Factors Influencing a 24-Hour Time-Budget for Wintering Atlantic Brant

Abstract

The wintering period is often a limiting time for waterfowl. To understand the behavioral dynamics of Atlantic brant Branta bernicla hrota wintering along coastal New Jersey, USA, we conducted observations across the full 24-h diel period in an effort to construct an accurate time-budget model for the wintering population. In most behavioral studies, it is only possible to collect diurnal and crepuscular behavior data, forcing the assumption that these data are representative of nocturnal behavior in order to model the full 24-h diel period. We collected behavior data in 5,902 instantaneous observational scans across 4 time periods (morning crepuscular, diurnal, evening crepuscular, and nocturnal) from the third week in October to the third week in February 2009–2010 and 2010–2011. Brant primarily allocated time toward swimming (43.5%), feeding (26.4%), resting (15.4%), and flying (7.7%); these proportions differed significantly across times of day. Brant exhibited decreased flight (4.8% vs. 9.3%) and feeding (22.3% vs. 29.6%) and increased resting behavior (24.4% vs. 10.5%) nocturnally compared with diurnal periods. We further modeled explanatory environmental variables, hunting effects (open vs. closed seasons, locations open vs. closed to hunting), and time of day (diurnal and nocturnal only) on wintering behaviors. Feeding, resting, and swimming behavior presence were most influenced by a predictive model of (Hunt Season × Hunt Location × Period) + (Tide × Period). Flight behavior presence were most influenced by a predictive model of (Hunt Season × Hunt Location × Period) + (Tide × Temperature). There is an interactive effect of hunting pressure and period of day on observed activity; therefore, our results demonstrate that not accounting for nocturnal variation in behavior can lead to biases when extrapolating to energy expenditure models. Additionally, hunting areas proved to be nocturnally valuable because these areas contain valuable energy resources that may be unavailable diurnally, and our observations show that brant will shift their activities around hunting pressures to make use of these areas.

Environmental and Social Influences on the Behaviour of Free-Living Mandarin Ducks in Richmond Park

“…Animals respond to environmental stimuli by altering their behaviour to improve their chances of survival and reproduction [ 27 ]. Temporal—i.e., time of day [ 21 , 22 , 28 ], seasonal [ 29 , 30 ]) and environmental factors (including pond size [ 31 ]) all affect how birds will partition their time to different behaviours. Social period (i.e., the different sections of the Mandarin’s breeding season when different social behaviours are performed) [ 21 ], prevailing weather conditions [ 28 , 32 , 33 , 34 , 35 ], vegetation structure [ 29 , 36 ], the presence of humans [ 37 , 38 ] and other species of bird [ 39 ] also influence behaviour patterns.…”

“…Temporal—i.e., time of day [ 21 , 22 , 28 ], seasonal [ 29 , 30 ]) and environmental factors (including pond size [ 31 ]) all affect how birds will partition their time to different behaviours. Social period (i.e., the different sections of the Mandarin’s breeding season when different social behaviours are performed) [ 21 ], prevailing weather conditions [ 28 , 32 , 33 , 34 , 35 ], vegetation structure [ 29 , 36 ], the presence of humans [ 37 , 38 ] and other species of bird [ 39 ] also influence behaviour patterns. Finally, individual animal factors such as energy requirements and sex-specific breeding activity [ 21 , 22 ] affect the time-budgets of birds and thus influence the breeding success and survival.…”

The demography of Atlantic brant (Branta bernicla hrota)

“…For holistic perspective on limitations to demography of migratory animal populations, it is essential to examine environmental drivers across the annual cycle and at multiple spatial scales (Hostetler et al 2015, Weegman et al 2017. Recent studies of brant have focused on developing an IPM to assist with harvest management ) and wintering ecology and energetics (Ladin et al 2011, Ladin et al 2014, Heise et al 2019). Practitioners also could modify the structure of two-season JE models to better address small sample sizes by leveraging overall means or using variance-covariance matrices for example.…”