10.5061/DRYAD.9GHX3FFDJ
Class, Barbara
0000-0002-2361-9821
University of Turku
Brommer, Jon
0000-0002-2435-2612
University of Turku
Contrasting multi-level relationships between behavior and body mass in
blue tit nestlings
Dryad
dataset
2020
Academy of Finland
289456
en
960280 bytes
1
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Repeatable behaviors (i.e. animal personality) are pervasive in the animal
kingdom and various mechanisms have been proposed to explain their
existence. Genetic and non-genetic mechanisms, which can be equally
important, predict correlations between behavior and body mass on
different levels (e.g. genetic, environmental) of variation. We
investigated multi-level relationships between body mass measured on weeks
1, 2, and 3 and three behavioral responses to handling, measured on week
3, and which form a behavioral syndrome in wild blue tit nestlings. Using
7 years of data and quantitative genetic models, we find that all
behaviors and body mass on week 3 are heritable (h2= 0.18-0.23) and
genetically correlated, whereas earlier body masses are not heritable. We
also find evidence for environmental correlations between body masses and
behaviors. Interestingly, these environmental correlations have different
signs for early and late body masses. Altogether, these findings indicate
genetic integration between body mass and behavior, and illustrate the
impacts of early environmental factors and environmentally-mediated growth
trajectory on behaviors expressed later in life. This study therefore
suggest that the relationship between personality and body mass in
developing individuals is due to various underlying mechanisms which can
have opposing effects. Future research on the link between behavior and
body mass would benefit from considering these multiple mechanisms
simultaneously.
Data collection Data used for these analyses was collected between 2012
and 2018 in a wild population of blue tits breeding in nest boxes in
south-west Finland (Tammisaari, 60°01′N, 23°31′E). This population has
been monitored yearly since 2003 during the breeding season (first broods
from end of April to end of June), following a standard protocol for nest
box-breeding passerines (Brommer and Kluen 2012). Nest boxes were visited
weekly in May to assess laying dates, clutch sizes and estimate expected
hatching dates. Nests from first broods were visited daily starting from
their expected hatching date until at least one hatchling was observed
(D0). Two days after the hatching day (D2), nestlings were weighed (using
a scale with 0.1g precision) and their nails were clipped following unique
combinations to allow their identification at later stages of development.
Parents were caught and identified when nestlings were at least 5 days
old. One week later (D9), nestlings were weighed and banded by putting a
metal ring with a unique alphanumeric code on their left leg after their
nail code was read. A few days before fledging (D16), nestlings were
transferred all together in a large paper bag and various measurements of
each nestling were taken following a fixed sequence (cf. Brommer and Kluen
2012). Firstly, each individual was held still on its back in the
observer’s palm. Stopwatch was started and the number of struggles during
10 seconds was counted. Docility was expressed as -1 time this
number/second. Immediately after this 10 seconds assay, the time each bird
took to take 30 breaths was measured twice using a stopwatch. Breath rate
was calculated as 30 divided by the average of these two measures and
expressed in number of breath/second. A higher breath rate reflects a
higher stress response to handling (Carere et al. 2004). The bird’s right
tarsus and head-bill length were then measured using a digital sliding
caliper (0.1mm accuracy) before measuring its wing and tail length using a
ruler (1mm accuracy). During these morphometric measurements, the bird’s
aggressive behavior (struggling, flapping wings) was observed and a
handling aggression score (1-5) was given to the bird. This score, which
is 1 for a completely passive bird and 5 for a bird struggling
continuously, reflects the time it takes for each bird to calm down during
these measurements. Each nestling was then weighed using a Pesola spring
balance (0.1g accuracy) and then placed in a second large paper bag where
measured nestlings remained until the entire brood was processed and put
back to its nest. Pedigreed population Phenotypic data was available for
5404 individuals, which were connected through a social pedigree based on
social parenthood. The pruned pedigree, which retains only informative
individuals holds record for 6205 individuals, 5464 maternities, 5107
paternities, 25107 full sibs, 43411 maternal sibs, 38543 paternal sibs,
18284 maternal half-sibs, 13416 paternal half-sibs, a mean family size of
10.8, a mean pairwise relatedness of 2.54e-3 and a maximum pedigree depth
of 11. In this population, 11% to 22% of offspring produced annually were
sired by extra-pair males (unpublished data). Based on simulations
(Charmantier & Réale 2005), such level of error in paternity
assignment is unlikely to cause substantial biases in quantitative genetic
parameters when using the social pedigree. Quantitative genetic analyses
Quantitative genetic analyses were carried out using animal models, which
are mixed effects models that use the relatedness matrix derived from a
population pedigree to estimate additive genetic (co) variance (Wilson et
al. 2010). Univariate animal models assuming Gaussian distribution were
run for each trait separately to estimate their variance components and
their ratios to phenotypic variance. Then, a multivariate animal model was
run for all six traits to estimate their correlation on various levels. In
all models, brood identity, maternal identity, and additive genetic
effects were fitted as random effects to estimate (co)variance due to
common environment, maternal, and additive genetic effects while fixed
effects included time of measurement in minutes and year as continuous and
categorical covariates, respectively. For behavioral responses, fixed
effects also included observer identity and handling order (continuous).
In univariate models, box was fitted as an additional random effect to
account for consistent differences between territories. Animal models were
solved using Restricted Maximum Likelihood (REML), and implemented in
ASReml-R version 3 (Butler et al. 2009; VSN International, Hemel
Hempstead, U.K.). Statistical significance of fixed and random effects was
tested using conditional Wald F tests and likelihood ratio tests (LRT)
with one degree of freedom, respectively. Heritability (h2) of each trait
was calculated as the ratio VA /VP where VP, the phenotypic variance, is
defined as the sum of the REML estimates of additive genetic effects,
maternal, common environment effects and residuals (VA , VPE, and VR
respectively) and is conditional on the fixed-effect structure of the
model. Correlations between pairs of traits on each level were calculated
based on the corresponding covariance matrix estimated by the multivariate
animal model. In this multivariate model, each response was corrected by
the same fixed effects as in its corresponding univariate model. Random
effects box and mother identities were not fitted in this model due to not
being estimable for the former, and due to model convergence issues for
the latter. Three 4-trait animal models including maternal effects were
however fitted to verify that the relationships between each separate mass
and behavior on other levels were consistent with the relationships found
using the 6 trait animal model excluding maternal effects. Standard errors
(SE) of variance ratios and correlations were approximated using the delta
method (Fischer et al. 2004). Coefficients of variation (CV=sd*100/mean)
were calculated for the different variance components in. All statistical
analyses were performed in R (R development core team 2019). Residuals of
all animal models were approximately normally distributed (Shapiro-Wilk
test values >0.92, Figure S1). Structural equation models SEMs
were used to investigate, on each level, different hypotheses for the
relationships between behavior and body masses at different ages (Figure
1). SEMs have been previously used in behavioral studies to explore the
structure of behavioral syndromes using predicted individual values
derived from mixed models (Dochtermann & Jenkins 2007, Dingemanse
et al. 2010). Here, each covariance matrix estimated by the multivariate
model was converted into a correlation matrix, which was used as input
data (cf. Class, Kluen and Brommer 2019, Moirón et al. 2019). In all SEMs
variance of latent factors was fixed to 1. Because a correlation matrix
was used as input data, the residual variance of each indicator (the
variance unexplained by the latent factor) was fixed to 1 minus its
squared factor loading. Each SEM was fitted in R using the package
“lavaan” (Rosseel 2012). Sample size in these models was nominally set at
642 for the common environment level (number of broods) and 5404 (number
of individuals) for the residual level and the all SEMs were compared
using AIC. The sample size in a SEM will not affect the inferred loadings
or correlations between latent variables but can impact their uncertainty
and the model AIC. We verified that the model rankings were similar if
sample size was assumed to be lower. Parametric bootstrap simulations were
conducted to estimate median and 95% confidence intervals (CI) the model’s
loadings and assess model selection uncertainty. Because our findings
indicated that the genetic covariance matrix was much reduced (see
results), we focused on the common-environment and residuals covariances.
Multivariate data for the 6 traits was simulated 1000 times using the
inferred common environment and residual covariance matrices to generate a
simulated dataset of the same dimension as the observations. Each
simulated data was analyzed using a multivariate mixed model. At each
iteration, and on each level, SEMs were run based on the estimated
correlation matrices and ranked by AIC. Model selection uncertainty was
assessed by calculating bootstrap selection rates (Lubke et al. 2017),
which indicate whether model ranking is consistent to sampling
variability. The selection rates have no a priori cut-off value for
“significance”, but instead provide an indication of model selection
uncertainty (Lubke et al. 2017). For example, a selection rate of a SEM of
50% would indicate it is the top model in half the simulations, and 100%
would suggest consistent support for this one SEM hypothesis over the
others in all simulations. R code for performing SEMs and simulations are
provided in Text S1.