Download Understanding the demographic mechanisms underlying effective

Appendix 6
Objective Three: to quantify the relationship between survival and body condition and food
provision
Literature review: the role of body condition in mediating relationships between food
availability and abundance
Introduction
There are several difficult steps in determining the mechanisms underlying relationships between
food supplies and bird abundance. The main difficulties lie in operating at a sufficiently large scale
and in (re-)capturing birds in sufficient numbers to allow the measurement of survival rates. A
parallel approach would be to divide the putative mechanism of enhanced food availability leading
to enhanced body condition leading to increased survival leading to increased abundance into its
component parts. These might then be easier to study in the field, although some of the links are
still likely to be problematical. Fortunately, there are many common factors among bird life-history
strategies, especially considering a species group such as temperate passerines, which mean that
general patterns can sensibly be used to infer the nature of specific relationships for, for example,
farmland birds in Britain.
Here, we review the literature on the components of relationships between food and abundance,
concentrating on the pivotal intermediate that is body condition. This is an individual characteristic
that is likely to be linked to probabilities of survival but is easier to measure than survival itself, and
to be influenced by food availability. We concentrate on the determinants and consequences of
condition outside the breeding season because the context of our study is that of effects of food in
winter; although there is a considerable body of literature on effects of food on breeding success,
we consider this to be outside the scope of this project.
Definitions and measures of body condition
a) Definitions
The term “body condition” has been used to refer to a wide range of characteristics, or suites
thereof, relating to the physical health, phenotypic fitness or nutritional status of an individual bird.
Gosler & Harper (2000) separated “broad-sense condition”, i.e. a composite of features of health,
nutritional state, experience and wear-and-tear from “narrow-sense”, i.e. specific measures of
condition components such as weight corrected for size. The former is commonly what is meant
when “condition” is discussed, whereas one of the latter is what is usually measured. This
difference between operational and conceptual definitions of condition could be critical (Brown
1996): conceptually, it is related to Darwinian fitness, but proof of relationships with fitness is
unlikely to exist for operational definitions of fitness for many species or situations.
In the context of effects of food on breeding abundance, it is most likely that intermediate effects on
condition would manifest themselves in measures of nutritional state, but it is also possible that
greater food availability (or greater ease of access) would permit the growth or development of
better quality “ornaments”, such as sexually selected plumage features, reduce the wearing of
existing plumage or body parts, or improve health in some other way.
b) Indices
A wide range of indices designed to measure different aspects of body condition have been used by
different researchers, reflecting differences in definitions (reviews in Brown 1996, Stejskalová
2001). The most frequently used indices attempt to describe nutritional state or energy reserves.
1
These range from remotely observed measures of body profiles as measures of fat stores (Owen
1981) to complete chemical breakdowns of carcasses (Evans 1969, Conway et al. 1994, Redfern et
al. 2000). Simple body mass can provide an index of condition in itself (e.g. Slagsvold 1982, Hõrak
et al. 2002) because heavier birds are likely to dominate in competitive interactions as well as,
perhaps, to have larger energy reserves. However, larger bodies need more energy for maintenance
and locomotion, making energy reserve effects complex, and a simple measure of mass does not
allow separation of the fitness consequences of structural size and nutritional state, or the
elimination of other sources of variation such as water content (Blem 1990). Accordingly, attempts
to quantify energy reserves have tended to focus either on correcting mass for structural size or on
direct estimation of fat and/or protein stores.
Birds store energy in both fat and protein (i.e. muscle tissue): fat stores are more easily mobilized
and contain more energy per unit weight, so tend to be used for short-term energy storage, leading
to large changes on short timescales (within a day, for example; Brown 1996). Fat also therefore
makes up the bulk of the variation in total energy reserves (Blem 1990), but the fact that protein
levels also vary mean that both need to be measured to provide a complete picture (Brittas &
Marcström 1982). However, proteins tend to be used only when carbohydrates and lipids are
exhausted because breaking them down is inefficient (in fact, it is uncertain whether they represent
a genuine resource store or a loss that is tolerated temporarily in extreme circumstances; Blem
1990). Measures of mass corrected for body size take account of variation in both fat stores and
amounts of muscle and are often calculated from data that are easily and accurately collected in the
field. Ideally, the body size component will account for the variation in energy demand that is
associated with differences in structural size or lean dry mass, leaving an index that relates directly
to the amount of “spare” resources an individual has (Blem 1990). Although such indices do not
discriminate between the fat and muscle components of body mass (Blem 1990), fat content tends
to make up much of the variation between individuals (e.g. Ardia 2005).
The simplest indices consist of ratios between mass and a linear measure of size such as wing
length, tarsus length or skull diameter. Slagsvold (1982) compared several body parts as the
structural variable in ratios to body mass and found that bill depth provided the best predictor of
abdominal fat content in a sample of dead crows. However, it seems likely that absolute levels of
variation would be much lower and measurement error much higher if this structural variable were
used for small passerines. Ormerod & Tyler (1990) compared tarsus and wing lengths as indicators
of skeletal size in condition indices and found that tarsus was the better measure, because wing
length varied with age, and Ardia (2005) found that tarsus length performed better in predicting
lipid levels in nestling starlings than a principal component axis combining it with head and bill
length and first primary length. One problem with simple ratios is that differences in the ratio can
still reflect variation in structural size, partly because not all variation in size is typically accounted
for by a single variable (Green 2001), but more fundamentally because mass scales as the cube of
linear measures of size. This has been accounted for by using the cube of wing length as the
structural part of the ratio (Senar et al. 1992) or by taking logs before the ratio is calculated (Jakob
et al. 1996, Ormerod & Tyler 1990).
Even after transforming measures of structural size, differences in ratios to mass can still reflect
variation in size, because parameters such as wing length and tarsus can show a degree of
independent variation (Green 2001, Schulte-Hostedde et al. 2005). The use of residual values from
fitted relationships between mass and a skeletal size variable has therefore been recommended (e.g.
Jakob et al. 1996) and such residuals been shown to predict subcutaneous fat well in at least two
studies (Merilä & Svensson 1997, Ardia 2005). This approach is not without problems, however,
because mass does not always increase linearly with single, linear size variables, even after the
latter have been transformed (Green 2001). This means that residuals from the relationship tend to
be higher as the size indicator increases, potentially making structurally larger birds appear to have
2
greater energy reserves (Green 2001). To a degree, the extent to which this is a real problem
depends on the definition of condition that is being used: components of body mass such as water
and skeletal variation can also be viewed as components of overall condition (Schulte-Hostedde et
al. 2005). Nevertheless, if energy stores are the main focus of interest, a lack of correlation between
structural size and the residuals used to indicate condition needs to be demonstrated, for example by
showing a lack of correlation with a different size variable (Green 2001). This relies on an
appropriate relationship being fitted between mass and size from which to calculate residuals, which
need not be linear, or even parametric (Green 2001, Schulte-Hostedde et al. 2005). Green (2001)
recommends combining multiple structural variables and fitting relationships using type II
regression, in which both variables are assumed to have been measured and thus to have associated
measurement errors (Sokal & Rohlf 1991), although comparing the results of multiple approaches
to fitting relationships would be advisable. Indeed, Schulte-Hostedde et al. (2005) suggest that type
I regression (assuming a fixed y-variable: Sokal & Rohlf 1991) outperforms type II regression as a
generator of residuals because it is better suited to making predictions.
If data on variations in energy stores were of primary interest, it would clearly be most useful to
have direct measures of those energy stores themselves. Measures of true fat and protein content
are, generally, invasive and destructive, involving dissection and chemical analysis (e.g. Conway et
al. 1994, Redfern et al. 2000), so are unsuitable for many field applications. Exceptions are the use
of ultrasound (Newton 1993) or total body electrical conductivity (e.g. Conway et al. 1994),
although equipment costs and availability probably limit the extent to which this technique can be
used in practice.
Another approach consists of the scoring of fat or protein deposits based on subtle variations in
body shape or of tissues visible through the skin. The least invasive such approach involves remote
observation of the shape, or profile, of the abdominal area, which is a major location for fat storage,
in geese (Owen 1981). Although well suited for use with large, terrestrial, slow-moving species
such as geese, this method would not be practical in other situations, such as with small passerines.
Further, it is fat in the tracheal pit that is most closely associated with variation in overall mass in at
least one passerine (Redfern et al. 2000). Tracheal fat can readily be assessed visually on passerines
in the hand (Brown 1996). Visual fat scores consider the areas in which most fat is stored (Blem
1990) and are both linearly correlated with body mass (Redfern et al. 2000) and a better predictor of
true total body fat than mass-biometric residuals (Conway et al. 1994, but see Merilä & Svensson
1997), although relationships with total body lipid are variable (Brown 1996). They are also a direct
measurement of the most mobilizable form of energy reserves and can be recorded rapidly in the
field once birds are in the hand. The weaknesses with fat scores, such as on the seven-point scale
used in the UK ringing scheme (Coiffait et al 2008), are that they are assessed potentially subject to
observer bias, and that they are measured on an interval scale, with the intervals not necessarily
representing a constant quantity of fat (Brown 1996). Such problems can be minimized by observer
training and checking and by calibration against a restricted sample. A more fundamental restriction
is that fat can be mobilized rapidly, leading to changes in fat score too, so scores reflect
environmental conditions and individual strategies in the recent past rather than, necessarily, future
fitness (Brown 1996).
Visual scoring of muscle deposits has been used less frequently than fat scoring, but is equally easy
to do in respect of pectoral muscles (Gosler 1991). Pectoral muscle is likely to be particularly
important given its role in powered flight and its mass has been shown to be correlated with the lean
mass of the whole body in three unrelated passerines, independent of the influence of fat stores
(Newton 1993, Redfern et al. 2000). Comparison of pectoral muscle scores with indices of moult
status has shown correlations suggesting that both may reflect past protein stress (Gosler 1991).
3
All the above approaches to measuring quantities of soft tissues are intended to reflect current body
condition, but indicators of condition in the past can also be of interest, both because such indicators
could reflect aspects of individual quality that are relevant throughout life and because influences
on structural development could have long-lasting effects. Two aspects of bird physiology that form
such indicators are feather quality, often assessed by pigmentation or the width of growth bars in
the feathers (Grubb1989, 1992, 1995), and the symmetry of bilateral structures such as matched
feathers, reflecting developmental stability (e.g. Swaddle & Witter 1994, Polo & Carrascal 1999).
Both of these approaches consider health or condition at particular times in the past, when the
structures in question were formed.
The growth of feathers, quantified using rates of change after experimental removal (e.g. Grubb &
Cimprich 1990) or lengths before and after moult (Harper 1999), has been shown to be related to
other measures of physiological condition and to environmental variation. However, the utility of
measures such as rates of feather re-growth as indicators of condition in practice is limited because
they only apply to the period over which the growth occurred: the impact of short-term
physiological stresses might have passed and become irrelevant before the measure is taken. The
principal relevance of such measures would therefore lie in any longer-term effects on fitness that
occur (e.g. Hinsley et al. 2003). This is particularly problematical for indices such as Harper’s
(1999) before-and-after-moult comparison, which make use of naturally ongoing processes instead
of experiments, so may not encompass periods of the year when conditions are critical for
determining fitness. Such periods might be particularly unlikely to co-occur with moult both
because of the degree of random chance associated with assessing condition based on one short
period in the year and because birds are probably unlikely to evolve to moult when resources are
scarce. Moreover, all such measures could be limited, in practice, by the need to minimize
measurement error in feather lengths that may differ by millimetres at the most (especially if
multiple observers are involved) and by the need for multiple captures of individuals, which are
often difficult to obtain in free-living populations, both because of movements and trap-shyness.
Alternative approaches to measuring plumage quality that require only single captures of
individuals are provided by “ptilochronology” (Grubb 1989) and measurements of fluctuating
asymmetry. Visible bars in feathers show 24-hour cycles of feather growth and the width of the bars
reflect nutritional status while the feather is growing (Grubb 1989, 1992, 1995). Experiments with
caged birds on controlled diets have suggested that the assumptions about relationships between
feather growth and diet do not hold (Murphy & King 1991, Murphy 1992), but the apparent
problems have been refuted (Grubb 1992). Overall, while other influences can affect final feather
length, bar width is a reliable indicator of general nutritional status (Grubb 1995). However, the
details of relationships with nutritional factors and whether these factors are correlated with survival
or fecundity are still the subject of active research (Grubb 1995).
Asymmetry in plumage features can be indicative of environmental or physiological stresses during
feather growth because growth is bilaterally simultaneous and because asymmetry is likely to be
energetically costly, especially in feathers used in flight (Møller & Swaddle 1997). This means that
birds are likely to try to minimize asymmetry most in feathers (and other structures) that most
influence their efficiency of movement and therefore of foraging (e.g. Polo & Carrascal 1999).
Features (such as ornaments) that influence mate choice directly might also be prioritized. These are
the characteristics in which asymmetry is most likely to reflect unavoidable stresses on birds during
feather growth and which are therefore likely to be the best indicators of condition at that time.
Effects on feathers important for flight, such as primaries and tail feathers, especially in aerial
feeders, could therefore also be important determinants of future condition, until the feathers
concerned are moulted.
4
Observations of fluctuating asymmetry have led to a huge range of studies in fields like sexual
selection, heredity and development (review in Møller & Swaddle 1997), but in the context of
measuring body condition, it is relevant as an analogous index to one based on ptilochronology. A
key difference, however, could be that feather growth bars merely reflect past stresses and are not
relevant to present fitness, whereas asymmetry also potentially drives present foraging efficiency
(and, potentially, competitive ability/dominance). Thus, individuals with less asymmetry have
higher fat scores than those with more asymmetry, perhaps because they are better foragers
(Swaddle & Witter 1994, Prentice et al. 2008). Note, however, that the evidence on fat levels would
suggest that such a pattern would only occur where food was very scarce or unpredictable (see
below), so it may not be a general result: one of these studies measured fat scores during moult
(Swaddle & Witter 1994) and the other on migration (Prentice et al. 2008). Interestingly, Prentice et
al. also found that the occurrence of fault bars in feathers was not related to the observed degree of
asymmetry, suggesting that (at least) two forms of developmental stress during feather development
can be detected and that these have different influences on present fitness. As with growth bars, the
latter is probably not surprising because feather fault bars probably have less impact on flight
performance than asymmetric wings or tails.
In general, characteristics of feathers can provide useful information about body condition while the
feathers were being grown and can have knock-on effects on the present quality of individual, so
could be considered as aspect of condition per se. Fluctuating asymmetry and ptilochronology could
be particularly useful because the bird effectively tells the observer about its own condition, rather
than requiring interpretation of variation in observed fat, muscle or weight. However, all indices
based on ptilochronology or fluctuating asymmetry, like the approaches based on moult, at least
mostly consider physiological features during the period of the year during which feathers are
grown, unless feathers are removed to induce re-growth, which then necessitates recapture, limiting
applications to the measuring condition in free-living birds.
Determinants of condition
Which factors determine condition clearly depend, first, on the definition of condition that is
adopted. We consider four key alternatives independently.
a) Fat
Lipid stores form the major energy reserve for birds and a buffer against variation in food
availability, effectively smoothing out fluctuations in food uptake (Newton 1972, Blem 1990).
During migration, fat stores generally form birds’ principal fuel reserves: weighing up to 27% of
lean body mass in a medium-distance migrant (robin: Lind et al. 1999) and even exceeding 100% of
lean mass in long-distance migrants crossing large barriers (Kullberg et al. 2000). This represents
the upper extreme of the weight/fat load spectrum found in nature.
Supplementing food availability for pheasants allows them to maintain high fat levels into the
spring, when seed food is scarcest (Draycott 2002), black-capped chickadees fed in a winter field
experiment were heavier than control birds (Brittingham & Temple 1988) and the passerine
communities at fed sites in a North American experiment tended to be slightly, but significantly,
fatter than those at control sites (Rogers & Heath-Coss 2003). However, although constraints on
resource availability must restrict the deposition and maintenance of fat reserves in some
circumstances, field and aviary research indicates that fat levels tend to vary according to strategies
adopted by birds in different social or environmental situations rather than with absolute food
availability: there is no simple, consistent relationship between food availability and fat deposits.
For example, two species of quail respond differently to experimental increases in food quality, one
5
putting on more fat and the other instead reducing liver size, reflecting physiological plasticity in
the species concerned that facilitates energy extraction from poorer food (Leif & Smith 1993).
Previous research has considered a range of potential influences on extra-migratory fat stores.
Because they are most easily mobilizable, their disappearance, in combination with very low total
body weight, is likely to be an early sign of poor physical health. Clearly, poor health could be
caused by a great many factors, one of which is exposure to toxins such as selenium, realistic levels
of which in food have been shown to have direct effects on fat stores in a raptor (Yamamoto &
Santolo 2000). Health (and other) influences on fat levels may be superimposed on individual
differences in propensity to lay down or to retain fat due to genetic and developmental factors. Food
intake in nestlings depends on what foraging parents bring back and, therefore, on factors such as
breeding territory quality and weather. The rearing environment is, therefore, a major influence on
nestling condition (e.g. Ardia 2005), but its effects probably do not persist long into later life.
Hochachka & Smith (1991), in common with other studies they reviewed, found that song sparrow
survival probabilities subsequent to independence were independent of the condition in which they
had left the nest. Merilä & Svensson (1997) found that nestling condition in blue tits was correlated
with that on their first migration four months later, although there were also other influences and the
effect varied in size with sex. Given the range of potential influences on condition, it seems unlikely
that such effects would persist for much longer, unless they actually represent genetic factors, such
as poor parental foraging ability being inherited by offspring. Considering the nestling phase itself,
Ardia (2005) found that genetic factors were less important than environmental ones for starlings,
and Merilä et al. (2001) showed that, although there was a significant genetic component in
condition for pied flycatcher, there was also a large non-genetic contribution. Gosler & Harper
(2000) examined the heritability of adult condition in a long-term study of a great tit population and
found significant, but low, heritability of breeding season fat levels, although reviewing evidence
from other studies revealed that laboratory experiments on poultry and gamebirds have shown
higher heritability. Gosler & Harper (op. cit.) considered that the effect for great tits probably
reflects parental (mostly maternal) effects during development or the inheritance of disease
resistance, etc., but that a different mechanism may underlie the pattern seen in gamebirds. Overall,
genetic and developmental effects will probably always be confounded to some extent because
parental quality could both affect development and be heritable, but the evidence suggests that
either type of effect is likely to be small in comparison to other influences on fat stores, especially
after the first winter. Even if there are long-term effects of genetics on body condition, it is certain
that variation from many other potential sources will be superimposed upon them: genetics are
highly unlikely to explain everything.
The predictability of food resource availability, and various factors that tend to make food supplies
unpredictable for particular individuals, have been implicated as controlling factors for fat stores in
several studies. Theoretical models predict that birds should be fatter when food is less predictable,
to hedge against periods of food shortage, which also means, paradoxically, that birds might be
expected to be fattest when starvation is most likely (Lima 1986, Houston & McNamara 1993).
Modelling also suggests that variability in the demand for food, in general, has the same effect as
variability in supply: more fat is predicted to be stored, as an insurance policy, where day-to-day
food requirements are less predictable (Bednekoff & Houston 1994). Effects of resource
predictability have been shown by analyses comparing guilds of ground foragers (which should be
affected by snow cover restricting foraging) and tree foragers (which should not) in regions with
different frequencies of snow cover: with mild weather, birds in both guilds tend to have little fat,
while ground foragers alone are fatter in colder climates where the availability of their food
becomes unpredictable (Rogers 1987, Rogers & Smith 1993). Hake (1996) found experimental
support for the hypothesis using captive greenfinches and Gosler (1996) found a similar pattern
with wild great tits. In contrast, Cucco et al. (2002) found the opposite pattern for magpies, perhaps
because larger, more generalist species are intrinsically buffered against variation in a particular
6
food resource. Supplementary feeding increases the predictability of food supplies as well as their
availability and Rogers & Heath-Coss (2003) attributed the increased fat levels that they found at
fed sites to the specific nature of the unpredictability to which birds in their study area were subject.
In warmer conditions (usually above freezing), foraging tends to be interrupted by within-day
events, such as disturbance or heavy rain, while in colder conditions, as found where Rogers &
Heath-Coss worked, higher probabilities of snow and freezing conditions mean that birds may be
adapted to interruptions on a day-to-day scale (i.e. longer periods without foraging). Brittingham &
Temple (1988) attributed their result that experimentally fed birds were heavier to a response to
increased potential foraging rates, as predicted by Lima (1986), although the result also seems to
contradict Lima’s prediction about effects of predictability. Karpouzos et al. (2005) reduced food
availability for captive birds and found that fat levels increased, but that they fell again after three
weeks on the new food regime, suggesting that predictability was a more important influence than
availability per se. It may also be important that different climatic conditions are likely to provide
different baseline foraging requirements: a given pattern of interruptions to foraging may be more
serious and necessitate greater resource storage levels where the climate is colder. In addition, in
any given ecological context, it is presumably how birds perceive the predictability of resource
availability that is key, rather than that predictability per se. Effects of particular environmental
conditions, especially those that are outside the normal experience of study species, such as
supplementary feeding, could well therefore reflect how birds perceive them more than how they
actually change the resource base.
Winter is likely to be a time of particular nutritional stress for many species because of low
temperatures and increased costs of thermoregulation (Lima 1986), thus affecting fat storage and
weight (Evans 1969, Lahti 1988). However, fat storage regimes can also change simply because
days (and therefore the time available for foraging) are shorter, and because there is a longer period
of night through which reserves accumulated during the day have to last (Newton 1972). Thus, Polo
et al. (2007) found that coal tits from different latitudes had different fattening responses with
respect to daylength, but that these differences disappeared when birds from the two populations
were subjected to similar photoperiods. This tends to lead to a daily pattern of fattening, in which
reserves are often at their highest just prior to roosting (e.g. Newton 1972, McNamara et al. 1994,
Lahti 1998, Koivula et al. 2002, Polo et al. 2007), which may be superimposed on any other
influences on fat levels. Estimation of the metabolic value of fat reserves in yellowhammers in
winter roosts has suggested that they have sufficient fat reserves to maintain them through the night,
but not through the whole of the next day (Evans 1969). An increased metabolic cost at lower
ambient temperatures in winter has been taken as a basic assumption in some modelling studies
(e.g. Bednekoff & Houston 1994). In many climates, temperatures can vary considerably from day
to day, so rapid responses to such fluctuations are likely to be beneficial. However, bird physiology
may not allow such a strategy in practice. Evans (1969) analysed yellowhammer carcasses
“collected” at different times during the winter and found that fat levels were correlated with longterm mean temperature, the likely proximate control being a cue from daylength. However, the
shortest days were not the coldest, so the effect was not confounded with responses to the time
available for foraging and the mechanism was not a simple response to the latter. Other studies have
found evidence for increased fat deposition with experimental reductions in daylength, however
(e.g. Karpouzos et al 2005). Peach et al. (1992) found that fat levels in roosting starlings were also
correlated with long-term variation in temperature, but that daily fluctuations had an additional
influence. Rogers (1995) reviewed the evidence on this issue: his own field data and an aviary
experiment on dark-eyed junco and song sparrow suggested a response to proximate temperature,
but there was also strong evidence from other species for endogenous mechanisms, suggesting
evolutionary adaptation to average seasonal changes in temperature. He concluded that some
species predict variation in temperature, some respond to short-term fluctuations and some do both,
which perhaps reflects evolutionary responses to the predictability of each species’ environment.
7
Social factors can be a further key influence on resource predictability, because dominant
individuals may be able to able to control their access to limited food resources at the expense of
subordinates, for whom food supplies are therefore less predictable. This would suggest that
dominant individuals should carry less fat and such a pattern has been found in great tits (Gosler
1996), as well as mixed flocks of dominant crested tits and subordinate willow tits, wherein both
inter- and intraspecific differences in fat are consistent with the expected effects of dominance and
removal of crested tits induces increased fat levels in the subordinate species (Krams 1998). Hake
(1996) found that subordinate greenfinches carried more fat than dominants, but the latter
responded to cold weather by increasing their fat stores (subordinates may be unable to do so in
such conditions), while only subordinates gained fat (from a lower baseline) in response to
experimentally increased unpredictability. Social status, therefore, modulates responses to
environmental variation. Aviary experiments on starlings in groups of three have confirmed that
dominance is inversely related to fat levels and that subordinates reduce their fat stores when
dominants are removed (Witter & Swaddle 1995). However, although one experiment that
increased the level of intra-group competition by further restricting food access and using groups of
four showed still larger effects on fat stores, a second found evidence for more complex effects,
whereby both the most dominant and most subordinate birds were relatively fat (Witter & Swaddle
1995). This suggests that there is no simple, monotonic relationship between dominance status and
optimal fat storage levels and, accordingly, other studies have revealed different patterns. Rogers &
Heath-Coss (2003) found no effects of the suite of more dominant species present in assemblages at
fed and control sites on patterns of fat storage in a supplementary feeding field experiment. In
partially migratory blackbirds, Lundberg (1985) found that young and female birds were fatter in
autumn, in line with dominance status, which also determines migratory strategy. This difference is,
therefore, potentially confounded with migratory fattening. In mid-winter, moreover, adults tended
to be fatter than the juveniles that had not migrated, perhaps reflecting the greater competitive
ability of adults allowing them to maintain fat as insurance against poor conditions (Lundberg
1985). Birds resident in wintering areas, which are generally more dominant individuals, have also
been shown to be fatter than transient ones in both white-throated sparrows (Piper & Wiley 1990)
and siskins (Senar et al. 1992). Further, wintering subordinate American redstarts, when “upgraded”
to better winter territories by the removal of dominants, maintained higher weights through the
winter than controls, left the wintering grounds earlier and were more likely to return the following
year (Studds & Marra 2005), suggesting a direct, positive relationship between fat levels and
fitness.
Taken together, the evidence for effects of social status suggests that a complex range of patterns
exist. It is possible that these patterns reflect differences in strategy at the population or species
level, but it may also be that there is a general pattern in which social status affects fat storage in
different ways given different balances between food demand and supply, as suggested by Hake’s
(1996) results. Differences in foraging ability are also expected to affect fat levels, because better
foragers are likely to be able to maintain lower weights under a given level of food availability
(Lima 1986), a pattern that is supported by Cresswell’s (2003) results for blackbird. Significant
regional variation in fat levels was found by Piper & Wiley (1990), alongside the effect of
dominance that they found, suggesting a role for variation in local conditions. Indeed, there must be
a level of resource availability where sheer shortages have more effect than unpredictability per se,
such that it is better for dominants to use their competitive advantages to maintain higher fat levels
if possible. This could explain Rogers & Heath-Coss’ (2003) result that individuals of multiple
species in areas with supplementary feeding tended to be slightly fatter than birds in control areas.
Without independent data on food predictability, this means that a plausible story about
predictability of resources could potentially be used to explain any observed variation in fat (Witter
& Swaddle 1995, Cuthill & Houston 1997). An operational determination of whether fatter should
be considered to be better may, however, be suggested by whether dominants carry less or more fat:
the individuals with a choice would be expected to adopt the most optimal strategy.
8
A further factor that can might be expected to elicit dual responses in terms of fattening strategies is
predation risk, because heavier birds (i.e. those carrying more fat) may pay a cost in terms of
reduced manoeuvrability (e.g. Houston & McNamara 1993) but also benefit from being able to
avoid feeding when predators are present, i.e. being less susceptible to starvation risk due to
unpredictable interruptions in foraging from predator attacks (e.g. Cuthill & Houston 1997). It is
also possible that the highest quality individuals can store more fat and still be able to escape
predators better (Cuthill & Houston 1997). Considering the costs of fattening early in the day in
terms of increasing predation risk, along with the benefits in terms of insurance against fluctuating
and unpredictable food availability, dynamic modelling analyses have returned patterns of morningbiased or bimodal (morning and evening) foraging and fattening that match those commonly
observed in nature (McNamara et al. 1994). This is supported by aviary experiments showing that,
considering only the range of natural, daily variation in weight, unalarmed flights by heavier birds
are slower (Veasey et al. 1998, Metcalfe & Ure 1995) and manoeuvrability is reduced (Metcalfe &
Ure 1995). Further, migratory fuel loads affect escape flights, with birds trading off the angle of
ascent against velocity in response to increases in weight caused by carrying up to 50-70% of their
lean body mass in fat (Lind et al. 1999, Kullberg et al. 1996, 2000, Witter & Cuthill 1993, Witter et
al. 1994). There is also some evidence that heavier birds are less likely to flush from cover (Witter
et al. 1994), perhaps indicating a change in anti-predator strategy as weight increases. Different
species in different contexts seem to be able to trade off speed against angle of take-off. Fatter
sedge warblers sacrificed speed when “attacked” by an aerial predator stimulus (Kullberg et al.
2000), starlings with added artificial weights on their backs sacrificed the angle of ascent and those
deprived of food to reduce body mass increased it (Witter et al. 1994), while both parameters were
reduced more in fatter blackcaps, although the angle of ascent effect was stronger (Kullberg et al.
1996). The angle of predator attack is one key influence on take-off angle (Kullberg et al. 1998) and
it seems likely that the type of attack that occurs (or is perceived to represent the greatest risk) and
the nature of the cover that escape aims to reach will affect the trade-off, so exactly how variation in
weight affects predation risk may depend on the forms of attack that are common in specific
contexts.
A caveat to the above evidence for direct costs of carrying fat on flight performance, and therefore
presumably on risks of predation, is that there is little evidence for an effect on escape flights of
variations in weight within natural ranges amongst non-migrating birds and, more specifically,
within the variation undergone by individuals (Van der Veen & Lindstrøm 2000). Several studies
have looked for such effects, using a range of species and a combination of field and aviary
experiments, but have failed to find them (Veasey et al. 1998, Van der Veen & Lindstrøm 2000,
Macleod 2006, Kullberg et al. 1998, Kullberg 1998). Only Metcalfe & Ure (1995) found some
evidence for a weak effect in captive zebra finches, but their sample size was small, and the results
of Kullberg et al. (1996) for migratory blackcaps suggest that effects occur with small fuel loads
(proportionally smaller than those found with high fat), but that these would be hard to detect
without the large loads also found in the same dataset. Further, Macleod (2006) considered the
physics underlying flight with variable wing-loadings and concluded that the effects expected
would be small and likely to be hard to detect with the experimental sample sizes possible in
practice. Overall, this suggests that the order of variation in fat levels found within and among
individual birds (outside periods of migration, if applicable) is unlikely to influence the risk of
predation greatly via effects on escape flights per se. Birds are probably either not much affected by
such variation or well able to compensate physiologically, at least during brief, isolated escape
events. Effects like those seen with migratory fat loads could still occur given comparison between,
for example, winter and spring weights, but the real cost, in terms of predation risk, of day-to-day
fattening is perhaps the increased exposure associated with feeding (Van der Veen & Lindstrøm
2000).
9
Lima (1986) formulated the “mass-dependent predation hypothesis”, which holds that optimal body
mass should fall as predation risk rises, as well as increasing with resource unpredictability, falling
food intake rate and falling temperature. However, Bednekoff & Houston (1994) found no effect of
varying predation risk per unit food on strong relationships between food
requirements/predictability and fat reserves in a modelling exercise, and Witter et al. (1994) found
increased fat levels in aviaries with increased cover, probably because costs of being fat are lower
where predation risk is reduced. Further, MacLeod & Gosler (2006) found that great tits increased
their weight after capture events, which are probably perceived similarly to predator attacks, so
seem to respond to the foraging interruption aspect of predator encounters rather than a need to
facilitate future escapes by losing weight. Lilliendahl (1998) and Van der Veen (1999) obtained
similar results in experiments attempting to manipulate perceived predation risk using brief
exposure to model predators: birds subsequently fed more, apparently responding to an interruption
to their foraging and hedging against further such interruptions later in the day. Conversely, Gentle
& Gosler (2001) found that wild great tits exposed to elevated apparent risk at feeding stations (via
simulated accipiter attacks) maintained lower fat levels than control birds and Lilliendahl (1997)
recorded lower weights in captive greenfinches when both exposed to a predator-stimulus and when
handled repeatedly. The differences between these sets of results could reflect the difficulty in
mimicking appropriate environmental variations or cues to manipulate perceived predation risk.
Indeed, in different contexts, and with different “background” levels of predator exposure, the best
response to a predator attack could vary between optimizing escape ability and merely
compensating for interruptions in foraging. Presumably, factors such as the frequency of predator
attack, the type of predator that is perceived to be the source of danger, the proximity and quality of
cover and the local flock size will affect the risk of attack that an individual perceives and also,
therefore, the best physiological response to changes in that risk. For example, Koivula et al. (2002)
found that willow tits given supplementary food were fatter than those in control areas and
proposed that this pattern reflects a release from predation pressure from mustelids and owls where
feeders were present: a different fattening pattern might have been observed if aerial predators like
sparrowhawks were present, because this species tends to be attracted to foraging flocks around
feeding stations. An experiment to investigate the variation in responses to predators might begin by
establishing conditions favouring high fat levels, such as low and unpredictable food supplies, and
subsequently apply experimental treatments with different levels of predator exposure (separately
using both aerial and terrestrial threats) and availabilities of cover. Care would have to be taken
that effective predation risk stimuli were used: some studies may have failed to find effects because
stimuli such as perched raptors or predator calls are not perceived as a real threat by subject birds.
Considering all the potential influences on fat storage discussed above, the sum of the evidence
seems to indicate that birds that need more fat to buffer against environmental extremes or
unpredictability (which may be mediated by social factors) then store more fat, if absolute food
availability, health and, perhaps, genetic quality, permit. During migration, far larger fat reserves
are laid down for short periods. Variation in the risk of predation seems often to act as a cause of
unpredictability or interruptions in foraging rather than as a direct influence determining fat levels
via costs of flight, but there is evidence that the latter type of effect occurs where exposure to aerial
predators is frequent. Gosler (1996) used long-term great tit data to investigate the various
predictions of the hypothesis that fat storage (i.e. visual fat scores) depends on variability in
resource availability as well as environmental factors, namely that fat should increase with falling
temperature, the rate of diurnal fattening should fall when food is abundant, fat should increase
when food is scarce and subordinates should be fatter than dominants. All these predictions were
supported by field data. In addition, winter fat did not predict over-winter survival when food was
abundant, suggesting that birds maintained their optimal fat levels when they were able to do so;
however, when food was scarce, while there was still no relationship for dominant birds, fatter
subordinates survived better than thinner ones.
10
b) Protein/muscle
Muscle tissue, as an energy store and an indicator of condition, has received much less research
attention than fat. Variation in muscle also has the potential to be more complex because it could be
influenced by most of the same factors, such as food availability and costs of increased body
weight, but it is also important in driving body movements, including flight. Thus, a bird with welldeveloped pectoral muscle (the muscle tissue that is most often scored) could be viewed as having
relatively large resource stores, as might be expected of a sub-ordinate individual or a migrant, or as
physically fit and a strong flyer, as might be expected of a dominant.
The genetic, heritable component of variation in muscle in great tits has been shown to be higher
than that of fat, but still lower than the value for body size, probably again reflecting a combination
of maternal effects and inheritance of factors associated with good health, such as resistance to
disease (Gosler & Harper 2000). Merilä et al. (2001) did not discriminate between fat and muscle in
determining that variation in the non-skeletal component of body size was, in part, determined
genetically, but it seems likely that the more important component of this variation was that in the
less mobilizable and more functional tissue, i.e. muscle.
Considering other influences on pectoral muscle, Harper (1999) found evidence of a dependence on
health: wild woodland birds with heavier mite loads had lower muscle scores. Latta & Faaborg
(2002) and Carrascal et al. (1998) both considered the influence of habitat quality. Latta & Faaborg
compared preferred and secondary habitats for Cape May warblers, defining the former on the basis
of the presence of more dominant individuals for longer through the winter: body mass was higher
in this habitat and pectoral muscle scores increased with time (as opposed to declining in secondary
habitat). Carrascal et al. compared great tits in warmer and colder areas, the latter seeming to be
poorer because birds there spent longer at feeders and took part in more competitive interactions,
and found, conversely, that birds from the colder area tended to have larger muscle stores. They
also found that dominant birds tended to have higher pectoral muscle scores. Taken together, these
studies suggest that variation in muscle is subject to rather different controls to that in fat: rather
than a costly deposit that is minimized by dominant birds, it could be an indicator of the strength
that enables them to dominate. It is also possible that both mechanisms operate, but in different
circumstances. The differences in the management of fat and protein reserves might be explained by
one being a readily mobilizable and rapidly deposited reserve and the other only a reserve in the
sense that it is metabolized at times of great need: at other times, it may be maintained at an
adequate level and built up further if food availability allows. This is supported by Heath & Dufty’s
(1998) finding that birds (juvenile American kestrels) in poorer condition, having been maintained
on a poorer diet, respond more slowly to stress, which may reflect the difference between
mobilizing fat and protein reserves to provide energy.
c) Feathers and fluctuating asymmetry
Feathers are subject to continual wear and birds must replace them, often using a more-or-less fixed
annual cycle of replacement. The production of new feathers must entail a cost in energy and
resources, both directly and, perhaps, indirectly as a result of impaired flight or thermoregulation.
This means that there is likely to be variation in ease with which birds are able to produce new
plumage, reflecting differences in environmental conditions such as food availability and/or
individual quality. Differences in plumage quality therefore have the potential both to indicate
nutritional condition at the time that the feathers were produced and to drive current differences in
fitness.
At their simplest, such effects have been investigated by measuring specific feathers, such as in
standardized measures of wing length. Harper (1999) found that change in wing length after moult
11
was correlated with mite load and other measures of plumage quality and physical health that vary
over a similar timescale. More general evidence for physiological condition being reflected in
feather growth comes from experiments in which feather growth was induced by removal on
capture and then measured on recapture. Ambient variation in habitat quality, i.e. food availability,
was positively associated with rates of induced feather growth in great tits in habitats varying in
climate and altitude (Carrascal et al. 1998) and also in loggerhead shrikes in sprayed and unsprayed
citrus groves (insecticide spraying being the chief influence on prey abundance: Grubb & Yosef
1994). Feather growth in house sparrows has also been found to be negatively affected by the
experimental introduction of immune challenges (Martin 2005). Experimental winter food
supplementation was also positively associated with the growth rate of induced feathers in a range
of species in North American woodland bird communities (Grubb & Cimprich 1990). These
patterns were most detectable in subordinate age- and sex-classes (Carrascal et al. 1998, Grubb &
Cimprich 1990), suggesting that there is a mediating effect of social dominance status. Further, it
suggests that this measure of plumage quality reflects a measure (like pectoral muscle score) that
birds aim to maximize, so it is likely to be directly related to fitness (unlike, necessarily, fat storage
levels). Whether plumage quality (as reflected by speed of feather growth) is directly related, per se,
to fitness or whether it is simply correlated to other aspects of physiological health that are more
critical is unknown, although it seems unlikely that subtle effects on small numbers of feathers not
critical for flight (or even flight feathers themselves in species that do not feed on the wing) could
have a large influence. Nevertheless, Hinsley et al. (2003) found that plumage quality was affected
by habitat quality via the time of year at which British woodland birds were able to moult in woods
of differing sizes, and that subsequent breeding success and survival were lower in smaller woods.
The plumage quality effect represents a plausible mechanism by which later breeding in the poorer
habitats affects demographic rates up to a year later (Hinsley et al. 2003, Ferns & Hinsley 2008).
d) Behavioural effects
Birds in poor physiological condition (and those identifying environmental stresses that could
induce it) are likely to work to try to improve their status by altering their behaviour. Increases in
the occurrence of behaviours involved with foraging or decreases in that of non-essential
behaviours may also be associated with poor condition. In principle, these behavioural changes
could provide indices of body condition and perhaps provide a more complete guide to condition as
perceived by the focal bird than physical or physiological measures. Examples of such indices
might include singing rates (Berg et al. 2005) or time devoted to vigilance (Enoksson 1990).
Demonstrating relevant effects requires measuring environmental variation (or controlling it
experimentally), as well as measuring behaviour, however, and such effects are quite likely to vary
with species and context. This means that more direct measures of condition will be investigated in
the process of identifying behavioural indices and that the later might still not be applicable in many
contexts. This may explain why only a few studies have examined relationships between
environmental variations, condition and behaviour. Berg et al. (2005) found that supplementary
feeding allowed behaviour to be more flexible in the Australian reed warbler, which was reflected
in increased rates of song production, but Enoksson (1990) found no effect of feeding on time
budgets in nuthatches. In general, physiological condition seems certain to be associated with
changes in behaviour in most situations, but condition would most often probably better be
measured directly rather than via behavioural indices.
Relationships between body condition and fitness (survival)
A range of studies have investigated various ways in which aspects of body condition relate to
survival. Brittingham & Temple (1988)’s supplementary feeding experiment with black-capped
chickadees demonstrated both increases in body weight (i.e. a response to feeding in terms of body
12
condition, probably reflecting fat reserves) and increases in monthly survival rates (despite
suggestions of increased predation rates at feeders), suggesting that there was a causal link. While
various other studies have also demonstrated significant increases in survival or abundance with
increased food availability, using either natural variation or experimental feeding (e.g. Siriwardena
et al. 2007, van Balen 1980, Desrochers et al. 1988, Källander 1981, Smith et al. 1980, Lahti 1998,
Lahti et al. 1998, Jansson et al. 1981), there is little other evidence linking measures of condition in
terms of fat reserves or weight to such increases. Altwegg et al. (2000) studied dispersal of house
sparrows between Norwegian islands: dispersing individuals had higher survival rates but there was
no relationship between dispersal probability and body condition (measured as residuals from a
function relating mass and body size). Thompson et al. (2003) found that nestling weight and
survival were not correlated in starlings, but were correlated for house wrens. However, the
heaviest, best-surviving birds were the largest, not the fattest, indicating that social dominance was
more closely related to survival than fat reserves. Similarly, Piper & Wiley (1990) found that only
social dominance of a range of potential influences, including fat score, was a significant predictor
of return rates in white-throated sparrows. Miller et al. (2003) also found no effect of fat score on
blackbird survival and Schmidt & Wolff (1985) found no firm evidence of an effect of weight on
great tit survival in a supplementary feeding experiment, although the analysis used could have
been more sensitive (Brittingham & Temple 1988). Indeed, several of these studies did not use such
sophisticated analyses of survival probabilities as are available now, so it is possible that some
subtle effects or relationships requiring modelling with complex control terms were missed in some
cases. However, it is likely that, if effects of condition on survival had been strong and clear in
general, they would have been detected in more of these studies than has been the case.
Another study that found no clear relationship between a simple measure of survival over winter
(present or not in spring after capture in autumn) and weight corrected for size considered dippers in
Scotland (Newton 1993). Larger body size was again associated with a higher survival probability
in females, but surviving males had more pectoral muscle, suggesting that this measure might be a
good predictor of fitness. Newton’s result involved just one sex of one species at one study site in
one year, however, and no other studies have considered pectoral muscle with respect to survival
rates, so conclusions about the generality of the pattern cannot yet be drawn.
Measures of condition in the sense of physiological health should, intuitively, be strongly related to
survival and, in the case of indicators of extreme morbidity, this is trivial. Møller & Saino (2004)
reviewed the evidence for more subtle associations between non-specific immune responses and
bird survival, conducting a meta-analysis of published results, and found good evidence for a
positive relationship. Further evidence comes from a study of indirect effects of helminth infection:
Millan et al. (2002) found that released pheasants that were predated (in a context of extremely high
predation rates: at least 55% within three days) had higher nematode parasite loads than birds that
died by other causes. Møller & Erritzøe (2000) compared various physiological and physical
variables between birds of a range of species killed by cats and those killed in other ways and found
that the samples differed very significantly in spleen size, showing that predated birds had poorer
immune systems (and were marginally more likely to be juveniles), but that there were no such
relationships with weight, size, fat score, sex or liver weight for most species. Relationships may
not always be so neat, however: Brown et al. (2006) measured feather mite loads on cliff swallows
and found positive associations with survival. They concluded that mites are beneficial because they
consume old feather oil, pollen, fungi and bacteria or compete for resources with fungi and bacteria,
so are mutualistic with the bird rather than parasitic on it.
Conclusions: relationships between (winter) food, condition and survival
13
Bird body condition can be defined and measured in a wide variety of ways. Although it is feasible
that feather growth or physiological health could be affected by supplementary feeding, the effects
most likely to occur and to be readily detectable involve reserves held in soft tissues, i.e. fat and
muscle. Other aspects of condition such as indices of plumage quality do not vary over periods
suitable for assessing effects of winter food. Variations in fat scores and in weight standardized for
structural size (which are likely mostly to reflect changes in fat reserves) could reflect a wide range
of situations in terms of food availability and predictability, social status, weather conditions and
predation pressure. This makes fat scores and weights difficult to interpret in terms of what they
mean for fitness and survival prospects in any given context without complete information about the
environmental influences perceived by the individual birds being studied. For example, studies of
predictability have often used rather simple systems, such as effects of snow cover on ground
foragers (Rogers & Smith 1993), which are quite different to the temperate farmland context, where
habitats like seed-rich fields may be predictable from day to day, but could disappear at any time
through ploughing. It is unknown how birds of different species and with different patterns of
movement and social organization perceive this sort of environment in terms of resource
availability and predictability. More generally, it would seem possible to explain any observed
pattern of variation in fat or weight with respect to food supply in terms of current theory by
presuming that particular patterns of social status and resource predictability, for example, prevail
in the sample of birds considered. In other words, fed birds could be found to be thinner or fatter
than control birds and either possibility could be taken to show positive effects of food provision on
fitness: the evidence suggests that birds will have higher fitness in some circumstances (e.g. given
low dominance, unpredictable food or low predation pressure) if they are fatter and higher fitness in
other circumstances if they are thinner (e.g. high food availability, ability to outcompete the other
birds present, early in the day or warm weather). Thus, it may not be particularly significant that
evidence of clear relationships between weight or fat content and fitness is rare. It may be safest
simply to assume that any significant effect of food on condition could have fitness benefits: only if
the birds responded non-adaptively would this conclusion be wrong.
Variation in muscle scores or content has received less research attention but could potentially be
more reliable as a guide to likely survival probabilities. Muscle will only be metabolized as a last
resort and will be built up when resources allow, and it may also be directly related to flying ability
or strength (i.e. dominance). Correspondingly, in at least one study, a pattern for dominants to have
more pectoral muscle stores contrasts with reported fat storage pattern whereby subordinates store
more (Carrascal et al. 1998). Measuring muscle content as well as fat content and/or standardized
weight would be recommended in studies of condition, for example as a potential response to
supplementary food. Unlike fat, however, it could be confidently predicted that increases in muscle
content would reflect increases in fitness if they occurred.
14