Data from: Effects of immune status on stopover departure decisions are subordinate to those of condition, cloud cover and tailwind in autumn-migrating common blackbirds (Turdus merula)
Data files
Nov 22, 2024 version files 48.38 KB
-
data_v2.xlsx
42.48 KB
-
README.md
5.90 KB
Abstract
Migratory birds encounter a large variety of parasites and pathogens en route and invest in immune defences to limit the risk and fitness costs of infection. Since both migration and immune defences carry costs, individuals on tight budgets may face trade-offs between migratory progress and immune status. Many species alternate legs of strenuous migratory flight with stopovers during which birds refuel, rest, and recover physiologically. Despite this, most time and energy consumed during migration are actually spent on stopovers. As a result, identifying what determines stopover duration is key in understanding how migratory birds balance investments in immune defences and migration. Yet, it is unknown under what conditions an individual’s immune status may affect migratory progress through the duration of stopovers. We explored whether immune status at arrival affects stopover duration by radio-tagging and blood-sampling common blackbirds (Turdus merula) during autumn stopovers on the Dutch island of Vlieland. To measure immune status, we quantified levels of bacterial killing ability, natural antibodies, complement, and haptoglobin, as well as heterophil-lymphocyte ratios. We show that stopover departures peaked during periods with low cloud cover and strong tailwinds. While lean birds prolonged stopovers, we only found a weak tendency of prolongation in birds with elevated haptoglobin levels. We conclude that effects of immune status on minimum stopover durations are subordinate to those of condition, cloud cover, and tailwinds in autumn-migrating common blackbirds. Hence, future studies on the link between immune defences and stopover durations should take weather conditions into account.
https://doi.org/10.5061/dryad.ffbg79d3q
The data file (“data_v2”): Minimum stopover durations of Common blackbirds are based on tag detections by the Motus receiver system. These form the basis of this dataset. Included covariates include several indices of immune status/function (haptoglobin, lysis and agglutionation scores, bacterial killing ability and heterophil-lymphocyte ratio’s), scaled mass index (based on tarsus and body weight), weather conditions (cloud cover, from the local KNMI weather station, and interpolated tailwind components derived from the u and v wind components in the NCEP dataset).The R script (“Analysis”): includes the time-dependent Cox proportional hazard models we used to analyse these data.
Description of the data and file structure
The R script ‘Analysisv2.R’ *includes the time-dependent Cox proportional hazard models we used to analyse these data, and uses the ‘data*_v2’ Excel file. Data in this Excel file are stored in two different data sheets: ‘wnd’ and ‘data’, which we describe in detail below. Abbreviations in the descriptions below for the measurement units are: m = meters, nm = nanometers, s = seconds, mg = milligrams, mL = milliliters, YYYY = year, MM = month, DD = day, hh = hour, mm = minute, ss = second. UTC = universal time coordinated time standard.
The sheet named ‘wnd’ includes data that are required to reproduce the figures in the manuscript by using the R script. These data may also be obtained via the RNCEP package, and from the KNMI repository, but upon request we include these here too, so figures can easily be reproduced. Each row in this data frame refers to a single time point, indicated by the colum ‘datetime’, for which the cloud cover score ‘N’ is given, as well as the maximum tailwind component ‘tailwind.max’. That is, the following columns are included in this datasheet:
datetime: the date and time at which cloud cover score ‘N’, and maximum tailwind component ‘tailwind.max’ were measured (YYYY-MM-DD hh:mm:ss UTC)
N: cloud cover at ‘datetime’ (scores: 0-8, indicating the range of scores from no cloud cover [0] to completely overcast skies[8])
tailwind.max: maximum tailwind component from 3 NCEP pressure bands at ‘datetime’, assuming 10 m/s air speed, and 247.05 degrees optimal migration heading (m/s).
The sheet named ‘data’, stores everything needed to run the statistical models of the paper. These data are structured in rows: one row for each night an individual was present at the stopover site, and in the position (‘at risk’) to depart. Specifically, the columns in this datafile refer to:
weather.time: timestamp for which the weather was obtained from interpolated RNCEP and KNMI weather station data (YYYY-MM-DD hh:mm:ss UTC)
day: day of weather being obtained (DD-MM-YYYY UTC)
rnr: unique ring number that identifies each individual bird
bka: bacterial killing ability capture for each individual (proportion, calculated as: ([colony count sample] - [colony count controls])/[colony count controls])
hl: hemolysis score (titer, as described in Matson et al. 2006 Dev. Comp. Immunol. DOI:10.1016/j.dci.2004.07.006)
ha: hemagglutination score (titer, as described in Matson et al. 2006 Dev. Comp. Immunol. DOI:10.1016/j.dci.2004.07.006)
pc1hlha: first principal component of hl and ha
hp: residual haptoglobin concentration (hp_original) over 450 nm prescan value (pre450) to correct for absorbance due to sample redness/cloudiness (mg/mL)
hp_original: haptoglobin values (mg/mL, not corrected for variation in redness as quantified by absorbance in pre450)
pre450: prescan value for haptoglobin concentration at 450 nm (absorbance value from plate reader)
sex: sex of bird (M or F, based on plumage characteristics)
age: age class of bird (1cy or >1cy, based on moult in greater coverts)
smi: scaled mass index of bird (based on median tarsus length and body weight)
hlratio: heterophil to lymphocyte ratio (ratio: number of heterophils per 100 leukocytes divided by the number of lymphocytes per 100 leukocytes based on blood smear)
tag.date: date of tagging (DD-MM-YYYY UTC)
dep.date: date of departure (DD-MM-YYYY UTC)
dep.date.full: date and time of departure (DD-MM-YYYY hh:mm:ss UTC)
sunset: date and time of sunset on ‘day’ (DD-MM-YYYY hh:mm:ss UTC)
N: cloud cover at ‘weather.time’ (scores: 0-8, indicating the range of scores from no cloud cover [0] to completely overcast skies[8])
date_utc: identical to ‘day’ (DD-MM-YYYY UTC)
tailwind.max: maximum tailwind component from 3 NCEP pressure bands at ‘weather.time’, assuming 10 m/s air speed, and 247.05 degrees optimal migration heading (m/s).
Sharing/Access information
The used NCEP data can be acquired through the RNCEP library in R (as indicated in our script. We also include the used numbers in our data sheet (see above).
The used local weather data (on cloud cover) from KNMI, was derived from the following source (website in Dutch):
Code/Software
The R script “Analysis.R” uses the data file described above, and was run in R v. 4.4.1.
By deploying radio transmitters on common blackbirds during stopover on the Dutch Island of Vlieland, we tested the relationships between body condition (scaled mass index) and several indices of immune status/function and stopover departures. We include temporal covariates for cloud cover and tailwind components in the model, which are included for each potential night of departure.