2.2 | Statistical analyses
The basic population composition of captured wintering robins at Hutu
Ranch was constructed after testing for the sex of the bird using
molecular analysis. To determine potential morphological differences
between females and males with the same-colored plumage, independent
comparison tests were conducted based on the normality and
homoscedasticity of the data for the eight morphological traits
measured.
The optimal indexes to describe fat reserve on birds may vary among
different species and populations (Labocha and Hayes, 2012). Therefore,
it is necessary to test the significance of different body condition
indices on body fat reserve. In this study, we dissected the carcass of
robins (n = 9) which had unexpectedly died and weighed the fat reserve
in the furcular and abdominal region. The sum of the fat reserve in the
furcular and abdominal region was recorded as fat mass (±0.001 g).
Furthermore, the proportion of fat mass in the whole body (fat mass /
body mass) was recorded as fat percentage (%). Fat mass and fat
percentage were used as response variables to explore the relationship
between fat reserve and other body condition indices using ordinary
least squares (OLS) regressions models. The significance of indices was
mainly confirmed using the
R2and p -value.
To study the dynamic body condition patterns in winter, we conducted an
OLS regression analysis of suitable body condition indices with ordinal
days. I Then, a comparison test was performed to compare the body
condition indices among different winter stages. The research period in
this study was divided into five stages: pre-winter (2022.11.6 –
2022.11.28), cold wave (2022.11.29 – 2022.12.1), early winter
(2022.12.2 – 2023.1.13), midwinter (2023.1.14 – 2023.1.28) and late
winter (2023.1.29 -2023.3.1), each stage based on the temperature change
trend in that period. However, no more robins were captured or recorded
after 2023.3.1. It is possible that these robins had already started
their spring migration northwards in early March.
For further analysis of predictor variables, fat score was chosen as the
response variable based on its significant regressive relationship with
ordinal days and its significant variance among winter stages. The
effects of the structural size of birds and daily capture time had been
discussed in some earlier studies, as the structurally larger birds are
usually heavier and can hold larger amounts of fat (Labocha and Hayes,
2012). Small passerine birds usually acquire more fat reserve throughout
the day in winter (Colorado Z. and Rodewald, 2017). Therefore, we tested
the effect of structural size (the pca1 result of eight morphological
traits) and daily capture time on selected body condition indices and
determined that only daily capture time had a significant positive
effect on fat score (β = 1.2844, p = 0.007). The residuals of the fat
score that extracted daily capture time by OLS regression were then used
as dependent variables for further analysis.
The predictor variables were divided into two groups: external and
internal. External factors consisted of environmental conditions,
including local temperature and humidity, snowfall events, and
invertebrate biomass. Internal factors consisted of the sex and capture
status of the birds. Detailed descriptions of the factors mentioned
above are available in Table S2 of the supplementary material. The
effect of those predictor variables on the fat score residual was
analyzed using a multiple linear regression model for the day of
capture, the three-day average before capture, and the seven-day average
before capture. Predictor variables were scaled using the scaling method
in the R ‘arm’ package. All potential predictor variables were included
in the initial version of the full model. A VIF test was then conducted
to check for multicollinearity, and predictors were sequentially
eliminated until all the VIF values of predictor variables were below
10. Then the full model final version was determined. All possible
submodels were constructed from the full model, and an Akaike
information criterion value (AIC) was then calculated for each submodel.
Using the model averaging method, models having a ΔAIC < 2
were retained and averaged, and finally an optimal model was
constructed. All statistical analyses were conducted in R version 4.2.1.
The function ‘dredge’ and ‘model.avg’ in the R package MuMIn was used
for model selection.