Arctic birds and mammals are physiologically adapted to survive in cold environments but live in the fastest-warming region on the planet. They should therefore be most threatened by climate change. Combining modelling and physiological measurements in dovekies (Alle alle) from East Greenland, we demonstrate that cold adaptation in this small Arctic seabird does not handicap acute tolerance to air temperatures more than 10 °C above their current maximum. We predict that climate warming will reduce the energetic costs of thermoregulation for dovekies, but their capacity to cope with rising temperatures will be constrained by water intake and salt balance. Dovekies evolved 15 million years ago, and their thermoregulatory physiology might reflect adaptation to paleoclimates that were substantially warmer than the present-day.
Overview
The data provided are described in three sections. We provide a brief overview of the type of data described in each section, followed by a more detailed description of the data below.
- Field-based physiological measurements: respirometry data and associated metadata.
- Comparative species data set: previously published data for upper critical temperature (TUC) measured in 255 bird species (Khaliq et al. 2014 Proc. B 281: 20141097) combined with published data quality categories pertaining to that data set (McKechnie et al. (2017). J. Biogeogr. 44, 2424-2426).
- Future climate scenarios: predicted land surface air temperatures under historical and future climate scenarios at the East Greenland study site, extracted from the HadGEM2-ES climate model.
Field-based physiological measurements
Column headings in .csv file
date: date of measurement
channel: a factor representing each flow-through system, from flow controller to chamber and then gas analyser channel.
test.period: an integer representing discrete measurements at a given acute test temperature in the chronological order of measurements for a given individual.
file: the file name of the raw data .txt file from which the processed data were derived.
flow.rate.set.point: the nominal flow rate at which the volumetric flow controller was set (i.e. uncalibrated flow rate).
time.s_start: the time at which a given measurement period commenced (in seconds since midnight).
time.s_end: the time at which a given measurement period ceased (in seconds since midnight).
vCO2.ml.h: uncalibrated rate of carbon dioxide production (VCO2) in millilitres per hour (standard temperature and pressure).
vH2O.ml.h: uncalibrated rate of water vapour production (VH2O) in millilitres per hour (standard temperature and pressure).
vO2.ml.h: uncalibrated rate of oxygen consumption (VO2) in millilitres per hour (standard temperature and pressure).
mean.temp: the mean air temperature inside a given respirometry chamber during a given measurement period (i.e. test period) as measured by a temperature data logger placed inside the respirometry chamber.
min.temp: the minimum air temperature inside a given respirometry chamber during a given measurement period (i.e. test period) as measured by a temperature data logger placed inside the respirometry chamber.
max.temp: the maximum air temperature inside a given respirometry chamber during a given measurement period (i.e. test period) as measured by a temperature data logger placed inside the respirometry chamber.
sd.temp: the standard deviation in the air temperature inside a given respirometry chamber during a given measurement period (i.e. test period) as measured by a temperature data logger placed inside the respirometry chamber.
id: unique identifier for each individual bird in the study.
capture.time: the time that each bird was captured prior to respirometry measurements.
release.time: the time that each bird was released after respirometry measurements.
mass.g: the mass of each bird in grams, measured immediately prior to respirometry measurements.
anipil.id: the unique identifier for the device used to measure internal body temperature in a subset of the birds in the study (measured simultaneously with respirometry measurements).
flow.calibration.factor: a factor used to calibrate VCO2, VO2, and VH2O measurements to account for differences in flow rate between flow controllers.
analyser.calibration.factor: a factor used to calibrate VCO2 measurements to account for differences between gas analysers, as measured by precision span gases.
barometric.calibration.factor: a factor used to adjust VCO2 and VH2O measurements to account for daily variation in barometric pressure on the measured concentrations of CO2 and H2O.
final.VCO2.ml.h: calibrated measurements of the rate of carbon dioxide production (i.e., estimates of resting metabolic rate).
final.VH2O.ml.h: calibrated measurements of the rate of water vapour production (i.e., estimates of resting evaporative water loss rate).
final.VO2.ml.h: calibrated measurements of the rate of oxygen consumption (which we used for calculating respiratory exchange ratio in the present study).
Comparative species data set
Column names in the .csv file
Species: Latin species name for each bird in the data set
Body.Mass..g.: mean mass of the individuals used in each study.
UCT…C.: mean estimated upper critical temperature for each species (in the present study we shorten it to TUC).
References: a reference to the study from which the data were obtained.
Category: data quality categories as proposed by McKechnie et al. (2017). Includes four levels, including ‘Good’, ‘Insufficient data’, ‘No TUC’, and ‘—–’. Details are provided in the paper.
Latitude…N.: the latitude at which each study was conducted.
Future climate scenarios
We provide a separate .csv file for each of the five combinations of time period and climate scenarios. Note that the files are formatted with each year as a row, with 3-hourly land surface air temperature predictions separated by semicolons.
Phylogenetic modelling
We fitted phylogenetic mixed models to existing data (Khaliq et al. 2017 Proc B 281: 20141097) for upper critical temperature (TUC) in 255 bird species. We also included information on potential sources of variation in TUC estimates due to differences in methodology among studies (McKechnie et al. 2017 J Biogeogr 44:10). Specifically, different studies estimated TUC from respirometric data obtained across different temperature ranges, which potentially influences the precision of estimates of TUC. To account for this potential source of error in TUC, we used the ‘data quality’ categories proposed by McKechnie et al. (2017) in their examination of the primary data from the Khaliq et al. (2014) dataset. Their categories were ‘Good’ (i.e., an increase in metabolic rate above thermoneutrality with a clear inflection point defining TUC), ‘Insufficient data’ (i.e., some evidence of an increase in metabolic rate above thermoneutrality, but based on measurements at too few air temperatures to reliably estimate TUC), ‘No TUC’ (i.e., no breakpoint in the data, TUC taken as highest measurement temperature), or ‘------’ (i.e., not able to assess the data). We included ‘data quality’ (which we called ‘methodology’) as a fixed factor (with the four levels described above) in our models. Methodology explained 2.8% of the variance in TUC and the standardized effect sizes (Cohen’s d) for each methodology category varied between 0.03 and 0.07. In other words, differences in methodology had little to neglible influence on the variance in TUC relative to that associated with latitude and log mass. See Methods in the paper associated with this data repository for more details.
Physiological measurements
We performed respirometry on individual dovekies (Alle alle, n = 36) captured from a breeding colony at Ukaleqarteq, East Greenland (70° 43' N, 21° 33 W) between 11 July and 8 August 2019 (during the late incubation and chick-rearing period). On each day of measurement, four breeding birds (as indicated by the presence of a brood patch) were captured between 8 am and 12:30 pm GMT, and mass and morphometrics were recorded at the time of capture. Each bird was then kept in a cotton bag in a quiet location for 2 to 3 hours before being placed inside a 5 L plastic respirometry chamber and placed inside a temperature-controlled box. The temperature-controlled box was made from an insulated icebox fitted with a Peltier-thermoelectric air cooler (TE Technology, model AC-027, Traverse City, MI, USA) and temperature controller (TE Technology, model TC-48-20, Traverse City, MI, USA). The temperature-controlled box provided a completely dark space in which to estimate the resting metabolic rate and water loss rate in the dovekies exposed to a range of air temperatures. Measurements were completed between ~4 pm and 4 am each day, which corresponds with the period of lowest activity on the colony from which the birds were collected.
The resting metabolic rate and water loss rate of each bird were estimated from the rate of carbon dioxide production (
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VCO2, ml CO2 h
-1) and rate of water vapour production (
VH2O, ml H2O h-1), respectively, during open-flow respirometry. Air was drawn from the outside air at the field lab using volumetric flow-controlled pumps (Sensidyne, model Gilian Gilair-5, St. Petersburg, FL, USA) at a flow rate set to 4.5 L min
-1 and calibrated with a wet-cell air flow calibrator (Sensidyne, Gilibrator-2, St. Petersburg, FL, USA). From the flow-controlled pump, air was delivered directly into the respirometry chambers (5 L plastic container). A subsample of the air leaving the respirometry chambers was passed through CO
2 / H
2O gas analyzers (LI-COR, model LI-840A, Lincoln, NE) interfaced with an analogue-to-digital converter (ADInstruments, model PowerLab 8/30 A/D convertor, Bella Vista, NSW, Australia) that was connected to a laptop computer. We were able to observe our measurements in real time via the LabChart software (ADInstruments, Bella Vista, NSW, Australia). We measured the excurrent air from chambers containing individual birds, as well as an empty reference chamber to obtain continuous measurements of atmospheric concentrations of CO
2 and H
2O during all measurements. The use of a reference chamber enabled us to mathematically compensate for fluctuations in atmospheric concentrations of CO
2 and H
2O as well as compensate for the effects of fluctuations in atmospheric pressure.
The laptop computer was placed outside the room in which birds were being held, to minimize disturbance to the birds and so that the respiration of the birds could be observed in real-time to monitor the welfare of the birds in each trial. We also monitored internal body temperature (Tb) in real-time using data loggers (Animals Monitoring, model Anipill, Caen, France) with a data receiver to ensure the birds did not overheat at any point. The Anipill loggers were ingested by birds between capture and being put in the respirometry chambers and recorded body temperature continuously (1 sample min-1). The Anipills are the same as those swallowed by human athletes to monitor their body temperature during activity (e.g. marathons). They are calibrated by the manufacturer, which provides a calibration certificate. In seabirds, the devices reside in the stomach and do not enter the intestine, because the pylorus is extremely small and only lets liquid transfer from the stomach into the intestine. The device therefore remains in the stomach until regurgitated by the bird, usually after a few days. Regurgitation is a natural process in seabirds, to feed chicks and/or to evacuate undigested hard parts of prey, such as fish otoliths. All birds survived the respirometry trials and flew away upon release at the end of each trial. Several birds on which respirometry had been performed were re-sighted at the colony in the following days, and some of those were observed with full gular pouches, meaning they had returned to feeding behavior and attending to their chicks at the nest. The period of time between when an individual was first caught and when it was released and had the opportunity to return to the nest was 17 hours. Dovekies exhibit biparental care, with both male and female adults provisioning their chick with food, meaning that chicks would not have been without food during the time the other parent was in captivity.
Upon being introduced to the respirometry chambers, birds were given 3.5 hours to settle at a temperature between 4 and 10 ℃ (the mean air temperature at the colony during our study was 7.5 ℃) before the temperature in the temperature-controlled box was changed. It took approximately 1.5 hours for the air temperature to stabilize at each new test temperature, after which birds were held at that temperature for approximately 1 hour. Hence, the first measurement period occurred at 5 hours after birds were introduced to the respirometry chamber, and birds were kept in respirometry chambers for approximately 14 hours and exposed to 4 – 5 acute test temperatures (in total 184 measurements from 36 individual birds). Estimates for resting metabolic rate, water loss rate, and internal body temperature were obtained from measurements taken during the 1-hour test period when air temperature was stable. We used a custom algorithm that searched for a 30-minute period corresponding to the lowest mean value of metabolic rate within the 1-hour measurement period. While each individual was exposed to 4 – 5 test temperatures, we randomised the test temperatures that individuals were exposed to between 5 °C and 35 °C. We randomised test temperatures to maximise the spread of samples across the continuous range of ambient temperatures rather than arbitrarily ‘binning’ all individuals to a subset of 4 – 5 temperatures, which would reduce the precision of the fit of the regression model to the data. We also randomised the order in which we were exposed to test temperatures so that any potential changes in the contribution of stress over the 14-hour measurement period were randomly distributed across the range and order of test temperatures in our study. The temperature was recorded separately inside each respirometry chamber with a temperature data logger (OneTemp, model Hobo MX2201, Adelaide, South Australia, Australia), which was connected via Bluetooth to a smartphone (Apple, model iPhone 6, Cupertino, California, US) and temperatures could be tracked in real-time.
No birds that were used for respirometry were observed with full gular pouches when captured prior to respirometry. This does not rule out that the birds might have fed recently prior to the trial but at least the birds had not fed immediately prior to capture. Even if birds had fed immediately prior to capture, the initial phase of the specific dynamic action response (during which time metabolic rate is elevated due to the energetic cost of digestion) would have passed prior to measurement of metabolic rate, which occurred at least 7 hours after capture. To the extent that it is possible in wild birds, our measurement protocols sought to minimise inter-individual variation in metabolic rate associated with energy expenditure on activities other than that required for thermoregulation, including digestion and stress.
Modelling dovekie physiology under future climate scenarios
We used one of the best-performing climate models for the Arctic climate (HadGEM2-ES) to extract land surface air temperatures every 3 hours for the summer months (June – August) at our study site at Ukaleqarteq, East Greenland (70° 43' N, 21° 33 W). We extracted 3 hourly summer air temperatures each year for a decade during an historical baseline (1996 – 2005), as well as mid-century (2036 – 2045) and a late-century (2090-2099). In the mid- and late-century decades, summer air temperatures were considered under both low (RCP2.6) and high (RCP8.5) radiative forcing scenarios, creating a total of five climatic conditions (one baseline, mid-century low and high global greenhouse gas emissions, and late century low and high global greenhouse gas emissions). We used the predicted air temperature data and empirical physiological data to calculate the total amount of energy expended (CO2 total) and water lost (H2O total) by an average individual over the summer breeding season and across a range of TUC values from 5 to 35 °C. See Methods in the paper associated with this data repository for more details.
