Moving in a moving medium: new perspectives on flight

Shepard, Emily L. C.; Ross, Andrew; Portugal, Steven J.

doi:10.1098/rstb.2015.0382

Cited by 29 publications

(37 citation statements)

References 66 publications

(87 reference statements)

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“…daily distance covered, time spent at stopovers) according to external and internal factors, to avoid detours triggered by adverse weather, to minimize energy consumption, to maximize speed of migration, and thus to enhance their chances of survival (Sergio et al , Vansteelant et al ; but see, Lok et al , Goymann et al ). Such modulation of migratory behaviours has been recorded in relation to windscapes (Sinelschikova et al , Klaassen et al , Yamaguchi et al ), food resources availability (van der Graaf et al , Duriez et al , Tøttrup et al ), presence/absence of ecological barriers (Deppe et al ) and exceptional weather events (Tøttrup et al , Vansteelant et al , 2016).…”

mentioning

confidence: 89%

Migration distance affects stopover use but not travel speed: contrasting patterns between long‐ and short‐distance migrating ospreys

Monti

Grémillet

Sforzi³

et al. 2018

Journal of Avian Biology

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The development of migratory behaviour is a continuous process which is not only determined by genes, but also moulded by individual differences based on life‐history variations occurring at each ontogenetic stage. Assessing consistency and plasticity in migratory traits between long distance (LDM) and short distance migratory (SDM) populations within the same species that may express dissimilarities in the leeway of annual schedules is essential to understand the evolution and ontogeny of migratory strategies. We studied the migration strategies in autumn regarding flight speed and the use of stopovers (number and duration of stop‐overs across the whole journey) at the intra‐specific level, by tracking with GPS loggers the intercontinental migration of 43 adult and juvenile ospreys Pandion haliaetus from both LDM and SDM populations. LDM ospreys travelled distances five times larger than SDM ospreys, but their total migration speed was 2.4 times slower. While daily distance travelled did not differ between populations, the reduced total migration speed by LDMs was due to higher stopover use compared to SDM birds. SDM birds used more direct routes, crossing open sea at higher flight speeds, even though both populations largely benefitted from wind assistance across their journey. Across populations, adult birds travelled longer distances per day and displayed less sinuous migratory paths than juveniles, suggesting that migratory capabilities improve with age and experience of the bird. Overall, the time constraint related to total migration distance was not the main driver of the total migration speed, and other factors such as physiological needs to rest and refuel at stopover sites may play an important role. Our study underlines the importance of investigating variability in migration strategies in partially migratory species, for a better understanding of avian migratory ecology.

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mentioning

confidence: 89%

Migration distance affects stopover use but not travel speed: contrasting patterns between long‐ and short‐distance migrating ospreys

Monti

Grémillet

Sforzi³

et al. 2018

Journal of Avian Biology

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“…While we have a relatively strong understanding of how mean flows affect the locomotion and ecology of flying animals, we know comparatively little about how turbulence impacts animal flight performance [16]. Recently, a number of wind tunnel studies have helped elucidate the effects of variable, but structured flows such as von Kármán vortex trails that form behind cylinders, on flight in both hummingbirds [13,17] and insects [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Foraging in an unsteady world: bumblebee flight performance in field-realistic turbulence

et al. 2017

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Natural environments are characterized by variable wind that can pose significant challenges for flying animals and robots. However, our understanding of the flow conditions that animals experience outdoors and how these impact flight performance remains limited. Here, we combine laboratory and field experiments to characterize wind conditions encountered by foraging bumblebees in outdoor environments and test the effects of these conditions on flight. We used radio-frequency tags to track foraging activity of uniquely identified bumblebee (Bombus impatiens) workers, while simultaneously recording local wind flows. Despite being subjected to a wide range of speeds and turbulence intensities, we find that bees do not avoid foraging in windy conditions. We then examined the impacts of turbulence on bumblebee flight in a wind tunnel. Rolling instabilities increased in turbulence, but only at higher wind speeds. Bees displayed higher mean wingbeat frequency and stroke amplitude in these conditions, as well as increased asymmetry in stroke amplitude-suggesting that bees employ an array of active responses to enable flight in turbulence, which may increase the energetic cost of flight. Our results provide the first direct evidence that moderate, environmentally relevant turbulence affects insect flight performance, and suggest that flying insects use diverse mechanisms to cope with these instabilities.

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“…Considerable progress has been made in developing methods that can categorize behaviors based on simple movement metrics (Edelhoff, Signer, & Balkenhol, 2016). These methods commonly identify multiple states and ascribe these to predefined behaviors such as search, rest, or travel (Evans, Dall, Bolton, Owen, & Votier, 2015;Guilford et al, 2008;Hamer, Phillips, Wanless, Harris, & Wood, 2000;King, Glahn, & Andrews, 1995;Palmer & Woinarski, 1999;Shepard, Ross, & Portugal, 2016;Weimerskirch et al, 2006). However, Gurarie et al (2016) argued for closer and more detailed exploratory analysis of movement data to prevent mis-specification of behavior, suggesting that the strengths of particular methods need to be more carefully considered so they are suitably attuned to the specific questions being asked by researchers.…”

Section: Introductionmentioning

confidence: 99%

Search and foraging behaviors from movement data: A comparison of methods

Bennison

Bearhop

Bodey

et al. 2017

Ecology and Evolution

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Search behavior is often used as a proxy for foraging effort within studies of animal movement, despite it being only one part of the foraging process, which also includes prey capture. While methods for validating prey capture exist, many studies rely solely on behavioral annotation of animal movement data to identify search and infer prey capture attempts. However, the degree to which search correlates with prey capture is largely untested. This study applied seven behavioral annotation methods to identify search behavior from GPS tracks of northern gannets (Morus bassanus), and compared outputs to the occurrence of dives recorded by simultaneously deployed time–depth recorders. We tested how behavioral annotation methods vary in their ability to identify search behavior leading to dive events. There was considerable variation in the number of dives occurring within search areas across methods. Hidden Markov models proved to be the most successful, with 81% of all dives occurring within areas identified as search. k‐Means clustering and first passage time had the highest rates of dives occurring outside identified search behavior. First passage time and hidden Markov models had the lowest rates of false positives, identifying fewer search areas with no dives. All behavioral annotation methods had advantages and drawbacks in terms of the complexity of analysis and ability to reflect prey capture events while minimizing the number of false positives and false negatives. We used these results, with consideration of analytical difficulty, to provide advice on the most appropriate methods for use where prey capture behavior is not available. This study highlights a need to critically assess and carefully choose a behavioral annotation method suitable for the research question being addressed, or resulting species management frameworks established.

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Moving in a moving medium: new perspectives on flight

Cited by 29 publications

References 66 publications

Migration distance affects stopover use but not travel speed: contrasting patterns between long‐ and short‐distance migrating ospreys

Migration distance affects stopover use but not travel speed: contrasting patterns between long‐ and short‐distance migrating ospreys

Foraging in an unsteady world: bumblebee flight performance in field-realistic turbulence

Search and foraging behaviors from movement data: A comparison of methods

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