Predicting the dynamics of animal behaviour in field populations

Henson, Shandelle M.; Dennis, B. R.; Hayward, James L.; Cushing, J. M.; Galusha, Joseph G.

doi:10.1016/j.anbehav.2006.11.015

Cited by 19 publications

(18 citation statements)

References 19 publications

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“…The r ij functions can be different in the time periods we designate as 'Morning' (5 ≤ t ≤ 10), 'Midday' (10 < t < 14) and 'Evening' (14 ≤ t ≤ 20) [7]. Thus,…”

Section: Colony-preen Modelmentioning

confidence: 99%

“…Administration (NOAA). We nondimensionalized all environmental variables x so that 1 ≤ x ≤ 2 [1,3,4,6,7].…”

Section: Hourly Datamentioning

confidence: 99%

“…We maximized the log-likelihood function numerically by minimizing its negative with the Nelder-Mead algorithm [14] under three different types of stochasticity: mostly demographic (ψ = 0.01), a mixture of demographic and environmental (ψ = 0.5), and purely environmental (ψ = 1) [7]. Fixed values of = 0.01, 0.5, and 1 for the best model yielded log-likelihood values of −260, 65.0, and 175, respectively.…”

Section: Parameter Estimationmentioning

confidence: 99%

“…Section 6 provides a discussion of the results and lists a number of important caveats. For an in-depth perspective on the methodology used in this study, see [7].…”

Section: Introductionmentioning

confidence: 99%

“…Henson et al [6] modeled the same system on two time scales, and studied recovery from disturbances as a function of environmental conditions. Henson et al [7] used a system of differential equations to predict the dynamics of colony occupancy and sleep in the Protection Island gull colony. In terms of R 2 values, the model explained 65% and 52% of the variability in colony occupancy and sleep data, respectively, as a function of abiotic environmental factors.…”

Section: Introductionmentioning

confidence: 99%

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Modeling territory attendance and preening behavior in a seabird colony as functions of environmental conditions

Henson

Galusha

Hayward

et al. 2007

Journal of Biological Dynamics

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In previous studies we developed a general compartmental methodology for modeling animal behavior and applied the methodology to marine birds and mammals. In this study we used the methodology to construct a system of two differential equations to model the dynamics of territory attendance and preening in a gull colony on Protection Island, Strait of Juan de Fuca, Washington. We found that colony occupancy was driven primarily by abiotic environmental conditions, including tide height, time of day, solar elevation, and wind speed over open water. For birds in the colony, preening behavior was driven to some extent by abiotic environmental conditions (including time of day, solar elevation, humidity, and wind speed on the colony), but apparently was driven primarily by local and/or biotic effects not included in the model. In terms of R(2) values, the model explained 65% and 37% of the variability in colony occupancy and preening data, respectively, as a function of these six abiotic environmental factors.

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“…The r ij functions can be different in the time periods we designate as 'Morning' (5 ≤ t ≤ 10), 'Midday' (10 < t < 14) and 'Evening' (14 ≤ t ≤ 20) [7]. Thus,…”

Section: Colony-preen Modelmentioning

confidence: 99%

“…Administration (NOAA). We nondimensionalized all environmental variables x so that 1 ≤ x ≤ 2 [1,3,4,6,7].…”

Section: Hourly Datamentioning

confidence: 99%

Section: Parameter Estimationmentioning

confidence: 99%

“…Section 6 provides a discussion of the results and lists a number of important caveats. For an in-depth perspective on the methodology used in this study, see [7].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Modeling territory attendance and preening behavior in a seabird colony as functions of environmental conditions

Henson

Galusha

Hayward

et al. 2007

Journal of Biological Dynamics

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Mathematical modeling of appendicular bone growth in glaucous‐winged gulls

Hayward

Henson

Banks

et al. 2008

Journal of Morphology

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Development of locomotor activity is crucial in tetrapods. In birds, this development leads to different functions for hindlimbs and forelimbs. The emergence of walking and flying as very different complex behavior patterns only weeks after hatching provides an interesting case study in animal development. We measured the diaphyseal lengths and midshaft diameters of three wing bones (humerus, ulna, and carpometacarpus) and three leg bones (femur, tibiotarsus, and tarsometatarsus) of 79 juvenile (ages 0-42 days) and 13 adult glaucous-winged gulls (Larus glaucescens), a semiprecocial species. From a suite of nine alternative mathematical models, we used information-theoretic criteria to determine the best model(s) for length and diameter of each bone as a function of age; that is, we determined the model(s) that obtained the best tradeoff between the minimized sum of squared residuals and the number of parameters used to fit the model. The Janoschek and Holling III models best described bone growth, with at least one of these models yielding an R(2) > or = 0.94 for every dimension except tarsometatarsus diameter (R(2) = 0.87). We used the best growth models to construct accurate allometric comparisons of the bones. Early maximal absolute growth rates characterize the humerus, femur, and tarsometatarsus, bones that assume adult-type support functions relatively early during juvenile development. Leg bone lengths exhibit more rapid but less sustained relative growth than wing bone lengths. Wing bone diameters are initially smaller than leg bone diameters, although this relationship is reversed by fledging. Wing bones and the femur approach adult length by fledging but continue to increase in diameter past fledging; the tibiotarsus and tarsometatarsus approach both adult length and diameter by fledging. In short, the pattern of bone growth in this semiprecocial species reflects the changing behavioral needs of the developing organism.

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