Altitudinal variation in metabolic parameters of a small Afrotropical bird

    Altitudinal variation in metabolic parameters of a small Afrotropical bird Lindy J. Thompson, Colleen T. Downs PII: DOI: Reference:

S1095-6433(17)30167-8 doi:10.1016/j.cbpa.2017.07.015 CBA 10254

To appear in:

Comparative Biochemistry and Physiology, Part A

Received date: Revised date: Accepted date:

2 May 2017 24 July 2017 26 July 2017

Please cite this article as: Thompson, Lindy J., Downs, Colleen T., Altitudinal variation in metabolic parameters of a small Afrotropical bird, Comparative Biochemistry and Physiology, Part A (2017), doi:10.1016/j.cbpa.2017.07.015

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ACCEPTED MANUSCRIPT Altitudinal variation in metabolic parameters of a small Afrotropical bird Lindy J. Thompson and Colleen T. Downs*

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School of Life Sciences, University of KwaZulu-Natal, P/Bag X01, Scottsville, Pietermaritzburg,

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3209, South Africa.

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* Corresponding Author: Colleen T. Downs Email: [email protected]

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Tel: +27 (0)33 260 5127

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ORCID: http://orcid.org/0000-0001-8334-1510

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Other Email: [email protected]

Running header: Altitudinal variation in avian resting metabolic rate

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ACCEPTED MANUSCRIPT Abstract Of the numerous factors affecting avian metabolic rate, altitude is one of the least studied. We used

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mass-flow respirometry to measure resting metabolic rate (RMR), evaporative water loss (EWL)

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and respiratory exchange ratio (RER) in two populations of a small (10-12g) Afrotropical bird, the Cape white-eye (Zosterops virens), in summer and in winter. In total, 51 freshly wild-caught adult

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Cape white-eyes were measured overnight. Altitude was included as a source of variation in the

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best approximating models for body mass, whole-animal RMR, RER, whole animal standard EWL and whole-animal basal EWL. RER was significantly lower values in winter, suggesting a greater

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proportion of lipid oxidation at lower ambient temperatures (Ta). Cape white-eyes were 0.8g heavier at the higher altitude site and 0.5g heavier in winter, suggesting they may have increased

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this to cope with cooler temperatures. EWL was generally significantly lower in winter than in summer, suggesting that birds may increase EWL with increasing Ta, as the need for evaporative

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cooling increases. Our results support the argument that the subtle and complex effects of altitude (and ambient temperature) should be taken into account in studies on avian metabolic rate.

Keywords altitude; altitudinal variation; seasonal variation; Cape White-eye; Zosterops virens; resting metabolic rate; evaporative water loss; respiratory exchange ratio

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ACCEPTED MANUSCRIPT What is already known Of the numerous studies known to affect avian metabolic rate, altitude is one of the least studied.

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Although trends are not always clear, generally, at higher altitudes, avian metabolic rate

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increases.

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What the study adds

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There were statistically significant seasonal and altitudinal differences in various physiological parameters of Cape white-eyes. These results highlight the importance of accounting for altitude

BMR Basal metabolic rate

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EWL Evaporative water loss

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Abbreviations

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in studies avian metabolic rate.

Mb

Body mass

RER

Respiratory exchange ratio

RMR Resting metabolic rate Ta

Ambient temperature

V̇CO2 Rate of CO2 production V̇O2

Rate of O2 consumption

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ACCEPTED MANUSCRIPT Introduction There are numerous environmental factors that may reversibly affect avian metabolic rate (see

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reviews by McKechnie, 2008; McKechnie and Swanson, 2010). One of these factors is altitudinal variation, which can significantly affect various avian physiological parameters, including rate of

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oxygen consumption (V̇O2), rate of carbon dioxide production (V̇CO2), respiratory exchange ratio

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(RER) and body temperature (Chappell and Bucher, 1987), however studies on the effects of

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altitudinal variation on metabolic rate within species are few (see Table 1). For example, resting metabolic rates (RMR) of Rosy Finches (Leucosticte arctoa), house finches (Carpodacus

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mexicanus) and Amethyst Sunbirds (Chalcomitra amethystina) increased significantly with increased altitude (Clemens, 1988; Lindsay et al., 2009). Similarly, in 13 bird of paradise species

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(Family Paradisaeidae), those that were limited to lower altitudes had lower basal metabolic rates (BMRs) than those found at higher altitudes (McNab, 2003, 2005). In each case, this increase in

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metabolic rate with increased altitude may be due to variation in ambient temperature (Ta) along the altitudinal gradient.

In contrast to the results of the aforementioned studies, Londoño et al. (2015) found that avian BMR does not always increase with an increase in altitude; contrary to expectations, the BMR of 253 Peruvian forest bird species did not differ significantly between altitudes of 400, 1500 and 3000 m above sea level. The authors concluded that tropical birds have a ‘consistently ‘slow’ energy metabolism’ across a wide range of altitudes (and thus temperatures). Furthermore, a study on four altitudinal subpopulations of Common Fiscals (Lanius collaris) showed that birds from lower altitudes had higher BMR and EWL than conspecifics from higher altitudes (Soobramoney et al., 2003), a finding these authors related to unpredictable climate and food availability. Thus altitudinal trends in avian metabolism are not always clear, and warrant further investigation.

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ACCEPTED MANUSCRIPT Relatively few studies have examined the phenotypic plasticity of avian metabolic rate in terms of both season and altitude, despite increasing recognition of avian RMR and BMR as a

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highly flexible traits (Piersma, 2002; Vézina et al., 2006), reversibly affected by a multitude of

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factors (Broggi et al., 2009). Furthermore, future work predicting the effects of climate change on avian physiology will require baseline data that is currently largely lacking. Thus, we investigated

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altitudinal and seasonal differences in RMR of a small Afrotropical bird species, the Cape White-

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eye (Zosterops virens) (Sundevall, 1850; Thompson and Taylor, 2014). We also scored birds for primary moult, since the maintenance of tissues necessary for moult may be associated with

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elevated avian metabolic rate (Klaassen, 1995; Lindström et al., 1993). We hypothesised that Cape White-eyes would show altitudinal differences in whole animal

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RMR, and we predicted that birds from higher altitudes (and consequently lower Ta) would have higher whole animal RMR than conspecifics from lower altitudes, as was the general trend from

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studies listed in Table 1. We further hypothesised that Cape White-eyes would show altitudinal variation in body mass (Mb) and RER. We predicted that birds from higher altitudes would have greater Mb (Soobramoney et al., 2003) and lower RER than those from lower altitudes, since higher altitudes have lower mean Tas irrespective of season, and thus an increased need for thermoregulation, and this may require the catabolism of substrates with greater energy yields, which would result in lower RER (Tucker, 1968). Cape White-eyes are generalist feeders; their diet includes nectar, insects, spiders, spider eggs and fruit (Kopij, 2004; Skead, 1967), so we assume that carbohydrates, lipids and proteins should be included in their diets (and available for catalysis) in all altitudinal locations. Only one of the studies listed in Table 1 examined altitudinal effects on RER, and so we further aimed to help fill this knowledge gap. Finally, we hypothesised that Cape White-eyes would show altitudinal and seasonal variation in EWL, since higher

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ACCEPTED MANUSCRIPT metabolic rates may require increased ventilation which is achieved by more frequent breathing or by increased tidal volume, leading to higher respiratory water loss (Williams and Tieleman, 2000).

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For example, cold-acclimated Hoopoe Larks (Alaemon alaudipes) have significantly higher EWL

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than warm-acclimated conspecifics when measured at the same Ta (Williams and Tieleman, 2000). Thus the higher metabolic rates we predicted for Cape White-eyes from the higher altitude site

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should be linked to higher EWL. We predict that when measured at 30°C (a Ta within their

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thermoneutral zone, Thompson et al., 2015d), EWL would be higher in Cape White-eyes from the high altitude site, since they would be acclimatised to lower Ta, and may therefore have a greater

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need for evaporative cooling when exposed to an overnight Ta of 30°C. We predicted that birds from both altitudinal sites would increase their EWL in summer (following Thompson et al.,

Study animals

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Methods

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2015d) to aid with heat dissipation through evaporative cooling.

Cape White-eyes are widespread throughout southern Africa and are found at a range of altitudes, from sea-level to 2770 m (Johnson and Maclean, 1994), making them a good model species for this study. RMR has been shown to differ between long-term captive and freshly wild-caught Cape White-eyes (Thompson et al., 2015b). Thus, only wild-caught Cape White-eyes were used in this study. Furthermore, there is a delay between acclimatization to the maximum/minimum in a particular season’s temperatures, and the corresponding maximum/minimum in RMR of Cape White-eyes (Thompson et al., 2015a). Specifically, RMR of Cape White-eyes reaches a maximum after the austral summer (i.e. in March), and a minimum after the austral winter (i.e. in October)

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ACCEPTED MANUSCRIPT (Thompson et al., 2015a), hence we decided to catch and measure our study birds just after summer (in March), and after winter (September).

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In September 2013, two groups of Cape White-eyes were caught using mist-nets (Ecotone,

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Gdynia, Poland) under permit number OP5122/2012 from the provincial authority, Ezemvelo KwaZulu-Natal Wildlife. The first group of 16 adult birds was caught in Pietermaritzburg,

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KwaZulu-Natal, South Africa, either at the botanical gardens of the University of KwaZulu-Natal

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(29°37’S and 30°24’E; 655 m a.s.l.), or at the Darvill Bird Sanctuary (29°36’S and 30°26’E; 624 m a.s.l.). The second group of 11 adult birds was caught in a garden in Howick, KwaZulu-Natal,

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South Africa (29°28’S and 30°13’E; 1079 m a.s.l.). Both sites were also used for capturing 14 (Pietermaritzburg) and 11 (Howick) adult Cape White-eyes from 8 February to 9 March 2014. One

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of the birds caught in Howick in winter 2013 was caught and measured in both seasons. In this

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paper, we will refer to the Howick capture site as the ‘High altitude site’, and the Darvill Bird

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Sanctuary and University of KwaZulu-Natal sites as the ‘Low altitude site’. Weather data for both areas were provided by the South African Weather Service and summarized in Table 2. Each Cape White-eye was ringed with a metal ring to enable individual identification. Cape White-eyes are mostly sedentary (Chittenden, 2007), with strong site fidelity (Symes et al., 2001) and little genetic exchange between populations (Brown et al., 2000). However, there are rare records of some individuals being recaptured > 100 km from previous ringing sites (Symes et al., 2001), and some altitudinal migration has been recorded (Johnson and Maclean, 1994). Therefore, we consulted the publicly available online-records of the South African Ringing Unit to check whether our study birds had been recaptured at different altitudinal sites or not (SAFRING, 2016). Of the 51 birds used in the study (one bird measured in winter 2013 was caught and measured again in summer 2014), 8 were recaptured once, and 1 was recaptured twice. For each individual,

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ACCEPTED MANUSCRIPT all captures were at the same location, suggesting strong site fidelity and no movement between populations.

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Cape White-eyes were transported by car to the University of KwaZulu-Natal,

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Pietermaritzburg (at 666 m above sea level). Distances the birds were transported from capture sites to the laboratory varied from 0.21 km and 4.11 km for the two low altitude sites (the

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University of KwaZulu-Natal’s Botanical Gardens and Darvill Bird Sanctuary respectively), to

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24.57 km for the high altitude site. Metabolic measurements began either on the same day that Cape White-eyes were caught, or as soon as possible thereafter. In instances where birds were held

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overnight prior to measurements being taken (due to a limit in the number of birds that could be measured each night), birds were kept in clean cages with wooden perches, and supplied with

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drinking water and a variety of ripe grated and whole fruit, including apples, bananas, oranges and pawpaws. All birds were released at their capture sites on the morning following their overnight

Moult

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metabolic measurements.

Cape White-eye was scored for primary feather moult immediately prior to metabolic trials. Birds that were actively moulting one or more of their primaries were given a score of 1, and individuals that were not moulting, with either all new or all old feathers, were given a score of 0. In winter, Cape White-eyes from both altitudinal groups had primary flight feathers that were all new and fully grown. In contrast, in summer, half of our Cape White-eyes were moulting their primaries. Our metabolic measurements were taken from fasted, resting birds during their rest phase, at 30°C, which is within their thermoneutral zone (Thompson et al., 2015d), however, some of our birds were moulting when our measurements were made. Moult is energetically expensive and it

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ACCEPTED MANUSCRIPT is correlated with increased metabolic rate (Cyr et al., 2008; Dolnik and Gavrilov, 1979; Guozhen and Hongfa, 1986; Lindström et al., 1993; Portugal et al., 2007). We therefore cannot refer to our

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measurements as BMR, which is defined as the minimum maintenance metabolism of a

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postabsorptive, normothermic, resting endotherm, measured at thermoneutrality, in the absence of circadian, thermoregulatory or other increments in metabolic heat production (sensu McKechnie,

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2008). Instead, we refer to our metabolic measurements as resting metabolic rate (RMR).

Sleep recording

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To confirm whether Cape White-eyes were indeed sleeping/resting during metabolic measurements, we video-recorded sleep behaviour of two Cape White-eyes from the low altitude

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sites, and four from the high altitude site, during overnight metabolic measurements. Two infraredsensitive video cameras recorded the behaviour of each bird from approximately 18:00 to 06:00

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the following morning. We used two video cameras per bird so that both sides of each bird’s head could be monitored separately, to determine whether each eye was open or closed. This is important since various avian species exhibit unihemispheric sleep (Rattenborg et al., 2001). A single observer (L.J.T.) evaluated video recordings of sleep behaviour for each Cape White-eye. Within each 12 h period, the duration of each ‘sleep bout’ (that is, the interval between eyelid closure and opening) was recorded to the nearest second, and these sleep bouts were summed for each night, and expressed as a percentage of the night spent asleep.

Gas exchange measurements At 15:00, Cape White-eyes were weighed to the nearest 0.01 g with digital scales (model: AFB3100L, Adam Equipment S.A. Pty Ltd, Johannesburg). Individual Cape White-eyes were then

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ACCEPTED MANUSCRIPT placed into 2.8 l Perspex respirometers, which in turn were placed inside an environmental chamber (CMP2244, Conviron, Winnipeg, Canada). The light : dark cycle of the environmental

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chamber was standardised between seasons, that is, lights went off at 18:00 and on at 06:00. The

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times of onset of light and darkness in our environmental chamber differed from the times of start and end of daylight at our study sites by 7 to 36 min; lights in our environmental chamber went on

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7 min earlier than the start of daylight in September, and went off 36 min earlier than end of

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daylight in February (https://www.timeanddate.com/sun/south-africa/pietermaritzburg). The temperature of the environmental chamber was set to 30°C, which falls within the thermoneutral

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zone of Cape White-eyes in Pietermaritzburg in both summer and winter (Thompson et al., 2015d). Cape White-eyes have digestive transit times of up to 49 min (Wellman and Downs, 2009),

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therefore in this study, individual Cape White-eyes were fasted for 3 h before respirometry measurements began, to ensure that the birds were post-absorptive. Each respirometer contained a

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plastic mesh platform positioned approximately 10 cm above a 1 cm layer of mineral oil (AlphaPharm, Pietermaritzburg), to reduce evaporation from excreta (Chappell and Bucher, 1987; Whitfield et al., 2015).

Metabolic rate was measured indirectly, using mass-flow, push-mode respirometry, as described by Thompson et al. (2015c). We used an interrupted sampling regime, starting with a 6 min baseline measurement, and then four birds respectively for 6 min each. This sequence took 30 min to complete, and was repeated continually, so that each of the four birds being measured on a certain night was measured twice (for 12 min in total) per hour. From 18:00 h to 06:00 h the following morning, O2 and CO2 concentrations (%), flow rate (ml min−1) and water vapour density (μg ml−1) were recorded at 5 s intervals.

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ACCEPTED MANUSCRIPT Silica gel, soda lime and more silica gel were used to remove water vapour and CO2 from environmental air, which was then pumped (model PP2, Sable Systems, Las Vegas, NV, USA)

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into the first five inlets of a flow measurement system (model FB8, Sable Systems). Flow rates

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were set at ∼800 ml min−1, which maintained O2 depletion in each respirometer between 0.1 and 1.0% (Lighton, 2008). Effluent air flowed into a flow multiplexer (model MUX, Sable Systems).

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Excess air escaped through a manifold, and the remainder was pumped into a subsampler (model

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SS4, Sable Systems) at 200 ml min−1. This air then passed into a water vapour analyser (model RH300, Sable Systems), which was regularly zeroed using pure N2, and spanned using

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atmospheric air bubbled through water of a known temperature, and thus known water vapour density, following Cory Toussaint and McKechnie (2012) and (Levesque and Tattersall, 2010).

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Minimal quantities of self-indicating Drierite (Hammond Drierite Co. Ltd, Xenia, Ohio) that had been previously recharged (following White et al., 2006) were used to dry the air which

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then passed through a CO2 analyser (model CA-10, Sable Systems). This analyser was regularly zeroed with N2, and spanned with a certified gas of 964 ppm CO2 in N2 (AFROX, Pietermaritzburg, South Africa). Finally, air passed through an O2 analyser (model FC-10, Sable Systems). A Universal Interface (model UI2, Sable Systems) transferred data from the flow meter and gas analysers to a computer using ExpeData data acquisition software (Sable Systems). A custom-written macro was used to perform lag and drift correction in ExpeData. We calculated a 95% equilibration time of 11 min (Lasiewski et al., 1966), and therefore, since we only measured each bird for 6 min, we Z-transformed V̇O2, V̇CO2 and EWL before the calculation of hourly means to correct for washout effects (following Lighton, 2008; Lighton and Halsey, 2011; Tøien et al., 2011). Samples (O2 and CO2 concentrations (%), flow rate (ml min−1) and water vapour density) were taken every 5 s, and data were recorded for each individual bird for 6 min

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ACCEPTED MANUSCRIPT out of every 30 min, so we obtained 72 values per bird, per 30 min. To objectively obtain steadystate values during each 6 min measurement period for each bird, we used a pre-recorded, custom-

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written macro to select the most stable section of 30 continuous samples (i.e. a 2.5 min period),

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and we calculated a mean of these 30 samples. Therefore, for each parameter and for individual bird, we obtained means from two 2.5 min sections of data (five mins in total) per hour. The lowest

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hourly V̇O2 value per bird on a certain night was taken as the RMR for that bird on that particular

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night. V̇CO2 and V̇H2O (hereafter termed standard EWL) were noted at the same time as RMR was recorded. V̇O2, V̇CO2 and standard EWL were calculated according to the configuration of

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the system, using equations from Withers (2001):

 V̇O2 = V̇I (FIO2 – [FEO2 (1 – FIO2 – FICO2 – FIH2O) / (1 – FEO2 – FECO2 – FEH2O)])

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 EWL = V̇I ([FEH2O (1 – FIO2 – FICO2 – FIH2O) / (1 – FEO2 – FECO2 – FEH2O)] – FIH2O)

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where V̇I was the measured mass flow.

Mass-specific rates are not presented here, since the implicit assumption of mass-specific units is a linear scaling with mass (Packard and Boardman, 1988, 1999). V̇CO2 data were only used to calculate RER, where RER = V̇CO2/ V̇O2, and thus V̇CO2 data are not presented here. Birds were reweighed at 06:00 in the morning following each overnight trial. Mb was taken at approximately the same time as RMR each night (i.e. if RMR occurred closer to the start of the trial, we used Mb recorded at the start of the trial; if RMR occurred towards the end of the trial, we used Mb at the end of the trial; if RMR occurred towards the middle of the night, then we used an average of Mb recorded at the start and end of the night). The lowest mean hourly EWL each night (hereafter referred to as basal EWL) was also recorded, since previous work (Thompson et al., 2015b) showed that standard and basal EWL did not necessarily occur at the same time each night. 12

ACCEPTED MANUSCRIPT Statistical analyses We identified the best statistical models of Mb, RER, RMR and EWL using an information-

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theoretic approach (Burnham and Anderson, 2002). For each response variable, we used analysis

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of variance (ANOVA) to compare the global model (i.e., one containing all main effects and interactions) using (i) linear regression and (ii) linear generalised least squares (GLS, weighted

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linear regressions which account for heterogeneity of variance of the residuals). For a particular

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response variable, if the global model using GLS was significantly better (p < 0.05) than the global model using linear regression, then we defined our set of candidate models using GLS, but if the

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two were not significantly different then we used linear regression. Thus, for whole-animal RMR and whole-animal standard EWL, we estimated the effects of ‘Season’ (either summer or winter),

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‘Altitude’ (at the respective capture site, either high or low), ‘Season*Altitude’ (the interaction

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between Season and Altitude), ‘Mb’ (g, taken at the time of RMR), ‘DaysHeld’ (the number of

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days that an individual bird was held in captivity prior to measurement, ranging from zero to three days) and ‘Moult’ (either 0 for no primary moult or 1 for some primary moult) using GLS, where the variance structure was different for each stratum (i.e. the ‘varIdent’ variance structure, Zuur et al., 2009). For Mb, RER and whole-animal basal EWL, we estimated the effects of ‘Season’, ‘Altitude’, ‘Season*Altitude’, ‘Mb’, ‘DaysHeld’ and ‘Moult’ using linear regression (for the response variable ‘Mb’ we excluded ‘Mb’ as a fixed effect). We defined a set of candidate models a priori following Burnham and Anderson (2002) and Zuur et al. (2010), and compared the candidate models to the global model. We used Akaike’s information criterion to select the best approximating models, following Burnham and Anderson (2002) and Wagenmakers and Farrell (2004). All models were fitted using the nlme package (Pinheiro et al., 2016), and models with ΔAICc < 2 were averaged for multimodel inference using

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ACCEPTED MANUSCRIPT the MuMIn package (Bartoń, 2016) in R version 3.2.5 (R Core Team, 2016). We used the zero method (Burnham and Anderson, 2002; Grueber et al., 2011) to weight the estimates and errors of

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our model-averaged parameters, since our aim was to determine which factors had the strongest

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effect on each respective response variable (Nakagawa and Freckleton, 2011).

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Results

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For whole-animal RMR, the likelihood ratio test indicated that the variance structure in the global model created with GLS was considerably better than the constant variance in the global model

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using linear regression, L = 6.14 (df = 2, p < 0.05). The best approximating models of wholeanimal RMR contained altitude, season, the interaction between altitude and season, moult,

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number of days held in laboratory prior to measurement, and Mb as sources of variation (Table 3).

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Mb affected whole-animal RMR more than any other factor, with a 0.96 ml O2.h-1 increase for

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each 1 g increase in Mb. There was a 2.37 ml O2.h-1 difference (i.e. 0.006 ml O2.h-1 for each m increase in altitude) between low and high altitude sites, a 7.00 ml O2.h-1 increase in winter, and a drop of 1.38 ml O2.h-1 with each successive day that a bird was held in captivity prior to measurement (Table 4). Freshly-caught Cape White-eyes from the high and low altitude sites spent 97.1 ± 2.5 % (n = 4) and 95.8 ± 0.0 % (mean ± SD, n = 2) of the night asleep, and so we are confident that metabolic measurements were conducted on resting birds during their rest phase. Body mass (Mb) of individual Cape White-eyes (taken before the start of overnight measurements) ranged from 9.70 to 13.15 g. We modelled Mb using linear regression, since the likelihood ratio test showed no significant difference between a global model specified with GLS and one specified using linear regression, L = 0.27 (df = 2, p > 0.05). Birds were 0.50 g heavier in

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ACCEPTED MANUSCRIPT winter, 0.10 g heavier when their primaries were moulting, and 0.82 g heavier (i.e. 0.0004 g per m of altitude) at the high altitude site (Fig. 1, Table 4).

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RER of individual Cape White-eyes ranged from 0.71 to 1.12. We modelled RER using

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linear regression, since the likelihood ratio test showed no significant difference between a global model specified with GLS or with linear regression, L = 0.84 (df = 2, p > 0.05). The best

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approximating models of RER contained number of days held in laboratory prior to measurement,

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season, altitude and the interaction between season and altitude. RER decreased by 0.30 in winter, and by 0.085 at higher altitude (a decrease of 0.0002 with each m increase in altitude), and

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increased by 0.08 at the low altitude site in winter (i.e. an increase of 0.0002 per m of altitude in winter, Fig. 1, Table 4).

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The likelihood ratio test indicated that the variance structure in the global model created with GLS was considerably better than the constant variance in the global model created using

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linear regression, L = 6.80 (df = 2, p < 0.05). Conversely, for basal whole-animal EWL, there was no significant difference between a global model specified with GLS or with linear regression, L = 3.55 (df = 2, p > 0.05). Both measurements of EWL were affected mainly by season, with Cape White-eyes reducing their EWL in winter (Fig. 2, Table 4). Birds moulting their primaries had higher values for all four measurements of EWL, and there was a decrease in standard whole animal EWL with altitude, whereas basal whole-animal EWL increased with altitude (Table 4). There were no significant differences in mean monthly rainfall between the two altitudinal sites (t(30) = 0.514, P = 0.611), however, mean monthly temperature maxima were significantly higher at the low altitude site than at the high altitude site, t(30) = 2.430, P = 0.021). Furthermore, mean summer temperature range (that is, mean difference between monthly maxima and minima) was significantly higher at high altitude site than at low altitude site (t(4) = 5.004, P = 0.004, Table

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ACCEPTED MANUSCRIPT 5), although there was no altitudinal difference in mean winter temperature range (t(4) = 0.888, P = 0.212). At both altitudinal sites, temperature ranges were significantly higher in winter than in

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summer (t(4) = 6.769, P = 0.011, and t(4) = 5.849, P = 0.014 respectively, Table 5).

Discussion

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The altitudinal difference between sampling sites in this study (albeit relatively small) was

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associated with variation in the whole animal RMR Mb, RER, and whole-animal EWL (basal and standard) of freshly wild-caught Cape White-eyes. Seasonal and altitudinal differences in

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physiological parameters were apparently temperature-related, and our data suggest that it is warmer temperatures in particular (in summer and at lower altitudes) that may be driving

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altitudinal and seasonal differences in physiological variables: (i) we found a significant altitudinal difference in RER in summer, but not in winter, and (ii) there was a significant seasonal difference

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in whole-animal RMR at low altitude, but not at high altitude. There is a significant negative correlation between temperature and altitude (Lancaster, 1980), and so the influence of altitude on avian metabolic rate and other physiological variables may reflect temperature differences between our study birds’ sites of origin.

Cape White-eyes from both altitudinal sites generally increased their standard and basal whole-animal EWL in summer, in accordance with previous work on this species (Thompson et al., 2015d). Although birds may be expected to increase evaporative cooling at warmer Ta s under field conditions, our birds were measured at the same Ta in both summer and winter. We therefore speculate that the higher summer EWL could be due to seasonal differences in body temperature and/or conductance (neither of which were measured here), and/or as a result of higher summer RMR and associated heat production or other factors.

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ACCEPTED MANUSCRIPT The mean whole-animal RMR values of freshly wild-caught Cape White-eyes presented in this study were slightly higher than those of a similarly-sized (11 g) congeneric species from

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Australia, the Silvereye (Zosterops lateralis) (Maddocks and Geiser, 2000). However, the BMR

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values of the latter and other species from arid Australia and the humid tropics were lower than predicted values (Ambrose et al., 1996; Maddocks and Geiser, 1999; Williams and Main, 1976).

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Our summer RER results are similar to those found in chukars (Alectoris chukar), where RER was

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significantly lower at high altitude than at low altitude (Chappell and Bucher, 1987). RER values in both seasons and at both altitudinal sites suggest the catabolism of a mixture of metabolic fuels:

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proteins, fats and carbohydrates (Gessaman and Nagy, 1988). However, the lower RER values at high altitude and in winter suggest increased catabolism of fats which have a high caloric content

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per weight unit (Tucker, 1968), thus providing more energy to the birds when their demands would have been greater due to lower Ta. Since our study birds were post-absorptive during metabolic

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measurements, we expected RER values at the time of RMR to be close to 0.7 (Buck and Barnes, 2000; Powers, 1991) in both seasons and in birds from both altitudinal sites, suggesting the catabolism of pure lipids. Our summer values in particular are surprisingly high, suggesting a possible error in CO2 calibration or measurement, although some avian species do exhibit RER values that are significantly higher than expected during their nocturnal fasts (Walsberg and Wolf, 1995), and it is important to note that in our study the lowest RER value during the night did not necessarily fall at the same time as RMR. We did not expect the slight delays in measurement to affect the physiological variables of our birds (Thompson et al., 2016), and although whole-animal RMR decreased and RER increased with increasing time in captivity, these effects were minimal. The increase in RER with time in captivity does not seem large enough to suggest a switch to catabolism of a different substrate.

17

ACCEPTED MANUSCRIPT Ta was significantly higher and Cape White-eyes were 0.8 g lighter at the low altitude site. Lower Ta may increase energy demands so that birds ingest more food, resulting in the hypertrophy

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of organs involved in catabolism (stomach, intestine and liver), oxygen transport to tissues (heart

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and lungs) and waste elimination (kidneys) (see Williams and Tieleman, 2000). These organs all have high energy consumption, thermogenic capacity and tissue-specific metabolic rates (Bech et

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al., 1999; Burness et al., 1998; Daan et al., 1990; Hammond et al., 2000; Tieleman et al., 2003;

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Vézina and Williams, 2003; Williams and Tieleman, 2000). Total oxygen demand therefore increases as these structures become larger (Liknes et al., 2002; Williams and Tieleman, 2000),

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which could explain the altitudinal variation in RMR of Cape White-eyes in this study. A difference in diet between altitudinal sites could also have affected avian gut morphology, which

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may in turn affect metabolic rate (Garland and Adolph, 1991). Indeed there may have been various other differences between altitudinal sites which may have affected the metabolic parameters of

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Cape White-eyes: humidity, predation, survival, parasitism, clutch size or available food resources (McNab, 2013). Since the causes for altitudinal effects that we have listed here are speculative, we suggest that future work could include (i) the determination of Cape White-eye diet at the two altitudinal sites, (ii) observations of behaviour (including sleeping postures) which may (or may not) affect daily energy expenditure, (iii) the inclusion of additional (intermediate and/or more extreme) altitudinal sites. The lack of any substantial altitudinal or seasonal variation in body mass of Cape Whiteeyes may be partly due to the trade-off between minimising predation risk and minimising the risk of starvation during inclement weather; in an environment where food resources are not particularly scarce then minimising predation risk is more important (Rogers, 1987; Witter and Cuthill, 1993). Experimental studies with Greenfinches (Carduelis chloris) (Ekman and Hake,

18

ACCEPTED MANUSCRIPT 1990), Great Tits (Parus major) (Bednekoff and Krebs, 1995) and Blackbirds (Turdus merula) (Macleod et al., 2005) showed that the birds adjusted their energy reserves according to their

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metabolic requirements, the predictability of foraging success and the reduction of predation risk.

Conclusion

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The effects of altitude on avian RMR may be subtle (McNab, 2013) and complex, given that Ta,

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and presumably a suite of other environmental factors including the availability of food resources, will covary with altitude. An increase in food intake and subsequent increase in gut size is one

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possible response of small vertebrates to the cooler Tas associated with higher altitudes (Hammond et al., 2001). In this study, Cape White-eyes were slightly (0.82 g) heavier at the higher altitude

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site, supporting the ideas of Hammond et al. (2001). Warmer temperatures associated with summer and the lower altitude capture site seemed to be driving variation in metabolic parameters. A deeper

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understanding of the more subtle effects of altitude on avian metabolic parameters is important in determining how birds at different altitudes may respond physiologically to a host of environmental factors, including imminent threats such as the increased temperatures associated with accelerated climate change.

Acknowledgements We are indebted to Stacey Hallam for a discussion that sparked the idea for this paper. P. Nicol and the late B. Nicol kindly allowed us to trap Cape White-eyes in their garden in Howick, and we are grateful to the University of KwaZulu-Natal (UKZN) for permission to trap birds in UKZN’s Botanical Garden in Pietermaritzburg. We thank B. Taylor, S. Hallam, K. Nelson and the Darvil Bird Sanctuary bird ringers for assistance with trapping birds. M. Brown, M. Hampton, C. Clark,

19

ACCEPTED MANUSCRIPT E. Ally, J. Davies and B. Lovegrove provided technical advice, and B. Joos, D. Levesque, C. Canale, J. Lighton, A. McKechnie, B. Smit, M. C. Whitfield and B. Wolf gave advice on the

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respirometry methods. LJT was supported by a National Research Foundation (NRF) (ZA)

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Innovation Doctoral scholarship (grant number 83805). Opinions expressed and conclusions arrived at are those of the authors and are not necessarily to be attributed to the NRF. The authors

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declare no competing financial interests. Raw weather data were provided by the South African

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Weather Service. We are grateful to the anonymous referees whose comments greatly improved

Compliance with ethical standards

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this manuscript.

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Conflict of interest The authors declare that they have no conflict of interest. Ethical approval Birds were caught with a SAFRING ringing license and a permit (number OP

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5122/2012) to capture, monitor, ring, release and transport Cape White-eyes was granted by the provincial authority, Ezemvelo KwaZulu-Natal Wildlife. Ethical approval for this study was granted by the Animal Ethics Sub-committee of the University of KwaZulu-Natal’s Ethics Committee, reference: 071/13/Animal. All permits were in hand when the study was conducted.

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Table 1. Studies on the effect of altitude on avian metabolic rate. In some instances, birds came from more than two sites, however we

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present only the lowest and highest altitudes from which birds were taken, as well as the maximum altitudinal difference (‘Altitude

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Diff.’, m) between sites, for each study.

Altitude

Species

(m)

measurements

Origin

Ref

Captive

1

-20, -10, 0, 10, 20, 30, 35,

BMR 

40

1012

RMR 

5 and 25 (in summer)

Freshly wild-caught

2

424

RMR 

30 (in summer)

Freshly wild-caught

3

1012

no effect (RMR)

5 and 25 (in winter)

Freshly wild-caught

4

424

no effect (RMR)

30 (in winter)

Freshly wild-caught

3

Rufous-collared Sparrow Zonotrichia capensis

4290

no effect (RMR)

5, 10, 15, 20, 25, 30

Wild-caught 10 days prior

5

1670

BMR 

5, 10, 15, 20, 25, 30, 35, 38

Wild-caught 2 weeks prior

6

3650

RMR 

-10, 5, 20, 30

Wild-caught 2 weeks prior

7

House Finch Carpodacus mexicanus

3650

RMR 

-10, 5, 20, 30

Wild-caught >2 weeks prior

7

79 species in greater New Guinea

2860

BMR 

2-4 Tas per night

Both1

8

13 species of Paradisaeidae

n/a

BMR 

not stated

Captive

9

13 species of Paradisaeidae

726

BMR 

not stated

Captive

10

253 Peruvian forest bird species

2820

no effect (BMR)

10, 20, 30, 32-34

Freshly wild-caught

11

3460

Amethyst Sunbird Chalcomitra amethystina

Cape White-eye Zosterops virens

Common Fiscal Lanius collaris Rosy Finch Leucosticte arctoa

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Amethyst Sunbird Chalcomitra amethystina

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Chukar Partridges Alectoris chukar

Cape White-eye Zosterops virens

>1

Ta (°C) of MR

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1

As altitude

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No. of spp.

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difference

27

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References cited: 1 = Chappell and Bucher (1987), 2 = Lindsay et al. (2009), 3 = this study, 4 = Lindsay (2007), 5 = Castro et al.

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(1985), 6 = Soobramoney et al. (2003), 7 = Clemens (1988), 8 = McNab (2013), 9 = McNab (2003), 10 = McNab (2005), 11 =

AC

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SC

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Londoño et al. (2015). 1 In the study by McNab (2013) there were 47 freshly-wild caught and 32 captive species.

28

ACCEPTED MANUSCRIPT Table 2. Maximum, mean, minimum and range of seasonal temperatures (°C) at the high and low

21.5 ± 2.0

23.6 ± 1.9

min

5.5 ± 1.0

8.5 ± 1.3

mean

13.5 ± 1.0

16.1 ± 1.3

range

16.0 ± 1.1

15.2 ± 1.2

max

26.6 ± 0.9

28.7 ± 1.1

min

14.8 ± 1.0

17.8 ± 0.9

mean

20.7 ± 1.0

range

11.8 ± 0.2

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max

23.3 ± 0.9

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11.0 ± 0.2

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summer

Low altitude site

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winter

High altitude site

29

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altitude sites.

ACCEPTED MANUSCRIPT

Table 3. Ranking of models predicting metabolic parameters of Cape White-eyes

Whole-animal RMR

AICcWt ΔAICc

K

AICc

Altitude + Season + Altitude*Season + Moult + Mb + DaysHeld Altitude + Season + Moult + Mb + DaysHeld Altitude + Season + Altitude*Season + Moult + DaysHeld Altitude + Season + Altitude*Season + Mb + DaysHeld Season + Moult + Mb + DaysHeld Season + Mb + DaysHeld Altitude + Season + DaysHeld Altitude + Season + Altitude*Season + DaysHeld Season + Moult + DaysHeld Altitude + Mb Altitude + Season + Altitude*Season + Mb Season + Mb Altitude + Season + Moult + Mb Season + Moult + Mb Altitude + Moult + Mb Altitude + Season

10 9 9 9 8 7 7 8 7 6 8 6 8 7 7 6

286.89 287.43 287.79 287.96 287.97 288.51 289.50 289.68 289.79 290.03 290.89 291.36 292.53 292.55 292.56 294.04

0.18 0.14 0.12 0.11 0.11 0.08 0.05 0.05 0.04 0.04 0.02 0.02 0.01 0.01 0.01 0.01

0.00 0.54 0.90 1.07 1.08 1.62 2.61 2.79 2.90 3.14 4.00 4.47 5.64 5.66 5.67 7.15

Body mass

Altitude + Season Season Altitude + Season + Moult Season + Moult Altitude + Season + Altitude*Season Altitude Altitude + Moult

4 3 5 4 5 3 4

112.17 112.62 113.63 113.72 114.40 115.74 117.75

0.30 0.24 0.15 0.14 0.10 0.05 0.02

0.00 0.45 1.46 1.55 2.24 3.57 5.58

RER

Altitude + Season + Altitude*Season + DaysHeld

6

-161.03

0.54

0.00

PT

Fixed effects

AC

CE

PT ED

MA

NU

SC

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Response variable

30

ACCEPTED MANUSCRIPT

SC

RI

PT

Altitude + Season + Altitude*Season Altitude + Season + Altitude*Season + Moult Altitude + Season + Altitude*Season + Mb Altitude + Season + Altitude*Season + Moult + Mb Altitude + Season Altitude + Season Altitude + Season + Altitude*Season + Mb Altitude + Season + Altitude*Season Season + Mb Season + Moult Altitude + Moult

Whole-animal basal EWL

Season + Moult Altitude + Season Altitude + Season + Altitude*Season Season + Mb Altitude + Season + Altitude*Season + Moult + Mb Altitude + Season + Altitude*Season + Mb

-159.43 -157.68 -157.17 -155.21 -152.66

0.24 0.10 0.08 0.03 0.01

1.6 3.35 3.86 5.82 8.37

5 7 6 5 5 5

462.04 462.61 462.63 462.70 462.83 468.75

0.25 0.19 0.19 0.18 0.17 0.01

0.00 0.57 0.59 0.66 0.79 6.71

4 4 5 4 7 6

382.42 384.32 386.79 386.88 387.58 389.30

0.58 0.23 0.07 0.06 0.04 0.02

0.00 1.90 4.37 4.46 5.16 6.87

AC

CE

PT ED

MA

NU

Whole-animal standard EWL

5 6 6 7 4

Models were ranked according to their corrected Akaike information criterion weights (AICcWt), and on the difference between each candidate model and the best model (ΔAICc). AICc scores and the number of parameters in each model (k) are presented for all models with an AICcWt > 0. Models with ΔAICc < 2 (highlighted in bold) have substantial empirical support (Burnham and Anderson, 2002), and were averaged for multimodel inference.

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ACCEPTED MANUSCRIPT Table 4. Estimate sizes of fixed effects contained in the best approximating model(s) fitted by restricted maximum-likelihood estimation (REML) for each respective response variable. Fixed effect Estimate size Altitude 0.006 Season 6.995 Moult 1.950 Mb 0.961 DaysHeld -1.384 Altitude*Season -0.004

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Altitude Season Moult

SEM 0.004 4.789 1.620 0.681 0.456 0.005

0.0004 0.500 0.096

0.0005 0.213 0.208

Altitude Season DaysHeld Altitude*Season

-0.0002 -0.303 0.009 0.0002

0.0000 0.053 0.008 0.0001

Whole-animal standard EWL Altitude Season Mb Altitude*Season Moult

-0.005 -11.935 2.129 -0.014 1.475

0.015 22.346 3.772 0.024 4.646

Whole-animal basal EWL

-28.269 6.905 0.003

4.092 5.675 0.007

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Response variable Whole-animal RMR

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RER

Season Moult Altitude

Note: In each case, ‘Altitude’ refers to the altitude at the respective capture site (m), ‘Season’ (refers to winter, as opposed to summer), ‘Season*Altitude’ refers to the interaction between ‘Altitude’ and ‘Season’), ‘Mb’ (refers to body mass (g), taken at the time of RMR), ‘DaysHeld’ refers to the number of days birds were held in captivity before measurement (ranging from 0 to 3), and ‘Moult’ is an index of primary moult (either 0 for no primary moult or 1 for some primary moult). The estimate sizes given above are the result of model averaging (for all response variables except RER).

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ACCEPTED MANUSCRIPT Table 5. Body mass (Mb), resting metabolic rate (RMR), standard and basal evaporative water loss (EWL) and respiratory exchange ratio (RER), measured overnight at 30°C, of freshly wild-

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caught adult Cape White-eyes from two different altitudes (the low altitude site at 624 - 655 m

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above sea level, and the high altitude site at 1079 m above sea level). Values presented are mean ± standard deviation. Results for birds from the low altitude site were presented in Thompson et

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al. (2015b). Here we provide a comparison with birds caught and measured over the same period,

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at the high altitude site.

Low altitude (16)

High altitude (11)

Low altitude (14)

High altitude (11)

Mb (g)

11.71 ± 0.66

12.13 ± 0.73

11.34 ± 0.65

11.55 ± 0.57

RMR (ml O2 h-1)

33.54 ± 4.76

33.89 ± 2.57

29.58 ± 4.32

33.10 ± 2.31

RMR (ml O2 h-1 g-1)

2.87 ± 0.41

2.80 ± 0.17

2.61 ± 0.35

2.87 ± 0.18

69.36 ± 28.56

54.37 ± 13.39

84.83 ± 14.64

84.81 ± 17.82

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Capture site (n)

Summer

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Winter

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Standard EWL (ml H2O h-1) Standard EWL (ml H2O g-1 h-1)

5.92 ± 2.37

4.47 ± 1.00

7.49 ± 1.33

7.35 ± 1.49

Basal EWL (ml H2O h-1)

33.34 ± 6.73

38.53 ± 11.56

65.06 ± 12.07

69.92 ± 11.20

Basal EWL (ml H2O g-1 h-1)

2.86 ± 0.62

3.02 ± 1.01

5.74 ± 1.02

6.08 ± 0.94

RER

0.75 ± 0.04

0.77 ± 0.04

0.93 ± 0.06

0.86 ± 0.05

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ACCEPTED MANUSCRIPT Figure Legends Fig. 1 Mean body mass (Mb, g), respiratory quotient (RQ), and whole animal resting metabolic

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rate (RMR, mL O2 h-1) of freshly adult wild-caught Cape White-eyes at low and high altitudes, in the austral summer and winter. Medians are indicated by the thick lines, and boxes contain the

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25th to 75th percentiles. Whiskers show the maximum and minimum values, and outliers are shown with small circles. Similar letters above boxplots indicate a lack of statistically significant

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differences, as determined using t-tests in R.

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Fig. 2 Whole-animal basal evaporative water loss (basal EWL, mg H2O h–1), whole-animal standard evaporative water loss (std EWL, mg H2O h–1) in wild-caught adult Cape White-eyes

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from high and low altitudes, in the austral summer and winter. Medians are indicated by the thick lines, and boxes contain the 25th to 75th percentiles. Whiskers show the maximum and minimum values, and outliers are shown with small circles. Similar letters above boxplots indicate a lack of

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statistically significant differences, as determined using t-tests in R.

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Fig. 1

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Fig. 2

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