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Genetic structure and sex-biased dispersal of a declining
cooperative-breeder, the Grey-crowned Babbler,
Pomatostomus temporalis, at the southern edge of its range
Kate P. Stevens
A,D
, Katherine A. Harrisson
B
, Rohan H. Clarke
B
, Raylene Cooke
A
and Fiona E. Hogan
C
A
School of Life and Environmental Sciences, Deakin University, 221 Burwood Highway,
Melbourne, Vic. 3125, Australia.
B
School of Biological Sciences, Monash University, Melbourne, Vic. 3800, Australia.
C
School of Applied and Biomedical Sciences, Federation University, PO Box 3191,
Gippsland Mail Centre, Churchill, Vic. 3148, Australia.
D
Corresponding author. Email: kstevens@deakin.edu.au
Abstract. Loss and fragmentation of habitat can disrupt genetic exchange between populations, which is reflected in
changes to the genetic structure of populations. The Grey-crowned Babbler (Pomatostomus temporalis) is a cooperatively
breeding woodland bird, once common and widespread in south-eastern Australia. The species has suffered population
declines of >90% across its southern distribution as a result of loss and fragmentation of habitat. We investigated patterns of
genetic diversity and population structure of Grey-crowned Babblers in fragmented habitats at the southernmost extent of its
range. We sampled blood from 135 individual Babblers from 39 groups stratified into six subpopulations in three regions.
Genotypic data were used to estimate genetic diversity, population substructure, local relatedness and dispersal patterns.
Individuals showed high heterozygosity within regions, and varying numbers of private alleles among regions suggested
differences in levels of connectivity between regions. Four genetic clusters revealed population substructure consistent with
treeless landscapes acting as strong barriers to gene flow. In contrast to previous studies, we identified a male-biased dispersal
pattern and significant isolation-by-distance patterns for females at fine spatial scales. We recommend that conservation
plans for this species incorporate opportunities to increase and enhance corridor areas to facilitate genetic exchange among
subpopulations.
Additional keywords: corridors, functional connectivity, genetic diversity, habitat fragmentation, isolation-by-distance,
regions.
Received 8 January 2015, accepted 5 March 2016, published online 28 July 2016
Introduction
The geographical structure of the range of a species is influenced
by biotic (e.g. habitat availability) and abiotic (e.g. climate
change) factors. Such factors change over time and can exert
differing pressures on population demographics, such as the size
of populations and gene flow (Donald et al.2001; Crozier and
Dwyer 2006; Wittmer et al.2007). Genetic diversity and gene
flow are critical for population viability, evolutionary potential,
resistance to disease and reducing the negative effects of inbreed-
ing and genetic drift (Frankham 1996). Loss and fragmentation of
habitat are frequently associated with loss of genetic diversity,
disrupted dispersal and reduced gene flow among populations
(Palstra and Ruzzante 2008; Jaquiéry et al.2009). Small, isolated
populations are especially vulnerable to extirpation as a result of
environmental stochasticity and/or demographic effects, such as
reduced reproductive rates. Such populations can also experience
increased mortality and negative genetic effects, such as a loss of
genetic diversity through drift and associated reductions in fitness
and evolutionary potential (Epps et al.2005; Méndez et al.2014;
Duncan et al.2015). Furthermore, the size and connectedness of
populations can often decline towards the edge of their ranges
(Guo et al.2005). Edge-of-range populations can become genet-
ically impoverished and differentiated owing to population de-
cline and loss of genetic connectivity with nearby populations
and larger core populations (Lesica and Allendorf 1995; Alda
et al.2013). Understanding the effects of loss and fragmentation
of habitat on gene flow and genetic diversity will improve
estimates of effective population size and strengthen our capacity
to manage population connectivity, both of which are integral to
biodiversity conservation (Palstra and Ruzzante 2008).
Species vulnerable to habitat fragmentation include restricted
dispersers, habitat specialists, sedentary species and species with
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complex mating systems, for example, cooperative breeders
(Frankham 2005; Amos et al.2014). Cooperatively breeding
species can also be vulnerable to habitat loss and fragmentation as
their breeding success is associated with helpers at the nest
(Stevens et al.2015); to increase the success of recolonisation
of habitat patches following local extinction, groups (i.e. more
than two birds) must recolonise a patch. In Australian ecosystems,
loss and fragmentation of habitat have transformed formerly large
and connected woodlands into small, isolated remnants within a
matrix of farmland (Robinson 1993; Robinson and Traill 1996).
Landscapes across south-eastern Australia have lost up to 85% of
native vegetation since the mid-1800s and remnant patches now
provide habitat refugia for species vulnerable to fragmentation
effects (Ford et al.2001). Species reliant on woodland habitat
have subsequently declined to small, isolated populations at
risk of local extirpation (Radford and Bennett 2007). Effective
conservation management for vulnerable species experiencing
habitat loss and fragmentation relies on understanding the impli-
cations of critical population processes for the species, such as
dispersal and changing population sizes.
The Grey-crowned Babbler (Pomatostomus temporalis)isa
cooperatively breeding woodland bird sensitive to the effects of
loss and fragmentation of habitat (Robinson 1993; Blackmore
and Heinsohn 2008; Garnett et al. 2011). In cooperatively
breeding systems, offspring from one or more generations delay
natal dispersal to help raise future generations of young, and local
neighbourhoods are characterised by closely related individuals
(Koenig et al.1992). Offspring of the Grey-crowned Babbler
remain as helpers at the nest for up to 3 years (Blackmore and
Heinsohn 2007) and the number of helpers increases the likeli-
hood of breeding success (Stevens et al.2015). The global range
of the Grey-crowned Babbler incorporates the Trans-Fly Region
of southern New Guinea and approximately two-thirds of main-
land Australia (Fig. 1). Two subspecies are recognised: P. t.
rubeculus in central-western, central and northern Australia and
P. t. temporalis in eastern Australia (Edwards 1993a; Fig. 1). This
study is focused on the eastern subspecies (P. t. temporalis) at the
southernmost edges of the range of the species in Victoria (Fig. 1).
The Grey-crowned Babbler was once common throughout much
of eastern Australia (NSW Scientific Committee 2001)but popu-
lations have declined by >90% in the southern part of its distri-
bution (i.e. south of 35.2820S) from the loss and fragmentation
of box and ironbark eucalypt woodlands (Robinson 1993,2006;
Environment Conservation Council 2001; Garnett et al. 2011). In
contrast to the species’distribution in northern parts of its range,
where Grey-crowned Babblers inhabit interior areas of large
reserves (Blackmore et al.2011), in southern landscapes it is
restricted to roadside or riparian vegetation, small remnant wood-
land patches (<0.5 ha) and edges of the few existing large (>5 ha)
conservation reserves (Tzaros 1995,2001; Robinson 2006;
N. Lacey unpubl. data; D. Robinson unpubl. data).
The Grey-crowned Babbler has been the subject of research
for over 30 years (Counsilman and King 1977; Gill and Dow
1985; Robinson 1993,2006; Davidson and Robinson 2009).
Early studies focused mainly on their cooperative breeding
system (Brown et al.1978; Counsilman 1979; King 1980; Brown
et al.1983), whereas later studies included genetic methods
to investigate social structure, breeding ecology and dispersal
(Johnson and Brown 1980; Edwards 1993a; Blackmore 2006;
Kawano et al.2007). Research in the north- and central-eastern
distribution of the species has mostly been conducted in contig-
uous habitat (Edwards 1993b; Blackmore and Heinsohn 2007,
2008; Eguchi et al.2007). Studies conducted within an 85-km
2
area of continuous habitat in northern New South Wales, in the
central-east of the distribution of the species, revealed spatial
genetic clustering of related individuals at a local scale (<1.5 km)
but detected no evidence of an isolation-by-distance effect at
larger spatial scales of up to 10 km (Blackmore et al.2011).
Dispersal distances recorded from recoveries of marked indivi-
duals have typically been short (mean = 1 km), although a few
longer distance movements have been recorded (maximum
26 km) (Higgins and Peter 2003; Department of the Environment
2010). Grey-crowned Babblers live in family groups, typically of
2–12 birds (mean ~5), and occupy territories of 2–53 ha (Higgins
and Peter 2003; Blackmore and Heinsohn 2008).
Populations experiencing sudden or large population declines
and reduced connectivity are at higher risk of local extirpation as a
result of genetic drift, stochasticity and inbreeding (Nei et al.
1975; Keller and Waller 2002). It is therefore notable that no
genetic studies have been conducted in the south of the distri-
bution of Grey-crowned Babblers. In particular, no investigations
have explored population structure and connectivity at the south-
ern edge of their range, where they have experienced major loss
and fragmentation of habitat (Bradshaw 2012), and populations
have undergone the largest declines (Robinson 1993,2006).
Here we investigated population genetic structure and con-
nectivity across the highly fragmented southern range of the
Grey-crowned Babbler. Our specific aims were to investigate:
(1) if population structure existed at the contemporary southern
edge of the range of the species; (2) if population structure was
explained by isolation-by-distance effects; and (3) if sex-biased
dispersal occurred. We compared our findings to those of similar
studies conducted in areas of continuous habitat cover to deter-
mine if more extensive loss and fragmentation of habitat in
southern parts of the range of the species may be reducing genetic
connectivity.
Methods
Study area, site selection and sampling of blood
Field (bird-banding) work was conducted from July 2010 to April
2012 and incorporated two annual breeding seasons, which
extend from June to April in Victoria. The study region encom-
passed an area of ~22250 km
2
in northern-central Victoria
(Fig. 1). Using long-term distribution and census data (Tzaros
1995,2001; N. Lacey unpubl. data; D. Robinson unpubl. data),
39 sampling sites were selected based on occupancy in three
geographical regions at the southernmost edge of the range of the
Grey-crowned Babbler: western, around Kerang and Boort
(n= 15 sampling sites); south-eastern, around Violet Town and
Lurg (n= 12 sites); and north-eastern, around Peechelba, Ruther-
glen and Chiltern (n= 13 sites) (Fig. 1). These regions represent
the three strongholds of the Grey-crowned Babbler in Victoria
(with 68 family groups in the western region; 155 in the south-
eastern region; and 40 in the north-eastern region), although
small numbers of family groups persist in the landscape between
regions (Tzaros 1995,2001; N. Lacey unpubl. data; D. Robinson
unpubl. data). Based on the identification of genetic clustering
BEmu K. P. Stevens et al.
among sites (see section Population genetic substructure among
regions), two subpopulations were identified within each of the
regions (west = Kerang north; Kerang south/Boort, south-east =
Lurg/Violet Town north/Peechelba; Violet Town south, north-
east = Rutherglen; Chiltern) (Fig. 1). Distances between sampling
sites within each of the three study regions ranged from 940 m to
75 km. Distances separating sites between regions ranged from
34 km (between a site in the south-eastern region and one in the
north-eastern region) to 258 km (between a site in the north-
eastern region and one in the western region).
Initial field work utilised call playback to confirm the presence
of Grey-crowned Babblers at each potential sampling site. Study
sites were chosen based on group occupancy determined from
a group’s nesting activity e.g. building/roosting. The size of a
family group was estimated by counts over an average of four
visits that varied in time from 1 to ~4 hours. At each sampling site,
searches using call playback were conducted in suitable habitat
within a 2-km radius of a family group’s territory centroid
(usually an active nest, i.e. roost or brood nest) to determine
distances to any neighbouring family groups. All locations were
recorded using a geographic positioning system (GPS). The mean
distance between the group centroid of a sampling site and that of
the nearest neighbouring family group (n= 39), which was not
necessarily a sampled group, was 1,294 m (172.7 s.e.).
At each sampling site, birds from the focal family group were
lured with call playback and trapped using mist-nets. Each
individual was banded with a numbered metal leg-band provided
by the Australian Bird and Bat Banding Scheme (Canberra, ACT)
and a unique combination of three coloured plastic leg-bands for
identification in the field. Individuals were weighed and measured
and a blood sample (~70 mL) collected from the brachial vein
using a Vitrex capillary tube. Blood was transferred to a Whatman
FTA Card and stored at room temperature in paper envelopes. We
weighed and obtained blood of 135 individual Grey-crowned
Western region
North-eastern region
South-eastern region
Western region South-eastern region North-eastern region
(a)
(b)
(c)
Fig. 1. (a) The global range of the Grey-crowned Babbler, (b) the study area and (c) population genetic structure of the subspecies P.t. temporalis within the study
area. (a) The thick black line shows the boundary between the two subspecies, with P.t. temporalis in eastern Australia and New Guinea and P.t. rubeculus in
central-western, central and northern Australia (Edwards 1993a); the study area is shown as a black rectangle. (b) The study area, showing: the three study regions
(within the black rectangles); sampling sites (black triangles); and tree cover (vegetation cover >2 m in height; grey shading). (c) The plot depicts the population
genetic structure of sampled birds, with each of the 135 individuals represented by a vertical bar, and shading indicating the genetic cluster represented (as shown
by cluster key at base of figure). Percentage of Qvalues for each individual is measured on the yaxis. The xaxis indicates: the family group to which individuals
belong (numbered 1–39, groups separated by the thin bars); subpopulation (bounded by medium-length thick black vertical lines); and study region (bounded by
longer thick black vertical lines).
Range-edge population genetics of Grey-crowned Babblers Emu C
Babblers from 39 family groups at 39 sampling sites. The number
of individuals sampled per family group ranged from 1 to 7, with
an average 60.7% of group members sampled.
Molecular methods
Genomic DNA was isolated from a 2-mm
2
sample of blood from
the stored Whatman cards, using the Qiagen DNeasy Blood and
Tissue kit as per the protocol of the manufacturer. DNA isolates
were quantified using an Invitrogen Qubit fluorometer kit. Sex
was determined by amplifying the CHD-1 (chromo-helicase-
DNA-binding) gene in 20 mL reactions containing 8 mL of Pro-
mega Go Taq, 1 mLof10mM avian sexing primers P2 and P8
(Griffiths et al.1998) and 2 mL of template. PCR thermal cycles
consisted of: 94C for 3 min, then 35 cycles of 94C for 30 s, 55C
for 30 s, 72C for 30 s, and a final elongation at 72C for 4 min.
Sexes were identified from PCR products visualised by gel
electrophoresis on Invitrogen E-Gel SYBR Safe 2% agarose
gels. DNA isolates were genotyped for 13 Grey-crowned Babbler
microsatellite loci: Pte101, Pte102, Pte103, Pte105, Pte106,
Pte108, Pte109 (Kawano et al.2007), and Pte17, Pte24, Pte42,
Pte47, Pte48, Pte50 (Blackmore et al.2006) (see Table S1 in
online supplementary material). The forward primer of each loci
was labelled with an appropriate fluorescent tag: FAM (Gene-
Works), NED, PET or Vic. (Applied Biosystems). Samples were
subsequently genotyped by the Australian Genomic Research
Facility (Brisbane, Qld) on an AB3730 capillary sequencer and
analysed using Applied Biosystems Genemapper 3.7.
Microsatellite descriptive statistics
Hardy–Weinberg equilibrium and linkage equilibrium were
assessed using Genepop version 4.2 (Raymond and Rousset
1995) with a Bonferroni correction for multiple comparisons
(Rice 1989). Hardy–Weinberg equilibrium and linkage equilib-
rium conformance tests were conducted for all loci (or locus pair)
and across the three regions (western, south-eastern, north-east-
ern). Loci were checked for the presence of null alleles by looking
for consistent departures from Hardy–Weinberg equilibrium in
the direction of homozygous excess. Genotypic data for loci were
manually checked for sex-linkage.
Analysis of cooperatively-breeding species sometimes re-
move putative offspring to reduce biasing results from including
closely related individuals (Harrisson et al.2013; Amos et al.
2014). However, following parentage assignments using CER-
VUS (Kalinowski et al.2007), we tested each analysis and
compared preliminary results for all individuals with the results
of unrelated individuals only. No differences between datasets
were detected in the results, so data analysed included all indi-
viduals (n= 135) to provide greater analytical power.
Genetic diversity
We used a range of metrics to explore patterns of genetic diversity
across the southernmost range of the Grey-crowned Babbler.
GenAlEx version 6.5 (Peakall and Smouse 2006) was used to
calculate mean number of alleles (A), mean expected heterozy-
gosity (H
e
) and observed heterozygosity (H
o
) and the number of
private alleles (P
a
; alleles unique to the region of sampling) across
loci for each of the three regions. Private alleles can provide
a simplistic measure of genetic distinctiveness. Allelic richness
(A
r
) was calculated in FSTAT (Goudet 1995) as a more robust
measure of allelic diversity standardised for differences in sample
size.
Population genetic structure
Analyses of individuals reflect population processes at finer
spatial and temporal scales than population analyses (Dutta
et al.2013). To investigate contemporary population structure
we used the individual Bayesian clustering method in TESS
version 2.3 (Durand et al.2009). TESS implements a spatially
explicit algorithm (i.e. uses geographical coordinates of sampling
locations) to resolve weaker population structure than compara-
tive non-spatial methods (e.g. STRUCTURE; Chen et al.2007)
(Durand et al.2009). TESS was run using the CAR admixture
model (100 iterations of 10
6
sweeps, discarding the first 30000),
with the spatial interaction parameter set to 0.6 and the number of
genetic clusters (K) set from 2 to 20. The point of greatest change
in a plot of mean deviance information criterion (DIC) across 100
runs against Kwas used to infer the most likely value of K. Cluster
probabilities were averaged for the 10 runs with the lowest DIC
values for the most likely Kvalue using the Greedy algorithm
option with 1000 random input orders in CLUMPP version 1.1.2
(Jakobsson and Rosenberg 2007). Results were visualised using
DISTRUCT version 1.1 (Rosenberg 2004). For each individual,
TESS calculates proportional assignment values (Q) to each
genetic cluster. We used a threshold of Q0.80 to assign an
individual to a particular cluster (labelled 1–4; Fig. 1).
Isolation-by-distance and dispersal patterns
To test for isolation-by-distance effects and evidence of sex-
biased dispersal at a fine scale, we compared the spatial genetic
structure of all individuals, and for males and females separately,
for distances of 0–60 km. We conducted spatial autocorrelation
analyses implemented in GenAlEx version 6.5 (Smouse and
Peakall 1999) to calculate the spatial autocorrelation coefficient
(r) as a measure of mean genetic similarity (relatedness levels)
among all pairs of individuals, partitioned into specified geo-
graphical distance-class bins. Following Peakall and Smouse
(2006), we selected distance-class bins based on a priori knowl-
edge of Grey-crowned Babbler breeding ecology and territory
size and, for comparative purposes, distances similar to those used
in previous investigations of isolation-by-distance patterns in the
Grey-crowned Babbler (Blackmore and Heinsohn 2008; Black-
more et al.2011). We also sought to achieve a relatively equal
spread of sample sizes across distance classes. The rcoefficient
value was calculated for all individuals, and for females and males
separately, across nine geographical distance classes range from
0.5 to 60 km. Random permutations were run 999 times to
generate upper and lower 95% confidence intervals bounding
the null hypothesis of no genetic structure (r= 0), and bootstrap
resampling was run 999 times to generate 95% confidence
intervals around r. Correlation coefficient values indicated a
positive spatial genetic structure if rvalues were >0, negative
structure if rwas <0 and random levels of genetic similarity if
r= 0. Permutated values were significant if they were either
outside of the 95% confidence intervals, or error bars did not
intersect zero (Smouse and Peakall 1999). Differences between
sexes were significant if their respective error bars did not overlap.
DEmu K. P. Stevens et al.
Landscape effects on gene flow
We investigated whether landscape patterns of tree cover might
influence the genetic connectivity of the species by estimating
differences of tree cover between the eastern and western areas of
the study region. Patterns and extent of tree cover were assessed
using FRAGSTATS version 4 (McGarigal et al. 2012). FRAG-
STATS estimated indices of tree cover for aggregated and
dispersed tree-cover patterns, which were calculated from
100-m pixel tree-cover rasters using ArcGIS version 10.1 (Esri
2010).
Results
Differences in landscape structure
The landscape extent and pattern of tree cover differed between
the combined eastern regions (south-eastern and north-eastern)
and the western region. In the two eastern regions, tree cover
was more dispersed and there was a larger total area of land with
tree cover (e.g. corridors, scattered trees, woodland patches). In
comparison, the western region had higher levels of aggregated
tree cover, in a small number (n= 7) of large patches (e.g.
conservation reserves) with less dispersed tree cover across the
landscape (Table S2).
Microsatellite loci
There were no consistent patterns of linkage disequilibrium
between any pairs of loci across regions. Conformance tests
across regions showed three loci deviated from Hardy–Weinberg
equilibrium in the west region only (Pte50, Pte102, Pte47;
Table S). These deviations probably reflect population-specific
effects and could arise from local population substructure (i.e.
Wahlund effect). No loci showed consistent linkage disequilib-
rium or Hardy–Weinberg equilibrium deviations across regions
and all loci were retained for analyses. Amplification of the CHD
gene identified 74 male and 61 female Grey-crowned Babblers.
Genetic diversity across regions
The mean observed heterozygosity (H
o
) and expected heterozy-
gosity (H
e
) for all regions were 0.73 and 0.72 respectively
(Table 1), and the mean allelic richness (A
r
) of the three regions
was similar (western = 8.23; south-eastern = 7.58; north-eastern =
8.20; Table 1). A total of 28 private alleles were found amongst all
loci. The western region recorded the highest number of private
alleles (n= 14), and the north-eastern region had similarly high
numbers of private alleles (n= 10). Conversely, the south-eastern
region recorded the smallest number of private alleles (n= 4),
indicating gene flow may occur from birds migrating to the south-
eastern region from other regions in the study area.
Population genetic substructure among regions
TESS found strong genetic substructure across the study region,
with the most parsimonious number of clusters being four (Fig. 1).
Individuals were assigned to a cluster if Q80%. In the western
region, 98% of individuals were assigned to either cluster 3 or 4
(Fig. 1, Table 2), with little evidence of admixture. This result
indicated a major barrier for dispersal between birds between
Kerang north and Kerang south/Boort subpopulations, which
were <20 km apart. In the south-eastern region, 67% of indivi-
duals were assigned to cluster 1 (Table 2). All but one individual
sampled from the subpopulation of Lurg/Violet Town north/
Peechelba were assigned to cluster 1, whereas individuals from
Violet Town south were admixtures of clusters 1 and 4, indicating
gene flow between the western and south-eastern regions.
In the north-eastern region individuals were assigned to
clusters 1 (5%), 2 (29%), 3 (2%), or an admixture of these
(63%) (Table 2), indicating gene flow between the sampled
locations. Individuals from Peechelba were either assigned to
cluster 1 or were an admixture of predominantly cluster 1 with
other clusters in smaller proportions. Individuals sampled from
Chiltern were mostly assigned to cluster 2, whereas individuals
sampled from Rutherglen had high admixture, with evidence
of all four clusters (cluster 2 prominent), suggesting gene flow
between the north-eastern region and both the western and south-
eastern regions.
Isolation-by-distance and dispersal patterns
The mean pairwise genetic similarity (relatedness) between all
individuals (Fig. 2a) was significantly positive up to a distance of
8 km (i.e. error bars did not intersect zero), and rvalues decreased
as geographical distances increased, indicative of an isolation-by-
distance effect. As geographical distances increased from 8 to
60 km, pairwise relatedness of individuals showed a pattern
expected under random mating until, at 60 km, individuals be-
came more dissimilar than expected under random mating (i.e.
error bar is below both zero and the lower confidence interval
bound).
When sexes were considered separately, an isolation-by-dis-
tance effect was observed for females only (Fig. 2b). Significant
differences between the pairwise relatedness for males and for
females (i.e. error bars do not overlap) at distances of 2–4km
Table 1. Genetic diversity metric averages of the Grey-crowned
Babbler within its southern edge-of-range population
A, number of alleles; A
r
, allelic richness; P
a
, number of private alleles;
H
o
, observed heterozygosity; and H
e
, expected heterozygosity
Study
region
Sample
size
AA
r
P
a
H
o
H
e
Western 51 8.50 8.23 14 0.69 0.72
South-eastern 43 6.50 7.58 4 0.75 0.73
North-eastern 41 7.64 8.20 10 0.74 0.72
Global 135 11.00 8.00 28 0.73 0.72
Table 2. Assignment of Grey-crowned Babblers to four genetic clusters
within the three study regions
Values represent the proportion of individuals assigned to each cluster (1–4;
Q0.80) or an admixture. Sample sizes are given in parentheses
Genetic cluster Study region
Western
(51)
South-eastern
(43)
North-eastern
(41)
1–65 5
2–– 29
325–2
473––
Admixture 2 33 63
Range-edge population genetics of Grey-crowned Babblers Emu E
(Fig. 2b) supports our assertion of an isolation-by-distance effect
on females. Female Grey-crowned Babbler pairs showed elevated
relatedness up to distances of 6–8 km. Between distances of
8–60 km, results indicate female pairwise genetic similarity to
be no different from that expected under random mating (i.e.
female error bars interact with male error bars, and male error bars
cross zero) (Fig. 2b). Males, on the other hand, did not show
elevated levels of relatedness beyond the group-level (<0.5 km)
and described a pattern of random genetic similarity for distances
>0.5–40 km. Between the two furthest distance classes (40–
60 km), males became more genetically dissimilar than expected
under random mating, suggesting male gene flow may be re-
stricted beyond 40 km. The presence of isolation-by-distance for
females at fine spatial scales and not for males suggests females
are driving the overall pattern of isolation-by-distance across the
study region, and is consistent with male-biased dispersal.
Discussion
Influences of geographical distance and barriers on genetic
diversity and population structure
Strong population substructure across the southern distribution
of the Grey-crowned Babbler was only partly explained by
geographical distance (patterns of isolation-by-distance). Strong
genetic breaks over short geographical distances observed in the
western region (e.g. ~20 km between Kerang north and Kerang
south/Boort) indicate that specific landscape elements are acting
as strong barriers to gene flow. Population substructure can result
from various factors, including anthropogenic or natural barriers,
such as loss of habitat, presence of roads or areas of non-habitat
(Sunnucks 2011; Taylor et al.2011). The genetic break between
northern and southern Kerang, in the western region, corresponds
with an area of ~5 km radius of agricultural landscapes and
roadsides with little or no tree cover. Differences in pattern and
extent of tree cover were evident between the western region
and the two eastern regions (Table S2). Relative to the known
dispersal distance average of the Grey-crowned Babbler (~1 km,
ABBBS), the western region consists of large areas devoid of tree
cover, and cropping farmscapes, and lacks wooded vegetation
along the major (>2000 vehicle movements per day (VPD);
Murray Valley Highway) and minor (1000–2000 VPD; Kerang–
Koondrook Road, Kerang–Quambatook Road and Boort–
Kerang Road; K. Stevens pers. obs.) roads in the region. In
contrast, the population of the north-eastern region and the Lurg
subpopulation in the south-eastern region indicated the occurr-
ence of gene flow despite being situated either side of the Hume
Highway, a major four-lane freeway (>5000 VPD), which also
separates the two Violet Town subpopulations in the south-
eastern region. The roadsides of the Hume Highway provide
high levels of structural habitat connectivity (physical habitat
connectedness across landscapes) along continuous stretches of
revegetated roadside habitat and median-strip vegetation in the
centre of the roadway. In the study area, the Hume Highway is
located adjacent to large (>50 ha) conservation reserves, habitat
along nearby roadsides, grazing land with remnant patches of
suitable habitat, scattered paddock trees and long-term revege-
tation works (Vesk et al.2015). It would appear that roadside
habitat, including woodland remnants, acts as a conduit for the
Grey-crowned Babbler, notwithstanding the threats associated
with high numbers of VPD (e.g. noise disturbance, vehicle strike)
(Develey and Stouffer 2001; Parris and Schneider 2009). Higher
levels of genetic admixture in the two eastern regions compared
with the western region are indicative of higher levels of gene
flow in the east, which could be facilitated by a greater extent
of riparian and roadside corridors, coupled with higher levels of
dispersed tree cover connecting areas of habitat than was apparent
in the west (Fig. 1, Table S2).
In contrast to the abrupt genetic discordance over very short
distances in the western region, shared cluster membership
between the western and north-eastern regions (cluster 3; Fig. 1)
provided evidence for gene flow over comparatively larger
geographical distances (~220 km). Grey-crowned Babblers are
considered to be sedentary, with the capacity to cross gaps >300 m
wide in farmland (Robinson 1993; Radford 2008). Fragmented
landscapes can necessitate responses, such as increased dispersal
distances and larger gap-crossing, in species that would otherwise
avoid such behaviours (Van Houtan et al.2007). Given the
biology of the species and the short distances of previous records
of mark and recovery, however, we suggest that observed gene
flow at these large spatial scales is more likely to reflect multi-
generational dispersal in a stepping-stone fashion rather than
single migration events (Department of the Environment 2015).
Gene flow over multiple generations at large spatial scales despite
high levels of habitat loss and fragmentation is consistent with
findings for a suite of other woodland bird species in Victoria
(Amos et al.2014). Long-distance movements, however, are
often difficult to detect through observational banding studies and
only a small number of migrants per generation (e.g. 1–10 per
generation; Mills and Allendorf 1996) are required to homogenise
genetic structure. Consequently, the dispersal ability of indivi-
duals may be greater than previously inferred (Robinson 1993;
Radford 2008).
The observed long-distance genetic connectivity may be
explained by instances where groups are found close together
–0.1
0
0.1
0.2
0.3
0.4
0.5
0.6
r
(a)
–0.1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.5 2.0 4.0 6.0 8.0 10.0 20.0 40.0 60.0
Distance class (km)
(b)
Fig. 2. Correlogram plots of the spatial genetic autocorrelation coefficient
ras a function of distance for (a) all individuals; and (b) males (unbroken
black line) and females (unbroken grey line) separately. The error bars are
the upper and lower 95% of ras determined by bootstrap resampling; and
the grey broken lines show the 95% confidence intervals of the null hypothesis
of no spatial structure (r= 0).
FEmu K. P. Stevens et al.
and where there is landscape connectivity to major riparian
corridors. For example, most of the individuals assigned to cluster
3 in the western and north-eastern regions (separated by 220 km)
were in habitat <5 km from continuous riparian corridors along
the Murray River or a major tributary, the Goulburn River (Fig. 1).
Avian species are known to rely on riparian corridors for move-
ment in fragmented landscapes (Vergara et al.2013; Volpe et al.
2014) and riparian corridors may play a role in the functional
connectivity of Grey-crowned Babbler populations in our study
area as has been demonstrated for different species elsewhere
(Cushman et al.2009).
Levels of genetic diversity were similar across the three study
regions. However, differences in the number of private alleles
across the three regions may indicate reduced gene flow among
subpopulations. The small number of private alleles in the south-
eastern region (n= 4) suggests that this region is predominantly
a genetic subset of the other two regions.
Fine-scale population structure and sex-specific effects
of habitat loss and fragmentation
Fine-scale population genetic structure and an isolation-by-dis-
tance effect were evident across our study region. When tested
separately, females displayed significant, positive, pairwise re-
latedness for distances up to 8 km whereas males showed no
relatedness to each other beyond their family groups (>0.5 km). In
comparison, significant positive relatedness between individuals
of each sex in continuous habitat in the central-east of the range of
the species in northern New South Wales, did not extend beyond
1.5 km (Blackmore et al.2011). Therefore, as the observed fine-
scale population structure in our study appeared to be driven by
elevated relatedness among females, results here could be indi-
cative of male-biased dispersal in the study area. As this study is
the first to describe both sex-biased dispersal and strong isolation-
by-distance effects for females, we suggest such patterns could be
explained by high levels of habitat loss and declines in landscape
connectivity. Other avian studies have also described restricted
mobility across fragmented landscapes leading to adverse sex-
specific demographic effects (Dale 2001; Harrisson et al.2012;
Amos et al.2014). Alternatively, in contrast to several previous
studies suggesting a lack of sex-bias dispersal for the Grey-
crowned Babbler (King 1980; Eguchi et al.2007; Blackmore
et al.2011), females in this study area are less dispersive than
males.
Conclusions and management recommendations
Fragmented landscapes are not dichotomies of habitat and non-
habitat, but rather represent a gradient of habitat qualities that may
display temporal plasticity (Bennett et al.2006; Sunnucks 2011).
Gene flow was disrupted over distances as little as <20 km in our
study landscape, indicating strong barriers to gene flow were
present (e.g. treeless areas). Despite strong barrier effects over
short distances, we detected evidence of long-distance gene flow
(~220 km), which is likely to have been influenced by combina-
tions of factors, including an the sex of an individual, structural
connectivity or distance. The strong evidence of isolation-by-
distance for females suggests distance is a limiting factor at fine
spatial scales for this sex of Grey-crowned Babbler. In contrast,
a lack of isolation-by-distance for male Grey-crowned Babblers
at distances <40 km could suggest higher levels of landscape
permeability for males, compared with females, at finer spatial-
scales.
Given numbers of Grey-crowned Babblers are estimated to be
only ~10% of historical levels in the south of its range (i.e. south
of 351605500S) (Robinson 2006), populations across our study
area are likely to be experiencing the negative effects of small
effective population size (Frankham 1996). Our study suggests
that loss of genetic connectivity as a result of habitat loss and
fragmentation may have contributed to the decline of the Grey-
crowned Babbler. Increasing functional connectivity across
the study landscape would be effective in counteracting genetic
drift and improving the chances of population persistence. Al-
though levels of genetic diversity did not differ across the three
study regions, different regions were associated with private
alleles. The formation of small, isolated populations as a result
of landscape modification can lead to a reduction in the overall
genetic diversity of a species (Frankham 1996). Our results
identify dispersal barriers and conduits that may be manipulated
to help facilitate the movements of the Grey-crowned Babbler
across the landscape and provide scope for genetic diversity to be
increased within regions by increasing gene flow among regions.
Habitat corridors are known to be a critical factor in maintaining
genetic exchange (Epps et al.2007) and we suggest that conser-
vation management plans, particularly in fragmented landscapes,
need to consider opportunities to increase and enhance corridors.
Further research into the extent of movements and the ancestry
of individuals can provide important knowledge of spatial and
temporal dispersal within the study area, and comparisons of
contemporary and historical patterns of gene flow will add to our
understanding of the effects of fragmentation on this species.
Conservation management plans for southern populations now
occupying highly fragmented landscapes, may benefit from
comparisons with other populations to determine if they contain
unique genetic diversity relative to more northern populations.
Acknowledgements
Data on the location of babbler territories was kindly provided by
D. Robinson, C. Tzaros and N. Lacey. We thank many enthusiastic field
assistants. Funding was provided by a Stuart Leslie Bird Research Award
(BirdLife Australia); a Professor Allen Keast Research Award (BirdLife
Australia); the Holsworth Wildlife Research Endowment; and a Jill Landsberg
Trust Fund Scholarship (Ecological Society of Australia), for which we are
most grateful. Research was conducted under Deakin University Animal
Welfare Committee approval A66-2009; Australian Bird and Bat Banding
Scheme authority 1762; and Department of Sustainability and Environment
(Victoria) bird banding and research permit 10005380.
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