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# Oxidative state is associated with migration distance, but not traits linked to flight energetics

Péter László Pap, Orsolya Vincze, Csongor Vágási

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Péter László Pap, Orsolya Vincze, Csongor Vágási. Oxidative state is associated with migration distance, but not traits linked to flight energetics. Journal of Avian Biology, 2024, 2025 (2), 10.1111/jav.03325. hal-05314555

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Distributed under a Creative Commons Attribution 4.0 International License
JOURNAL OF

AVIAN BIOLOGY

Research article

Oxidative state is associated with migration distance, but not
traits linked to flight energetics

Péter László Pap✉<fnref n="1" />, Orsolya Vincze<fnref n="1" />,<fnref n="2" />,<fnref n="3" /> and Csongor I. Vágási✉<fnref n="1" />

Correspondence: Péter László Pap (peterlpap@gmail.com) and Csongor I. Vágási (csvagasi@gmail.com)

Journal of Avian Biology

2024: e03325

doi: 10.1111/jav.03325

Subject Editor: Pablo Salmon

Editor-in-Chief: Jan-Åke Nilsson

Accepted 15 August 2024

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Flight can be highly energy demanding, but its efficiency depends largely on flight style, wing shape and wing loading, and a range of morphological and lifestyle adaptations that can modify the cost of sustained flight. Such behavioural and morphological adaptations can also influence the physiological costs associated with migration. For instance, during intense flight and catabolism of reserves, lipid damage induced by pro-oxidants increases, and to keep oxidative physiological homeostasis under control, the antioxidant machinery can be upregulated. Studies on the oxidative physiology of endurance flight have produced contradictory results, making generalization difficult, especially because multispecies studies are missing. Therefore, to explore the oxidative cost of flight and migration, we used samples collected during the breeding season from 113 European bird species and explored the associations of measures of antioxidant capacity (total antioxidant status, uric acid and glutathione concentration) and oxidative damage of lipids (malondialdehyde) with variables reflecting flight energetics (year-round or specifically during migration) using a phylogenetic framework. We found that none of the traits predicting year-round flight energy expenditure (flight style, wing morphology and flight muscle morphology) explained any measures of oxidative state. Our results suggest that birds endure their everyday flight exercise without or with low oxidative cost. However, oxidative damage to lipids and one component of the endogenous antioxidant system (uric acid), measured after the end of spring migration on breeding adult birds, increased with migration distance. Our results suggest that migration could have oxidative consequences that might be carried over to subsequent life-history stages (breeding).

Keywords: antioxidants, birds, flight style, migration, oxidative stress, wing morphology

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<footnote n="1">¹Evolutionary Ecology Group, Hungarian Department of Biology and Ecology, Babeş-Bolyai University, Cluj-Napoca, Romania</footnote>

<footnote n="2">²ImmunoConcEpT, CNRS UMR 5164, University of Bordeaux, Bordeaux, France</footnote>

<footnote n="3">³Centre for Ecological Research-DRI, Debrecen, Hungary</footnote>
Introduction

Flight is one of the most efficient forms of locomotion in
the animal kingdom, allowing birds to wander long dis-
tances and conquer remote areas. Extensive flight during
brood provisioning or endurance flight during migration is
enabled through a set of adaptations, including morphologi-
cal, physiological and behavioural traits aiming to minimize
the costs of flight (Cooper-Mullin and McWilliams 2016,
Vágási et al. 2016, Eikenaar et al. 2018, Hegemann et al.
2019, McWilliams et al. 2021). Despite these adaptations,
flight is still an energetically demanding type of locomo-
tion (Norberg 1996). Indeed, the metabolic rate of flight
exercised by migrating birds increases well above the basal
metabolic rate (Butler et al. 2004). The energetic expenses
of flight might induce oxidative stress by the overproduction
of a variety of reactive oxygen species (ROS) in mitochon-
dria (Halliwell and Gutteridge 2015, Cooper-Mullin and
McWilliams 2016). Yet, whether increased rate of metabo-
lism increases or reduces ROS production is still hotly
debated (Hickey et al. 2012). ROS are neutralized through
a complex antioxidant system, which includes endogenous
enzymatic and non-enzymatic antioxidants produced within
the body, as well as exogenous antioxidants obtained from
the diet (Pamplona and Costantini 2011, Cooper-Mullin
and McWilliams 2016, McWilliams et al. 2021). Oxidative
stress occurs when ROS production overwhelms the antioxi-
dant defence system and causes oxidative damage to vital cell
constituents (DNA, protein and lipid molecules), which ulti-
mately impair cellular functioning, physiological processes
and fitness (Halliwell and Gutteridge 2015, Cooper-Mullin
and McWilliams 2016). Natural selection shapes the ability of
animals to keep oxidation challenges under control, produc-
ing variance across species in levels of antioxidants and oxida-
tive damage (Cohen et al. 2008, 2009, Vágási et al. 2019).
For instance, mitochondria are generally considered as major
sources of ROS (Balaban et al. 2005), but eventually they also
act as antioxidant systems. For instance, greater mitochon-
drial mass in species with high metabolic needs, such as birds,
was suggested to act as a substantial sink for ROS, mitigating
the oxidative cost of flight (Hickey et al. 2012).

Currently available observational and experimental stud-
ies on this topic provide inconsistent conclusions regarding
whether flight exercise or migration induces oxidative stress
in birds. This question was primarily investigated experimen-
tally using birds exposed to flight exercise (Costantini et al.
2008, Dick and Guglielmo 2019, McWilliams et al. 2020,
Frawley et al. 2021), and correlatively by comparing the
physiological status of birds at different stages of migration
(e.g. before migration or after recovery at stopover sites, e.g.
Jenni-Eiermann et al. 2014, Cooper-Mullin et al. 2019).
Moreover, some studies have compared migratory and non-
migratory species or populations regarding their physiology
(Eikenaar et al. 2017, 2022). While certain studies have dem-
onstrated the oxidative costs (damage) of flight in exercised
birds (Costantini et al. 2008, Dick and Guglielmo 2019),
others have shown weak, undetectable or tissue-specific

effects (Cooper-Mullin et al. 2019, Frawley et al. 2021,
Marasco et al. 2021). Similarly, migratory activity has been
sometimes associated in correlational studies with increased
level of oxidative damage despite a parallel upregulation of
the antioxidant levels (Jenni-Eiermann et al. 2014), but lack
of such costs has also been reported (Eikenaar et al. 2017).
The association of general daily flight routine and migratory
flights with the oxidative state is therefore more complex than
previously thought, and drawing a general conclusion might
be difficult owing to species-differences in aerobic lifestyle
and adaptations to flight (Liechti 2006, Bowlin and Wikelski
2008, Hickey et al. 2012).

The physiological costs of flight potentially depend on
flight efficiency and the amount of time spent flying. Flight
efficiency varies with multiple traits, including flight style,
wing morphology (wing loading and aspect ratio), the relative
size of the flight muscles (pectoral-to-supracoracoid muscle
ratio) and body mass. Flight style of birds can be classified as
flapping or gliding/soaring, the former being associated with
higher energy use than the latter (Guigueno et al. 2019). We
may therefore expect that the cost of flight for flapping flyers
(e.g. ducks, geese, cormorants or most songbirds) is higher
than that of soarers and gliders (e.g. storks, herons, rap-
tors, gulls, terns, swifts or swallows). Furthermore, it is well
established that wing shape and wing loading are important
determinants of flight energetics (Hedenström and Alerstam
1997, Kvist et al. 2001, Hedenström 2008). Indeed, long-
distance migrants evolved higher wing aspect ratios and
lower wing loadings, probably through selection for a lower
flight cost (Vágási et al. 2016). Consequently, it is reasonable
to assume that traits of the flight apparatus might explain
variation in the oxidative costs of flight among bird species.
Furthermore, we anticipate that this effect will be observable
throughout the annual cycle of birds, as the flight apparatus
of birds undergoes minimal changes year-round, thereby it
consistently may determine the costs of flight not only dur-
ing migration but during other life-history stages as well (e.g.
breeding). Finally, avian flight is powered by a downstroke
force generated by the pectoral muscle and an upstroke force
generated by the supracoracoid muscle. Some taxa, particu-
larly gliders and soarers, have very high pectoral-to-supracor-
acoid muscle ratios (i.e. disproportionately larger pectoralis
mass), indicating a greater power for generating lift, there-
fore a lower energy expenditure of sustained flight (Deeming
2023). Therefore, we expect that the high pectoral-to-supra-
coracoid muscle ratio is associated with energy saving flight
style and therefore is associated with lower oxidative cost.

Besides the morphology of the flight apparatus, the
amount of time birds spend flying is another key factor influ-
encing the oxidative costs of flight (Costantini et al. 2008).
However, estimating the time spent flying is inherently com-
plex due to variations across seasons, breeding statuses and
annual cycles. Migration, in particular, is a significant endur-
ance challenge, associated with excessive physical activity with
potential long-term physiological consequences that persist
beyond the migratory period. Migration distance is generally
considered as a good proxy for the time and energy invested
in flight across species, because it correlates with the energy expenditure of flight, with species varying from residents to long-distance migrants (Cooper-Mullin and McWilliams 2016). Consequently, it is plausible to assume that the oxidative cost of flight is evolutionarily linked to migratory strategy, and in particular to migration distance.

Comparing a large number of species with variable flight types, flight-associated morphologies and migratory behaviours might bring valuable insight into the association between oxidative physiology and flight or migration at the macroevolutionary scale. Studies at this scale are currently lacking, yet they represent powerful tools to discover evolutionary links between physiological, morphological and behavioural traits. In the present study, we fill this gap using data from 113 European bird species from which we measured three non-enzymatic antioxidant parameters (total antioxidant status (TAS), uric acid (UA) and total glutathione (tGSH)) and the oxidative lipid damage (malondialdehyde (MDA)) in the blood. We used phylogeny-controlled analyses to investigate whether the oxidative physiology of birds is explained by traits associated with flight demand, describing the global, year-round flight energetics and migration strategy. If the energetic expense of flight induces the overproduction of ROS, we can hypothesize that traits associated with higher flight energy demand will be associated with elevated oxidative damage and/or will drive the evolution of more efficient antioxidant defences to prevent potential damages. Alternatively, physiological adaptations of birds to a life with high metabolic demands, including the resistance to oxidative stress, ROS production, metabolic management, the high density of mitochondria functioning as sink for ROS, or the composition of fatty acids of the energy reserve during migration may mitigate the oxidative cost of aerial locomotion across species (Hickey et al. 2012, McWilliams et al. 2020, 2021).

# Material and methods

## Sample collection

We used data from 113 European species, comprising a total of 5068 physiological measurements (1172 for TAS, 1286 for UA, 1254 for tGSH and 1356 for MDA) from blood samples of wild, adult birds captured at various sites across Romania, between 2011 and 2013, and between 2016 and 2019. Sampling was restricted to the breeding season (April–July), to the same geographic region, and was performed using identical field protocols (minimizing handling stress) and uniform laboratory measurements (Vincze et al. 2022). While the life-history stage we focused on does not coincide with migration, we are confident that our data are suitable for testing the oxidative costs of flight for multiple reasons. First, breeding represents one of the most physiologically stressful periods for birds, especially since the feeding of the young elevates considerably the need for foraging, most often increasing the time spent flying. Second, while physiological

traits, including oxidative parameters, can rapidly change according to the environmental conditions or individual state (Eikenaar et al. 2016), they remain considerably consistent within individuals between seasons (Møller et al. 2003, Pap et al. 2014). Sampled birds were confirmed to be in breeding state by observation of breeding behaviour (e.g. feeding), by the presence of a brood patch, especially in females (sometimes in males as well) and by the presence of cloacal protuberances in males.

## Physiological variables and predictors

Oxidative state was assessed by measuring the levels of three non-enzymatic antioxidant markers (TAS and UA in the plasma and tGSH in erythrocytes) and the level of peroxidative damage to membrane lipids (MDA in the plasma). We detailed the protocols of laboratory assays in Bókony et al. (2014). Briefly, TAS level (mmol l⁻¹ Trolox equivalents) was measured from 10 µl plasma spectrophotometrically at 660 nm using a commercial kit (Cayman Chemical, Ann Arbor, MI, USA) according to the method described by Erel (2004), with slight modifications as per Sepp et al. (2010). UA concentration (mg dl⁻¹) was determined spectrophotometrically from 5 µl of plasma by a uricase/peroxidase method (Uric Acid liquicolor kit, Human, Wiesbaden, Germany). MDA concentration (µg ml⁻¹) was determined from 10 µl of plasma by high performance liquid chromatography (HPLC) on an HPLC SUPELCOSILTM LC-18 column (5 µm particle size; SigmaAldrich) with UV detection at 254 nm (Jasco, UV-2075 Plus, Japan). The concentration of tGSH (nmol l⁻¹ mg⁻¹ of erythrocyte pellet) was assayed using a commercial kit (SigmaAldrich, St Louis, MO, USA) and according to Galván and Alonso-Alvarez (2008) and Hórak et al. (2010) with the modifications detailed in Bókony et al. (2014). Duplicate measurements (both within and between plates) of a subset of samples provided highly significant repeatabilities (Bókony et al. 2014).

The values of these oxidative state variables can quickly respond to extrinsic or intrinsic factors (environmental stress or change in condition, respectively) making possible adjustments to maximize fitness. Despite this temporal lability of these traits, it has been previously demonstrated that species-specific mean values are representative to the species because values of conspecifics resemble each other significantly more than members of other species as quantified by repeatability (Vincze et al. 2022). Thus, individual differences due to capture date, capture time or breeding phase are negligible compared to the wide cross-species differences in oxidative state markers. The level and significance of repeatabilities fulfils the species-specificity prerequisite of phylogenetic comparative studies, ensuring that macroevolutionary relationships among traits can be detected despite the within-species noise.

Species were classified regarding their flight type according to Pennycuick (2008) and Bruderer et al. (2010) with small modifications suggested by Pap et al. (2015): flapping-soaring, flapping-gliding, continuous flapping and passerine-type flight. Because of the limited sample size for
the flapping-soaring and flapping-gliding categories, we
merged these two groups. Wing morphology was described
by wing loading (kg m⁻²) and wing aspect ratio (dimen-
sionless), which were calculated from photographs of fully
stretched wings using ImageJ (http://rsbweb.nih.gov/ij/;
see Vágási et al. 2016 for details). Wing loading was calcu-
lated by dividing average body mass (kg) by wing area (m²),
while wing aspect ratio was estimated as squared wingspan
divided by wing area (both m²). Wingspan and wing area
were measured excluding the body between the wings, but
these measurements are strongly positively correlated with
those that include the body (Vágási et al. 2016). Data on the
mass of pectoralis major and the supracoracoid muscles were
extracted from Vágási et al. (2016). Migration distance was
calculated using the distribution maps of the breeding and
wintering ranges of western Palaearctic bird species (shape
files) which were retrieved from BirdLife International and
Handbook of the Birds of the World (2019). Geometric
centroids of the spatial polygons for breeding (i.e. breed-
ing and resident) and wintering (i.e. wintering and resident)
ranges were then calculated from these maps using the gCen-
troid function of the R package 'rgeos' (https://rdrr.io/cran/
rgeos/), while migration distance was calculated as the geo-
graphic distance between the two centroids using a custom-
written function in R (Vincze et al. 2019).

The relationship between oxidative physiology and proxies
of flight energetics (flight type, wing morphology, flight mus-
cle morphology and migration distance) can be affected by
potentially confounding variables, which can covary with the
amount of antioxidants or affect oxidative damage, such as
the fat load during migration, diet and reproductive rate. Fat
load during migration can augment the cost of flight because
birds that accumulate fat stores equal to their lean body
mass before migration are subject to higher energy use than
birds that migrate in smaller leaps and thus accumulate less
fat (Pennycuick 1989, Kvist et al. 2001, Vincze et al. 2019).
Using fat as an energy reserve during migration can also be
costly because lipids are prone to oxidation (Eikenaar et al.
2022). Species-specific diet can also determine the oxidative
state because it determines the amounts of exogenous antiox-
idants (Cohen et al. 2009, Cooper-Mullin and McWilliams
2016). Because oxidative physiology covaries with life history
at the macroevolutionary scale (Vágási et al. 2019), the rela-
tionship between proxies of flight energetics and oxidative
state can be affected by the pace of life. Finally, given that
across-species variation in physiology and energy demand is
strongly dependent on body size (Hedenström 2008), we also
controlled for the effect of body mass.

Accumulated fuel load of migrating birds was extracted
from Vincze et al. (2019) and the diet of breeding birds was
extracted from Storchová and Hořák (2018) as categori-
cal variable with three levels: animal (mostly animal-based),
plant (mostly plant-based) or mixed (quasi equal consump-
tion of animal- and plant-derived food). We used fecundity
as a proxy of pace of life, which was calculated as the aver-
age clutch size multiplied by the average number of breed-
ing events per year as given by Storchová and Hořák (2018).

Body mass was extracted from Storchová and Hořák (2018),
but data were verified using alternative sources and some
were updated using recent primary literature or our field
measurements.

Statistical analyses

All analyses were run in the R computing environment ver. 4.0.4 (www.r-project.org). Despite standardized sampling and laboratory processing of samples, differences in physiological parameters could be detected across years of sample collection (Vincze et al. 2022). To control for this potentially confounding effect, we extracted species-specific average physiology measures as best linear unbiased estimates (BLUEs). To do so, we constructed general linear mixed models (using R function *lmer*, package 'lme4'; Bates et al. 2015). All models contained a physiological parameter as a dependent variable and two random factors: year and the combination of species and sex. BLUEs were then extracted from these models as the conditional means (from linear models) for each species-by-sex combination (using the *ranef* function, package 'lme4'). To ensure model fit, all oxidative state variables were log(x + 1) transformed in the models from which BLUEs were extracted. Migration distance was standardized to increase the model fit.

Phylogenetic generalized least squares (PGLS) models
were constructed (using *gls* function from package 'nlme';
Pinheiro et al. 2021) for each physiological parameter using
BLUEs as dependent variables. We constructed four alterna-
tive models due to the incompleteness of some of the predic-
tors. In the first model, we included the following predictors:
flight style and diet as fixed factors, and wing parameters (wing
loading and aspect ratio), migration distance and body mass
as continuous predictors. Given that flight style is expected
to modulate oxidative physiology and its association with the
energetic demand of flight, and because the passerine-type
flight style is one of the most energy demanding, in a second
model, we tested the consistency of our results by limiting
the analyses to the passerine type flyers and removing flight
type as predictor from the model (83 out of 112 species in
the case of TAS, UA and MDA; and 84 of the 113 species
in the case of tGSH). In the third model, we tested whether
the covariation between migration distance and oxidative
physiology traits are affected by measures of flight energet-
ics other than flight style and wing morphology by replac-
ing flight style, wing loading and aspect ratio with the flight
muscle morphology, while body mass and diet were retained
in the models. Finally, we tested the effect of fat load and
fecundity on the relationship between oxidative physiology
parameters and migration distance, while body mass and diet
were retained in the models. Because UA is a major compo-
nent of TAS (Cohen et al. 2008, Vágási et al. 2019), we con-
trolled for the possible confounding effect of UA on TAS in a
separate model. However, because the main results remained
unchanged, we present only the results of the non-corrected
models. Full and minimal (obtained through stepwise back-
ward selection, with a threshold of p < 0.1) models are both
presented. This p-value threshold allows for a better balance between including and highlighting potentially relevant predictors that this study might fail to identify, while avoiding overfitting. Weighting models by species-specific sample size of each response variable did not improve model fit for any of the models, therefore we present the outcomes of unweighted models. To control for the non-independence of species, all models were controlled for common descent using a phylogeny. To do so, we obtained 1000 equally parsimonious phylogenies from http://birdtree.org (Jetz et al. 2012) using the Hackett backbone tree (Hackett et al. 2008), and we assembled a rooted, ultrametric consensus tree using the *consensus. edges* function of the 'phytools' R package (Revell 2012). This consensus tree was then used in all presented PGLS models. Phylogenetic signal (Pagel's lambda, λ) was estimated by maximum likelihood approximation in each model; λ values that converged to negative estimates were fixed at zero. Post hoc tests, aiming to establish the pairwise differences between levels of the predictors, were performed using the 'emmeans' R package (Lenth 2024). No correction for multiple testing was performed in this study due to the hypothesis-driven tests, low sample size and non-independence of the oxidative parameters.

## Results

Flight style and wing morphology (i.e. wing loading and aspect ratio) had no effect on any measures of the oxidative physiology except of TAS (Table 1). TAS was significantly explained by flight style in the minimal model: continuous flappers (CF) had the lowest; passerine-type (PT) flyers the highest; and flapping-soarers and flapping-gliders (FS-FG) had intermediate TAS concentration (CF versus

FS-FG: β (SE)=0.02 (0.01), t=1.40, p=0.1648; CF versus PT: β (SE)=0.03 (0.01), t=3.10, p=0.0025). UA and MDA were positively correlated with migration distance in both full and minimal multivariate PGLS models where the possible confounding effect of flight style, wing morphology, body mass and diet were controlled for (Table 1, Fig. 1). The non-significant positive relationship between migration distance and TAS turned significant after stepwise elimination of other non-significant predictors (full versus minimal models). The concentration of TAS, UA and MDA decreased and the concentration of tGSH increased with the body mass, but the relationship reaches significance only for tGSH (TAS, full model: β (SE)=-0.00 (0.00), t=-0.46; UA, full model: β (SE)=-0.02 (0.03), t=-0.65, minimal model: β (SE)=-0.06 (0.03), t=-1.89; tGSH, full model: β (SE)=0.09 (0.04), t=2.13, minimal model: β (SE)=0.10 (0.04), t=2.74; MDA, full model: β (SE)=-0.02 (0.02), t=-1.18; Table 1). Diet explained the concentration of UA and MDA (Table 1). In the second set of analyses, where only species with passerine type flight were included in the models, the significant association of migration distance with UA and MDA persisted, while the significant correlation with TAS disappeared (Supporting information). The concentration of UA significantly decreased with wing loading in passerine type flyers in both full and minimal models (full model: β (SE)=-0.05 (0.02), t=-2.29; minimal model: β (SE)=-0.05 (0.02), t=-2.42). In the third set of models, we tested whether the significant associations of migration distance with TAS, UA and MDA persist after replacing flight style, wing loading and aspect ratio with the pectoral-to-supracoracoid muscle ratio, while body mass and diet was retained in the models. Pectoral-to-supracoracoid muscle ratio significantly and positively correlated to MDA concentration (full model: β (SE)=0.03 (0.01), t=2.01, minimal

<table><caption>Table 1. Full and minimum adequate phylogenetic generalized least squares (PGLS) models used to investigate the relationships of oxidative physiology markers with flight style, wing parameters and migration distance, while controlling for body mass and diet. Significant associations between explanatory variables describing flight, wing morphology, migration distance and oxidative physiology measures are bolded.</caption><thead><tr><th rowspan="3"></th><th rowspan="3">df</th><th colspan="4">TAS</th><th colspan="4">UA</th></tr><tr><th colspan="2">Full model</th><th colspan="2">Minimal model</th><th colspan="2">Full model</th><th colspan="2">Minimal model</th></tr><tr><th>F</th><th>p</th><th>F</th><th>p</th><th>F</th><th>p</th><th>F</th><th>p</th></tr></thead><tbody><tr><td>Intercept</td><td>1</td><td>2.55</td><td>0.1132</td><td><strong>7.84</strong></td><td><strong>0.0061</strong></td><td>1.80</td><td>0.1828</td><td>0.02</td><td>0.8819</td></tr><tr><td>Flight style</td><td>2</td><td>1.74</td><td>0.1808</td><td><strong>5.41</strong></td><td><strong>0.0058</strong></td><td>1.49</td><td>0.2295</td><td></td><td></td></tr><tr><td>Wing loading</td><td>1</td><td>0.00</td><td>0.9714</td><td></td><td></td><td>2.79</td><td>0.0980</td><td></td><td></td></tr><tr><td>Aspect ratio</td><td>1</td><td>0.25</td><td>0.6196</td><td></td><td></td><td>1.86</td><td>0.1754</td><td></td><td></td></tr><tr><td>Migration distance</td><td>1</td><td>1.81</td><td>0.1818</td><td><strong>4.82</strong></td><td><strong>0.0303</strong></td><td><strong>6.30</strong></td><td><strong>0.0136</strong></td><td><strong>4.26</strong></td><td><strong>0.0415</strong></td></tr><tr><td>Body mass</td><td>1</td><td>0.21</td><td>0.6466</td><td></td><td></td><td>0.43</td><td>0.5151</td><td>3.56</td><td>0.0620</td></tr><tr><td>Diet</td><td>2</td><td>0.83</td><td>0.4373</td><td>n=112, Pagel's λ=0.00</td><td>n=112, Pagel's λ=0.00</td><td><strong>3.17</strong></td><td><strong>0.0461</strong></td><td><strong>4.21</strong></td><td><strong>0.0174</strong></td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td>n=112, Pagel's λ=0.04</td><td>n=112, Pagel's λ=0.04</td><td>n=112, Pagel's λ=0.30</td><td>n=112, Pagel's λ=0.30</td></tr><tr><th rowspan="3"></th><th rowspan="3"></th><th colspan="4">tGSH</th><th colspan="4">MDA</th></tr><tr><th>F</th><th>p</th><th>F</th><th>p</th><th>F</th><th>p</th><th>F</th><th>p</th></tr><tr><td>0.01</td><td>0.9273</td><td>0.61</td><td>0.4351</td><td>0.13</td><td>0.7188</td><td>0.00</td><td>0.9870</td></tr><tr><td>Flight style</td><td>2</td><td>0.66</td><td>0.5192</td><td></td><td></td><td>0.20</td><td>0.8212</td><td></td><td></td></tr><tr><td>Wing loading</td><td>1</td><td>0.89</td><td>0.3468</td><td></td><td></td><td>1.20</td><td>0.2757</td><td></td><td></td></tr><tr><td>Aspect ratio</td><td>1</td><td>0.55</td><td>0.4586</td><td></td><td></td><td>0.11</td><td>0.7383</td><td></td><td></td></tr><tr><td>Migration distance</td><td>1</td><td>0.15</td><td>0.6993</td><td></td><td></td><td><strong>11.33</strong></td><td><strong>0.0011</strong></td><td><strong>19.36</strong></td><td><strong>&lt; 0.0001</strong></td></tr><tr><td>Body mass</td><td>1</td><td><strong>4.53</strong></td><td><strong>0.0356</strong></td><td><strong>7.51</strong></td><td><strong>0.0072</strong></td><td>1.40</td><td>0.2397</td><td></td><td></td></tr><tr><td>Diet</td><td>2</td><td>2.40</td><td>0.0956</td><td>n=113, Pagel's λ=0.75</td><td>n=113, Pagel's λ=0.77</td><td><strong>4.88</strong></td><td><strong>0.0094</strong></td><td><strong>5.83</strong></td><td><strong>0.0039</strong></td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td>n=112, Pagel's λ=0.41</td><td>n=112, Pagel's λ=0.41</td><td>n=112, Pagel's λ=0.47</td><td>n=112, Pagel's λ=0.47</td></tr></tbody></table>
<figure><img src="image_5.png" /><figcaption>Figure 1. Associations of malondialdehyde (MDA) and uric acid (UA) with migration distance in birds. Phylogenetic distribution of MDA, UA and migration distance is shown across bird species (a). Estimates of regression coefficients from the full models presented in Table 1 for MDA (b) and UA (c) are: $β (SE) = 0.05$ (0.01) and $β (SE) = 0.06$ (0.02). Values of oxidative parameters are best linear unbiased estimates (Material and methods). Fitted lines were obtained from the respective full models.</figcaption></figure>
model: β (SE) = 0.03 (0.01), t = 2.01). Migration distance was retained as significant predictors of UA and MDA (Supporting information). Finally, in the fourth set of models where the possible confounding effect of fat load and pace of reproduction was tested for, again we found that the association of migration distance with UA and MDA concentration persisted, while the effect on TAS turned to be non-significant (Supporting information).

Discussion

Our study brings novel insights into the oxidative costs of
flight and migration in birds. For instance, contrary to our
expectations, we found that general traits associated with
flight energetics, such as flight style, wing morphology (i.e.
wing loading and aspect ratio) and flight muscle size (pec-
toral-to-supracoracoid muscle ratio) did not significantly
explain, or at best only weakly explain, the variation in the
four measures of oxidative physiology across species. This
suggests that birds have evolved efficient mechanisms to
avoid the potential oxidative stress (at least as measured by
our parameters) associated with their everyday flight activi-
ties, although breeding activity may interplay and potentially
confound this finding (see below). On the other hand, we
observed a significant increase in oxidative damage, as indi-
cated by elevated levels of MDA, and a component of the
antioxidant defence system, UA, with increasing migration
distance across 113 European bird species encompassing a
wide spectrum of flight styles, flight morphology, migratory
fuelling strategies, life histories and diet. The latter result
indicates that while routine flight does not seem to impose
substantial oxidative stress, the prolonged and strenuous
exercise required for migration does have measurable oxida-
tive costs. These results confirm former correlative and exper-
imental studies at the within-species scale, showing evidence
for the oxidative cost of endurance flight and migration
(Costantini et al. 2008, Jenni-Eiermann et al. 2014, Dick
and Guglielmo 2019). These findings also imply that oxida-
tive stress during migration might influence subsequent life-
history stages, such as breeding, potentially affecting overall
fitness and survival. Our study highlights the importance of
considering the oxidative challenges of migration in the con-
text of avian physiology and ecology.

The present study represents a pioneering effort to under-
stand the effect of flight, flight morphology and migration on
oxidative physiology from an evolutionary perspective. So far,
only a limited number of studies explored the oxidative con-
sequences of migration and these mostly compared migrant
and resident populations of some fish and bird species. For
instance, partially migrating brown trout *Salmo trutta* indi-
viduals had, on average, higher oxidative stress compared to
residents when measured at the spawning grounds shortly
after arrival (Peiman et al. 2017). Elevated oxidative stress in
these individuals might have been the direct consequence of
the freshly completed migratory journey and its associated
physical demands, potentially supporting the hypothesis that

migration causes oxidative stress. Another study focusing on
the cost of migration in European blackbirds *Turdus merula*
found that migrants invest more in antioxidant defences
in order to reduce oxidative damage to lipids compared to
residents during the autumn migration period, likely rep-
resenting a physiological response to diminish the costs of
migration (Eikenaar et al. 2017). Our results show that the
oxidative cost of migration is carried over to upcoming life-
history stages (including breeding), indicating that the daily,
seasonal or across life stages fluctuations in oxidative physiol-
ogy traits can be modulated by the cost of migration (Jenni-
Eiermann et al. 2014). The macroevolutionary association
of migration distance with UA and MDA can arise owing
to migratory behaviour being integrated with several mor-
phological, physiological and life-history traits into a migra-
tory syndrome (Piersma et al. 2005, Dingle 2006). However,
this hypothesis is seemingly contradicted by the fact that
the positive relationship of migration distance with UA and
MDA persists even after controlling for several possible con-
founding effects, such as fat load, fecundity, body mass or
diet. Spring migration shortly preceded the period when
samples for measuring oxidative physiology were collected,
potentially influencing our results. Spring migration is fol-
lowed by the energetically highly demanding reproduction,
for which long-distance migratory species have limited time
to prepare, leaving less room for physiological recovery after
spring migration (McWilliams et al. 2022). This swift transi-
tion between energetically costly life-history stages and the
tight annual schedule of long-distance migrants might set the
scene for migration-related physiological costs to be carried
over to and still be sizable in the reproductive period. Future
studies should also address how differences in stopover stra-
gies among species might either augment or mitigate the
potential oxidative costs of migration. Migrants that do not
utilize stopover sites or only occasionally stop for recovery
and refuelling along their route might suffer more oxidative
stress, while species with short flight bouts interrupted fre-
quently by stopover stages ('stepping-stone' migrants) might
better withstand the oxidative challenge of migratory flight
(within-species example in Eikenaar et al. 2020). The recent
upsurge of studying tagged birds can provide new opportuni-
ties in the study of migration physiology in birds.

We detected increased levels of UA in species with lon-
ger migration distances, suggesting that the role of this
endogenous non-enzymatic antioxidant in oxidative defence
increases with flight distance. Alternatively, because UA is a
product of protein catabolism, our results could also indicate
that longer migratory distances necessitate protein breakdown
to fuel strenuous flights as compared with sedentary or short-
distance migratory species (McWilliams et al. 2020). MDA
also showed positive evolutionary covariation with migration
distance, which indicates that the evolution of increased UA
concentrations cannot neutralise the increased oxidative chal-
lenge faced by long-distance migrants, ultimately leading to
lipid damage. To understand in depth the role of the com-
plex antioxidant system in regulating oxidative stress during
migration, it is important to study other determinants of the
oxidative state, such as the flight-dependent production of ROS, changes in the concentration of additional (exogenous and endogenous) antioxidants and the physiological adaptations that allow resistance against oxidative stress (e.g. the ratio of monounsaturated fatty acids to polyunsaturated fatty acids; McWilliams et al. 2020, 2021).

All flight parameters, except migration distance, explored in our study depict the general, year-round flight behaviour and associated exigencies of different species. The potential physiological costs, if any, of such year-round flight traits are pronounced during the breeding period because chick provisioning is one of the most physically active life-history stages. The potential costs of breeding-associated flight exercises are also dependent on the species-specific breeding effort (i.e. fecundity). Along with this reasoning, the physiological state during breeding should reflect the cost of flight exercise especially after fecundity is factored out. Alternatively, breeding in birds might not be metabolically as costly as expected; therefore costs of reproduction, and any physiological signature of these costs, might be quite rare (Williams 2018). Our findings that most traits associated with flight energetics were only weakly associated with oxidative physiology at the macroevolutionary scale seemingly support the latter notion. Nevertheless, our findings are somehow unexpected because these traits determine flight efficiency and energy expenditure of birds all over the year (Liechti 2006, Bowlin and Wikelski 2008). Because spring migration precedes our sampling period, our results should be treated accordingly. Spring migration ends with the arrival of birds to the breeding ground, when the amount of daily flight is expected to drop transiently. Sampling the birds several weeks after the spring migration, we expected that flight style, wing morphology and flight muscle sizes would best explain the oxidative physiology parameters, as opposed to migration distance. One possible explanation for the absence of relationship between flight-related behavioural and morphological traits on oxidative physiology in birds is that flight-driven aerobic adaptations might mitigate the potential oxidative costs of daily flight exercise (Hickey et al. 2012, Williams 2018). Mitochondrial mass, depressed insulin level and sensitization to glucagon, and the resulting high blood glucose level may represent such adaptations to high aerobic life in birds. Furthermore, the composition of fatty acids may differ between bird species, influencing not only the mobilisation rate of fat, but also its susceptibility to free radicals (McWilliams et al. 2020, 2021). Unfortunately, the variation in physiological set-up and fat store composition among species, and how these vary with migration distance, flight style and wing morphology, are not yet known. A wider coverage across the avian tree including non-volant species and more diverse physiological measurements, such as the mitochondrial mass, glucose and insulin concentration, or sensitivity to insulin and glucagon, are needed to better understand the complex regulation of oxidative physiology of flight (Hickey et al. 2012).

To conclude, our comparative study on 113 European bird species, covering a wide range of volant species characterized

by different flight styles, wing and flight muscle morphology, including residents and migrants, shows that none of the traits explaining year-round flight energy expenditure explain any measures of oxidative state, suggesting that birds endure their everyday exercise without oxidative cost. However, oxidative damage to lipids and one component of the endogenous antioxidant system (UA) increased with migration distance. Our results suggest that migration might have oxidative consequences that are carried over to upcoming life-history stages.

**Acknowledgements** – We are grateful to many students and colleagues who faithfully helped during fieldwork and to Janka Pénzes for biochemical analyses.

**Funding** – The research was supported by the Romanian Ministry of Research, Innovation and Digitization (CNCS – UEFISCDI; project no. PN-III-P4-ID-PCE-2016-0404 to PLP, OV and CIV, and project no. PN-III-P1-1.1-TE-2021-0502 to CIV and OV). PLP was financed by the János Bolyai Research Scholarship of the Hungarian Academy of Sciences (HAS). The work of OV was supported by the National Scientific Research Fund (OTKA K143421).

**Permits** – Bird trapping and blood sampling was performed as licensed by the Romanian Academy of Sciences (permit no. 2257) and in accordance with current animal welfare laws of Romania.

## Author contributions

**Péter László Pap**: Conceptualization (equal); Data curation (equal); Formal analysis (supporting); Funding acquisition (lead); Methodology (equal); Visualization (supporting); Writing – original draft (lead); Writing – review and editing (lead). **Orsolya Vincze**: Conceptualization (equal); Data curation (equal); Formal analysis (lead); Funding acquisition (supporting); Methodology (equal); Visualization (lead); Writing – original draft (equal); Writing – review and editing (equal). **Csongor I. Vágási**: Conceptualization (equal); Data curation (equal); Formal analysis (supporting); Funding acquisition (equal); Methodology (equal); Visualization (supporting); Writing – original draft (equal); Writing – review and editing (equal).

## Transparent peer review

The peer review history for this article is available at https://www.webofscience.com/api/gateway/wos/peer-review/10.1111/jav.03325.

## Data availability statement

Data are available from the Dryad Digital Repository: https://doi.org/10.5061/dryad.6djh9w19s (Pap et al. 2024).

## Supporting information

The Supporting information associated with this article is available with the online version.
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