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Year-round at-sea movements of fairy prions from south-eastern Australia

Cite this dataset

Fromant, Aymeric et al. (2022). Year-round at-sea movements of fairy prions from south-eastern Australia [Dataset]. Dryad.


Effective conservation assessments require detailed information of species’ ecological niches during the whole annual cycle. For seabirds, this implies investigating the at-sea distribution and foraging behaviour during both the breeding and non-breeding periods. However, until recently, collecting information about the smallest species has been precluded by the excessive size of the required devices. This lack of knowledge is exacerbated in the case of polytypic genera with species sharing very similar appearance and behaviour, such as the super-abundant prions (Pachyptila spp.). The present study investigates the year-round at-sea distribution and foraging ecology of the fairy prion (Pachyptila turtur) in south-eastern Australia. Using miniaturized GPS during the breeding season and geolocators (GLS) during the non-breeding period, the results highlight the importance of the continental shelf-edge waters for fairy prions throughout the year. In addition, contrary to previous assumptions, the GLS data revealed an unsuspected post-breeding migration to the waters south of Australia, during which individuals likely undergo a rapid moult of flight feathers. Understanding the at-sea distribution and ecology of prions during the whole annual cycle will be fundamental to their conservation as it can reveal species- or population-specific threats that have been overlooked because of their status of abundant species.


Animal handling and instrumentation

The study was conducted during breeding (incubation and chick-rearing) and non-breeding periods over four consecutive years at Kanowna Island (39º15’ S, 146º30’ E) in northern Bass Strait, south-eastern Australia. Seven seabird species breed on this island (Fromant et al. 2020b), including 1000-4000 breeding pairs of fairy prion, which represent 1-4% of the Bass Strait population (Schumann et al. 2014).

To evaluate the at-sea movements of fairy prions during the breeding season (incubation and chick-rearing periods), adult breeding birds were equipped with a miniaturized GPS data logger (nanoFix-GEO, Pathtrack Ltd, Otley, United-Kingdom), attached to two central tail feathers using waterproof tape (Tesa 4651; Beiersdorf AG). The GPS loggers were programmed to record locations at 20 min and 10 min interval during the incubation and chick-rearing periods, respectively. The total mass of logger attachments corresponded to 2.4 ± 0.2% of body mass (124 ± 10 g), which was unlikely to have impacted the foraging and breeding parameters of the equipped individuals (Fromant et al. 2021). Individuals were weighed (± 2 g; Pesola), and bill, tarsus (± 0.1 mm; Vernier callipers) and wing length (± 1 mm; ruler) were measured.

To determine the at-sea distribution of fairy prions during the non-breeding period, adult birds were equipped with leg-mounted GLS (Migrate Technology, model C65, United Kingdom). The total mass of logger attachments corresponded to 1.2 ± 0.1% of body mass of the equipped birds. Breeding individuals were equipped at the end of the breeding season and were recaptured during the following breeding season. In addition, the timing of wing moult was inferred from information provided by the GLS on the time spent on the sea surface (wet and dry sensor). Since flight feather renewal directly affects flying ability (Cherel et al. 2016), the peak of time spent on the water during the non-breeding period was used to identify the period when fairy prions moult their wing flight feathers. Finally, the dates of last and first burrow attendance, as well as the periods of burrow attendance during the non-breeding period, were determined by combining information from the GLS of activity (wet-dry) and movement data (presence of the bird in the breeding area) (Fromant et al. 2020c). When a burrow attendance was detected during the non-breeding period, it was not possible to confirm that the individuals returned to the exact same breeding colony due to the low spatial accuracy of GLS (Phillips et al. 2004). However, fairy prions are highly philopatric at the colony level (Ovenden et al. 1991) and it was assumed that tracked individuals were attending burrows at their colony during the non-breeding season.

When individuals were recaptured, six body feathers and blood (0.2 mL from the brachial vein) were collected for sexing and stable isotope analysis. Sex was determined by DNA analysis (DNA solutions, Wantirna South, Australia) from either blood or a single body feather. Stable isotope ratios of carbon (δ13C) and nitrogen (δ15N) in whole blood and body feathers were used as proxies of foraging habitat and diet/trophic level, respectively. Specifically, isotopic values of whole blood (hereafter blood) reflect dietary integration of approximately two to four weeks, while body feathers reflect dietary intake when they were synthesized (Cherel et al. 2000) from the end of the breeding season and throughout the non-breeding period (Marchant and Higgins 1990). Therefore, isotopic values of blood were used to determine the trophic ecology of the fairy prion throughout the breeding season (incubation and chick-rearing), while body feathers were used for the non-breeding period. One body feather per individual was analysed to determine the inter-annual variations. In addition, in 2018-2019, four feathers per individual were analysed to investigate intra-individual variation.

Data processing and analyses

All data analyses were processed within the R statistical environment (R Core Team 2021). A speed filter with a threshold at 20 m·s-1 was applied to remove erroneous locations (Spear and Ainley 1997). Because of poor satellite reception when the birds are feeding or sitting on the water, linear interpolation was applied to correct for unequal sampling frequencies between foraging and commuting. For each complete foraging trip (defined as the time spent at sea between the departure from, and the return to, the burrow), the following basic parameters were calculated: trip duration (h), total horizontal distance travelled (km), and maximum distance from the colony (km). Incomplete records of trips were only used to estimate maximum distance from the colony. During the chick-rearing period, trips were classified in two categories based on the data distribution of trip duration: short (≤ 2 d at sea) or long (> 2 d at sea). Such a dual foraging strategy (alternating short and long trips) is common in procellariiform species, including prions (Weimerskirch et al. 1994).

The expectation maximization binary clustering (EMBC) was used to infer fairy prion at-sea foraging behaviour (R package EMbC; Garriga et al. 2016, R Core Team 2021). This method classifies four different movement types based on travel speed and turning angle between subsequent locations: travelling−commuting (high-speed low turn, HL), resting on the water (low-speed low turn, LL), and intensive (low-speed high turn, LH) and extensive searching (high-speed high turn, HH). This method has been shown to be well suited to interpreting ecologically meaningful behaviours from movement data for a range of procellariiform species (de Grissac et al. 2017, Clay et al. 2019, Weimerskirch et al. 2020).

Processing and calculations of GLS data were conducted using the GeoLight package in the R statistical environment (Lisovski et al. 2012, R Core Team 2021). The device records the maximum light intensity for each 5 min interval, and the determination of morning and evening twilights enables longitude (timing of local midday and midnight) and latitude (duration of day and night) to be estimated, providing two positions per day with an average accuracy of 186 ± 114 km (mean ± SD; Phillips et al. 2004). Before spatial analyses were conducted, data for two weeks before and after the autumn and spring equinoxes were excluded because latitude estimations around these periods are unreliable (Wilson et al. 1992). The dates of last and first burrow attendance were determined by combining information on activity (wet-dry: 100% dry for a period > 8 h) and movement data (presence of the bird within 200 km from the breeding colony). These data were then used to estimate the duration and the total distance travelled during the post-breeding migration. The moulting period of flight feathers was determined for each individual using the period of maximum proportion of time spent on the water (wet-dry sensor being wet > 90% per day; Cherel et al. 2016). Wet-dry data were sampled every 30 s with the number of samples wet and maximum conductivity recorded every 4 h.

For both GPS and GLS data, filtered locations were used to generate kernel utilization distribution (UD) estimates. For GPS data, a smoothing parameter of h = 0.2 was used with a grid of 0.1° × 0.1° cells (to avoid over-fragmentation), while for GLS data a h of 1.8 was selected (corresponding to a search radius of ~ 200 km) with a 1º x 1º grid cell size (based on the mean accuracy of the device). The 50% (core foraging area) and 95% (home range) kernel UD contours were obtained. Spatial analyses were performed using the adehabitatHR R package (Calenge 2006; R Core Team 2021).

For stable isotope analyses, blood samples were freeze-dried, ground to powder and homogenized. Body feathers were cleaned of surface lipids and contaminants using a 2:1 chloroform:methanol solution in a ultrasonic bath, followed by two successive methanol rinses and air dried 24 h at 50°C (Fromant et al. 2016). Each feather was then cut with scissors to produce a fine powder for homogenization before carbon and nitrogen isotope ratio determination using a continuous flow mass spectrometer (Delta V Plus or Delta V Advantage both with a Conflo IV interface, Thermo Scientific, Bremen, Germany) coupled to an elemental analyser (Flash 2000 or Flash EA 1112, Thermo Scientific, Milan, Italy) at the LIENSs laboratory (La Rochelle Université, France). Stable isotope values were expressed in conventional notation (δX = [(Rsample/Rstandard) – 1]) where X is 13C or 15N and R represent the corresponding ratio 15N/14N or 13C/12C. Rstandard values were based on Vienna Pee Dee Belemnite for 13C, and atmospheric nitrogen (N2) for 15N. Replicates of internal laboratory standards (Caffeine USGS-61 and USGS-62) indicate measurement errors < 0.10 ‰ for δ13C and 0.15 ‰ for δ15N.


Deakin University

Sea World Research and Rescue Foundation Inc.

BirdLife Australia