Search

For studies of how birds control their altitude, seabirds are of particular interest because they forage offshore where the visual environment can be simply modelled by a flat world textured by waves then generating only ventral visual cues. This study suggests that optic flow, i.e. the rate at which the sea moves across the eye's retina, can explain gulls' altitude control over seas. In particular, a new flight model that includes both energy and optical invariants helps explain the gulls' trajectories during offshore takeoff and cruising flight. A linear mixed model applied to 352 flights from 16 individual lesser black backed gulls ( Larus fuscus ) revealed a statistically significant optic flow set-point of ca 25° s -1 . Thereafter, an optic flow-based flight model was applied to 18 offshore takeoff flights from nine individual gulls. By introducing an upper limit in climb rate on the elevation dynamics, coupled with an optic flow set-point, the predicted altitude gives an optimized fit factor value of 63% on average (30-83% in range) with respect to the GPS data. We conclude that the optic flow regulation principle helps gulls to adjust their altitude over sea without having to directly measure their current altitude.

Many migrating birds divide their journeys into nocturnal flights interspersed by stopovers where they build up energy reserves (fuel) for subsequent flights. Given the difficulty in monitoring fuel loads of individual migrants over long distances, theoretical models are often used to interpret observed relations between departure fuel loads (DFLs) and fuel deposition rates (FDRs) in the context of time-minimised migration. Models applicable to nocturnal migration have hitherto considered only the departure decision, i.e. ignored interim stopover possibilities before fuel loads are depleted. This results in 'risk-prone' migratory schedules in terms of choice of stopover. In this study we assess 'risk-averse' behaviour, whereby nocturnal migrants minimise migration time by adjusting not only DFLs to experienced and expected FDRs, but also stopping fuel loads (SFLs), below which they stop to refuel at high-quality sites. We developed analytical formulae to solve for maximal risk-prone and risk-averse migration speeds in modelled environments comprised of two stopover qualities (high- and low-quality), and a constant probability of encountering a high-quality site (encounter probability). Risk-aversion was beneficial to migration speeds in over 99% of the modelled environments, with median ratios of risk-averse to risk-prone migration speeds ranging from 1.5 to 2.8. Among modelled environments, this benefit increased with increasing FDRs at high-quality sites, and was highest with low probabilities of encountering high-quality sites. Time-minimising risk-averse DFLs at low-quality sites were minimal in nearly all modelled environments, and whenever encounter probabilities were low, risk-averse SFLs typically indicated that migrants should stop immediately on encountering high-quality sites. Modelled fuel loads in environments with high and low encounter probabilities resembled observed fuel loads of migratory populations with ubiquitous and scarce food availability, respectively. This study demonstrates the benefits of risk-averse exploitation of variable resources, presents a new approach to model time-minimised migration in heterogeneous environments and emphasises the importance of premium stopover sites to nocturnally migrating birds., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

The impact that flows of air and water have on organisms moving through these environments has received a great deal of attention in theoretical and empirical studies. There are many behavioral strategies that animals can adopt to interact with these flows, and by assuming one of these strategies a researcher can quantify the instantaneous assistance an animal derives from a particular flow. Calculating flow-assistance in this way can provide an elegant simplification of a multivariate problem to a univariate one and has many potential uses; however, the resultant flow-assistance values are inseparably linked to the specific behavioral strategy assumed. We expect that flow-assistance may differ considerably depending on the behavioral strategy assumed and the accuracy of the assumptions associated with that strategy. Further, we expect that the magnitude of these differences may depend on the specific flow conditions. We describe equations to quantify flow-assistance of increasing complexity (i.e. more assumptions), focusing on the behavioral strategies assumed by each. We illustrate differences in suggested flow-assistance between these equations and calculate the sensitivity of each equation to uncertainty in its particular assumptions for a range of theoretical flow conditions. We then simulate trajectories that occur if an animal behaves according to the assumptions inherent in these equations. We find large differences in flow-assistance between the equations, particularly with increasing lateral flow and increasingly supportive axial flow. We find that the behavioral strategy assumed is generally more influential on the perception of flow-assistance than a small amount of uncertainty in the specification of an animal's speed (i.e. <5 ms(-1)) or preferred direction of movement (i.e. <10°). Using simulated trajectories, we show that differences between flow-assistance equations can accumulate over time and distance. The appropriateness and potential biases of an equation to quantify flow-assistance, and the behavioral assumptions the equation implies, must be considered in the context of the system being studied, particularly when interpreting results. Thus, we offer this framework for researchers to evaluate the suitability of a particular flow-assistance equation and assess the implications of its use., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

Many soaring bird species migrate southwards in autumn from their breeding grounds in Europe and Central Asia towards their wintering grounds. Our knowledge about interactions between migrating birds, thermal selection during migration and mechanisms that lead to flocking or convergent travel networks is still very limited. To start investigating these aspects we developed an individual-based simulation model that describes the local interactions between birds and their environment during their migratory flight, leading to emergent patterns at larger scales. The aim of our model is to identify likely decision rules with respect to thermal selection and navigation. After explaining the model, it is applied to analyse the migration of white storks (Ciconia ciconia) over part of its migration domain. A model base-run is accompanied by a sensitivity analysis. It appears that social interactions lead to the use of fewer thermals and slight increases in distance travelled. Possibilities for different model extensions and further model application are discussed., (Copyright © 2010 Elsevier Ltd. All rights reserved.)