Electronic supplementary material
Brain region associated with visual cues is important for bird migration
Orsolya Vincze1,2, Csongor I. Vágási1,2, Péter L. Pap11,2, Gergely Osváth2,3, Anders Pape Møller4
1MTA-DE
“Lendület” Behavioural Ecology Research Group, Department of Evolutionary Zoology,
University of Debrecen, Egytem tér 1, H–4032, Debrecen, Hungary;
2
Evolutionary Ecology Group, Hungarian Department of Biology and Ecolgy, Babeş-Bolyai University,
Clinicilor street 5–7, RO–400006, Cluj Napoca, Romania;
3
Museum of Zoology, Babeş-Bolyai University, Clinicilor street 5–7, RO–400006, Cluj Napoca,
Romania;
4Laboratoire
d'Ecologie, Systématique et Evolution, CNRS UMR 8079, Université Paris-Sud, Bâtiment
362, F-91405 Orsay Cedex, France
Appendix S1 – Detailed methodology of the conducted study
Brain size data
We extracted brain region size data of 140 species published in [1], additionally 13 other species, not
available from the latter source was extracted from [2]. The two sources used identical methodology for
brain region measurement. Since [2] presents sex-specific means, species average was calculated as
standard mean of male and female averages. Note that the optic lobe also includes the underlying inferior
colliculus, as well as some parts of the tegmentum due to dissection methodology applied, which have
implications for the interpretation of our results. Nevertheless, we presume that optic tectum, the
elaborately laminated supraventricular part of the lobe will increase with the size of the lobe itself and
discuss our results accordingly in the main text. Coturnix chinesensis [1] had no distribution map
available from BirdLife International, therefore was excluded from analyses. Body mass of adult birds
was extracted from [3].
Migration distance
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We obtained distribution maps (shape files) for each species from
http://www.birdlife.org/datazone/info/spcdownload [4]. Using these distribution maps, we calculated the
geometric centroids of the spatial polygons of breeding (breeding and resident) and wintering (wintering
and resident) ranges for each species using the ‘gCentroid’ function of the R package ‘rgeos’ [5].
Migration distance was then calculated as the geographic distance between breeding and wintering
centroids using a custom function [6] written in R 3.2 [7]. Migratory distance calculated here represents
the average distance travelled by a given species during migration.
Wing morphology
Aerodynamic theory predicts that wing architecture defines flight capacity, and, therefore, wing
morphology is often linked to migration distance [8]. To control for this effect, we measured wing area
(m2) and aspect ratio as wing area divided by wingspan (m) squared (therefore, dimensionless measure
[m2/m2]) using photographs or drawings of fully stretched wings with the leading edge perpendicular to
the body axis (for the details see Vágási et al. submitted). Note that data on wing morphology was only
available for 91 out of the 152 species, and, therefore, models containing any of these two variables are
restricted to 91 species.
Statistical analyses
We log10-transformed all variables prior to analyses in order to ensure model residual normality. A
constant of one was added to migration distance prior to transformation to avoid problems with migration
distances of 0 (i.e. residents).
All statistical analyses were conducted using R [7]. To assess how migration distance relates to wing
morphology and brain region sizes we used multivariate phylogenetic generalized least squares
regressions (PGLS), as implemented in the R package “caper” [9]. Migration distance was used as
dependent variable, while all others variables were included as explanatory variables. In order to control
for similarity due to common phylogenetic descent, we used an ultrametric, rooted phylogenetic tree,
calculated based on 1000 random trees with Hackett backbone [10], accessed from www.birdtree.org
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[11], using the ‘sumtrees’ software [12]. Phylogenetic dependence was estimated using Pagel's λ and was
set to the most appropriate value assessed by maximum likelihood in each model. We performed p-value
based stepwise backward model selection on multivariate models, with a threshold value of 0.05. Body
mass was retained in all models to control for body size effects. We present both full and minimal
models.
Note that all four brain region sizes are highly correlated with body mass, potentially causing
multicollinearity problems in multivariate models. Therefore, we repeated each analysis using massadjusted brain sizes. For this, scaling exponents were extracted from PGLS regressions between log brain
(region) size and log body mass as implemented in R package “caper” [9]. Scaling exponents (b; i.e. the
slope of the PGLS regression) were the following: whole brain: 0.615; telencephalon: 0.658; optic lobe:
0.454; cerebellum: 0.599. Scaling exponents for only passerines (n = 38 species, whole brain: 0.674;
telencephalon: 0.719; optic lobe: 0.546; cerebellum: 0.632). Mass-adjusted brain region values were
computed as raw brain (region) size divided by body mass raised to power b. Models based on massadjusted brain sizes yielded highly consistent results with models based on log-transformed data,
indicating that collinearity is not a major issue in our analyses (Table S1 b–c).
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