Time allocation and foraging behaviour of chick‐rearing Brünnich's Guillemots <i>Uria lomvia</i> in high‐arctic Greenland

Falk, Knud; Benvenuti, Simone Messerotti; Dall’Antonia, Luigi; Kampp, Kaj; Ribolini, Alberto

doi:10.1111/j.1474-919x.2000.tb07687.x

Cited by 74 publications

(33 citation statements)

References 13 publications

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“…During 1999-2000, we used TDRs identical to those described by Falk et al (2000Falk et al ( , 2002 and Benvenuti et al (1998). The 'Benvenuti' TDRs (length: 80 mm;width: 23 mm (tip) to 30 mm (base); depth: 13 to 18.5 mm) housed a pressure sensor and 2 motion recorders.…”

Section: Methodsmentioning

confidence: 99%

“…We converted flight time into distance assuming 75 km h -1 flight speed, ignoring variation in flight velocity with wind speed, load and other factors (Elliott & Gaston 2005). This calculation assumed that murres returned in a straight line from their foraging destination, as predicted from theoretical considerations for single-prey loaders and shown for murres from colonies in Iceland (Benvenuti et al 1998) and Greenland (Falk et al 2000(Falk et al , 2002. We assumed that the maximum depth of the last dive prior to returning to the colony represented the depth at which the prey item was collected because for all prey types final dives were on average shorter in duration, but not consistently shallower than the average for the entire final dive bout, suggesting that the final dive was aborted once a prey item was captured.…”

Section: Methodsmentioning

confidence: 99%

“…Back-mounted TDRs are known to impact murre provisioning rates, trip duration and mass loss (Falk et al 2000, 2002, Tremblay et al 2003, Paredes et al 2004, Elliott et al 2007). To deter- Depth contours are in 20 m intervals.…”

Section: Methodsmentioning

confidence: 99%

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Seabird foraging behaviour indicates prey type

Elliott¹,

Woo²,

Gaston³

et al. 2008

Mar. Ecol. Prog. Ser.

116

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To investigate how a generalist marine predator modifies its foraging behaviour by prey type, we attached time-depth-temperature recorders to chick-rearing thick-billed murres (n = 204) at Coats Island, Nunavut, Canada from 1999 to 2007. Predators varied their behavior along 3 major 'axes': foraging effort, prey depth and prey lifestyle (benthic/pelagic). Dive behaviours for different prey -fish doctor, squid, sandlance, amphipods, snakeblenny, daubed shanny, sandlance and Arctic shanny -were discriminated from one another in a discriminant analysis of dive variables and these prey were therefore considered 'specialist' prey items. Specifically, amphipods were captured during V-shaped dives near the colony with a slow bird descent rate, squid were captured during deep V-shaped dives in cold water and fish doctor were captured during a long series of U-shaped dives in relatively warm water far from the colony. Arctic shanny and snakeblenny tended to be taken at moderate distances from the colony, with snakeblenny taken at deeper depths. Daubed shanny captures showed a bimodal distribution, with some taken at shallow depths far from the colony and others at deep depths close to the colony. Dive behaviours for Arctic cod, capelin and sculpin overlapped both with each other and the behaviours for specialist prey items and, therefore, were classified as 'generalist' prey items. In general, V-shaped dives preceded deliveries of pelagic prey items and U-shaped dives preceded deliveries of benthic prey items. Our results strongly suggest that generalist marine predators use stereotypic behaviour to forage for prey items, based on previous knowledge about what locations/strategies maximized intake for a given prey type.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Seabird foraging behaviour indicates prey type

Elliott¹,

Woo²,

Gaston³

et al. 2008

Mar. Ecol. Prog. Ser.

116

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“…eggs or milk), digestion, thermoregulation or, conversely, energy-saving processes such as torpor. For example, elephants spend less than 20% of their time walking (Shannon et al, 2008), seabirds commonly spend over 50% of their day resting (Falk et al, 2000;Grémillet et al, 1995) even when busy provisioning nestlings, and red deer rest for at least 45% of the day, depending on the time of year (Pépin et al, 2006). So while the relationship between ODBA and energy expenditure has been investigated during activity for a number of species (Fahlman et al, 2008;Halsey et al, 2008a;Halsey et al, 2008b;Halsey et al, 2008c;Wilson et al, 2006), it is well recognised that, during periods of inactivity, estimates of energy expenditure might need to be derived from laboratory studies or allometric estimates .…”

Section: Introductionmentioning

confidence: 99%

Estimating energy expenditure of animals using the accelerometry technique: activity, inactivity and comparison with the heart-rate technique

Green

Halsey

Wilson

et al. 2009

Journal of Experimental Biology

128

100

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Several errors were published in the original online version of J. Exp. Biol. 212,[471][472][473][474][475][476][477][478][479][480][481][482]. These errors occurred in both the PDF and fulltext versions of the online article but have now been corrected. The print version is correct.The captions of Figs 9 and 10 were truncated, and the caption published under Fig. 11 referred to Fig. 10.Figs 9-11, together with the correct captions, are printed below.We sincerely apologise to all authors and readers of this article for any inconvenience this has caused. Erratum Fig. 9. Rate of oxygen consumption as a function of heart rate in eight bantam chickens. Data were recorded while the chickens walked on a treadmill (filled squares), ate a meal of food pellets (filled triangles), digested the meal of food pellets (open triangles) or thermoregulated (open squares). Also plotted are two best-fit regression lines (solid line) and 95% confidence intervals (black dashed lines) and 95% prediction intervals (grey broken lines). 95% confidence intervals were calculated as if sVO 2 was estimated from one measurement of heart rate, during one additional behaviour by one additional chicken. 95% prediction intervals were calculated as if sVO 2 was estimated from 10,000 measurements of heart rate, during four additional behaviours by 100 additional chickens, effectively the smallest possible prediction interval for this model. . Also plotted are best-fit regression lines (solid line) and 95% confidence intervals (black broken lines) and 95% prediction intervals (grey broken lines). 95% confidence intervals were calculated as if sVO 2 was estimated from one measurement of heart rate, during one behaviour by one additional chicken. 95% prediction intervals were calculated as if sVO 2 was estimated from 10,000 measurements of heart rate, during four additional behaviours by 100 additional chickens, effectively the smallest possible prediction interval for this model. the standard error of the estimate (s.e.e.) and (C) the coefficient of variation (CV=100*s.e.e./Estimate) were calculated for this range of activity using four predictive approaches. Each of the four approaches used either partial dynamic body acceleration in the x and z axis (PDBAxz) or heart rate (fH) to predict VO 2 . The approaches used were (1) one-model using PDBAxz (black solid lines), (2) two-model using PDBAxz (grey solid lines), (3) one-model using fH (black broken lines), (4) two-model using fH (grey broken lines). See text for further details of the four predictive approaches.

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“…It is notable that many authors detailing information on the durations and depths of penguin dives disregard any that do not exceed 3 m (e.g. Bost et al 1994, Moore et al 1999, Radl & Culik 1999, Falk et al 2000, Deagle et al 2008). This may reflect, in part, a perception that such 'surface' dives are irrelevant for actual feeding, something that is certainly not true for Adélie Pygoscelis adeliae, chinstrap P. antarctica, gentoo P. papua or African penguins (R. P. Wilson unpubl.…”

Section: Changes In Capacities In Solid-state Devicesmentioning

confidence: 99%

Technological innovation in archival tags used in seabird research

Wilson¹,

Vandenabeele²

2012

Mar. Ecol. Prog. Ser.

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The first archival tags were used on seabirds in the 1960s. Since that time, when recording systems were primitive, various technological advances, culminating in solid state devices, have provided the seabird research community with extraordinarily sophisticated and powerful tags. This work documents the progress and development of archival tags used on seabirds. Major advances and the insights that these have provided into seabird behavioural ecology are highlighted, followed by an examination of the current limitations to our ability to study seabird ecology and speculation on what the future will bring to this exciting and dynamic field.

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Time allocation and foraging behaviour of chick‐rearing Brünnich's Guillemots Uria lomvia in high‐arctic Greenland

Cited by 74 publications

References 13 publications

Seabird foraging behaviour indicates prey type

Seabird foraging behaviour indicates prey type

Estimating energy expenditure of animals using the accelerometry technique: activity, inactivity and comparison with the heart-rate technique

Technological innovation in archival tags used in seabird research

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