Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

The high-energy aerial insectivore lifestyle of swallows does not produce clear thermogenic side effects

The high-energy aerial insectivore lifestyle of swallows does not produce clear thermogenic side... Abstract Ecological traits related to pace of life, such as foraging strategies and activity levels, influence daily energy expenditure (DEE) and can affect fitness. A fast pace of life tends to be supported by high-energy aerobic activity and is positively correlated with high DEE and basal and maximal metabolic rates in some endotherms. Given that maximal capacities for exercise and thermogenesis are both functions of aerobic muscle output and are often positively correlated with each other, high-energy aerobic lifestyles might be associated with high aerobic capacities, which would be expected to produce high thermogenic capacities as a side effect. We tested whether the high-energy aerial insectivore lifestyle in swallows is correlated with elevated basal and maximal thermogenic metabolic rates. We measured basal (BMR) and summit (Msum = maximum cold-induced metabolic rate) metabolic rates in 6 species of swallows (Hirundinidae) and combined these data with literature data for additional swallows (n = 10 for BMR; n = 8 for Msum) and non-aerial insectivore birds (n = 215 for BMR; n = 64 for Msum) to address the hypothesis that swallows have higher BMR and Msum than non-aerial insectivores. BMR in swallows was significantly higher than for non-aerial insectivore birds for phylogenetically adjusted analyses after correcting for body mass and region of origin (tropical vs. temperate). In contrast, Msum did not differ significantly between swallows and non-aerial insectivores. Thermogenic scope (Msum – BMR), however, was lower in tropical non-aerial insectivore birds compared with tropical swallows and temperate birds. This suggests that the aerial insectivore lifestyle elevates maintenance costs, but maximum thermogenic capacities are not clearly upregulated, despite tropical swallows having higher thermogenic scope than tropical non-aerial insectivores. These data suggest that the high-energy aerial insectivore lifestyle does not produce strong thermogenic side effects in swallows. RÉSUMÉ Les traits écologiques associés au rythme de vie, tels que les stratégies de recherche alimentaire et les niveaux d’activité, influencent la dépense énergétique quotidienne et peuvent avoir une incidence sur la condition physique. Un rythme de vie rapide a tendance à être soutenu par une activité aérobique à haute énergie et est positivement corrélé à une dépense énergétique quotidienne élevée et des taux métaboliques basal et maximal chez certains endothermes. Étant donné que les capacités maximales d’exercice et de thermogénèse sont toutes deux des fonctions de la production musculaire aérobie et sont souvent corrélées positivement, des modes de vie aérobie à haute énergie pourraient être associés à des capacités aérobies élevées, qui devraient produire des capacités thermogènes élevées comme effet secondaire. Nous avons testé si le mode de vie insectivore aérien à haute énergie chez les hirondelles est corrélé avec des taux métaboliques basal et maximal thermogènes élevés. Nous avons mesuré les taux métaboliques basal (BMR) et maximal (Msum = taux métabolique maximum qui est induit par le froid) chez 6 espèces d’hirondelles (Hirundinidés) et nous avons combiné ces données aux données de la littérature pour d’autres hirondelles (n = 10 pour BMR; n = 8 pour Msum) et d’autres oiseaux non insectivores (n = 215 pour BMR; n = 64 pour Msum) pour vérifier l’hypothèse que les hirondelles ont un BMR et un Msum plus élevé que les insectivores non aériens. Le BMR des hirondelles était significativement plus élevé que celui des oiseaux insectivores non aériens dans les analyses ajustées phylogénétiquement après correction pour la masse corporelle et la région de provenance (tropicale vs tempérée). Par contre, le Msum ne différait pas significativement entre les hirondelles et les insectivores non aériens. L’étendue thermogène (Msum – BMR) était cependant plus faible chez les oiseaux insectivores non aériens tropicaux en comparaison des hirondelles tropicales et des oiseaux des régions tempérées. Ceci suggère que le mode de vie insectivore aérien augmente les coûts de maintenance, mais les capacités thermogènes maximales ne sont pas clairement régulées vers le haut, malgré que les hirondelles tropicales aient une étendue thermogène plus élevée que les insectivores non aériens tropicaux. Ces données suggèrent que le mode de vie insectivore aérien à haute énergie ne produit pas d’effets secondaires thermogènes marqués chez les hirondelles. Lay Summary • Animals have different lifestyles or paces of life that influence the amount of energy used per day. • Swallows are aerial insectivores and have an active lifestyle that could lead to higher metabolic capacities at both basal and maximal levels. • We measured basal and maximum cold-induced metabolic rates in swallows and compared these with non-aerial insectivore birds to test if swallows have higher basal and maximum metabolic capacities. • We found that swallows have higher basal metabolic rates but not maximum cold-induced metabolic rates compared with other non-aerial insectivore birds. • The results suggest that the active lifestyle of swallows required more energy for maintenance but did not produce high thermogenic capacities as a byproduct. INTRODUCTION Ecological or life-history traits associated with energy acquisition, such as foraging strategies and activity levels, contribute to lifestyle differences among species that can affect energetics and fitness and link organisms to the environment (Dobson 2012). Species with different lifestyles will have different energy demands for activities such as foraging, predator avoidance, and locomotor patterns, and this may, in turn, lead to the evolution of differences in metabolic rate. There are several studies on mammals, fishes, and reptiles, which indicate that elevated metabolic capacities are associated with greater activity levels (Hailey and Davies 1986, Killen et al. 2010). In addition, positive correlations among basal metabolic rate (BMR), daily energy expenditure (DEE), and maximal metabolic rate responses to cold (summit metabolism, Msum) or maximal metabolic rate responses to exercise (MMR) may occur in birds (Dutenhoffer and Swanson 1996, Rezende et al. 2002, Portugal et al. 2016 but see Ricklefs et al. 1996, Wiersma et al. 2007a, Auer et al. 2017). Lower and upper metabolic limits, basal and maximal metabolic rates, respectively, are often measured to indicate the energetic profile of an organism for comparison with other organisms (Swanson 2010, Barceló et al. 2017, Swanson et al. 2017). BMR, defined as the minimum metabolic rate required for maintenance, is largely determined by the function of central organs and is widely used as a measure of the physiological baseline or the “rate of living” for an animal (McNab 1997, McKechnie 2008, Petit and Vézina 2014b, Swanson et al. 2017). A faster rate of living might, therefore, be expected to produce a higher BMR. Furthermore, because maximal metabolic rate produced by aerobic activities (e.g., exercise or thermogenesis) is mainly a function of skeletal muscles, these metabolic rates serve as measures of the energetic capacity of an endotherm for exercise or thermogenesis. The maximum metabolic rate during thermogenesis (i.e. cold-induced summit metabolic rate: Msum) has recently become a common measure for birds and has been standardized among studies, thus allowing comparisons among different bird groups (McKechnie and Swanson 2010). Both shivering thermogenesis and flight are functions of skeletal muscle activity and share similar metabolic pathways and substrates (Wiersma et al. 2007a, Guglielmo 2010, Zhang et al. 2015a). Indeed, evidence from recent studies suggests that MMR and Msum may increase in tandem in response to increasing energy demands from exercise or cold exposure (Petit and Vézina 2014a, Zhang et al. 2015b). For example, cold- and exercise-trained house sparrows (Passer domesticus) showed positive intraspecific correlations between Msum and MMR, and exercise enhanced both exercise and thermogenic maximal metabolic rates in this species (Zhang et al. 2015b). Experimentally increased flight costs can also increase Msum in birds (Petit and Vézina 2014a). Pre-migratory fattening may also result in increases in Msum and produce thermogenic side effects in birds (Vézina et al. 2007). Moreover, Msum was higher during spring migration than during non-migratory seasons for northern waterthrush (Parkesia noveboracensis (Corder and Schaeffer 2015) and was higher during spring than fall migration for several bird species, potentially associated with a faster pace of migration during spring (Swanson 1995, Swanson and Dean 1999). Finally, Wiersma et al. (2007a) found that the aerial insectivore mangrove swallow (Tachycineta albilinea) had the highest Msum among tropical bird species. Thus, enhanced heat production can apparently occur as a byproduct of adaptation to high-energy portions of the annual cycle or to a high-activity lifestyle in birds. The avian family Hirundinidae includes swallows and martins and, collectively, is distributed on all continents except Antarctica. Swallows and martins are characterized by adaptation to aerial feeding. Unlike most other bird species, swallows fly continuously during foraging, whereas foraging in most other terrestrial birds involves perching and resting between foraging bouts. Consequently, the aerial foraging lifestyle of swallows involves extended periods of high-energy flight. Aerial foraging, such as that documented for tree swallows, involves rapid turns and maneuvers, which comprise an energetically expensive type of flight (Carey 1996). DEE of breeding swallows is positively associated with the amount of time spent flying and DEE in 7 species of Hirundinidines was higher than that in non-aerial insectivorous bird species (Williams 1988). Such a metabolically expensive lifestyle might necessitate higher maintenance costs (Auer et al. 2017), so the aerial insectivore lifestyle of swallows might come at the cost of a higher BMR. Given the often positive interspecific relationships between BMR, DEE, and Msum in birds (Rezende et al. 2002, Wiersma et al. 2007a), we hypothesized that swallows would have high BMR and thermogenic capacities as a byproduct of their high-energy aerobic lifestyle. In the present study, we collected wild, free-living swallows in southeastern South Dakota and measured both BMR and Msum for 6 Hirundinidae species. We supplemented these data with literature values for both other swallows and for non-aerial insectivorous birds to compare basal and maximal thermogenic (Msum) metabolic levels. We employed phylogenetically informed statistical analyses to examine whether the energetically expensive lifestyle of swallows affects metabolic capacities. We predicted that birds from the family Hirundinidae would have: (1) higher BMR than non-aerial insectivorous birds to support the high energetic demand of their aerial foraging lifestyle and (2) higher Msum as a byproduct of adaptation to the high aerobic energy demands for prolonged foraging on the wing. MATERIALS AND METHODS Sampling We collected BMR and Msum data for 6 species in the family Hirundinidae in this study. These species included Purple Martin (n = 6; Progne subis), Tree Swallow (n = 6; Tachycineta bicolor), Bank Swallow (n = 5; Riparia riparia), Northern Rough-winged Swallow (n = 5; Stelgidopteryx serripennis), Cliff Swallow (n = 7; Hirundo pyrrhonota), and Barn Swallow (n = 6; Hirundo rustica). We collected all species, except purple martin, from wild populations near Vermillion, Clay County, South Dakota (~42.47°N, 97°W). We collected purple martins from a colony near Madison, Lake County, South Dakota (~44°N, 97.7°W). We captured adult swallows for all species by mist net. We captured all swallows in the morning (before 1,100 central daylight time) between May 20 and June 30 in 2010 and 2011. After capture, we transported birds to the laboratory (<30 min for all species but purple martin, which required about 105 min transport time) and caged them at room temperature (20–25°C). We did not feed birds after capture but did provide water in the holding cage. We conducted Msum measurements during the daytime (between 1200 and 1800 hours) within 8 hr of collection. We measured BMR the following night and all birds were fasted for at least 6 hr before BMR measurement. After both Msum and BMR measurements, we banded birds and released them at the site of capture the following morning. Msum and BMR Measurement We measured metabolic rate using open-circuit respirometry as described previously (Zhang et al. 2015b). We measured body mass before and after each metabolic rate measurement. Mass loss was assumed to be constant throughout the test period and we corrected body mass to the time for which we recorded BMR or Msum (see below). The respirometry system for Msum and BMR measurements consisted of 1.9-L paint cans with the inner surface painted flat black as metabolic chambers. Metabolic chambers were immersed into an ethylene glycol bath for temperature control to ±0.2°C. Fractional concentrations of oxygen in excurrent air were sampled with an Ametek S-3A oxygen analyzer (Applied Electrochemistry, Pittsburgh, Pennsylvania, USA) at 1-s intervals and we collected data with Expedata 2.0 (Sable Systems, Henderson, Nevada, USA) software. We analyzed oxygen consumption data with Expedata 2.0 software after correcting to STPD, using steady-state calculations for BMR and instantaneous calculations for Msum (Bartholomew et al. 1981, Lighton 2008). We performed Msum with a sliding cold exposure protocol with helox (79% helium/21% oxygen) (Swanson et al. 1996), which has higher thermal conductivity than air (Arens and Cooper 2005). For the sliding cold exposure protocol, flow rates of dry, CO2-free helox (79% helium/21% oxygen) at 1,010–1,030 mL min−1 were maintained through the chambers. We controlled the flow rate by a Cole-Parmer Precision Rotameter (Model FM082-03ST; Cole-Parmer, Vernon Hills, IL, USA) calibrated to ±1% accuracy with a soap bubble meter. After flushing the chamber with helox for 5 min, we initiated the cold exposure by immersing the metabolic chamber into the ethylene glycol bath, with the cold exposure initiated at 5°C. We continued the sliding cold exposure treatment by decreasing the temperature at a rate of ~1°C every 5 min until we detected a steady decline in oxygen consumption over several minutes, which is indicative of hypothermia. We then removed birds from the metabolic chamber and recorded body temperatures cloacally. We considered cloacal temperatures of ≤36°C as hypothermic (Swanson and Liknes 2006), and all birds were hypothermic at the end of cold exposure trials, which validated that Msum had been attained. We considered the highest 5-min running mean value for oxygen consumption over the test period as Msum (Swanson et al. 2012). We conducted BMR measurements at night (at least 1 hr after darkness) on birds fasted for at least 6 hr prior to metabolic measurements at 30°C, which is within the thermoneutral zone for swallows (Bryant et al. 1984, Williams 1988, Wiersma et al. 2007b). We maintained flow rates of dry, CO2-free air at 280–300 mL min−1 for BMR. We allowed birds to equilibrate for at least 1 hr within the metabolic chamber before we initiated metabolic measurements. We measured BMR for all birds for a period of at least 1 hr (between 2200 and 0500 hours) following the 1-hr equilibration periods and we calculated 10-min running mean values for oxygen consumption over the test period, with the lowest 10-min running mean designated as BMR (Swanson et al. 2012). From BMR and Msum data, we calculated thermogenic scope as Msum – BMR for each individual. Phylogenetic Tree Construction To compare metabolic rates of Hirundines with other birds, we collected BMR, Msum, and thermogenic scope data from the literature (see Supplementary Material Tables 1–3) for both additional Hirundines and for other non-aerial insectivore birds. Ten species of Hirundines and 215 species of non-aerial insectivores were included for BMR analyses and 8 species of Hirundines and 64 species of species of non-aerial insectivores for Msum analyses. We used uncorrected raw BMR data from McKechnie (2008), Stager et al. (2016), and Wiersma et al. (2007a), and Msum data from Swanson and Garland (2009), Swanson and Bozinovic (2011), Stager et al. (2016), and Wiersma et al. (2007a). We included metabolic trait data from literature values only from summer measurements for temperate-zone bird species because temperate-zone birds exhibit seasonal variation in metabolic rates (Swanson 2010, Pollock et al. 2019), and our metabolic measurements were from summer-acclimatized swallows. We created a consensus phylogenetic tree from birdtree.org for all species considered in all 3 sets of analyses (BMR, Msum, and thermogenic scope) in this study. The consensus tree was derived from 100 randomly sampled phylogenetic trees for all species included in this study using the Ericson All Species backbone posterior distribution from a global phylogeny of birds (Jetz et al. 2014). We first trimmed the global phylogeny of birds to a subset including the species included in our analyses and then created 100 randomly sampled trees using a pseudo-posterior distribution (see Jetz et al. 2014 for detailed methods). The consensus phylogenetic trees for all 3 sets of analyses are shown in Supplementary Figures S1–S3. Statistics Analyses were carried out using R 0.99.467 (R Core Team 2013) and the packages “ape,” “geiger,” “nlme,” “phytools,” “lmtest,” and “multcomp.” Residuals were first visually examined for normality using quantile–quantile plots and log-transformed if they were not normally distributed. We also used Levene’s test to test for equal variances among groups; variances did not differ significantly among groups for any metabolic trait (all P > 0.34). To test whether metabolic rates differed between swallows and other birds, we employed phylogenetically informed statistical analyses, which adjust for phylogenetic nonindependence of raw data (Felsenstein 1985, Garland et al. 1999, Symonds and Blomberg 2014). Linear regressions were computed using phylogenetic generalized least squares (PGLS) models in which the residuals were modeled as having evolved via either Brownian Motion or Ornstein–Uhlenbeck (OU) processes, the latter of which simulates stabilizing selection (Symonds and Blomberg 2014). Both the PGLS models (Brownian Motion and OU) were run incorporating the maximum likelihood estimate of lambda. The range of estimated values for lambda is generally 0 to 1, where 0 indicates no phylogenic signals and 1 indicates that traits vary directly with phylogenetic distance. Lambda values > 1, however, can occur if, for example, traits are more similar to each other than expected by phylogenetic distance (Freckleton et al. 2002). We first tested whether BMR, Msum, and thermogenic scope scaled with Mb by including Mb as a predictor of BMR, Msum, and thermogenic scope in our regression models. To investigate how BMR, Msum, and thermogenic scope varied between swallows and non-aerial insectivore birds, we ran generalized linear regression models using group (i.e. swallow vs. non-swallow) and region (i.e. temperate vs. tropical) as predictors and Mb as a covariate. Residuals in the interaction were also tested for normality and log-transformed if they were not normally distributed. We report degrees of freedom of the residuals, slope, intercept, R-squared values, and P-values for regressions with a continuous predictor (i.e. Mb). We report F-statistics for GLMs with the categorical variables “group” and “region.” Additionally, we also report estimates of the phylogenetic signal associated with all variables (Pagel’s λ), which indicates the extent to which closely related species tend to resemble each other. For BMR, Msum, and thermogenic scope, we also report Pagel’s λ of the residuals (i.e. differences from predicted values after correcting for body mass, region, and group). corrected Akaike Information Criterion (AICc) and Likelihood ratio tests were conducted to compare PGLS regressions using the Ornstein–OU and Brownian motion models. RESULTS Phylogenetic Signal for Body Mass and Metabolic Traits Estimates of Pagel’s λ for BMR, Msum, and thermogenic scope were 0.99, 1.01, and 0.94, respectively. Because we used 3 different datasets and 3 different phylogenetic trees for each metabolic trait, we generated 3 separate estimates of phylogenetic signal for Mb, one for each dataset. These values for Pagel’s λ for Mb were 1.01 (BMR dataset), 1.01 (Msum dataset), and 1.01 (thermogenic scope dataset). Since Estimates of Pagel’s λ for BMR, Msum, and thermogenic scope were close to 1, which indicated strong phylogenetic signals, we only included PGLS–OU and PGLS–Brownian models, rather than ordinary least squares models, for our analyses (Freckleton 2009). Estimates of Pagel’s λ for residual BMR, Msum, and thermogenic scope, after correcting for body mass, region, and group, were 0.006, 0.075, and 0.090, respectively. Correlation between BMR and Msum BMR was positively correlated with Msum in both PGLS–Brownian and PGLS–OU models (P < 0.01; Table 2). Equations for PGLS–OU: LogBMR = 0.66 LogMsum – 0.38; PGLS–Brownian: LogBMR = 0.87 LogMsum – 0.31. The significant correlations between BMR and Msum were lost when including body mass as a covariant (P > 0.61). Allometric Relationships for Metabolic Traits When assessing relationships between Mb and all 3 metabolic variables (Table 1), likelihood ratio tests did not reveal any significant differences between PGLS using the OU model and PGLS using the Brownian motion model (P > 0.1 in cases). According to the best-fit Brownian motion PGLS model, there was a significant positive association between log Mb and log BMR (Figure 1A), between log Mb and log Msum (Figure 1B), and between log Mb and thermogenic scope (Figure 1C). Table 1. Mean (± SD) body mass (Mb) and basal and summit metabolic rates (mL O2 min−1) for the 6 species of swallows measured in this study. Species . n . Mb BMR . BMR . Mb Msum . Msum . Barn Swallow 6 16.2 ± 1.5 0.894 ± 0.200 15.1 ± 1.5 4.666 ± 1.932 Bank Swallow 5 12.6 ± 0.7 0.868 ± 0.280 12.6 ± 0.8 3.416 ± 0.544 Cliff Swallow 7 20.1 ± 1.4 1.036 ± 0.118 18.7 ± 1.3 4.768 ± 0.774 Northern Rough-winged Swallow 5 14.6 ± 0.5 1.109 ± 0.196 15.6 ± 1.5 4.487 ± 0.766 Tree Swallow 6 17.4 ± 3.0 0.881 ± 0.131 18.7 ± 2.9 4.623 ± 0.488 Purple Martin 6 44.6 ± 2.4 1.552 ± 0.171 46.0 ± 2.5 8.679 ± 0.819 Species . n . Mb BMR . BMR . Mb Msum . Msum . Barn Swallow 6 16.2 ± 1.5 0.894 ± 0.200 15.1 ± 1.5 4.666 ± 1.932 Bank Swallow 5 12.6 ± 0.7 0.868 ± 0.280 12.6 ± 0.8 3.416 ± 0.544 Cliff Swallow 7 20.1 ± 1.4 1.036 ± 0.118 18.7 ± 1.3 4.768 ± 0.774 Northern Rough-winged Swallow 5 14.6 ± 0.5 1.109 ± 0.196 15.6 ± 1.5 4.487 ± 0.766 Tree Swallow 6 17.4 ± 3.0 0.881 ± 0.131 18.7 ± 2.9 4.623 ± 0.488 Purple Martin 6 44.6 ± 2.4 1.552 ± 0.171 46.0 ± 2.5 8.679 ± 0.819 Open in new tab Table 1. Mean (± SD) body mass (Mb) and basal and summit metabolic rates (mL O2 min−1) for the 6 species of swallows measured in this study. Species . n . Mb BMR . BMR . Mb Msum . Msum . Barn Swallow 6 16.2 ± 1.5 0.894 ± 0.200 15.1 ± 1.5 4.666 ± 1.932 Bank Swallow 5 12.6 ± 0.7 0.868 ± 0.280 12.6 ± 0.8 3.416 ± 0.544 Cliff Swallow 7 20.1 ± 1.4 1.036 ± 0.118 18.7 ± 1.3 4.768 ± 0.774 Northern Rough-winged Swallow 5 14.6 ± 0.5 1.109 ± 0.196 15.6 ± 1.5 4.487 ± 0.766 Tree Swallow 6 17.4 ± 3.0 0.881 ± 0.131 18.7 ± 2.9 4.623 ± 0.488 Purple Martin 6 44.6 ± 2.4 1.552 ± 0.171 46.0 ± 2.5 8.679 ± 0.819 Species . n . Mb BMR . BMR . Mb Msum . Msum . Barn Swallow 6 16.2 ± 1.5 0.894 ± 0.200 15.1 ± 1.5 4.666 ± 1.932 Bank Swallow 5 12.6 ± 0.7 0.868 ± 0.280 12.6 ± 0.8 3.416 ± 0.544 Cliff Swallow 7 20.1 ± 1.4 1.036 ± 0.118 18.7 ± 1.3 4.768 ± 0.774 Northern Rough-winged Swallow 5 14.6 ± 0.5 1.109 ± 0.196 15.6 ± 1.5 4.487 ± 0.766 Tree Swallow 6 17.4 ± 3.0 0.881 ± 0.131 18.7 ± 2.9 4.623 ± 0.488 Purple Martin 6 44.6 ± 2.4 1.552 ± 0.171 46.0 ± 2.5 8.679 ± 0.819 Open in new tab Table 2. Statistical output for regression models. . Model . Residual DF . Slope . Intercept . R2 . P-value . BMR  Log BMR ~ log Mb PGLS–OU 233 0.63 –1.36 0.67 <0.001  Log BMR ~ log Mb PGLS–Brownian 233 0.66 –1.46 0.67 <0.001 Msum  Log Msum ~ log Mb PGLS–OU 70 0.79 –0.84 0.68 <0.001  Log Msum ~ log Mb PGLS–Brownian 70 0.68 –0.71 0.72 <0.001 Thermogenic scope  Thermogenic scope ~ log Mb PGLS–OU 48 1.49 –0.89 0.2 <0.001  Thermogenic scope ~ log Mb PGLS–Brownian 48 1.38 –0.84 0.2 <0.001 . Model . Residual DF . Slope . Intercept . R2 . P-value . BMR  Log BMR ~ log Mb PGLS–OU 233 0.63 –1.36 0.67 <0.001  Log BMR ~ log Mb PGLS–Brownian 233 0.66 –1.46 0.67 <0.001 Msum  Log Msum ~ log Mb PGLS–OU 70 0.79 –0.84 0.68 <0.001  Log Msum ~ log Mb PGLS–Brownian 70 0.68 –0.71 0.72 <0.001 Thermogenic scope  Thermogenic scope ~ log Mb PGLS–OU 48 1.49 –0.89 0.2 <0.001  Thermogenic scope ~ log Mb PGLS–Brownian 48 1.38 –0.84 0.2 <0.001 Open in new tab Table 2. Statistical output for regression models. . Model . Residual DF . Slope . Intercept . R2 . P-value . BMR  Log BMR ~ log Mb PGLS–OU 233 0.63 –1.36 0.67 <0.001  Log BMR ~ log Mb PGLS–Brownian 233 0.66 –1.46 0.67 <0.001 Msum  Log Msum ~ log Mb PGLS–OU 70 0.79 –0.84 0.68 <0.001  Log Msum ~ log Mb PGLS–Brownian 70 0.68 –0.71 0.72 <0.001 Thermogenic scope  Thermogenic scope ~ log Mb PGLS–OU 48 1.49 –0.89 0.2 <0.001  Thermogenic scope ~ log Mb PGLS–Brownian 48 1.38 –0.84 0.2 <0.001 . Model . Residual DF . Slope . Intercept . R2 . P-value . BMR  Log BMR ~ log Mb PGLS–OU 233 0.63 –1.36 0.67 <0.001  Log BMR ~ log Mb PGLS–Brownian 233 0.66 –1.46 0.67 <0.001 Msum  Log Msum ~ log Mb PGLS–OU 70 0.79 –0.84 0.68 <0.001  Log Msum ~ log Mb PGLS–Brownian 70 0.68 –0.71 0.72 <0.001 Thermogenic scope  Thermogenic scope ~ log Mb PGLS–OU 48 1.49 –0.89 0.2 <0.001  Thermogenic scope ~ log Mb PGLS–Brownian 48 1.38 –0.84 0.2 <0.001 Open in new tab Figure 1. Open in new tabDownload slide Allometric relationships among (A) log BMR, (B) log Msum, and (C) thermogenic scope and log Mb. Symbols represent individual species data and statistics are provided for the best-fit Brownian regression. Figure 1. Open in new tabDownload slide Allometric relationships among (A) log BMR, (B) log Msum, and (C) thermogenic scope and log Mb. Symbols represent individual species data and statistics are provided for the best-fit Brownian regression. Do BMR, Summit Metabolic Rate, and Thermogenic Scope Differ between Swallows and Other Birds? PGLS–OU models provided better fits than PGLS–Brownian model for the data comparing BMR, Msum and thermogenic scope between swallows and non-aerial insectivore birds. AICc and log likelihood (LL) values for the metabolic trait models were BMR: OU:AICc = –254.7, Brownian AICc = –127.8; OU LL = 133.4, Brownian LL = 68.9; Msum: OU:AICc = –83.5, Brownian AICc = –53.8; OU LL = 47.8, Brownian LL = 31.9; Thermogenic Scope: OU:AICc = 65.7, Brownian AICc = 86.1; OU LL = –26.8, Brownian LL = –38.1. BMR was significantly higher (9.8%) in swallows than in non-aerial insectivore birds for PGLS–OU (Table 3, Figure 2A) models. BMR was also significantly higher (17.2 %) in temperate compared with tropical birds (Table 3, Figure 3A), regardless of group. There was no significant group by region interaction for BMR for PGLS–OU (P > 0.1 in both cases). Although the PGLS–Brownian motion model showed a significant group by region interaction for BMR (Table 3), PGLS–OU provided a better fit. Table 3. Statistical output for best-fit PGLS via an OU process. Corresponding predictors . Num DF . Residual DF . F-value . P-value . Log BMR ~ group + region + log Mb  Group 1 231 8.51 0.004  Region 1 231 93.65 <0.0001  Log Mb 1 231 1,102.26 <0.0001  Group * region 1 230 0.08 0.77 Log Msum ~ group + region + log Mb  Group 1 68 0.85 0.36  Region 1 68 3.01 0.09  Log Mb 1 68 178.23 <0.0001  Group * region 1 67 0.95 0.33 Thermogenic scope ~ group + region + log Mb  Group 1 46 5.25 0.03  Region 1 46 25.84 <0.0001  Log Mb 1 46 39.41 <0.0001  Group * region 1 45 5.13 0.03 Corresponding predictors . Num DF . Residual DF . F-value . P-value . Log BMR ~ group + region + log Mb  Group 1 231 8.51 0.004  Region 1 231 93.65 <0.0001  Log Mb 1 231 1,102.26 <0.0001  Group * region 1 230 0.08 0.77 Log Msum ~ group + region + log Mb  Group 1 68 0.85 0.36  Region 1 68 3.01 0.09  Log Mb 1 68 178.23 <0.0001  Group * region 1 67 0.95 0.33 Thermogenic scope ~ group + region + log Mb  Group 1 46 5.25 0.03  Region 1 46 25.84 <0.0001  Log Mb 1 46 39.41 <0.0001  Group * region 1 45 5.13 0.03 Open in new tab Table 3. Statistical output for best-fit PGLS via an OU process. Corresponding predictors . Num DF . Residual DF . F-value . P-value . Log BMR ~ group + region + log Mb  Group 1 231 8.51 0.004  Region 1 231 93.65 <0.0001  Log Mb 1 231 1,102.26 <0.0001  Group * region 1 230 0.08 0.77 Log Msum ~ group + region + log Mb  Group 1 68 0.85 0.36  Region 1 68 3.01 0.09  Log Mb 1 68 178.23 <0.0001  Group * region 1 67 0.95 0.33 Thermogenic scope ~ group + region + log Mb  Group 1 46 5.25 0.03  Region 1 46 25.84 <0.0001  Log Mb 1 46 39.41 <0.0001  Group * region 1 45 5.13 0.03 Corresponding predictors . Num DF . Residual DF . F-value . P-value . Log BMR ~ group + region + log Mb  Group 1 231 8.51 0.004  Region 1 231 93.65 <0.0001  Log Mb 1 231 1,102.26 <0.0001  Group * region 1 230 0.08 0.77 Log Msum ~ group + region + log Mb  Group 1 68 0.85 0.36  Region 1 68 3.01 0.09  Log Mb 1 68 178.23 <0.0001  Group * region 1 67 0.95 0.33 Thermogenic scope ~ group + region + log Mb  Group 1 46 5.25 0.03  Region 1 46 25.84 <0.0001  Log Mb 1 46 39.41 <0.0001  Group * region 1 45 5.13 0.03 Open in new tab Figure 2. Open in new tabDownload slide Relationship between (A) group and BMR and (B) group and Msum. Data shown are individual species data and PGLS–OU means. Different letters denote statistical significance. Figure 2. Open in new tabDownload slide Relationship between (A) group and BMR and (B) group and Msum. Data shown are individual species data and PGLS–OU means. Different letters denote statistical significance. Figure 3. Open in new tabDownload slide Relationship among (A) region and BMR, (B) region and Msum, and (C) group, region, and thermogenic scope. Data shown are individual species data and PGLS–OU means. Different letters denote statistical significance. Figure 3. Open in new tabDownload slide Relationship among (A) region and BMR, (B) region and Msum, and (C) group, region, and thermogenic scope. Data shown are individual species data and PGLS–OU means. Different letters denote statistical significance. None of the models showed significant differences in Msum between swallows and non-aerial insectivores (Table 3). According to the best-fit PGLS–OU model, Msum was 20.6% higher in swallows than in non-aerial insectivore birds, but this difference was not statistically significant (Table 3, Figure 2B). Furthermore, no models indicated significant differences in Msum between tropical and temperate birds (Table 3), although this difference in the best-fit PGLS–OU model approached significance (F1, 68 = 3.011, P = 0.087, Figure 3B), with 32% higher Msum in temperate birds. No significant group by region interaction occurred for Msum for any model (P > 0.1 in all cases). For thermogenic scope, a significant group by region interaction occurred between swallows and non-aerial insectivores (Figure 3C), where tropical non-aerial insectivores had significantly lower thermogenic scope compared with tropical swallows and to temperate-zone birds, including both swallows and non-aerial insectivores (Table 3). Thermogenic scope was significantly higher (30.9%) in swallows compared with non-aerial insectivores for the PGLS–OU model, but this result was driven by differences in tropical birds, where thermogenic scope in swallows exceeded that in non-aerial insectivores by 64.8%. For temperate birds, thermogenic scope in swallows only exceeded that in that in non-aerial insectivore birds by only 4.3% (Table 3). DISCUSSION The PGLS–OU models fit the data better than the PGLS–Brownian motion models. After correcting for body mass and temperate vs. tropical affiliation, BMR was significantly higher for swallows than for non-aerial insectivore birds in PGLS–OU model. These results suggest that swallows, as a taxon, have high BMR relative to non-aerial insectivore birds. The high BMR of swallows supports our hypothesis that the energetically expensive aerial insectivore lifestyle does incur increased maintenance costs to support metabolic machinery associated with spending much of the active period each day on the wing. Our results also confirm results from other studies that, after correcting for body mass, temperate-zone bird species have higher BMR than tropical species (Wiersma et al. 2007b, Londoño et al. 2015). One methodological factor, however, might impact our comparisons of BMR between swallows and non-aerial insectivore birds. We measured BMR in swallows for 2 hr of the night on birds fasted for at least 6 hr prior to measurements. These methods meet the strict definition of BMR, but other studies show that metabolic rates often decrease to their lowest values later in the night (Jacobs and McKechnie 2014). Rates of oxygen consumption for birds in our study were uniformly stable and low during the second hour of measurement, and birds were asleep when removed from the metabolic chamber, but we cannot rule out that metabolic rates may have been slightly lower later in the night. To help address this issue, we measured BMR for 4 birds over the entire night (on days where only a single bird was captured). For 2 of these birds, the lowest 10-min period of oxygen consumption occurred during the second hour of measurement. For the remaining 2 birds, the lowest period of oxygen consumption occurred at 2.6 and 4.4 hr after the beginning of measurements. For these latter 2 birds, the lowest 10-min rate of oxygen consumption overnight was only 2.7% lower on average compared with the lowest rate for the second hour of measurement. In addition, for birds in our study measured for only the 2-hr period, the lowest 10-min period for oxygen consumption, which we considered BMR, did not always occur at the end of the second hour in the chamber (mean ± SD time at beginning of 10-min period = 89.8 ± 13.8 min), suggesting that metabolic rates had reached steady-state conditions before the end of our 2-hr measurement period. Measurements of BMR for the swallow species in this study are not available in the literature. Williams (1988) did estimate BMR in tree swallows (mean Mb = 21.6 g) as 0.992 mL O2 min−1 by subtracting 25% from resting metabolic rate values measured during the day. This compares to our value for tree swallows in the present study (mean Mb = 17.4 g) of 0.881 mL O2 min−1 (Table 1). If we use the PGLS exponent for BMR (0.65; Figure 1) to calculate expected differences in BMR based on body mass differences between the 2 studies (21.6 vs. 17.4 g), this method yields an expected BMR 15.3% higher for birds in the Williams (1988) study than that for the present study. The estimated BMR in the Williams (1988) study was 12.6% higher than in the present study, which differs by only 2.7% from predictions based on body masses, suggesting that our methods produced similar measures of BMR. In addition, a number of the BMR studies in the comparative dataset also report BMR values for periods not encompassing the entire night (Supplementary Material Table 1) but still meeting the strict definition of BMR. Thus, while we cannot rule out that slightly lower metabolic rates might have occurred with longer measurement periods, these are not likely to fully account for the magnitude of the difference in BMR between swallows and non-aerial insectivore birds (9.8%) detected by the PGLS–OU analyses in the present study. Species traits, such as diet, foraging mode, and migratory status, can contribute to energetically demanding lifestyles and may impact life-history strategies (Dobson 2007, Sibly et al. 2012). Different foraging strategies have different energetic costs and high-energy foraging strategies might be negatively correlated with other life-history traits (Yap et al. 2017, Zhang et al. 2018b). For example, the cost of foraging may influence other life-history traits such as reproduction and predation risk (Grémillet and Wilson 1999). A fast pace of life, associated with energetically demanding lifestyles, is often positively correlated with BMR (Wiersma et al. 2007b, Williams et al. 2010, Versteegh et al. 2012). This relationship likely exists because activities associated with fast lifestyles may involve higher support costs, such as larger nutritional and exercise organs (Wiersma et al. 2012), which, in turn, may necessitate higher BMR. Consequently, a high BMR would support high activity levels associated with a fast pace of life, which may be adaptive in variable environments by allowing rapid adjustments of energy provision when environmental factors change. Temperate bird species typically show higher Msum than their tropical counterparts (Wiersma et al. 2007a) as a component of the fast pace of life syndrome. The results in the present study provide marginal support for this finding, as we observed a nonsignificant trend (P = 0.087) toward higher Msum in temperate than in tropical species. Reasons for the absence of a significant difference in Msum between temperate and tropical birds in the present study are not known but likely involve higher variation in Msum than in BMR measurements (where a difference was detected) or differences in the phylogenies used for the different studies, although overlap in phylogenies occurred among studies. From an energetics point of view, it seems likely that natural selection should act to reduce BMR to the lowest level possible (Swanson et al. 2017). Thus, the high BMR in Hirundines in this study could result in potential tradeoffs with other life-history traits such as reproduction and lifespan. It is also worth noting here that even if there is a cost to a high-energy lifestyle, such a cost does not always result in tradeoffs (Zhang and Hood 2016). The tradeoff hypothesis assumes that the energy pool for the animal is fixed, so higher maintenance costs would result in compromising other traits. But if the animal is able to increase the energy pool, higher BMR could have significant beneficial effects in supporting other lifestyle characteristics. The insectivore diet of Hirundines contains high protein and lipid content (Turner 1981), so such an energy-rich diet could support the energetically demanding aerial insectivore lifestyle and any associated elevation of BMR. The increased intake hypothesis for changes in BMR supports the idea that high BMR increases support of higher activity, which, in turn, increases fitness (Burton et al. 2011). Highlighting the fitness consequences of such BMR elevation, however, needs further investigation. Flight is an energetically expensive form of locomotion, so birds foraging on the wing exhibit higher DEE than other birds (i.e. DEE) (Williams 1988, Bryant and Tatner 1991, Schmidt-Wellenburg et al. 2007). BMR and Msum in the present study were positively correlated. BMR and DEE or MMR may also be positively correlated in birds (Dutenhoffer and Swanson 1996, Rezende et al. 2002, Portugal et al. 2016, Nespolo et al. 2017; but see Ricklefs et al. 1996, Wiersma et al. 2007a). If positive correlations exist among BMR, DEE, and maximum metabolic outputs, species with energetically demanding lifestyles might also be expected to show higher maximal thermogenic metabolic rates than other birds. Flight capacity is also positively associated with heart size in birds, suggesting that energetically expensive flight modes are positively associated with aerobic capacity (Nespolo et al. 2018). Pectoralis muscle masses are often positively correlated with Msum (Vézina et al. 2007, Swanson et al. 2013, Petit et al. 2014, Petit and Vézina 2014a, Milbergue et al. 2018) and MMR (Chappell et al. 1999, Hammond et al. 2000) in birds, although this is not always the case (Swanson et al. 2014, Dubois et al. 2016, Zhang et al. 2018a). Similar positive correlations of heart mass with Msum or MMR also occur in birds (Chappell et al. 1999, Hammond et al. 2000, Swanson et al. 2014; but see Milbergue et al. 2018). These results suggest the potential for thermogenic side effects of an energetically expensive, high-activity lifestyle in birds. In the present study, however, we did not observe any significant differences in Msum between swallows and non-aerial insectivore birds even though Msum averaged 20.6% higher in swallows. This finding is consistent with recent data for shorebirds, which show energetically expensive long-distance migrant, high-latitude breeding lifestyles, and high BMR (Kersten and Piersma 1987, Lindström and Klaassen 2003) but not high exercise or cold-induced maximal metabolic capacities (Thomas and Swanson 2019). Thus, it appears that energetically demanding lifestyles are not phenotypically linked to high metabolic capacities for either exercise or thermogenesis in birds generally. Sample sizes for Msum dataset (8 swallow species, 72 species total) might contribute to the nonsignificant results, despite higher average Msum in swallows. However, variation in Msum was higher than for BMR in the present study (Figures 1 and 2), so other factors also likely contribute to the absence of a significant difference in Msum between swallows and non-aerial insectivore birds. For example, aerial insectivores spend the majority of their time on the wing, so they also show aerodynamically efficient flight with relatively low flight metabolic rate (Buchanan and Evans 2000, Hedenström et al. 2019). Swallows relying on glides and partial bounds during flight minimize flapping to reduce the average power expended for changes in speed or elevation (Warrick et al. 2016, Hedrick et al. 2018). These biomechanical adaptations could allow swallows to minimize their energy expenditure for flight while maintaining an aerial insectivore lifestyle (Hails 1979). Nevertheless, DEE is still higher for swallows than for other birds (Williams 1988, Bryant and Tatner 1991, Schmidt-Wellenburg et al. 2007). In addition, even though positive correlations between Msum and MMR have been documented both intra- and inter-specifically, as discussed above, this positive relationship between Msum and MMR is not uniform among birds, particularly for intraspecific comparisons, where both positive and no correlations have been observed (Swanson et al. 2012, Zhang et al. 2015b). Despite shivering and flight both being functions of skeletal muscle contraction that share similar metabolic pathways (Zhang et al. 2015a), isotonic flight and isometric shivering could still potentially result in different metabolic capacities (Iellamo et al. 1997). Indeed, thermogenic maximum metabolic rates are generally only 60% to 70% of exercise maximal metabolic rates in birds (Chappell et al. 1999, Swanson et al. 2012, Zhang et al. 2015b). Thus, whether both Msum and MMR vary similarly with BMR and how they correlate with each other remains an open question (McKechnie and Swanson 2010). Aerobic scope defines the capacity of an organism to elevate metabolic output to meet an energetic challenge. Species living at high latitudes and cold environmental temperatures or employing torpor or hibernation often exhibit high aerobic scope (Naya et al. 2012, Careau 2013, Stager et al. 2016). In the present study, the group by region interaction term was a significant effector of thermogenic scope. This result was driven primarily by differences in tropical birds where thermogenic scope in swallows exceeded that in non-aerial insectivores by 64.8%, whereas the difference between swallows and non-aerial insectivores for temperate birds was only 4.3%. The lower magnitude of the difference in thermogenic scope for temperate swallows vs. non-aerial insectivores suggests that other factors associated with the faster pace of life of temperate-zone birds (Wiersma et al. 2007a, 2007b, Stager et al. 2016) select for higher capacities to elevate metabolic rates to meet ecological and energetic demands imposed by temperate-zone climates and lifestyles in non-aerial insectivores. For tropical birds, with a generally slow pace of life (Wiersma et al. 2007b), the energetically demanding aerial insectivore lifestyle may not permit similar reductions of aerobic or thermogenic scope for tropical swallows. We caution, however, that this conclusion is based on a sample size of 2 for tropical swallows, so future research should examine metabolic capacities in additional tropical Hirundines. The differences in thermogenic scope in the present study result from changes at the upper, rather than lower, end of the metabolic expansibility spectrum. Metabolic expansibility values in the present study averaged 4.83 ± 0.25 (SE) in swallow species and were consistent with values for Msum exceeding BMR by 4–8 times in birds generally (Swanson 2010) and by an average of 4.83 ± 0.25 (SE) for the 6 Hirundinidae species measured in this study. These data also suggest that upper and lower metabolic bounds may be modulated independently (Petit et al. 2013, Petit and Vézina 2014b, Barceló et al. 2017). Such a scenario is consistent with the results of Stager et al. (2016), who demonstrated that latitudinal patterns of variation in thermogenic scope among birds were driven primarily by differences in maximum thermogenic output rather than BMR. It is also worth noting in this regard that the datasets for BMR, Msum, and thermogenic scope employed in this present study were different, with more BMR data than Msum or thermogenic scope data. Consequently, differences among the datasets might also contribute to different results for BMR, Msum, and thermogenic scope data regarding metabolic levels between swallows and non-aerial insectivore birds. In conclusion, swallows demonstrate high maintenance metabolic costs relative to non-aerial insectivore birds, but a concomitant increase in maximum thermogenic capacity was not observed. This pattern suggests that while increases in DEE may carry additional maintenance costs in swallows, this does not similarly extend to promote higher maximal thermogenic capacities. Perhaps these differences could be explained by differences in selective pressures, with maximum thermogenic capacities being under stabilizing selection with BMR being under directional selection to reduce maintenance costs (Nespolo et al. 2017, Swanson et al. 2017). In such a scenario, maintenance costs should always be as low as possible, so selection should act to drive BMR downward (Swanson et al. 2017). Maximal metabolic capacities, however, might be under different stabilizing selective pressures to reach an optimal level that produces fitness benefits (Nespolo et al. 2017, Petit et al. 2017, Latimer et al. 2018). In the present study, however, PGLS–OU models fit the data for both BMR and Msum better than PGLS–Brownian motion models, which suggests that both metabolic traits might be under similar stabilizing selection pressures (Nilsson and Nilsson 2016). Further studies are needed to address the question of whether the observed increase of BMR in swallows might produce tradeoffs with other life-history traits affecting reproduction and survival. ACKNOWLEDGMENTS We thank Ming Liu, Michael Moxnes, Steven Higgins, Aaron Gregor, Kenneth Renner, and Joe Vitt for their technical assistance in the field and laboratory. Funding statement: This research was funded by National Science Foundation (NSF) IOS-1021218 to D.L.S. Ethics statement: This research was conducted in compliance with the University of South Dakota Institutional Animal Care and Use Committee (Protocol number: 27-02-08-11B). Author contributions: Y.Z. and D.L.S. conceived and designed the experiments. Y.Z. performed the experiments. Y.Z., K.N.Y., and K.T.D. analyzed the data. Y.Z., K.N.Y., and D.L.S. wrote the manuscript; other authors provided editorial advice. Data availability: Analyses reported in this article can be reproduced using the data within the article and online Supplementary Material. LITERATURE CITED Arens , J. R. , and S. J. Cooper ( 2005 ). Metabolic and ventilatory acclimatization to cold stress in House Sparrows (Passer domesticus) . Physiological and Biochemical Zoology 78 : 579 – 589 . Google Scholar Crossref Search ADS PubMed WorldCat Auer , S. K. , S. S. Killen, and E. L. Rezende ( 2017 ). Resting vs. active: A meta-analysis of the intra- and inter-specific associations between minimum, sustained, and maximum metabolic rates in vertebrates . Functional Ecology 31 : 1728 – 1738 . Google Scholar Crossref Search ADS PubMed WorldCat Barceló , G. , O. P. Love, and F. Vézina ( 2017 ). Uncoupling basal and summit metabolic rates in White-throated Sparrows: Digestive demand drives maintenance costs, but changes in muscle mass are not needed to improve thermogenic capacity . Physiological and Biochemical Zoology 90 : 153 – 165 . Google Scholar Crossref Search ADS PubMed WorldCat Bartholomew , G. A. , D. Vleck, and C. M. Vleck ( 1981 ). Instantaneous measurements of oxygen consumption during pre-flight warm-up and post-flight cooling in sphingid and saturniid moths . Journal of Experimental Biology 90 : 17 – 32 . Google Scholar OpenURL Placeholder Text WorldCat Bryant , D. , C. Hails, and P. Tatner ( 1984 ). Reproductive energetics of two tropical bird species . The Auk 101 : 25 – 37 . Google Scholar Crossref Search ADS WorldCat Bryant , D. M. , and P. Tatner ( 1991 ). Intraspecies variation in avian energy expenditure: Correlates and constraints . Ibis 133 : 236 – 245 . Google Scholar Crossref Search ADS WorldCat Buchanan , K. L. , and M. R. Evans ( 2000 ). The effect of tail streamer length on aerodynamic performance in the Barn Swallow . Behavioral Ecology 11 : 228 – 238 . Google Scholar Crossref Search ADS WorldCat Burton , T. , S. S. Killen, J. D. Armstrong, and N. B. Metcalfe ( 2011 ). What causes intraspecific variation in resting metabolic rate and what are its ecological consequences? Proceedings. Biological Sciences 278 : 3465 – 3473 . Google Scholar PubMed OpenURL Placeholder Text WorldCat Careau , V . ( 2013 ). Basal metabolic rate, maximum thermogenic capacity and aerobic scope in rodents: Interaction between environmental temperature and torpor use . Biology Letters 9 : 20121104 . Google Scholar Crossref Search ADS PubMed WorldCat Carey , C . ( 1996 ). Avian Energetics and Nutritional Ecology . Chapman & Hall, New York, NY, USA . Google Scholar Crossref Search ADS Google Preview WorldCat COPAC Chappell , M. A. , C. Bech, and W. A. Buttemer ( 1999 ). The relationship of central and peripheral organ masses to aerobic performance variation in house sparrows . The Journal of Experimental Biology 202 (Pt 17) : 2269 – 2279 . Google Scholar PubMed OpenURL Placeholder Text WorldCat Corder , K. R. , and P. J. Schaeffer ( 2015 ). Summit metabolic rate exhibits phenotypic flexibility with migration, but not latitude in a Neotropical migrant, Parkesia noveboracensis . Journal of Ornithology 156 : 547 – 550 . Google Scholar Crossref Search ADS WorldCat Dobson , F. S . ( 2007 ). A lifestyle view of life-history evolution . Proceedings of the National Academy of Sciences USA 104 : 17565 – 17566 . Google Scholar Crossref Search ADS WorldCat Dobson , F. S . ( 2012 ). Lifestyles and phylogeny explain bird life histories . Proceedings of the National Academy of Sciences USA 109 : 10747 – 10748 . Google Scholar Crossref Search ADS WorldCat Dubois , K. , F. Hallot, and F. Vézina ( 2016 ). Basal and maximal metabolic rates differ in their response to rapid temperature change among avian species . Journal of Comparative Physiology B 186 : 919 – 935 . Google Scholar Crossref Search ADS WorldCat Dutenhoffer , M. S. , and D. L. Swanson ( 1996 ). Relationship of basal to summit metabolic rate in passerine birds and the aerobic capacity model for the evolution of endothermy . Physiological Zoology 69 : 1232 – 1254 . Google Scholar Crossref Search ADS WorldCat Felsenstein , J . ( 1985 ). Confidence limits on phylogenies: An approach using the bootstrap . Evolution 39 : 783 – 791 . Google Scholar Crossref Search ADS PubMed WorldCat Freckleton , R. P . ( 2009 ). The seven deadly sins of comparative analysis . Journal of Evolutionary Biology 22 : 1367 – 1375 . Google Scholar Crossref Search ADS PubMed WorldCat Freckleton , R. P. , P. H. Harvey, and M. Pagel ( 2002 ). Phylogenetic analysis and comparative data: A test and review of evidence . The American Naturalist 160 : 712 – 726 . Google Scholar Crossref Search ADS PubMed WorldCat Garland , T. Jr,, P. E. Midford, and A. R. Ives ( 1999 ). An introduction to phylogenetically based statistical methods, with a new method for confidence intervals on ancestral values . American Zoologist 39 : 374 – 388 . Google Scholar Crossref Search ADS WorldCat Grémillet , D. , and R. P. Wilson ( 1999 ). A life in the fast lane: Energetics and foraging strategies of the Great Cormorant . Behavioral Ecology 10 : 516 – 524 . Google Scholar Crossref Search ADS WorldCat Guglielmo , C. G . ( 2010 ). Move that fatty acid: Fuel selection and transport in migratory birds and bats . Integrative and Comparative Biology 50 : 336 – 345 . Google Scholar Crossref Search ADS PubMed WorldCat Hailey , A. , and P. M. C. Davies ( 1986 ). Lifestyle, latitude and activity metabolism of natricine snakes . Journal of Zoology 209 : 461 – 476 . Google Scholar Crossref Search ADS WorldCat Hails , C . ( 1979 ). A comparison of flight energetics in hirundines and other birds . Comparative Biochemistry and Physiology Part A: Physiology 63 : 581 – 585 . Google Scholar Crossref Search ADS WorldCat Hammond , K. A. , M. A. Chappell, R. A. Cardullo, R. Lin, and T. S. Johnsen ( 2000 ). The mechanistic basis of aerobic performance variation in Red Junglefowl . The Journal of Experimental Biology 203 : 2053 – 2064 . Google Scholar PubMed OpenURL Placeholder Text WorldCat Hedenström , A. , G. Norevik, G. Boano, A. Andersson, J. Bäckman, and S. Åkesson ( 2019 ). Flight activity in Pallid Swifts Apus pallidus during the non-breeding period . Journal of Avian Biology 50: 2 . doi:10.1111/jav.01972 Google Scholar OpenURL Placeholder Text WorldCat Hedrick , T. L. , C. Pichot, and E. De Margerie ( 2018 ). Gliding for a free lunch: Biomechanics of foraging flight in Common Swifts (Apus apus) . Journal of Experimental Biology 221 : jeb186270 . Google Scholar Crossref Search ADS WorldCat Iellamo , F. , J. M. Legramante, G. Raimondi, F. Castrucci, C. Damiani, C. Foti, G. Peruzzi, and I. Caruso ( 1997 ). Effects of isokinetic, isotonic and isometric submaximal exercise on heart rate and blood pressure . European Journal of Applied Physiology and Occupational Physiology 75 : 89 – 96 . Google Scholar Crossref Search ADS PubMed WorldCat Jacobs , P. J. , and A. E. McKechnie ( 2014 ). Experimental sources of variation in avian energetics: Estimated basal metabolic rate decreases with successive measurements . Physiological and Biochemical Zoology 87 : 762 – 769 . Google Scholar Crossref Search ADS PubMed WorldCat Jetz , W. , G. H. Thomas, J. B. Joy, D. W. Redding, K. Hartmann, and A. O. Mooers ( 2014 ). Global distribution and conservation of evolutionary distinctness in birds . Current Biology 24 : 919 – 930 . Google Scholar Crossref Search ADS PubMed WorldCat Kersten , M. , and T. Piersma ( 1987 ). High levels of energy expenditure in shorebirds: Metabolic adaptations to an energetically expensive way of life . Ardea 75 : 175 – 187 . Google Scholar OpenURL Placeholder Text WorldCat Killen , S. S. , D. Atkinson, and D. S. Glazier ( 2010 ). The intraspecific scaling of metabolic rate with body mass in fishes depends on lifestyle and temperature . Ecology Letters 13 : 184 – 193 . Google Scholar Crossref Search ADS PubMed WorldCat Latimer , C. E. , S. J. Cooper, W. H. Karasov, and B. Zuckerberg ( 2018 ). Does habitat fragmentation promote climate-resilient phenotypes? Oikos 127 : 1069 – 1080 . Google Scholar Crossref Search ADS WorldCat Lighton , J. R . ( 2008 ). Measuring Metabolic Rates: A Manual for Scientists . Oxford University Press, New York, NY, USA . Google Scholar Crossref Search ADS Google Preview WorldCat COPAC Lindström , Å. , and M. Klaassen ( 2003 ). High basal metabolic rates of shorebirds while in the Arctic: A circumpolar view . The Condor 105 : 420 – 427 . Google Scholar Crossref Search ADS WorldCat Londoño , G. A. , M. A. Chappell, M. d. R. Castaneda, J. E. Jankowski, and S. K. Robinson ( 2015 ). Basal metabolism in tropical birds: Latitude, altitude, and the ‘pace of life’ . Functional Ecology 29 : 338 – 346 . Google Scholar Crossref Search ADS WorldCat McKechnie , A. E . ( 2008 ). Phenotypic flexibility in basal metabolic rate and the changing view of avian physiological diversity: A review . Journal of Comparative Physiology B 178 : 235 – 247 . Google Scholar Crossref Search ADS WorldCat McKechnie , A. E. , and D. L. Swanson ( 2010 ). Sources and significance of variation in basal, summit and maximal metabolic rates in birds . Current Zoology 56 : 741 – 758 . Google Scholar Crossref Search ADS WorldCat McNab , B. K . ( 1997 ). On the utility of uniformity in the definition of basal rate of metabolism . Physiological Zoology 70 : 718 – 720 . Google Scholar Crossref Search ADS PubMed WorldCat Milbergue , M. S. , P. U. Blier, and F. Vézina ( 2018 ). Large muscles are beneficial but not required for improving thermogenic capacity in small birds . Scientific Reports 8 : 14009 . Google Scholar Crossref Search ADS PubMed WorldCat Naya , D. E. , L. Spangenberg, H. Naya, and F. Bozinovic ( 2012 ). Latitudinal patterns in rodent metabolic flexibility . The American Naturalist 179 : E172 – E179 . Google Scholar Crossref Search ADS PubMed WorldCat Nespolo , R. F. , C. González-Lagos, J. J. Solano-Iguaran , M. Elfwing, A. Garitano-Zavala, S. Mañosa, J. C. Alonso, and J. Altimiras ( 2018 ). Aerobic power and flight capacity in birds: A phylogenetic test of the heart-size hypothesis . Journal of Experimental Biology 221 : jeb162693 . Google Scholar Crossref Search ADS WorldCat Nespolo , R. F. , J. J. Solano-Iguaran, and F. Bozinovic ( 2017 ). Phylogenetic analysis supports the aerobic-capacity model for the evolution of endothermy . The American Naturalist 189 : 13 – 27 . Google Scholar Crossref Search ADS PubMed WorldCat Nilsson , J. F. , and J. Å. Nilsson ( 2016 ). Fluctuating selection on basal metabolic rate . Ecology and Evolution 6 : 1197 – 1202 . Google Scholar Crossref Search ADS PubMed WorldCat Petit , M. , S. Clavijo-Baquet, and F. Vézina ( 2017 ). Increasing winter maximal metabolic rate improves intrawinter survival in small birds . Physiological and Biochemical Zoology 90 : 166 – 177 . Google Scholar Crossref Search ADS PubMed WorldCat Petit , M. , A. Lewden, and F. Vézina ( 2013 ). Intra-seasonal flexibility in avian metabolic performance highlights the uncoupling of basal metabolic rate and thermogenic capacity . PLoS One 8 : e68292 . Google Scholar Crossref Search ADS PubMed WorldCat Petit , M. , A. Lewden, and F. Vézina ( 2014 ). How does flexibility in body composition relate to seasonal changes in metabolic performance in a small passerine wintering at northern latitude? Physiological and Biochemical Zoology 87 : 539 – 549 . Google Scholar Crossref Search ADS PubMed WorldCat Petit , M. , and F. Vézina ( 2014a ). Phenotype manipulations confirm the role of pectoral muscles and haematocrit in avian maximal thermogenic capacity . The Journal of Experimental Biology 217 : 824 – 830 . Google Scholar Crossref Search ADS WorldCat Petit , M. , and F. Vézina ( 2014b ). Reaction norms in natural conditions: How does metabolic performance respond to weather variations in a small endotherm facing cold environments? PLoS One 9 : e113617 . Google Scholar Crossref Search ADS WorldCat Pollock , H. S. , J. D. Brawn, T. J. Agin, and Z. A. Cheviron ( 2019 ). Differences between temperate and tropical birds in seasonal acclimatization of thermoregulatory traits . Journal of Avian Biology 50: 4 . doi:10.1111/jav.02067 Google Scholar Crossref Search ADS Google Preview WorldCat COPAC Portugal , S. J. , J. A. Green, L. G. Halsey, W. Arnold, V. Careau, P. Dann, P. B. Frappell, D. Grémillet, Y. Handrich, G. R. Martin, et al. ( 2016 ). Associations between resting, activity, and daily metabolic rate in free-living endotherms: No universal rule in birds and mammals . Physiological and Biochemical Zoology 89 : 251 – 261 . Google Scholar Crossref Search ADS PubMed WorldCat R Core Team ( 2013 ). R: A language and environment for statistical computing . R Foundation for Statistical Computing, Vienna, Austria . http://www.R-project.org/ Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Rezende , E. L. , D. L. Swanson, F. F. Novoa, and F. Bozinovic ( 2002 ). Passerines versus nonpasserines: So far, no statistical differences in the scaling of avian energetics . The Journal of Experimental Biology 205 : 101 – 107 . Google Scholar PubMed OpenURL Placeholder Text WorldCat Ricklefs , R. E. , M. Konarzewski, and S. Daan ( 1996 ). The relationship between basal metabolic rate and daily energy expenditure in birds and mammals . The American Naturalist 147 : 1047 – 1071 . Google Scholar Crossref Search ADS WorldCat Schmidt-Wellenburg , C. A. , H. Biebach, S. Daan, and G. H. Visser ( 2007 ). Energy expenditure and wing beat frequency in relation to body mass in free flying Barn Swallows (Hirundo rustica) . Journal of Comparative Physiology B 177 : 327 – 337 . Google Scholar Crossref Search ADS WorldCat Sibly , R. M. , C. C. Witt, N. A. Wright, C. Venditti, W. Jetz, and J. H. Brown ( 2012 ). Energetics, lifestyle, and reproduction in birds . Proceedings of the National Academy of Sciences USA 109 : 10937 – 10941 . Google Scholar Crossref Search ADS WorldCat Stager , M. , H. S. Pollock, P. M. Benham, N. D. Sly, J. D. Brawn, and Z. A. Cheviron ( 2016 ). Disentangling environmental drivers of metabolic flexibility in birds: The importance of temperature extremes versus temperature variability . Ecography 39 : 787 – 795 . Google Scholar Crossref Search ADS WorldCat Swanson , D. L . ( 1995 ). Seasonal variation in thermogenic capacity of migratory Warbling Vireos . The Auk 112 : 870 – 877 . Google Scholar Crossref Search ADS WorldCat Swanson , D. L . ( 2010 ). Seasonal metabolic variation in birds: Functional and mechanistic correlates . Current Ornithology 17 : 75 – 129 . Google Scholar OpenURL Placeholder Text WorldCat Swanson , D. L. , and F. Bozinovic ( 2011 ). Metabolic capacity and the evolution of biogeographic patterns in oscine and suboscine passerine birds . Physiological and Biochemical Zoology 84 : 185 – 194 . Google Scholar Crossref Search ADS PubMed WorldCat Swanson , D. L. , and K. L. Dean ( 1999 ). Migration-induced variation in thermogenic capacity in migratory passerines . Journal of Avian Biology 30 : 245 – 254 . Google Scholar Crossref Search ADS WorldCat Swanson , D. L. , M. W. Drymalski, and J. R. Brown ( 1996 ). Sliding vs static cold exposure and the measurement of summit metabolism in birds . Journal of Thermal Biology 21 : 221 – 226 . Google Scholar Crossref Search ADS WorldCat Swanson , D. L. , and T. Garland , Jr. ( 2009 ). The evolution of high summit metabolism and cold tolerance in birds and its impact on present-day distributions . Evolution 63 : 184 – 194 . Google Scholar Crossref Search ADS PubMed WorldCat Swanson , D. L. , and E. T. Liknes ( 2006 ). A comparative analysis of thermogenic capacity and cold tolerance in small birds . The Journal of Experimental Biology 209 : 466 – 474 . Google Scholar Crossref Search ADS PubMed WorldCat Swanson , D. L. , A. E. McKechnie, and F. Vézina ( 2017 ). How low can you go? An adaptive energetic framework for interpreting basal metabolic rate variation in endotherms . Journal of Comparative Physiology B 187 : 1039 – 1056 . Google Scholar Crossref Search ADS WorldCat Swanson , D. L. , N. E. Thomas, E. T. Liknes, and S. J. Cooper ( 2012 ). Intraspecific correlations of basal and maximal metabolic rates in birds and the aerobic capacity model for the evolution of endothermy . PLos One 7 : e34271 . Google Scholar Crossref Search ADS PubMed WorldCat Swanson , D. L. , Y. Zhang, and M. O. King ( 2013 ). Individual variation in thermogenic capacity is correlated with flight muscle size but not cellular metabolic capacity in American Goldfinches (Spinus tristis) . Physiological and Biochemical Zoology 86 : 421 – 431 . Google Scholar Crossref Search ADS PubMed WorldCat Swanson , D. , Y. Zhang, and M. King ( 2014 ). Mechanistic drivers of flexibility in summit metabolic rates of small birds . PLoS One 9 : e101577 . Google Scholar Crossref Search ADS PubMed WorldCat Symonds , M. R. , and S. P. Blomberg ( 2014 ). A primer on phylogenetic generalised least squares . In Modern Phylogenetic Comparative Methods and Their Application in Evolutionary Biology ( L. Z. Garamszegi, Editor). Springer-Verlag , Berlin, Heidelberg, Germany . pp. 105 – 130. Google Scholar Crossref Search ADS Google Preview WorldCat COPAC Thomas , N. E. , and D. L. Swanson ( 2019 ). Do the high energy lifestyles of shorebirds result in high maximal metabolic rates? Basal and maximal metabolic rates in least and pectoral sandpipers during migration . Journal of Avian Biology 50: 4 . Google Scholar OpenURL Placeholder Text WorldCat Turner , A. K . ( 1981 ). The Use of Time and Energy by Aerial-Feeding Birds . Doctoral dissertation , University of Stirling , Stirling, Scotland, UK . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Versteegh , M. A. , I. Schwabl, S. Jaquier, and B. I. Tieleman ( 2012 ). Do immunological, endocrine and metabolic traits fall on a single Pace-of-Life axis? Covariation and constraints among physiological systems . Journal of Evolutionary Biology 25 : 1864 – 1876 . Google Scholar Crossref Search ADS PubMed WorldCat Vézina , F. , K. M. Jalvingh, A. Dekinga, and T. Piersma ( 2007 ). Thermogenic side effects to migratory predisposition in shorebirds . American Journal of Physiology. Regulatory, Integrative and Comparative Physiology 292 : R1287 – R1297 . Google Scholar Crossref Search ADS PubMed WorldCat Warrick , D. R. , T. L. Hedrick, A. A. Biewener, K. E. Crandell, and B. W. Tobalske ( 2016 ). Foraging at the edge of the world: Low-altitude, high-speed manoeuvering in Barn Swallows . Philosophical Transactions of the Royal Society B 371 : 20150391 . Google Scholar Crossref Search ADS WorldCat Wiersma , P. , M. A. Chappell, and J. B. Williams ( 2007a ). Cold-and exercise-induced peak metabolic rates in tropical birds . Proceedings of the National Academy of Sciences USA 104 : 20866 – 20871 . Google Scholar Crossref Search ADS WorldCat Wiersma , P. , A. Muñoz-Garcia, A. Walker, and J. B. Williams ( 2007b ). Tropical birds have a slow pace of life . Proceedings of the National Academy of Sciences USA 104 : 9340 – 9345 . Google Scholar Crossref Search ADS WorldCat Wiersma , P. , B. Nowak, and J. B. Williams ( 2012 ). Small organ size contributes to the slow pace of life in tropical birds . The Journal of Experimental Biology 215 : 1662 – 1669 . Google Scholar Crossref Search ADS PubMed WorldCat Williams , J. B . ( 1988 ). Field metabolism of Tree Swallows during the breeding season . The Auk 105 : 706 – 714 . Google Scholar Crossref Search ADS WorldCat Williams , J. B. , R. A. Miller, J. M. Harper, and P. Wiersma ( 2010 ). Functional linkages for the pace of life, life-history, and environment in birds . Integrative and Comparative Biology 50 : 855 – 868 . Google Scholar Crossref Search ADS PubMed WorldCat Yap , K. N. , O. R. Kim, K. C. Harris, and T. D. Williams ( 2017 ). Physiological effects of increased foraging effort in a small passerine . The Journal of Experimental Biology 220 : 4282 – 4291 . Google Scholar Crossref Search ADS PubMed WorldCat Zhang , Y. , T. Carter, K. Eyster, and D. L. Swanson ( 2015a ). Acute cold and exercise training up-regulate similar aspects of fatty acid transport and catabolism in House Sparrows (Passer domesticus) . The Journal of Experimental Biology 218 : 3885 – 3893 . Google Scholar Crossref Search ADS WorldCat Zhang , Y. , K. Eyster, J. S. Liu, and D. L. Swanson ( 2015b ). Cross-training in birds: Cold and exercise training produce similar changes in maximal metabolic output, muscle masses and myostatin expression in House Sparrows (Passer domesticus) . The Journal of Experimental Biology 218 : 2190 – 2200 . Google Scholar Crossref Search ADS WorldCat Zhang , Y. , K. Eyster, and D. L. Swanson ( 2018a ). Context-dependent regulation of pectoralis myostatin and lipid transporters by temperature and photoperiod in Dark-eyed Juncos . Current Zoology 64 : 23 – 31 . Google Scholar Crossref Search ADS WorldCat Zhang , Y. , and W. R. Hood ( 2016 ). Current versus future reproduction and longevity: A re-evaluation of predictions and mechanisms . The Journal of Experimental Biology 219 : 3177 – 3189 . Google Scholar Crossref Search ADS PubMed WorldCat Zhang , Y. , K. N. Yap, T. D. Williams, and D. L. Swanson ( 2018b ). Experimental increases in foraging costs affect pectoralis muscle mass and myostatin expression in female, but not male, Zebra Finches (Taeniopygia guttata) . Physiological and Biochemical Zoology 91 : 849 – 858 . Google Scholar Crossref Search ADS WorldCat © 2021 American Ornithological Society. ISSN 0004-8038, electronic ISSN 1938-4254 Direct all requests to reproduce journal content to the AOS Publications Office at pubs@americanornithology.org This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Ornithology Oxford University Press

The high-energy aerial insectivore lifestyle of swallows does not produce clear thermogenic side effects

Ornithology , Volume Advance Article – Apr 23, 2021

Loading next page...
 
/lp/oxford-university-press/the-high-energy-aerial-insectivore-lifestyle-of-swallows-does-not-AIuwzQjpzu

References (104)

Copyright
Copyright © 2021 American Ornithological Society
eISSN
2732-4613
DOI
10.1093/ornithology/ukab022
Publisher site
See Article on Publisher Site

Abstract

Abstract Ecological traits related to pace of life, such as foraging strategies and activity levels, influence daily energy expenditure (DEE) and can affect fitness. A fast pace of life tends to be supported by high-energy aerobic activity and is positively correlated with high DEE and basal and maximal metabolic rates in some endotherms. Given that maximal capacities for exercise and thermogenesis are both functions of aerobic muscle output and are often positively correlated with each other, high-energy aerobic lifestyles might be associated with high aerobic capacities, which would be expected to produce high thermogenic capacities as a side effect. We tested whether the high-energy aerial insectivore lifestyle in swallows is correlated with elevated basal and maximal thermogenic metabolic rates. We measured basal (BMR) and summit (Msum = maximum cold-induced metabolic rate) metabolic rates in 6 species of swallows (Hirundinidae) and combined these data with literature data for additional swallows (n = 10 for BMR; n = 8 for Msum) and non-aerial insectivore birds (n = 215 for BMR; n = 64 for Msum) to address the hypothesis that swallows have higher BMR and Msum than non-aerial insectivores. BMR in swallows was significantly higher than for non-aerial insectivore birds for phylogenetically adjusted analyses after correcting for body mass and region of origin (tropical vs. temperate). In contrast, Msum did not differ significantly between swallows and non-aerial insectivores. Thermogenic scope (Msum – BMR), however, was lower in tropical non-aerial insectivore birds compared with tropical swallows and temperate birds. This suggests that the aerial insectivore lifestyle elevates maintenance costs, but maximum thermogenic capacities are not clearly upregulated, despite tropical swallows having higher thermogenic scope than tropical non-aerial insectivores. These data suggest that the high-energy aerial insectivore lifestyle does not produce strong thermogenic side effects in swallows. RÉSUMÉ Les traits écologiques associés au rythme de vie, tels que les stratégies de recherche alimentaire et les niveaux d’activité, influencent la dépense énergétique quotidienne et peuvent avoir une incidence sur la condition physique. Un rythme de vie rapide a tendance à être soutenu par une activité aérobique à haute énergie et est positivement corrélé à une dépense énergétique quotidienne élevée et des taux métaboliques basal et maximal chez certains endothermes. Étant donné que les capacités maximales d’exercice et de thermogénèse sont toutes deux des fonctions de la production musculaire aérobie et sont souvent corrélées positivement, des modes de vie aérobie à haute énergie pourraient être associés à des capacités aérobies élevées, qui devraient produire des capacités thermogènes élevées comme effet secondaire. Nous avons testé si le mode de vie insectivore aérien à haute énergie chez les hirondelles est corrélé avec des taux métaboliques basal et maximal thermogènes élevés. Nous avons mesuré les taux métaboliques basal (BMR) et maximal (Msum = taux métabolique maximum qui est induit par le froid) chez 6 espèces d’hirondelles (Hirundinidés) et nous avons combiné ces données aux données de la littérature pour d’autres hirondelles (n = 10 pour BMR; n = 8 pour Msum) et d’autres oiseaux non insectivores (n = 215 pour BMR; n = 64 pour Msum) pour vérifier l’hypothèse que les hirondelles ont un BMR et un Msum plus élevé que les insectivores non aériens. Le BMR des hirondelles était significativement plus élevé que celui des oiseaux insectivores non aériens dans les analyses ajustées phylogénétiquement après correction pour la masse corporelle et la région de provenance (tropicale vs tempérée). Par contre, le Msum ne différait pas significativement entre les hirondelles et les insectivores non aériens. L’étendue thermogène (Msum – BMR) était cependant plus faible chez les oiseaux insectivores non aériens tropicaux en comparaison des hirondelles tropicales et des oiseaux des régions tempérées. Ceci suggère que le mode de vie insectivore aérien augmente les coûts de maintenance, mais les capacités thermogènes maximales ne sont pas clairement régulées vers le haut, malgré que les hirondelles tropicales aient une étendue thermogène plus élevée que les insectivores non aériens tropicaux. Ces données suggèrent que le mode de vie insectivore aérien à haute énergie ne produit pas d’effets secondaires thermogènes marqués chez les hirondelles. Lay Summary • Animals have different lifestyles or paces of life that influence the amount of energy used per day. • Swallows are aerial insectivores and have an active lifestyle that could lead to higher metabolic capacities at both basal and maximal levels. • We measured basal and maximum cold-induced metabolic rates in swallows and compared these with non-aerial insectivore birds to test if swallows have higher basal and maximum metabolic capacities. • We found that swallows have higher basal metabolic rates but not maximum cold-induced metabolic rates compared with other non-aerial insectivore birds. • The results suggest that the active lifestyle of swallows required more energy for maintenance but did not produce high thermogenic capacities as a byproduct. INTRODUCTION Ecological or life-history traits associated with energy acquisition, such as foraging strategies and activity levels, contribute to lifestyle differences among species that can affect energetics and fitness and link organisms to the environment (Dobson 2012). Species with different lifestyles will have different energy demands for activities such as foraging, predator avoidance, and locomotor patterns, and this may, in turn, lead to the evolution of differences in metabolic rate. There are several studies on mammals, fishes, and reptiles, which indicate that elevated metabolic capacities are associated with greater activity levels (Hailey and Davies 1986, Killen et al. 2010). In addition, positive correlations among basal metabolic rate (BMR), daily energy expenditure (DEE), and maximal metabolic rate responses to cold (summit metabolism, Msum) or maximal metabolic rate responses to exercise (MMR) may occur in birds (Dutenhoffer and Swanson 1996, Rezende et al. 2002, Portugal et al. 2016 but see Ricklefs et al. 1996, Wiersma et al. 2007a, Auer et al. 2017). Lower and upper metabolic limits, basal and maximal metabolic rates, respectively, are often measured to indicate the energetic profile of an organism for comparison with other organisms (Swanson 2010, Barceló et al. 2017, Swanson et al. 2017). BMR, defined as the minimum metabolic rate required for maintenance, is largely determined by the function of central organs and is widely used as a measure of the physiological baseline or the “rate of living” for an animal (McNab 1997, McKechnie 2008, Petit and Vézina 2014b, Swanson et al. 2017). A faster rate of living might, therefore, be expected to produce a higher BMR. Furthermore, because maximal metabolic rate produced by aerobic activities (e.g., exercise or thermogenesis) is mainly a function of skeletal muscles, these metabolic rates serve as measures of the energetic capacity of an endotherm for exercise or thermogenesis. The maximum metabolic rate during thermogenesis (i.e. cold-induced summit metabolic rate: Msum) has recently become a common measure for birds and has been standardized among studies, thus allowing comparisons among different bird groups (McKechnie and Swanson 2010). Both shivering thermogenesis and flight are functions of skeletal muscle activity and share similar metabolic pathways and substrates (Wiersma et al. 2007a, Guglielmo 2010, Zhang et al. 2015a). Indeed, evidence from recent studies suggests that MMR and Msum may increase in tandem in response to increasing energy demands from exercise or cold exposure (Petit and Vézina 2014a, Zhang et al. 2015b). For example, cold- and exercise-trained house sparrows (Passer domesticus) showed positive intraspecific correlations between Msum and MMR, and exercise enhanced both exercise and thermogenic maximal metabolic rates in this species (Zhang et al. 2015b). Experimentally increased flight costs can also increase Msum in birds (Petit and Vézina 2014a). Pre-migratory fattening may also result in increases in Msum and produce thermogenic side effects in birds (Vézina et al. 2007). Moreover, Msum was higher during spring migration than during non-migratory seasons for northern waterthrush (Parkesia noveboracensis (Corder and Schaeffer 2015) and was higher during spring than fall migration for several bird species, potentially associated with a faster pace of migration during spring (Swanson 1995, Swanson and Dean 1999). Finally, Wiersma et al. (2007a) found that the aerial insectivore mangrove swallow (Tachycineta albilinea) had the highest Msum among tropical bird species. Thus, enhanced heat production can apparently occur as a byproduct of adaptation to high-energy portions of the annual cycle or to a high-activity lifestyle in birds. The avian family Hirundinidae includes swallows and martins and, collectively, is distributed on all continents except Antarctica. Swallows and martins are characterized by adaptation to aerial feeding. Unlike most other bird species, swallows fly continuously during foraging, whereas foraging in most other terrestrial birds involves perching and resting between foraging bouts. Consequently, the aerial foraging lifestyle of swallows involves extended periods of high-energy flight. Aerial foraging, such as that documented for tree swallows, involves rapid turns and maneuvers, which comprise an energetically expensive type of flight (Carey 1996). DEE of breeding swallows is positively associated with the amount of time spent flying and DEE in 7 species of Hirundinidines was higher than that in non-aerial insectivorous bird species (Williams 1988). Such a metabolically expensive lifestyle might necessitate higher maintenance costs (Auer et al. 2017), so the aerial insectivore lifestyle of swallows might come at the cost of a higher BMR. Given the often positive interspecific relationships between BMR, DEE, and Msum in birds (Rezende et al. 2002, Wiersma et al. 2007a), we hypothesized that swallows would have high BMR and thermogenic capacities as a byproduct of their high-energy aerobic lifestyle. In the present study, we collected wild, free-living swallows in southeastern South Dakota and measured both BMR and Msum for 6 Hirundinidae species. We supplemented these data with literature values for both other swallows and for non-aerial insectivorous birds to compare basal and maximal thermogenic (Msum) metabolic levels. We employed phylogenetically informed statistical analyses to examine whether the energetically expensive lifestyle of swallows affects metabolic capacities. We predicted that birds from the family Hirundinidae would have: (1) higher BMR than non-aerial insectivorous birds to support the high energetic demand of their aerial foraging lifestyle and (2) higher Msum as a byproduct of adaptation to the high aerobic energy demands for prolonged foraging on the wing. MATERIALS AND METHODS Sampling We collected BMR and Msum data for 6 species in the family Hirundinidae in this study. These species included Purple Martin (n = 6; Progne subis), Tree Swallow (n = 6; Tachycineta bicolor), Bank Swallow (n = 5; Riparia riparia), Northern Rough-winged Swallow (n = 5; Stelgidopteryx serripennis), Cliff Swallow (n = 7; Hirundo pyrrhonota), and Barn Swallow (n = 6; Hirundo rustica). We collected all species, except purple martin, from wild populations near Vermillion, Clay County, South Dakota (~42.47°N, 97°W). We collected purple martins from a colony near Madison, Lake County, South Dakota (~44°N, 97.7°W). We captured adult swallows for all species by mist net. We captured all swallows in the morning (before 1,100 central daylight time) between May 20 and June 30 in 2010 and 2011. After capture, we transported birds to the laboratory (<30 min for all species but purple martin, which required about 105 min transport time) and caged them at room temperature (20–25°C). We did not feed birds after capture but did provide water in the holding cage. We conducted Msum measurements during the daytime (between 1200 and 1800 hours) within 8 hr of collection. We measured BMR the following night and all birds were fasted for at least 6 hr before BMR measurement. After both Msum and BMR measurements, we banded birds and released them at the site of capture the following morning. Msum and BMR Measurement We measured metabolic rate using open-circuit respirometry as described previously (Zhang et al. 2015b). We measured body mass before and after each metabolic rate measurement. Mass loss was assumed to be constant throughout the test period and we corrected body mass to the time for which we recorded BMR or Msum (see below). The respirometry system for Msum and BMR measurements consisted of 1.9-L paint cans with the inner surface painted flat black as metabolic chambers. Metabolic chambers were immersed into an ethylene glycol bath for temperature control to ±0.2°C. Fractional concentrations of oxygen in excurrent air were sampled with an Ametek S-3A oxygen analyzer (Applied Electrochemistry, Pittsburgh, Pennsylvania, USA) at 1-s intervals and we collected data with Expedata 2.0 (Sable Systems, Henderson, Nevada, USA) software. We analyzed oxygen consumption data with Expedata 2.0 software after correcting to STPD, using steady-state calculations for BMR and instantaneous calculations for Msum (Bartholomew et al. 1981, Lighton 2008). We performed Msum with a sliding cold exposure protocol with helox (79% helium/21% oxygen) (Swanson et al. 1996), which has higher thermal conductivity than air (Arens and Cooper 2005). For the sliding cold exposure protocol, flow rates of dry, CO2-free helox (79% helium/21% oxygen) at 1,010–1,030 mL min−1 were maintained through the chambers. We controlled the flow rate by a Cole-Parmer Precision Rotameter (Model FM082-03ST; Cole-Parmer, Vernon Hills, IL, USA) calibrated to ±1% accuracy with a soap bubble meter. After flushing the chamber with helox for 5 min, we initiated the cold exposure by immersing the metabolic chamber into the ethylene glycol bath, with the cold exposure initiated at 5°C. We continued the sliding cold exposure treatment by decreasing the temperature at a rate of ~1°C every 5 min until we detected a steady decline in oxygen consumption over several minutes, which is indicative of hypothermia. We then removed birds from the metabolic chamber and recorded body temperatures cloacally. We considered cloacal temperatures of ≤36°C as hypothermic (Swanson and Liknes 2006), and all birds were hypothermic at the end of cold exposure trials, which validated that Msum had been attained. We considered the highest 5-min running mean value for oxygen consumption over the test period as Msum (Swanson et al. 2012). We conducted BMR measurements at night (at least 1 hr after darkness) on birds fasted for at least 6 hr prior to metabolic measurements at 30°C, which is within the thermoneutral zone for swallows (Bryant et al. 1984, Williams 1988, Wiersma et al. 2007b). We maintained flow rates of dry, CO2-free air at 280–300 mL min−1 for BMR. We allowed birds to equilibrate for at least 1 hr within the metabolic chamber before we initiated metabolic measurements. We measured BMR for all birds for a period of at least 1 hr (between 2200 and 0500 hours) following the 1-hr equilibration periods and we calculated 10-min running mean values for oxygen consumption over the test period, with the lowest 10-min running mean designated as BMR (Swanson et al. 2012). From BMR and Msum data, we calculated thermogenic scope as Msum – BMR for each individual. Phylogenetic Tree Construction To compare metabolic rates of Hirundines with other birds, we collected BMR, Msum, and thermogenic scope data from the literature (see Supplementary Material Tables 1–3) for both additional Hirundines and for other non-aerial insectivore birds. Ten species of Hirundines and 215 species of non-aerial insectivores were included for BMR analyses and 8 species of Hirundines and 64 species of species of non-aerial insectivores for Msum analyses. We used uncorrected raw BMR data from McKechnie (2008), Stager et al. (2016), and Wiersma et al. (2007a), and Msum data from Swanson and Garland (2009), Swanson and Bozinovic (2011), Stager et al. (2016), and Wiersma et al. (2007a). We included metabolic trait data from literature values only from summer measurements for temperate-zone bird species because temperate-zone birds exhibit seasonal variation in metabolic rates (Swanson 2010, Pollock et al. 2019), and our metabolic measurements were from summer-acclimatized swallows. We created a consensus phylogenetic tree from birdtree.org for all species considered in all 3 sets of analyses (BMR, Msum, and thermogenic scope) in this study. The consensus tree was derived from 100 randomly sampled phylogenetic trees for all species included in this study using the Ericson All Species backbone posterior distribution from a global phylogeny of birds (Jetz et al. 2014). We first trimmed the global phylogeny of birds to a subset including the species included in our analyses and then created 100 randomly sampled trees using a pseudo-posterior distribution (see Jetz et al. 2014 for detailed methods). The consensus phylogenetic trees for all 3 sets of analyses are shown in Supplementary Figures S1–S3. Statistics Analyses were carried out using R 0.99.467 (R Core Team 2013) and the packages “ape,” “geiger,” “nlme,” “phytools,” “lmtest,” and “multcomp.” Residuals were first visually examined for normality using quantile–quantile plots and log-transformed if they were not normally distributed. We also used Levene’s test to test for equal variances among groups; variances did not differ significantly among groups for any metabolic trait (all P > 0.34). To test whether metabolic rates differed between swallows and other birds, we employed phylogenetically informed statistical analyses, which adjust for phylogenetic nonindependence of raw data (Felsenstein 1985, Garland et al. 1999, Symonds and Blomberg 2014). Linear regressions were computed using phylogenetic generalized least squares (PGLS) models in which the residuals were modeled as having evolved via either Brownian Motion or Ornstein–Uhlenbeck (OU) processes, the latter of which simulates stabilizing selection (Symonds and Blomberg 2014). Both the PGLS models (Brownian Motion and OU) were run incorporating the maximum likelihood estimate of lambda. The range of estimated values for lambda is generally 0 to 1, where 0 indicates no phylogenic signals and 1 indicates that traits vary directly with phylogenetic distance. Lambda values > 1, however, can occur if, for example, traits are more similar to each other than expected by phylogenetic distance (Freckleton et al. 2002). We first tested whether BMR, Msum, and thermogenic scope scaled with Mb by including Mb as a predictor of BMR, Msum, and thermogenic scope in our regression models. To investigate how BMR, Msum, and thermogenic scope varied between swallows and non-aerial insectivore birds, we ran generalized linear regression models using group (i.e. swallow vs. non-swallow) and region (i.e. temperate vs. tropical) as predictors and Mb as a covariate. Residuals in the interaction were also tested for normality and log-transformed if they were not normally distributed. We report degrees of freedom of the residuals, slope, intercept, R-squared values, and P-values for regressions with a continuous predictor (i.e. Mb). We report F-statistics for GLMs with the categorical variables “group” and “region.” Additionally, we also report estimates of the phylogenetic signal associated with all variables (Pagel’s λ), which indicates the extent to which closely related species tend to resemble each other. For BMR, Msum, and thermogenic scope, we also report Pagel’s λ of the residuals (i.e. differences from predicted values after correcting for body mass, region, and group). corrected Akaike Information Criterion (AICc) and Likelihood ratio tests were conducted to compare PGLS regressions using the Ornstein–OU and Brownian motion models. RESULTS Phylogenetic Signal for Body Mass and Metabolic Traits Estimates of Pagel’s λ for BMR, Msum, and thermogenic scope were 0.99, 1.01, and 0.94, respectively. Because we used 3 different datasets and 3 different phylogenetic trees for each metabolic trait, we generated 3 separate estimates of phylogenetic signal for Mb, one for each dataset. These values for Pagel’s λ for Mb were 1.01 (BMR dataset), 1.01 (Msum dataset), and 1.01 (thermogenic scope dataset). Since Estimates of Pagel’s λ for BMR, Msum, and thermogenic scope were close to 1, which indicated strong phylogenetic signals, we only included PGLS–OU and PGLS–Brownian models, rather than ordinary least squares models, for our analyses (Freckleton 2009). Estimates of Pagel’s λ for residual BMR, Msum, and thermogenic scope, after correcting for body mass, region, and group, were 0.006, 0.075, and 0.090, respectively. Correlation between BMR and Msum BMR was positively correlated with Msum in both PGLS–Brownian and PGLS–OU models (P < 0.01; Table 2). Equations for PGLS–OU: LogBMR = 0.66 LogMsum – 0.38; PGLS–Brownian: LogBMR = 0.87 LogMsum – 0.31. The significant correlations between BMR and Msum were lost when including body mass as a covariant (P > 0.61). Allometric Relationships for Metabolic Traits When assessing relationships between Mb and all 3 metabolic variables (Table 1), likelihood ratio tests did not reveal any significant differences between PGLS using the OU model and PGLS using the Brownian motion model (P > 0.1 in cases). According to the best-fit Brownian motion PGLS model, there was a significant positive association between log Mb and log BMR (Figure 1A), between log Mb and log Msum (Figure 1B), and between log Mb and thermogenic scope (Figure 1C). Table 1. Mean (± SD) body mass (Mb) and basal and summit metabolic rates (mL O2 min−1) for the 6 species of swallows measured in this study. Species . n . Mb BMR . BMR . Mb Msum . Msum . Barn Swallow 6 16.2 ± 1.5 0.894 ± 0.200 15.1 ± 1.5 4.666 ± 1.932 Bank Swallow 5 12.6 ± 0.7 0.868 ± 0.280 12.6 ± 0.8 3.416 ± 0.544 Cliff Swallow 7 20.1 ± 1.4 1.036 ± 0.118 18.7 ± 1.3 4.768 ± 0.774 Northern Rough-winged Swallow 5 14.6 ± 0.5 1.109 ± 0.196 15.6 ± 1.5 4.487 ± 0.766 Tree Swallow 6 17.4 ± 3.0 0.881 ± 0.131 18.7 ± 2.9 4.623 ± 0.488 Purple Martin 6 44.6 ± 2.4 1.552 ± 0.171 46.0 ± 2.5 8.679 ± 0.819 Species . n . Mb BMR . BMR . Mb Msum . Msum . Barn Swallow 6 16.2 ± 1.5 0.894 ± 0.200 15.1 ± 1.5 4.666 ± 1.932 Bank Swallow 5 12.6 ± 0.7 0.868 ± 0.280 12.6 ± 0.8 3.416 ± 0.544 Cliff Swallow 7 20.1 ± 1.4 1.036 ± 0.118 18.7 ± 1.3 4.768 ± 0.774 Northern Rough-winged Swallow 5 14.6 ± 0.5 1.109 ± 0.196 15.6 ± 1.5 4.487 ± 0.766 Tree Swallow 6 17.4 ± 3.0 0.881 ± 0.131 18.7 ± 2.9 4.623 ± 0.488 Purple Martin 6 44.6 ± 2.4 1.552 ± 0.171 46.0 ± 2.5 8.679 ± 0.819 Open in new tab Table 1. Mean (± SD) body mass (Mb) and basal and summit metabolic rates (mL O2 min−1) for the 6 species of swallows measured in this study. Species . n . Mb BMR . BMR . Mb Msum . Msum . Barn Swallow 6 16.2 ± 1.5 0.894 ± 0.200 15.1 ± 1.5 4.666 ± 1.932 Bank Swallow 5 12.6 ± 0.7 0.868 ± 0.280 12.6 ± 0.8 3.416 ± 0.544 Cliff Swallow 7 20.1 ± 1.4 1.036 ± 0.118 18.7 ± 1.3 4.768 ± 0.774 Northern Rough-winged Swallow 5 14.6 ± 0.5 1.109 ± 0.196 15.6 ± 1.5 4.487 ± 0.766 Tree Swallow 6 17.4 ± 3.0 0.881 ± 0.131 18.7 ± 2.9 4.623 ± 0.488 Purple Martin 6 44.6 ± 2.4 1.552 ± 0.171 46.0 ± 2.5 8.679 ± 0.819 Species . n . Mb BMR . BMR . Mb Msum . Msum . Barn Swallow 6 16.2 ± 1.5 0.894 ± 0.200 15.1 ± 1.5 4.666 ± 1.932 Bank Swallow 5 12.6 ± 0.7 0.868 ± 0.280 12.6 ± 0.8 3.416 ± 0.544 Cliff Swallow 7 20.1 ± 1.4 1.036 ± 0.118 18.7 ± 1.3 4.768 ± 0.774 Northern Rough-winged Swallow 5 14.6 ± 0.5 1.109 ± 0.196 15.6 ± 1.5 4.487 ± 0.766 Tree Swallow 6 17.4 ± 3.0 0.881 ± 0.131 18.7 ± 2.9 4.623 ± 0.488 Purple Martin 6 44.6 ± 2.4 1.552 ± 0.171 46.0 ± 2.5 8.679 ± 0.819 Open in new tab Table 2. Statistical output for regression models. . Model . Residual DF . Slope . Intercept . R2 . P-value . BMR  Log BMR ~ log Mb PGLS–OU 233 0.63 –1.36 0.67 <0.001  Log BMR ~ log Mb PGLS–Brownian 233 0.66 –1.46 0.67 <0.001 Msum  Log Msum ~ log Mb PGLS–OU 70 0.79 –0.84 0.68 <0.001  Log Msum ~ log Mb PGLS–Brownian 70 0.68 –0.71 0.72 <0.001 Thermogenic scope  Thermogenic scope ~ log Mb PGLS–OU 48 1.49 –0.89 0.2 <0.001  Thermogenic scope ~ log Mb PGLS–Brownian 48 1.38 –0.84 0.2 <0.001 . Model . Residual DF . Slope . Intercept . R2 . P-value . BMR  Log BMR ~ log Mb PGLS–OU 233 0.63 –1.36 0.67 <0.001  Log BMR ~ log Mb PGLS–Brownian 233 0.66 –1.46 0.67 <0.001 Msum  Log Msum ~ log Mb PGLS–OU 70 0.79 –0.84 0.68 <0.001  Log Msum ~ log Mb PGLS–Brownian 70 0.68 –0.71 0.72 <0.001 Thermogenic scope  Thermogenic scope ~ log Mb PGLS–OU 48 1.49 –0.89 0.2 <0.001  Thermogenic scope ~ log Mb PGLS–Brownian 48 1.38 –0.84 0.2 <0.001 Open in new tab Table 2. Statistical output for regression models. . Model . Residual DF . Slope . Intercept . R2 . P-value . BMR  Log BMR ~ log Mb PGLS–OU 233 0.63 –1.36 0.67 <0.001  Log BMR ~ log Mb PGLS–Brownian 233 0.66 –1.46 0.67 <0.001 Msum  Log Msum ~ log Mb PGLS–OU 70 0.79 –0.84 0.68 <0.001  Log Msum ~ log Mb PGLS–Brownian 70 0.68 –0.71 0.72 <0.001 Thermogenic scope  Thermogenic scope ~ log Mb PGLS–OU 48 1.49 –0.89 0.2 <0.001  Thermogenic scope ~ log Mb PGLS–Brownian 48 1.38 –0.84 0.2 <0.001 . Model . Residual DF . Slope . Intercept . R2 . P-value . BMR  Log BMR ~ log Mb PGLS–OU 233 0.63 –1.36 0.67 <0.001  Log BMR ~ log Mb PGLS–Brownian 233 0.66 –1.46 0.67 <0.001 Msum  Log Msum ~ log Mb PGLS–OU 70 0.79 –0.84 0.68 <0.001  Log Msum ~ log Mb PGLS–Brownian 70 0.68 –0.71 0.72 <0.001 Thermogenic scope  Thermogenic scope ~ log Mb PGLS–OU 48 1.49 –0.89 0.2 <0.001  Thermogenic scope ~ log Mb PGLS–Brownian 48 1.38 –0.84 0.2 <0.001 Open in new tab Figure 1. Open in new tabDownload slide Allometric relationships among (A) log BMR, (B) log Msum, and (C) thermogenic scope and log Mb. Symbols represent individual species data and statistics are provided for the best-fit Brownian regression. Figure 1. Open in new tabDownload slide Allometric relationships among (A) log BMR, (B) log Msum, and (C) thermogenic scope and log Mb. Symbols represent individual species data and statistics are provided for the best-fit Brownian regression. Do BMR, Summit Metabolic Rate, and Thermogenic Scope Differ between Swallows and Other Birds? PGLS–OU models provided better fits than PGLS–Brownian model for the data comparing BMR, Msum and thermogenic scope between swallows and non-aerial insectivore birds. AICc and log likelihood (LL) values for the metabolic trait models were BMR: OU:AICc = –254.7, Brownian AICc = –127.8; OU LL = 133.4, Brownian LL = 68.9; Msum: OU:AICc = –83.5, Brownian AICc = –53.8; OU LL = 47.8, Brownian LL = 31.9; Thermogenic Scope: OU:AICc = 65.7, Brownian AICc = 86.1; OU LL = –26.8, Brownian LL = –38.1. BMR was significantly higher (9.8%) in swallows than in non-aerial insectivore birds for PGLS–OU (Table 3, Figure 2A) models. BMR was also significantly higher (17.2 %) in temperate compared with tropical birds (Table 3, Figure 3A), regardless of group. There was no significant group by region interaction for BMR for PGLS–OU (P > 0.1 in both cases). Although the PGLS–Brownian motion model showed a significant group by region interaction for BMR (Table 3), PGLS–OU provided a better fit. Table 3. Statistical output for best-fit PGLS via an OU process. Corresponding predictors . Num DF . Residual DF . F-value . P-value . Log BMR ~ group + region + log Mb  Group 1 231 8.51 0.004  Region 1 231 93.65 <0.0001  Log Mb 1 231 1,102.26 <0.0001  Group * region 1 230 0.08 0.77 Log Msum ~ group + region + log Mb  Group 1 68 0.85 0.36  Region 1 68 3.01 0.09  Log Mb 1 68 178.23 <0.0001  Group * region 1 67 0.95 0.33 Thermogenic scope ~ group + region + log Mb  Group 1 46 5.25 0.03  Region 1 46 25.84 <0.0001  Log Mb 1 46 39.41 <0.0001  Group * region 1 45 5.13 0.03 Corresponding predictors . Num DF . Residual DF . F-value . P-value . Log BMR ~ group + region + log Mb  Group 1 231 8.51 0.004  Region 1 231 93.65 <0.0001  Log Mb 1 231 1,102.26 <0.0001  Group * region 1 230 0.08 0.77 Log Msum ~ group + region + log Mb  Group 1 68 0.85 0.36  Region 1 68 3.01 0.09  Log Mb 1 68 178.23 <0.0001  Group * region 1 67 0.95 0.33 Thermogenic scope ~ group + region + log Mb  Group 1 46 5.25 0.03  Region 1 46 25.84 <0.0001  Log Mb 1 46 39.41 <0.0001  Group * region 1 45 5.13 0.03 Open in new tab Table 3. Statistical output for best-fit PGLS via an OU process. Corresponding predictors . Num DF . Residual DF . F-value . P-value . Log BMR ~ group + region + log Mb  Group 1 231 8.51 0.004  Region 1 231 93.65 <0.0001  Log Mb 1 231 1,102.26 <0.0001  Group * region 1 230 0.08 0.77 Log Msum ~ group + region + log Mb  Group 1 68 0.85 0.36  Region 1 68 3.01 0.09  Log Mb 1 68 178.23 <0.0001  Group * region 1 67 0.95 0.33 Thermogenic scope ~ group + region + log Mb  Group 1 46 5.25 0.03  Region 1 46 25.84 <0.0001  Log Mb 1 46 39.41 <0.0001  Group * region 1 45 5.13 0.03 Corresponding predictors . Num DF . Residual DF . F-value . P-value . Log BMR ~ group + region + log Mb  Group 1 231 8.51 0.004  Region 1 231 93.65 <0.0001  Log Mb 1 231 1,102.26 <0.0001  Group * region 1 230 0.08 0.77 Log Msum ~ group + region + log Mb  Group 1 68 0.85 0.36  Region 1 68 3.01 0.09  Log Mb 1 68 178.23 <0.0001  Group * region 1 67 0.95 0.33 Thermogenic scope ~ group + region + log Mb  Group 1 46 5.25 0.03  Region 1 46 25.84 <0.0001  Log Mb 1 46 39.41 <0.0001  Group * region 1 45 5.13 0.03 Open in new tab Figure 2. Open in new tabDownload slide Relationship between (A) group and BMR and (B) group and Msum. Data shown are individual species data and PGLS–OU means. Different letters denote statistical significance. Figure 2. Open in new tabDownload slide Relationship between (A) group and BMR and (B) group and Msum. Data shown are individual species data and PGLS–OU means. Different letters denote statistical significance. Figure 3. Open in new tabDownload slide Relationship among (A) region and BMR, (B) region and Msum, and (C) group, region, and thermogenic scope. Data shown are individual species data and PGLS–OU means. Different letters denote statistical significance. Figure 3. Open in new tabDownload slide Relationship among (A) region and BMR, (B) region and Msum, and (C) group, region, and thermogenic scope. Data shown are individual species data and PGLS–OU means. Different letters denote statistical significance. None of the models showed significant differences in Msum between swallows and non-aerial insectivores (Table 3). According to the best-fit PGLS–OU model, Msum was 20.6% higher in swallows than in non-aerial insectivore birds, but this difference was not statistically significant (Table 3, Figure 2B). Furthermore, no models indicated significant differences in Msum between tropical and temperate birds (Table 3), although this difference in the best-fit PGLS–OU model approached significance (F1, 68 = 3.011, P = 0.087, Figure 3B), with 32% higher Msum in temperate birds. No significant group by region interaction occurred for Msum for any model (P > 0.1 in all cases). For thermogenic scope, a significant group by region interaction occurred between swallows and non-aerial insectivores (Figure 3C), where tropical non-aerial insectivores had significantly lower thermogenic scope compared with tropical swallows and to temperate-zone birds, including both swallows and non-aerial insectivores (Table 3). Thermogenic scope was significantly higher (30.9%) in swallows compared with non-aerial insectivores for the PGLS–OU model, but this result was driven by differences in tropical birds, where thermogenic scope in swallows exceeded that in non-aerial insectivores by 64.8%. For temperate birds, thermogenic scope in swallows only exceeded that in that in non-aerial insectivore birds by only 4.3% (Table 3). DISCUSSION The PGLS–OU models fit the data better than the PGLS–Brownian motion models. After correcting for body mass and temperate vs. tropical affiliation, BMR was significantly higher for swallows than for non-aerial insectivore birds in PGLS–OU model. These results suggest that swallows, as a taxon, have high BMR relative to non-aerial insectivore birds. The high BMR of swallows supports our hypothesis that the energetically expensive aerial insectivore lifestyle does incur increased maintenance costs to support metabolic machinery associated with spending much of the active period each day on the wing. Our results also confirm results from other studies that, after correcting for body mass, temperate-zone bird species have higher BMR than tropical species (Wiersma et al. 2007b, Londoño et al. 2015). One methodological factor, however, might impact our comparisons of BMR between swallows and non-aerial insectivore birds. We measured BMR in swallows for 2 hr of the night on birds fasted for at least 6 hr prior to measurements. These methods meet the strict definition of BMR, but other studies show that metabolic rates often decrease to their lowest values later in the night (Jacobs and McKechnie 2014). Rates of oxygen consumption for birds in our study were uniformly stable and low during the second hour of measurement, and birds were asleep when removed from the metabolic chamber, but we cannot rule out that metabolic rates may have been slightly lower later in the night. To help address this issue, we measured BMR for 4 birds over the entire night (on days where only a single bird was captured). For 2 of these birds, the lowest 10-min period of oxygen consumption occurred during the second hour of measurement. For the remaining 2 birds, the lowest period of oxygen consumption occurred at 2.6 and 4.4 hr after the beginning of measurements. For these latter 2 birds, the lowest 10-min rate of oxygen consumption overnight was only 2.7% lower on average compared with the lowest rate for the second hour of measurement. In addition, for birds in our study measured for only the 2-hr period, the lowest 10-min period for oxygen consumption, which we considered BMR, did not always occur at the end of the second hour in the chamber (mean ± SD time at beginning of 10-min period = 89.8 ± 13.8 min), suggesting that metabolic rates had reached steady-state conditions before the end of our 2-hr measurement period. Measurements of BMR for the swallow species in this study are not available in the literature. Williams (1988) did estimate BMR in tree swallows (mean Mb = 21.6 g) as 0.992 mL O2 min−1 by subtracting 25% from resting metabolic rate values measured during the day. This compares to our value for tree swallows in the present study (mean Mb = 17.4 g) of 0.881 mL O2 min−1 (Table 1). If we use the PGLS exponent for BMR (0.65; Figure 1) to calculate expected differences in BMR based on body mass differences between the 2 studies (21.6 vs. 17.4 g), this method yields an expected BMR 15.3% higher for birds in the Williams (1988) study than that for the present study. The estimated BMR in the Williams (1988) study was 12.6% higher than in the present study, which differs by only 2.7% from predictions based on body masses, suggesting that our methods produced similar measures of BMR. In addition, a number of the BMR studies in the comparative dataset also report BMR values for periods not encompassing the entire night (Supplementary Material Table 1) but still meeting the strict definition of BMR. Thus, while we cannot rule out that slightly lower metabolic rates might have occurred with longer measurement periods, these are not likely to fully account for the magnitude of the difference in BMR between swallows and non-aerial insectivore birds (9.8%) detected by the PGLS–OU analyses in the present study. Species traits, such as diet, foraging mode, and migratory status, can contribute to energetically demanding lifestyles and may impact life-history strategies (Dobson 2007, Sibly et al. 2012). Different foraging strategies have different energetic costs and high-energy foraging strategies might be negatively correlated with other life-history traits (Yap et al. 2017, Zhang et al. 2018b). For example, the cost of foraging may influence other life-history traits such as reproduction and predation risk (Grémillet and Wilson 1999). A fast pace of life, associated with energetically demanding lifestyles, is often positively correlated with BMR (Wiersma et al. 2007b, Williams et al. 2010, Versteegh et al. 2012). This relationship likely exists because activities associated with fast lifestyles may involve higher support costs, such as larger nutritional and exercise organs (Wiersma et al. 2012), which, in turn, may necessitate higher BMR. Consequently, a high BMR would support high activity levels associated with a fast pace of life, which may be adaptive in variable environments by allowing rapid adjustments of energy provision when environmental factors change. Temperate bird species typically show higher Msum than their tropical counterparts (Wiersma et al. 2007a) as a component of the fast pace of life syndrome. The results in the present study provide marginal support for this finding, as we observed a nonsignificant trend (P = 0.087) toward higher Msum in temperate than in tropical species. Reasons for the absence of a significant difference in Msum between temperate and tropical birds in the present study are not known but likely involve higher variation in Msum than in BMR measurements (where a difference was detected) or differences in the phylogenies used for the different studies, although overlap in phylogenies occurred among studies. From an energetics point of view, it seems likely that natural selection should act to reduce BMR to the lowest level possible (Swanson et al. 2017). Thus, the high BMR in Hirundines in this study could result in potential tradeoffs with other life-history traits such as reproduction and lifespan. It is also worth noting here that even if there is a cost to a high-energy lifestyle, such a cost does not always result in tradeoffs (Zhang and Hood 2016). The tradeoff hypothesis assumes that the energy pool for the animal is fixed, so higher maintenance costs would result in compromising other traits. But if the animal is able to increase the energy pool, higher BMR could have significant beneficial effects in supporting other lifestyle characteristics. The insectivore diet of Hirundines contains high protein and lipid content (Turner 1981), so such an energy-rich diet could support the energetically demanding aerial insectivore lifestyle and any associated elevation of BMR. The increased intake hypothesis for changes in BMR supports the idea that high BMR increases support of higher activity, which, in turn, increases fitness (Burton et al. 2011). Highlighting the fitness consequences of such BMR elevation, however, needs further investigation. Flight is an energetically expensive form of locomotion, so birds foraging on the wing exhibit higher DEE than other birds (i.e. DEE) (Williams 1988, Bryant and Tatner 1991, Schmidt-Wellenburg et al. 2007). BMR and Msum in the present study were positively correlated. BMR and DEE or MMR may also be positively correlated in birds (Dutenhoffer and Swanson 1996, Rezende et al. 2002, Portugal et al. 2016, Nespolo et al. 2017; but see Ricklefs et al. 1996, Wiersma et al. 2007a). If positive correlations exist among BMR, DEE, and maximum metabolic outputs, species with energetically demanding lifestyles might also be expected to show higher maximal thermogenic metabolic rates than other birds. Flight capacity is also positively associated with heart size in birds, suggesting that energetically expensive flight modes are positively associated with aerobic capacity (Nespolo et al. 2018). Pectoralis muscle masses are often positively correlated with Msum (Vézina et al. 2007, Swanson et al. 2013, Petit et al. 2014, Petit and Vézina 2014a, Milbergue et al. 2018) and MMR (Chappell et al. 1999, Hammond et al. 2000) in birds, although this is not always the case (Swanson et al. 2014, Dubois et al. 2016, Zhang et al. 2018a). Similar positive correlations of heart mass with Msum or MMR also occur in birds (Chappell et al. 1999, Hammond et al. 2000, Swanson et al. 2014; but see Milbergue et al. 2018). These results suggest the potential for thermogenic side effects of an energetically expensive, high-activity lifestyle in birds. In the present study, however, we did not observe any significant differences in Msum between swallows and non-aerial insectivore birds even though Msum averaged 20.6% higher in swallows. This finding is consistent with recent data for shorebirds, which show energetically expensive long-distance migrant, high-latitude breeding lifestyles, and high BMR (Kersten and Piersma 1987, Lindström and Klaassen 2003) but not high exercise or cold-induced maximal metabolic capacities (Thomas and Swanson 2019). Thus, it appears that energetically demanding lifestyles are not phenotypically linked to high metabolic capacities for either exercise or thermogenesis in birds generally. Sample sizes for Msum dataset (8 swallow species, 72 species total) might contribute to the nonsignificant results, despite higher average Msum in swallows. However, variation in Msum was higher than for BMR in the present study (Figures 1 and 2), so other factors also likely contribute to the absence of a significant difference in Msum between swallows and non-aerial insectivore birds. For example, aerial insectivores spend the majority of their time on the wing, so they also show aerodynamically efficient flight with relatively low flight metabolic rate (Buchanan and Evans 2000, Hedenström et al. 2019). Swallows relying on glides and partial bounds during flight minimize flapping to reduce the average power expended for changes in speed or elevation (Warrick et al. 2016, Hedrick et al. 2018). These biomechanical adaptations could allow swallows to minimize their energy expenditure for flight while maintaining an aerial insectivore lifestyle (Hails 1979). Nevertheless, DEE is still higher for swallows than for other birds (Williams 1988, Bryant and Tatner 1991, Schmidt-Wellenburg et al. 2007). In addition, even though positive correlations between Msum and MMR have been documented both intra- and inter-specifically, as discussed above, this positive relationship between Msum and MMR is not uniform among birds, particularly for intraspecific comparisons, where both positive and no correlations have been observed (Swanson et al. 2012, Zhang et al. 2015b). Despite shivering and flight both being functions of skeletal muscle contraction that share similar metabolic pathways (Zhang et al. 2015a), isotonic flight and isometric shivering could still potentially result in different metabolic capacities (Iellamo et al. 1997). Indeed, thermogenic maximum metabolic rates are generally only 60% to 70% of exercise maximal metabolic rates in birds (Chappell et al. 1999, Swanson et al. 2012, Zhang et al. 2015b). Thus, whether both Msum and MMR vary similarly with BMR and how they correlate with each other remains an open question (McKechnie and Swanson 2010). Aerobic scope defines the capacity of an organism to elevate metabolic output to meet an energetic challenge. Species living at high latitudes and cold environmental temperatures or employing torpor or hibernation often exhibit high aerobic scope (Naya et al. 2012, Careau 2013, Stager et al. 2016). In the present study, the group by region interaction term was a significant effector of thermogenic scope. This result was driven primarily by differences in tropical birds where thermogenic scope in swallows exceeded that in non-aerial insectivores by 64.8%, whereas the difference between swallows and non-aerial insectivores for temperate birds was only 4.3%. The lower magnitude of the difference in thermogenic scope for temperate swallows vs. non-aerial insectivores suggests that other factors associated with the faster pace of life of temperate-zone birds (Wiersma et al. 2007a, 2007b, Stager et al. 2016) select for higher capacities to elevate metabolic rates to meet ecological and energetic demands imposed by temperate-zone climates and lifestyles in non-aerial insectivores. For tropical birds, with a generally slow pace of life (Wiersma et al. 2007b), the energetically demanding aerial insectivore lifestyle may not permit similar reductions of aerobic or thermogenic scope for tropical swallows. We caution, however, that this conclusion is based on a sample size of 2 for tropical swallows, so future research should examine metabolic capacities in additional tropical Hirundines. The differences in thermogenic scope in the present study result from changes at the upper, rather than lower, end of the metabolic expansibility spectrum. Metabolic expansibility values in the present study averaged 4.83 ± 0.25 (SE) in swallow species and were consistent with values for Msum exceeding BMR by 4–8 times in birds generally (Swanson 2010) and by an average of 4.83 ± 0.25 (SE) for the 6 Hirundinidae species measured in this study. These data also suggest that upper and lower metabolic bounds may be modulated independently (Petit et al. 2013, Petit and Vézina 2014b, Barceló et al. 2017). Such a scenario is consistent with the results of Stager et al. (2016), who demonstrated that latitudinal patterns of variation in thermogenic scope among birds were driven primarily by differences in maximum thermogenic output rather than BMR. It is also worth noting in this regard that the datasets for BMR, Msum, and thermogenic scope employed in this present study were different, with more BMR data than Msum or thermogenic scope data. Consequently, differences among the datasets might also contribute to different results for BMR, Msum, and thermogenic scope data regarding metabolic levels between swallows and non-aerial insectivore birds. In conclusion, swallows demonstrate high maintenance metabolic costs relative to non-aerial insectivore birds, but a concomitant increase in maximum thermogenic capacity was not observed. This pattern suggests that while increases in DEE may carry additional maintenance costs in swallows, this does not similarly extend to promote higher maximal thermogenic capacities. Perhaps these differences could be explained by differences in selective pressures, with maximum thermogenic capacities being under stabilizing selection with BMR being under directional selection to reduce maintenance costs (Nespolo et al. 2017, Swanson et al. 2017). In such a scenario, maintenance costs should always be as low as possible, so selection should act to drive BMR downward (Swanson et al. 2017). Maximal metabolic capacities, however, might be under different stabilizing selective pressures to reach an optimal level that produces fitness benefits (Nespolo et al. 2017, Petit et al. 2017, Latimer et al. 2018). In the present study, however, PGLS–OU models fit the data for both BMR and Msum better than PGLS–Brownian motion models, which suggests that both metabolic traits might be under similar stabilizing selection pressures (Nilsson and Nilsson 2016). Further studies are needed to address the question of whether the observed increase of BMR in swallows might produce tradeoffs with other life-history traits affecting reproduction and survival. ACKNOWLEDGMENTS We thank Ming Liu, Michael Moxnes, Steven Higgins, Aaron Gregor, Kenneth Renner, and Joe Vitt for their technical assistance in the field and laboratory. Funding statement: This research was funded by National Science Foundation (NSF) IOS-1021218 to D.L.S. Ethics statement: This research was conducted in compliance with the University of South Dakota Institutional Animal Care and Use Committee (Protocol number: 27-02-08-11B). Author contributions: Y.Z. and D.L.S. conceived and designed the experiments. Y.Z. performed the experiments. Y.Z., K.N.Y., and K.T.D. analyzed the data. Y.Z., K.N.Y., and D.L.S. wrote the manuscript; other authors provided editorial advice. Data availability: Analyses reported in this article can be reproduced using the data within the article and online Supplementary Material. LITERATURE CITED Arens , J. R. , and S. J. Cooper ( 2005 ). Metabolic and ventilatory acclimatization to cold stress in House Sparrows (Passer domesticus) . Physiological and Biochemical Zoology 78 : 579 – 589 . Google Scholar Crossref Search ADS PubMed WorldCat Auer , S. K. , S. S. Killen, and E. L. Rezende ( 2017 ). Resting vs. active: A meta-analysis of the intra- and inter-specific associations between minimum, sustained, and maximum metabolic rates in vertebrates . Functional Ecology 31 : 1728 – 1738 . Google Scholar Crossref Search ADS PubMed WorldCat Barceló , G. , O. P. Love, and F. Vézina ( 2017 ). Uncoupling basal and summit metabolic rates in White-throated Sparrows: Digestive demand drives maintenance costs, but changes in muscle mass are not needed to improve thermogenic capacity . Physiological and Biochemical Zoology 90 : 153 – 165 . Google Scholar Crossref Search ADS PubMed WorldCat Bartholomew , G. A. , D. Vleck, and C. M. Vleck ( 1981 ). Instantaneous measurements of oxygen consumption during pre-flight warm-up and post-flight cooling in sphingid and saturniid moths . Journal of Experimental Biology 90 : 17 – 32 . Google Scholar OpenURL Placeholder Text WorldCat Bryant , D. , C. Hails, and P. Tatner ( 1984 ). Reproductive energetics of two tropical bird species . The Auk 101 : 25 – 37 . Google Scholar Crossref Search ADS WorldCat Bryant , D. M. , and P. Tatner ( 1991 ). Intraspecies variation in avian energy expenditure: Correlates and constraints . Ibis 133 : 236 – 245 . Google Scholar Crossref Search ADS WorldCat Buchanan , K. L. , and M. R. Evans ( 2000 ). The effect of tail streamer length on aerodynamic performance in the Barn Swallow . Behavioral Ecology 11 : 228 – 238 . Google Scholar Crossref Search ADS WorldCat Burton , T. , S. S. Killen, J. D. Armstrong, and N. B. Metcalfe ( 2011 ). What causes intraspecific variation in resting metabolic rate and what are its ecological consequences? Proceedings. Biological Sciences 278 : 3465 – 3473 . Google Scholar PubMed OpenURL Placeholder Text WorldCat Careau , V . ( 2013 ). Basal metabolic rate, maximum thermogenic capacity and aerobic scope in rodents: Interaction between environmental temperature and torpor use . Biology Letters 9 : 20121104 . Google Scholar Crossref Search ADS PubMed WorldCat Carey , C . ( 1996 ). Avian Energetics and Nutritional Ecology . Chapman & Hall, New York, NY, USA . Google Scholar Crossref Search ADS Google Preview WorldCat COPAC Chappell , M. A. , C. Bech, and W. A. Buttemer ( 1999 ). The relationship of central and peripheral organ masses to aerobic performance variation in house sparrows . The Journal of Experimental Biology 202 (Pt 17) : 2269 – 2279 . Google Scholar PubMed OpenURL Placeholder Text WorldCat Corder , K. R. , and P. J. Schaeffer ( 2015 ). Summit metabolic rate exhibits phenotypic flexibility with migration, but not latitude in a Neotropical migrant, Parkesia noveboracensis . Journal of Ornithology 156 : 547 – 550 . Google Scholar Crossref Search ADS WorldCat Dobson , F. S . ( 2007 ). A lifestyle view of life-history evolution . Proceedings of the National Academy of Sciences USA 104 : 17565 – 17566 . Google Scholar Crossref Search ADS WorldCat Dobson , F. S . ( 2012 ). Lifestyles and phylogeny explain bird life histories . Proceedings of the National Academy of Sciences USA 109 : 10747 – 10748 . Google Scholar Crossref Search ADS WorldCat Dubois , K. , F. Hallot, and F. Vézina ( 2016 ). Basal and maximal metabolic rates differ in their response to rapid temperature change among avian species . Journal of Comparative Physiology B 186 : 919 – 935 . Google Scholar Crossref Search ADS WorldCat Dutenhoffer , M. S. , and D. L. Swanson ( 1996 ). Relationship of basal to summit metabolic rate in passerine birds and the aerobic capacity model for the evolution of endothermy . Physiological Zoology 69 : 1232 – 1254 . Google Scholar Crossref Search ADS WorldCat Felsenstein , J . ( 1985 ). Confidence limits on phylogenies: An approach using the bootstrap . Evolution 39 : 783 – 791 . Google Scholar Crossref Search ADS PubMed WorldCat Freckleton , R. P . ( 2009 ). The seven deadly sins of comparative analysis . Journal of Evolutionary Biology 22 : 1367 – 1375 . Google Scholar Crossref Search ADS PubMed WorldCat Freckleton , R. P. , P. H. Harvey, and M. Pagel ( 2002 ). Phylogenetic analysis and comparative data: A test and review of evidence . The American Naturalist 160 : 712 – 726 . Google Scholar Crossref Search ADS PubMed WorldCat Garland , T. Jr,, P. E. Midford, and A. R. Ives ( 1999 ). An introduction to phylogenetically based statistical methods, with a new method for confidence intervals on ancestral values . American Zoologist 39 : 374 – 388 . Google Scholar Crossref Search ADS WorldCat Grémillet , D. , and R. P. Wilson ( 1999 ). A life in the fast lane: Energetics and foraging strategies of the Great Cormorant . Behavioral Ecology 10 : 516 – 524 . Google Scholar Crossref Search ADS WorldCat Guglielmo , C. G . ( 2010 ). Move that fatty acid: Fuel selection and transport in migratory birds and bats . Integrative and Comparative Biology 50 : 336 – 345 . Google Scholar Crossref Search ADS PubMed WorldCat Hailey , A. , and P. M. C. Davies ( 1986 ). Lifestyle, latitude and activity metabolism of natricine snakes . Journal of Zoology 209 : 461 – 476 . Google Scholar Crossref Search ADS WorldCat Hails , C . ( 1979 ). A comparison of flight energetics in hirundines and other birds . Comparative Biochemistry and Physiology Part A: Physiology 63 : 581 – 585 . Google Scholar Crossref Search ADS WorldCat Hammond , K. A. , M. A. Chappell, R. A. Cardullo, R. Lin, and T. S. Johnsen ( 2000 ). The mechanistic basis of aerobic performance variation in Red Junglefowl . The Journal of Experimental Biology 203 : 2053 – 2064 . Google Scholar PubMed OpenURL Placeholder Text WorldCat Hedenström , A. , G. Norevik, G. Boano, A. Andersson, J. Bäckman, and S. Åkesson ( 2019 ). Flight activity in Pallid Swifts Apus pallidus during the non-breeding period . Journal of Avian Biology 50: 2 . doi:10.1111/jav.01972 Google Scholar OpenURL Placeholder Text WorldCat Hedrick , T. L. , C. Pichot, and E. De Margerie ( 2018 ). Gliding for a free lunch: Biomechanics of foraging flight in Common Swifts (Apus apus) . Journal of Experimental Biology 221 : jeb186270 . Google Scholar Crossref Search ADS WorldCat Iellamo , F. , J. M. Legramante, G. Raimondi, F. Castrucci, C. Damiani, C. Foti, G. Peruzzi, and I. Caruso ( 1997 ). Effects of isokinetic, isotonic and isometric submaximal exercise on heart rate and blood pressure . European Journal of Applied Physiology and Occupational Physiology 75 : 89 – 96 . Google Scholar Crossref Search ADS PubMed WorldCat Jacobs , P. J. , and A. E. McKechnie ( 2014 ). Experimental sources of variation in avian energetics: Estimated basal metabolic rate decreases with successive measurements . Physiological and Biochemical Zoology 87 : 762 – 769 . Google Scholar Crossref Search ADS PubMed WorldCat Jetz , W. , G. H. Thomas, J. B. Joy, D. W. Redding, K. Hartmann, and A. O. Mooers ( 2014 ). Global distribution and conservation of evolutionary distinctness in birds . Current Biology 24 : 919 – 930 . Google Scholar Crossref Search ADS PubMed WorldCat Kersten , M. , and T. Piersma ( 1987 ). High levels of energy expenditure in shorebirds: Metabolic adaptations to an energetically expensive way of life . Ardea 75 : 175 – 187 . Google Scholar OpenURL Placeholder Text WorldCat Killen , S. S. , D. Atkinson, and D. S. Glazier ( 2010 ). The intraspecific scaling of metabolic rate with body mass in fishes depends on lifestyle and temperature . Ecology Letters 13 : 184 – 193 . Google Scholar Crossref Search ADS PubMed WorldCat Latimer , C. E. , S. J. Cooper, W. H. Karasov, and B. Zuckerberg ( 2018 ). Does habitat fragmentation promote climate-resilient phenotypes? Oikos 127 : 1069 – 1080 . Google Scholar Crossref Search ADS WorldCat Lighton , J. R . ( 2008 ). Measuring Metabolic Rates: A Manual for Scientists . Oxford University Press, New York, NY, USA . Google Scholar Crossref Search ADS Google Preview WorldCat COPAC Lindström , Å. , and M. Klaassen ( 2003 ). High basal metabolic rates of shorebirds while in the Arctic: A circumpolar view . The Condor 105 : 420 – 427 . Google Scholar Crossref Search ADS WorldCat Londoño , G. A. , M. A. Chappell, M. d. R. Castaneda, J. E. Jankowski, and S. K. Robinson ( 2015 ). Basal metabolism in tropical birds: Latitude, altitude, and the ‘pace of life’ . Functional Ecology 29 : 338 – 346 . Google Scholar Crossref Search ADS WorldCat McKechnie , A. E . ( 2008 ). Phenotypic flexibility in basal metabolic rate and the changing view of avian physiological diversity: A review . Journal of Comparative Physiology B 178 : 235 – 247 . Google Scholar Crossref Search ADS WorldCat McKechnie , A. E. , and D. L. Swanson ( 2010 ). Sources and significance of variation in basal, summit and maximal metabolic rates in birds . Current Zoology 56 : 741 – 758 . Google Scholar Crossref Search ADS WorldCat McNab , B. K . ( 1997 ). On the utility of uniformity in the definition of basal rate of metabolism . Physiological Zoology 70 : 718 – 720 . Google Scholar Crossref Search ADS PubMed WorldCat Milbergue , M. S. , P. U. Blier, and F. Vézina ( 2018 ). Large muscles are beneficial but not required for improving thermogenic capacity in small birds . Scientific Reports 8 : 14009 . Google Scholar Crossref Search ADS PubMed WorldCat Naya , D. E. , L. Spangenberg, H. Naya, and F. Bozinovic ( 2012 ). Latitudinal patterns in rodent metabolic flexibility . The American Naturalist 179 : E172 – E179 . Google Scholar Crossref Search ADS PubMed WorldCat Nespolo , R. F. , C. González-Lagos, J. J. Solano-Iguaran , M. Elfwing, A. Garitano-Zavala, S. Mañosa, J. C. Alonso, and J. Altimiras ( 2018 ). Aerobic power and flight capacity in birds: A phylogenetic test of the heart-size hypothesis . Journal of Experimental Biology 221 : jeb162693 . Google Scholar Crossref Search ADS WorldCat Nespolo , R. F. , J. J. Solano-Iguaran, and F. Bozinovic ( 2017 ). Phylogenetic analysis supports the aerobic-capacity model for the evolution of endothermy . The American Naturalist 189 : 13 – 27 . Google Scholar Crossref Search ADS PubMed WorldCat Nilsson , J. F. , and J. Å. Nilsson ( 2016 ). Fluctuating selection on basal metabolic rate . Ecology and Evolution 6 : 1197 – 1202 . Google Scholar Crossref Search ADS PubMed WorldCat Petit , M. , S. Clavijo-Baquet, and F. Vézina ( 2017 ). Increasing winter maximal metabolic rate improves intrawinter survival in small birds . Physiological and Biochemical Zoology 90 : 166 – 177 . Google Scholar Crossref Search ADS PubMed WorldCat Petit , M. , A. Lewden, and F. Vézina ( 2013 ). Intra-seasonal flexibility in avian metabolic performance highlights the uncoupling of basal metabolic rate and thermogenic capacity . PLoS One 8 : e68292 . Google Scholar Crossref Search ADS PubMed WorldCat Petit , M. , A. Lewden, and F. Vézina ( 2014 ). How does flexibility in body composition relate to seasonal changes in metabolic performance in a small passerine wintering at northern latitude? Physiological and Biochemical Zoology 87 : 539 – 549 . Google Scholar Crossref Search ADS PubMed WorldCat Petit , M. , and F. Vézina ( 2014a ). Phenotype manipulations confirm the role of pectoral muscles and haematocrit in avian maximal thermogenic capacity . The Journal of Experimental Biology 217 : 824 – 830 . Google Scholar Crossref Search ADS WorldCat Petit , M. , and F. Vézina ( 2014b ). Reaction norms in natural conditions: How does metabolic performance respond to weather variations in a small endotherm facing cold environments? PLoS One 9 : e113617 . Google Scholar Crossref Search ADS WorldCat Pollock , H. S. , J. D. Brawn, T. J. Agin, and Z. A. Cheviron ( 2019 ). Differences between temperate and tropical birds in seasonal acclimatization of thermoregulatory traits . Journal of Avian Biology 50: 4 . doi:10.1111/jav.02067 Google Scholar Crossref Search ADS Google Preview WorldCat COPAC Portugal , S. J. , J. A. Green, L. G. Halsey, W. Arnold, V. Careau, P. Dann, P. B. Frappell, D. Grémillet, Y. Handrich, G. R. Martin, et al. ( 2016 ). Associations between resting, activity, and daily metabolic rate in free-living endotherms: No universal rule in birds and mammals . Physiological and Biochemical Zoology 89 : 251 – 261 . Google Scholar Crossref Search ADS PubMed WorldCat R Core Team ( 2013 ). R: A language and environment for statistical computing . R Foundation for Statistical Computing, Vienna, Austria . http://www.R-project.org/ Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Rezende , E. L. , D. L. Swanson, F. F. Novoa, and F. Bozinovic ( 2002 ). Passerines versus nonpasserines: So far, no statistical differences in the scaling of avian energetics . The Journal of Experimental Biology 205 : 101 – 107 . Google Scholar PubMed OpenURL Placeholder Text WorldCat Ricklefs , R. E. , M. Konarzewski, and S. Daan ( 1996 ). The relationship between basal metabolic rate and daily energy expenditure in birds and mammals . The American Naturalist 147 : 1047 – 1071 . Google Scholar Crossref Search ADS WorldCat Schmidt-Wellenburg , C. A. , H. Biebach, S. Daan, and G. H. Visser ( 2007 ). Energy expenditure and wing beat frequency in relation to body mass in free flying Barn Swallows (Hirundo rustica) . Journal of Comparative Physiology B 177 : 327 – 337 . Google Scholar Crossref Search ADS WorldCat Sibly , R. M. , C. C. Witt, N. A. Wright, C. Venditti, W. Jetz, and J. H. Brown ( 2012 ). Energetics, lifestyle, and reproduction in birds . Proceedings of the National Academy of Sciences USA 109 : 10937 – 10941 . Google Scholar Crossref Search ADS WorldCat Stager , M. , H. S. Pollock, P. M. Benham, N. D. Sly, J. D. Brawn, and Z. A. Cheviron ( 2016 ). Disentangling environmental drivers of metabolic flexibility in birds: The importance of temperature extremes versus temperature variability . Ecography 39 : 787 – 795 . Google Scholar Crossref Search ADS WorldCat Swanson , D. L . ( 1995 ). Seasonal variation in thermogenic capacity of migratory Warbling Vireos . The Auk 112 : 870 – 877 . Google Scholar Crossref Search ADS WorldCat Swanson , D. L . ( 2010 ). Seasonal metabolic variation in birds: Functional and mechanistic correlates . Current Ornithology 17 : 75 – 129 . Google Scholar OpenURL Placeholder Text WorldCat Swanson , D. L. , and F. Bozinovic ( 2011 ). Metabolic capacity and the evolution of biogeographic patterns in oscine and suboscine passerine birds . Physiological and Biochemical Zoology 84 : 185 – 194 . Google Scholar Crossref Search ADS PubMed WorldCat Swanson , D. L. , and K. L. Dean ( 1999 ). Migration-induced variation in thermogenic capacity in migratory passerines . Journal of Avian Biology 30 : 245 – 254 . Google Scholar Crossref Search ADS WorldCat Swanson , D. L. , M. W. Drymalski, and J. R. Brown ( 1996 ). Sliding vs static cold exposure and the measurement of summit metabolism in birds . Journal of Thermal Biology 21 : 221 – 226 . Google Scholar Crossref Search ADS WorldCat Swanson , D. L. , and T. Garland , Jr. ( 2009 ). The evolution of high summit metabolism and cold tolerance in birds and its impact on present-day distributions . Evolution 63 : 184 – 194 . Google Scholar Crossref Search ADS PubMed WorldCat Swanson , D. L. , and E. T. Liknes ( 2006 ). A comparative analysis of thermogenic capacity and cold tolerance in small birds . The Journal of Experimental Biology 209 : 466 – 474 . Google Scholar Crossref Search ADS PubMed WorldCat Swanson , D. L. , A. E. McKechnie, and F. Vézina ( 2017 ). How low can you go? An adaptive energetic framework for interpreting basal metabolic rate variation in endotherms . Journal of Comparative Physiology B 187 : 1039 – 1056 . Google Scholar Crossref Search ADS WorldCat Swanson , D. L. , N. E. Thomas, E. T. Liknes, and S. J. Cooper ( 2012 ). Intraspecific correlations of basal and maximal metabolic rates in birds and the aerobic capacity model for the evolution of endothermy . PLos One 7 : e34271 . Google Scholar Crossref Search ADS PubMed WorldCat Swanson , D. L. , Y. Zhang, and M. O. King ( 2013 ). Individual variation in thermogenic capacity is correlated with flight muscle size but not cellular metabolic capacity in American Goldfinches (Spinus tristis) . Physiological and Biochemical Zoology 86 : 421 – 431 . Google Scholar Crossref Search ADS PubMed WorldCat Swanson , D. , Y. Zhang, and M. King ( 2014 ). Mechanistic drivers of flexibility in summit metabolic rates of small birds . PLoS One 9 : e101577 . Google Scholar Crossref Search ADS PubMed WorldCat Symonds , M. R. , and S. P. Blomberg ( 2014 ). A primer on phylogenetic generalised least squares . In Modern Phylogenetic Comparative Methods and Their Application in Evolutionary Biology ( L. Z. Garamszegi, Editor). Springer-Verlag , Berlin, Heidelberg, Germany . pp. 105 – 130. Google Scholar Crossref Search ADS Google Preview WorldCat COPAC Thomas , N. E. , and D. L. Swanson ( 2019 ). Do the high energy lifestyles of shorebirds result in high maximal metabolic rates? Basal and maximal metabolic rates in least and pectoral sandpipers during migration . Journal of Avian Biology 50: 4 . Google Scholar OpenURL Placeholder Text WorldCat Turner , A. K . ( 1981 ). The Use of Time and Energy by Aerial-Feeding Birds . Doctoral dissertation , University of Stirling , Stirling, Scotland, UK . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Versteegh , M. A. , I. Schwabl, S. Jaquier, and B. I. Tieleman ( 2012 ). Do immunological, endocrine and metabolic traits fall on a single Pace-of-Life axis? Covariation and constraints among physiological systems . Journal of Evolutionary Biology 25 : 1864 – 1876 . Google Scholar Crossref Search ADS PubMed WorldCat Vézina , F. , K. M. Jalvingh, A. Dekinga, and T. Piersma ( 2007 ). Thermogenic side effects to migratory predisposition in shorebirds . American Journal of Physiology. Regulatory, Integrative and Comparative Physiology 292 : R1287 – R1297 . Google Scholar Crossref Search ADS PubMed WorldCat Warrick , D. R. , T. L. Hedrick, A. A. Biewener, K. E. Crandell, and B. W. Tobalske ( 2016 ). Foraging at the edge of the world: Low-altitude, high-speed manoeuvering in Barn Swallows . Philosophical Transactions of the Royal Society B 371 : 20150391 . Google Scholar Crossref Search ADS WorldCat Wiersma , P. , M. A. Chappell, and J. B. Williams ( 2007a ). Cold-and exercise-induced peak metabolic rates in tropical birds . Proceedings of the National Academy of Sciences USA 104 : 20866 – 20871 . Google Scholar Crossref Search ADS WorldCat Wiersma , P. , A. Muñoz-Garcia, A. Walker, and J. B. Williams ( 2007b ). Tropical birds have a slow pace of life . Proceedings of the National Academy of Sciences USA 104 : 9340 – 9345 . Google Scholar Crossref Search ADS WorldCat Wiersma , P. , B. Nowak, and J. B. Williams ( 2012 ). Small organ size contributes to the slow pace of life in tropical birds . The Journal of Experimental Biology 215 : 1662 – 1669 . Google Scholar Crossref Search ADS PubMed WorldCat Williams , J. B . ( 1988 ). Field metabolism of Tree Swallows during the breeding season . The Auk 105 : 706 – 714 . Google Scholar Crossref Search ADS WorldCat Williams , J. B. , R. A. Miller, J. M. Harper, and P. Wiersma ( 2010 ). Functional linkages for the pace of life, life-history, and environment in birds . Integrative and Comparative Biology 50 : 855 – 868 . Google Scholar Crossref Search ADS PubMed WorldCat Yap , K. N. , O. R. Kim, K. C. Harris, and T. D. Williams ( 2017 ). Physiological effects of increased foraging effort in a small passerine . The Journal of Experimental Biology 220 : 4282 – 4291 . Google Scholar Crossref Search ADS PubMed WorldCat Zhang , Y. , T. Carter, K. Eyster, and D. L. Swanson ( 2015a ). Acute cold and exercise training up-regulate similar aspects of fatty acid transport and catabolism in House Sparrows (Passer domesticus) . The Journal of Experimental Biology 218 : 3885 – 3893 . Google Scholar Crossref Search ADS WorldCat Zhang , Y. , K. Eyster, J. S. Liu, and D. L. Swanson ( 2015b ). Cross-training in birds: Cold and exercise training produce similar changes in maximal metabolic output, muscle masses and myostatin expression in House Sparrows (Passer domesticus) . The Journal of Experimental Biology 218 : 2190 – 2200 . Google Scholar Crossref Search ADS WorldCat Zhang , Y. , K. Eyster, and D. L. Swanson ( 2018a ). Context-dependent regulation of pectoralis myostatin and lipid transporters by temperature and photoperiod in Dark-eyed Juncos . Current Zoology 64 : 23 – 31 . Google Scholar Crossref Search ADS WorldCat Zhang , Y. , and W. R. Hood ( 2016 ). Current versus future reproduction and longevity: A re-evaluation of predictions and mechanisms . The Journal of Experimental Biology 219 : 3177 – 3189 . Google Scholar Crossref Search ADS PubMed WorldCat Zhang , Y. , K. N. Yap, T. D. Williams, and D. L. Swanson ( 2018b ). Experimental increases in foraging costs affect pectoralis muscle mass and myostatin expression in female, but not male, Zebra Finches (Taeniopygia guttata) . Physiological and Biochemical Zoology 91 : 849 – 858 . Google Scholar Crossref Search ADS WorldCat © 2021 American Ornithological Society. ISSN 0004-8038, electronic ISSN 1938-4254 Direct all requests to reproduce journal content to the AOS Publications Office at pubs@americanornithology.org This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

Journal

OrnithologyOxford University Press

Published: Apr 23, 2021

There are no references for this article.