Research Article

Morphology, muscle capacity, skill, and maneuvering ability in hummingbirds

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Science  09 Feb 2018:
Vol. 359, Issue 6376, pp. 653-657
DOI: 10.1126/science.aao7104
  • Fig. 1 Flight performance and evolution of Central and South American hummingbirds.

    (A) A tracking system recorded body position (blue dots) and orientation (red lines) at 200 frames per second. These data were used to identify stereotyped translations, rotations, and complex turns. The sequence in (A) and movie S1 shows a bird performing a pitch-roll turn (PRT) followed by a deceleration (DecHor), an arcing turn (Arc), and an acceleration (AccHor). The sequence duration is 2.5 s, and every 5th frame is shown. (B) The number of translations, rotations, and turns recorded in this study (vertical ticks show the means). (C) Example translations and rotations illustrating the performance metrics. (D) We obtained performance metrics for 207 individuals from 25 species, ranging in mass from ~2 to 10 g. The phylogenetic tree at left was derived from a recent multilocus analysis (23), with eight principal clades denoted by color. The vertical ticks in (D) show the mean and range for 263 individuals assessed for species average muscle capacity and morphological traits (figs. S1 and S2). The bird in (A) is not drawn to scale. Alternative smoothing of tracking data is provided in figs. S3 and S4 and movie S2.

  • Fig. 2 Performance of different maneuvers is positively correlated.

    (A) If birds differ in overall maneuverability, we expect trial-average performance to be positively correlated among individuals. Positive correlations are particularly strong for the rotations and translations [means ± 95% confidence intervals (CIs), n = 200 individuals]. The dashed line is the average for all correlations. (B) Positive correlations could be caused by coupling due to constraints of flight in a confined space; if so, it would generate strong within-flight bout correlations. Each column in (B) compares the strength of the average within-flight bout correlation (± 95% CI) with the corresponding among-individual correlation. This shows that most of the strong covariance among maneuvers is not due to coupling within bouts of flight. Full analysis is provided in fig. S5.

  • Fig. 3 Species differ in maneuvers and morphology.

    (A to B) We used discriminant functions (DFs) to classify 25 species based on (A) maneuvering and (B) morphological phenotypes (n = 180 birds). Each species is represented by a different color-symbol combination. The x axis (DF1; arbitrary units) accounts for most of the variation in the data space [33% in (A); 85% in (B)]. In both (A) and (B), DF1 has a Pagel’s λ significantly greater than 0, indicating that closely related species resemble each other more than species drawn at random (all P < 0.03). (Left insets) The top DF1 loadings, demonstrating that species mainly differ in (A) their performance of complex turns and rotations and (B) body mass. (Right insets) The results of cross-validation, as the median percent of test data that was classified to the correct species (±95% central range). In both (A) and (B), classification accuracy was significantly better than expected (inset histograms = 10,000 randomized permutations). However, morphological classification was nearly twice as accurate, demonstrating that the species differ more in morphology than maneuvering style. Additional loadings are available in fig. S6.

  • Fig. 4 Different flight maneuvers are determined by different biomechanical traits.

    (A) Species mass is positively associated with several maneuvers. (B) Effect sizes from scaling models (mass only) and full models (mass + other traits). Each row is a maneuver, ordered to match (C). Effect sizes are standardized to be comparable across traits (columns) and maneuvers (rows). The positive effects of species mass (blue) are attenuated when the other traits are added in the full model, except for PRT%. The negative effects of individual mass (red) are consistent. (C) Results of the full models show how maneuvers are clustered according to their associations with different biomechanical traits. AR stands for wing aspect ratio, a measure of wing shape. The bootstrap support (percent) is shown for each node in the dendrogram. (D) The agglomerative coefficient measures the strength of clustering and is significantly greater than that of randomized data (black histogram; shading shows the 95% central range for 10,000 permutations). (E) The mean effect magnitude for each trait, averaged across all maneuvers. The outlined circles in (B) and (C) show the results of analyses that account for phylogenetic relationships (n = 187 birds, except 180 for Arc). Full analysis is provided in figs. S7 to S9.

  • Fig. 5 Species and individual wing size affect different maneuvers.

    Partial effect plots illustrate the relative effect of wing loading given the other effects in the statistical model. Dotted lines indicate nonsignificant effects. (A) Yaw rotations, the use of complex turns, and downward rotations are strongly dependent on species wing loading (top row). However, these same maneuvers are not significantly associated with an individual’s wing loading relative to conspecifics (bottom row). (B) Horizontal acceleration, deceleration, and centripetal acceleration are not associated with species wing loading (top row); however, these are the metrics that are most strongly associated with individual wing loading (bottom row). Colors indicate clade, as in Fig. 1. Bubble diameters represent the sample sizes for the 25 species.

  • Fig. 6 Complex turns are associated with the environment, morphology, and skill.

    (A) The use of sharp versus smooth turns, PRT%, was the only behavior that differed among high- and low-elevation species. Effect sizes account for phylogenetic relationships (±95% highest posterior density intervals). (B and C) Partial effect plots illustrate the relative effect of each variable on PRT%, given the other effects in the statistical model (n = 180 individuals). (B) All else being equal, low-elevation species, larger-mass species, and those with lower wing loading use proportionately more sharp PRTs (and fewer Arcs). (C) Individuals with longer wings (high AR) use more sharp turns, although further analysis shows how this association can vary, depending on the species (fig. S11). Species and individuals that perform Arcs with greater centripetal acceleration also use proportionately more Arcs [(B) and (C), rightmost column], indicating that skill predominates over fatigue in the use of these complex turn maneuvers. Results are robust to removing the high-leverage species with n = 1 individual in (B).

Supplementary Materials

  • Morphology, muscle capacity, skill, and maneuvering ability in hummingbirds

    Roslyn Dakin, Paolo S. Segre, Andrew D. Straw, Douglas L. Altshuler

    Materials/Methods, Supplementary Text, Tables, Figures, and/or References

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    • Materials and Methods
    • Figs. S1 to S12
    • Table S1
    • References

    Images, Video, and Other Media

    Movie S1
    Maneuvers of two hummingbird species recorded in Costa Rica. The first clip shows an individual Eugenes fulgens, a high elevation specialist with a body mass of 8.8g. Body position (blue) and orientation (red) are reprojected onto the videos of four synchronized cameras. The sequence of maneuvers illustrated in Fig. 1A occurs at 11-13s. The second clip shows an individual Phaethornis striigularis, a much smaller lowland species with a body mass of only 2.9g. All maneuvers are shown in real time. Note that while the cameras in the tracking system occasionally drop frames (indicated by white frames), continuous 3D tracking was obtained using the other cameras.
    Movie S2
    Comparison of smoothing parameters. The matrices Q, Rpos, and Rori are implemented in the extended Kalman filters used to smooth body position and orientation data. These parameter values were chosen by reprojecting hummingbird body position and orientation onto sample videos, and validated by verifying the gravitational acceleration of dropped objects. Next, a sequence of maneuvers is shown by an individual Eugenes fulgens under nine alternate smoothing scenarios. The first clip uses Rpos and Rori smoothing parameters implemented in the main text. The sequence repeats for each alternate smoothing scenario (two-fold and ten-fold over- and under-smoothing for Rpos and Rori, for a total of eight alternate scenarios). The original 200 fps video is modified so that the bird’s motion is shown in real time. Note that while one of the cameras occasionally dropped frames, continuous 3D tracking was obtained from the other cameras.

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