Identifying at-sea foraging areas is a longstanding goal for seabird ecology and conservation. GPS tracks can reveal behaviour because slow, tortuous flight often indicates searching linked to feeding attempts, but two-dimensional (2D) paths may oversimplify three-dimensional (3D) flight. Here, we present the first 3D tracks for Ascension Frigatebirds Fregata aquila and assess whether incorporating flight altitude improves our ability to describe putative behavioural states. We compare results using altitude derived from GPS loggers and barometric altimeters deployed simultaneously. Tracked birds (three females) travelled at a mean altitude of 178 m and attained maximum heights of 1,658–1,871 m (measured by barometric altimeters). Hidden Markov models based on 2D tracks defined three states (interpreted as ‘search’, ‘slow travel’ and ‘fast travel’). However, with 59.5% of locations defined as ‘search’, identifying the most important foraging hotspots would be challenging. Including altitude was informative, allowing models to define two further states by introducing high-altitude ‘soaring/thermalling’ behaviour (3.4% of locations) and dividing ‘search’ into mid-altitude (44.7%) and low-altitude (12.2%), the latter being more likely to represent prey capture. Barometric altitude was less prone to large errors than GPS, but GPS altitude was highly correlated (r = 0.94) and state assignments overlapped by 88% overall. Using GPS altitude would reduce potential tag effects and allow us to model tracks in 3D for males and lighter females, which are too small to carry both loggers. Overall, incorporating flight height from either barometric altimeters or GPS loggers into behavioural models improved our ability to distinguish putative foraging events from high-altitude flight.

Breeding seabirds are central place foragers that can travel vast distances from the nest to feed. Many factors influence the distance travelled to forage, including species ecology (Thaxter et al. 2012; Oppel et al. 2018), prey availability (Hamer et al. 2007; Paiva et al. 2013; Thorne et al. 2015), and competition (Ashmole 1963; Lewis et al. 2001; Wakefield et al. 2013; Oppel et al. 2015; Corman et al. 2016). Our understanding of the patterns and processes involved in determining foraging behaviour is crucial to conserving seabirds both at their breeding colonies and their often-distant foraging areas (Croxall et al. 2012; Lewison et al. 2012). A key goal is to identify geographical areas or predictable environmental features associated with foraging to prioritise areas for conservation measures (BirdLife International 2010). Foraging is often classified from tracking data by identifying slow and tortuous flight as searching behaviour associated with feeding attempts (Andersson 1981; Fauchald & Tveraa 2003). However, this approach generally operates within a two-dimensional (2D) plane, whereas aerial and marine species move in three dimensions (3D) (Bailleul et al. 2010; Belant et al. 2012). Many wide-ranging species have evolved strategies such as thermalling and dynamic soaring that improve flight efficiency when travelling over large distances (Weimerskirch et al. 2003; Sachs et al. 2012; Yonehara et al. 2016). These movements may not follow the direct trajectories that many species exhibit during transit behaviour, so additional data streams may be needed to confidently interpret behavioural patterns (McClintock et al. 2017).

Identifying important areas for seabirds is particularly challenging in the tropics, where low productivity and unpredictable prey distributions mean that foraging is often diffuse and not linked to stable environmental features (Ashmole 1963; Boekelheide & Ainley 1983; Weimerskirch 2007). Frigatebirds provide an example of extreme specialisation to such sparse environments. They are the only marine animals that are physically unable to enter the water despite relying entirely on marine resources, such as flying fish and squid (Weimerskirch et al. 2003, 2010), which can be brought to the surface by other aquatic predators (Au & Pitman 1986; Miller et al. 2018). Frigatebirds may also predate on seabird chicks or turtle hatchlings and feed through kleptoparasitism, but this is more common in immature birds (Stonehouse & Stonehouse 1963; Osorno et al. 1992; Lagarde et al. 2001). As their plumage is not waterproof and they cannot reliably take off when wet (Mahoney 1984), they cannot rest on the water and must remain in flight for the duration of a foraging trip (Weimerskirch et al. 2003; De Monte et al. 2012; Weimerskirch et al. 2016). Consequently, frigatebirds have extremely low wing loadings, allowing them to remain airborne for many days with very low energetic output (Brewer & Hertel 2007). They use thermals to reach high altitudes, allowing them to glide and soar to efficiently cover large distances (Weimerskirch et al. 2016), even sleeping on the wing in rising air currents (Rattenborg et al. 2016). However, foraging can only take place when birds are near to sea level, and so altitude data is likely to provide relevant information for behavioural models.

The Ascension Frigatebird Fregata aquila is endemic to Ascension Island, an isolated peak in the central tropical Atlantic, 1,300 km from the nearest land. The species is regarded as ‘vulnerable’ due to its restricted range (BirdLife International 2018a). The Ascension Frigatebird only recently recolonised the main island from Boatswainbird Islet after the successful eradication of Feral Cats Felis catus in 2006 (Ratcliffe et al. 2008, 2010). A previous study described the at-sea foraging distributions of this species, showing them to roam over a large marine area with some trips extending up to 1,100 km from the colony (Oppel et al. 2017). However, more detailed behavioural analyses are required to locate foraging hotspots within this very large area. In this study, we use a combination of GPS and barometric altimeter data to reconstruct the first 3D foraging tracks of the Ascension Frigatebird. GPS altitude is less accurate than latitude and longitude because four satellites are required for 3D positions, compared to three for 2D positions, and the location of those satellites affects accuracy (Dussault et al. 2019). These GPS altitude errors can occur at the scale of frigatebird flight (De Monte et al. 2012). Barometric pressure loggers are less prone to large errors but are affected by changing sea level air pressure (Berberan-Santos et al. 1997). Consequently, we first compare the distribution of error in the altitude recorded at a fixed point by GPS loggers and barometric pressure loggers. We then evaluate the implications of incorporating altitude into behavioural classifications performed using hidden Markov models (HMMs): a commonly used technique for decomposing tracking data into discrete movement patterns based primarily on speed and turning angle (Michelot et al. 2016; Bennison et al. 2017). Finally, we assess whether altitudes recorded by GPS loggers can be reliably used in place of barometric pressure data in future studies of this species (e.g. Rattenborg et al. 2016; Weimerskirch et al. 2016; Parr et al. 2017).

This project was funded by the Darwin Initiative project DPLUS063, The Ascension Island Ocean Sanctuary Project (ASIOS) jointly managed by Ascension Island Government Conservation and Fisheries Department (AIGCFD) and the University of Exeter, in-kind funding from AIGCFD. Altitude loggers were previously used for a NERC GW4+ Doctoral Training Partnership studentship awarded to Bethany Clark from the Natural Environment Research Council [NE/L002434/1]. For assistance in the field, we thank Sophie Tuppen, as well as Andy Richardson, Jolene Sim, Natasha Williams, Megan Benjamin, Diane Baum and Matthew Stritch. We thank Annette Broderick and Stephen Votier for advice, and two anonymous reviewers for their valuable input. An Environmental Research Permit was provided by the Ascension Island Government, and ethical approval was granted by the University of Exeter.

Amélineau, F., Péron, C., Lescroël, A., Authier, M., Provost, P. & Grémillet, D. 2014. Windscape and tortuosity shape the flight costs of northern gannets. Journal of Experimental Biology 217: 876–85. [Crossref]

Andersson, M. 1981. On optimal predator search. Theoretical Population Biology 19: 58–86. [Crossref]

Ashmole, N. P. 1963. The regulation of numbers of tropical oceanic birds. Ibis 103b: 458–473. [Crossref]

Au, D. W. K. & Pitman, R. L. 1986. Seabird interactions with dolphins and tuna in the eastern tropical Pacific. The Condor 88: 304–317. [Crossref]

Austin, R. E., De Pascalis, F., Arnould, J. P. Y., Haakonsson, J., Votier, S. C., Ebanks-Petrie, G., Austin, T., Morgan, G., Bennett, G. & Green, J. A. 2019. A sex-influenced flexible foraging strategy in a tropical seabird, the magnificent frigatebird. Marine Ecology Progress Series 611: 203–214. [Crossref]

Bailleul, F., Lesage, V. & Hammill, M. O. 2010. Spherical first passage time: A tool to investigate area-restricted search in three-dimensional movements. Ecological Modelling 221: 1665–1673. [Crossref]

Belant, J. L., Millspaugh, J. J., Martin, J. A. & Gitzen, R. A. 2012. Multi-dimensional space use: The final frontier. Frontiers in Ecology and the Environment 10: 11–12. [Crossref]

Bennison, A., Bearhop, S., Bodey, T. W., Votier, S. C., Grecian, W. J., Wakefield, E. D., Hamer, K. C. & Jessopp, M. 2017. Search and foraging behaviors from movement data: A comparison of methods. Ecology and Evolution 8: 13–24. [Crossref]

Berberan-Santos, M. N., Bodunov, E. N. & Pogliani, L. 1997. On the barometric formula. American Journal of Physics 65: 404–412. [Crossref]

BirdLife International. 2010. Marine Important Bird Areas toolkit: Standardised techniques for identifying priority sites for the conservation of seabirds at sea. Version 1. BirdLife International, Cambridge UK

BirdLife International, 2018a. Fregata aquila, Ascension Frigatebird. The IUCN Red List of Threatened Species 2018: e.T22697728A132597828.

BirdLife International, 2018b. Fregata andrewsi, Christmas Frigatebird. The IUCN Red List of Threatened Species 2018: e.T22697742A132599384.

Bodey, T. W., Cleasby, I. R., Bell, F., Parr, N., Schultz, A., Votier, S. C. & Bearhop, S. 2018. A phylogenetically controlled meta-analysis of biologging device effects on birds: Deleterious effects and a call for more standardized reporting of study data. Methods in Ecology and Evolution 9: 946–955. [Crossref]

Boekelheide, R. J. & Ainley, D. G. 1983. An ecological comparison of oceanic seabird communities of the South Pacific Ocean. Studies in Avian Biology 8, 2–23.

Brewer, M. L. & Hertel, F. 2007. Wing morphology and flight behavior of pelecaniform seabirds. Journal of Morphology 268: 866–877. [Crossref]

Cleasby, I. R., Wakefield, E. D., Bearhop, S., Bodey, T. W., Votier, S. C. & Hamer, K. C. 2015. Three-dimensional tracking of a wide-ranging marine predator: flight heights and vulnerability to offshore wind farms. Journal of Applied Ecology 52: 1474–1482. [Crossref]

Corman, A. M., Mendel, B., Voigt, C. C. & Garthe, S. 2016. Varying foraging patterns inresponse to competition? A multicolony approach in a generalist seabird. Ecology and Evolution 6: 974–986. [Crossref]

Cox, S. L., Miller, P. I., Embling, C. B., Scales, K. L., Bicknell, A. W. J., Hosegood, P. J., Morgan, G., Ingram, S. N. & Votier, S. C. 2016. Seabird diving behaviour reveals the functional significance of shelf-sea fronts as foraging hotspots. Royal Society Open Science 3: 160317. [Crossref]

Croxall, J. P., Butchart, S. H. M., Lascelles, B., Stattersfield, A. J., Sullivan, B., Symes, A. & Taylor, P. 2012. Seabird conservation status, threats and priority actions: A global assessment. Bird Conservation International 22: 1–34. [Crossref]

De Monte, S., Cotté, C., D’Ovidio, F., Lévy, M., Le Corre, M. & Weimerskirch, H. 2012. Frigatebird behaviour at the ocean-atmosphere interface: integrating animal behaviour with multi-satellite data. Journal of the Royal Society Interface 9: 3351–3358. [Crossref]

Dean, B., Freeman, R., Kirk, H., Leonard, K., Phillips, R. A., Perrins, C. M. & Guilford, T. 2013. Behavioural mapping of a pelagic seabird: combining multiple sensors and a hidden Markov model reveals the distribution of at-sea behaviour. Journal of the Royal Society Interface 10: 20120570. [Crossref]

Dussault, C., Courtois, R., Ouellet, J. & Huot, J. 2001. lnfluence of satellite geometry and differential correction on GPS location accuracy. Wildlife Society Bulletin 29: 171–179.

Fairbairn, J. & Shine, R. 1993. Patterns of sexual size dimorphism in seabirds of the Southern Hemisphere. Oikos 68: 139–145. [Crossref]

Fauchald, P. & Tveraa, T. 2003. Using first-passage time in the analysis of area restricted search and habitat selection. Ecology 84: 282–288. [Crossref]

Hamer, K. C., Humphreys, E. M., Garthe, S., Hennicke, J., Peters, G., Grémillet, D., Phillips, R. A., Harris, M. P. & Wanless, S. 2007. Annual variation in diets, feeding locations and foraging behaviour of gannets in the North Sea: flexibility, consistency and constraint. Marine Ecology Progress Series 338: 295–305. [Crossref]

Hennicke, J. C., James, D. J. & Weimerskirch, H. 2015. Sex-specific habitat utilization and differential breeding investments in Christmas Island frigatebirds throughout the breeding cycle. PLoS One 10: e0129437. [Crossref]

Hijmans, R. J. 2018. raster: Geographic Data Analysis and Modeling. R package version 2.8-4.

Hijmans, R. J. 2017. geosphere: Spherical Trigonometry. R package version 1.5-7.

Kay, W. P., Naumann, D. S., Bowen, H. J., Withers, S. J., Evans, B. J., Wilson, R. P., Stringell, T. B., Bull, J .C., Hopkins, P. W. & Börger, L. 2019. Minimizing the impact of biologging devices: Using computational fluid dynamics for optimizing tag design and positioning. Methods in Ecology and Evolution 10, 1222–1233. [Crossref]

Kuhn, M., Wing, J., Weston, S., Williams, A., Keefer, C., Engelhardt, A., Cooper, T., Mayer, Z., Kenkel, B., Team, T. R. C., Benesty, M., Lescarbeau, R., Ziem, A., Scrucca, L., Tang, Y., Candan, C. & Hunt, T. 2019. caret: Classification and Regression Training. R package version 6.0-84.

Lagarde, F., Le Corre, M. & Lormée, H. 2001. Species and sex-biased predation on hatchling green turtles by frigatebirds on Europa Island, Western Indian Ocean. The Condor 103: 405–408. [Crossref]

Lewis, S., Sherratt, T. N., Hamer, K. C. & Wanless, S. 2001. Evidence of intra-specific competition for food in a pelagic seabird. Nature 412: 816–819. [Crossref]

Lewison, R., Oro, D., Godley, B. J., Underhill, L., Bearhop, S., Wilson, R. P., Ainley, D., Arcos, J. M., Boersma, P. D., Borboroglu, P. G., Boulinier, T., Frederiksen, M., Genovart, M., González-Solís, J., Green, J. A., Grémillet, D., Hamer, K. C., Hilton, G. M., Hyrenbach, K. D., Martínez-Abraín, A., Montevecchi, W. A., Phillips, R. A., Ryan, P. G., Sagar, P., Sydeman, W. J., Wanless, S., Watanuki, Y., Weimerskirch, H. & Yorio, P. 2012. Research priorities for seabirds: Improving conservation and management in the 21st century. Endangered Species Research 17: 93–121. [Crossref]

Mahoney, S. A. 1984. Plumage wettability of aquatic birds. The Auk 101: 181–185. [Crossref]

McClintock, B. T. & Michelot, T. 2018. momentuHMM: R package for generalized hidden Markov models of animal movement. Methods in Ecology and Evolution 9: 1518–1530. [Crossref]

McClintock, B. T., London, J. M., Cameron, M. F. & Boveng, P. L. 2017. Bridging the gaps in animal movement: hidden behaviors and ecological relationships revealed by integrated data streams. Ecosphere 8: e01751. [Crossref]

Michelot, T., Langrock, R. & Patterson, T. A. 2016. moveHMM: an R package for the statistical modelling of animal movement data using hidden Markov models. Methods in Ecology and Evolution 7: 1308–1315. [Crossref]

Miller, M. G., Carlile, N., Phillips, J. S., Mcduie, F. & Congdon, B. C. 2018. Importance of tropical tuna for seabird foraging over a marine productivity gradient. Marine Ecology Progress Series 586: 233–249. [Crossref]

Morgan-Wall, T. 2019. rayshader: Create and visualize hillshaded maps from elevation matrices. R package version 0.11.5. https://CRAN.R-project.org/package=rayshader.

Oppel, S., Beard, A., Fox, D., Mackley, E., Leat, E., Henry, L., Clingham, E., Fowler, N., Sim, J., Sommerfeld, J., Weber, N., Weber, S. & Bolton, M. 2015. Foraging distribution of a tropical seabird supports Ashmole’s hypothesis of population regulation. Behavioral Ecology and Sociobiology 69: 915–926. [Crossref]

Oppel, S., Bolton, M., Carneiro, A. P. B., Dias, M. P., Green, J. A., Masello, J. F., Phillips, R. A., Owen, E., Quillfeldt, P., Beard, A., Bertrand, S., Blackburn, J., Boersma, P. D., Borges, A., Broderick, A. C., Catry, P., Cleasby, I., Clingham, E., Creuwels, J., Crofts, S., Cuthbert, R. J., Dallmeijer, H., Davies, D., Davies, R., Dilley, B. J., Dinis, H. A., Dossa, J., Dunn, M. J., Efe, M. A., Fayet, A. L., Figueiredo, L., Frederico, A. P., Gjerdrum, C., Godley, B. J., Granadeiro, J. P., Guilford, T., Hamer, K. C., Hazin, C., Hedd, A., Henry, L., Hernández-Montero, M., Hinke, J., Kokubun, N., Leat, E., McFarlane Tranquilla, L., Metzger, B. Militão, T., Montrond, G., Mullié, W., Padget, O., Pearmain, E. J., Pollet, I. L., Pütz, K., Quintana, F., Ratcliffe, N., Ronconi, R. A., Ryan, P. G., Saldanha, S., Shoji, A., Sim, J., Small, C., Soanes, L., Takahashi, A., Trathan, P., Trivelpiece, W., Veen, J., Wakefield, E., Weber, N., Weber, S., Zango, L., Daunt, F., Ito, M., Harris, M. P., Newell, M. A., Wanless, S., González-Solís, J. & Croxall, J. 2018. Spatial scales of marine conservation management for breeding seabirds. Marine Policy 98: 37–46. [Crossref]

Oppel, S., Weber, S., Weber, N., Fox, D., Leat, E., Sim, J., Sommerfeld, J., Bolton, M., Broderick, A. C. & Godley, B. J. 2017. Seasonal shifts in foraging distribution due to individual flexibility in a tropical pelagic forager, the Ascension frigatebird. Marine Ecology Progress Series 585: 199–212. [Crossref]

Osorno, J., Torres, R. & Macias Garcia, C. 1992. Kleptoparasitic behaviour of the Magnificent Frigatebird: Sex bias and success. The Condor 94: 692–698. [Crossref]

Paiva, V. H., Geraldes, P., Ramirez, I., Werner, A. C., Garthe, S. & Ramos, J. A. 2013. Overcoming difficult times: the behavioural resilience of a marine predator when facing environmental stochasticity. Marine Ecology Progress Series 486: 277–288. [Crossref]

Paredes, R., Orben, R. A., Suryan, R. M., Irons, D. B., Roby, D. D., Harding, A. M. A., Young, R. C., Benoit-Bird, K., Ladd, C., Renner, H., Heppell, S., Phillips, R. A. & Kitaysky, A. 2014. Foraging responses of black-legged kittiwakes to prolonged food-shortages around colonies on the Bering Sea shelf. PLoS One 9: e92520. [Crossref]

Parr, N., Bearhop, S., Douglas, D. C., Takekawa, J. Y., Prosser, D. J., Newman, S. H., Perry, W. M., Balachandran, S., Witt, M. J., Hou, Y., Luo, Z. & Hawkes, L. A. 2017. High altitude flights by ruddy shelduck Tadorna ferruginea during trans-Himalayan migrations. Journal of Avian Biology 48: 1–6. [Crossref]

Pavlis, N. K., Holmes, S. A., Kenyon, S. C. & Factor, J. K. 2012. The development and evaluation of the Earth Gravitational Model 2008 (EGM2008). Journal of Geophysical Research 117: B04406. [Crossref]

Ratcliffe, N., Bell, M., Pelembe, T., Boyle, D., Benjamin, R., White, R., Godley, B. J., Stevenson, J. & Sanders, S. 2010. The eradication of feral cats from Ascension Island and its subsequent recolonization by seabirds. Oryx 44: 20–29. [Crossref]

Ratcliffe, N., Pelembe, T. & White, R. 2008. Resolving the population status of Ascension Frigatebird Fregata aquila using a ‘virtual ecologist’ model. Ibis 150: 300–306. [Crossref]

Rattenborg, N. C., Voirin, B., Cruz, S. M., Tisdale, R., Dell’Omo, G., Lipp, H.-P., Wikelski, M. & Vyssotski, A. L. 2016. Evidence that birds sleep in mid-flight. Nature Communications 7: 12468. [Crossref]

R Core Team. 2019. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. www.R-project.org.

Sachs, G., Traugott, J., Nesterova, A. P., Dell’Omo, G., Kümmeth, F., Heidrich, W., Vyssotski, A. L. & Bonadonna, F. 2012. Flying at no mechanical energy cost: disclosing the secret of wandering albatrosses. PLoS One 7, e41449. [Crossref]

Schreiber, E. 1999. Problems encountered when banding frigatebirds and boobies. Waterbirds 22: 310–313. [Crossref]

Stonehouse, B. & Stonehouse, S. 1963. The Frigate Bird Fregata aquila of Ascension Island. Ibis 103b: 409–422. [Crossref]

Thaxter, C. B., Lascelles, B., Sugar, K., Cook, A. S. C. P., Roos, S., Bolton, M., Langston, R. H. W. & Burton, N. H. K. 2012. Seabird foraging ranges as a preliminary tool for identifying candidate Marine Protected Areas. Biological Conservation 156: 53–61. [Crossref]

Thorne, L. H., Hazen, E. L., Bograd, S. J., Foley, D. G., Conners, M. G., Kappes, M. A., Kim, H. M., Costa, D. P., Tremblay, Y. & Shaffer, S. A. 2015. Foraging behavior links climate variability and reproduction in North Pacific albatrosses. Movement Ecology 3: 27. [Crossref]

Trefry, S. A., Diamond, A. W. & Jesson, L. K. 2013. Wing marker woes: A case study and meta-analysis of the impacts of wing and patagial tags. Journal of Ornithology 154: 1–11. [Crossref]

Vandenabeele, S., Wilson, R. & Grogan, A. 2011. Tags on seabirds; how seriously are we considering instrument-induced behaviors? Animal Welfare 20: 559–571. [Crossref]

Wakefield, E. D., Bodey, T. W., Bearhop, S., Blackburn, J., Colhoun, K., Davies, R., Dwyer, R. G., Green, J. A., Grémillet, D., Jackson, A. L., Jessopp, M. J., Kane, A., Langston, R. H. W., Lescroël, A., Murray, S., Le Nuz, M., Patrick, S. C., Péron, C., Soanes, L. M., Wanless, S., Votier, S. C. & Hamer, K. C. 2013. Space partitioning without territoriality in gannets. Science 341: 68–70. [Crossref]

Weimerskirch, H. 2007. Are seabirds foraging for unpredictable resources? Deep Sea Research Part II: Topical Studies in Oceanography 54: 211–223. [Crossref]

Weimerskirch, H., Guionnet, T., Martin, J., Shaffer, S. A. & Costa, D. P. 2000. Fast and fuel efficient? Optimal use of wind by flying albatrosses. Proceedings of the Royal Society B: Biological Sciences 267: 1869–1874. [Crossref]

Weimerskirch, H., Chastel, O., Barbraud, C. & Tostain, O. 2003. Frigatebirds ride high on thermals. Nature 421: 333–334. [Crossref]

Weimerskirch, H., Le Corre, M., Jaquemet, S., Potier, M. & Marsac, F. 2004. Foraging strategy of a top predator in tropical waters: great frigatebirds in the Mozambique Channel. Marine Ecology Progress Series 275: 297–308.[Crossref]

Weimerskirch, H., Le Corre, M., Tew Kai, E. & Marsac, F. 2010. Foraging movements of great frigatebirds from Aldabra Island: Relationship with environmental variables and interactions with fisheries. Progress in Oceanography 86: 204–213. [Crossref]

Weimerskirch, H., Bishop, C., Jeanniard-du-Dot, T., Prudor, A. & Sachs, G. 2016. Frigate birds track atmospheric conditions over months-long transoceanic flights. Science 353: 74–78. [Crossref]

Yonehara, Y., Goto, Y., Yoda, K., Watanuki, Y., Young, L. C., Weimerskirch, H., Bost, C. & Sato, K. 2016. Flight paths of seabirds soaring over the ocean surface enable measurement of fine-scale wind speed and direction. PNAS 113: 9039–9044. [Crossref]