Ornis Hungarica 2014. 22(1): 15–35.
DOI: 10.2478/orhu-2014-0008

Phylogeny, historical biogeography and theevolution of migration in accipitrid birds ofprey (Aves: Accipitriformes)Jenő Nagy1 & Jácint Tökölyi2*Jenő Nagy & Jácint Tökölyi 2014. Phylogeny, historical biogeography and the evolution ofmig­ration in accipitrid birds of prey (Aves: Accipitriformes). – Ornis Hungarica 22(1): 15–35.
Abstract Migration plays a fundamental part in the life of most temperate bird species. The re­gu­lar, large-scale seasonal movements that characterize temperate migration systems appear tohave originated in parallel with the postglacial northern expansion of tropical species. Migratoriness is also influenced by a number of ecological factors, such as the ability to survive harsh winters. Hence, understanding
the origins and evolution of migration requires integration of the biogeographic history and ecology of birds in aphylogenetic context. We used molecular dating and ancestral state reconstruction to infer the origins and evolutionary changes in migratory behavior and ancestral area reconstruction to investigate historical patterns of range
evolution in accipitrid birds of prey (Accipitriformes). Migration evolved multiple times in birds of prey, the earliest of which occurred in true hawks (Accipitrinae), during the middle Miocene period, according to our analyses. In most cases, a tropical ancestral distribution was inferred for the non-migratory ancestors of migratory lineages. Results from directional evolutionary tests indicate that migration evolved in the tropics and then increased
the rate of colonization of temperate habitats, suggesting that temperate species might be descendants of tropical ones that dispersed into these seasonal habitats. Finally, we found that diet generalization predicts migratoriness in this group.
Keywords: ancestral area reconstruction, annual cycle, comparative, diet specialization, diurnal birds of prey, molecular dating, seasonalityÖsszefoglalás A legtöbb mérsékelt övi madárfaj életciklusában alapvető szerepet tölt be a vonulás. A rendszeres, nagy
kiterjedésű mozgások, melyek a mérsékelt övi vonulási rendszereket jellemzik, egyes feltételezések szerint a trópusi
fajok poszt-glaciális, északi irányú terjeszkedésével párhuzamosan jelentek meg. Ezen felül a vonulás előfordulását
számos ökológiai tényező is befolyásolhatja, mint például a környezet szezonalitásának mértéke vagy a téli túlélést
befolyásoló tényezők. A vonulás eredete és evolúciója ezért csak úgy érthető meg, ha a madarak biogeográfiai történetiségét és ökológiáját filogenetikai kontextusban tanulmányozzuk. Jelen vizsgálatban a vágómadár-alakúak (Accipitriformes) vonulásának evolúcióját elemeztük komparatív módszerekkel. Első lépésben létrehoztunk egy fosszilis
adatok alapján datált molekuláris törzsfát, amelyen jellegrekonstrukciót végeztünk és rekonstruáltuk a fajok ősi elterjedési területét. Az elemzéseink alapján a vonulás többször alakult ki a ragadozók esetében, legkorábban a héjaformákon (Accipitrinae) belül, vélhetően a Miocén közepén. A legtöbb esetben a vonuló leszármazási vonalak nem vonuló őseinél trópusi elterjedésre következtethetünk. A direkcionális evolúciós teszt alapján a vonulás a trópusokon
jelent meg és megnövelte a mérsékelt égöv kolonizációjának rátáját. Eszerint tehát a mérsékelt övi ragadozómadár fajok vonuló trópusi fajok leszármazottainak tekinthetők, melyek az erősen szezonális, északi élőhelyek irányába terjeszkedtek. Végezetül negatív kapcsolatot találtunk a vonulás megjelenése és a táplálékspecializáció mértéke között.
Kulcsszavak: éves ciklus, jellegrekonstrukció, komparatív, molekuláris datálás, nappali ragadozómadarak, szezonalitás, táplálékspecializáció
MTA-ELTE-MTM Ecology Research Group, 1117 Budapest, Pázmány Péter sétány 1/C, Hungary, e-mail: jenonagy.off@gmail.com
2
MTA-DE “Lendület” Behavioural Ecology Research Group, Department of Evolutionary Zoology and Human
Biology, University of Debrecen, 4032 Debrecen, Egyetem tér 1., Hungary, jtokolyi@vocs.unideb.hu*corresponding author
1

16

ORNIS HUNGARICA 2014. 22(1)Introduction
Birds originated, according to our current
knowledge of the fossil record, about 150200 million years ago during the geological era of Jurassic (Padian & Chiappe 1998).
The appearance of powered flight, pro­bab­ly in combination with several other avian fea­tures such as warm-bloodedness andthe presence of extensive parental care has
fuelled the diversification of this group ofvertebrates, which seems to have accele­
rated around, or shortly after the CretaceousPa­leo­gene boundary (Ericson et al. 2006,
Brown et al. 2008, Jetz et al. 2012).
The widespread occurrence of birds isgreatly facilitated by their excellent dispersal capabilities. This is perhaps mostclearly seen in migratory birds, which can
travel thousands of kilometres on continental scale within a single year. Migration isa characteristic feature of birds that is extremely common especially in species inhabiting the Northern Hemisphere tempe­
rate zone and the Arctic, but it also occurs,
although in less extreme forms, in other regions of the globe in the form of intratropical migration systems (Alerstam 1993, Ber­
thold 2001, Newton 2008).
Migration itself profoundly influences the
distribution, ecology and diversification of
birds (Newton 2008), hence it is not surpri­sing that a great deal of information has accumulated on its internal, proximate determinants and its phenology ever since Hans
Christian Mortensen started ringing birds atthe very end of the 19th century. These stu­dies
revealed that the migratory phenotype is determined by a set of complex and tightly re­gu­lated mechanisms (Gwinner 1990), which
includes, among others (1) sensory elements
underlying orientation and navigation, (2)mechanisms responsible for the regulation of

migratory restlessness (‘zugunruhe’) du­ring
the annual cycle and (3) a range of physiological adaptations that cover the metabolic
requirements of long-distance flights during
migration. Understanding how such a comp­lex phenotype could have evolved is a major
challenge in ornithology.
While a wealth of information has accumulated on the details of the process of mig­ration, comparatively little is known about
how migration originated and evolved inbirds. This is not surprising, since behavioral traits, such as the migratory ha­bits of a
species do not fossilize and hence our current ideas of it are strictly inferred fromphylogenetic or biogeographic studies. Current theories of the evolution of migration can be divided into two groups (Rappole & Jones 2002, Bruderer & Salewski2008). The ‘tropical origin’ hypothesis proposes that migratory birds derive from species inhabiting regions where environmental factors were constant during the year,
so there was no need for migration. These
species could have colonized more seasonal, northern habitats, which, during thesummer months provided appropriate conditions for reproduction. However, during
winter food availability decreased, hence
these birds were forced to return to southernlatitudes (Rappole & Jones 2002, Bruderer& Salewski 2008). Cox (1985) developeda stepping-stone model of this hypothesis.
According to this model, resource-limitation due to competition for food forced certain tropical resident species to expand theirrange to the subtropics. These birds conti­nu­
ed to return to the tropics during the winter,
resulting in the formation of partial mig­rant
species. These partial migrants then continu­
ed to spread to higher latitudes where they
were able to breed successfully while still
returning to the original area in the winterJ. Nagy & J. Tökölyi(Stiles 1980, Cox 1985). Thus, this hypo­the­sis predicts that migratory species evolved
from tropical ancestors.
Several lines of evidence support the tro­pi­cal origin of long-distance migrants. Joseph et al. (1999), in a study of waders,
analyzed the evolution of breeding and wintering distribution of 16 species from thegenus Charadrius using phylogenetic me­
thods. By reconstructing the hypothesized
distribution of the ancestors of these birds,
they showed that species that are migratory
today derive from ancestors whose bree­ding
and non-breeding ranges were located in thetropical zone. Another similar study investigated the evolution of migration in Catharus thrushes (Outlaw et al. 2003). This
study showed that North American (migratory) thrushes are sister to tropical species,
and the ancestral area for the whole lineage
was inferred to be in the Neotropics, providing further support for the ‘tropical origin’ hypothesis. This pattern is not restric­
ted to interspecific comparisons but is also
seen among populations differing in migratory status. For instance, in a study of North
American Chipping Sparrows (Spizaellapasserina), Milá et al. (2006) have shownthat the northern, long-distance migrant
populations descend from non-migratory Mexican populations, which colonized
North America after the last glacial maximum 18 000 years ago. Thus, long-distancemigration and colonization of temperate regions in this species developed in tandem.
Other hypotheses on the origin of migration emphasize the importance of chan­ges
on the breeding territories of birds with anorthern distribution (‘northern origin’ hypothesis) (Bell 2000, 2005, Bruderer &Salewski 2008). According to these suppositions, climatic or other ecological chan­ges(e.g. global cooling) could have led to the17

evolution of migration by forcing non-mig­ratory temperate and arctic species to leave
there home ranges during the winter (Bell2000, 2005), resulting in migratory strategies which allowed the survival of populations in a strongly seasonal milieu (Bell2000, 2005). Thus, this hypothesis predicts
that migration evolved from temperate resi­
dent species, a prediction that has received
relatively low support to date. It is clear,
however, that migration can evolve without the expansion of the breeding ranges,
as examplified by the large number of intra-tropical migrants (e.g. Boyle & Conway
2007, Boyle et al. 2011). Comparative stu­
dies of the occurrence of migration amongsome of these tropical taxa, such as the passerine group Tyranni revealed that a number of ecological traits, specifically diet andhabitat, predict whether a species is migratory or not in the tropics (Boyle & Conway
2007, Boyle et al. 2011). Thus, year-roundvariation and predictability of food sources(Boyle & Conway 2007, Boyle et al. 2011),
as well as the ability to exploit these sour­ces (Bell 2011, Boyle et al. 2011) appears
to predispose some avian taxa for migration.
As the examples above suggest, the evolution of migration in birds is a complex problem that requires an integrative approachcombining aspects of the historical biogeo­
graphy (range expansions), ecology (habitat, food availability) and behavioral ecology(diet specialization) of birds. Yet, phylogenetic studies often target only one of these
aspects, while ignoring others. Here, we describe an attempt for such an integrative app­roach using accipitrid birds of prey (Accipi­
triformes) as a model group. This taxon is
ideal for our purpose because it includes both
tropical and temperate species and there is
wide variation in migratory behavior, habitat and diet within the group (Ferguson-Lees

18

ORNIS HUNGARICA 2014. 22(1)& Christie 2001). Specifically, we investigate
the following: (1) the phylogeny and histori­
cal biogeography of diurnal birds of prey;(2) the evolutionary origins of migration in
raptors in a phylogenetic context and (3)the ecological and behavioral traits that areassocia­ted with migratoriness in this group.
The traits we investigate are body mass, dietand habitat. Body mass influences nearly all
aspects of birds’ life and could be a crucial
factor determining which species can survive periods of food shortage and/or cold
wheather (e.g. Newton 2008). Therefore, wehypothesize that larger birds are more likely to become residents (or vice versa: residents might be selected to become larger)(Tökölyi & Barta 2011). Diet can be important for two reasons: first, raptors feeding on
warm-blooded prey or carcass are more likely to survive the winter at temperate latitudes
and hence be non-migratory. Second, species
with a generalist diet should be more likely
to subsist during periods of resource shortageby finding alternative food sources, hence
we predict that food generalists are more fre-quent in mig­rants. Lastly, habitat type was
hypothesized to be important in the evolution of tropical-temperate migration systems
because it could have determined the availability of sui­table corridors for tropical-temperate dispersal routes (Rappole & Jones
2002). Rappole and Jones (2002) noted thatthe majo­rity of long-distance migrants in the
Nearctic spend the winter in forests, whereas
almost none of the Palearctic/Asian migrants
do so. They proposed that the lack of forested
habi­tats in North Africa could have acted asa dispersal barrier, effectively filtering range
expansions from south. Therefore, we tested
whether habitat type is associated with mig­ratoriness in birds of prey.

Methods
Phylogenetic reconstruction andmolecular dating
The list of genes used to reconstruct themolecular phylogeny of birds of prey forGene (Abbreviation)

No species

No. bpModel

12S ribosomal RNA (12S)
16S ribosomal RNA (16S)

74

900

TIM2+I+G

53

1527

GTR+I+G

ATP synthase F0 subunit 6 (ATP6)

56

684

TrN+I+G

ATP synthase F0 subunit 8 (ATP8)

56

168

TrN+I+Gβ-fibronigen intron 7 (BFI7)

69

922

TVM+G

Cytochrome c oxidase subunit 1 (COX1)

86

1551

TIM2+I+G

Cytochrome b (CYTB)

164

1146

TIM3+I+G

NADH-ubiquinone oxidoreductase subunit 2 (ND2)

151

1047

GTR+I+G

NADH-ubiquinone oxidoreductase subunit 2 (ND6)

66

525

GTR+I+G

Recombinase activating gene 1 (RAG1)

87

2872

GTR+I+G

Table 1.

Gene sequences used to reconstruct the phylogeny of Accipitriformes, their availability,
length and the most appropriate evolutionary model of sequence evolution applying tothem, as evaluated by jModelTest
1. táblázat A vágómadár-alakúak törzsfa-rekonstrukciójához használt génszekvenciák neve, elérhetősége (fajok száma), hossza és a jModelTest által meghatározott legmegfelelőbb evolúciós
modellJ. Nagy & J. Tökölyi
which genetic data is available is shown in
Table 1. All sequences were retrieved from
GenBank (http://ncbi.nlm.nih.gov/). Sequences were aligned using MAFFT-LINSI(Katoh et al. 2005) with default parameters
and alignments were visually checked. Twoalignments (12S and 16S) contained multiple indels and were run through Gblocks(Castresana 2000) to remove poorly aligned
positions. Sequence management was donein the R statistical environment (R Development Core Team 2012) using functions fromlibraries ape (Paradis et al. 2004) and seqinr(Charif & Lobry 2007).
Alignments were concatenated and species with few data (<500 nucleotides) were
removed. The median sequence length forthe remaining 180 species was 1038 basepairs (range 519-2872) (Table 1). This taxo­
nomic sample represents approximately
70% of extant species.
The resulting sparse supermatrix wasused to reconstruct the phylogenetic relationships of the 180 species. First, RAxML(Stamatakis 2006) was used to obtain astarting tree for phylogeny estimation. We
used a rapid bootstrap analysis with 100
bootstrap replicates followed by a search forthe best-sco­ring maximum likelihood tree,using the GTR+I+G model of evolution.
The Secretary Bird Sagittarius serpentinus(Sagit­tariidae) was used as an outgroup in
this process; the sister relationship between
Sagit­tariidae and the rest of the Accipitriformes (Pandionidae and Accipitridae) iswell supported from molecular phylogene­tic studies (e.g. Ericson et al. 2006, Brown
et al. 2008, Hackett et al. 2008).
Next, the best-scoring tree obtained fromthis analysis was used as a starting tree ina Bayesian MCMC analysis (implemented
in BEAST; Drummond & Rambaut 2007)
to simultaneously reconstruct the phylo­ge­

19

ny and divergence times of birds of prey.
The ten gene segments were partitioned
separately and each gene segment was assigned its own best-fit evolutionary mo­del,as evaluated by Akaike Information Criterion (AIC) in the software jModelTest 2(Guindon & Gascuel 2003, Darriba et al.
2012) (Table 1).
Molecular dating was done using an uncorrelated relaxed molecular clocks app­roach, which takes into account variation in
the rate of molecular evolution among line­
ages (Drummond et al. 2006). Three fossil constraints were used to date the phylogenetic tree (following do Amaral et al.
2009 and references therein): (1) the mini­
mum age of divergence between Pandionidae and Accipitridae was set to 37 Mya,
based on the oldest known fossil belon­ging
to Pandionidae (Harrison & Walker 1976);(2) the maximum age of divergence for Buteo galapagoensis was set to 4 Mya and(3) the maximum age of divergence for B.
soli­tarius was set to 5.1 Mya. B. galapa­go­ensis and B. solitarius are both island species (restricted to the Galapagos Islands
and Hawaii, respectively) and the latter two
age constraints are based on the assumption that these species cannot be older thenthe islands which they inhabit (do Ama­
ral et al. 2009). Two independent BEAST
runs were performed, each allowed to run
for 50 000 000 generations with a thinninginterval of 5000 generations. Convergence
was evaluated by checking effective sample
size (ESS) of parameters in Tracer (Rambaut & Drummond 2012). All parame­ters
had ESS values >100 and most were >>200.
The two runs were combined (after removing 10% burnin) and resampled at intervalsof 10 000 generations to yield 9000 treesthat represents a sample of the posterior
distribution of phylogenetic trees. A maxi-

20

ORNIS HUNGARICA 2014. 22(1)mum clade credibility tree was gene­rated
from this sample in TreeAnnotator (Rambaut & Drummond 2012).
To visualize diversification rate throughtime, we created a lineages-through-timeplot for 100 trees selected randomly fromthe posterior sample and the maximum
clade credibility tree.
Ancestral area reconstruction
We collected breeding season distribution
data on 180 species from Ferguson-Lees
and Christie (2001). Species were scoredas present/absent in the following biogeographical realms: Nearctic, Palearctic, Neo­tropical, Afrotropical, Malagasy, Indomalayan, Australasian. The delimitation ofthese realms is based on Ferguson-Lees andChristie (2001).
We inferred ancestral ranges based on
this distribution data by employing pro­ba­bilistic historical biogeography methods
using the BioGeoBEARS R package (Matzke 2013). These methods model geographic
range evolution by assuming different formsof anagenetic and cladogenetic changes in
geographic distribution during speciation
events: dispersal, extinction, vicariance,
sympatric speciation and founder-event speciation. We evaluated which of the traditio­
nally used historical biogeographic models
best fits range evolution in birds of prey by
calculating and comparing six models using
Akaike Information Criterion. These mo­dels are the Dispersal-Vicariance (DIVA)
model (Ronquist 1997), the Dispersal-Extinction-Cladogenesis (DEC) model (Ree etal. 2005, Ree & Smith 2008) and the Bay­Area model (Landis et al. 2013), toge­therwith the combination of these three withfounder-event speciation. The three baseline models all assume dispersal, extinction,

sympatric speciation and vicariance as possible range evolution mechanisms but differ
in the way they treat sympatric and vicariant
speciation events: the DIVA model allows
narrow-scale sympatry but both narrow andwidespread vicariance. The DEC model assumes narrow-scale and subset sympatry,
but only narrow-scale vicariance whereas
BayArea assumes narrow-scale and wide­
scale sympatry to occur (Matzke 2013).
We used the best fit of these models to estimate the most likely ancient distributions at
each node (ancestor state) of the phylogeny.
The method also gives a relative probability, ranging from 0 to 1, which gives the pro­bability that the node was in the given state.
The higher this value, the higher is our confidence in the actual reconstuction is correct and uncertainity in the ancestral range
reconstruction is low. These analyzes weredone using the maximum clade credibility
tree.
Life history data
All data, with the exception of body massinformation, were collected from Ferguson-Lees and Christie (2001), complemen­ted from the Global Raptor Information
Network (2013), if necessary. Migratory behavior was categorized based on previous phylogenetic studies (e.g. Kondo &Omland 2007, do Amaral et al. 2009) as:(1) non-mig­ratory (no seasonal movements
pre­sent), (2) partially migratory (part of the
populations, or part of the individuals within the species perform regular seasonal
movements) and (3) completely migratory(all populations and individuals migratory).
We used this variable to infer rates of evolution to and from complete migration (seebelow). However, the number of completely
migratory species was relatively low in our

J. Nagy & J. Tökölyi
sample (N=13), therefore, migration was binarized in all other analyzes as either migratory or non-migratory.
Information on body mass was obtained
from Dunning (2008), and in a few cases
from Ferguson-Lees and Christie (2001).
We used the average of male and female
body masses (log-transformed) when they
were available; however, in 8 cases data on
male or female body mass was available only. For 29 species no reliable body mass data could be found.
Diet (winter diet) was categorized following Roulin and Wink (2004). These authors
assigned a relative importance value ran­ging from 1 to 9 to each of nine food cate­gories (live birds, mammals, reptiles, fish,
amphibians, crustaceans, insects, worms
and carrion) based on descriptions of indivi­
dual species’ diet in Ferguson-Lees andChristie (2001). Food types that do not appear in the diet of a species received a scoreof 9, whereas the most important food typereceived a score of 1. From these values, we
calculated reliance on warm-blooded preyand carrion as the minimum of the importance scores received for bird or mammal
prey or carrion. Diet specificity was estimated by counting the number of food types
in the diet of a given species that received ascore <9.
Finally we classified habitat type as open
or closed based on descriptions in Ferguson-Lees and Christie (2001).
Comparative analyzes
We performed Bayesian ancestral state reconstruction in BEAST (Drummond &Rambaut 2007) to infer the migratory behavior at ancestral nodes in the phylogenyof birds of prey. Migratory behavior was recoded as a binary variable for this analysis

21(as either migratory or non-migratory, thus
complete and partial migrants were colla­ted). Bayesian ancestral state reconstruction
takes into account phylogenetic uncertainty and calculates the probability that a gi­
ven node was migratory or non-migratory,
based on the trait values of its ancestor anddescendants. By taking into account uncertainity in phylogenetic reconstruction, thismethod is substantially better than parsimony-based reconstructions, whose outcome
is conditional on a single (possibly erroneous) topology. In addition, Bayesian ancestral state reconstruction also takes into account differences in branch lengths, whichmakes them more realistic than parsimony-based methods.
Next, we investigated how changes in migratory behavior occurred on the phylogeny by estimating transition rates between
the three levels of migratory behavior (i.e.
the rate of transition from migratory to partial or complete migrant and vice versa, andthe rate of transition from partial to comp­lete migrant and vice versa). This analysis
was done using the MultiState module ofBayesTraits 1.0 (Pagel et al. 2004).
We determined whether body size (log-­transformed), habitat type, diet breadth,reliance on warm-blooded prey and geographical location (Old vs. New World) affects migratory behavior by constructing a
multivariate phylogenetic generalized linear mixed models as implemented in the MCMCglmm package in R (Hadfield & Naka­
gawa 2010), with these traits as dependent
variables. We also included the interaction
between habitat and geographical occurrence to model the differences in habitat use
among Old World and New World migrants.
Lastly, we tested the correlated evolution among migration and explanatory vari­ables found to be significant in the multiva­

22

ORNIS HUNGARICA 2014. 22(1)riate analyses using the Discrete module ofBayesTraits 1.0 (Pagel & Meade 2006). Thismethod evaluates transition rates among
pairs of binary traits on a phylogeny revea­
ling details of correlated evolution among
traits. For example, when analy­zing the correlated evolution between migration and diet specificity one can ask whe­ther mig­ration
is more likely evolve in gene­ralist (or specialist) lineages or vice versa: does a gene­ralist (or specialist) diet evolve more likely
in migrants? Since this analysis can hand­le only binary traits we dichotomized diet
specificity as specialist (<5 food types consumed) or generalist (at least 5 food types
consumed).

Results
Phylogeny and diversification of
Accipitriformes
Figure 1 shows the relationship between
major lineages of raptors. Our analysis recovered the relationships among major line­
ages of birds of prey described in previous molecular phylogenetic studies (Wink& Sauer-Gürth 2004, Lerner & Mindell
2005, Griffiths et al. 2007). Crown group
Accipit­riformes (i.e. the split between Sa­
git­tarius and the rest of the species) is inferred to have originated ~44 million years
ago (95% highest posterior density interval:
56.4 – 37.4), during the Eocene period. The

Figure 1. Simplified phylogeny showing major sublineages of Accipitriformes
1. ábra Egyszerűsített törzsfa a vágómadár-alakúak főbb csoportjainak filogenetikai viszonyairól

J. Nagy & J. Tökölyi

23

Figure 2. Lineages-through-time plot showing the pattern of diversification and accumulation of raptor species through time as reconstructed by our multi-gene relaxed molecular dating ana­
lysis. Grey lines show random trees (N=100) from the posterior sample of dated ultrametrictrees obtained from BEAST; the black line denotes the maximum clade credibility tree
2. ábra A vágómadár-alakúak diverzifikációja a molekuláris datálás alapján. A szürke vonalak atörzsfa-rekonstrukció során létrehozott poszterior mintából 100 véletlenszerűen kiválasztott ultrametrikus fa, a fekete vonal pedig az összegzett mintából számolt ultrametrikus faágainak számát mutatja az idő függvényében

split between Pandion and the rest of Accipitriformes occurred ~39.5 million years
ago (95% highest posterior density interval:
49.5–37). The two earliest branchings within Accipitridae resulted in the appearance of
elanid kites (Elaninae) ~34.7 million years
ago (95% highest posterior density interval:
44.3–29.4) and the group containing Gypaetinae and Perninae (~27.7 million years
ago; 95% highest posterior density interval:
35.3–23.2). Then, around the start of the
Miocene period the diversification of raptors accelerated and continued at a high rateuntil recent times (Figure 2).
Ancestral area reconstruction
The best model describing range evolution
in birds of prey was the DEC model containing founder-event speciation. Based on

this model, a southern origin was inferred
for all raptor subfamilies. These analyses
suggest that Accipitrinae, Aegypiinae andGypaetinae have an Afrotropical origin,whereas Elaninae and Perninae derive fromthe Neotropics. Buteoninae and Harpiinae
had a joint Afrotropical/Neotropical distribution according to our reconstruction,
whereas Aquilinae have an Afrotropical/
Neotropical and Indomalayan origin, although we note that the accuracy of thesereconstructions is quite low (<0.3) (Table
2). Circeatinae were assigned an Indomalayan origin with relatively high probabili­ty (0.87) (Table 2). Lastly, the most likely
ancestral distribution of Haliaeetinae wasAustralasia.

24

ORNIS HUNGARICA 2014. 22(1)Subfamily

GeneraNode age(Mya; 95%
HPD interval)

Ancestral
distribution

Prob.

Accipitrinae

Accipiter, Circus, Kaupifalco, Melierax,
Urotriorchis

15.1; 23.0

At

0.79

Aegypiinae

Aegypius, Gyps, Necrosyrtes, Sarcogyps,
Torgos, Trigonoceps

7.4; 12.6

At

0.78

Aquilinae

Aquila, Hieraaetus, Ictinaetus,
Lophaetus, Nisaetus, Oroaetus,
Polemaetus, Spizaetus, Spizastur,
Stephanoaetus

9.8; 15.4

NtAtIm0.26

Buteoninae

Busarellus, Butastur, Buteo, Buteogallus,
Geranoaetus, Geranospiza,
Harpyhaliaetus, Ictinia, Leucopternis,
Parabuteo, Rostrhamus

10.3; 16.7

NtAt

0.24

Circaetinae

Circaetus, Dryotriorchis, Pithecophaga,
Spilornis, Terathopius

13.8; 22.5

Im

0.87

Elaninae

Elanus, Gampsonyx

14.4; 29.4

Nt

0.15

Gypaetinae

Gypaetus, Gypohierax, Neophron,
Polyboroides

18.5; 29.0

At

0.99

Haliaeetinae

Haliaeetus, Haliastur, Ichthyophaga,
Milvus9.8; 15.7

Au

0.30

Harpiinae

Harpia, Harpyopsis, Macheiramphus,
Morphnus

13.6; 24.4

NtAt

0,20

Perninae

Aviceda, Chondrohierax, Elanoides,
Eutriorchis, Hamirostra, Leptodon,
Lophoictinia, Pernis

18.7; 29.4

Nt

0.15

Table 2.

Geographic origin of major sublineages of Accipitriformes as inferred from the ancestralarea reconstruction following a dispersal-extinction-cladogenesis-founder-event speciation (DEC+J) model of geographic area evolution. This table lists the major subfamiliesof Accipitriformes, the genera they contain, the inferred age of these groups (age of thecrown group, i.e. the most recent common ancestor of all extant species), shown as 95%
highest posterior density interval (HPD) and the most likely ancestral area inferred for thecrown group, along with the probability that the node was in the given state. Geogra­phic
distribution is abbreviated as: Pa – Palaearctic; Na – Nearctic; At – Afrotropical; Nt – Neotropical; Im – Indomalayan; Au – Australasian
2. táblázat A vágómadár-alakúak főbb csoportjainak rekonstruált ősi elterjedési területe. A táblázata főbb alcsaládok nevét, az ezekbe sorolt génuszokat, az alcsaládok molekuláris datálásáltal becsült korát, a legvalószínűbb ősi elterjedési területet, illetve ennek a valószínűségét mutatja. A földrajzi elterjedések rövidítése: Pa – Palearktisz; Na – Nearktisz; At – Afro­tro­pisz; Nt – Neotropisz; Im – Indomaláj; Au – Ausztrálázsiai

J. Nagy & J. Tökölyi

25

Table/táblázat 3aSpecies

Distribution

Ancestor’s age(Mya; 95% HPDinterval)

Prob. ofAncestor’sancestor
Ancestor’sposterior
being non- distribution
node support
migratory

Prob.

Accipiter gularisPa0; 0.5

0.95

0.98

PaIm

0.56

Accipiter nisus

PaAtIm2.4; 5.2

0.42

0.84

NtPaAtIm

0.21

Accipiter soloensis

PaIm

6.3; 13.3

0.92

0.96

ImAu

0.18

Accipiter striatus

NaNt

1.3; 3.3

0.53

0.84

NaNtAt0.51

Aegypius monachus

PaAt

2; 4

1.00

0.98

At

0.46

Aquila chrysaetos

NaPaAt3.6; 6.2

1.00

0.99

At

0.91

Aquila heliaca

Pa0.5; 1.6

0.94

0.99

Pa

1.00

Aquila nipalensis

Pa

1.6; 3.5

1.00

0.97

Pa

0.99

Hieraaetus wahlbergiAt

3.8; 6.6

1.00

0.59

At

1.00

Butastur indicus

PaIm

2.1; 5.1

0.83

0.56

Im

0.32

Butastur rufipennisAt

2.9; 6.4

1.00

0.57

At

0.29

Buteogallus anthracinus

NaNt

0; 0.1

1.00

1.00

Nt

0.95

Buteogallus meridionalis

Nt

2.7; 4.5

1.00

1.00

Nt

1.00

Buteo lineatusNa1.1; 2.1

1.00

0.79

NaNt

0.38

Buteo nitidus

NaNt

3; 4.8

0.32

0.66

Na

0.71

Buteo platypterus

NaNt

2.8; 4.5

0.99

0.70

Na

0.72

Buteo polyosoma

Nt0.4; 1.1

1.00

0.99

Nt

1.00

Buteo swainsoni

Na0.2; 0.4

1.00

0.99

Nt

0.44

Circaetus fasciolatusAt1.5; 3.9

1.00

0.91

At

1.00

Circaetus gallicus

PaAtIm2.8; 6.1

1.00

0.78

At

0.78

Circaetus pectoralisAt

4.7; 8.8

1.00

0.79

At

0.99

Elanoides forficatus

NaNt

15.5; 25.1

0.53

0.84

NtPa

0.22

Gypohierax angolensis

At18; 27.8

0.43

0.88

At

0.90

Gyps coprotheres

At

0.3; 0.8

0.35

1.00

At

0.40

Gyps fulvus

PaAt

0.3; 0.9

0.66

1.00

At

0.96

Haliaeetus leucogasterImAu

0; 0.3

1.00

0.89

Au

0.87

Haliaeetus vociferAt

0.7; 1.9

1.00

0.91

At

1.00

Haliastur sphenurusAu

2.3; 4.8

1.00

0.70

Au

0.37

Hieraaetus ayresii

At2.4; 4.7

1.00

0.51

At

0.88

Hieraaetus pennatus

PaAt

0.7; 1.7

1.00

0.53

PaAtAu0.28

Macheiramphus alcinusAt

13.6; 24.4

0.99

1.00

NtAt

0.20

Neophron percnopterus

PaAtIm14.4; 24.7

1.00

0.87

At

0.42

Pandion haliaetus

NaNtPaAtImAu

37; 49.5

1.00

0.91

At

0.37

Parabuteo unicinctus

NaNt

4.5; 7.5

1.00

0.98

Nt

0.95

Polyboroides typusAt18.5; 29

0.95

0.91

At

0.99

Rostrhamus sociabilis

NaNt

7.5; 12

0.96

0.99

Nt

0,96

ORNIS HUNGARICA 2014. 22(1)26Table/táblázat 3b

Prob. ofAncestor’sancestorposterior node
being nonsupport
migratory

NodenumberDistribution

Ancestor’s age(Mya; 95% HPDinterval)

185

NaNtPaAtImAu

14.4; 29.4

1.00

217

Nt

11.7; 18.2

1.00

251

NaPa

7.1; 11.9

255

PaAtImAu

5.3; 9.3

261

NaNt

344

Pa

359

Pa

Ancestor’s
distribution

Prob.

0.77

Nt

0.15

0.63

Nt

0.71

1.00

0.93

Im

0.10

1.00

0.69

Au

0.38

9.5; 14.5

0.90

0.99

Nt

0.93

2.1; 4.4

0.98

1.00

Pa

0.81

15.5; 25.1

0.84

0.53

NtPa

0.22

Table 3.

Possible cases of independent appearance of migration in Accipitriformes. This table lists
migratory species (a) or nodes that were inferred to be migratory with a probability >0.8(b) with non-migratory ancestors. The probability that the ancestor was non-migratory,
as inferred from ancestral character estimation, is shown along each possible case. Notethat only those cases are listed where this probability is >0.5, i.e. the node is more likely to be non-migratory than migratory. The greater this value, the higher is our confidence that migration appeared on this branch. Also shown are the ancestral nodes’ posterior support, the 95% highest posterior density (HPD) interval of the nodes’ age, alongwith the most likely distribution of these nodes and the probability that the node wasin this state, as inferred in the ancestral area reconstruction. Geographic distribution isabbrevia­ted as: Pa – Palaearctic; Na – Nearctic; At – Afrotropical; Nt – Neotropical; Im – Indomalayan; Au – Australasian. Node numbers appearing in (b) are shown in Figure 3
3. táblázat A vágómadár-alakúak törzsfejlődése során a vonulás egymástól függetlenül többször
megjelent. Ez a táblázat azokat a vonuló fajokat (a) vagy közös ősöket (b) mutatja, amelyek nagy valószínűséggel vonulók voltak (>0.8 valószínűséggel). Mindegyik esetben fel
van tüntetve: (i) a vonuló faj (ős) jelenlegi (becsült) előfordulása, (ii) a közvetlen ősük becsült kora, (iii) a közvetlen ős filogenetikai helyzetének pontossága, (iv) annak a valószínűsége, hogy a közvetlen ős nem-vonuló volt, (v) a közvetlen ős rekonstruált ősi elterjedési területe, illetve (vi) annak a valószínűsége, hogy az ősi elterjedés ténylegesen ebbenaz állapotban volt. Csak azok az esetek szerepelnek, ahol a közvetlen ős legalább 0.5 valószínűséggel helytülő volt. Minél nagyobb ez az érték, annál bizonyosabb, hogy a vonulás
ténylegesen ezen az ágon alakulhatott ki. A földrajzi elterjedések rövidítése: Pa – Palearktisz; Na – Nearktisz; At – Afrotropisz; Nt – Neotropisz; Im – Indomaláj; Au – Ausztrálázsiai.
A (b)-ben szereplő számok közös ősöket jelölnek, amelyeknek helyzete a 3. ábrán látható
Figure 3. Bayesian ancestral state reconstruction of migratory behavior in Accipitriformes. Tip labelsshow migratory behavior in extant species (white: non-migratory; black: partial or complete migrant). Pie charts labelling the nodes show the probability that the given species
was migratory (black) or non-migratory (white). A completely black chart indicates that theancestor was migratory with high posterior probability. The size of the charts is proportio­
nal to the posterior support of specific nodes: small charts indicate high uncertainity in phylogenetic reconstruction
3. ábra Vonulási viselkedés rekonstrukciója a vágómadár-alakúak törzsfáján. A fajnevek melletti
négyzetek színe az illető fajok vonulási viselkedését jelöli (fehér – helytülő; fekete – részleges vagy teljes vonuló). A közös ősöket jelölő kördiagrammok mutatják annak a valószínűségét, hogy az illető faj vonuló volt (fekete). A kördiagrammok mérete egyenesen arányos
az illető csomópontok rekonstrukciójának pontosságával

J. Nagy & J. Tökölyi
Evolution of migration in Accipitriformes
Table 3 shows possible cases of independent
appearance of migratory behavior in Accipit­
riformes. Since the presence of migrationis quite variable among species, our reconstruction of ancestral states involves conside­
Figure/ábra 3a27

rable uncertainty in some nodes. On the other
hand, in several cases (e.g. Buteo hawks, Accipiter hawks or Haliaeetus eagles), closely related species are all migratory, with theconsequence that the most likely state for theancestor of these species is being migratory.
Two important patterns can be seen fromthis list of evolutionary events. First, most of

28

Figure/ábra 3b

ORNIS HUNGARICA 2014. 22(1)J. Nagy & J. Tökölyi

29

Figure 4. Evolutionary transition rates between levels of
migratoriness inbirds of prey
4. ábra Vonulási stratégiák közötti evolúciós tranzíciós ráták

the independent events leading to the appearance of migration involve single species (Table 3a). In these situations, the upperbound for the time of appearance of migration is the age of that particular species (i.e.
when it split off from its ancestor). Hence,migration could have appeared in these line­
ages recently; this possibility is supportedby the observation that most of these upperbounds are not older than the beginning of
the Pliocene epoch (~5.3 Mya). On the other
hand, nodes that are reconstructed as migratory provide lower estimates for the originof migration (Table 3b). The oldest of thesenodes is the common ancestor of the Goshawk and the harriers, with an estimated ageof 12.9 Mya (95% highest posterior density
interval: 16.3–10.1 Mya).
Second, a tropical or partly tropical ancestral area was inferred for the ancestor ofmost migratory lineages. There are very few
exceptions from this pattern. For instance,
our results suggest that Aquila heliaca andA. nipalensis (both migratory) evolved within the Palearctic from non-migratory ancestors. Similarly, two migratory Buteo hawks(B. platypterus and B. nitidus) seem to have
evolved in the Nearctic from non-migratory
ancestors, although in these latter cases posterior support for the phylogenetic reconstruction is quite low (0.32 for the ancestorof B. nitidus). In sum, our results provide
broad support for a tropical origin of migratory species.
Comparative analyzes
Analysis of the transition rates using
BayesTraits Multistate module revealed
that both complete and partial migrations
evolved from a non-migratory state, butevolutionary transitions between partial andcomplete migration or vice versa are verylow (Figure 4), suggesting that partial mig­ration is not a transitional state between
complete migration or lack of migration.
Multivariate analysis of the ecological factors influencing migration suggests that only
diet breadth and habitat are associated with
migration (Table 4). However, when thismultivariate model is simplified by backward elimination of nonsignificant parameters, habitat type does not remain significant.
Hence, the only ecological factor associatedsignificantly with migration was winter dietbreadth: species with a more generalist winter diet are more likely to be migratory.
Finally, we performed directional tests bet­
ween migratory behavior on one hand andgeographic distribution and diet breadth,res­pectively, on the other. In the first case,
we found that evolutionary transitions to30

ORNIS HUNGARICA 2014. 22(1)Parameter
estimate

lower 95%
confidence
interval

upper 95%
confidence
interval

P-value

Habitat (Forest/Open)

1.30

0.13

2.27

0.02

Old/New World

0.78

-0.75

1.75

0.16

log(body mass)

-0.06

-0.51

0.33

0.78

Diet generalism

0.44

0.21

0.75

<0.01

Reliance on warm-blooded prey or carcass

0.00

-0.21

0.23

0.99

Habitat : Old/New World interaction

-1.39

-3.35

0.40

0.18

Factors affecting the occurrence of migration in diurnal birds of prey (N=151 species); parameter estimates, their 95% confidence intervals and P-values from a multivariate mixed
effect models controlling for phylogeny
4. táblázat A vonulás előfordulását befolyásoló tényezők vágómadár-alakúaknál (N=151 faj); filogenetikai viszonyokra kontrollált többváltozós kevert lineáris modellből származó becsültértékek, azok 95%-os konfidenciaintervalluma és a P-értékek

Table 4.

mig­ration occured with a higher rate in line­
ages with a tropical distribution (9.08 vs.
0.73 in lineages with a non-tropical distribution) (Figure 5a). Furthermore, transitions to a non-tropical distribution are much
more likely in migratory (30.01) than in
non-mig­ratory lineages (0.15). The tran-

sition rates also revealed that switches in
the geogra­phic distribution from a tropicalto a non-tropical distribution or vice versa
are virtually lacking in non-migratory lineages, whereas they occur at a relatively highrate in migrants (Figure 5a). In the second
case, transition rates indicate that migrationab

Figure 5. Results from pairwise directional test between migratory behavior (migratory or non-migratory) and (a) geographic distribution: tropical (species not present in the Nearctic or the
Palearctic) or non-tropical (species present in the Nearctic or the Palearctic); (b) diet specificity: specialist (consumes <5 types of food) or generalist (at least 5 food types consumed).
The graph show transition rates among pairs of traits indicating the rate with which theseevolutionary changes are inferred to have occurred on the phylogeny
5. ábra A vonulási viselkedés és a földrajzi elterjedés (a) illetve a táplálékspecializáció (b) közötti direkcionális tesztek eredményei. A vonulási viselkedés kódolása: vonuló (részlegesen vagy
teljesen) vagy nem vonuló. A földrajzi elterjedés kódolása: trópusi (nem fordul elő sem a Palearktiszban sem a Nearktiszban) vagy nem trópusi (az előző ellentettje). A táplálékspecializáció kódolása: specialista (<5 tápláléktípust fogyaszt) vagy generalista (az előző ellentettje). Az ábra a különböző jellegpárok közötti evolúciós tranzíciós rátákat mutatja

J. Nagy & J. Tökölyi
is more likely to arise in specialist lineages
and that a generalist diet is more likely to
evolve in migrants than in non-migrants.
Hence, it appears that diet breadth evolves
as in response to the selective environments
imposed by migratoriness, rather than predisposing species for migration.

Discussion
Phylogeny of Accipitriformes
The phylogenetic relationships among andwithin major lineages of Accipitriformes
have been extensively studied before (e.g.
Wink & Sauer-Gürth 2004, Helbig et al.
2005, Lerner & Mindell 2005, Griffths etal. 2007, do Amaral et al. 2009). Here, we
combined all available genetic information
to produce a multi-gene phylogeny of Accipitriformes with a broad taxonomic sampling, including approximately two-thirds ofextant species of accipitrid birds of prey. The
phylogenetic hypothesis obtained from thisanalysis is broadly congruent with previous
reports showing that several traditionally established clades are in fact polyphyletic orparaphyletic. For instance, Old World vultures form a polyphyletic clade comprisedof: (1) Gypaetinae which is monophyletic with Perninae and includes the Bearded
Vulture Gypaetus barbatus and the Egyptian
Vulture Neophron percnopterus and (2) Aegypiinae which contains all remaining Old
World vultures and is the sister clade of Circaetinae. Accipiter hawks are paraphyle­tic
and should include harriers (Circus spp.),which are closely related to the clade containing goshawks (see also Breman et al.
2013 for a more detailed analysis). In addition, we also observed widespread paraphyly in aquiline eagles and buteonine hawks, as

31repor­ted previously (Helbig et al. 2005, do
Amaral et al. 2009).
On the other hand, we also observed seve­ral discrepancies in the higher level relationships of Accipitridae between our reconstructions and those of obtained from previous
studies (e.g. Lerner & Mindell 2005, Griffiths et al. 2007). For instance, we recovered
Aqui­linae and Harpiinae as sister clades, albeit with relatively low support (posterior
probability: 0.45). Harpagus kites were inferred as the sister group of the clade containing Buteoninae and Haliaeetinae withrelatively high support (posterior probability:0.83). Lastly, the sister relationship between
the clade containing Aquilinae and Harpiinae on one hand and Buteoninae, Haliaeetinae and Accipitrinae on the other was strongly supported (posterior probability: 1).
Historical biogeography and evolution of
migration in Accipitriformes
Our ancestral state reconstruction suggeststhat migratory behavior in birds of prey
evolved multiple times. Most of these appear to be relatively recent events (occurring during the Pliocene or Pleistocene, i.e.
<5 million years ago). In one case howe­ver,migratory behavior appears to be much moreancient. In true hawks (Accipitrinae) migration appears to have evolved approxi­mately
14-12 million years ago, during the middleof the Miocene period. By compari­son, do
Amaral et al. (2009) reconstructed the origin of migration in one Buteo clade at app­roximately 5 million years ago, a result thatis supportedby our analyses (Figure 3b).
More direct estimates based on the age distribution of fossilized individuals (specifically, the lack of juveniles) found at Olduvai
Gorge, in Tanzania, suggest that this site wasa wintering location of shorebirds belon­

32

ORNIS HUNGARICA 2014. 22(1)ging to Charadriidae 1.9-1.74 million years
ago, implying that migration was present at
this time (Louchart 2008). Hence, our estimate for the origin of migration in accipitrid
hawks appears to be one of the oldest dates
published so far. Such estimates are important (yet remarkably lacking) if we are to understand the evolution of migration in a constantly changing spatiotemporal context at a
global scale (Louchart 2008).
The middle of the Miocene period saw aseries of global cooling events (Zachos etal. 2001), which resulted in the expansionof grasslands and contraction of forest habi­tats, possibly opening new niches for birdsof prey. Our analyses suggest that accipitrid
hawks appeared shortly before this period,
probably in the Afrotropical realm and colonized other parts of the world shortly thereafter. Since most extant species belonging
to this lineage are at least partly migratory today, it is likely that their ancestor also performed seasonal migratory movements. Alternatively, migration could have
evolved separately in these lineages due
to similar selective environments (i.e. as aconsequence of convergent evolution ra­
ther than shared phylogenetic background).
Since migration is a phylogenetically labile
trait that can evolve very quickly (see e.g.
Zink 2011), independent evolution in multiple lineages experiencing similar selective
environments is a plausible scenario for theoccurrence of migratory behavior in clusters
of closely related species. However, this explanation is clearly less parsimonious in explaining the evolution of migration in true
hawks, since this group contains both temperate-tropical and intra-tropical migrants
on different continents, which would imply simultaneous, independent selection formig­ration in a wide variety of different environments on different parts of the world.

Joint reconstruction of ancestral distribution and migratory behavior suggests that in
raptors, migration appeared mostly in species with a southern origin. This is further
strengthened by our directional analyses,
which suggest that migration is more likely to evolve in tropical species and that mig­ratory raptors are more likely to switch to a
non-tropical breeding range, hence sugges­
ting that migratory behavior and range expansions are evolving in parallel. Interes­tingly, we also found that the transition ratefrom a tropical to non-tropical distribution(and vice versa) is very low in non-migratory raptors, but not in migrants, suggestingthat migration greatly enhanced range expansions in this group of birds.
The southern origin of migratory raptors is
in line with previous studies obtaining similar results in a variety of taxonomic groups(Joseph et al. 1999, Outlaw et al. 2003, Miláet al. 2006). We have to emphasize, howe­ver,that this result helps little in understanding
the evolution of migration in birds of prey.
As we have shown, all major lineages within Accipitridae trace back their origin to oneof the southern biogeographic realms. Hence,both migratory and non-migratory species
currently inhabiting the temperate zone des­cend from the tropics. Our analyses do suggest, however, that migration is more likely
to emerge in the tropics than in the tempe­
rate zone, and that migratory birds are more
likely to disperse and switch from a tropical distribution to a non-tropical one. Hence,
the relationship between colonization of thetemperate zone and the evolution of migration could be the reverse of what traditionally is assumed, i.e. migratory birds (intratropi­
cal migrants) more likely to colonize novel
habitats and expand to North. This hypothesis could be tested in the future by investiga­
ting the ecological and behavioral traits pro-

J. Nagy & J. Tökölyi
moting the colonization of temperate habitats
in a broader sample of birds.
Since all birds of prey are of tropical ancestry, their ancestors must have undergone
range expansions to the temperate region.
Yet, not all of these species became migratory. We found that winter diet specialization
predicts the occurrence of migration in acci­
pitrid birds of prey, with migratory species
relying on more variable diets. The ability
to feed on a wide source of food types could
greatly enhance the probability that a species
survives the winter in the temperate zone
where food availability is much lower during
the winter (Newton 2008). Hence, this couldat least partly explain interspecific differen­
ces in migratoriness. Interestingly, we foundno association between migration and reliance on warm-blooded prey or carcass, suggesting that these food sources alone might
not be enough to sustain most species in thetemperate zone during winter. For instance,
two of the four vulture species that occur inEurope (the Griffon Vulture Gyps fulvus, andthe Egyptian Vulture) are migratory, despite
the fact that their major food source – carcass– is most likely available year-round. However, these food sources might also show sea-33

sonal fluctuations (e.g. Kendall et al. 2012).
Alternatively, other factors, such as selection
for early breeding or extended breeding season could generate differences in migratoriness between populations or species (e.g.
Tökölyi & Barta 2011, Camacho 2013). Further work is required to clarify ecological determinants of migration in birds of prey.

Acknowledgements
We would like to express our gratitude to
the two anonymous referees for their usefulcomments on our manuscript. J. N. was supported by a scholarship from the University of Debrecen’s Talent Development Prog­ram (DETEP). J. T. was supported by theTÁMOP 4.2.2.C-11/1/KONV-2012-0010
project; the project is co-financed by the European Social Fund and the European Regional Development Fund. Computations
were partly run on Hungarian National Infrastructure Institute supercomputers (http://niif.hu). We are grateful to József Büki, headof András Keve Library for Ornithology andNature Conservation for his help in acces­
sing literature in an early part of this study.

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