Predation represents one of the strongest forms of selection in nature (Grant and Clarke, 2001; Cook and Saccheri, 2013; Cuthill, 2019; Stevens and Merilaita, 2011; Hall et al., 2013; Dawkins and Krebs, 1979). As a result, animals have evolved sophisticated adaptations to modify the sensory information they emit (Stevens and Merilaita, 2011; Brooker and Wong, 2020; Ruxton, 2009; Garrouste et al., 2016; Caro et al., 2014). Camouflage has been widely studied as an anti-predator defence, with mechanisms including background matching, disruption, and self-shadow concealment facilitating predator avoidance (Stuart-fox et al., 2006; Stevens et al., 2011; Ruxton et al., 2018; Ancillotto et al., 2022). Predators also show adaptations to reduce detection by prey e.g., in their color, markings, and/ or behavior (Pembury Smith and Ruxton, 2020; Théry and Casas, 2002; San-Jose et al., 2019). However, in general, predator camouflage is far less understood due to the challenges of simulating predation in controlled settings (Nebel et al., 2019) and observing predation attempts in the wild (Morisaka and Connor, 2007). This has hindered our understanding of the evolutionary forces driving predator camouflage and explains why predator cues have yet to be linked to prey capture success.

Predation typically requires movements of a predator towards its prey, either during a pursuit or an ambush, which usually exposes chasing predators to detection (Pembury Smith and Ruxton, 2020; Anderson and McOwan, 2003). Indeed, motion makes individuals more conspicuous (Hall et al., 2013; Stevens et al., 2011; Rushton et al., 2007; Regan and Beverley, 1984). But motion also produces sound through the generation of vibrations and turbulence (Larsson, 2012; Clark, 2016), which can be detected by prey with acute hearing. Many predators alter their movements accordingly, for instance, by moving slowly during the pursuit, which may provide both acoustic and visual camouflage (Ruxton, 2009), particularly when combined with a background colour matching (Pembury Smith and Ruxton, 2020; Anderson and McOwan, 2003; Zylinski et al., 2009). While the direct link to hunting success remains unclear (Pembury Smith and Ruxton, 2020; Mizutani et al., 2003), selection should favour camouflage strategies that reduce sound emission in quiet environments. The resulting arms race may explain why many nocturnal species have acute senses of hearing, which they rely on to detect danger or prey (Ruxton, 2009; Gerkema et al., 2013; Popper and Fay, 1997).

The silent flight of owls is one of the most iconic examples of noise camouflage. Quiet flight is achieved through comb-like serrations on the leading edge of owls’ wing feathers that break up the turbulent air and minimize associated sound production (Ruxton, 2009; Clark et al., 2020). This should provide advantages when hunting on the wing. However, most owls also launch attacks from perches, which involves moving from one perch to the next as they approach their prey (Payne, 1971; Roulin, 2019; Taylor, 2004). Landing also produces vibrations, and hence sound, with the intensity being proportional to the landing force (Wernli et al., 2016). In this dynamic sit-and-wait strategy, landing likely becomes a key element of the prey approach. We use high-frequency GPS and accelerometer data to investigate the landing dynamics of this sit-and-wait strategy in wild barn owls (Tyto alba). Specifically, we quantify whether the landing force varies with (i) the time until the hunting strike (i.e. hunting motivation), (ii) perch type (i.e. environmental context), and (iii) body mass, which varies between males and females (Roulin et al., 2001). Finally, we test the extent to which the magnitude of the landing force affects hunting success.

We used GPS loggers and accelerometers to record high-resolution movement data during two consecutive breeding seasons (May to August in 2019 and 2020) from 163 wild barn owls (79 males and 84 females) breeding in nest boxes across a 1000  km² intensive agricultural landscape in the western Swiss plateau. Of these individuals, 142 belonged to pairs for which data were recovered from both partners (71 pairs in total, 40 in 2019, 31 in 2020). The remaining 21 individuals belonged to pairs with data from one partner (11 females and 1 male in 2019; four females and five males in 2020).

Measurement of landing force

We used the acceleration data to identify 84,855 landings. These were further categorized into perching events (n=56,874) and hunting strikes (n=27,981), depending on whether barn owls were landing on a perch or attempting to strike prey on the ground (Figure 1A and B, see methods for specific details on behavioural classification). We extracted the peak vectorial sum of the raw acceleration during each landing and converted this to ground reaction force (hereafter ‘landing force’ in Newtons) using measurements of individual body mass (see methods for detailed description).

Figure 1

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Hunting strikes had landing forces over four times higher than perching events (Figure 1C, Appendix 1—table 2, Appendix 1—table 3; ratio: 4.5, z-ratio: 486.3, p<0.001). When converted to multiples of body weight, hunting strikes had peak forces that were equivalent to approximately 13 times body weight, whereas perching events involved forces roughly three times body weight.

Determinants of landing force

We conducted two sets of analyses to investigate factors that influence the variation in landing force in different landing contexts: perching events and hunting strikes.

Barn owls employing a sit-and-wait strategy land on multiple perches before initiating an attack, with successive landings reducing the distance to the target prey (Figure 2C). We analysed the landing forces involved in sequences of perching events in relation to perch type (poles, buildings, and trees: identified using GPS data) and the time before an attack (i.e. pre-hunt time: an indication of hunting motivation). The most important predictor of landing force in perching events was perch type. Perching events on buildings were associated with the highest forces (8.96 N, CI: 8.90–9.01 N; Appendix 1—table 4, Appendix 1—table 5), closely followed by landing on trees (8.86 N, CI: 8.81–8.90 N; Appendix 1—table 4, Appendix 1—table 5). Poles were associated with the lowest landing force (8.33 N, CI: 8.28–8.38 N; Appendix 1—table 4, Appendix 1—table 5).

Figure 2

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Importantly, within perch types, there was a reduction in landing force with time until the next hunting attempt, with the pattern differing with perch type (EDF_poles = 4.22, p<0.001; EDF_buildings = 1.00, p<0.001; EDF_trees = 1.50, p=0.005; Figure 2A, Appendix 1—table 4, Appendix 1—table 5; n_tot = 40,306 perching events; see Appendix 1—figure 4 for the full representation and derivative plot). When barn owls perched on poles, the landing force showed a marked decrease in the last 30 min before the hunting strike, whereas landing force only showed a marginal linear reduction with time before the strike for landings on buildings (Figure 2A, Appendix 1—figure 4). Landing force did not show any significant reduction with time for perching events on trees (Figure 2A, Appendix 1—figure 4, Appendix 1—table 4, Appendix 1—table 5). Our analysis also revealed a clear temporal pattern in the birds' use of perch types: owls launched more attacks from poles than from trees, with the fewest attacks launched from buildings (Figure 2B). The pattern of variation in landing force according to perch type and hunting motivation, and the pattern of perch use, were consistent for both males and females, despite females consistently exhibiting greater landing forces than males (Appendix 1—table 4, Appendix 1—table 5).

Additionally, our analysis of hunting strike force showed that both hunting strategy and success were related to strike force (Appendix 1—table 6, Appendix 1—table 7). When hunting on the wing, successful strikes involved greater forces than unsuccessful strikes (n_tot = 24,464; successful strikes: n_succ = 5830, 40.3 N, CI: 39.5–41.2 N; unsuccessful strikes: n_unsucc = 18,634, 38.4 N, CI: 37.7–39.2 N). This was not the case when barn owls hunted from a perch (n_tot = 3517; successful strikes: n_succ = 1042, 38.8 N, CI: 37.7–40.0 N; unsuccessful strikes: n_unsucc = 2475, 38.5 N, CI: 37.6–39.5 N).

Sexual dimorphism and foraging behavior

Sexual dimorphism in body mass was marked among our sampled individuals. Males were lighter than females (84 females, average body mass: 322±22.6 g; 79 males, average body mass 281±16.5 g, Appendix 1—figure 6) and provided almost three times more prey per night than females (males: 8±5 prey per night; females: 3±3 prey per night; Appendix 1—figure 7). Males also displayed higher nightly hunting effort than females (Males: 46±16 hunting attempts per night, n=79; Females: 25±11 hunting attempts per night, n=84; Figure 3A, Appendix 1—figure 8). However, females were more likely to use a sit-and-wait strategy than males (females: 24%±15%, males: 13%±10%, Appendix 1—figure 9). As a result, the number of perching events per night was similar between males and females (Females: 76±23 perching events per night; Males: 69±20 perching events per night; Appendix 1—figure 8).

Figure 3

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We conducted two different analyses to assess whether hunting strategies differed in success and efficiency (i.e. foraging trip duration). To assess the influence of strategy on foraging trip duration, we extracted the number of sit-and-wait hunting attempts and divided this by the total number of hunting attempts, and analysed this in relation to the trip duration (min) and sex. Our analysis showed that trip duration increased with the use of the sit-and-wait strategy (Appendix 1—figure 11, Appendix 1—table 13): barn owls that only used the sit-and-wait strategy (sit-and-wait frequency = 1) took an average of 15 min longer to provide prey to the nest than those that only hunted on the wing (sit-and-wait frequency = 0). Nonetheless, barn owls were more successful when using a sit-and-wait strategy, with success also varying with sex. Males were more successful than females, both for the sit-and-wait strategy (males: 34.5%, CI: 31.6–37.5%; females: 26.8%, CI: 24.4–29.2%, Figure 3D, Appendix 1—table 8, Appendix 1—table 9) and hunting on the wing (males: 26.1%, CI: 24.7–27.6%; females: 19.1%, CI: 17.7–20.5%, Figure 3D, Appendix 1—table 8, Appendix 1—table 9).

Landing force also varied with sex, with females generating landing forces that were 26% higher than males on average during perching events (Figure 3C, Appendix 1—table 2, Appendix 1—table 3) (females: 9.94 N, CI: 9.63 N–10.27 N; males: 7.91 N, CI: 7.65 N–8.18 N; ratio F/M: 1.26, 95% CI: 1.2–1.31). Males and females had similar landing forces during hunting strikes, with females generating forces that were only 6% higher than males on average (females: 40.8 N, CI: 39.49 N–42.18 N; males: 38.41 N, CI: 37.14 N–39.71 N; ratio F/M: 1.06, 95% CI: 1.01–1.11; Appendix 1—figure 10). However, when considered per unit of body mass (see methods), males exhibited lower forces than females when perching (males: 28.2 N/Kg, CI: 27.4 N/Kg–29.0 N/Kg; females: 30.9 N/Kg, CI: 30.1 N/Kg–31.8 N/Kg; Appendix 1—figure 10), but higher forces than females in hunting strikes (males: 136.2 N/kg, CI: 132.3 N/Kg–140.2 N/Kg; females: 126.4 N/kg, CI: 122.8 N/Kg–130.1 N/Kg, Appendix 1—figure 10).

Additionally. we performed two additional analyses to investigate potential variations in landing force and flight speed between sexes. We extracted the median ground speed (in m s^–1) of each flight prior to each hunting attempt and analysed this in relation to the sex of the individual. This showed that males flew slightly more slowly than females when searching for prey on the wing. Males flew slower than females by 0.23 ms-1 (Average flight speed males: 5.24 ms^–1, CI: 5.15 ms^–1–5.33 ms^–1; average flight speed females: 5.47 ms^–1, CI: 5.38 ms^–1–5.56 ms^–1, Figure 3B, Appendix 1—table 12).

Pre-hunt landing force predicts hunting success for sit-and-wait strategy

Finally, we analysed whether the landing force in the last perching event before each hunting attempt (i.e. pre-hunt perching force) predicted variation in hunting success. Our results showed that hunting strategy was the strongest predictor of success (Figure 4, Appendix 1—table 10, Appendix 1—table 11, n=3040 hunting strikes from 151 individuals, see methods for details on data filtering). When hunting from the wing, the force applied during pre-hunt perching events had no effect on hunting success (Figure 4, Appendix 1—table 10, Appendix 1—table 11, odds ratio: 1.07, CI: 0.97–1.17, p=0.19). However, during sit-and-wait hunts, where the distance between the last perch and the prey is rather short (median distance 6.5 m, Appendix 1—figure 5), pre-hunt perching force predicted hunting success (Figure 4, Appendix 1—table 10 , Appendix 1—table 11). When barn owls hunted directly from a perch, the chance of success decreased by 15% for every 1 N increase in pre-hunt perching force (odds ratio: 0.85, CI: 0.79–0.99, p=0.04). Perch type and wind speed were dropped from the final model after model selection (Appendix 1—table 10, Appendix 1—table 11).

Figure 4

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Silent flight is considered crucial for owls hunting on the wing (Clark et al., 2020). But these predators also use a sit-and-wait strategy, with owls in this study achieving greater hunting success when launching attacks from a perch. Here, owls typically approach prey by moving between multiple perches. They must, therefore, avoid detection both in flight and as they land, as the benefits of silent flight may be negated if owls are detected during touchdown. We found that barn owls hunting from pasture poles reduced their landing force as they got closer to their prey. This suggests that soft landings are a novel form of acoustic camouflage, with predators reducing their motion-induced sound production in response to information on prey presence, which they gather as they move between perches in the final phase of the hunt. The landing force also affected the success of the subsequent strike, demonstrating the link between the predator camouflage and hunting success. However, the relatively low R-squared value (Appendix 1—table 10) suggests that hunting success is affected by additional factors such as prey behaviour, substrate type, and grass length (Low-Décarie et al., 2014).

Owls appeared to vary their perch use in relation to their motivation to hunt. For instance, barn owls that landed on a perch 30–90 min before a strike may have done so without the immediate intention of hunting. This phase was associated with greater use of buildings and trees, which are much higher structures than poles. Owls may, therefore, preferentially use these perches to rest or gather information over a wider area. The shift in predominant perch type to pasture poles 5–10 min before hunting strikes appears to represent a shift to periods of active prey searching. In some systems, the choice to hunt from pasture poles may be driven by prey availability, as pasture poles may be embedded in hedgerows or areas with longer grass where prey density might be higher. However, this is unlikely to be the case in our system as pasture poles generally occur in the middle of short-cropped grass (Appendix 1—figure 2). Instead, poles may offer advantages in being close to the ground, enhancing opportunities for owls to refine their estimates of prey location, prey type, or size. Indeed, the opportunity to gather information from perches could help explain the greater overall hunting success in attacks launched from a perch, compared to hunting on the wing.

In birds, landings are primarily governed by the need to maintain flight control and minimize the risk of injury (KleinHeerenbrink et al., 2022; Lee et al., 1993; Paskins et al., 2007),. For instance, Harris’ hawks (Parabuteo unicinctus) landing in controlled conditions postponed the stall until they were as close to the landing perch as possible (KleinHeerenbrink et al., 2022). Such a strategy could serve two functions in barn owls using sit-and-wait hunting; minimizing both the energy dissipated on impact and the associated sound production (Wernli et al., 2016). This raises the question of why owls would ever land with anything above the minimal force. To date, almost all studies have examined landings in controlled conditions (KleinHeerenbrink et al., 2022; Roderick et al., 2017), yet in the wild, birds are faced with a range of perch types and landing conditions. Perch characteristics are likely to play a pivotal role, as forces tend to be absorbed to a greater degree by compliant substrates (Demes et al., 1995). In support of this, landings on buildings were associated with the highest mean forces, and higher forces than tree branches (Appendix 1—table 4), which would be more compliant, with the extent varying with branch type and diameter (Paskins et al., 2007). It was, therefore, notable that forces were lowest for landings on poles, which, like buildings, are rigid. However, poles occur in open habitats (Appendix 1—figure 2), providing a predictable landing surface that can be approached from all directions, facilitating control through optimal use of the wind vector. Landing force may, therefore, be influenced by the access options as well as the substrate type. There may also be greater incentive to reduce landing force on poles, since they are close to the ground and sound attenuates with distance (Clark et al., 2020; Wahlberg and Larsen, 2017; Larsen et al., 2017).

The biggest difference in landing force was observed between perching events and hunting strikes. Strike forces in our study are the highest recorded in any bird, relative to body mass, with maximal force reaching more than 34 times the body weight (100 N). This exceeds estimates previously reported for captive barn owls (Usherwood et al., 2014), and the kicking strike of the secretary bird (Sagittarius serpentarius) that reached an average of 5.1 times body weight (Portugal et al., 2016). Unlike secretary birds, whose kicking strength depends solely on the muscular power of their lower limbs, owls use the dynamics of their entire body in flight. While this likely minimizes the chances of prey escape, it is also associated with a potential risk of injury (KleinHeerenbrink et al., 2022; Provini et al., 2014). Our results likely underestimate the true peak forces, as acceleration was recorded at 50 Hz (for reference, data on force development in controlled car crashes are typically recorded at >2 kHz). Nonetheless, our data can still provide new insight into the selective pressures that have influenced owl morphology. Indeed, the lower limbs of owls allow for the dual function of absorbing shock during pre-hunt perching and generating extremely powerful hunting strikes.

We find that males and females had very similar strike forces, despite their substantial difference in body mass. This indicates that there might be a selective pressure for a minimum strike force, which males may generate by increasing or maintaining their flight speed prior to a strike to a greater extent than females. Males had a lower flight speed during prey searching, most likely due to their lower body mass (Pennycuick, 2008). While the difference in flight speed was relatively small, slower flight could still have advantages in (i) providing additional time to localize prey, and (ii) enabling birds to manoeuvre into the strike phase (Amélineau et al., 2014; Machovsky-Capuska et al., 2012). This may help explain why males have higher success when hunting on the wing. Males also showed greater hunting success than females in the sit-and-wait strategy. Here, a lower body mass could also provide advantages by facilitating lower impact, and hence quieter landings.

Given that sit-and-wait hunting is associated with higher success, why do male barn owls not use this strategy more (it was associated with less than 10% of hunting attempts)? Male barn owls engage in intense hunting activity in the breeding season, providing over 15 prey per night in our study. Our results showed that foraging duration increases with the use of the sit-and-wait strategy. Thus, the time required to capture prey appears to be the key element influencing the choice of hunting strategy in males. Females provide fewer prey items and the additional time required for sit-and-wait hunting may, therefore, be less of a constraint. Furthermore, sit-and-wait hunting may require less flight time and hence effort, which is likely to be particularly advantageous for females due to their greater body mass (and higher flight costs per unit mass).

In conclusion, we use high-frequency movement data to propose a novel form of acoustic camouflage and demonstrate that the magnitude of predator cues can influence hunting success (English et al., 2024). Minimizing landing force, and associated sound production is likely to be particularly pertinent for nocturnal predators, which operate in quiet environments and target prey with an acute sense of hearing (Gerkema et al., 2013; Popper and Fay, 1997; Webster and Plassmann, 1992). Importantly, the ability to minimize landing force was modulated by the perch characteristics, providing a potential link between landing impact and habitat characteristics. This suggests there could be spatial patterns in the effectiveness of acoustic camouflage and, ultimately, hunting success. The availability of different perch types could, therefore, be an additional, and previously unrecognized, aspect of habitat and territory quality, and, in this case, one that is strongly linked to land-use practices.

All code and datasets used to produce the results, analyses, and figures presented in this manuscript are available from a GitHub repository at https://github.com/kimschalcher/data-availability-Schalcher-et-al-eLife, copy archived at Schalcher, 2024.

1. 1. Almasi B
   2. Béziers P
   3. Roulin A
   4. Jenni L

   (2015) Agricultural land use and human presence around breeding sites increase stress-hormone levels and decrease body mass in barn owl nestlings

   Oecologia 179:89–101.

   https://doi.org/10.1007/s00442-015-3318-2

   • PubMed
   • Google Scholar
2. 1. Amélineau F
   2. Péron C
   3. Lescroël A
   4. Authier M
   5. Provost P
   6. Grémillet D

   (2014) Windscape and tortuosity shape the flight costs of northern gannets

   The Journal of Experimental Biology 217:876–885.

   https://doi.org/10.1242/jeb.097915

   • PubMed
   • Google Scholar
3. 1. Ancillotto L
   2. Pafundi D
   3. Cappa F
   4. Chaverri G
   5. Gamba M
   6. Cervo R
   7. Russo D

   (2022) Bats mimic hymenopteran insect sounds to deter predators

   Current Biology 32:R408–R409.

   https://doi.org/10.1016/j.cub.2022.03.052

   • PubMed
   • Google Scholar
4. 1. Anderson AJ
   2. McOwan PW

   (2003) Model of a predatory stealth behaviour camouflaging motion

   Proceedings. Biological Sciences 270:489–495.

   https://doi.org/10.1098/rspb.2002.2259

   • PubMed
   • Google Scholar
5. 1. Banerjee D
   2. Daigle CL
   3. Dong B
   4. Wurtz K
   5. Newberry RC
   6. Siegford JM
   7. Biswas S

   (2014) Detection of jumping and landing force in laying hens using wireless wearable sensors

   Poultry Science 93:2724–2733.

   https://doi.org/10.3382/ps.2014-04006

   • PubMed
   • Google Scholar
6. 1. Bates D
   2. Mächler M
   3. Bolker B
   4. Walker S

   (2015) Fitting linear mixed-effects models using lme4

   Journal of Statistical Software 67:e7.i01.

   https://doi.org/10.18637/jss.v067.i01

   • Google Scholar
7. 1. Becciu P
   2. Troupin D
   3. Dinevich L
   4. Leshem Y
   5. Sapir N

   (2023) Soaring migrants flexibly respond to sea-breeze in a migratory bottleneck: using first derivatives to identify behavioural adjustments over time

   Movement Ecology 11:44.

   https://doi.org/10.1186/s40462-023-00402-4

   • PubMed
   • Google Scholar
8. 1. Brooker RM
   2. Wong BBM

   (2020) Non-visual camouflage

   Current Biology 30:R1290–R1292.

   https://doi.org/10.1016/j.cub.2020.08.001

   • PubMed
   • Google Scholar
9. Book

   1. Burnham KP
   2. Anderson DR

   (2002) Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach

   Springer.

   https://doi.org/10.1007/b97636

   • Google Scholar
10. 1. Burnham KP
    2. Anderson DR
    3. Huyvaert KP

    (2011) AIC model selection and multimodel inference in behavioral ecology: some background, observations, and comparisons

    Behavioral Ecology and Sociobiology 65:23–35.

    https://doi.org/10.1007/s00265-010-1029-6

    • Google Scholar
11. 1. Caro T
    2. Izzo A
    3. Reiner RC
    4. Walker H
    5. Stankowich T

    (2014) The function of zebra stripes

    Nature Communications 5:3535.

    https://doi.org/10.1038/ncomms4535

    • PubMed
    • Google Scholar
12. Book

    1. Clark CJ

    (2016)

    Locomotion-Induced Sounds and Sonations: Mechanisms, Communication Function, and Relationship with Behavior

    Springer Handbook of Auditory Research.

    • Google Scholar
13. 1. Clark CJ
    2. LePiane K
    3. Liu L

    (2020) Evolution and ecology of silent flight in owls and other flying vertebrates

    Integrative Organismal Biology 2:baa001.

    https://doi.org/10.1093/iob/obaa001

    • PubMed
    • Google Scholar
14. 1. Cook LM
    2. Saccheri IJ

    (2013) The peppered moth and industrial melanism: evolution of a natural selection case study

    Heredity 110:207–212.

    https://doi.org/10.1038/hdy.2012.92

    • PubMed
    • Google Scholar
15. 1. Cuthill IC

    (2019) Camouflage

    Journal of Zoology 308:75–92.

    https://doi.org/10.1111/jzo.12682

    • Google Scholar
16. 1. Dawkins R
    2. Krebs JR

    (1979) Arms races between and within species

    Proceedings of the Royal Society of London. Series B, Biological Sciences 205:489–511.

    https://doi.org/10.1098/rspb.1979.0081

    • PubMed
    • Google Scholar
17. 1. Demes B
    2. Jungers WL
    3. Gross TS
    4. Fleagle JG

    (1995) Kinetics of leaping primates: influence of substrate orientation and compliance

    American Journal of Physical Anthropology 96:419–429.

    https://doi.org/10.1002/ajpa.1330960407

    • PubMed
    • Google Scholar
18. 1. English HM
    2. Börger L
    3. Kane A
    4. Ciuti S

    (2024) Advances in biologging can identify nuanced energetic costs and gains in predators

    Movement Ecology 12:7.

    https://doi.org/10.1186/s40462-024-00448-y

    • PubMed
    • Google Scholar
19. Book

    1. Fair JM
    2. Jones J

    (2010)

    Guidelines to the Use of Wild Birds in Research

    Ornithological Council.

    • Google Scholar
20. 1. Frey C
    2. Sonnay C
    3. Dreiss A
    4. Roulin A

    (2011) Habitat, breeding performance, diet and individual age in Swiss Barn Owls (Tyto alba)

    Journal of Ornithology 152:279–290.

    https://doi.org/10.1007/s10336-010-0579-8

    • Google Scholar
21. 1. Garrouste R
    2. Hugel S
    3. Jacquelin L
    4. Rostan P
    5. Steyer JS
    6. Desutter-Grandcolas L
    7. Nel A

    (2016) Insect mimicry of plants dates back to the Permian

    Nature Communications 7:13735.

    https://doi.org/10.1038/ncomms13735

    • PubMed
    • Google Scholar
22. 1. Gerkema MP
    2. Davies WIL
    3. Foster RG
    4. Menaker M
    5. Hut RA

    (2013) The nocturnal bottleneck and the evolution of activity patterns in mammals

    Proceedings. Biological Sciences 280:20130508.

    https://doi.org/10.1098/rspb.2013.0508

    • PubMed
    • Google Scholar
23. Book

    1. Grant BS
    2. Clarke SCA

    (2001) Industrial Melanism. e LS

    Wiley.

    https://doi.org/10.1002/9780470015902.a0029393

    • Google Scholar
24. 1. Hall JR
    2. Cuthill IC
    3. Baddeley R
    4. Shohet AJ
    5. Scott-Samuel NE

    (2013) Camouflage, detection and identification of moving targets

    Proceedings. Biological Sciences 280:20130064.

    https://doi.org/10.1098/rspb.2013.0064

    • PubMed
    • Google Scholar
25. 1. KleinHeerenbrink M
    2. France LA
    3. Brighton CH
    4. Taylor GK

    (2022) Optimization of avian perching manoeuvres

    Nature 607:91–96.

    https://doi.org/10.1038/s41586-022-04861-4

    • PubMed
    • Google Scholar
26. 1. Kuznetsova A
    2. Brockhoff PB
    3. Christensen RHB

    (2017) lmerTest Package: Tests in Linear Mixed Effects Models

    Journal of Statistical Software 82:1–26.

    https://doi.org/10.18637/jss.v082.i13

    • Google Scholar
27. Book

    1. Larsen ON
    2. Wahlberg M
    3. Brown C
    4. Riede T

    (2017)

    Comparative bioacoustics: an overview

    In: Wahlberg M, editors. Sound and Sound Sources. Bentham Books. pp. 3–62.

    • Google Scholar
28. 1. Larsson M

    (2012) Incidental sounds of locomotion in animal cognition

    Animal Cognition 15:1–13.

    https://doi.org/10.1007/s10071-011-0433-2

    • PubMed
    • Google Scholar
29. 1. Lee DN
    2. Davies MNO
    3. Green PR
    4. Van Der Weel FRR

    (1993) Visual control of velocity of approach by pigeons when landing

    Journal of Experimental Biology 180:85–104.

    https://doi.org/10.1242/jeb.180.1.85

    • PubMed
    • Google Scholar
30. Software

    1. Lenth R
    2. Singmann H
    3. Love J
    4. Buerkner P

    (2021) Emmeans: estimated marginal means, aka least-squares means, version 1.10.3

    R Package.

    https://rvlenth.github.io/emmeans/
31. 1. Low-Décarie E
    2. Chivers C
    3. Granados M

    (2014) Rising complexity and falling explanatory power in ecology

    Frontiers in Ecology and the Environment 12:412–418.

    https://doi.org/10.1890/130230

    • Google Scholar
32. 1. Machovsky-Capuska GE
    2. Howland HC
    3. Raubenheimer D
    4. Vaughn-Hirshorn R
    5. Würsig B
    6. Hauber ME
    7. Katzir G

    (2012) Visual accommodation and active pursuit of prey underwater in a plunge-diving bird: the Australasian gannet

    Proceedings. Biological Sciences 279:4118–4125.

    https://doi.org/10.1098/rspb.2012.1519

    • PubMed
    • Google Scholar
33. 1. Mizutani A
    2. Chahl JS
    3. Srinivasan MV

    (2003) Motion camouflage in dragonflies

    Nature 423:604.

    https://doi.org/10.1038/423604a

    • Google Scholar
34. 1. Morisaka T
    2. Connor RC

    (2007) Predation by killer whales (Orcinus orca) and the evolution of whistle loss and narrow-band high frequency clicks in odontocetes

    Journal of Evolutionary Biology 20:1439–1458.

    https://doi.org/10.1111/j.1420-9101.2007.01336.x

    • PubMed
    • Google Scholar
35. 1. Nebel C
    2. Sumasgutner P
    3. Pajot A
    4. Amar A

    (2019) Response time of an avian prey to a simulated hawk attack is slower in darker conditions, but is independent of hawk colour morph

    Royal Society Open Science 6:190677.

    https://doi.org/10.1098/rsos.190677

    • PubMed
    • Google Scholar
36. 1. Paskins KE
    2. Bowyer A
    3. Megill WM
    4. Scheibe JS

    (2007) Take-off and landing forces and the evolution of controlled gliding in northern flying squirrels Glaucomys sabrinus

    The Journal of Experimental Biology 210:1413–1423.

    https://doi.org/10.1242/jeb.02747

    • PubMed
    • Google Scholar
37. 1. Payne RS

    (1971) Acoustic location of prey by barn owls (Tyto alba)

    The Journal of Experimental Biology 54:535–573.

    https://doi.org/10.1242/jeb.54.3.535

    • PubMed
    • Google Scholar
38. 1. Pembury Smith MQR
    2. Ruxton GD

    (2020) Camouflage in predators

    Biological Reviews of the Cambridge Philosophical Society 95:1325–1340.

    https://doi.org/10.1111/brv.12612

    • PubMed
    • Google Scholar
39. Book

    1. Pennycuick CJ

    (2008)

    Modelling the Flying Bird

    Academic Press.

    • Google Scholar
40. 1. Popper N
    2. Fay R

    (1997) Evolution of the ear and hearing: issues and questions

    Brain, Behavior and Evolution 50:213–221.

    https://doi.org/10.1159/000113335

    • Google Scholar
41. 1. Portugal SJ
    2. Murn CP
    3. Sparkes EL
    4. Daley MA

    (2016) The fast and forceful kicking strike of the secretary bird

    Current Biology 26:R58–R59.

    https://doi.org/10.1016/j.cub.2015.12.004

    • PubMed
    • Google Scholar
42. 1. Pouliot-Laforte A
    2. Veilleux LN
    3. Rauch F
    4. Lemay M

    (2014)

    Validity of an accelerometer as a vertical ground reaction force measuring device in healthy children and adolescents and in children and adolescents with osteogenesis imperfecta type I

    Journal of Musculoskeletal & Neuronal Interactions 14:155–161.

    • PubMed
    • Google Scholar
43. 1. Provini P
    2. Tobalske BW
    3. Crandell KE
    4. Abourachid A

    (2014) Transition from wing to leg forces during landing in birds

    The Journal of Experimental Biology 217:2659–2666.

    https://doi.org/10.1242/jeb.104588

    • PubMed
    • Google Scholar
44. 1. Regan D
    2. Beverley KI

    (1984) Figure–ground segregation by motion contrast and by luminance contrast

    Journal of the Optical Society of America A 1:433.

    https://doi.org/10.1364/JOSAA.1.000433

    • Google Scholar
45. 1. Roderick WRT
    2. Cutkosky MR
    3. Lentink D

    (2017) Touchdown to take-off: at the interface of flight and surface locomotion

    Interface Focus 7:20160094.

    https://doi.org/10.1098/rsfs.2016.0094

    • PubMed
    • Google Scholar
46. 1. Roulin A
    2. Dijkstra C
    3. Riols C
    4. Ducrest AL

    (2001) Female- and male-specific signals of quality in the barn owl

    Journal of Evolutionary Biology 14:255–266.

    https://doi.org/10.1046/j.1420-9101.2001.00274.x

    • Google Scholar
47. Book

    1. Roulin A

    (2019) Barn Owls: Evolution and Ecology

    Cambridge University Press.

    https://doi.org/10.1017/9781316694114

    • Google Scholar
48. 1. Rushton SK
    2. Bradshaw MF
    3. Warren PA

    (2007) The pop out of scene-relative object movement against retinal motion due to self-movement

    Cognition 105:237–245.

    https://doi.org/10.1016/j.cognition.2006.09.004

    • PubMed
    • Google Scholar
49. 1. Ruxton GD

    (2009) Non-visual crypsis: a review of the empirical evidence for camouflage to senses other than vision

    Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 364:549–557.

    https://doi.org/10.1098/rstb.2008.0228

    • PubMed
    • Google Scholar
50. Book

    1. Ruxton GD
    2. Allen WL
    3. Sherratt TN
    4. Speed MP

    (2018) Avoiding Attack: The Evolutionary Ecology of Crypsis, Aposematism, and Mimicry

    Oxford University Press.

    https://doi.org/10.1093/oso/9780199688678.001.0001

    • Google Scholar
51. 1. San-Jose LM
    2. Séchaud R
    3. Schalcher K
    4. Judes C
    5. Questiaux A
    6. Oliveira-Xavier A
    7. Gémard C
    8. Almasi B
    9. Béziers P
    10. Kelber A
    11. Amar A
    12. Roulin A

    (2019) Differential fitness effects of moonlight on plumage colour morphs in barn owls

    Nature Ecology & Evolution 3:1331–1340.

    https://doi.org/10.1038/s41559-019-0967-2

    • PubMed
    • Google Scholar
52. Software

    1. Schalcher K

    (2024) Data-availability-schalcher-et-al-elife, version swh:1:rev:59dec0d852a05a11783cc45a31e110a91d45b1f2

    Software Heritage.

    https://archive.softwareheritage.org/swh:1:dir:c446d6e0a6ae3a2f8d83fde54b00b8558fef4c6e;origin=https://github.com/kimschalcher/data-availability-Schalcher-et-al-eLife;visit=swh:1:snp:df154c1acc39a1f33df90f55021a9a1652045102;anchor=swh:1:rev:59dec0d852a05a11783cc45a31e110a91d45b1f2
53. 1. Shepard E
    2. Wilson R
    3. Halsey L
    4. Quintana F
    5. Gómez Laich A
    6. Gleiss A
    7. Liebsch N
    8. Myers A
    9. Norman B

    (2008) Derivation of body motion via appropriate smoothing of acceleration data

    Aquatic Biology 4:235–241.

    https://doi.org/10.3354/ab00104

    • Google Scholar
54. 1. Shepard E
    2. Wilson R
    3. Quintana F
    4. Gómez Laich A
    5. Liebsch N
    6. Albareda D
    7. Halsey L
    8. Gleiss A
    9. Morgan D
    10. Myers A
    11. Newman C
    12. McDonald D

    (2010) Identification of animal movement patterns using tri-axial accelerometry

    Endangered Species Research 10:47–60.

    https://doi.org/10.3354/esr00084

    • Google Scholar
55. 1. Simpson GL

    (2018) Modelling palaeoecological time series using generalised additive models

    Frontiers in Ecology and Evolution 6:e0149.

    https://doi.org/10.3389/fevo.2018.00149

    • Google Scholar
56. Book

    1. Stevens M
    2. Merilaita S

    (2011) Animal camouflage: mechanisms and function

    National Geographic.

    https://doi.org/10.1017/CBO9780511852053

    • Google Scholar
57. 1. Stevens M
    2. Searle WTL
    3. Seymour JE
    4. Marshall KLA
    5. Ruxton GD

    (2011) Motion dazzle and camouflage as distinct anti-predator defenses

    BMC Biology 9:81.

    https://doi.org/10.1186/1741-7007-9-81

    • PubMed
    • Google Scholar
58. 1. Stuart-fox D
    2. Whiting MJ
    3. Moussalli A

    (2006) Camouflage and colour change: antipredator responses to bird and snake predators across multiple populations in a dwarf chameleon

    Biological Journal of the Linnean Society 88:437–446.

    https://doi.org/10.1111/j.1095-8312.2006.00631.x

    • Google Scholar
59. 1. Sugiura N

    (1978) Further analysis of the data by Akaike’s information criterion and the finite corrections

    Communications in Statistics - Theory and Methods 7:13–26.

    https://doi.org/10.1080/03610927808827599

    • Google Scholar
60. Book

    1. Taylor I

    (2004)

    Barn Owls: Predator-Prey Relationships and Conservation

    Cambridge University Press.

    • Google Scholar
61. 1. Théry M
    2. Casas J

    (2002) Predator and prey views of spider camouflage

    Nature 415:133.

    https://doi.org/10.1038/415133a

    • PubMed
    • Google Scholar
62. 1. Usherwood JR
    2. Sparkes EL
    3. Weller R

    (2014) Leap and strike kinetics of an acoustically “hunting” barn owl (Tyto alba)

    The Journal of Experimental Biology 217:3002–3005.

    https://doi.org/10.1242/jeb.107169

    • PubMed
    • Google Scholar
63. 1. Wahlberg M
    2. Larsen ON

    (2017) Comparative bioacoustics: an overview

    Propagation of Sound 01:e0101.

    https://doi.org/10.2174/97816810831791170101

    • Google Scholar
64. Book

    1. Webster DB
    2. Plassmann W

    (1992) Parallel evolution of low-frequency sensitivity in old world and new world desert rodents

    In: Webster DB, Plassmann W, editors. In: The Evolutionary Biology of Hearing. Springer. pp. 633–636.

    https://doi.org/10.1007/978-1-4612-2784-7

    • Google Scholar
65. 1. Wernli K
    2. Ng L
    3. Phan X
    4. Davey P
    5. Grisbrook T

    (2016) The relationship between landing sound, vertical ground reaction force, and kinematics of the lower limb during drop landings in healthy men

    The Journal of Orthopaedic and Sports Physical Therapy 46:194–199.

    https://doi.org/10.2519/jospt.2016.6041

    • PubMed
    • Google Scholar
66. 1. Wilson RP
    2. Shepard ELC
    3. Liebsch N

    (2008) Prying into the intimate details of animal lives: use of a daily diary on animals

    Endangered Species Research 4:123–137.

    https://doi.org/10.3354/esr00064

    • Google Scholar
67. 1. Wilson RP
    2. Holton MD
    3. di Virgilio A
    4. Williams H
    5. Shepard ELC
    6. Lambertucci S
    7. Quintana F
    8. Sala JE
    9. Balaji B
    10. Lee ES
    11. Srivastava M
    12. Scantlebury DM
    13. Duarte CM

    (2018) Give the machine a hand: a boolean time‐based decision‐tree template for rapidly finding animal behaviours in multisensor data

    Methods in Ecology and Evolution 9:2206–2215.

    https://doi.org/10.1111/2041-210X.13069

    • Google Scholar
68. 1. Wood SN

    (2004) Stable and efficient multiple smoothing parameter estimation for generalized additive models

    Journal of the American Statistical Association 99:673–686.

    https://doi.org/10.1198/016214504000000980

    • Google Scholar
69. 1. Wood SN

    (2011) Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models

    Journal of the Royal Statistical Society Series B 73:3–36.

    https://doi.org/10.1111/j.1467-9868.2010.00749.x

    • Google Scholar
70. Book

    1. Wood SN

    (2017) Generalized additive models

    Chapman and Hall/CRC.

    https://doi.org/10.1201/9781315370279

    • Google Scholar
71. 1. Zylinski S
    2. Osorio D
    3. Shohet AJ

    (2009) Cuttlefish camouflage: context-dependent body pattern use during motion

    Proceedings. Biological Sciences 276:3963–3969.

    https://doi.org/10.1098/rspb.2009.1083

    • PubMed
    • Google Scholar

Author details

1. Kim Schalcher

   Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing

   For correspondence

   kim.schalcher@unil.ch

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0719-2765

2. Estelle Milliet

   Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland

   Contribution

   Formal analysis, Visualization, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

3. Robin Séchaud

   1. Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland
   2. Agroecology and Environment, Agroscope, Zurich, Switzerland

   Contribution

   Conceptualization, Writing – original draft

   Competing interests

   No competing interests declared

4. Roman Bühler

   Swiss Ornithological Institute, Sempach, Switzerland

   Contribution

   Investigation, Visualization

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4883-4923

5. Bettina Almasi

   Swiss Ornithological Institute, Sempach, Switzerland

   Contribution

   Resources, Funding acquisition, Writing – original draft

   Competing interests

   No competing interests declared

6. Simon Potier

   1. Department of Biology, Lund University, Lund, Sweden
   2. Les Ailes de l’Urga, Marcilly-la-Campagne, France

   Contribution

   Conceptualization, Methodology, Writing – original draft

   Competing interests

   No competing interests declared

7. Paolo Becciu

   Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland

   Contribution

   Supervision, Visualization, Methodology, Writing – original draft, Writing – review and editing

   Contributed equally with

   Alexandre Roulin and Emily LC Shepard

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2145-6667

8. Alexandre Roulin

   Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland

   Contribution

   Conceptualization, Resources, Supervision, Funding acquisition, Writing – original draft, Project administration

   Contributed equally with

   Paolo Becciu and Emily LC Shepard

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-1940-6927

9. Emily LC Shepard

   Department of Biosciences, Swansea University, Swansea, United Kingdom

   Contribution

   Conceptualization, Software, Supervision, Validation, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing

   Contributed equally with

   Paolo Becciu and Alexandre Roulin

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7325-6398

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