foraging, Lying, Standing and Walking). We subsequently used Classification and Regression Trees to classify the simultaneously obtained GPS data as these behaviour types, based on distances and turning angles between fixes. GPS data with a 1 min interval from the open field was classified correctly for more than 70% of the samples. Data from the 12 s and 2 s interval could not be classified successfully, emphasizing that the interval should be long enough for the behaviour to be defined by its characteristic movement metrics. Data obtained in the forested area were classified with a lower accuracy (57%) than the data from the open field, due to a larger positional error of GPS locations and differences in behavioural performance influenced by the habitat type. This demonstrates the importance of understanding the relationship between behaviour and movement metrics, derived from GNSS fixes at different frequencies and in different habitats, in order to successfully infer behaviour. When spatially accurate location data can be obtained, behaviour can be inferred from high-frequency GNSS fixes by calculating simple movement metrics and using easily interpretable decision trees. This allows for the combined study of animal behaviour and habitat use based on location data, and might make it possible to detect deviations in behaviour at the individual level.

Dataset Information

Manx shearwater (Puffinus puffinus) rafting behaviour revealed by GPS tracking and behavioural observations.

Publications

Manx shearwater (<i>Puffinus puffinus</i>) rafting behaviour revealed by GPS tracking and behavioural observations.

Similar Datasets

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