Contrasting
geographic structure in evolutionarily divergent Lake Tanganyika catfishes
Claire R. Peart123, Kanchon K.
Dasmahapatra4, Julia J. Day1
1. Department of Genetics, Evolution and Environment, University College London, London WC1E 6BT, U.K
2. Department of Life Sciences, The Natural History Museum, London SW7 5BD, U.K
3.
Current
address Division of Evolutionary Biology,
Ludwig-Maximilians-Universität München, Grosshaderner Strasse 2,
82152 Planegg-Martinsried, Germany
4. Department of Biology, University of York, Heslington, York, YO10
5DD, U.K
Correspondence: peart@biologie.uni-muenchen.de
Abstract
Geographic isolation is
suggested to be among the most important processes in the generation of cichlid
fish diversity in East Africa’s Great Lakes, both through isolation by distance and fluctuating
connectivity caused by changing lake levels. However, even broad scale
phylogeographic patterns are currently unknown in many non-cichlid littoral taxa
from these systems. To begin to address this we generated restriction-site
associated DNA sequence (RADseq) data to investigate phylogeographic structure
throughout Lake Tanganyika in two broadly sympatric rocky shore catfish species
from independent evolutionary radiations with differing behaviours: the
mouth-brooding claroteine, Lophiobagrus
cyclurus, and the
brood-parasite mochokid, Synodontis
multipunctatus. Our results indicated contrasting patterns between these species, with strong lake-wide phylogeographic signal in L. cyclurus including a deep divergence
between the northern and southern lake basins. Further structuring of these
populations was observed across a heterogeneous habitat over much smaller
distances. Strong population growth
was observed in L. cyclurus sampled from shallow
shorelines, suggesting population growth associated with the colonization of
new habitats following lake level rises. Conversely, S. multipunctatus, which occupies a broader depth range,
showed little phylogeographic structure and lower rates of population growth. Our
findings suggest that isolation by distance and/or habitat barriers may play a
role in the divergence of non-cichlid fishes in Lake Tanganyika but this effect
varies by species.
Keywords: East African Great Lakes, Phylogeography, RADseq, catfishes, diversification, Lake Tanganyika, lake level fluctuations
The study of species diversification in biodiverse
environments can reveal the importance of different factors influencing species
diversity and coexistence. In the large East African rift lakes multiple
factors have been implicated in species divergence, for example ecological
adaptation, sexual selection and allopatric divergence (Salzburger
et al., 2014 and refs therein). Habitat breaks creating
phylogeographically structured populations may provide the seed for future
speciation and increased genetic diversity, but what constitutes a habitat
break varies between species living in the same environment, as observed in
cichlid fishes and their parasites (Baric
et al., 2003; Grégoir et al., 2015; Koblmüller et al., 2011; Sefc
et al., 2007; Van Oppen et al., 1997; Wagner and McCune, 2009). While mechanisms of
diversification are intensively studied in these highly speciose radiations,
the extent to which the observed geographic patterns apply to taxa from
non-cichlid radiations remains to be tested, with even broad scale geographic
patterns poorly understood.
Lake Tanganyika (LT), the oldest African rift lake,
contains exceptional species richness with high levels of endemism and multiple
independent radiations (e.g.
Day et al., 2008; Meyer et al., 2015) even among non-cichlid taxa, for
example Platytelphusid crabs (Marijnissen
et al., 2006), multiple gastropod lineages (West
and Michel, 2000; Wilson et al., 2004) and mastacembelid eels (Brown
et al., 2010). The elevated diversity of LT
has been attributed in part to the lake’s complex history, since it was
formed from multiple basins varying in connectivity and size (Cohen
et al., 1993). During the Late Pleistocene
glaciations (≈106 Ka) lake levels dropped by ≈435m and while the
size of LT was considerably reduced, it remained a large and mostly connected
water body (McGlue
et al., 2008). These lake level fluctuations have
been shown to influence distributions and diversification in multiple cichlid
species (e.g.
Rüber et al., 1999; Sefc et al., 2017; Sturmbauer et al., 2017; Verheyen
et al., 1996) primarily through altered
habitat barriers and repeated periods of isolation followed by secondary
contact (e.g.,
Egger et al., 2007; Nevado et al., 2013). Lake level rises also correlate
with population expansions allowing the colonization of new habitats (Koblmüller
et al., 2011; Winkelmann et al., 2017)
Lake Tanganyika catfishes (~34 species), as with
cichlids from this lake, comprise multiple independent radiations (Day
et al., 2009; Day and Wilkinson, 2006; Peart et al., 2014; Wright and Bailey,
2012) albeit on a smaller scale. They
also share a similar habitat range, from deep to littoral waters, with littoral
species vulnerable to displacement by lake level changes. Here, we focus on two
species with lake-wide distributions selected from independent evolutionary
radiations, the claroteine (15 described LT species), Lophiobagrus cyclurus, and the mochokid (≈11 LT
species), Synodontis multipunctatus. Lophiobagruscyclurus is a mouthbrooder (Ochi et al., 2002), a trait shared with many cichlid species,
which may reduce dispersal ability through extended parental care
whereas S.
multipunctatus
is a known brood parasite of multiple cichlid species (Sato,
1986). Both species occur over rocky
shores, although while L. cyclurus is
thought to be restricted to the littoral zone (Bailey
and Stewart, 1984), S.
multipunctatus can also tolerate a greater depth range (sampled up
to 170m, Coulter,
1991). Fossil calibrated molecular
dating indicates that both L. cyclurus and
S. multipunctatus diverged from their
sister species before the onset of climatic fluctuations in the Late
Pleistocene (Day
et al., 2013; Peart et al., 2014), suggesting that the present day
geographic distributions of these species do not reflect the location of a
recent origin.
The high sample number required for the techniques
previously employed to investigate populations of LT taxa e.g., mitochondrial
sequences, microsatellites (Koblmüller
et al., 2015; Wagner and McCune, 2009), AFLPs (Egger
et al., 2007), has been a barrier to
undertaking intraspecific studies on poorly sampled non-cichlid taxa. Here we
use Restriction Site Associated sequencing (RADseq) to generate high numbers of
loci per individual, allowing robust estimates of differentiation to be
calculated using small numbers of individuals (e.g.
Willing et al., 2012). RAD loci were used to compare population
structure in the two focal species
A total of 57 individuals
from the focal species (33 L. cyclurus and 24 S.
multipunctatus specimens), sampled from four main locations around
Lake Tanganyika (Figure 1, Table S1) were included in this study. Samples were collected
from one steeper shoreline (Kigoma) and three shallower shorelines (Bujumbura
Rural, Mpulungu, Sumbu) (Figure
1 in Scholz et al., 2007).
Bioinformatic processing was conducted as in Hoffman
et al. (2014). In brief, the Stacks pipeline
v1.08 (Catchen
et al., 2013) was used for demultiplexing and
run using the forward reads following a superparent approach. The paired-end
reads for each locus were then assembled using Velvet (Zerbino
and Birney, 2008) and used to generate a reference
with the first read connected to the paired-end by a string of Ns. All tags
with more than one assembled contig were eliminated to avoid including
instances where several tags had been collapsed into one tag in the clustering
process, which ignores the second read. The Lophiobagrus
reference consisted of 33,833 contigs with lengths ranging from 595bp to
1140bp. The S. multipunctatus reference consisted of
26,472 contigs with lengths ranging from 592bp to 1133bp. The Burrows-Wheeler algorithm was used
to map each sample using BWA-MEM (Li,
2013), duplicates were removed using
picard-tools 1.102 (http://picard.sourceforge.net) and SNPs were called using
GATK UnifiedGenotyper (McKenna
et al., 2010). The resultant vcf file was
filtered to include only high quality calls with the following parameters; only
biallelic SNPs, SNP quality score of 30, genotype quality score of 20, mapping
quality score of 20, sites with coverage under five were excluded. In addition,
in an attempt to avoid including repeated transposon regions in all datasets,
sites with excessive coverage (the upper 5% of the coverage distribution) were
excluded. Sites that were present in at least two individuals were included as
it has been shown that stringent exclusion of missing data can bias the dataset
(Huateng
and Knowles, 2016). This resulted in very few
called sites for the individual C137 from the Lophiobagrus
dataset and so this individual was excluded from further analysis. The final Lophiobagrus dataset included 299,633 SNPs of which 266,434
SNPs were variable in L. cyclurus.
The S. multipunctatus dataset consisted of
107,321 SNPs.
Maximum likelihood (ML) trees were calculated with the
GTR+GAMMA model using RAxML 7.7.8 (Stamatakis,
2006) with 1000 non-parametric rapid
bootstraps. These analyses were performed on alignments containing both variant
and non-variant sites: 2,628,515 sites for the Lophiobagrus
dataset and 3,356,174 sites for the S. multipunctatus dataset.
This analysis was also repeated using alignments with no missing data across
individuals leading to 350,347 sites for the Lophiobagrus
dataset and 1,479,271 sites for the S. multipunctatus
dataset.
Population structure was investigated using Bayesian clustering in structure 2.3 (Pritchard et al., 2000) using a matrix of variable sites. Structure uses unlinked markers, so only one SNP per contig was retained, leading to datasets of 23,739 sites for Lophiobagrus and 16,443 for S. multipunctatus. Five replicates per K value (K 1-8) were run using 100,000 generations as burn-in before sampling 100,000 generations using a model of admixture with correlated allele frequencies (Falush et al., 2003). Runs were collated using STRUCTURE HARVESTER (Earl and VonHoldt, 2011). Independent runs for the suggested K values were combined and averaged in CLUMPP (Jakobsson and Rosenberg, 2007) and the output visualised in distruct (Rosenberg, 2004). An additional analysis was repeated using datasets of variable sites present in all individuals using only one SNP per contig (1,938 Lophiobagrus and 5,116 S. multipunctatus). The package fineRADstructure (v. 0.2) (Malinsky et al., 2016) was also used to investigate structure in both the Lophiobagrus and S. multipunctatus datasets. These analyses were performed using loci with one to four SNPs in the forward read (using loci defined by Stacks), with no missing data, leading to datasets of 10,380 sites for Lophiobagrus and 13,028 for S. multipunctatus. Samples were assigned to populations using 100,000 iterations as burn-in before sampling 100,000 iterations. The trees were built using 10,000 iterations and the output visualised using the fineradstructureplot.r and finestructurelibrary.r R scripts (http://cichlid.gurdon.cam.ac.uk/fineRADstructure.html).
Structure within L. cyclurus and S. multipunctatus
datasets was further investigated using Principal Component Analysis (PCA)
analyses in the R packages ‘adegenet’ (Jombart
et al., 2008) and ade4 (Dray
and Dufour, 2007). PCA analyses were conducted on
datasets of variable sites present in all individuals using only one SNP per
contig (2,065 L. cyclurus and
5,116 S. multipunctatus). FST values were
calculated using the index of Reich et al. (2009) as this method performed well on
small sample sizes in a recent simulation study (Willing
et al., 2012). Analyses were completed using
one SNP per contig with code modified from Rheindt
et al. (2013). 95% confidence intervals for
these FST values were calculated by jack-knifing.
Admixture between populations of L. cycluruswas investigated with D-Statistics (Durand
et al., 2011; Green et al., 2010) using each supported four-taxon
tree (L. aquilus as outgroup). The
significance of the D-statistic (from 0) is usually calculated by computing the
standard error of the D-statistic using a block jack-knife approach over
linkage groups. This approach was not possible in this case, as in the absence
of a reference genome the order of RAD contigs is unknown, so reliability was
assessed by randomly subsampling the dataset 1,000 times including 99%, 95%,
90%, 80% and 70% of the data and calculating the mean and standard error of theD-statistic. These analyses were conducted using a modified version of a python
script from Rheindt et al. (2013).
The statistical significance of the relationship
between geographic distance and genetic distance was assessed using Mantel
tests with 999 permutations in the R package ade4 (Dray
and Dufour, 2007). Straight line distances between
collection localities were calculated using the haversine method for
calculating distances on the surface of a sphere. Distances along the lakeshore
were calculated using ERSI ArcMap 10’s Network Analyst package using the
Detailed Water Bodies layer. Lakeshore distances were only calculated for L. cyclurus as this taxon was sampled from the littoral zone
(<15m). The analysis was performed using average genetic distance between
collection localities as within location samples are pseudoreplicates. The uncorrected
P-distances were calculated using the R package ape (Paradis
et al., 2004).
Population trends in each population were estimated
using a subset of the most variable loci by constructing Extended Bayesian
Skyline Plot (EBSP) in BEAST (Drummond
et al., 2012). We followed the protocol of
Trucchi et al. (2014) to avoid over parameterisation
when analysing RADseq datasets. For each population this analysis was performed
on three independent subsamples of 50 loci with four SNPs in the forward read(using loci defined by Stacks). Due to a shortage of four SNP loci found in all
individuals within a population, loci found in at least seven individuals were
used for S. multipunctatus samples from Mpulungu,
and loci found in at least six individuals were used for analyses on L. cyclurus samples from Kigoma and S. multipunctatus
samples from Bujumbura Rural and Kigoma. Each analysis was run
until convergence, with Tracer (Rambaut
and Drummond, 2009) used to visualise convergence
and effective population sizes. Sample C171 was excluded from this analysis due
to its placement in the phylogenetic and structuring analyses (Figure 1). In
the absence of any rapidly evolving markers with a known mutation rate to
calibrate this analysis it was not possible to date demographic events.
Lophiobagrus cyclurus is supported as monophyletic in
the maximum-likelihood tree (100%, Figure 1 and Figure S1), which was not
observed in previous analyses (Peart
et al., 2014), although the structure and FineRADstructure analysis (K=5, Figure 1, Figure
S2 and Figure S3) highlights
an admixed sample, C228, which is derived almost equally from the L. aquilus and Sumbu clusters. All analyses show a clear
separation between the northern and southern basins, as well as between Kigoma
and Bujumbura Rural within the northern basin. In the southern basin, the two
maximally supported clades correspond to collection locality, with a single
exception (C171). This sample, from Sumbu, nests within the Mpulungu clade, and
in the structure analysis is comprised entirely
of a cluster found in all Mpulungu samples (blue in Figure 1 and Figure S2).
The cluster most strongly associated with Sumbu (red) is also seen in the
remainder of the Mpulungu samples. The same patterns within L. cyclurus were observed in the structure
analysis at K=4 (Figure S4). This placement of C171 as close to but distinct
from the Mpulungu samples is also supported by the FineRADstructure analysis (Figure S3). The samples from the
southern basin are not supported as monophyletic in the maximum-likelihood tree
constructed with no missing data (Figure S1).
The population divisions in L. cyclurus
are further supported by the PCA results (Figure 2) that show three distinct
clusters relating to Kigoma, Bujumbura Rural and the Zambian sites on the first
two axes (19.3% and 13.7% of the variation respectively). The third PCA axis
also shows separation between the Zambian sites (Figure S5).
FST values between the Zambian populations and
the population at Kigoma (0.191 [0.183-0.195, 95% confidence intervals], 0.198
[0.188-0.205]) were larger than those between the Zambian populations and the
most northerly population (Bujumbura Rural) (0.162 [0.154-0.165], 0.170
[0.159-0.175]). Genetic distances (p-distance) in L. cyclurus increased significantly with straight line
distances (Mantel test r=0.762, p=0.004) and lake shore distances (Mantel test
r=0.759, p=0.001) around LT. This significant relationship between genetic
distance and straight line distance remained when only the Zambian samples were
considered (Mantel test r=0.482, p=0.041) despite the low number of pairwise
comparisons (not significant using lake shore distance, r=0.479, p=0.095). The FST value of 0.034 (0.024-0.040] indicates some
differentiation at this smaller geographic scale.
D-statistics indicated gene flow between all
populations (Table 1) with greater gene flow between Kigoma and the Zambian
sites than between Bujumbura and the Zambian sites; consistent with the larger
distance between the latter sites. Subsampling of the data yielded similar
values of the D-statistic with standard errors that do not include zero. The
D-statistics increased in value when the analysis was repeated without the
possible confounding sample C228 (Table S2).
In S.
multipunctatus population structure was weaker. The
maximum-likelihood tree strongly supports the separation of populations from
the northern and southern basins (100%) but finds no support for further
structure within the northern basin. Support for the separation of the northern
and southern basins is lower in the phylogeny built with no missing data (Figure
S6). The structure analysis indicates a single
population (K=1), however at K=2, the northern and southern basins clusterseparately (Figure 1 and Figure S7). The fineRADstructure analysis shows a
clear separation of the northern and southern basins and also weaker structure
within the northern basin (Figure S8). Similarly, the first two PCA axes
represent 8.7% and 5.6% of the variation (Figure S9) and show three clusters
corresponding to the sampled populations, although these are less tightly
clustered than in L. cyclurus (Figure 2). The FST values were below zero, indicating a lack of
population differentiation.
In general mixing in the EBSP analyses was poor,
requiring long run times (up to 2,150,000,000 generations). ESS values, with
the exception of some operators, were above 200 in all analyses with the
exception of one S. multipunctatus analysis from
Kigoma and one from Mpulungu. It was not possible to continue these runs and so
they were discarded. The null hypothesis of no population
change could be rejected for each analysis with the exception of two L. cyclurus runs from Kigoma. The L. cyclurus
results for Kigoma show either constant population or very weak growth, whereas
the other three populations all show similar signatures of population growth (Figure
3 and Figure S10). All S. multipunctatus
EBSP analyses show shallow population growth (Figure 4 and Figure S11). In
Bujumbura Rural and Mpulungu population growth is steeper in L. cyclurus than in S. multipunctatus
whereas both species show similar population trajectories in Kigoma.
Biodiversity hotspots contain a disproportionate
amount of the world’s diversity, and as a result have received
considerable attention regarding the patterns and processes underlying the
generation and maintenance of species diversity. For the East African Great Lakes
however, the vast majority of studies have focused on the hyper-diverse cichlid
fishes, which
may not exemplify the patterns of evolution seen in other fish groups, and it is not known if the same factors influence other taxa that
have diversified in these water bodies. To begin to remedy this knowledge gap we focused on non-cichlid taxa and show contrasting spatial patterns of
phylogeographic structure in two broadly sympatric rocky shore catfish species
from independent evolutionary radiations with different parental care
strategies. We report strong lake-wide population structure in L. cyclurus, including within basin differentiation.
Conversely, population structure in S. multipunctatus
is weak, and although there is some support for population differentiation
between the northern and southern basins from the phylogenetic and structure
analyses, this pattern is not reflected in the FST
values suggesting that it is driven by a small number of SNPs. Such weak
structure may be due to its dispersal ability at depth (Coulter,
1991). The only other LT species found at equivalent depths for which
population structure has been investigated is the cichlid Boulengerochromis
microlepis (Koblmüller et al., 2015) that showed similarly weak lake-wide
phylogeographic structure. The extent to which brood parasitism in S. multipunctatus (Sato, 1986) facilitates dispersal is
unclear, since this species parasitizes both stenotypic and less geographically
restricted species e.g., Tropheus moorii,
Simochromis diagramma (Sefc et al., 2007; Wagner and McCune,
2009).
Populations in shallower
lake regions are expected to be more affected by lake level fluctuations due to
the availability of new habitat as lake levels rise and potential fusion of
previously separated populations when lake levels fall. Our results from Kigoma
(steeper shoreline) are consistent with the idea that populations are more
stable at sites with steeper shorelines compared to shallow regions. However,
there are more missing data in the reconstructions from Kigoma and we cannot
discount the possibility that the weak or no population growth seen in L. cyclurus might be related to a lack of resolution to
accurately reconstruct the demographic history. Without an absolute calibration
of the EBSPs, it is not possible to date the reconstructions, however the
signatures of population growth at both Zambian sites in L. cyclurus is
consistent with results from several cichlid species where populations grew
following the most recent low stand as new habitat became available (Koblmüller et al., 2011; Winkelmann
et al., 2017)
The placement in both the
phylogenetic and structure analyses of L. aquilus-C228
suggested possible interbreeding between this species and L. cyclurus.
Notably, this specimen was collected in Mpulungu, despite its partial
assignment to a genetic cluster most common in the Sumbu L. cyclurus
population and was identified as L. aquilus based
on the key of Bailey and Stewart (1984). Within L. cyclurus there
was admixture between all populations with the overall patterns reflecting
geographic structure. Population subdivision in ancestral species can also
influence gene trees and the relative proportion of ABBA/BABA SNPs (Eriksson and Manica, 2012). It is possible that this affected our results as population structure
with L. aquilus has not been investigated and
this species was sampled only from Zambia.
Investigating diversity patterns both within and between species in biodiversity hotpots allow conclusions to be drawn as to which mechanisms are responsible for such elevated diversity. Here we report contrasting geographic structure at a broad spatial-scale in evolutionary divergent Lake Tanganyika catfishes using genomic data. Our study suggests that lake level fluctuations have a role in structuring their diversity, although the genomic consequences of these differing histories remain to be studied. We recommend the study of additional species from non-cichlid radiations with different ecologies to provide a more comprehensive understanding of evolutionary patterns and processes in these systems. Within LT catfishes restricted to the littoral zone, and in L. cyclurus in particular, fine scale population structure warrants further investigation with additional sampling localities required. This study is limited by low sample sizes, and further sampling would allow the timing and extent of gene flow between populations to be investigated plus the identification of habitat barriers for this species. Additional sampling localities in the southern basin of LT may allow the placement of sample C171 to be further understood in a broader geographic context. Further L. aquilus samples from these localities would also allow estimates of the extent of gene flow between these species to be investigated.
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Tables
Table 1. Nominal D-Statistics for L. cyclurus with mean and standard deviation from 1,000 random subsamples of the dataset at each percentage coverage. The taxa in bold show evidence of admixture based on the D-statistics.
|
Tree topology |
Overall D-statistic |
D-statistic ± standard
deviation at subsampling level |
||||
|
99% |
95% |
90% |
80% |
70% |
||
|
┌── Burundi ┌─│ ┌─│ └── Kigoma │
└──── Mpulungu └──────
L. aquilus |
0.0496 |
0.0496 |
0.0497 |
0.0496 |
0.0497 |
0.0495 |
|
┌── Burundi ┌─│ ┌─│
└── Kigoma │
└──── Sumbu └──────
L. aquilus |
0.0569 |
0.0569 |
0.0569 |
0.0568 |
0.0570 |
0.0570 |
|
┌── Mpulungu ┌─│ ┌─│
└── Sumbu │
└──── Burundi └──────
L. aquilus |
0.0110 |
0.0110 |
0.0110 |
0.0110 |
0.0110 |
0.0109 |
|
┌── Mpulungu ┌─│
└── Sumbu │
└──── Kigoma └──────
L. aquilus |
0.0188 |
0.0188 |
0.0188 |
0.0188 |
0.0188 |
0.0190 |
Figure Legends
Figure 1. Maximum-likelihood trees based on 2,628,515bp and 3,356,174bp alignments (bootstrap support: black circles 100%, grey circles >90%, white circles >80%) and structure plots, based on 23,739 SNPs and 16,443 SNPs for Lophiobagrus and Synodontis multipunctatus respectively. Colours in the phylogeny depict collection locality. Green in the S. multipunctatus plot comprises sites from the northern basin. Sampling localities: BR - Bujumbura Rural, KG - Kigoma SM - Sumbu, MP - Mpulungu. Major rivers are shown on the map, the inflow of the (smaller) Lufubu river is denoted with an “L”.
Figure 2. PCA plots using 2,065 and 5,116 SNPs for Lophiobagrus cyclurus and Synodontis multipunctatus respectively. Colours depict collection locality.
Figure 3. EBSP plots for Lophiobagrus
cyclurus. The y axis shows effective population size scaled by
mutation rate (Neµ). Grey area represents 95% confidence
interval.
Figure 4. EBSP plots for Synodontis
multipunctatus. The y axis shows effective population size scaled by
mutation rate (Neµ). Grey area represents 95% confidence
interval.
Acknowledgements
For facilitating fieldwork
we are grateful to Yohana Budeba, Ben Ngatunga and Amon Shoko (Tanzanian
Fisheries Research Institute, Tanzania), Lawrence Makasa and Danny Sinyinza
(Department of Fisheries, Mpulungu, Zambia), and Adelin Ntungumburanye
(Institut National pour L’Environnement et al Conservation de la Nature,
Burundi). We thank Roger Bills and George Kazumbe for their invaluable field
assistance and Saskia Marijnissen for facilitating collecting in Burundi. We
thank anonymous reviewers for improving earlier versions of this work.
Ethics
Conducted under COSTECH permit: no.
2010-03-NA-2009-207.
Data Accessibility
The data is stored on NCBI under BioProject PRJNA421168
Funding
This work was supported by
NERC funding NE/I528169/1. A National Geographic Society Waitts grant (1341-0)
supported JJD and CRP during field expeditions. JJD also acknowledges the Percy
Sladen Memorial Trust Fund for travel support. CRP acknowledges UCL’s
Graduate School.
Author contributions
All authors contributed to study design and wrote the
manuscript. CRP and JJD conducted fieldwork, CRP and KKD generated and analysed
the RAD data.