Sequence of perching and hunting strike events throughout a Barn owl foraging trip.

A) GPS tracks (black line) during one complete foraging trip (duration = 73 minutes) performed by a female barn owl, with perching events (squares), unsuccessful (circles) and successful (triangles) hunting strikes. Black arrows indicate the flight direction. Successful hunting strikes were identified by the presence of self-feeding events (identified from the acceleration data), or by the direct return to the nest box (identified from the acceleration data and validated with the nest box camera footage). Inset panels show an example of the tri-axial acceleration signals corresponding to both nest-box return and self-feeding behaviours (see Fig S3 for detailed representations). B) The heave acceleration and the associated force during a perching event (highlighted in orange) and a hunting strike (highlighted in dark purple). C) Variation in peak landing force for perching events (orange dots, n = 56,874) and hunting strikes (dark dots, n = 27,981). White dots show the estimated mean, and data distribution is represented by both violin and box plots. The owl picture at the top left of Panel A is courtesy of J. Bierer, and owl drawings are courtesy of L. Willenegger, all used with permission.

Sequential changes in perch type and landing force prior to hunting strikes during a sit-and-wait hunt

A) Landing force during perching events in relation to the time until the next hunting event and perch type. Each line represents the predicted mean for each perch type (here shown for males), with the 95% confidence intervals. B) The selection of perch type in relation to time until the next hunting strike, highlighting the change in perch type that occurs ∼10 minutes prior to a strike. C) A sequence of perching events (oranges circles) prior to a successful strike (purple circle) for a typical sit-and-wait hunt, showing the variation in peak landing force through time. White arrows indicate flight direction and numbers under each perching events indicate the time until the next hunting attempt (i.e pre-hunt time). The owl illustrations at the top of Panel A are courtesy of L. Willenegger, used with permission.

Sexual differences in foraging behaviour, landing force and hunting success.

A) GPS tracks showing the foraging activities of a barn owl breeding pair during one complete night. Movement patterns of both male (yellow lines) and female (blue lines) are shown, with colour scale changing from the first trip of the night (foraging trip 1) to the last one (Male: nmax = 11; Female: nmax = 4). Perching events (squares), unsuccessful (circle) and successful (triangle) hunting attempts are shown for each foraging trip. B) Variation in foraging flight speed for female (blues dots, n = 9,223) and male (orange dots, n= 18,019) barn owls (n. females: 84; n. males: 78). C) Variation in peak landing force during perching events (perching force) for female (blue dots, n= 30,378) and male (yellow dots, n = 26,496) barn owls. D) Variation in hunting success when barn owls hunted on the wing or used the sit-and-wait strategy for female (blue dots, non-the-wing = 8,136, nsit-and-wait = 1,981) and male (yellow dots, non-the-wing = 16,328, nsit-and-wait = 1,532) barn owls. For visualization purposes, each dot shows the average hunting success of each individual for both hunting strategies. Panels B to C) White dots and bars show the mean and the 95% CI around the mean respectively, and data distribution is represented by both violin and boxplots. Owl drawings are courtesy of L. Willenegger, all used with permission.

Pre-hunt perching force affects hunting success during sit-and-wait hunting.

Variation in hunting success according to the pre-hunt perching force (N), depending on whether owls hunted on the wing (yellow) or using the sit-and-wait strategy (blue). Solid lines show the estimated means (averaged over both sexes), and the shaded area corresponds to the 95% confidence intervals around each mean. Blue and yellow circles show the force, recorded during the last perching event before hunting (pre-hunt perching), pooled to the nearest integer N value for representation purposes, when hunting on the wing or using the sit-and-wait strategy, respectively. Circle size is related to the amount of data with the same value. The owl illustrations at the top right are courtesy of L. Willenegger, used with permission.

: GPS tracks (in black) of the 163 breeding barn owls used in this study recorded in 2019 and 2020 in western Switzerland.

Typical Barn owl foraging ground and nest location in western Switzerland.

A) Example of how pasture poles are usually located within the agricultural landscape which represents the main habitat for barn owls in western Switzerland. B) Typical barn in which nest boxes are usually installed on the Swiss plateau.

Behaviour classification from accelerometer data.

Time series data of the different behaviour classified using Boolean approach showing changes in the raw tri-axial acceleration, body pitch angle, and the vectorial sum of the dynamic body acceleration (VeDBA) corresponding to A) flight, B) hunting strikes, C) nest box visit and D) self-feeding events. Behaviour specific base element used in the Boolean classification are shown in grey bands. Note that hunting strikes involves greater acceleration amplitude, VeDBA and body pitch variation between landings in context of usual perching (here at the end of flight sequence).

Complete variation in landing force according to pre-hunt time.

Graphic representation of (A) the complete variation of the landing force calculated during perching and (B) the corresponding first derivative events in relation to the time (hours) until the next hunting event depending on whether owls perched on poles (in grey), trees (in green) and buildings (in yellow). Each line represents the predicted means for each perch type (averaged over male individuals), and shades show the 95% confidence intervals around each mean.

Differences in distance between the last perch and strike location according to the hunting strategy.

Box plots of the variation in distance between perching location and hunting strikes among both perching and flying hunting strategies, showing a significant difference in distance between perch and strike location according to the hunting strategy (Wilcoxon test: W = 2344881, p-value < 0.001). Boxes boundaries highlight the first and the third quartile of the range distribution of the data. The line within each box marks the median and whiskers above and below boxes indicate the 10th and 90th percentiles.

Sexual dimorphism in body mass.

Box plots of the variation in body mass between females (blue dots, n= 84) and males (orange dots, n= 79). White dots and bars respectively highlight the average and the standard deviation of body mass.

Sexual differences in food provisioning.

Box plots of the variation in the number of prey items delivered to the nest each night between females (blue dots, n = 1,226) and males (orange dots, n = 3,105). White dots and bars respectively highlight the average and the standard deviation of the number of prey items delivered to the nest each night.

Sexual differences in hunting activity.

Box plots of the variation in the number of hunting strikes and perching events performed each night by females (blue dots, nb perching events = 22,134, nb hunting strikes = 8176) and males (orange dots, nb perching events = 19,657, nb hunting strikes = 15,158). White dots and bars respectively highlight the average, and the standard deviation of the number of hunting strikes and perching events performed each night.

Sexual difference in the use of hunting strategy.

Box plots of the variation in the proportion of use of the sit-and-wait strategy for females (blue dots) and males (orange dots). White dots and bars respectively highlight the average and the standard deviation of the proportion of use of the sit-and-wait strategy.

Sexual comparison of landing force as a function of landing context.

Variation in peak landing force, on the log scale, involved in perching events and hunting strikes between female (blue dots, nb hunting strikes = 10,117, nb perching events = 30,378) and male (orange dots, nb hunting strikes = 17,864, nb perching events = 26,496) individuals. A) Shows landing force per unit of body mass and B) shows variation in peak landing force. White dots show the estimated mean, and data distribution is represented by both violin and box plots.

Influence of hunting strategy on barn owls foraging trip duration.

Variation in foraging trip duration (in min) as a function of the frequency of use of the sit-and-wait strategy (relative to hunting on the wing). Solid line shows the estimated mean (averaged over both sexes) and the grey shade corresponds to the 95% confidence intervals around the mean.

Flexible time algorithms in a Boolean approach for classification of barn owl behaviours.

Time series (TS) of base elements applied for the classification of flight, hunting strikes, nest return (indicating prey delivery to the nest), and self-feeding behaviours. Each base element (BE) has a defined temporal flexibility that includes a number of events over which the conditions of the element are met (Present), as well as a range to the next element (Range), a window of flexibility (Flexibility) and a period over which the element is extended (ETNE), where the units are in events, 50 Hz having 50 events per second. See Wilson et al.(54) for details of use of these algorithms in the Boolean approach.

Modelling the effect of landing context and sex on landing force.

Model output for the LMM predicting variations in landing force (log transformed) due to landing context (hunting strike vs perching) and sex (Males vs Females). The model also included a random effect of BirdID and NightID (nested in BirdID). Intercept is reported for both landing contexts (highlighted in grey) and give information about the averaged landing force considering female individuals. Estimates for interactions give the % of change between females and males for each landing contexts. Variance (σ2), intra-class correlation coefficient (ICC) and number of observations are provided for random effects.

Model selection results using Akaike Information Criteria corrected for small sample sizes (AICc) for all possible models evaluating the effect of landing context and sex on barn owl landing force.

A “*” in the variable columns indicates that the variable was included in that model. K is the number of variables included in each model. All LMM included a random effect of BirdID and NightID (nested in BirdID). Table includes all possible models, ranked by AICc. Models with Delta AICc < 2 are highlighted in grey and the final model is shown in bold.

Modelling the effect of time to hunt and perch type on landing force during perching events.

Model output of the GAMM predicting variations in pre-hunt perching force in N (log transformed) due to perch type (buildings, tree branches and road/pasture poles), wind speed, and sex as linear predictor (lme) and time to the next strike as an additive term (gam). The model also included a random effect of BirdID. Effective degrees of freedom (EDF) are shown for additive terms, providing the degree of non-linearity between pre-hunt perching force and time to hunt for each perch type.

Model selection results using Akaike Information Criteria corrected for small sample sizes (AICc) for all possible models evaluating the effect of time to hunt and perch type on landing force during perching events.

A “*” in the variable columns indicates that the variable was included in that model. K is the number of variables included in each model. All LMM included a random effect of BirdID and NightID (nested in BirdID). Table includes the top five models, ranked by AICc. Models with Delta AICc < 2 are highlighted in grey and the final model is shown in bold.

Modelling the effect of hunting success and hunting strategy on landing force during hunting strike.

Model output for the LMM predicting variations in hunting strike force (log transformed) due to hunting success (0 vs 1), hunting strategy (perching vs flying) and sex. The model also included a random effect of BirdID and NightID (nested in BirdID). Intercept provides the averaged strike force (N) considering female individuals hunting on the wing. Variance (σ2), intra-class correlation coefficient (ICC) and number of observations are provided for random effects.

Model selection results using Akaike Information Criteria corrected for small sample sizes (AICc) for all possible models evaluating the effect of hunting success and hunting strategy on landing force during hunting strike.

A “*” in the variable columns indicates that the variable was included in that model. K is the number of variables included in each model. All LMM included a random effect of BirdID and NightID (nested in BirdID). Table includes the top five models, ranked by AICc. Models with Delta AICc < 2 are highlighted in grey and the final model is shown in bold.

Modelling the effect of sex and hunting strategy on hunting success.

Model output for the GLMM predicting variations in hunting success (binary response 0,1) due to sex and hunting strategy (on the wing vs sit-and-wait). The model also included a random effect of BirdID and NightID (nested in BirdID). Intercept is reported for both sexes and give information about the averaged hunting success considering individuals hunting on the wing. Standardized estimates are provided for any additional terms in the model, representing % of change (odds ratio) of hunting success. The influence of landing force on hunting success is provided considering both hunting strategies. Variance (σ2), intra-class correlation coefficient (ICC) and number of observations of each group are provided for random effects.

Model selection results using Akaike Information Criteria corrected for small sample sizes (AICc) for all possible models evaluating the effect of sex, hunting strategy, and their interaction on hunting success.

A “*” in the variable columns indicates that the variable was included in that model. K is the number of variables included in each model. All LMM included a random effect of BirdID and NightID (nested in BirdID). Table includes the top five models, ranked by AICc. Models with Delta AICc < 2 are highlighted in grey and the final model is shown in bold.

Modelling the effect of landing force during pre-hunt perching on hunting success.

Model output for the GLMM predicting variations in hunting success (binary response 0,1) due to pre-hunt perching force, sex, hunting strategy (on the wing vs sit-and-wait) and wind speed. The model also included a random effect of BirdID and NightID (nested in BirdID). Intercept is reported for both sexes (highlighted in grey) and give information about the averaged hunting success considering individuals hunting on the wing. Standardized estimates are provided for any additional terms in the model, representing % of change (odds ratio) of hunting success. The influence of landing force on hunting success is provided considering both hunting strategies. Variance (σ2), intra-class correlation coefficient (ICC) and number of observations of each group are provided for random effects.

Model selection results using Akaike Information Criteria corrected for small sample sizes (AICc) for all possible models evaluating the effect of landing force during pre-hunt perching on hunting success.

A “*” in the variable columns indicates that the variable was included in that model. K is the number of variables included in each model. All LMM included a random effect of BirdID and NightID (nested in BirdID). Table includes the top five models, ranked by AICc. Models with Delta AICc < 2 are highlighted in grey and the final model is shown in bold.

Modelling the effect of sex on foraging flights speed.

Model output of the LMM predicting variation of barn owls foraging flights speed (in ms-1) as a function of the sex. Intercept provides the averaged foraging flight speed (in ms-1) considering female individuals. The model also included a random effect of BirdID and NightID (nested in BirdID). Variance (σ2), intra-class correlation coefficient (ICC) and number of observations of each group are provided for random effects.

Modelling the effect of sex and hunting strategy on barn owls foraging trip duration.

Model output of the LMM predicting variation of barn owls foraging trip duration (in min) as a function of the total number of hunting attempts per trip, the frequency of use of the sit-and-wait strategy (relative to hunting on the wing), and sex. The model also included a random effect of BirdID and NightID (nested in BirdID). Variance (σ2), intra-class correlation coefficient (ICC) and number of observations of each group are provided for random effects.