Available online at ww

w.sciencedirect.com

ANIMAL BEHAVIOUR, 2008, 76, 1103e1105doi:10.1016/j.anbehav.2008.08.008

IN FOCUS

Featured Articles in this Month’s Animal Behaviour

Flying Over the Radar and BeyondExpectation

Figure 1. Heiko Schmaljohann using radar to track songbirds over theSahara desert. Photo: Erich Bachler, Swiss Ornithological Institute.

If there’s one thing to be learned from the study ofanimal behaviour, it’s that one should always expect theunexpected. No matter how carefully we build our modelsor how rigorously we design our experiments, we mustalways be prepared to change our views in light of the factthat animals can apparently achieve the impossible. Theblue fin tuna, for example, can swim up to seven timesfaster than anatomical and physiological studies suggest ispossible. Luckily, as the old joke goes, no one has told thetuna. By generating, and then exploiting, vortices in thewater using their powerful tails, tuna can propel them-selves much faster through the water and exceed theirphysiological limits. In this month’s issue (pp. 1133–1138), Heiko Schmaljohann, Bruno Bruderer and FelixLiechti show that migrating songbirds are able to fly acrossthe Sahara at temperatures that are much higher thanexpected, and which appear to exceed the birds’ capacitiesto sustain continuous flight.

Songbirds crossing the Sahara desert are expected to flyat high altitudes in autumn to take advantage of cool andhumid air. The disadvantage of this strategy is that theyencounter head winds at high altitudes in autumn thatimpede flight and slow them down. Flying at lowaltitudes, however, where they would encounter beneficialtail winds, also exposes the birds to air that is hot and dry,resulting in heavy water loss. As most experiments havefound that birds refrain from flying at all when temper-atures climb above 25 �C, high-altitude flying has been as-sumed to be the norm. Schmaljohann and colleagues setout to test whether this was, in fact, the case by monitor-ing the flight of songbirds across the Sahara desert.

To obtain their measures, the researchers set up a radarsite in Mauritania that could detect the birds as they flewoverhead (Fig. 1). Moreover, they could identify birds assongbirds by their wing beat patterns. Birds passing overthe site had already completed around 1500e1700 km ofthe 2000 km Sahara crossing. Contrary to prediction,these measures showed that songbirds chose to fly at alti-tudes that provided the best wind conditions, rather thanthose that minimized water loss. Over 60% of migrationoccurred at only 1000 m above ground level, where thetemperature was around 30 �C. Schmaljohann and col-leagues had calculated that the maximum possible overallwater loss of a garden warbler, Sylvia borin, a typical trans-Sahara migrant, for the entire Sahara crossing would be in

11030003e3472/08/$34.00/0 � 2008 The Association for the Stu

the order of 0.29 g/h, but found that songbirds passingover their study site must have lost something in the orderof 0.62 g/h based on current knowledge of temperature-dependent water loss rates. With rates of water loss thishigh, birds should run out of water well before theyhave crossed the Sahara but, as these empirical data indi-cate, this isn’t the case.

What, then, can explain these findings? Schmaljo-hann and colleagues have two suggestions: human erroror bird adaptation. It is possible, for example, thatputting birds in a wind tunnel or making free-flyingbirds carry equipment to measure physiological variablesserves to increase the costs of flying, and hence leads toan overestimation of water loss. It’s also possible thatexpired air may not be fully saturated with water, asmost physiological models assume, so that rates of waterloss may actually be lower than we have calculated. It isalso the case that no experimental studies have actuallybeen carried out above 25 �C. Any or all of these factorscould explain why songbirds seem to achieve the impos-sible. If we can rule out experimental error, however,then the only alternative explanation is that songbirdsmust have some specific adaptations, much like the tu-na’s tail flicks, that allow them to sustain flight underhot conditions. It has been shown, for example, that

dy of Animal Behaviour. Published by Elsevier Ltd. All rights reserved.

ANIMAL BEHAVIOUR, 76, 41104

carrying large amounts of energy is less expensive forbirds than previously thought, so perhaps birds alsolose less water than expected. The birds may also pos-sess some other physiological adaptations that onlykick in at high temperatures and have therefore goneundetected so far. Either way, it is clear that these newdata will have a profound influence on the developmentof future physiological models. They also emphasize theimportance of studying animals in their natural environ-ment, which allows us the possibility of detecting exter-nal, environmental factors that enable animals toaugment and amplify their capacities in adaptive ways.

Louise BarrettExecutive Editor

Wary Elk Support the Risk AllocationHypothesis

Figure 2. Elk, especially females, become more vigilant on days thatwolves are locally present. Photo: John A. Winnie, Jr.

If you’re edible, predation risk appears to be everywhere.Yet risk may vary both temporally and spatially. Whenpredators are around, animals may allocate more time toantipredator vigilance. But how much? Animals may alsoavoid risky places to remain safe. But how does spatialvariation in risk really affect behaviour? And, whathappens when animals face both temporal and spatiallyvariable risk? For instance, it is possible to be safer in coveronly when predators are present and predators may notalways be present.

Although antipredator behaviour has been studied forcenturies, until recently, we had no quantitative theoryto predict how both temporal and spatial variation inpredation risk should influence antipredator behaviour.Around a decade ago, Steve Lima & Peter Bednekoff(1999) developed such a theory, which they called the‘risk allocation hypothesis’. This predicts that antipreda-tor behaviour should be particularly sensitive to riskvariation. For animals living in generally safe environ-ments, a pulse of increased risk should lead to a largerantipredator response than for animals living in a riskyarea. This makes sense because animals in a safe areacan expect a return to safe conditions quickly oncethe pulse of predation risk decreases, whereas animalsliving in a risky area should expect risk to persist be-yond the current high pulse. In a safe world, selectionwould favour those who dropped everything duringa rare period of risk. In a generally risky world, this lux-ury is not an option.

Until now this hypothesis has been tested only onanimals in captivity. In this issue (pp. 1139–1146), Creelet al. provide a strong test of the risk allocation hypothesisunder natural field conditions. They focused on elk, Cervuselaphus, living under risk of predation by wolves, Canis lu-pus, in what has become a remarkable natural laboratoryof risk: the Greater Yellowstone Ecosystem.

Creel et al. studied elk that were either regularly exposedto wolves in the Northern Range, were intermittentlyexposed in the Gallatin Canyon, or were never exposed towolves in the Elkhorn Mountains. As elk do not migratebetween these patches during the course of a winter, Creel

et al. could compare elk that lived under differentbackground levels of predation risk. Additionally, whilewolves might have been present in an area, the elk didn’tnecessarily encounter them on a daily basis. Over fourwinters, Creel et al. quantified both wolf activity and elkvigilance. They used a mix of radiotelemetry and readingsigns (tracks, scats and fresh kills) to determine whetherelk had experienced recent wolf exposure.

Creel et al. then explicitly evaluated alternative modelsto explain patterns of vigilance. Specifically, they com-pared the risk allocation hypothesis with a null model(vigilance should not be influenced by levels of risk), therisky times hypothesis (vigilance should be higher duringpulses of risk), and the risky places hypothesis (vigilanceshould be higher in higher-risk areas than lower-riskareas). Their results provided strong support for the riskallocation hypothesis, some support for the risky timeshypothesis, but no support for either the risky placeshypothesis or the null model (Fig. 2).

In addition to providing some of the strongest supportfor the risk allocation hypothesis, there is an importantecological lesson to be learned from this study. Muchresearch assumes that the ecological impact of predationcan be assessed via knowledge of the number of times thatprey encounter predators in a particular location. Thecurrent study, however, highlights the fact that, to un-derstand both the spatial and temporal patterns of pre-dation risk, we need to quantify the relative effect ofbehaviourally mediated risk. And, as Creel & Christianson(2008) point out elsewhere, responding to the risk of pre-dation could lead to larger demographic effects than the

IN FOCUS 1105

act of predation (specifically, the behavioural effectsshould be biggest when predators are rare enough to belargely avoidable).

Daniel T. BlumsteinEditor

References

Creel, S. & Christianson, D. 2008. Relationships between direct pre-

dation and risk effects. Trends in Ecology & Evolution, 23, 194e201.

Lima, S. L. & Bednekoff, P. A. 1999. Temporal variation in danger

drives antipredator behavior: the predation risk allocation hypoth-esis. American Naturalist, 153, 649e659.