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. 2024 May 23;19(5):e0293715. doi: 10.1371/journal.pone.0293715

Species-specific dynamics may cause deviations from general biogeographical predictions – evidence from a population genomics study of a New Guinean endemic passerine bird family (Melampittidae)

Ingo A Müller 1,2,3,*, Filip Thörn 1,2,3, Samyuktha Rajan 4, Per G P Ericson 2, John P Dumbacher 5, Gibson Maiah 6, Mozes P K Blom 3, Knud A Jønsson 2, Martin Irestedt 2
Editor: Sven Winter7
PMCID: PMC11115331  PMID: 38781204

Abstract

The family Melampittidae is endemic to New Guinea and consists of two monotypic genera: Melampitta lugubris (Lesser Melampitta) and Megalampitta gigantea (Greater Melampitta). Both Melampitta species have scattered and disconnected distributions across New Guinea in the central mountain range and in some of the outlying ranges. While M. lugubris is common and found in most montane regions of the island, M. gigantaea is elusive and known from only six localities in isolated pockets on New Guinea with very specific habitats of limestone and sinkholes. In this project, we apply museomics to determine the population structure and demographic history of these two species. We re-sequenced the genomes of all seven known M. gigantaea samples housed in museum collections as well as 24 M. lugubris samples from across its distribution. By comparing population structure between the two species, we investigate to what extent habitat dependence, such as in M. gigantaea, may affect population connectivity. Phylogenetic and population genomic analyses, as well as acoustic variation revealed that M. gigantaea consists of a single population in contrast to M. lugubris that shows much stronger population structure across the island. We suggest a recent collapse of M. gigantaea into its fragmented habitats as an explanation to its unexpected low diversity and lack of population structure. The deep genetic divergences between the M. lugubris populations on the Vogelkop region, in the western central range and the eastern central range, respectively, suggests that these three populations should be elevated to full species level. This work sheds new light on the mechanisms that have shaped the intriguing distribution of the two species within this family and is a prime example of the importance of museum collections for genomic studies of poorly known and rare species.

Introduction

What determines the build-up of biodiversity in space and through time is a long-standing question within biology. The accumulation of phenotypic and genetic differences between populations can only be generated through reproductive isolation that impedes genetic exchange between populations [1,2]. More explicitly, gene flow between diverging populations must be sufficiently limited so that genetic exchange does not exceed the accumulation of differentiation. Barriers underlying reproductive isolation, may differ markedly. They may be postzygotic and arise from genetic incompatibilities, which produce hybrid offspring that have either reduced fitness or are infertile [3–5]. Alternatively, barriers may be prezygotic and decrease the probability of mating events between populations, due to mating preferences, or through geographical (allopatric) or habitat barriers that separate different populations [4–6].

Mountains represent a classic example of geographical barriers both as physical barriers for populations but also because they harbour highly differentiated environments at different elevations. For sedentary lowland populations, mountains may represent unsurpassable barriers, which may over time lead to isolation and differentiation of separate lowland populations. Evidence for such montane barriers restricting gene flow between lowland populations are known from various organismal groups such as amphibians, spiders and coniferous trees [7–9]. Alternatively, extensive lowland valleys can also act as barriers to geneflow between populations adapted to high elevations. Lowland environments may be unsuitable for such mountain-adapted individuals, which over time become isolated on a series of mountaintops or “sky islands” [10,11] as known from some groups of birds, lizards and plants [12–16].

Related to this is the observation that within island systems such as on New Guinea older taxa are often found at higher elevations, while young lineages that are generally widespread, good dispersers and show little differentiation inhabit the lowlands (e.g. [16–19]. Such observations (mostly from island systems) have led to the formulation of the concept of taxon cycles, in which taxa pass through phases of expansions and contractions. The concept predicts that over time, taxa move into high elevation habitats either because they are outcompeted by new young generalist taxa in their original (lowland) habitats or because they specialise and adapt to new environments at higher elevations [17,18,20–22].

Recent work on the New Guinean avifauna has provided empirical evidence in favour of species originating in the lowlands from which they move into the highlands over time and become relictual specialists [16,22,23], although some colonisation from mountaintop to mountaintop has also been shown to occur [15]. In addition, recent Pleistocene speciation events on New Guinea are mainly the result of changes in habitat distributions due to climate fluctuations, as this has caused species with continuous distributions to become geographically fragmented [24–26]. Pliocene speciation events, on the other hand, are driven mainly by geological processes such as montane uplift, which is known to have caused barriers to gene flow [27–30].

The Melampittidae represents an example of an old passerine family with only two deeply diverged species each placed in monotypic genera. Their taxonomic affinities have been difficult to establish, but recent genetic results have placed the family as sister to the clade containing crows (Corvidae), white-crowned shrikes (Eurocephalidae), crested jayshrikes (Platylophidae) and shrikes (Laniidae) with an estimated divergence time from these at ca. 16.1 Mya [31,32]. One of the species, Melampitta lugubris (Lesser Melampitta) is relatively common at high elevations (1150–3500 m asl.), in accordance with the notion that older species tend to occupy higher elevations [21,22,33]. The other species, Megalampitta gigantea (Greater Melampitta) is only known from six localities at mid-elevations (650–1400 m asl.) scattered across New Guinea. Based on few field observations, it is considered to be sedentary and to have limited flight capabilities [34,35]. Within its range, M. gigantea is associated with very specific karstic habitats where it has been observed to spend considerable time nesting in narrow limestone sinkholes in which the birds have to climb in and out [34]. In contrast to M. lugubris the distribution of M. gigantea does not fit the general pattern that old taxa tend to occupy high elevation.

In this genomic study we determine the population structure and demographic patterns within the species M. lugubris and M. gigantea that each exhibit different levels of habitat connectivity. To answer this, we have re-sequenced 31 individuals at a median depth-of-coverage of 10.8 x and performed various population genetic (PCA, Admixture, diversity estimates) and phylogenetic analyses (mitochondrial and nuclear phylogenies). Based on the contemporary distributions of the two species we hypothesize that:

  1. The individuals of M. gigantea represent several distinct evolutionary entities/populations, as the species is a poor disperser and has a fragmented distribution across New Guinea where it is associated with specific karst limestone habitat with sinkholes.

  2. Individuals of M. lugubris represent a relatively cohesive group, yet with some population structure as deep lowland valleys may prevent gene flow between the various montane populations in the Central Range, the Huon mountains in the northeast and the Arfak mountains in the northwest.

Material & methods

DNA sampling, sequencing and read processing

In this study, we follow the taxonomy of the IOC World Bird List [36]. We sampled 24 individuals of Melampitta lugubris of which 22 were footpads from museum specimens and two were fresh tissue samples. Additionally, we sampled 7 individuals of Megalampitta gigantaea, which represent all known samples present in museum collections. One of these samples was the only available fresh blood sample for this species. The rest were footpads from historical samples (for a detailed list of samples and the museum collections in which they are stored see S1 Table). The work is mainly based on old museum specimens and therefore no ethical approval for animal research was required for this type of study. The few fresh tissue samples included in the study are from already preserved samples at natural history museums. Required permits were obtained through the Conservation and Environment Protection Authority (CEPA) of Papua New Guinea for research permits (99902749307 to K.A.J.) and export permits (017179 and 19069), which complied with all relevant regulations. The low number of modern tissue samples was due to the difficulty of acquiring fresh samples as both species are rather elusive.

DNA from fresh blood/tissue samples was extracted using Qiagen’s DNeasy Blood and Tissue kits. For DNA extraction and sequencing library preparation of historical samples, we followed a modified version of Meyer and Kircher [37] that proved suitable for avian museum samples [38]. In short, we extract DNA from toepad tissue mainly following the instructions from Qiagen for animal tissue with the addition of Dithiothreitol (DTT) to improve the ligation yield. During library preparation, we treat our samples with USER enzyme to reduce deamination patterns that are typical for fragmented DNA from historical or ancient samples [39]. For a detailed protocol see [38]. Whole genome re-sequencing was performed on Illumina NovaSeq 6000 machines on S4 flow cells going through 200 cycles with a read length of 2 x 100 bp at the National Genomics Infrastructure (NGI) in Stockholm. We consider this read length adequate, as our previous work has shown an average length of historical DNA fragments around 90–130 bp [38]. Up to 24 samples with four libraries each were multiplexed on a single flow cell lane yielding on average 108 reads per sample at a targeted depth-of-coverage of 10 x per individual (expected genome size: ∼ 1 Gb).

Sequenced reads were then polished using the reproducible Nextflow workflow nf-polish (https://github.com/MozesBlom/nf-polish) [40,41] (see S2 Table for specific github commits used). Besides providing a quality report through FastQC (v0.11.8, [42]), this pipeline performs multiple polishing steps, including deduplication (SuperDeduper, as part of HTStream v1.3.3, [43]), adapter- and quality-based trimming (Trimmomatic v0.39, [44]), read merging (PEAR v v0.9.11, [45]) and the removal of low-complexity reads, and calculates processing statistics after each step using seqkit (v0.16, [46]). See S2 File for the applied flags of each tool. Polished reads were then mapped onto a reference genome using nf-μmap (https://github.com/IngoMue/nf-umap [47]) applying bwa-mem2 (v2.2.1, [48]) as mapping algorithm (default settings, only adding read group information), merging and converting into bam format through samtools (v1.13, [49]) with defaults and evaluating the quality control after mapping with qualimap (v2.2.2d, [50]) and investigating damage patterns that are typical for historical DNA through DamageProfiler (v0.4.9, [51]). We used the hooded crow (Corvus cornix, Refseq GCF_000738735.5, [52]) as our reference genome as it represents the most closely related species with a high-quality chromosome level assembly [31].

Our evaluation of mapped reads against the Corvus cornix genome showed a median depth-of-coverage (DoC) for the nuclear genome of ∼ 10.822 x (min: 0.031 x, max: 31.324 x, SD: 7.513) and a median percentage of mapped reads at 89.5% (min 0.2%:, max: 95.8%, SD: 23.693). Detailed values for each individual and the mitochondria are listed in S1 Table.

Phylogenetic analyses on mitochondrial and nuclear DNA

To assemble the full mitogenome from our polished reads we used nf_mito-mania with default settings (https://github.com/FilipThorn/nf_mito-mania) [53]. In a first step, this workflow uses a random subset of 5 * 106 polished reads to assemble a de novo mitochondrial backbone using MITObim (v1.9.1, [54]). As a starting seed for MITObim we have used the Corvus cornix mitochondrial scaffold (Accession MT371428.1). Variant calling implemented in this pipeline filters sites with a depth-of-coverage below 20 or above three times the average depth-of-coverage across the whole mitogenome of each individual. The resulting consensus sequences of every individual were aligned using MAFFT (v7.407, [55], see S2 File for specific flags). An occasional artefact of MITObim where mitochondrial assemblies become longer than they are supposed to be resulted in overhanging sequences in some individuals. These overhangs were then cut out of the alignment after visual inspection using Geneious Prime 2023.0.4 so that the final alignment consisted only of overlapping reads (total length 17 112 bp including gaps), which were then used as input for RAxML-NG (v1.1.0, [56] using the GTR+G substitution model, 100 bootstrap replicates and ten parsimony-based randomised stepwise addition starting trees to generate a mitochondrial maximum likelihood phylogenetic tree. Mitochondrial assemblies were also forced into diploid variant calls to check for contamination in our samples. As mitochondria are haploid, heterozygote sites are not expected and could therefore be indicative of cross-contamination.

For the nuclear phylogenetic tree, we used the previously mapped .bam files excluding individuals with very low mean depth-of-coverage (n = 3, DoC < 4 x) to call variants for each individual using freebayes (v1.3.1-dirty, [57]). We used freebayes as variant caller in this step as it is suitable to use on non-model organism genomes and is more efficient when using samples with a low to intermediate depth-of-coverage [58]. Polymorphic sites were filtered based on their quality score (> 20), allelic balance (≥ 0.2), and minimum and maximum depth-of-coverage (3 x / 100 x). We also decomposed multiple nucleotide polymorphisms (MNPs) into single nucleotide polymorphisms (SNPs) and removed heterozygous positions and indels. These filtered .vcf files were then used as input files for the reproducible Nextflow workflow nf-phylo with default settings (https://github.com/MozesBlom/nf-phylo) [59]. This pipeline first creates consensus sequences for each individual and chromosome using samtools and bcftools (both v1.12, [49]) which are then combined into chromosomal alignments. Alignments were then divided into windows of different sizes (2000, 5000, 10000, 20000 base pairs, with 100 kbp between windows). Each window would undergo filtering and was included in a filtered concatenated alignment if at least half of the individuals were represented in more than 50% of an alignment column and if 80% or more of the individuals had missing data below 40%. The resulting filtered alignments were then used to generate phylogenetic trees for each window size using IQ-TREE (v2.0.3, [60]) while applying a GTR+I+G model. Using the same windows and model, a concatenated alignment of all autosomes and alignments for each chromosome were also generated and used to infer phylogenies with IQ-TREE. Additionally, summary coalescent phylogenies were generated based on all autosomal windows using ASTRAL3 (v5.7.8, [61,62]). Lastly, site and window concordance factors (sCF and gCF) were being calculated for all inferred phylogenies using IQ-TREE to complement bootstrapping. All implemented flags and filters are listed in S2 File.

Population structure and genetic diversity

To quantify population structure and to estimate genetic diversity between samples, we used a genotype likelihood approach as implemented in ANGSD (v0.938) as this is better suited for low coverage data and would allow us to include all of our samples [63]. Specific commands and the filters used are explained in the S2 File. The filters we used for admixture and principal component analyses (PCAs) were slightly different from those used to calculate nucleotide diversity, heterozygosity and Tajima’s D. As we did not have an ancestral genome available, we used the Corvus cornix reference genome as ancestral sequence and folded the site frequency spectra (SFS). PCAs were performed through PCAngsd [64] and plotted with custom R scripts through RStudio (v 2023.03.0 build 386, R version 4.1.1, [65,66]). Admixture analyses to determine population structure were run through NgsAdmix [67] running up to K = 10 with ten replicates for each K and visualised with custom R scripts. Individual heterozygosity was estimated by generating a site frequency spectrum for each individual and dividing the number of sites with one derived allele by the total number of sites as performed by e.g. Hansen et al. [68]. Using SFS for each species, nucleotide diversity and Tajima’s D were both estimated for each chromosome as well as in 20 kb windows sliding in steps of 10 kb using the thetaStat command. We divided the pairwise theta estimator (tP) by the total number of Sites (nSites) of each chromosome/window to calculate nucleotide diversity. Statistical significance of differences in heterozygosity and nucleotide diversity between the two species was checked using Welch’s t-test after verifying normal distributions and inequal variances within the data. Lastly, we also calculated absolute divergences (Dxy) between different population pairs by using allele frequency estimates (maf files) from ANGSD as input. First, we calculated allele frequencies within a dataset containing all individuals to obtain a list of polymorphic sites to be analysed even if a site may be fixed in one of the populations. Next, we calculated allele frequencies for each population (see S6 Table for each included individual). Different population pairs were then compared and Dxy was calculated using the R script calcDxy (available from https://github.com/mfumagalli/ngsPopGen/blob/master/scripts/calcDxy.R). Compared to other methods, this tool may provide underestimated values of Dxy which should therefore only be compared between the groups within this study.

Estimation of effective population sizes through time and divergence times

To estimate effective population sizes through time, we used Pairwise Sequentially Markovian coalescent (PSMC) [69] (for details on the method see S1 File). As an estimate of the neutral genomic mutation rate per generation we used 4.6*10−9 as obtained in a study of the collared flycatcher Ficedula albicollis [70]. We set the estimated generation time for M. lugubris to 3.90 years and for M. gigantea to 4.58 years [71]. The parameters for the PSMC analysis were set to “-N30 -t5 -r5 -p 4 + 30*2 + 4 + 6 + 10” following Nadachowska-Brzyska et al. [72]. The authors observed no significant change in curve shape when modifying the atomic vectors parameter (-p) and applied the same settings to several different avian species. We only ran PSMC for the two samples of M. gigantea with the highest depth-of-coverage and for each of the five identified clusters within M. lugubris (West, Central, East, Huon, Southeast) with 100 bootstrap replicates per individual. False negative rates (FNRs) were adjusted based on depth-of-coverage. If depth-of-coverage was higher than 15 x, FNR was kept at 0. However, if individual A had higher depth-of-coverage than individual B, then individual B would have an FNR of 0.1 * x, where x is the depth-of-coverage of individual A divided by depth-of-coverage of individual B.

To estimate divergence times between the two species, but also between the different subpopulations of M. lugubris, we first ran F1-hybrid PSMC (hPSMC, [73] using the same parameters as for the previous PSMC analyses and implementing 100 bootstraps replicates. Additionally, we estimated mitochondrial divergences within and between the two species and subpopulations of M. lugubris using the previously generated mitochondrial alignment and its nucleotide diversity matrix as obtained through Geneious Prime 2023.0.4. We applied a simple average divergence rate between two avian lineages for the whole mitochondria at 1.8% per million years as estimated by Lerner et al. [74] which refines the “2% rule” by estimating divergence rates for several mitochondrial regions and genes rather than just cytochrome b [75]. We compared these divergence time estimates with those obtained from the hPSMC analyses.

Acoustic recordings and analysis

Acoustic recordings of 10 M. gigantea individuals and 28 M. lugubris individuals were obtained from an online repository of avian vocalizations (https://xeno-canto.org/), which covered different locations across New Guinea. We included all types of vocalisations ‐ songs, calls and vocalisations of an unknown type in the analysis, unless the function of the vocalisation was specified by the recordist (eg: alarm). This is due to the high uncertainty in estimating the type of vocalisation in M. lugubris, and visual comparison between vocalisations classified as ‘songs’ versus ‘calls’ between individuals recorded in the same location, often showed that they were the same. The vocalisations of each individual (median = 9 vocalisations/individual) were measured by a single author (SR) using the Luscinia sound analysis program (version 2.17.11.22.01, [76]. Each vocalisation was visualised using a Gaussian windowing function with the following spectrogram settings: 13 kHz maximum frequency, 5 ms frame length, 221 spectrograph points, 80% spectrograph overlap, 80 dB dynamic range, 30% dereverberation, and 50 ms of dereverberation range. Elements, which are the smallest unit of a vocalisation, were measured as continuous sound traces and then grouped into syllables within each vocalisation (each vocalisation contained only one syllable) (see S14 Fig)

The structures of the acoustic vocalisations were then compared to each other using the dynamic time warping algorithm (DTW) in Luscinia. The DTW algorithm calculates the optimal alignment between all the vocalisations i.e., syllables in the dataset, based on multiple acoustic features, and then provides a final output of a syllable dissimilarity matrix [76]. We followed the same settings used in Wheatcroft et al. [77] that has provided reliable grouping outputs for other songbird species: compression factor = 0.0001, time SD weighting = 1, maximum warp = 25%, minimum element length = 25 samples; with the following weightings for time (5), mean frequency (1), mean frequency change (1), normalised mean frequency (1). All other acoustic features were left at the standard values. In order to examine acoustic variation in syllables, we converted the syllable dissimilarity matrix obtained from the DTW process, into 10 principal components using non-metric multidimensional scaling. These 10 components preserved the overall dissimilarity between the syllables well (kruskal stress value = 0.002, values < 0.1 are usually considered good [76]) and were used to infer acoustic variation among the two species.

Results

During contamination control using mitochondrial assemblies, we observed an increased amount of heterozygous sites across the libraries in 6 individuals (S3 Table). Upon manual inspection using Geneious Prime we found that these heterozygote positions mostly appear in blocks and often within the same regions. This suggests that they were in fact nuclear mitochondrial sequences (NUMTs) that were wrongly mapped onto the mitochondrial genome instead of being a result of contamination. We also observed that non-reference alleles often appeared at a lower frequency (98.093% of heterozygote sites had a reference allele frequency > 0.5, median reference allele frequency across all heterozygote sites at 0.874) and therefore disappeared during consensus calling, as the more frequent allele gets chosen during this step. Nonetheless, we manually excluded two regions from all samples with blocks (in total 5 700 bp out of the entire alignment’s 17 112 bp) of heterozygote sites shared across the majority of individuals. The remaining 11 412 bp were used to generate the mitochondrial phylogenies.

Phylogenetic analyses on mitochondrial and nuclear DNA

We found high congruence between phylogenies built from mitochondrial and nuclear genomes (Figs 1A and S1). Different window sizes and summary coalescent vs concatenated nuclear phylogenies also had little effect on the topology. We recovered three main clusters within M. lugubris that correspond to the geographic location of the samples on an east to west axis (Fig 1). These clusters also align with previously described subspecies of M. lugubris [35]. The first cluster within M. lugubris consists of individuals inhabiting the Birds-Head of north-western New Guinea as well as an individual in the westernmost part of the Central Range. The next cluster inhabits the western and central parts of the Central Range of New Guinea, and the third cluster inhabits the eastern and south-eastern section of the Central Range as well as the isolated outlying Huon mountains. M. gigantaea, on the other hand, shows very short branch lengths between individuals compared to M. lugubris indicating less diversity within this species. Relationships within M. gigantaea are also in accordance with the geographical locality of the samples.

Fig 1.

Fig 1

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A Distribution map and sampling sites of M. gigantaea (orange distribution) and M. lugubris (blue distribution), subclusters of M. lugubris are also coloured differently. Shapefiles for administrative boundaries were obtained from geoBoundaries [78] under a CC BY license, with permission from Dan Runfola, original copyright CC BY 4.0 (2020), the map was created using QGIS [79]. B Nuclear phylogeny of Melampittidae highlighting the subdivisions within M. lugubris (West, Central, East, Huon, Southeast), the tree was constructed using IQ-TREE on concatenated 5 kbp window alignments with a GTR+I+G substitution model, support values next to the main branches show bootstraps/site concordance factors (sCF)/window concordance factors (wCF).

Population structure and genetic diversity

As observed in the phylogenies, we recover a similar pattern in the PCAs (Fig 2A), heterozygosity (Figs 2B and S13), nucleotide diversity (π, S4 Table and S2 Fig), absolute divergence (Dxy, S6 Table) and admixture (Fig 2C) where M. gigantaea exhibits lower genetic diversity in compared to M. lugubris. For M. lugubris Tajima’s D was consistently negative with a mean value of -0.902 (SD: 0.126, median: -0.897, S5 Table and S3 Fig) which is indicative of either population expansion or a selective sweep. In M. gigantaea values for Tajima’s D were slightly above zero in the range of 0–0.2 (mean 0.103, SD: 0.040, median: 0.112, S5 Table and S3 Fig). Positive values of Tajima’s D could indicate a reduction in population size or balancing selection acting, however as the values are so close to zero the population may just evolve neutrally. In the PCA (Fig 2A), PC1 separates the two species, afterwards M. gigantaea remains closely clustered up to PC4, while subgroups corresponding to geographic localities make up clusters within M. lugubris (S4 Fig). The two distinct clusters of M. lugubris on PC2 (Fig 2A) separate eastern New Guinean populations and northwestern New Guinean populations as also observed in the phylogenetic tree (Fig 1B). Both heterozygosity and nucleotide diversity were significantly lower in M. gigantaea than in M. lugubris. Although we observed a clear trend of increasing heterozygosity with higher depth-of-coverage, the slopes for each population were similar and consistently higher in all but one population of M. lugubris (S5 Fig). Admixture analysis revealed no substructure within M. gigantaea from K = 2 to K = 7. For M. lugubris the observed clusters between K = 2–6 align with the clusters observed in the phylogenetic trees and in the PCAs. Further subdivisions within the main clusters of M. lugubris at higher values of K are also corresponding to the populations’ geographical location. Estimates of mean absolute divergence (Dxy) per-site across the entire genome ranged from a minimum of 0.0005 between the geographically most distant M. gigantaea individuals to a maximum of 0.012 between the two species. Dxy between different pairs of (sub)populations within M. lugubris (min: 0.002, max: 0.005) was higher than within M. gigantaea and about half as much as the divergence between M. gigantaea and M. lugubris in the most divergent pair of M. lugubris (West vs. East). An overview of all pairwise comparisons is given in S6 Table.

Fig 2. Genetic diversity and population structure in Melampittidae.

Fig 2

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A) PCA showing the first two principal components for both species (M. gigantaea in orange, subclusters of M. lugubris in shades of blue/purple). B) Admixture analysis from K = 2 to K = 6 C) Heterozygosity for all individuals between both species.

Estimation of effective population size in time and divergence times

PSMC curves (Fig 3) for samples from the same populations had similar shapes, but not entirely overlapping as depth-of-coverage varied between samples. Within M. lugubris the shape of the curves varied, but most of this variation could be ascribed to population specific events. In M. gigantaea, we observe an effective population size peak at around 200 Kya followed by a steady decline in effective population size up until around 40 Kya.

Fig 3.

Fig 3

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PSMC plots for two representative individuals of A) M. gigantaea (solid line: AMNH 590764, dashed line: PNGNM 26694) B) M. Lugubris western population (solid line: AMNH 293751, dashed line: AMNH 293748) C) M. lugubris central population (solid line: AMNH 340368, dashed line: Bishop 98503) D) M. lugubris eastern population (solid line: Bishop 100613, dashed line: Bishop 55612), E) M. lugubris Huon population (solid line: NHMD 615247, dashed line: NHMD 616019) F) M. lugubris southeastern population (solid line: Bishop 54821, dashed line: AMNH 590750) Thinner lines depict the curves of bootstrap runs.

The divergence time obtained from hPSMCs curves for the split between M. gigantaea and M. lugubris was estimated to about 10 mya (S6 Fig). Splits between subgroups within M. lugubris were estimated more recently with the split between Western+Vogelkop and Eastern populations at around 4–5 mya (S7 Fig) and between Western and Vogelkop populations at about 3–4 mya (S8 Fig). The next divisions within Eastern M. lugubris populations (East, Huon and Southeast) happened at similar times around 1 mya (S8 and S9 Figs).

Mitochondrial divergences were, as expected, highest for comparisons between M. gigantaea and M. lugubris and its subpopulations at a range of 9.8–13.3%. Divergence within M. gigantaea was also lower (mean 0.912%) than within M. lugubris (mean 4.979%) or even in some of its subpopulations. (For an extensive table with all comparisons of mitochondrial divergence see S11 and S12 Figs). Divergence times obtained by assuming an average rate of mitochondrial divergence of 1.8% were 5.5–7.4 mya for the split between M. gigantaea and M. lugubris, 4.1–6.2 mya for splits between Western+Vogelkop populations from Eastern populations of M. lugubris and Western populations from Vogelkop populations at 4–4.2 mya. Subdivisions within the Eastern populations were estimated at 0.2–2.9 mya.

Acoustic recordings and analysis

The first ten principal components collectively explained 97% of the variation in vocalisations across the two Melampitta species. PC1, which explained 44.5% of the variation in all vocalisations, was more varied for M. lugubris (standard deviation (SD) = 0.086) compared to M. gigantaea (SD = 0.036). The same was true for PC2, where the standard deviation was once again higher for M. lugubris (SD = 0.11) compared to M. gigantaea (SD = 0.017). These results show that M. lugubris has greater acoustic diversity than M. gigantaea (Fig 4), highlighting the need for a more in-depth examination of life history trait variation between the two species. In addition to obtaining higher quality recordings, future work should examine whether this preliminary observation of acoustic variation corresponds to different populations.

Fig 4. Acoustic variation across species.

Fig 4

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Principal component space (PC1-2) of vocalisations from M. gigantaea and M. lugubris. PC1 and PC2 scores are averaged within individuals and triangles represent species centroids. Ellipses contain 95% of vocalisations of each species. The significant outlier within M. lugubris may represent an odd vocalisation that is not directly comparable with the other vocalisations included here. Note that vocalisations for M. gigantaea were only available from three localities (the Fakfak mountains in the Bird’s Neck, a locality in the southern Bird’s Neck and Tabubil in the central highlands) and vocalisations for M. lugubris were only available from two of the three distinct clades (samples were available from the western and central but no vocalisation data was available from the eastern and Huon populations).

Discussion

The formation of the avifauna on New Guinea largely follows the predictions of taxon cycles [15,16] whereby new species form in or colonise through the lowlands and over time move upwards and become relictual at high elevations. The family Melampittidae is a species-poor old endemic lineage of New Guinea [31]. The family includes two extant species of which one (Melampitta lugubris) follows the general taxon-cycle expectation in that it is an old lineage that inhabits montane forests of New Guinea. The other species, Megalampitta gigantea, however, has a distribution associated with specific karst habitats at lower elevations and in foothills [35].

Our divergence time estimates suggest that M. gigantea and M. lugubris diverged from each other in the Miocene (at approximately 10 Mya based on hPSMC results), which is slightly younger than the divergence time estimated by Jønsson et al. [80] and slightly older than the divergence time estimated by McCullough et al. [81]. The three main populations of M. lugubris (Fig 1B) diverged from each other in the early Pliocene (at approximately 4–5 Mya based on hPSMC curves). A Pliocene divergence of M. lugubris populations coincides with major uplift of various mountain regions on New Guinea [82–84], which may have shaped the present population structure of M. lugubris. The distributional pattern of populations of M. lugubris, with one distinct Vogelkop population and a division of an eastern and a western population along the central mountain range, is also a pattern similar to that found in other New Guinean mountain birds with Pliocene divergences [30,85,86].

The PSMC curves of the three main populations of M. lugubris differ (Fig 3), yet with a general trend of increasing population sizes towards the present. The exception to this is the population of the Huon mountains, which shows a continuous decrease in population size since approximately 100 Kya. Our interpretation is that eastern and south-eastern populations of the Central Range have maintained continuous gene flow, while the connectivity with the Huon population was broken or at least severely reduced as this population became isolated in the outlying Huon Mountain range.

Given a presumed poor dispersal capacity [34] and a patchy distribution at mid-elevations, we initially hypothesised that M. gigantea would exhibit a clear population structure. However, contrary to expectations, all our samples of M. gigantea, from localities scattered across New Guinea, cluster tightly together genetically (Figs 1 and 2). Analysis of vocalisations also shows a similar pattern, as M. gigantea exhibits less acoustic diversity compared to M. lugubris (Fig 4). This is fascinating and difficult to explain. Below, we discuss three scenarios that may provide possible explanations for these patterns. First, it is possible that continuous migration (or high rates of juvenile dispersal) of M. gigantea individuals maintains contact and gene flow between populations. However, an exclusively ground-dwelling lifestyle and the lack of long-distance flight capabilities, suggested by its morphology and field observations contradict this scenario [34,35]. Second, it is possible that their presently known fragmented distribution does not properly reflect their actual distribution, which may be more extensive [35,87]. Karst regions are generally species-poor in comparison to the species-rich tropical forests of New Guinea and such localised karstic areas dispersed throughout New Guinea may therefore have commanded less attention by ornithological surveys. Finally, it is possible that M. gigantea once had a wider more continuous distribution and that a recent decline has left scattered populations in small pockets of Karst habitat. The PSMC analyses support this scenario by showing that the population size of M. gigantea has dropped dramatically within the last 200 Ky (Fig 3). The fact that M. gigantea is highly adapted to a very specific habitat type (nesting in deep holes in karst limestone that they have to climb out of [33] is, however, difficult to reconcile with this scenario. However, one may speculate that M. gigantea in the past had broader habitat preferences not only restricted to the present karst limestone habitats. Perhaps during the last 200 Ky, increased competition from other species forced M. gigantea to retract to a particular low-diversity habitat type, leaving behind the scattered distribution that we see today. Overall, we find it most plausible, that M. gigantea had a larger and more continuous distribution in the past, yet we acknowledge that the present distribution may be underestimated. Additional ornithological surveys to suitable habitats may, thus, reveal further M. gigantea populations.

Conclusions

In this study, the rather surprising population structure of the two species of an old New Guinean avian family have been elucidated by genomic data largely obtained from historical museum collections. While the population structure of Melampitta lugubris is similar to those found in other mountain birds of New Guinea with similar age, the population structure of Megalampitta gigantea is intriguing. The study is an example of how intrinsic properties, such as demographic history as exhibited by M. gigantea, may cause their population dynamics to deviate from general biogeographical predictions. The study is also an example of how important museum collections are for increasing the knowledge of rare taxa that occur in remote regions. The levels of divergence between the three major populations of M. lugubris are well above those at which ornithologists would normally assign species rank. Consequently, we tentatively propose that these three populations should be elevated to species rank, Melampitta lugubris (Schlegel, 1871) in the Vogelkop region, Melampitta rostrata (Ogilvie-Grant, 1913) in the western central range and Melampitta longicauda (Mayr & Gilliard, 1952) in the eastern central range.

Supporting information

S1 Fig. Mitochondrial phylogeny for all individuals based on an alignment of mitochondrial consensus sequences.

The tree was constructed using RaxML-NG applying a GTR+G substitution model.

(TIF)

pone.0293715.s001.tif (620KB, tif)
S2 Fig. Individual nucleotide diversity (π) is significantly lower in M. gigantaea (orange) than in M. lugubris (blue).

The applied statistical test was a Welch’s two sample t-test for unequal variances.

(TIF)

pone.0293715.s002.tif (277.8KB, tif)
S3 Fig. Tajima’s D across all chromosomes.

Chromosomes are divided into macrochromosomes (> = 40 Mbp), intermediate chromosomes (> = 20 Mbp, < 40 Mbp) and microchromosomes (< 20 Mbp). Values are consistently negative across most of each chromosome in M. lugubris (blue) and slightly positive in M. gigantaea (orange).

(TIF)

pone.0293715.s003.tif (2.3MB, tif)
S4 Fig. Principal components 1 to 4 describing divisions within subpopulations of M. lugubris (shades of blue/purple) while M. gigantaea (orange) remains a tight cluster.

Weyland represents one individual (AMNH 301956) that was collected between our Western and Central populations and is assigned to the Western population in most other analyses.

(TIF)

pone.0293715.s004.tif (588.9KB, tif)
S5 Fig. Heterozygosity shows a correlation with increasing depth of coverage (DoC).

Slopes are similar between populations/species and M. gigantaea still shows lower heterozygosity than most M. lugubris populations when comparing individuals with similar DoC. To fit regression lines, we applied Kendall’s rank correlation coefficient as it is recommended for smaller sample sizes containing outliers [Kendall MG. A New Measure of Rank Correlation. Biometrika. 1938;30(1/2):81–93.].

(TIF)

pone.0293715.s005.tif (539.3KB, tif)
S6 Fig. PSMC for M. lugubris (blue) and M. gigantaea (red) and their hybrid PSMC curve (purple) to show the divergence time between the two species.

(TIF)

pone.0293715.s006.tif (763.1KB, tif)
S7 Fig. PSMC for M. lugubris NHMD616019 from Huon (red) and M. lugubris B98503 from Central New Guinea (blue) and their hybrid PSMC curve (purple) to show the divergence time between Eastern populations and Western + Central populations of M. lugubris.

(TIF)

pone.0293715.s007.tif (880.7KB, tif)
S8 Fig. PSMC for M. lugubris B98503 from Central New Guinea (red) and M. lugubris AMNH293751 from Western New Guinea (blue) and their hybrid PSMC curve (purple) to show the divergence time between Central populations and Western populations of M. lugubris.

(TIF)

pone.0293715.s008.tif (965.7KB, tif)
S9 Fig. PSMC for M. lugubris NHMD616019 from Huon (red) and M. lugubris AMNH590750 from the Southeast (blue) and their hybrid PSMC curve (purple) to show the divergence time between Huon populations and Southeastern populations of M. lugubris.

(TIF)

pone.0293715.s009.tif (1.2MB, tif)
S10 Fig. PSMC for M. lugubris NHMD616019 from Huon (red) and M. lugubris B100613 from the East (blue) and their hybrid PSMC curve (purple) to show the divergence time between Huon populations and East populations of M. lugubris.

(TIF)

pone.0293715.s010.tif (1.1MB, tif)
S11 Fig. Mitochondrial divergence matrix showing the minimum (first value) and maximum (second value) for each comparison of populations and species, M. gigantaea (Meg), M. lugubris (Mel), Eastern populations (EPops) include the subpopulations East, Southeast and Huon.

(TIF)

pone.0293715.s011.tif (1MB, tif)
S12 Fig. Mitochondrial divergence matrix showing the mean divergence for each comparison of populations and species, M. gigantaea (Meg), M. lugubris (Mel), Eastern populations (EPops) include the subpopulations East, Southeast and Huon.

(TIF)

pone.0293715.s012.tif (1,017.1KB, tif)
S13 Fig. Heterozygosity between M. gigantaea and populations of M. lugubris. M. gigantaea is shown in orange, populations of M. lugubris in shades of blue/purple. Statistical significance is only shown for significant differences between pairwise comparisons of M. gigantaea and each population of M. lugubris applying Welch’s t-test.

(TIF)

pone.0293715.s013.tif (505.1KB, tif)
S14 Fig. Representative acoustic vocalisations of M. gigantaea and M. lugubris.

For each species, vocalisations from four different individuals are depicted. The top panel illustrates how vocalisations were measured in the acoustic software Luscinia: The user manually traces out the elements, the smallest unit within each vocalisation (in green), after which they are grouped into syllables (in red). Each vocalisation typically contains only 1 syllable for both species. The dynamic time warping algorithm in Luscinia creates a matrix of syllable dissimilarities using multiple frequency and time measurements that are extracted from these measured syllables.

(TIF)

pone.0293715.s014.tif (712.4KB, tif)
S1 Table. List of samples.

Additional information such as sample locality, museum voucher, tissue type, etc. are included. The table also shows mapping statistics (e.g. mapping percentage and depth-of-coverage) for each individual.

(XLSX)

pone.0293715.s015.xlsx (14.9KB, xlsx)
S2 Table. Used github commits when running Nextflow workflows.

(XLSX)

pone.0293715.s016.xlsx (8.8KB, xlsx)
S3 Table. Filtered individuals with heterozygous blocks in mtDNA.

(XLSX)

pone.0293715.s017.xlsx (8.4KB, xlsx)
S4 Table. Individual nucleotide diversity (π).

Sheet 1 (Individual) contains statistics calculated for each species using i) all chromosomes and ii) only autosomes (aut.). Sheet 2 (Species-wide) contains estimates averaged across i) all chromosomes and ii) only autosomes (aut.).

(XLSX)

pone.0293715.s018.xlsx (10.2KB, xlsx)
S5 Table. Species-wide Tajima’s D.

Values averaged across i) all chromosomes and ii) only autosomes (aut.).

(XLSX)

pone.0293715.s019.xlsx (9.1KB, xlsx)
S6 Table. Mean per-site Dxy etimates.

Sheet 1 (Dxy) shows pairwise comparisons between all major population splits as well as an estimate within M. gigantaea. Sheet 2 (Populations) lists the individuals that were included in each population.

(XLSX)

pone.0293715.s020.xlsx (13.7KB, xlsx)
S1 File. Details on PSMC methodology.

(DOCX)

pone.0293715.s021.docx (31.4KB, docx)
S2 File. Codes and parameters settings.

(DOCX)

pone.0293715.s022.docx (34.9KB, docx)
S3 File. List of adapters that were removed during trimming.

(DOCX)

pone.0293715.s023.docx (12.1KB, docx)

Acknowledgments

We thank all the staff and field assistants that facilitated fieldwork in Papua New Guinea. Notably, the Binatang Research Center and local communities in the YUS conservation area from the villages of Towet and Yawan. We are also grateful for the assistance provided by the PNG National Museum and Art Gallery and the Conservation and Environment Protection Authority (CEPA) of Papua New Guinea for research permits (99902749307 to K.A.J.) and export permits (017179 and 19069). We would like to acknowledge the following museum collections and their managers that have generously provided us with tissue samples for this study: American Museum of Natural History, New York, USA (Paul Sweet, Pete Capainolo, Tom Trombone and Brian T. Smith); Bernice Pauahi Bishop Museum, Honolulu, USA (Molly Hagemann); Natural History Museum, London, UK (Robert Prys-Jones, Hein van Grouw and Mark Adams); Papua New Guinea National Museum and Art Gallery, Port Moresby, Papua New Guinea; Swedish Museum of Natural History, Stockholm, Sweden (Ulf Johansson) and the Natural history museum of Denmark, Copenhagen, Denmark (Peter Hosner). Lastly, we would like to thank Nikolay Oskolkov for assistance with bioinformatic analyses as part of the drop-in services and the Swedish Bioinformatics Advisory Program of the National Bioinformatics Infrastructure Sweden (NBIS).

Data Availability

Sequences that were obtained as part of this study have been deposited at the European Nucleotide Archive (ENA) under accession numbers ERS17855383 - ERS17855413, project accession PRJEB72101. Distributional data for both Melampittidae species were extracted through the IUCN’s red list webpage (https://www.iucnredlist.org/). Avian distributional data on the IUCN red list are originally provided by BirdLife (https://www.birdlife.org/). Shapefiles for administrative boundaries of Indonesia and Papua New Guinea were obtained from geoBoundaries (https://www.geoboundaries.org/). Topographic data of New Guinea was extracted from the United States Geological Survey (https://www.usgs.gov). All codes and software version numbers used for this article are mentioned within the paper and described in more detail in the supplementary material.

Funding Statement

The project has been funded by the Swedish Research Council (Vetenskapsrådet, https://www.vr.se/) grant 2019-03900 (MI), the Villum Foundation, young Investigator Programme (https://veluxfoundations.dk/) project No. 15560 (KAJ), Albert & Maria Bergström Foundation 2022 (IM), Alice and Lars Siléns fund 2021+2022 (IM) and Riksmusei Vänner 2022 (http://riksmuseivanner.se/) (IM). Computations were enabled by resources provided by the National Academic Infrastructure for Supercomputing in Sweden (NAISS) and the Swedish National Infrastructure for Computing (SNIC) at UPPMAX partially funded by the Swedish Research Council through grant agreements no. 2022-06725 and no. 2018-05973. Furthermore, the authors acknowledge support from the National Genomics Infrastructure in Stockholm funded by Science for Life Laboratory, the Knut and Alice Wallenberg Foundation and the Swedish Research Council, and SNIC/Uppsala Multidisciplinary Center for Advanced Computational Science for assistance with massively parallel sequencing and access to the UPPMAX computational infrastructure. These funding sources had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Decision Letter 0

Sven Winter

11 Dec 2023

PONE-D-23-33496Species-specific dynamics may cause deviations from general biogeographical predictions – evidence from a population genomics study of a New Guinean endemic passerine bird family (Melampittidae).PLOS ONE

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Reviewer #2: Yes

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Reviewer #1: Interesting study, in which the authors seem to have managed to produce good quality data from museum samples. However, in terms of the analyses, and interpretation and presentation of results, the study/paper needs in my opinion substantial improvement. See my comments below. The major comments are indicated with 'MAJOR'.

Abstract:

MAJOR: 50% of the abstract is intro, 30% methods, with effectively only one sentence describing the results. If in the conclusion you claim that certain populations should be raised to species level, this should be mentioned in the abstract. Also, refer back to the title: which species-specific dynamics? And how do the study species deviate?

Introduction

Line 68-70: what would young lineages be better adapted to lowland habitats?

Line 85: why ‘in accordance’? Why do you assume that the Lesser M. is an old lineage?

Line 91-93: Are mid-elevations to be considered here as ‘lowland’?

Line 95-98: specify here number of individuals sequenced, and to which depth. Also specify roughly which kind of analyses.

Line 96: MAJOR: while genomic data allows indeed to determine present-day population structure with the methods employed in this study (pca, phylogeny, admixture), but more methods are needed to determine how ‘habitat connectivity across space and time has shaped differentiation’

Line 99-106: Wouldn’t you expect a priori the opposite? Namely, the high-elevation species (Lesser. M) to have low connectivity between mountain-tops and hence to have population structure?

Methods

Line 109-117: why so few modern samples? Are samples difficult to acquire?

Line 126: 2x100bp read length is deliberate?

Line 127-128: How much data per sample? Expected genome size, targeted depth?

Line 129-138: While I appreciate the efforts made to make the work reproducible using a pipeline, the authors still need to describe here the settings of each software used by the pipeline (e.g., samtools and bwa). For instance, which base quality, mapping quality, and alignment score thresholds were used? Furthermore, all software used by the pipeline, needs to be mentioned here, and referenced.

Line 142: Because the authors so far did not mention the mean sequencing depth, it is hard to evaluate whether these thresholds are appropriate. The mitogenome depth is usually much higher than the nuclear depth, and hence 20x could be low. Could the authors present a summary of mean coverage of both the nuclear and mito genome?

Line 152: Mitochondrial genomes (!) might be effectively haploid, but in reality they are highly polyploid: each cell contains hundreds or thousands of copies which are independently proliferating, which may also cause heterozygous calls.

Line 155: for museum samples, a depth below 4x is not too bad. Why freebayes? Other genotyping software (like bcftools, GATK or ANGSD) have been reported to have higher accuracy.

Line 160-163: Again, it does not suffice to only mention the pipeline. Even though this information could be looked up at the Github page, it needs to be specified here which settings were used to run IQtree2 and ASTRAL.

Line 162: Running IQtree (maximum likelihood approach) on an input dataset of SNPs (diploid, recombining loci), violates underlying assumptions of ML tree building.

Line 162: Better to use here the word ‘locus tree’ rather than ‘gene tree’

Line 165: MAJOR: which of the below mentioned statistics estimate levels of differentiation? In reality, all statistics (nucleotide diversity, heterozygosity and SFS) estimate genetic diversity (with Tajima’s D testing for neutrality). PCA and admixture show population structure, but do not estimate the level of population differentiation.

Line 170: MAJOR: With only 6 individuals per population, does it really make sense to try to reconstruct the SFS?

Line 173: Admixture analyses were run…

Line 178: MAJOR: Does it really make sense to calculate Tajima’s D for an entire chromosome? This test is designed for a single, non-recombining locus.

Results

Line 232-247: move to methods section.

Line 295-317: a population split of 4-5 Mya, would this not imply these populations are in fact different species? Else, is it possible that the PSMC settings inflate the divergence time estimate?

Discussion

Line 340: how do we know it is an old lineage? When does a lineage quality as ‘old’?

Line 349: MAJOR: a population split of 4-5 Mya, would this not imply these populations are in fact different species? Else, is it possible that the PSMC settings inflate the divergence time estimate?

Line 368: See my previous comment for line 99-106: Wouldn’t you expect a priori the opposite? Namely, the high-elevation species (Lesser. M) to have low connectivity between mountain-tops and hence to have population structure?

Conclusion:

Line 396: Which intrinsic properties?

Line 399-401: MAJOR: Perhaps I overread, but how did you estimate levels of divergence, other than using hPSMC to estimate divergence times? As mentioned in a previous comment, a split time of 4-5 Mya would indeed suggest species status. But to be better able to evaluate this claim, it would be useful if the authors would estimate levels of genetic divergence between the populations (i.e. Dxy).

Figures

Figure 1. MAJOR colour coding of eastern populations does not correspond between A and B. Add to the figure legend (following the species names) the elevation ranges of the two species. Throughout the text, the authors could make clearer (or reminder the reader) that the Lesser M. occurs at high elevation, and the Greater M at mid elevation. In the caption of figure 1, specify which software/method has been used to construct the phylogeny.

Figure 2. MAJOR These analyses only qualitatively assess population structure, but do not provide quantitative estimates (e.g. Dxy or Fst). The legend (e.g. Vogelkop, Weyland, etc) does not correspond with the labels of the admixture plot. Regarding the admixture plot: I find it suspicious that there is no evidence for admixture whatsoever. Especially between the central populations, which are geographically close to each other, should there not be any exchange? Regarding the He-plot, could you present He per population (e.g., west, east, central) rather than per species?

Figure 3. MAJOR Since PSMC is used to evaluate split times, why not plot the curves on top of each so that it is better possible to evaluate when the lines diverge? It should also be made more clear which populations/species each tile represents (the sample names are not informative). Where are the bootstraps? In figure 3c, one of the samples need to be corrected, in order to overlap (which they clearly do, just need to be corrected for difference in depth). Where is the hPSMC plot?

Reviewer #2: This study provides a novel genetic analysis of a so-far poorly-studied passerine bird family (Melampittidae). It is a huge merit of the team that they managed to analyse all seven known museum specimens of Megalampitta gigantaea. Although this species is currently not considered to be under threat (least concern, IUCN), there is a considerable knowledge gap on that species’ distribution and intraspecific diversification (e.g. whether it comprises more than one evolutionary significantly unit). This emphasizes the relevance of museomics for current biodiversity research. The research team is known for their strong expertise of collection-based genetics/genomics and the use (and development) of specific protocols for historic DNA analysis. The results shade new light on the cryptic diversification in this group, e.g. despite being considered a monotypic species M. lugubris turned out to comprise several distinct genetic lineages restricted to different mountain ranges on New Guinea. This is a fine study that is highly recommendable for publication. In the following I am commenting on a few aspects of the study that should be outlined or explained in a little more detail in a revised manuscript.

l. 63: “older taxa are often found at higher elevations, while young lineages that are generally widespread, good dispersers and show little differentiation inhabit the lowlands”. Following a general introduction on mountain biodiversity and evolutionary patters in mountain specialists, this reads as if it was a generalized statement. However, this seems to be a rather characteristic pattern on New Guinea (see l. 71), “island systems” (l. 65) or in other tropical ecosystems, whereas in other big mountain systems of the Earth, this does not seem to be the common pattern. For the Himalayas (and the Andes) see Fjieldsa et al. (2012: Fig. 1) with both mountain systems showing highest richness of both youngest and oldest species. Furthermore, for the Himalayas Price et al. (2014) stated that „we found that the average age of separation of species in the assemblage declines monotonically with elevation, rather than being lowest in the most species-rich elevational band”. I think in this introductory paragraph it is important to emphasize that it is not the general pattern for any mountain system that evolutionary ancient species occur on top of the mountain.

l. 82: “recent genetic results have placed the family as sister to crows (Corvidae) and shrikes (Laniidae) with an estimated divergence time from these at ca. 17 Mya [31]”

This statement refers to Oliveros et al. (2019), however, this year a new study has been published by

McCollough et al. 2023: Ornithology, 2023, 140, 1–11 https://doi.org/10.1093/ornithology/ukad025. Their phylogeny based on UCEs placed Eurocephalidae as the sister to Corvidae, Platylophidae sister to Laniidae and Melampittidae was sister to all four. For completeness, this study should be acknowledged here.

l. 115: “the work is mainly based on old museum specimens for which the Nagoya Protocol does not apply.” I do not expect a reply from the authors to this comment, because this is just a friendly, but to my feeling important reminder. I would like to send out a warning to the authors rather not to use such vague statements. The word “mainly” implies that “not all” samples used are old and could evoke the impression of decision makers (including the persons in charge on the NFPs of a country of origin), that for some of the material the Nagoya Protocol might theoretically apply and that this must have been checked before publication (even before performing any genetic work on the material). Moreover, I think this statement is unnecessary, because at least to my information Papua New Guinea (PNG) is not a signatory country of the Nagoya Protocol (check here)

https://www.cbd.int/abs/nagoya-protocol/signatories/

whereas, nevertheless PNG has established an NFP for the Nagoya Protocol. So, this is one of the many very tricky cases and potential pitfalls for us scientists (check here)

https://www.cbd.int/countries/?country=pg

I assume that even the two “fresh tissue samples” (l. 111) had been assessed before October 2014; if so, then I would suggest either stating that Nagoya does not apply to the study material, because all samples were assessed before that date – however, in that case, that remark is rather unnecessary and I would recommend rather deleting this statement, if journal policies do not require adding a disclaimer on compliance with the Nagoya Protocol.

l. 117: “permits are available”; it would be good to cite these (including permit numbers) in the acknowledgements (collecting or research permits in the country of origin etc.).

l. 207: “We applied the simple 2% rule…” Indeed, this is a very simplistic approach, and I am not sure whether I correctly understand, how this was actually done. I think we can infer from the reference to Weir & Schluter (2008) that the empirical cytochrome-b rate was apparently applied across the entire mitogenome. To me it seems that pairwise distances (inferred from whole mitogenomes including tRNAs, rRNAs and non-coding regions like the D-loop) among taxa/clades simply were transferred into split ages using the cytb rate (the statement in l. 205/206 evokes this impression). Considering that mitochondrial genes evolve a different substitution rates (in fact quite different between coding and non-coding regions; ; compare Lerner et al. 2011: Curr Biol 21, 1838-1844), this is not a very precise approach (and I would not consider this the state of the art). The more convincing method would be reconstructing a time-calibrated mitochondrial phylogeny, using for example the thirteen coding-markers of the mitogenome (as thirteen separate partitions in BEAUti) and applying empirical rates to each partition (e.g. from Lerner et al. 2011). If the simple approach is preferred, then it should be limited to the cytb fragments of the Melampittidae mitogenomes, because the Weir & Schluter estimate refers only to cytb.

l. 224: “Elements were measured as continuous sound traces and then grouped into syllables within each vocalisation (each vocalisation contained only one syllable).”

It is unclear to me what was done here. From that scarce information I think it is not possible to infer, how song characters were quantified, i.e. which sound parameters (frequency, time) were actually measured. Maybe this information is hidden in the statement on the DTW algorithm and the link to reference 62 (l. 226/227). Nevertheless, this vague information is not helpful to understand the differences in songs among the two species (except that one is more variable than the other). If 10 principal components were extracted these should normally correlate with song parameters (to be inferred from factor loads). So, as the reader I would like to know: What does differentiation along PC1 actually tell us? (Fig. 4; ~ 44% of variation) Which acoustic traits correlate with PC1? Are these differences in pitch, speed, complexity? Given that many acoustic features change with habitat density that might also relate to the two study species (mountain forests versus [more open?] karst habitats), this seems relevant information to me. This should be outlined in much more detail. A figure showing sonagrams would be actually helpful, because from the text I have no idea of the specific vocalizations (traits measured could then also be shown in one of the sonagrams). A closer check of sonagrams, could also be helpful for interpretation of the “significant outlier” in M. lugubris (legend of fig. 4; page 14). What means an “odd vocalization” in this context? Given the apparently high uncertainty of the context of a recorded vocalization (as outlined in Acoustic recordings and analysis), this one vocalization might not even be homologous to the other vocalizations of M. lugubris. Considering that even the distinction between “calls” and “songs” was unclear in this study, the vocalizations in general should be described and illustrated in much more detail.

l. 324: “greater acoustic diversity” Indeed, that can be inferred from the scatterplot, but this is not the same as a greater “vocal differentiation” (l. 367/68). The latter would imply diversified vocal groups, e.g. among mountain systems. At least, in the context of that paragraph it comes across that way, because the previous sentence directly refers to differences among the two species in “populations structure” (M. gigantea does not show a clear [genetic] population structure in contrast to M. lugubris; l. 364). The next sentence says that the vocal pattern is similar, however, although clear genetic clusters have been shown for M. lugburis, this is not true for the songs. Only because the songs are more variable, this does not mean, that this greater variation corresponds to different acoustic entities (dialects for example). At least this has not been shown for songs in the analysis.

l. 269: “We recover the same pattern of lower levels of differentiation in M. gigantaea compared to M. lugubris in the PCAs (Fig. 2 A) …” I do not understand this statement. Isn’t the whole paper on the clear differences between the species, e.g. M gigantaea being one very homogenic cluster in the PCA, and M. lugubris being represented by two clear PCA clusters and even a clear clustering structure for k=6. How does this conform with the statement in l. 281 that “Both heterozygosity and nucleotide diversity were significantly lower in M. gigantaea than in M. lugubris”? How can that paragraph be started with the statement that patterns of low (intraspecific) differentiation were the same in the two study species?

Minor

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PLoS One. 2024 May 23;19(5):e0293715. doi: 10.1371/journal.pone.0293715.r002

Author response to Decision Letter 0

26 Jan 2024

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RESPONSE: We have updated our methods section and ethics statement to clarify that our research has been performed on samples stored in museum collections for which no ethical approval is required. Relevant collection and export permits were also added.

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RESPONSE: We will make sure our sequences will be uploaded onto the European Nucleotide Archive (ENA) which is synchronised with NCBI until a potential acceptance of the manuscript and will provide you with the relevant accession numbers once available.

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RESPONSE: Map and satellite images for this figure were obtained from the United States Geological Survey (USGS, open domain) and geoBoundaries (https://www.geoboundaries.org/), an open database built by the geoLab at the College of William & Mary, which makes all data available under a CC BY 4.0 license. Nonetheless, we have attached a signed permission form by the geoLab confirming their permission to use this data.

Reviewer #1: Interesting study, in which the authors seem to have managed to produce good quality data from museum samples. However, in terms of the analyses, and interpretation and presentation of results, the study/paper needs in my opinion substantial improvement. See my comments below. The major comments are indicated with 'MAJOR'.

Abstract:

MAJOR: 50% of the abstract is intro, 30% methods, with effectively only one sentence describing the results. If in the conclusion you claim that certain populations should be raised to species level, this should be mentioned in the abstract. Also, refer back to the title: which species-specific dynamics? And how do the study species deviate?

RESPONSE: We have revised the abstract to accommodate this critique. Specifically, we have shortened the intro and added information about the potential elevation of Lesser Melampitta populations to full species. We also refer back to the title.

Introduction

Line 68-70: what would young lineages be better adapted to lowland habitats?

RESPONSE: This sentence summarises the concept of taxon cycles in which taxa go through phases of expansions (lowland generalists) and contractions (highland specialisation). The idea is that from time-to-time lowland generalists colonise new islands. As they are generalists, they tend to colonise peripheral (coastal) habitat. Over time these taxa move inland and upwards and become specialists. Little is known about the order of this. In any case, when it comes to taxon cycles, the new colonisers are thought to be “pioneer species” that can exploit the unstable coastal habitats better than more specialised old species. We have tried to summarise this very briefly in the introduction and have now added the word “generalist” to the text to indicate that the new lowland colonisers are expected to be generalists.

Line 85: why ‘in accordance’? Why do you assume that the Lesser M. is an old lineage?

RESPONSE: The family Melampittidae with only the two species investigated herein represent a very deep linage within Corvides and is one of a few very distinct and old lineages (16.1 My) in New Guinea. Moreover, the two currently recognised species within this family are placed in different genera and diverged from each other about 10 Mya. Although old is a relative term, these two genera represent old linages within Corvides and the divergence between these two species/genera is also old compared to other sister taxa divergences in this region.

Line 91-93: Are mid-elevations to be considered here as ‘lowland’?

RESPONSE: We apologise that this was not clear from the text, but we do not consider the habitat of M. gigantaea as lowlands, but neither as high elevations. We wanted to point out that this species has moved out of the coastal lowlands to some extent, but not into the high elevations that we normally observe for old/relictual taxa (as frequently observed in other avian groups on New Guinea). We consider here a scenario where rather than moving even higher up, M. gigantaea moved into suboptimal karst habitats (at mid-elevations) where they would also encounter less competition.

Line 95-98: specify here number of individuals sequenced, and to which depth. Also specify roughly which kind of analyses.

RESPONSE: This is a valid point and we have added this information to the introduction. More detailed numbers can be found at the beginning of the results and in supplementary table S1.

Line 96: MAJOR: while genomic data allows indeed to determine present-day population structure with the methods employed in this study (pca, phylogeny, admixture), but more methods are needed to determine how ‘habitat connectivity across space and time has shaped differentiation’

RESPONSE: Thank you for this important note. We have deleted and rephrased this segment to be more precise in the aim of this study and which questions can be answered with the applied methodology.

Line 99-106: Wouldn’t you expect a priori the opposite? Namely, the high-elevation species (Lesser. M) to have low connectivity between mountain-tops and hence to have population structure?

RESPONSE: Intuitively, we agree that we would expect a high-elevation species to be more isolated across a mountain range than a species inhabiting lower elevations. However, in this study - due to the large distances between the few distributional data points - M. gigantaea was expected to exhibit a stronger structure between populations. Additionally, since M. gigantaea is specialised on karst environments, we would expect the species to struggle outside these environments and be strongly isolated.

Methods

Line 109-117: why so few modern samples? Are samples difficult to acquire?

RESPONSE: Yes, the low number of modern samples is due to the difficulty of collection. For example, co-author Knud Jønsson spent 6 months in the field in Papua New Guinea within the distribution of M. lugubris and only ever collected two M. lugubris samples. Additionally, sampling in Indonesia has become almost impossible due to government regulations. For M. gigantaea sampling is even more difficult as we were able to obtain the only fresh sample collected within the last 50 years as well as all 6 historical specimens that are available in museum collections worldwide.

Line 126: 2x100bp read length is deliberate?

RESPONSE: We choose a read length of 2x100 bp in our museomics projects as our fragments rarely reach sizes longer than 200 bp (the average length of our fragments ranges from about 90 bp to 130 bp, see e.g. Irestedt et al. (2022)) that would make it worthwhile to sequence with the 2x150 bp chemistry.

Irestedt, M., Thörn, F., Müller, I. A., Jønsson, K. A., Ericson, P. G., & Blom, M. P. (2022). A guide to avian museomics: Insights gained from resequencing hundreds of avian study skins. Molecular Ecology Resources, 22(7), 2672-2684.

Line 127-128: How much data per sample? Expected genome size, targeted depth?

RESPONSE: We have added the requested information to the text. Our expected genome size was about 1 GB, given that the genome sizes in passerine birds is conserved and by the size of our chosen reference genome (Corvus cornix, genome size: 1.031 GB). As we did not sequence any de novo genome of Melampittidae we do not have a more precise estimate.

Line 129-138: While I appreciate the efforts made to make the work reproducible using a pipeline, the authors still need to describe here the settings of each software used by the pipeline (e.g., samtools and bwa). For instance, which base quality, mapping quality, and alignment score thresholds were used? Furthermore, all software used by the pipeline, needs to be mentioned here, and referenced.

RESPONSE: Thank you for the comment. We have added each tool and its version of all tools that are part of the Nextflow workflows. All implemented flags are now included in supplementary file S2. Bwa-mem2 and samtools were both run with default settings as we perform the relevant filtering steps in subsequent downstream analyses anyway.

Line 142: Because the authors so far did not mention the mean sequencing depth, it is hard to evaluate whether these thresholds are appropriate. The mitogenome depth is usually much higher than the nuclear depth, and hence 20x could be low. Could the authors present a summary of mean coverage of both the nuclear and mito genome?

RESPONSE: We have expanded supplementary table S1 to distinguish depth-of-coverage (DoC) of the nuclear genome and the mitochondria. The median DoC 786 x of the mitochondria is of course much higher than in the nuclear genome, a minimum DoC of 20 x is in our opinion still sufficient to make confident variant calls with low biases from erroneous reads.

Line 152: Mitochondrial genomes (!) might be effectively haploid, but in reality they are highly polyploid: each cell contains hundreds or thousands of copies which are independently proliferating, which may also cause heterozygous calls.

RESPONSE: This is absolutely true and a valid point. While we expect a certain number of heterozygous calls due to heteroplasmy, we saw a significantly higher number of such sites in six individuals (> 50 sites although most of these had more than 100-200 heterozygous sites, compared to 0-5 sites in most other individuals) and these sites were always concentrated in the same region across these individuals. As the flanking regions of the reads that produce these blocks were not of mitochondrial origin, we consider it highly likely that they represent NUMTs and has thus been deleted. We would not expect such a concentrated pattern due to random mutations in independent mitochondria especially not if only occurred in some individuals.

Line 155: for museum samples, a depth below 4x is not too bad. Why freebayes? Other genotyping software (like bcftools, GATK or ANGSD) have been reported to have higher accuracy.

RESPONSE: A depth of coverage around 4 x is indeed not too bad, but it’s also not great in comparison to coverages typically seen for sequencing data of fresh tissue samples. Genotyping software such as GATK was developed for analysing data from model organisms where we often have a good reference point to calibrate our variant calling. In other words, we can optimise our parameter settings for variant call recalibration using truth and training sets. However, this is much more difficult to do for non-model organisms and for historical datasets where there is a higher error rate. At the same time, 4 x depth of coverage is indeed much higher than typically seen for ancient DNA data where genotype likelihood methods such as employed by ANGSD excel. We therefore opted for a holistic approach using both ANGSD (for admixture analyses, PCAs and estimates of π, Tajima’s D, Heterozygosity) as well as freebayes, because the latter allows the use of a population prior when variant calling. This population information is used for the imputation of genotypes across a population and should therefore limit potential residual deamination patterns in historical samples. We are unaware of any reports that have demonstrated a higher accuracy for other genotyping software with this kind of datasets.

Line 160-163: Again, it does not suffice to only mention the pipeline. Even though this information could be looked up at the Github page, it needs to be specified here which settings were used to run IQtree2 and ASTRAL.

RESPONSE: Thank you, we have now expanded this section to mention all major steps and tools citing the respective creators. We have also modified supplementary file S2 to include all implemented flags and filters.

Line 162: Running IQtree (maximum likelihood approach) on an input dataset of SNPs (diploid, recombining loci), violates underlying assumptions of ML tree building.

RESPONSE: We don’t think that a concatenated dataset violates the underlying assumptions of ML tree building itself. In fact, this is a very common approach to infer the prevailing phylogenetic signal across a (whole-genome) dataset (see e.g. Lopes, F., et al. Systematic Biology (2021), Bein, B., et al. Marine Biology (2023)). However, we do agree that it does not account for any possible variation in coalescent histories between loci across and between chromosomes (i.e. where there has been a recombination event). We therefore complement the ML inference of concatenated data with a summary-coalescent species tree estimation based approach (ASTRAL3) which is based on genomic windows of different lengths (here we ran 2000, 5000, 10000 and 20000 bp windows) rather than concatenation.

Lopes, F., Oliveira, L. R., Kessler, A., Beux, Y., Crespo, E., Cárdenas-Alayza, S., ... & Bonatto, S. L. (2021). Phylogenomic discordance in the eared seals is best explained by incomplete lineage sorting following explosive radiation in the southern hemisphere. Systematic biology, 70(4), 786-802.

Bein, B., Lima, F. D., Lazzarotto, H., Rocha, L. A., Leite, T. S., Lima, S. M., & Pereira, R. J. (2023). Population genomics of an Octopus species identify oceanographic barriers and inbreeding patterns. Marine Biology, 170(12), 161.

Line 162: Better to use here the word ‘locus tree’ rather than ‘gene tree’

RESPONSE: You are correct, since we are doing a window-based the term gene tree is not correct here. In the rephrased segment we have avoided this term.

Line 165: MAJOR: which of the below mentioned statistics estimate levels of differentiation? In reality, all statistics (nucleotide diversity, heterozygosity and SFS) estimate genetic diversity (with Tajima’s D testing for neutrality). PCA and admixture show population structure, but do not estimate the level of population differentiation.

RESPONSE: Thank you very much for pointing out this issue. We realise that the term differentiation was not used correctly here as we haven’t employed a direct measure of it. We have corrected all mentions of differentiation to either refer to population structure or genetic diversity.

Line 170: MAJOR: With only 6 individuals per population, does it really make sense to try to reconstruct the SFS?

RESPONSE: While the sample size is close to the recommended minimum for population level estimates, we still believe that our sampling scheme allows for a decent estimate of our calculated statistics as we included all available samples of M. gigantaea which cover its entire distribution across New Guinea as well as M. lugubris individuals that all cover different localities within the distribution of its populations.

Line 173: Admixture analyses were run…

RESPONSE: Thank you for pointing out this grammatical error, we have now corrected it in the text.

Line 178: MAJOR: Does it

Attachment

Submitted filename: ResponseToReviewers.docx

pone.0293715.s024.docx (36.5KB, docx)

Decision Letter 1

Sven Winter

23 Feb 2024

PONE-D-23-33496R1Species-specific dynamics may cause deviations from general biogeographical predictions – evidence from a population genomics study of a New Guinean endemic passerine bird family (Melampittidae).PLOS ONE

Dear Dr. Müller,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

 The last revision greatly improved the manuscript. Reviewer #1 has a few more minor requests that should be easily implemented before acceptance. 

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Reviewer #1: All comments have been addressed

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Reviewer #1: Yes

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Reviewer #1: Yes

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Reviewer #1: Yes

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Reviewer #1: I am satisfied with the sensibly replies to my comments, as well as the corrections made to the manuscript. I have just one remaining request/suggestion, namely to estimate the level of divergence between lineages using the Dxy-estimate. This is crucial to assess the species status. The authors have argued that sample size prohibits this calculation, but actually Dxy is insensitive to sample size (see also one of my replies below). For reference, see also: Roux et al. 2016 Shedding Light on the Grey Zone of Speciation along a Continuum of Genomic Divergence

A few replies (referring to line numbers of comments of first revision round):

Line 109-117: The authors could consider adding this information to the methods section, so the reader better appreciate the efforts that the authors have put in acquiring this dataset.

Line 126: same: useful information, why not include in methods section?

Line 142: Agreed, 20x is sufficient, but on the other hand, which factors could cause a site or region to have a depth of 20 if the mean is 786? It makes such regions look ‘suspicuous’.

Line 152: Thanks for the clarification, which even gives me a potential explanation of patterns observed in my own datasets.

Line 155: Again, a concise summary of these considerations could be added to the methods section, in order for the reader to appreciate the underlying rationale.

Line 162: I am not fully convinced by this answer. Common approaches are not necessarily correct approaches. ML-phylogenetic inferences aims to reconstruct the most likely phylogenetic model, including the parameters u (mutation rate) and t (branch length), given the data. For a concatenated set of independently evolving loci, each with its own u and t, would this mean that in practice the method aims to infer the most likely average values of u and t (?). And if the input dataset is unphased, how does the method deal with ambiguous sites stemming from heterozygosity?

Line 178: Actually, I have to correct here myself. In the original paper, Tajima applies the test both to single-locus and multi-locus datasets.

Line 295-317: As a ‘second-opinion’, the authors could calculate Dxy (mean absolute genetic distance), for example using the software PIXY, or using the python scripts from the github page of Simon Martin (distMat.py, or popgenWindows.py –analysis popPairDist). Note that when inputting snp data, you would afterwards have to correct for this by multiplying the output estimates by the proportion of variable sites.

Line 399-401: Unlike Fst, Dxy is not sensitive to sample size. This is one of the main advantages of Dxy over Fst. (Another advantage is that it is not effective by Ne.) Thus, Dxy is even valid in case each population is represented by a single individual only. When using entire genomes, single-locus stochastics are cancelled out by the law or large numbers.

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Reviewer #1: No

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PLoS One. 2024 May 23;19(5):e0293715. doi: 10.1371/journal.pone.0293715.r004

Author response to Decision Letter 1

22 Mar 2024

Journal Requirements: Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

RESPONSE: We have double checked our reference list for completion and we did not see any citation that has been retracted at this point. Based on the reviewer’s suggestions we have additionally included the following references that were not used in the original manuscript (no reference has been removed):

32. McCullough JM, Hruska JP, Oliveros CH, Moyle RG, Andersen MJ. Ultraconserved elements support the elevation of a new avian family, Eurocephalidae, the white-crowned shrikes. Ornithology. 2023 Jul 11;140(3):ukad025.

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44. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014 Aug 1;30(15):2114–20.

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46. Shen W, Le S, Li Y, Hu F. SeqKit: A Cross-Platform and Ultrafast Toolkit for FASTA/Q File Manipulation. PLOS ONE. 2016 Oct 5;11(10):e0163962.

49. Danecek P, Bonfield JK, Liddle J, Marshall J, Ohan V, Pollard MO, et al. Twelve years of SAMtools and BCFtools. GigaScience [Internet]. 2021 Feb;10(2). Available from: https://doi.org/10.1093/gigascience/giab008

50. Okonechnikov K, Conesa A, García-Alcalde F. Qualimap 2: advanced multi-sample quality control for high-throughput sequencing data. Bioinformatics. 2016 Jan 15;

32(2):292–4.

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Reviewer #1: I am satisfied with the sensibly replies to my comments, as well as the corrections made to the manuscript. I have just one remaining request/suggestion, namely to estimate the level of divergence between lineages using the Dxy-estimate. This is crucial to assess the species status. The authors have argued that sample size prohibits this calculation, but actually Dxy is insensitive to sample size (see also one of my replies below). For reference, see also: Roux et al. 2016 Shedding Light on the Grey Zone of Speciation along a Continuum of Genomic Divergence

RESPONSE: We hope we have addressed all of your suggestions and have now included estimates of Dxy. See below for our responses to each point.

A few replies (referring to line numbers of comments of first revision round):

Line 109-117: The authors could consider adding this information to the methods section, so the reader better appreciate the efforts that the authors have put in acquiring this dataset.

RESPONSE: We have added some additional information in the manuscript to explain our low number of modern samples.

Line 126: same: useful information, why not include in methods section?

RESPONSE: We now include a sentence in the methods in which we explain our choice of sequencing read length.

Line 142: Agreed, 20x is sufficient, but on the other hand, which factors could cause a site or region to have a depth of 20 if the mean is 786? It makes such regions look ‘suspicuous’.

RESPONSE: We agree that such a heavy dip from the average depth of coverage would look odd. Even in the mitochondria, we would expect certain regions to be more difficult to sequence and assemble, such as the control region, but also repetitive regions. Additionally, our chosen method takes a random subset of 5 000 000 reads, which is normally more than sufficient for a mitochondrial assembly, but due to its random nature there may still be a low chance that a certain region is covered by less reads within the subset.

Line 152: Thanks for the clarification, which even gives me a potential explanation of patterns observed in my own datasets.

RESPONSE: Happy to help, we have discussed this issue extensively in our group.

Line 155: Again, a concise summary of these considerations could be added to the methods section, in order for the reader to appreciate the underlying rationale.

RESPONSE: We have added a short justification to each of the applied variant calling methods.

Line 162: I am not fully convinced by this answer. Common approaches are not necessarily correct approaches. ML-phylogenetic inferences aims to reconstruct the most likely phylogenetic model, including the parameters u (mutation rate) and t (branch length), given the data. For a concatenated set of independently evolving loci, each with its own u and t, would this mean that in practice the method aims to infer the most likely average values of u and t (?). And if the input dataset is unphased, how does the method deal with ambiguous sites stemming from heterozygosity?

RESPONSE: The first step of the used workflow (nf-phylo) is to generate a consensus sequence for every chromosome/scaffold based on the vcf of each individual, with heterozygous and low/high coverage positions masked. These consensus sequences are then used as input for phylogenetic inference, not the SNP-data itself. Although we do not reach the point of parameters for each independent locus, we try to accommodate for this issue by inferring phylogenies at different windows sizes (between 2 000 – 20 000 bp) and verify that the topology remains consistent. Additionally, we compare topologies between concatenated and summary coalescent phylogenies to make sure that these are also similar. Another measure that gives us a sense of support from each locus is obtained through the site concordance factor which measures the proportion of sites within each window that support a certain branch.

Ambiguous heterozygous sites are not an issue in our approach as we remove all heterozygous sites during the filtering of our SNP data. We definitely lose some information from these sites by removing them, however it gives us more security due to the relatively low coverage of some of our samples. We have clarified this part in the text (previously it only said “masked”).

Line 178: Actually, I have to correct here myself. In the original paper, Tajima applies the test both to single-locus and multi-locus datasets.

RESPONSE: Thank you for the correction. Your original comment was still very much valid, and we have also discussed with other colleagues that it is unusual to present Tajima’s D as a single value for the entire genome as one may miss regions with a strong different signal otherwise. Although we did not intend on investigating the genomes on this scale within this study, it is still important to show changes in Tajima’s D across the entire genome.

Line 295-317: As a ‘second-opinion’, the authors could calculate Dxy (mean absolute genetic distance), for example using the software PIXY, or using the python scripts from the github page of Simon Martin (distMat.py, or popgenWindows.py –analysis popPairDist). Note that when inputting snp data, you would afterwards have to correct for this by multiplying the output estimates by the proportion of variable sites.

RESPONSE: We have included estimates of Dxy which are now part of the supplementary material (S6 Table). We chose to calculate it through a genotype likelihood-based approach so that we could include as many individuals as possible. The script is based on https://github.com/mfumagalli/ngsPopGen/blob/master/scripts/calcDxy.R and was only modified to perform multiple pairwise comparisons and provide a summary of global estimates. The main calculation of Dxy remains the same. The approach is described in the methods and codes are listed in S2 File.

Line 399-401: Unlike Fst, Dxy is not sensitive to sample size. This is one of the main advantages of Dxy over Fst. (Another advantage is that it is not effective by Ne.) Thus, Dxy is even valid in case each population is represented by a single individual only. When using entire genomes, single-locus stochastics are cancelled out by the law or large numbers.

RESPONSE: Thank you for the comment, although we were sceptical whether Dxy would really be representative for single individuals, we were pleasantly surprised to find that pairwise estimates were around the same range even when using different subpopulations (smaller or even consisting of a single individual) for the calculation.

Attachment

Submitted filename: ResponseToReviewers.docx

pone.0293715.s025.docx (24KB, docx)

Decision Letter 2

Sven Winter

27 Mar 2024

Species-specific dynamics may cause deviations from general biogeographical predictions – evidence from a population genomics study of a New Guinean endemic passerine bird family (Melampittidae).

PONE-D-23-33496R2

Dear Dr. Müller,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Reviewers' comments:

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 Fig. Mitochondrial phylogeny for all individuals based on an alignment of mitochondrial consensus sequences.

    The tree was constructed using RaxML-NG applying a GTR+G substitution model.

    (TIF)

    pone.0293715.s001.tif (620KB, tif)
    S2 Fig. Individual nucleotide diversity (π) is significantly lower in M. gigantaea (orange) than in M. lugubris (blue).

    The applied statistical test was a Welch’s two sample t-test for unequal variances.

    (TIF)

    pone.0293715.s002.tif (277.8KB, tif)
    S3 Fig. Tajima’s D across all chromosomes.

    Chromosomes are divided into macrochromosomes (> = 40 Mbp), intermediate chromosomes (> = 20 Mbp, < 40 Mbp) and microchromosomes (< 20 Mbp). Values are consistently negative across most of each chromosome in M. lugubris (blue) and slightly positive in M. gigantaea (orange).

    (TIF)

    pone.0293715.s003.tif (2.3MB, tif)
    S4 Fig. Principal components 1 to 4 describing divisions within subpopulations of M. lugubris (shades of blue/purple) while M. gigantaea (orange) remains a tight cluster.

    Weyland represents one individual (AMNH 301956) that was collected between our Western and Central populations and is assigned to the Western population in most other analyses.

    (TIF)

    pone.0293715.s004.tif (588.9KB, tif)
    S5 Fig. Heterozygosity shows a correlation with increasing depth of coverage (DoC).

    Slopes are similar between populations/species and M. gigantaea still shows lower heterozygosity than most M. lugubris populations when comparing individuals with similar DoC. To fit regression lines, we applied Kendall’s rank correlation coefficient as it is recommended for smaller sample sizes containing outliers [Kendall MG. A New Measure of Rank Correlation. Biometrika. 1938;30(1/2):81–93.].

    (TIF)

    pone.0293715.s005.tif (539.3KB, tif)
    S6 Fig. PSMC for M. lugubris (blue) and M. gigantaea (red) and their hybrid PSMC curve (purple) to show the divergence time between the two species.

    (TIF)

    pone.0293715.s006.tif (763.1KB, tif)
    S7 Fig. PSMC for M. lugubris NHMD616019 from Huon (red) and M. lugubris B98503 from Central New Guinea (blue) and their hybrid PSMC curve (purple) to show the divergence time between Eastern populations and Western + Central populations of M. lugubris.

    (TIF)

    pone.0293715.s007.tif (880.7KB, tif)
    S8 Fig. PSMC for M. lugubris B98503 from Central New Guinea (red) and M. lugubris AMNH293751 from Western New Guinea (blue) and their hybrid PSMC curve (purple) to show the divergence time between Central populations and Western populations of M. lugubris.

    (TIF)

    pone.0293715.s008.tif (965.7KB, tif)
    S9 Fig. PSMC for M. lugubris NHMD616019 from Huon (red) and M. lugubris AMNH590750 from the Southeast (blue) and their hybrid PSMC curve (purple) to show the divergence time between Huon populations and Southeastern populations of M. lugubris.

    (TIF)

    pone.0293715.s009.tif (1.2MB, tif)
    S10 Fig. PSMC for M. lugubris NHMD616019 from Huon (red) and M. lugubris B100613 from the East (blue) and their hybrid PSMC curve (purple) to show the divergence time between Huon populations and East populations of M. lugubris.

    (TIF)

    pone.0293715.s010.tif (1.1MB, tif)
    S11 Fig. Mitochondrial divergence matrix showing the minimum (first value) and maximum (second value) for each comparison of populations and species, M. gigantaea (Meg), M. lugubris (Mel), Eastern populations (EPops) include the subpopulations East, Southeast and Huon.

    (TIF)

    pone.0293715.s011.tif (1MB, tif)
    S12 Fig. Mitochondrial divergence matrix showing the mean divergence for each comparison of populations and species, M. gigantaea (Meg), M. lugubris (Mel), Eastern populations (EPops) include the subpopulations East, Southeast and Huon.

    (TIF)

    pone.0293715.s012.tif (1,017.1KB, tif)
    S13 Fig. Heterozygosity between M. gigantaea and populations of M. lugubris. M. gigantaea is shown in orange, populations of M. lugubris in shades of blue/purple. Statistical significance is only shown for significant differences between pairwise comparisons of M. gigantaea and each population of M. lugubris applying Welch’s t-test.

    (TIF)

    pone.0293715.s013.tif (505.1KB, tif)
    S14 Fig. Representative acoustic vocalisations of M. gigantaea and M. lugubris.

    For each species, vocalisations from four different individuals are depicted. The top panel illustrates how vocalisations were measured in the acoustic software Luscinia: The user manually traces out the elements, the smallest unit within each vocalisation (in green), after which they are grouped into syllables (in red). Each vocalisation typically contains only 1 syllable for both species. The dynamic time warping algorithm in Luscinia creates a matrix of syllable dissimilarities using multiple frequency and time measurements that are extracted from these measured syllables.

    (TIF)

    pone.0293715.s014.tif (712.4KB, tif)
    S1 Table. List of samples.

    Additional information such as sample locality, museum voucher, tissue type, etc. are included. The table also shows mapping statistics (e.g. mapping percentage and depth-of-coverage) for each individual.

    (XLSX)

    pone.0293715.s015.xlsx (14.9KB, xlsx)
    S2 Table. Used github commits when running Nextflow workflows.

    (XLSX)

    pone.0293715.s016.xlsx (8.8KB, xlsx)
    S3 Table. Filtered individuals with heterozygous blocks in mtDNA.

    (XLSX)

    pone.0293715.s017.xlsx (8.4KB, xlsx)
    S4 Table. Individual nucleotide diversity (π).

    Sheet 1 (Individual) contains statistics calculated for each species using i) all chromosomes and ii) only autosomes (aut.). Sheet 2 (Species-wide) contains estimates averaged across i) all chromosomes and ii) only autosomes (aut.).

    (XLSX)

    pone.0293715.s018.xlsx (10.2KB, xlsx)
    S5 Table. Species-wide Tajima’s D.

    Values averaged across i) all chromosomes and ii) only autosomes (aut.).

    (XLSX)

    pone.0293715.s019.xlsx (9.1KB, xlsx)
    S6 Table. Mean per-site Dxy etimates.

    Sheet 1 (Dxy) shows pairwise comparisons between all major population splits as well as an estimate within M. gigantaea. Sheet 2 (Populations) lists the individuals that were included in each population.

    (XLSX)

    pone.0293715.s020.xlsx (13.7KB, xlsx)
    S1 File. Details on PSMC methodology.

    (DOCX)

    pone.0293715.s021.docx (31.4KB, docx)
    S2 File. Codes and parameters settings.

    (DOCX)

    pone.0293715.s022.docx (34.9KB, docx)
    S3 File. List of adapters that were removed during trimming.

    (DOCX)

    pone.0293715.s023.docx (12.1KB, docx)
    Attachment

    Submitted filename: ResponseToReviewers.docx

    pone.0293715.s024.docx (36.5KB, docx)
    Attachment

    Submitted filename: ResponseToReviewers.docx

    pone.0293715.s025.docx (24KB, docx)

    Data Availability Statement

    Sequences that were obtained as part of this study have been deposited at the European Nucleotide Archive (ENA) under accession numbers ERS17855383 - ERS17855413, project accession PRJEB72101. Distributional data for both Melampittidae species were extracted through the IUCN’s red list webpage (https://www.iucnredlist.org/). Avian distributional data on the IUCN red list are originally provided by BirdLife (https://www.birdlife.org/). Shapefiles for administrative boundaries of Indonesia and Papua New Guinea were obtained from geoBoundaries (https://www.geoboundaries.org/). Topographic data of New Guinea was extracted from the United States Geological Survey (https://www.usgs.gov). All codes and software version numbers used for this article are mentioned within the paper and described in more detail in the supplementary material.

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