Using metabolic data to investigate the role of brood size in the development of endothermy
Data files
Oct 07, 2024 version files 25.40 KB
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D14.csv
10.15 KB
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D3.csv
1.84 KB
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metabolic_and_temperature_data.csv
9 KB
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README.md
4.41 KB
Abstract
Altricial songbirds transform themselves from naked poikilotherms to fully feathered endothermic homeotherms over a matter of days from hatching to fledging. The ontogeny of endothermy is a developmental milestone for birds that not only face warmer average temperatures, but also increasingly frequent cold snaps and extreme weather. The timing of development of endothermy has been studied in altricial birds for over half a century. However, the determinants and constraints of the onset of endothermy are not yet fully understood. We experimentally investigated whether brood size influences the ontogeny of endothermic heat production in 4-8 day-old nestling blue tits (Cyanistes caeruleus) in southern Sweden. The thermogenic response to a cooling challenge (15°C) increased with age overall. We found that 8-day-old nestlings from reduced broods had a slightly increased capacity for endothermic heat production compared to enlarged broods. This difference cannot be explained by body mass because this trait did not differ between brood size categories. Although a metabolic response was present in most nestlings by day 6, it was brief, not lasting more than a few minutes, and not sufficient to maintain a stable body temperature in any age group. Our study shows that incipient endothermy is present at an early age in nestling blue tits and may advance faster in reduced broods, but that individual nestlings lack sufficient insulation and thermogenic performance to maintain homeothermy independently during the first week of life.
https://doi.org/10.5061/dryad.1ns1rn924
Description of the data and file structure
Results and figures should be reproducible using the files and script listed below.
Files included in this deposition:
1) D3.csv
Data obtained during brood size manipulations of blue tits on day 3 after hatching
Columns:
#ID = Nestbox ID
#tx = treatment group, e = enlarged (nestlings were added to this nest), r = reduced (nestlings were taken from this nest)
#hdate = hatching date (mm/dd/yy)
#eggs = clutch size
#broodsize = brood size on day 3
#moved = number of nestlings transferred to or from another nest
#dm = total mass of donated nestlings (g)
#idm = mean individual mass of donated nestlings (g)
#om = total mass of nestlings pre-manipulation (g)
#iom = mean individual nestling mass pre-manipulation (g)
#lm = total leftover nestling mass (reduced broods only, g)
#ilm = mean leftover individual nestlings mass (reduced broods only, g)
#to.from = nest that the nestlings were transferred to or from
#aprild = April day - hatching date as number of days after march 30th
#eb = experimental brood size (brood size after manipulation)
2) D14.csv
Data from nestling measurements of blue tits on day 14 after hatching
#ID = Nestbox ID
#date (mm/dd/yy)
#ringnr = nestling ID
#tars = tarsus length (mm)
#wing = wing length (flattened, straightened, mm)
#mass = nestling mass (g)
3) metabolic_and_temperature_data.csv
Metabolic and skin temperature data from cooling challenge experiment of blue tit nestlings
#ID = Nestbox ID
#IDday = experiment ID (nest and day)
#Date (mm/dd/yy)
#Time (hh:mm:ss)
#VO2_tnz = minimum oxygen consumption in the warm chamber (mL per minute)
#FR_tnz = mean flow rate during measurement in the warm chamber (mL per minute)
#Thermo_1_tnz = mean skin temperature during metabolic measurement in the warm chamber (degrees Celcius)
#Thermo_2_tnz = mean air temperature in the warm chamber during metabolic measurement (degrees Celcius)
#VO2_coldmax = maximum 0xygen consumption in the cold chamber (mL per minute)
#FR_coldmax = mean flow rate during metabolic measurement in the cold chamber (mL per minute)
#Thermo_4_coldmax = mean air temperature in cold chamber during metabolic measurement (degrees Celcius)
#ts_start = skin temperature at the start of cooling challenge (degrees Celcius)
#ts_end = skin temperature after 5 minutes of cooling challenge (degrees Celcius)
#day = age (days)
#tc = binary for if thermocouple stayed attached for the first 5 minutes of the cooling challenge (y = yes, n = no)
#tx = treatment group (e = enlarged, r = reduced)
#avgmass = mean mass of individual nestlings (g)
#aprild = April day - hatching date as number of days after march 30th
4) brood_day3_data.R
R script for analysis of data collected on day 3, the day of brood size manipulations
5) nestlings_day14_data.R
R script for analysis of data collected on day 14 in blue tit nestlings
6) metabolic_and_cooling_analysis.R
R scripts for analysis and figures from metabolic and cooling rate data in blue tit nestlingsCode/Software
Software
ExpeData (v1.9.27)
R version 4.3.2 (2023-10-31)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Sonoma 14.1
Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: Europe/Stockholm
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] ggeffects_1.4.0 gridExtra_2.3 rmcorr_0.6.0 MuMIn_1.47.5 emmeans_1.10.0 MVN_5.9
[7] rstatix_0.7.2 ggstatsplot_0.12.2 lubridate_1.9.3 forcats_1.0.0 stringr_1.5.1 purrr_1.0.2
[13] readr_2.1.5 tibble_3.2.1 tidyverse_2.0.0 lmerTest_3.1-3 lme4_1.1-35.1 Matrix_1.6-1.1
[19] ggpubr_0.6.0 here_1.0.1 ggplot2_3.4.4 dplyr_1.1.4 tidyr_1.3.1 car_3.1-2
[25] carData_3.0-5
Metabolic data and skin temperature data was collected and processed using ExpeData (v1.9.27). We used the software's built-in functions to span calibrate baseline measurements and correct for drift in the oxygen sensor. Metabolic data was collected as %O2, %CO2 and was mathematically dried and converted to VO2 (mL O2 per minute). Data was selected automatically in ExpeData for minimum and maximum mean values.