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Full-Text Articles in Animal Sciences
Foraging-Related Activity Of Bald Eagles At A Washington Seabird Colony And Seal Rookery, James L. Hayward, Joseph G. Galusha, Shandelle M. Henson
Foraging-Related Activity Of Bald Eagles At A Washington Seabird Colony And Seal Rookery, James L. Hayward, Joseph G. Galusha, Shandelle M. Henson
Faculty Publications
From 1980 to 1998, Washington's Bald Eagle (Haliaeetus leucocephalus) population increased at an annual rate of 10. Over the same time period, foraging activity of Bald Eagles at marine bird breeding colonies also increased. From 1993 to 2008, we observed foraging-related behavior of Bald Eagles on Violet Point, Protection Island. This island hosts more than 70 of the breeding seabirds in Washington's inner seaways and serves as an important rookery for harbor seals (Phoca vitulina). We found that (1) eagles landed more frequently in seal haul-out (beach) areas than in gull-nesting (non-beach) areas of Violet Point, and that subadult eagles …
Socially Induced Synchronization Of Every-Other-Day Egg Laying In A Seabird Colony, Shandelle M. Henson, James L. Hayward, J. M. Cushing, Joseph C. Galusha
Socially Induced Synchronization Of Every-Other-Day Egg Laying In A Seabird Colony, Shandelle M. Henson, James L. Hayward, J. M. Cushing, Joseph C. Galusha
Faculty Publications
Spontaneous oscillator synchrony has been documented in a wide variety of electrical, mechanical, chemical, and biological systems, including the menstrual cycles of women and estrous cycles of Norway Rats (Rattus norvegicus). In temperate regions, many colonial birds breed seasonally in a time window set by photoperiod; some studies have suggested that heightened social stimulation in denser colonies can lead to a tightened annual reproductive pulse. It has been unknown, however, whether the analog of menstrual synchrony occurs in birdsthat is, whether avian ovulation cycles can synchronize on a daily timescale within the annual breeding pulse. We report every-other-day clutch-initiation and …
Habitat Patch Occupancy Dynamics Of Glaucous-Winged Gulls (Larus Glaucescens) I: A Discrete-Time Model, Karl W. Phillips, Smruti P. Damania, James L. Hayward, Shandelle M. Henson, Clara J. Logan
Habitat Patch Occupancy Dynamics Of Glaucous-Winged Gulls (Larus Glaucescens) I: A Discrete-Time Model, Karl W. Phillips, Smruti P. Damania, James L. Hayward, Shandelle M. Henson, Clara J. Logan
Faculty Publications
Diurnal habitat occupancy dynamics of Glaucous-winged Gulls were evaluated in a system of six habitats on and around Protection Island, Washington. Data were collected on the rates of gull movement between habitat patches, and from these data the probabilities of transitions between habitats were estimated as functions of tide height and time of day. A discrete-time matrix model based on the transition probabilities was used to generate habitat occupancy predictions, which were then compared to hourly census data. All model parameters were estimated directly from data rather than through model fitting. The model made reasonable predictions for two of the …
Habitat Patch Occupancy Dynamics Of Glaucous-Winged Gulls (Larus Glaucescens) Ii: A Continuous-Time Model, Smruti P. Damania, Karl W. Phillips, Shandelle M. Henson, James L. Hayward
Habitat Patch Occupancy Dynamics Of Glaucous-Winged Gulls (Larus Glaucescens) Ii: A Continuous-Time Model, Smruti P. Damania, Karl W. Phillips, Shandelle M. Henson, James L. Hayward
Faculty Publications
The diurnal distribution and abundance dynamics of loafing Glaucous-winged Gulls (Larus glaucescens) were examined at Protection Island National Wildlife Refuge, Strait of Juan de Fuca, Washington. Asynchronous movement of gulls among three habitat patches dedicated to loafing was modeled as a function of environmental variables using differential equations. Multiple time scale analysis led to the derivation of algebraic models for habitat patch occupancy dynamics. The models were parameterized with hourly census data collected from each habitat patch, and the resulting model predictions were compared with observed census data. A four-compartment model explained 41% of the variability in the data. Models …