The effects of intense wing molt on diving in alcids and potential influences on the evolution of molt patterns
University of Minnesota Department of Ecology, Evolution, and Behavior, 100 Ecology Building, 1987 Upper Buford Circle, Saint Paul, MN 55108, USA
Address for correspondence: University of Memphis Biology Department, 5700 Walker Avenue, 103 Ellington Hall, Memphis, TN 38152-3540, USA (e-mail: ebridge{at}memphis.edu)
Accepted 26 May 2004
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Summary |
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Key words: molt, wing-propelled diving, Uria aalge, Fratercula cirrhata, biomechanics, video analysis, mechanical efficiency, flap cycle
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Effects of moult on diving |
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Medium-sized and large alcids (Alcidae) undergo a relatively intense wing
molt, wherein the flight feathers are shed in short succession (usually within
2 weeks). In most alcids this molt takes place in the fall, shortly after the
breeding season (Ewins, 1993;
Gaston and Jones, 1998
;
Thompson et al., 1998
;
Thompson and Kitaysky, 2004
),
and causes a substantial loss of wing surface area rendering a molting bird
temporarily flightless. Yet these birds must continue to dive underwater
throughout wing molt to forage and perhaps to avoid predators. This situation
begs the question of how a molt-induced loss of wing area affects the capacity
for the wings to propel an alcid underwater. However, prior to this study
there has been only one examination of the effect of wing molt on diving, and
dive speed was the only parameter measured
(Swennen and Duiven,
1991
).
Studies of the effects of wing molt on aerial flight generally indicate
that molt reduces flight ability and/or efficiency
(Tucker, 1991;
Swaddle and Witter, 1997
;
Hedenström and Sunada,
1998
; Chai et al.,
1999
; Swaddle et al.,
1999
; Bridge,
2003
), and we might expect the same to be true for underwater
diving. However, some have speculated that the opposite may be true for the
effects of flight-feather molt on wing-propelled diving
(Thompson et al., 1998
;
Keitt et al., 2000
;
Montevecchi and Stenhouse,
2002
). Birds that use their wings underwater must balance
morphological trade-offs between evolutionary pressures associated with aerial
flight and underwater diving (Storer,
1960
; Stresemann and
Stresemann, 1966
; Ashmole,
1971
; Pennycuick,
1987
; Raikow et al.,
1988
; Kovacs and Meyers,
2000
). A presumed indication of such a trade-off is the fact that
many diving birds have small wings relative to body size (i.e. high wing
loading). Although these small wings allow for little maneuverability and
require high speeds and flap rates for aerial flight, they may be more
effective than large wings for underwater propulsion as large wings would
create inordinate drag (Thompson et al.,
1998
; Keitt et al.,
2000
; Montevecchi and
Stenhouse, 2002
). Following this line of reasoning, a molt-induced
loss of wing area could cause an increase in diving ability and/or
efficiency.
The goal of this study was to use a video triangulation technique to closely monitor the underwater movements of captive alcids in such a way that I could measure parameters such as flap duration, speed, work per flap, cost of transport, and power throughout a period of wing molt. Because the effects of molt on these diving parameters are difficult to predict, I tested the null hypothesis that molt would have no effect on any of these diving parameters.
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Materials and methods |
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During the period of data collection for this study, the birds were fed a fish diet consisting of several species (e.g. Atlantic silversides Menidia menidia and herring, Clupea spp.). Food was available ad libitum in trays located throughout the aviary, and husbandry staff threw thawed fish into the diving pool at four regular feedings every day. Diving activity generally peaked during these feeding times, but there was usually sporadic diving throughout the day.
Camera array and filming
I used an array of four small black-and-white CCD video cameras (model
YK-3027D, Iou Ken Electronic Co., Taipei, Taiwan) to film diving activity
within a small portion of the pool. The cameras were arranged as two pairs,
which viewed the same section of the pool from two different angles. One
camera of each pair was mounted in front of the viewing window (referred to
henceforth as a frontal camera), and the other was mounted above the pool
pointing straight down (referred to as an overhead camera). Thus, a bird
diving within the observational field of the cameras would be filmed from
above and from the side simultaneously.
Signals from the cameras were routed through a video processor (model YK-9003, Iou Ken Electronic Co., Taipei, Taiwan), which combined the inputs from the four video cameras into a composite image with the views of each video camera occupying a fourth of the video screen. This composite footage was then digitally recorded on a Sony Digtial8 Camcorder at 30 frames s-1. An example of a video-taped dive is available at http://jeb.biologist.org.
Generation of three-dimensional position data
In order to calibrate the measurement system to obtain measurements in SI
units from the video footage, I fashioned a T-shaped apparatus made of white
2.5 cm (diameter) polyvinyl chloride (PVC) tubing (henceforth called the
T-pipe), and with assistance from the SeaWorld staff, had it placed in
strategic positions within the diving pool. The T-pipe measured 1.12 m across
the top and had several detachable sections incorporated into the stem that
allowed the video cameras to record known lengths marked on the T-pipe at
known distances from the inside surface of the viewing window or from the
surface of the water. For the frontal cameras, this calibration procedure
involved placing the stem of the T-pipe against the glass and extending it
horizontally directly away from each camera, such that the top of the T-pipe
was distanced from the glass surface by an amount equal to the length of the
stem. By adding 0.45 m and 0.70 m sections of tubing to the stem in any
combination, the distance between the span of tubing at the top of the T-pipe
and the video camera could be altered by known intervals. I used similar
procedures to calibrate the overhead cameras, but in this case the T-pipe was
held vertically and upside-down directly beneath the cameras.
To generate measures of the speed and depth of each dive from the digital video footage, I first had to plot the birds' positions from each camera onto a two-dimensional coordinate system. After trimming the dive footage to remove unwanted parts of a dive (i.e. turning, stopping, gliding and the first few flaps of a dive), I converted the image into QuickTime JPEG format with a screen size of 1000x750 pixels. I used the program CamMotion v0.9.5 (TERC, Cambridge, MA, http://projects.terc.edu/cam/cam_homepage.html) to map the position of a bird throughout a dive from both overhead and frontal views onto a Cartesian coordinate system that used pixels as units.
The coordinate data from both the overhead and frontal cameras went through
several transformations in order to yield three-dimensional coordinates in SI
units. Firstly, the video cameras I used generated a somewhat distorted image
in which a flat surface would have a convex appearance. This phenomenon is
known as barrel distortion (so named because a rectangle would be made to
appear barrel-shaped) and is a common shortcoming of inexpensive camera
lenses. I corrected for this barrel distortion by converting the Cartesian
coordinates to radial coordinates and applying a second-order polynomial
function that increased each point's radial distance as a function of the
initial radial distance (r):
![]() | (1) |
The values used in this equation are particular to the cameras employed in this study and were derived by a calibration process wherein I filmed a flat wall with an array of dots spaced at known intervals that allowed me to calculate the extent to which the image was distorted as a function of the radial distance from the center of the field of view.
A second correction was needed to ensure that the angles of each dive were calculated with respect to true horizontal. I used the surface of the water as viewed from the frontal cameras as a standard for true horizontal and rotated the radial coordinates from the frontal cameras according to the slope of the water's surface in the video. I performed a similar correction on data from the overhead cameras except that the correction was made with reference to the surface of the viewing window (i.e. perpendicular to the associated frontal camera).
After conversion of the transformed and rotated radial coordinates back to x-y coordinates, the data were scaled such that the surface of the water was set to y=0 and that all y coordinates for a diving bird were negative numbers. These new Cartesian coordinates were then converted from pixels to meters using scale factors generated from the calibration footage.
Triangulation of a bird's position in the pool was complicated by the fact
that light from within the water was refracted as it moved through the
water-air interface or the water-glass-air interface before reaching a camera.
I dealt with this problem by using the calibration footage to estimate the
apparent position of the camera relative to the water. This apparent position
is where the camera appeared to be according to how the length of a piece of
plastic pipe changed on the video screen as its proximity to the camera was
increased by known intervals. I used the following equation to model the
visual length of the pipe (L) as a function of its distance
(D) from the water's surface or the water-window interface:
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where the constants L0 and DC are the length of the pipe at D=0 and the apparent distance of the camera from the water, respectively. Thus, as the pipe's distance from the camera doubles such that D=DC, its length on the screen is reduced by half. After fitting the data from calibration footage (leveled and corrected for barrel distortion as described above) to Equation 2, I estimated the apparent distance of each camera from the water by extrapolating the value of D for which L was halved and using this value of D to derive DC.
Once the apparent distance of the camera was known, the two-dimensional surface maps of bird positions from the overhead and frontal points of view were then combined to triangulate the true positions of a bird in three-dimensional space. I defined coordinates according to three axes: x, the horizontal axis parallel to the viewing window; y, the vertical axis (depth); and z, the horizontal axis perpendicular to the viewing window. Fig. 1 illustrates how the actual z and y coordinates (zA and yA) can be found, given z and y coordinates as they appear in the video footage (zV and yV) and the apparent positions of the overhead and frontal cameras relative to the surface of the water and the water-window interface, respectively. The perpendicular distances between the water and the camera are defined as DO with respect to the overhead camera and DF with respect to the frontal camera. The surface of the water defined the zero value for y, such that all y values for diving birds were negative. The zero value of the z axis was defined by the position of the overhead camera such that the z equaled 0 directly below the camera and z increased with the distance from the water-window interface. The water-window interface defined the lowest possible z value, zMIN. To derive zA and yA, I used the coordinates of the apparent camera positions along with zV and yV to establish equations for lines that link the actual position of the bird with each of the apparent camera positions, which are shown below in slope-intercept form with zA as the independent variable:
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For the frontal camera:
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and for the overhead camera:
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These equations can then be used to solve for zA as
follows:
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One can then find yA using either Equation 3 or 4. I used a similar series of calculations to determine the actual value for x. Because the paired sets of cameras overlapped in their fields of view to a small extent, I was able to consolidate position data from birds that swam through both fields of view by simply appending the relative position data from the second half of a dive to that of the first half.
Before using this system of video triangulation to measure dive parameters I tested the system with the calibration footage by finding the three-dimensional positions of the opposite ends of a 0.56 m piece of PVC tubing in a variety of locations throughout the monitored area of the diving pool. After establishing the accuracy of the measurement system (see Results), I generated coordinate data for diving birds using CamMotion to record the initial coordinates and an Excel spreadsheet to perform the necessary transformations and triangulation of the data.
Derivation of dive parameters
As with other attempts to measure biomechanical properties of
wing-propelled diving (Lovvorn et al.,
1999; Lovvorn,
2001
; Lovvorn and Liggins,
2002
), I employed a model that conceives a bird's body and wings
as a fuselage and a propulsion system, respectively. The propulsion system can
then be evaluated by observing its capacity to counter the forces that resist
forward movement of the fuselage. This approach avoids the need to invoke
complex models of fluid vorticity around the wings, which would have to take
into account varying wing shape, surface area and rotational velocity
(Dickinson, 1996
). My approach
was further simplified by using the flap cycle as the measurement interval of
interest. As average speed remained relatively constant from one flap cycle to
the next, I ignored inertial work (work associated with changes in velocity)
in my calculations as this parameter would sum to zero over a flap, with
negative work from deceleration during passive phases of the flap cycle
counteracting positive work from acceleration during active stroke phases.
Although I present several measures of diving efficiency in this paper,
including measures of work and power, they are all generated from three basic
measurements: displacement, dive angle (relative to horizontal), and absolute
depth. For example, estimates of drag were calculated based on rates of
displacement or speed. These estimates were generated using equations specific
to common guillemots and tufted puffins derived from empirical tests of drag
versus speed performed on frozen specimens in a tow tank
(Lovvorn et al., 2001;
Lovvorn and Liggins,
2002
).
The measurements of dive angle and absolute depth were necessary to
calculate the degree to which buoyancy was opposing the forward motion of the
bird. Buoyancy in diving birds decreases with depth in direct proportion to
the reduction of air volumes in the plumage and lungs. I estimated these air
volumes using published data from common guillemots and other diving birds.
Estimates of plumage air volume came from a measurement of 0.33 l
kg-1 obtained by Wilson et al.
(1992), who compared water
displacement of dead common guillemots before and after flooding the plumage.
In estimating this residual respiratory air volume I followed the procedures
of Wilson et al. (1992
), who
employed an allometric equation from Lasiewski and Calder
(1971
) to derive an estimate
of 0.173 l for a common guillemot weighing 1.087 kg (i.e. 0.160 l
kg-1).
The contribution of the body tissues to buoyancy was calculated from body
composition following the same procedures
(Lovvorn et al., 1999) used to
estimate buoyancy in Brünnich's guillemots Uria lomvia. Briefly,
I used the absolute densities of water, protein, lipid and ash
(DeVries and Eastman, 1978
;
Lovvorn and Jones, 1991
;
Lovvorn et al., 1999
) in
conjunction with the mass ratios of these components in a common guillemot
(Furness et al., 1994
), to
find a composite estimate of the volume of the body tissues.
I summed the volume of the body tissues with the plumage and respiratory
air volumes to calculate total body volume (L), and I divided this
sum by the approximate mass of the bird (in kg) to find the bird's above-water
density (kg L-1). During a dive, air volumes decrease by a
factor of 10/(n+10) where n is depth in meters. Thus, the
absolute density of a bird was calculated by reducing the air volumes as a
function of depth and dividing this depth-dependent body volume estimate by
the bird's body mass. Per kilogram buoyancy force was then calculated as
(w-
d)9.806/
d, where
w is the density of water (1 kg l-1),
d is depth-dependent density, and 9.806 is the force of
gravity (in N kg-1). Multiplying this result by the bird's
approximate mass gave the absolute buoyancy force adjusted for depth.
Because capturing the SeaWorld birds would entail a stressful disturbance, I could not weigh the birds observed in this study. Thus for the purposes of calculating buoyancy, I assumed a mass of 1 kg for common murres and 0.8 kg for tufted puffins. I examined the potential for violation of this assumption to reduce the accuracy of my results by calculating dive parameters for 12 haphazardly selected common murre dives, once with the mass of the bird changed to 1.15 kg (15% increase) and again with the bird's mass set at 0.85 kg. I then compared work-per-flap estimates from these calculations using the increased and decreased masses with estimates based on the original masses to determine how sensitive my results would be to changing the mass parameter.
To move themselves forward diving birds must generate a propulsion force equal to the sum of the drag on the bird's body and the upward force of buoyancy. Given that the pull of buoyancy was always directly upward and that the drag vector was always in the opposite direction of the bird's displacement, it was possible to calculate instantaneous resistance force that must be equaled by wing propulsion by summing the buoyancy and drag vectors. Assuming that the rate of displacement was constant over the given time interval (this assumption is addressed in the Discussion), I could then calculate the work (forcexdisplacement) done during a flap cycle, and generate an estimate of cost of transport (COT), a dimensionless measure of the amount of work involved in moving a given mass a given distance (workxmass-1xdisplacement-1). I could also examine average power over a flap cycle (workxtime-1). The beginning of a given flap cycle as well as the end of the previous one was delineated by the wing tips reaching their lowest position relative to the bird's body. Only dives with constant flapping were analyzed in this study, and dives or parts of dives with noticeable glide phases were excluded from the data set.
I generated dive parameters from the video footage using two different general techniques. Initially I took position coordinates of the bird's head (the most reliable morphological landmark in my low-resolution video) at each frame throughout a dive. After generating three-dimensional coordinates, I applied a mild smoothing function (a moving average that spanned 5 coordinates for each axis) to these data to minimize the effects of small mouse-clicking errors when using CamMotion. I used these smoothed coordinates to calculate the amount of displacement that occurred between each video frame as well as the angle of descent and absolute depth. I then revisited the video footage and used CamMotion to record the time at which each flap cycle was completed (i.e. when the wing tip was at its lowest position relative to the bird's body). The total displacement that occurred over each flap cycle was found by summing the displacement values from each frame. Dividing the displacement over each interval by 1/30 of a second yielded a speed estimate, which could be used to calculate drag. From these nearly instantaneous drag estimates followed estimates for work and power associated with each 1/30 s time interval, which I summed over each flap cycle to generate per-flap estimates of these dive parameters.
Because this technique generally required the digitization of over 100 coordinates for each dive, I devised a less labor-intensive means of generating estimates of dive speed and depth, which I refer to as the shortcut technique. The shortcut technique involved recording the position of a bird's head only at the end of each flap cycle, rather than recording coordinates for each frame. Thus, the time marks for each flap and the coordinate data were recorded simultaneously, and there were generally less than 15 data points for each dive one for each flap. Unlike the first technique, values for displacement, work, power, etc. were not derived by summing incremental values over a dive, but were simply calculated based on the change in a bird's position at the end of each flap cycle.
Molt monitoring
The SeaWorld husbandry staff at the Penguin Encounter generally try to
avoid all unnecessary disturbances to their birds. Thus, it was not possible
to capture birds in order to monitor molt in a quantitative way (e.g. by
measuring the growth of new feathers). However, because feather loss was rapid
and quite obvious in the birds I studied and because the birds were easily
observed at close range through the viewing window, I was able to assess
qualitatively the extent of wing molt in individual birds using binoculars or
a video camera. These observations involved closely watching individual birds
when they seemed prone to frequent stretching and preening of the wings and
noting the extent to which the primaries and secondaries had been lost or
replaced. From these observations I identified four somewhat distinct stages
of flight-feather molt as follows: stage 1, all primaries missing and all or
most secondaries remaining; stage 2, all primaries and secondaries missing;
stage 3, new primaries emerged just beyond primary coverts; stage 4, primaries
visible well beyond primary coverts and secondaries visible beyond secondary
coverts (Fig. 2). I designated
pre- and post-wing-molt plumages as stage 0 and stage 5, respectively.
Fig. 2 shows approximate
changes in wing surface area associated with each molt stage, based on
photographs of a dead tufted puffin. Intermediate stages (e.g. roughly half of
the primaries missing) were noted as such, and dives performed during these
intermediate molt stages were not used for biomechanical analyses.
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I calculated the approximate duration of each molt stage in both species by averaging the molt stage durations for which I had sufficient observations to distinguish an approximate beginning and end. The durations of intermediate molt stages were divided equally between the preceding and following stages for the purpose of calculating the average duration of each molt stage.
Statistical analysis of dive parameters
Because individual birds were identified as they dived, I tested for the
effects of wing molt on the dive parameters using repeated-measures analysis
of variance. This analysis used a within-subjects approach to test whether
measurements made during any of the four stages of molt deviated significantly
from baseline measurements obtained when a bird's wings were intact. Due to
limited time for video footage collection, molt stage 0 was represented by
only two birds among the common guillemots and molt stage 5 was not
represented among the tufted puffins. Thus, I combined molt stages 0 and 5 to
produce baseline values representative of the fully plumaged wing for
comparison with different molt stages. I used Tukey-Kramer adjustments for
multiple comparisons to determine statistical differences among the molt
stages based on the magnitude of the deviations from baseline
measurements.
The amount of usable dive footage obtained varied greatly among individual birds for different molt stages. Of the 40 common guillemots in the Penguin Encounter, there was sufficient footage for only 15 birds. Similarly the data for tufted puffins came from a total of 12 birds. For many of the birds that contributed to the analyzed data set, there was not sufficient dive footage to provide sets of dive parameters for some stages of molt. Molt stage 4 for tufted puffins was especially poorly represented by only three birds. Although I did not define a minimum acceptable quantity of data (i.e. a minimum number of flaps or dives) for inclusion of a bird mean in the analyzed data set, I endeavored to ensure that each bird's mean for a given molt stage was derived from 20 or more flaps when sufficient footage was available. Table 1 lists the quantities of data that contributed to the individual bird means used for statistical testing.
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Results |
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Twenty-one measurements of a 0.56 m length of pipe positioned haphazardly in various positions throughout the visible portion of the diving pool averaged 0.556±0.017 m(mean ± S.D., range 0.534-0.595 m). There was no apparent pattern to errors in these measurements associated with the position of the pipe with respect to any of the three axes. Thus, coordinate data throughout the visible portion of the diving pool were equally valid.
Comparisons of the two techniques used to generate dive parameters (i.e. using coordinates from each frame of video versus using only the frames that depict the end of a flap cycle) indicated that they give equivalent qualitative results (Fig. 3). However, the shortcut technique yielded slightly lower values for some parameters, primarily because the displacement during any flap cycle was minimized (i.e. straightened). Nevertheless, the labor-saving advantages of the shortcut technique outweigh this minor inaccuracy, which is unlikely to have an appreciable effect of the results. Thus, with the exception of Fig. 3, all data presented here were generated by the shortcut technique.
|
Changes in dive parameters associated with wing molt
Wing molt was associated with clear decreases in flap duration for both
common guillemots (F4,62=8.24, P<0.001) and
tufted puffins (F4,38=5.85, P<0.001) for all
stages of molt except stage 4 (Fig.
4A). For both species, the relative decreases in flap duration
associated with molt stages 1, 2 and 3 did not differ significantly from one
another according to Tukey-Kramer multiple comparisons tests (=0.05).
Some stages of wing molt also appeared to decrease the average displacement
achieved during a flap in both species (guillemots
F4,62=6.36, P<0.001; puffins:
F4,38=4.80, P=0.003;
Fig. 4B). Despite these changes
in flap duration and displacement during wing molt, average speed (calculated
as displacement/duration) did not change significantly in association with
molt stage (guillemots F4,62=1.23, P=0.31;
puffins: F4,38=1.00, P=0.42;
Fig. 4C). Because molt
shortened the distance covered over each flap cycle, one would expect a
corresponding decrease in work per flap. However, work per flap did not
decrease significantly in common guillemots when comparing means from all molt
stages (F4,62=1.73, P=0.15;
Fig. 4D), perhaps because of
small insignificant increases in speed associated with some molt stages, and
there was only a near-significant decrease in work per flap in tufted puffins
(F4,38=2.43, P=0.064;
Fig. 4D). Notably, 14 of 15
guillemots in molt stage 2 showed a decrease in work per flap when compared to
baseline values, and this general decrease is significantly different from
zero in a simple two-tailed t-test that ignores multiple comparisons
(t14=-3.62, P=0.003). Dive angle, which was also
a potentially important determinant of work, did not change significantly in
response to molt (guillemots F4,62=0.66, P=0.62;
puffins: F4,38=0.42, P=0.80; not shown). COT did
not change significantly as a result of wing molt (guillemots
F4,62=0.99, P=0.42; puffins:
F4,38=0.1.00, P=0.42), and was roughly equal for
both guillemots and puffins (Fig.
4E). Similarly, power averaged over the flap interval was not
strongly affected by wing molt (guillemots F4,62=1.32,
P=0.27; puffins: F4,38=0.1.07, P=0.38;
Fig. 4F)
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Molt observations
Average durations for each molt stage of each species are shown in
Fig. 5. Interestingly, the
onset of primary and secondary molt were offset in both species such that
secondary molt began roughly 12 days after primary molt. Because many of the
primaries are considerably longer than the secondaries, the primaries required
more time to grow, so growth of both primaries and secondaries culminated at
roughly the same time.
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Discussion |
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Because wing molt had no positive effects on any of the diving parameters,
it is unlikely that the evolution of intense molt in alcids was favored by
diving biomechanics as some have speculated
(Gaston and Jones, 1998;
Thompson et al., 1998
). The
more generally accepted explanation for intense wing molt in alcids and other
species with high wing loading (e.g. waterfowl) postulates that almost any
reduction in wing area, even that resulting from a relatively slow sequential
wing molt, would cause wing-loading to approach the theoretical maximum for
aerial flight, approximately 2.5 g cm-2
(Meunier, 1951
; Storer,
1960
,
1971
;
Warham, 1977
;
Pennycuick, 1987
;
Livezey, 1988
). In other
words, the loss of one or two feathers, as would occur with a gradual molt,
may render a bird with especially high wing loading flightless, or nearly so.
Thus, intense molt probably evolved as a means of shortening the molting
period and of avoiding some of the potential costs associated with a prolonged
molt-induced disruption of flight ability.
Although there often was not sufficient resolution in the data to
distinguish the relative degrees to which different stages of molt diminished
diving efficiency, it is notable that flap duration was at its lowest during
molt stage 2 in both study species, and measurements associated with molt
stage 4 indicated less severe affects on most diving parameters. Thus, the
detrimental effects of wing molt are to some degree proportional to the extent
of wing-area reduction, and the fact that secondary molt began roughly 12 days
after the initiation of primary molt could be viewed as an adaptation to
reduce the duration of molt stage 2, wherein both primaries and secondaries
are missing (or have yet to emerge beyond the coverts). Delaying the onset of
secondary molt allows partial regrowth of the primaries before the secondaries
are lost, preserving wing surface area to some degree and shortening the
period of time during which neither primaries nor secondaries extend beyond
their respective coverts. Feathers of all bird species grow at roughly the
same rate (3-5 mm day-1), regardless of the size of the fully grown
feather (Prevost, 1983;
Langston and Rohwer, 1996
;
Prum and Williamson, 2001
;
Dawson, 2003
). Therefore,
because most of the primaries are considerably longer than the secondaries,
the primaries take longer to completely regrow. Consequently, delaying
secondary molt results in both primary and secondary growth culminating at
approximately the same time, so delaying the onset of secondary molt does not
appear to lengthen the molting period (Fig.
5).
The fact that reduced wing area failed to improve diving efficiency
suggests that the evolution of small wings and high wing loading may not be an
adaptation for improved diving ability as previously thought
(Storer, 1960;
Stresemann and Stresemann,
1966
; Ashmole,
1971
; Pennycuick,
1987
). Lovvorn and Jones
(1994
) argue convincingly that
the small, pointed wings of alcids and many other open-water birds are an
adaptation associated with maximizing high-speed, long-distance flight
efficiency at the expense of maneuverability. They point out that an
open-water habitat relaxes selective pressure for highly maneuverable flight
at low speeds, as is needed to land on perches and to be able to escape
predators by rapid, vertical take-off. As a consequence, many aquatic birds
have adopted a style of flight that uses small, rapidly beating wings to
generate high flight speed, which may be useful for rapid movements between
specialized habitats. The extremely long-winged seabirds such as shearwaters
and albatrosses (Procellariiformes) have presumably adopted an alternative
flight strategy wherein they use their long wings for energetically efficient
dynamic soaring, sacrificing both high flight speed and some degree of
maneuverability. Lovvorn and Jones
(1994
) also note that many
birds that do not practice wing-propelled diving, such as waterfowl
(anseriformes), grebes (Podicepidiformes), and loons (Gaviformes), have
adopted similarly high wing loading and rapid-stroke flying dynamics in
association with an open water habitat. Furthermore, there are several
long-winged seabirds that demonstrate diving behavior comparable to that of
alcids. For instance, wedge-tailed shearwaters Puffinus pacificus,
Audubon's shearwaters Puffinus lherminieri, short-tailed shearwaters
Puffinus tenuirostris, black-vented shearwaters Puffinus
opisthomelas, and sooty shearwaters Puffinus griseus all dive to
depths exceeding 20 m, with some individuals reaching 70 m depth
(Morgan, 1982
;
Weimerskirch and Sagar, 1996
;
Keitt et al., 2000
;
Burger, 2001
), and there is
evidence of wing-propelled diving among boobies and gannets as well
(Adams and Walter, 1993
;
Le Corre, 1997
). Thus, it
would appear that the evolution of small wings is less relevant to
wing-propelled diving than many have suggested (e.g.
Storer, 1960
;
Stresemann and Stresemann,
1966
; Ashmole,
1971
; Pennycuick,
1987
).
Limitations of study system
A critical assumption to the quasi-steady modeling approach that allowed
for my calculations of drag is that flow over the surface of the bird is fully
developed at each different speed over each time interval. Because fully
developed flow does not occur instantaneously, this assumption is technically
incorrect. Direct studies of unsteady flow are thus far limited to rigid
structures under carefully controlled conditions
(Dickinson, 1996), and the
complexity of unsteady (as opposed to quasi-steady) flow theory makes it
unsuitable for most applied problems involving flapping propulsors. Thus, the
quasi-steady approach is the best available method for investigating the
effects of wing molt on diving.
This study differs from the standard approach to quantitative biomechanical
studies in that it forgoes any attempt to generate highly accurate
measurements from a small number of samples in favor of generating a large
data set with less accurate measurements. Due to limitations in the study
system, there are several potential sources of measurement error in this study
that should be addressed. First of all, it is important to regard the dive
parameters reported in this study more as indices than absolute measurements.
One reason for this distinction is that the relationship between speed and
drag is not linear. In calculating the work done over an entire flap I used
the average speed to derive the drag force, which amounts to assuming a
uniform speed throughout a flap cycle. However, it has been demonstrated that
instantaneous velocity varies considerably during a flap in association with
both upstroke and downstroke components (Lovvorn et al.,
1999,
2001
;
Lovvorn, 2001
;
Lovvorn and Liggins, 2002
).
Because drag increases exponentially with speed, fluctuations in speed could
give rise to disproportionate fluctuations in drag, such that only
instantaneous speed and drag measurements can provide extremely accurate
estimates of work and power by integrating over a time interval.
Secondly, the shortcut method used to generate coordinate data may have
resulted in underestimated speed, work, and power (see
Fig. 4). Average non-molting
dive speeds in this study were 1.32 m s-1 and 0.99 m s-1
for common guillemots and tufted puffins respectively. In other studies of
alcids, estimates of dive speed are generally higher. Swennen and Duiven
(1991) estimated the mean
level swimming speed of common guillemots at 2.18 m s-1, and
according to Croll et al.
(1992
) free-swimming
Brünnich's guillemots averaged 1.52 m s-1. Similarly, Atlantic
puffins Fraturcula arctica swimming in a confined dive tank
demonstrated speeds ranging from 1.02 to 2.14 m s-1
(Johansson and Wetterholm Aldrin,
2002
).
Finally, as mentioned earlier, actual masses of the birds (a component of buoyancy calculations) were unknown because frequent weighing would violate SeaWorld's husbandry policy. However, I suspect that the minor inaccuracies in some dive parameters associated with the lack of mass data are rendered negligible in terms of testing the effects of molt because of the within-subjects experimental design, which negates variation among birds.
Despite these potential problems, my approach seems suitable for testing
for effects of molt on diving ability, at least in a qualitative sense.
Discrepancies between dive speeds observed in this study and others may be due
to factors other than measurement techniques, such as the nature and intensity
of a bird's motivation to dive. There was rarely any clear motivation for any
of the dives examined in this study, whereas the birds in other studies were
diving for food (Swennen and Duiven,
1991; Croll et al.,
1992
) or in response to a startle stimulus
(Johansson and Wetterholm Aldrin,
2002
).
Extrapolation to wild birds
Perhaps of greater concern than measurement error is the possibility that
wing molt as I observed it in SeaWorld's captive alcids may differ from what
occurs in wild birds. Molt is generally triggered by changes in day length,
and SeaWorld's birds were exposed to natural daylight via several
large skylights in their aviary. SeaWorld's birds included individuals
captured as wild adults (mostly from high-latitude populations) as well as
captive-born birds. Because of the skylights in the birds' habitat, it was
impossible to shorten day lengths during the winter. Thus, molting schedules
in these birds evolved under a day-length regime with greater seasonal
variability than occurs in SeaWorld's location in southern California.
Nevertheless, my observations of molt at SeaWorld do not appear to differ
substantially from the reports of molt in wild alcids. The most comprehensive
treatment of molt in common guillemots is that of Thompson et al.
(1998), who determined the
pattern and timing of molt from wild birds killed by gill net fisheries. They
found that the duration of a molt cycle was highly variable, ranging from 24
to 81 days with large differences between the 2 years of their study. The
findings of this study and other estimates of wing-molt duration in common
terns generally fall within this range
(Glutz von Blotzheim and Bauer,
1982
; Ginn and Melville,
1983
; Harris and Wanless,
1988
). Furthermore, the delaying of secondary molt with respect to
primary molt was documented by Thompson et al.
(1998
), who observed secondary
molt beginning and finishing when the primaries were 27% and 99% grown,
respectively. Birkhead and Taylor
(1977
) offer a similar finding,
with secondary molt beginning and finishing when the primaries were 38% and
99% grown, respectively. Thus, my observations of molt in SeaWorld's common
guillemots correspond with what occurs in the wild, and I think that my
conclusions regarding the evolutionary significance of offsetting primary and
secondary molt are relevant to wild common guillemots.
It is less clear whether captive tufted puffins accurately reflect wing
molt in wild birds. My observations generally agree in terms of molt duration
and offsetting primary and secondary molt with a separate study of captive
tufted puffins undergoing their first wing molt
(Thompson and Kitaysky, 2004).
Unfortunately, detailed molt data from wild tufted puffins is lacking, but
there is documentation of molt in Atlantic puffins that states that these
birds do not lose their secondaries until their primaries have emerged from
their sheaths (Harris and Yule,
1977
), indicating a delay in the onset of secondary molt as
observed in SeaWorld's tufted puffins.
Suggestions for future studies
Although this study reveals increases in flap rates and relatively constant
or slightly reduced work per flap for birds in molt, the effects of the
substantial loss of wing area caused by intense molt were surprisingly small
with regard to cost of transport and power output. The absence of strong
effects on these and other parameters suggests that alcids may alter their
wing-stroke biomechanics to compensate for their missing wing feathers. Thus,
in addition to studying the gross effects of wing molt on wing-propelled
diving, it would also be of interest to investigate whether alcids make
adjustments in diving parameters such as stroke volume (i.e. the volume that
contains the plane of the wing throughout a stroke), angles of attack at given
points of the upstroke and downstroke, and rotational velocity of the wings in
response to wing molt. Molting alcids may also extend their wings to a greater
extent when diving to effectively increase their wing surface area. Techniques
outlined by Johansson (2003)
and Johansson et al. (2002
)
would allow for a good evaluation of these variables with regard to the
potential effects of wing molt.
It would also be of interest to examine how molt affects diving ability in
smaller alcids that undergo rapid wing molt. This excludes the Aethia
and Ptychoramphus auklets, which molt their wing feathers gradually
(Thompson and Kitaysky, 2004),
but includes species such as dovekies Alle alle and marbled murrelets
Brachyramphus marmoratus, which are roughly half the body length and
a third the mass of common murres. Diving petrels, which are of similar size
to the smallest alcids, might also be considered for further study of
wing-molt biomechanics as some species appear to have a rapid wing molt
similar to that of alcids (Watson,
1968
). Because these smaller birds carry much less inertial mass
through the water and have somewhat lower wing loading than many larger
alcids, rapid wing molt may have different effects on diving efficiency.
A molting alcid must contend with the energetic demands of feather growth in addition to the lack of mobility associated with flightlessness. Thus, an improved understanding of the effects of intense wing molt on diving and foraging could provide key insights into how these birds survive this potentially stressful period. As researchers pursue the development of biomechanical models for describing wing-propelled diving, I suggest that they consider the effects of wing molt and generate testable hypotheses regarding how this phenomenon affects birds in the field.
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References |
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