Journal
Fault bars and the risk of feather damage in cranes
R. Jovani1,2, J. Blas1, M. J. Stoffel3, L. E. Bortolotti3 & G. R. Bortolotti3
1 Department of Conservation Biology, Estación Biológica de Doñana, CSIC, Sevilla, Spain
2 UFZ, Department of Ecological Modelling, Helmholtz Centre for Environmental Research—UFZ, Leipzig, Germany
3 Department of Biology, University of Saskatchewan, Saskatoon, SK, Canada
Keywords
fault bars; feathers; flight; natural selection;
sandhill crane; stress.
Correspondence
Roger Jovani, Department of Evolutionary
Ecology, Estación Biológica de Doñana,
CSIC, Avda. Americo Vespuccio s/n 41092,
Sevilla, Spain.
Email: jovani@ebd.csic.es
Abstract
Fault bars are translucent areas across feathers grown under stressful conditions.
They are ubiquitous across avian species and feather tracts. Because fault bars
weaken feather structure and can lead to feather breakage, they may reduce flight
performance and lower fitness. Therefore, natural selection might prime mechanisms aimed at reducing the cost of fault bars, penalizing their occurrence in those
feathers more relevant for flight. Here, we tested one prediction of this ‘fault bar
allocation hypothesis’: that the prevalence, abundance and risk of damage of fault
bars change across the wing feathers of a long-distance migrant, the sandhill crane
Grus canadensis as a function of the strength requirements of feathers for flight. We
analysed 2411 wing feathers with 4676 fault bars from 39 cranes in active
migration. Fault bars did not increase feather damage with feather age. The
occurrence of fault bars decreased from proximal to distal wing portions, both in
flight feathers and in coverts, according to the presumed greater strength requirements of external wing feathers during flight. The occurrence of fault bars was
variable when producing low feather damage (o2%) but was consistently low for
fault bars with a higher damage probability (42–30%). Altogether, our results
suggest that fault bars are common on the feathers of birds even after millions of
years of evolution because natural selection seems to penalize birds with particularly harmful fault bars in certain feathers and of a certain magnitude, but is
unable to eliminate less harmful fault bars according to their strength and position.
Fault bars are common structural abnormalities of feathers
produced as they grow (Riddle, 1908). Although the proximal factors generating fault bars are still poorly understood, poor nutrition and stress are commonly invoked
(Riddle, 1908; Slagsvold, 1982; King & Murphy, 1984;
Machmer et al., 1992). Feathers grow by the accumulation
of keratin produced in the follicle collar by keratinocytes.
Fault bars are a deficit of keratin that results in the absence
or the slimming of some barbules, appearing as narrow
(o2 mm) translucent bands across the feather vane, almost
perpendicular to the feather rachis (Riddle, 1908; Prum &
Williamson, 2001). Because fault bars weaken the feather
structure, they increase the probability of partial or even
complete feather breakage along the fault bar line (Slagsvold, 1982; Newton, 1986; Machmer et al., 1992; Sarasola &
Jovani, 2006; Møller, Erritzøe & Nielsen, 2009). The timing
of moult does not change for damaged feathers, and therefore fault bars may decrease the wing area for long periods
of time. This might be particularly relevant for species that
do not undergo a complete annual moult of flight feathers,
as occurs in most large birds (Baker, 1993; Rohwer et al.,
2009). Because wing load (i.e. body weight/wing area) is
crucial for flight performance (Pennycuick, 1989), and
experimental reductions of wing area have increased the
energetic demands of birds and reduced their reproductive
success (Mauck & Grubb, 1995; Velando, 2002), fault bars
could potentially jeopardize individual fitness (Bortolotti,
Dawson & Murza, 2002).
Jovani & Blas (2004) proposed that fault bars may have
imposed a selective pressure on birds to evolve adaptive
strategies that minimize the negative consequences of fault
bars on fitness. The ‘fault bar allocation hypothesis’ (Jovani
& Blas, 2004) was originally proposed in a study of white
storks Ciconia ciconia, where the abundance of fault bars
along the wing feathers followed a non-random pattern,
consistent with specific feather functions and strength requirements for flight. Subsequent evidence in support of the
hypothesis emerged from studies in a migratory passerine
(Serrano & Jovani, 2005) and a migratory hawk (Sarasola &
Jovani, 2006). Here, we studied the occurrence of faults bars
on wing feathers of sandhill cranes Grus canadensis to test
the ‘fault bar allocation hypothesis’ (Jovani & Blas, 2004)
and discuss the results from an evolutionary perspective.
Materials and methods
Study species and data collection
The sandhill crane is a large (3400–4900 g) long-lived
(420 years) bird widely distributed in North America
(Tacha, Nesbitt & Vohs, 1992). The nominal subspecies
breeds in arctic and subarctic North America and eastern
Siberia, and performs a long-distance migration to wintering quarters in the south-west USA and north-central
Mexico (del Hoyo, Elliot & Sargatal, 1996). Stable isotope
analyses suggest that the breeding grounds of our study
population span the northern parts of the western Canadian
provinces and northward through the Arctic (Hobson et al.,
2006).
In October 2004, we collected 39 crane carcasses hunted
during fall migration in southern Saskatchewan (Canada).
The carcasses were salvaged from a commercial hunting
camp, and so no animals were harmed or killed for
this study. Upon collection, carcasses were transported to
Saskatoon and frozen until examination in the laboratory.
Some feathers were completely or partially missing (n = 51),
or too dirty to be analysed (n = 34), and were excluded
from further analyses. The remaining primaries, secondaries, tertials, scapulars and their associated coverts of
one wing per bird were carefully inspected for fault
bars under constant light conditions by a single observer
(M. J. S.) unaware of the hypothesis being tested. Wing
flight feathers were classified into five groups: Pext: from the
outermost first to the fifth primary, Pint: primaries
six to the innermost number 10, S: secondaries, T: tertials
and Sc: scapulars. Primaries were divided into two
groups because of their distinct morphology and function
during flight, the distal ones being more asymmetrical
and curving up strongly during flight. This distinction
did not apply to coverts and were thus classified as:
PC, primary coverts; SC, secondary coverts; TC, tertial
coverts and ScC, scapular coverts. A given crane specimen
often had different feather generations from different
moulting episodes, and we recorded the age of the feathers,
that is feathers grown this year, last year or earlier according
to feather wear. Fault bars were categorized following
Sarasola & Jovani (2006) as light (absence of some barbules
producing a visible discontinuity on the structure of the
feather), medium (a narrow, i.e. o1 mm, translucent
line across the feather) or strong (Z1 mm, translucent line
across the feather). We also recorded whether fault bars
produced a break in a portion of the vane from its position
up to the distal edge of the feather vane (damage), or
the complete breakage of the rachis, thus shortening the
feather (cut).
Statistical analyses
Because individual bird behaviour or feather quality would
vary among birds, we treated the fault bars of the same bird
as non-independent units in the analyses. To do so, we
performed a generalized linear mixed model (GLMMs)
using the GLIMMIX macro of SAS, where bird identity was
treated as a random factor (Littell et al., 1996). Whether the
fault bar did (1) or did not (0) produce feather ‘damage’ or
‘cut’ was introduced as the dependant variable, and feather
age, fault bar strength and feather type were introduced as
independent categorical variables. We used a binomial
distribution of errors and a logit link function leading a
model equivalent to a logistic regression.
The proportion of feathers with fault bars was analysed
with a binomial distribution of errors and a logit link
function, and the number of fault bars per feather with a
Poisson distribution of errors and a log link function.
Because the occurrence of fault bars in a particular feather
is potentially related to the occurrence of fault bars in the
other feathers of an individual, whenever possible, we
considered feathers as non-independent units in the analyses. Moreover, unpredictable sources of ‘noise’ could arise
on the number of fault bars in feathers of different ages due
to a ‘year effect’ (e.g. different environmental conditions
among years). To overcome these statistical difficulties, bird
identity and the age of the feather were used as random
factors in generalized linear mixed models (GLMMs), using
GLIMMIX macro of SAS (Littell et al., 1996). Feather group
(e.g. secondaries, scapular coverts) was fitted into the model
as a factor.
For the analysis of the probability of fault bars producing
damage, fault bars in growing feathers were considered to be
of the same age as those in fresh ones (i.e.
1 year old).
When GLMM models did not converge in these analyses,
GLM models were used. However, when comparing the
occurrence of fault bars in different feathers, the growing
feathers were not considered because the number of fault
bars could be underestimated.
Results
Overall, we analysed 2411 feathers: 195 Pext, 193 Pint, 603
S, 114 T, 112 Sc, 383 PC, 592 SC, 114 TC and 105 ScC, from
which we recorded a total of 4676 fault bars: 3664 light, 664
medium and 348 strong; see the legend of Fig. 2 for sample
sizes according to feather groups. Feather damage was
detected for 96 fault bars and 13 feathers were cut by a fault
bar.
Feather age and fault bars
We first tested whether older feathers showed more damage
by fault bars as a consequence of a longer exposure to the
wear and tear of flight. We did not find, though, any
evidence that fault bars increase damage probability with
feather age (F3,4634 = 0.69, P = 0.561), and this held true
when we controlled for the strength of the fault bar
(F3,4632 = 0.76, P = 0.518), feather type (F3,4626 = 0.10,
P = 0.958) and both variables together (F3,4625 = 0.01,
P = 0.998). Thus, we were confident that our results on fault
bar risk of damage were not biased by feather age.
Fault bar prevalence
In flight feathers, the prevalence of medium and strong fault
bars was low (o20%) in secondaries and internal primaries,
and slightly higher (20–30%) in tertials, scapulars and external primaries (GLMM, medium: F4,1135 = 5.92, Po
0.0001; strong: F4,1135 = 3.08, P = 0.016). Both light and
overall fault bar prevalence were higher in flight feathers,
Feather damage
Figure 1 Prevalence (a) and abundance (b) of fault bars according to
feather group and fault bar magnitude.
but particularly so on tertials and scapulars (GLMM, light:
F4,1135 = 7.56, Po0.0001; all: F4,1135 = 10.45, Po0.0001).
The same patterns of fault bar abundance observed in flight
feathers were found in coverts (GLMM, light: F3,1059
= 17.99, Po0.0001; medium: F3,1059 = 8.40, Po0.0001;
strong: F3,1059 = 19.99, Po0.0001; all: F3,1059 = 20.97,
Po0.0001; Fig. 1a).
Thirteen feathers were broken due to fault bars (one light,
four medium and eight strong fault bars). Because of this
small number of feathers broken by fault bars, we analysed
feather breakage and vane damage together (n = 109). As
expected, the risk of feather damage on flight feathers
differed among fault bar strengths (w22 = 1231.64,
Po0.0001). There was a low risk for light fault bars (0.2%
of feathers), intermediate for medium (4.0%) and moderate
(14.4%) for strong fault bars. A similar pattern occurred in
covert feathers (w22 = 122.83, Po0.0001): light (1.1%), medium (8.0%) and strong (17.8%) fault bars.
Within a fault bar strength category, the risk of feather
breakage was equal across flight feathers (GLM: light:
w42 = 3.46, P = 0.484, medium: w24 = 6.19, P = 0.185, strong:
w24 = 5.26, P = 0.262; Fig. 2). The same pattern held true for
fault bars in the coverts, although slight differences emerged
(GLM: light: w32 = 4.36, P = 0.225, medium: w23 = 7.59,
P = 0.055, strong: w23 = 6.64, P = 0.084; Fig. 2).
The risk of feather breakage was negatively correlated
with fault bar prevalence (Spearman’s r =—0.673, n = 27,
P = 0.0001) and abundance (Spearman’s r =—0.666, n = 27,
P = 0.0002) (Fig. 3). Harmless fault bars, that is those
producing feather damage in o2% of the cases (left of
dashed line in Fig. 3) occurred at variable prevalence and
abundance in different feathers, from being almost completely absent to a high prevalence and abundance. However,
fault bars constituting a higher risk of feather damage (those
on the right of the dashed line in Fig. 3) occurred at a very
low prevalence and abundance on feathers.
The proportion of feathers with at least some damage was
similarly low across flight feathers (between 3 and 6%;
w24 = 5.17, P = 0.270; Fig. 4); however, it was more variable
within wing coverts (between 2 and 14%; w32 = 29.82,
Po0.0001), being especially high in tertial coverts (Fig. 4).
Number of fault bars
The number of strong fault bars was equally low in all flight
feathers (GLMM: F4,1135 = 1.60, P = 0.172); however, the
number of medium fault bars was higher in tertials and
scapulars (GLMM: F4,1135 = 2.42, P = 0.0467). Light, and
overall fault bar abundance, was higher in scapulars and
tertials and slightly higher in external than in internal
primaries (GLMM, light: F4,1135 = 12.07, Po0.0001; all:
F4,1135 = 8.83, Po0.0001). The same pattern was repeated
in coverts, with the differences in fault bar abundance being
even more pronounced (GLMM: light: F3,1059 = 48.01,
Po0.0001; medium: F3,1059 = 13.23, Po0.0001; strong:
F3,1059 = 29.41, Po0.0001; all: F3,1059 = 58.24, Po0.0001;
Fig. 1b).
Figure 2 Percentage of fault bars producing feather damage. The
sample sizes (number of fault bars) for feather groups from left to right
were: light: 242, 161, 1031, 272, 287, 409, 544, 400 and 318; medium:
66, 40, 174, 52, 52, 92, 90, 59 and 39; strong: 43, 20, 79, 18, 18, 24,
41, 68 and 37.
Figure 3 Relationship between percentage of fault bars producing
breakage (risk of feather breakage by fault bars) and fault bar
prevalence (left axis) and abundance (right axis).
Figure 4 Abundance and prevalence of feather breakage by fault bars
along wing flight feathers and coverts. Note the same scale as in Figs
1 and 2 for comparison.
The abundance of feather damage was equally low, between
0.03 and 0.06 breaks per feather, among flight feathers
(Kruskal–Wallis w42 = 5.09, P = 0.278; Fig. 4), but different
within covert feather groups (between 0.02 and 0.17 damages per feather; Kruskal–Wallis w32 = 29.70, Po0.0001),
and being especially high in tertial coverts (Fig. 4).
Discussion
Fault bars were equally or more prevalent on inner wing
feathers than on outer ones, consistent with the studies on a
stork, passerine and raptor (Jovani & Blas, 2004; Serrano &
Jovani, 2005; Sarasola & Jovani, 2006). The fault bar
allocation hypothesis proposes that such a pattern is the
result of the higher cost of fault bars in these feathers
(Jovani & Blas, 2004). Accordingly, we found that the
occurrence of fault bars was dependent on their risk of
producing feather damage. Fault bars occurred infrequently
at intensities or positions prone to undergoing or producing
rachis breakage or loss of vane portions. As a result, the
prevalence and abundance of feather damage due to fault
bars was low and similar along the wing feathers. However,
despite the negative association between damage risk and
fault bar occurrence across feathers (Fig. 3), the prevalence
and abundance of feather damage was not uniform among
the feather types studied that is feather damage was highest
in the inner coverts, particularly TC and ScC (Fig. 4). Thus,
it seems that natural selection was weaker on these inner
feathers, which presumably have lower strength demands
during fight, and perhaps lower fitness-related costs if they
break.
We assessed for the first time the probability of feather
damage due to fault bars across time, and found no effects
of feather age. This result is particularly interesting because
we studied feathers ranging from those that were growing to
more than 3 years old. Also, the probability of feather
breakage was similar between long wing feathers and their
corresponding coverts despite the fact that wing coverts are
less likely to bend during flight and are exposed less to
friction with abrasive materials. Therefore, if a fault bar is
going to cause damage to the feather, it seems that this
occurs shortly after feather production. This suggests that
fault bars have a potential impact on flight performance
during the entire period of feather use. Moreover, this
finding represents a considerable methodological advantage
for future studies, because feathers of different ages can be
compared without bias regarding feather damage. This also
allows for more valid comparisons between studies conducted with moulted feathers collected in the field and those
conducted with whole birds.
What do these results explain about the evolution of
feathers? Long-distance migration is a challenge for the
evolution of bird morphology and physiology, and sandhill
cranes cover several thousand kilometres annually between
breeding and wintering grounds (Tacha et al., 1992). It thus
seems plausible that natural selection has favoured some
mechanism to reduce the harmful consequences of increased
wing loading and decreased manoeuvrability due to partial
or complete feather gaps (Hedenstrom & Sunada, 1999) and
their associated wing asymmetry consequences (Thomas,
1993). Thus, it is difficult to imagine a single advantage to
the presence of fault bars; the optimal solution is to have no
fault bars. However, feather evolution has not eliminated
the occurrence of fault bars in birds; as we have found here,
fault bars are abundant in cranes and continue producing
feather damage. This suggests that the mechanisms by which
such feather growth errors are produced, which remain
largely unknown, are difficult to avoid.
Although natural selection has not eliminated fault bar
formation, it has succeeded in reducing to a large extend
their occurrence on feathers with a high risk of breaking due
to fault bars, in turn reducing damage on feathers that must
be in good condition for flight. Fault bars are still produced
on important flight feathers, where breakage may result in
negative fitness consequences, but in lower numbers. Overall, this shows that natural selection does not ‘select’ optimal
solutions (e.g. mechanisms eliminating fault bars), but
rather punishes bad designs (e.g. fault bars occurring on
feathers that are important for flight). Finally, fault bar
occurrence is highly variable among feathers, suggesting
that adaptive mechanisms that successfully reduce fault bar
occurrence in some feathers are not used in those other
feathers with a higher abundance of fault bars. This suggests
that either fault bar avoidance mechanisms are costly to
implement or that the characteristics of feather growth
leading to fault bars have evolved separately in different
feather tracts or even at the individual feather level.
Acknowledgements
This work was supported by a grant from Telefonica
Moviles, Vodafone and Amena and a postdoctoral grant
from the Secretarıa de Estado de Educacion y Universidades
and Fondo Social Europeo to R.J., a postdoctoral I3P
contract (CSIC/European Union) to J.B., an NSERC grant
to G.R.B. and the Stuart and Mary Houston Professorship
in Ornithology to G.R.B.
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