The efficiency of detecting seabird behaviour from movement patterns: the effect of sampling frequency on inferring movement metrics in Procellariiformes.

BACKGROUND: Recent technological advances have resulted in low-cost GPS loggers that are small enough to be used on a range of seabirds, producing accurate location estimates (± 5 m) at sampling intervals as low as 1 s. However, tradeoffs between battery life and sampling frequency result in studies using GPS loggers on flying seabirds yielding locational data at a wide range of sampling intervals. Metrics derived from these data are known to be scale-sensitive, but quantification of these errors is rarely available. Very frequent sampling, coupled with limited movement, can result in measurement error, overestimating movement, but a much more pervasive problem results from sampling at long intervals, which grossly underestimates path lengths. METHODS: We use fine-scale (1 Hz) GPS data from a range of albatrosses and petrels to study the effect of sampling interval on metrics derived from the data. The GPS paths were sub-sampled at increasing intervals to show the effect on path length (i.e. ground speed), turning angles, total distance travelled, as well as inferred behavioural states. RESULTS: We show that distances (and per implication ground speeds) are overestimated (4% on average, but up to 20%) at the shortest sampling intervals (1-5 s) and underestimated at longer intervals. The latter bias is greater for more sinuous flights (underestimated by on average 40% when sampling > 1-min intervals) as opposed to straight flight (11%). Although sample sizes were modest, the effect of the bias seemingly varied with species, where species with more sinuous flight modes had larger bias. Sampling intervals also played a large role when inferring behavioural states from path length and turning angles. CONCLUSIONS: Location estimates from low-cost GPS loggers are appropriate to study the large-scale movements of seabirds when using coarse sampling intervals, but actual flight distances are underestimated. When inferring behavioural states from path lengths and turning angles, moderate sampling intervals (10-30 min) may provide more stable models, but the accuracy of the inferred behavioural states will depend on the time period associated with specific behaviours. Sampling rates have to be considered when comparing behaviours derived using varying sampling intervals and the use of bias-informed analyses are encouraged.

Identifying prey capture events of a free-ranging marine predator using bio-logger data and deep learning.

Marine predators are integral to the functioning of marine ecosystems, and their consumption requirements should be integrated into ecosystem-based management policies. However, estimating prey consumption in diving marine predators requires innovative methods as predator-prey interactions are rarely observable. We developed a novel method, validated by animal-borne video, that uses tri-axial acceleration and depth data to quantify prey capture rates in chinstrap penguins (Pygoscelis antarctica). These penguins are important consumers of Antarctic krill (Euphausia superba), a commercially harvested crustacean central to the Southern Ocean food web. We collected a large data set (n = 41 individuals) comprising overlapping video, accelerometer and depth data from foraging penguins. Prey captures were manually identified in videos, and those observations were used in supervised training of two deep learning neural networks (convolutional neural network (CNN) and V-Net). Although the CNN and V-Net architectures and input data pipelines differed, both trained models were able to predict prey captures from new acceleration and depth data (linear regression slope of predictions against video-observed prey captures = 1.13; R 2 ≈ 0.86). Our results illustrate that deep learning algorithms offer a means to process the large quantities of data generated by contemporary bio-logging sensors to robustly estimate prey capture events in diving marine predators.

A novel approach to seabird posture estimation: finding roll and yaw angles of dynamic soaring albatrosses using tri-axial magnetometers.

With advances in bio-logging technology, the posture of animals is now commonly described by inertial measurement units, which include tri-axial accelerometers to estimate pitch and roll angles. Many large seabirds use dynamic soaring flight to travel long distances, but this low-cost flight mode results in high centripetal acceleration, which obscures posture derived from accelerometers. Tri-axial magnetometers are not influenced by acceleration and might provide a way to estimate the posture of animals that experience high centripetal acceleration. We propose a new method to estimate the posture of dynamic soaring seabirds using tri-axial magnetometer data, with the assumption that they do not have large pitch angles during routine flight. This method was field-tested by deploying a combination of bio-logging devices on three albatross species breeding on Marion Island, using bird-borne video loggers to validate the roll angles. Validated data showed that the method worked well in most instances, but accuracy decreased when the heading was close to magnetic north or south. Accurate, fine-scale posture estimates may provide insight into dynamic soaring flight and allow estimates of fine-scale tracks using dead-reckoning, not only for seabirds, but potentially for other species where centripetal acceleration limits the use of accelerometers to estimate posture.

Seabird morphology determines operational wind speeds, tolerable maxima, and responses to extremes.

Storms can cause widespread seabird stranding and wrecking,1,2,3,4,5 yet little is known about the maximum wind speeds that birds are able to tolerate or the conditions they avoid. We analyzed >300,000 h of tracking data from 18 seabird species, including flapping and soaring fliers, to assess how flight morphology affects wind selectivity, both at fine scales (hourly movement steps) and across the breeding season. We found no general preference or avoidance of particular wind speeds within foraging tracks. This suggests seabird flight morphology is adapted to a "wind niche," with higher wing loading being selected in windier environments. In support of this, wing loading was positively related to the median wind speeds on the breeding grounds, as well as the maximum wind speeds in which birds flew. Yet globally, the highest wind speeds occur in the tropics (in association with tropical cyclones) where birds are morphologically adapted to low median wind speeds. Tropical species must therefore show behavioral responses to extreme winds, including long-range avoidance of wind speeds that can be twice their operable maxima. By contrast, Procellariiformes flew in almost all wind speeds they encountered at a seasonal scale. Despite this, we describe a small number of cases where albatrosses avoided strong winds at close range, including by flying into the eye of the storm. Extreme winds appear to pose context-dependent risks to seabirds, and more information is needed on the factors that determine the hierarchy of risk, given the impact of global change on storm intensity.6,7.

The role of wingbeat frequency and amplitude in flight power.

Body-mounted accelerometers provide a new prospect for estimating power use in flying birds, as the signal varies with the two major kinematic determinants of aerodynamic power: wingbeat frequency and amplitude. Yet wingbeat frequency is sometimes used as a proxy for power output in isolation. There is, therefore, a need to understand which kinematic parameter birds vary and whether this is predicted by flight mode (e.g. accelerating, ascending/descending flight), speed or morphology. We investigate this using high-frequency acceleration data from (i) 14 species flying in the wild, (ii) two species flying in controlled conditions in a wind tunnel and (iii) a review of experimental and field studies. While wingbeat frequency and amplitude were positively correlated, R2 values were generally low, supporting the idea that parameters can vary independently. Indeed, birds were more likely to modulate wingbeat amplitude for more energy-demanding flight modes, including climbing and take-off. Nonetheless, the striking variability, even within species and flight types, highlights the complexity of describing the kinematic relationships, which appear sensitive to both the biological and physical context. Notwithstanding this, acceleration metrics that incorporate both kinematic parameters should be more robust proxies for power than wingbeat frequency alone.

Decadal changes in plastic litter regurgitated by albatrosses and giant petrels at sub-Antarctic Marion Island.

Plastic ingestion by seabirds is an efficient way to monitor marine plastics. We report temporal variation in the characteristics of marine litter regurgitated by albatrosses and giant petrels on sub-Antarctic Marion Island between 1996 and 2018. Both fishery and other litter peaked during the height of the Patagonian toothfish fishery around the island (1997-1999). Comparing the two subsequent decades of reduced fishing effort (1999-2008 and 2009-2018), fishing litter decreased while other litter increased across all species. Litter increased most in grey-headed albatrosses, followed by giant petrels and wandering albatrosses. Similar ranked responses were found in the same species at South Georgia, but non-fishery-related litter has increased faster in the Indian Ocean than the southwest Atlantic, indicating regional changes in litter growth rates. These seabirds' regurgitations provide an easy, non-invasive way to track changes in oceanic litter in a remote area that is otherwise difficult to monitor.