Time budget and 24-h temporal rest–activity patterns of captive red deer hinds

Pépin, Dominique; Renaud, Pierre‐Cyril; Dumont, Bertrand; Decuq, Francis

doi:10.1016/j.applanim.2006.02.002

Cited by 21 publications

(14 citation statements)

References 30 publications

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“…''*''mark a significant increasing comparing with previous month (b, c) timing, community dynamics, social activity, and environmental conditions (Aschoff 1963;Horn and Rubenstein 1984). For deer species, peak activity usually occurs over 4 h around sunrise and sunset (Georgii and Schröder 1983;Pépin et al 2006). Here, we found that Eld's deer inhabiting a reserve unfrequented by people deviated from this trend and were active for two much longer periods of time (approximately 7 h each).…”

Section: Discussionmentioning

confidence: 70%

Eld’s Deer Translocated to Human-Inhabited Areas Become Nocturnal

Pan

Teng

Cui

et al. 2010

AMBIO

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As human populations expand and nonhuman animals decline, understanding the interactions between people and wildlife is essential. For endangered species, appreciating the effect of human disturbance can be important for their conservation. However, a human disturbance angle is often absent from ecological research, despite growing evidence of the negative impact of nonfatal human interference. Here, we monitored Hainan Eld's deer living within a reserve and translocated animals living amongst villagers. We show that translocated deer deviated from a crepuscular activity pattern and became increasingly nocturnal, and most active when villagers were not. It appears that translocated deer adapted over time to human disturbance and this pattern is similar to that of other species during periods of hunting. People do not pose an actual threat to Eld's deer, but their presence triggered a response akin to predator avoidance and may be interfering with broader aspects of their biology and conservation.

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Section: Discussionmentioning

confidence: 70%

Eld’s Deer Translocated to Human-Inhabited Areas Become Nocturnal

Pan

Teng

Cui

et al. 2010

AMBIO

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“…Ashoff 1966Ashoff , 1989. In red deer, a bimodal rhythm with prominent peaks around sunrise and sunset has been found in both free-ranging (Bubenik and Bubenikova 1967;Georgii 1981;Georgii and Schröder 1983) and farmed animals (Pépin et al 2006). …”

Section: Introductionmentioning

confidence: 97%

Seasonal and daily walking activity patterns of free-ranging adult red deer (Cervus elaphus) at the individual level

Pépin

Morellet

Goulard³

2009

Eur J Wildl Res

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We studied the walking activity over the year of free-ranging adult red deer (Cervus elaphus) in a mountainous area with the aim of describing the dynamics of movement patterns at the individual level. We monitored the distance walked by two males and two females fitted with global positioning system collars to test the hypothesis that deer adopt behaviours to reduce costs of locomotion. We predicted that both sexes would travel less in winter when disadvantageous environmental conditions occurred. We also predicted that the males would (1) reduce their movement soon after the rut due to very high energy expenditure during the breeding season and (2) travel less than the females due to their larger body mass. As we expected, minimum walking activity occurred after the rut from November to February for the males and in late February for the females. The walking activity of males peaked during the rut whereas that of females decreased. But compared to males, females moved more both during winter and daylight hours. Although our study stems from just four individuals, these results and the methodology used can be inspirational for red deer research as well as for ungulate research in general.

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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

130

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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Time budget and 24-h temporal rest–activity patterns of captive red deer hinds

Cited by 21 publications

References 30 publications

Eld’s Deer Translocated to Human-Inhabited Areas Become Nocturnal

Eld’s Deer Translocated to Human-Inhabited Areas Become Nocturnal

Seasonal and daily walking activity patterns of free-ranging adult red deer (Cervus elaphus) at the individual level

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

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